Technology, Public Policy, and the

Changing Structure of American Agriculture

March 1986

NTIS order #PB86-184637
Recommended Citation:

U.S. Congress, Office of Technology Assessment, Technology, Public Policy, and the

Changing Structure of American Agriculture, OTA-F-285 (Washington, DC: U.S. Government Printing Office, March 1986).

Library of Congress Catalog Card Number 85-600632

For sale by the Superintendent of Documents

U.S. Government Printing Office, Washington, DC 20402
American agriculture is undergoing significant change and stress. Much of the

recent change has been attributed to the financial farm crisis caused mainly by

declining agricultural exports. However, underlying these financial difficulties are

strong technological and structural forces which will cause further changes and

adjustments in American agriculture for the remainder of this century.

Congress, concerned about the nature of these adjustments, requested the Office

of Technology Assessment (OTA) to analyze the underlying technological, structural, and political forces which impact American agriculture and to determine

the industry’s probable future direction. Committees requesting the study include:

the Senate Committee on Agriculture, the Senate Small Business Committee (the

Subcommittee on the Family Farm), the Joint Economic Committee, the House Committee on Science and Technology, and the House Committee on Agriculture (the

Subcommittee on Livestock, Dairy, and Poultry; the Subcommittee on Department

Operations, Research, and Foreign Agriculture; and the Subcommittee on Forests,

Family Farms, and Energy).

In the course of preparing this report, an interim report entitled A Special Report for the 1985 Farm Bill was transmitted to the requesting committees for their

use during the debates and the writing of the Food Security Act of 1985 (1985 Farm

Bill). The special report focused on assessment findings that were particularly relevant for issues debated in that legislation.

This report addresses the longer run issues that technology and certain other

factors will have on American agriculture during the remainder of this century.

It focuses on the relationship of technology to: agricultural production, structural

change, rural communities, environment and natural resource base, finance and

credit, research and extension, and public policy. The assessment identifies many

benefits that new technologies will create, but these benefits will also exact substantial costs in potential adjustment problems. This report is a first step toward

understanding these interrelated problems and identifying policies to ameliorate

them.

OTA greatly appreciates the contribution of the advisory panel, workgroups, workshop participants, authors of the technical background papers, and the many other

advisors and reviewers who assisted OTA from the public and private sector. Their

guidance and comments helped develop a comprehensive report. As with all OTA

studies, however, the content of this report is the sole responsibility of OTA.

- JOHN H. GIBBONS

Director

iii
Technology, Public Policy, and the Changing Structure of

American Agriculture: Advisory Panel

Frank Baker

Director, International Stockmen’s School

Winrock International Livestock Research

and Training Center

Arkansas

James Bonnen

Professor

Department of Agricultural Economics

Michigan State University

William Brown

Chairman of the Board

Pioneer Hi-Bred International, Inc.

Iowa

Frederick Buttel

Associate Professor

Department of Rural Sociology

Cornell University

Willard Cochrane

Consultant

California

Jack Doyle

Director

Agricultural Resources Project

Environmental Policy Institute

Washington, DC

Marcia Dudden

Dudden Farms, Inc.

Iowa

Walter Ehrhardt

Ehrhardt Farms

Maryland

Dean Gillette

Professor

Harvey Mudd College

Claremont College

Roger Granados

Executive Director

La Coopertiva

California

Richard Harwood

Program Officer

Winrock International

Virginia

Charles Kidd

Dean

College of Engineering Science,

Technology, and Agriculture

Florida A&M University

Robert Lanphier III

Chairman of the Board

DICKEY-john Corp.

Illinois

Edward Legates

Dean, College of the Agriculture and

Life Sciences

North Carolina State University

John Marvel’

president and General Manager

Research Division

Monsanto Agriculture products Co.

Missouri

Donella Meadows

Adjunct Professor

Resources Policy Center

Dartmouth College

Don Paarlberg

Consultant

Indiana

Don Reeves

Consultant, Interreligious Taskforce on

U.S. Food Policy

Nebraska

Milo Schanzenbach

Schanzenbach Farms

South Dakota

* Resigned May 1985.

iv
Technology, Public Policy, and the Changing Structure

of American Agriculture: OTA Project Staff

Roger C. Herdman, Assistant Director, OTA

Health and Life Sciences Division

Walter E. Parham, Food and Renewable Resources Program Manager

Michael J. Phillips, Project Director

Yao-chi Lu, Senior Analyst

Robert C. Reining, Analyst

Juliette Linzer,l Research Assistant

Kathryn M. Van Wyk, Editor and Writer

Administrative Staff

Patricia Durana2 and Beckie Erickson,3 Administrative Assistants

Nellie Hammond, Secretary

Carolyn Swarm, Secretary

1Through May 1984.

2Through July 1985.

3After July 1985,
Contents

Chapter Page

1. Summary..... . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3

Part I: The Emerging Technologies

Z. Emerging Technologies for Agriculture..., . . . . . . . . . . . . . . . . . . . . . 2 7

3, impacts of Emerging Technologies on Agricultural Production . . . . . . . 75

Part II: The Changing Structure of American Agriculture

4. Dynamic Structure of Agriculture...... . . . . . . . . . . . . . . . . . . . . . . . . . . 91

5. Factors Contributing to Structural Change in Agriculture ., , ., ..., . . . 109

Part III: Analyses of Technology, public Policy, and

Agricultural Structure

6. Emerging Technologies and Agricultural Structure . . . . . . . . . ..., . . . . . 123

7. Impacts of Agricultural Finance and Credit , ., ., ..., , ., ., . . . . . ., . . . 137

8. Emerging Technologies, Public Policy, and Various Size Crop Farms , . 163

9. Emerging Technologies, Public Policy, and Various Size Dairy Farms . 189

10. Impacts on the Environment and Natural Resources. , , . . . , , , ., ., , . . . 205

11. Impacts on Rural Communities . . . ., . . . . . ..., . . . . . . . . ., ., ..., . . . ., 221

12. Impacts on Agricultural Research and Extension. , ., ., , , ., ., ., ., ., ., 253

Part IV: Implications and Policy Options for Agriculture

13. Implications and Policy Options for Agriculture , , ., , . . . , ..., , . . . . . . 285

Appendixes

A:

B:

c:

D:

E:

F:

G:

Animal and Plant Technology Workshop Methodology

and Procedures . . . . . . . . . . . , . . . . . . . . . . . , . . . . . . . . . . , , . , . , . . . , . , . , . 297

U.S. Regional Agricultural Sales by Sales Class and Commodity , ... , . . 305

Participants in OTA Workshops ... , . . . . . . . . . . . . . . . . ., ., ., ., ., . . . . . 310

Analysis of Size Economies and Comparative Advantage in Crop

Production in Various Areas of the United States ., ., ., ... , ... , ... , ., 317

Methodology and Detailed Results of Macroeconomic Impacts of Technology and Public Policy for Crop Farms . , . . . . . . . , . , . . . . . , , . . . . . . . . 333

Detailed Results of Macroeconomic Impacts of Technology and

Public Policy on Dairy Farms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 348

Workgroups, Background Papers, and Acknowledgments . . . . . . . . . . . . . 358

Index , , , . . . . . . . . . . . . . . . . . . . ., , . . . . . . , , . . . . . . . . . . . . . . . . . . . . . ., ... , 369
Chapter 1
Contents

Page

Agricultural Dependency on World Markets . . . . . . . . . . . . . . . . . . . . . . . . . . . 4

Emerging Technologies for Agriculture . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4

Biotechnology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4

Information Technology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6

The Changing Structure of Agriculture . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8

Major Findings . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10

Emerging Technologies and Future Agricultural Production . . . . . . . . . . . . 10

Emerging Technologies and the Future Structure of Agriculture . . . . . . . . 12

Impacts of Agricultural Finance and Credit . . . . . . . . . . . . . . . . . . . . . . . . . . 12

Emerging Technologies, Policy, and Survival of Various Size Farms . . . . . 14

Impacts on the Environment and Natural Resources . . . . . . . . . . . . . . . . . . 15

Impacts on Rural Communities . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16

Impacts on Agricultural Research and Extension . . . . . . . . . . . . . . . . . . . . . 18

Implications and Policy Options.. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20

The Issue of Farm Structure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20

Required Policy Adjustments.. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21

Summary Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25

Tables

Table No. Page

1-1.

1-2.

1-3.

1-4

1-5.

1-6.

Distribution of Farm Sizes, Percent of Cash Receipts, Percent of Farm -

Income, and Farm and Off-Farm Income by Sales Class, 1982 . . . . . . . . 8

Most Likely Projection of Total Number of U.S. Farms in Year 2000,

by Sales Class . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9

Impact of Emerging Technology on Animal Production Efficiency

in Year 200 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10

Impact of Emerging Technology on Crop Yields in Year 2000 . . . . . . . . 11

Projections of Major Crop Production . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11

Comparison of Commodities With Current Economies of Size and

Future Technological Gains . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14
Chapter 1

Summary

Over the next 15 years, American farmers will

be offered an extensive array of new biotechnologies and information technologies that

could revolutionize animal and plant production. The adoption of these technologies will be

critical for shoring up the United States’ lagging ability to compete in the international marketplace. Indeed, 83 percent of the estimated

1.8-percent annual increase in agricultural production needed to meet world agricultural demand by year 2000 must come from increases

in agricultural yields, yields that can only be

possible through the development and adoption

of emerging technologies.

Yet if current agricultural policies remain in

force, this new biotechnology and information

technology era will also generate marked changes

in the structure of the agricultural sector and

of the rural communities that support farming.

Some of these changes are’ already evident:

Farming is becoming more centralized, more

vertically integrated. Large farms, though small

in number, now produce most of this country’s

agricultural output. Operators of small and

moderate-size farms, the so-called backbone of

American agriculture, are becoming increasingly less able to compete, partly because they

lack access to the information and finances necessary for adopting the new technologies effectively. Many such farmers must relocate, change

to other kinds of farming, or give up farming

altogether. The disappearance of these farm

operations is causing repercussions for other

businesses in the rural community and for the

labor pool in general, which must absorb all

those whose livelihood once depended on agricultural production.

This report is the first step toward understanding the social and economic costs, as well as

the benefits, of the emerging technologies for

U.S. agriculture. It analyzes the dynamic forces

influencing change in the structure of agriculture. Although technology was found to be an

important force in such change, it is only one

of several such forces. Public policy, institutions, and economics have had and will continue to have important roles in shaping agriculture. OTA analyzed the relationships between

all these factors, focusing on the 150 production technologies that are likely to be available

commercially over the next 15 years. The study

results are presented in this report in four parts.

Part I identifies and analyzes the productive

capacity of those emerging technologies that

will help shape and define American agriculture to the year 2000. Chapters 2 and 3 describe

the emerging technologies, discuss how they

will be used in agriculture, and analyze the impact these technologies will have on animal and

plant agriculture.

Part II traces the historical changes in agricultural structure. It provides a perspective for

analyzing technology’s distributional impacts

on agricultural structure by surveying the characteristics of that structure and the factors that

affect it.

How the emerging technologies, the policies,

and structural change relate to one another is

the subject of chapters 6 through 12 in part III.

The chapters analyze the results of this relationship on: 1) future structure, 2) agricultural finance and credit, 3) survivability of crop and

dairy farms of various sizes, 4) environment,

5) rural communities, and 6] agricultural research and extension.

Part IV draws the implications of the analysis for policy makers. It shows the direction in

which agriculture is headed and concludes with

congressional policy options for improving the

picture of U.S. agriculture.

3
4 ● Technology, Public Policy, and the Changing Structure of American Agriculture

AGRICULTURAL DEPENDENCEY ON WORLD MARKETS

The financial condition of many American

farmers in the 1980s has significantly deteriorated during a long period of surpluses. The decline in agricultural exports is largely responsible for this situation. And although exports

are not this report’s central focus, the future of

U.S. agricultural exports loom large in the background of this report.

Agricultural exports have historically been responsible for lessening the negative trade balance caused primarily by the manufacturing

and energy sectors. This importance of agriculture to the balance of trade has increased significantly over the past 30 years. However, the

past several years have witnessed a drop both

in the value of U.S. agricultural exports and in

agriculture’s share of total U.S. exports.

Several key factors are causally related to recent declines in U.S. agriculture:

1. a weak world economy,

2. the strong value of the dollar,

3. the enhanced competitiveness of other

countries,

4. an increase in trade agreements, and

5. price support levels that permit other countries to undersell the United States.

Although all of the factors are important, agricultural experts are beginning to focus on the

lower costs of production in other countries as

the long-term primary factor in the decline of

this country’s competitiveness. The United

States faces strong competition in wheat, corn,

rice, soybeans, and cotton. Each of these major

export commodities has been produced by at

least one country at or below the U.S. average

production costs since 1981. Estimates suggest

that any historic cost advantage that the United

States may have enjoyed in these commodities

is now tenuous.

Future exports will depend on the ability of

American farmers to use new technology to produce commodities more efficiently than competing countries can. If the United States cannot effectively compete with other countries in

the export market, reduced exports will magnify the structural change and adjustment that

U.S. farmers and the rural communities will face

because of technological change.

EMERGING TECHNOLOGIES FOR AGRICULTURE

Technology has made U.S. agriculture one of

the world’s most productive and competitive

industries. Americans have already witnessed

the dramatic results of two major technological eras in agriculture. The mechanical era of

1920 to 1950 allowed farmers to make the transition from horsepower to mechanical power

and greatly increased the productive capacity

of U.S. agriculture. The chemical era of 1950

to 1980 further increased agricultural productivity by increasing the farmers’ ability to control pests and disease and by increasing the use

of chemical fertilizers. Now, in the 1980s, American agriculture is being propelled by a new major technological thrust—the biotechnology and

information technology era. The effects of this

new era on agricultural productivity may be

more profound than those experienced from either the mechanical or chemical eras.

Below is a brief summary of the technologies

examined for this study. A more complete description of the 150 technologies can be found

in chapter 2.

BIOTECHNOLOGY

Biotechnology, broadly defined, includes any

technique that uses living organisms or processes to make or modify products, to improve

plants or animals, or to develop micro-organisms for specific uses. It focuses on two powerful molecular genetic techniques: recombinant

deoxyribonucleic acid (rDNA) and cell fusion
Ch. 1—Summary ● 5

technologies. Using these techniques scientists

can visualize the gene—to isolate, clone, and

study the structure of the gene and the gene’s

relationships to the processes of living things.

Such knowledge and skills will give scientists

much greater control over biological systems,

leading to significant improvements in the production of plants and animals,

Animal Agriculture

In animal agriculture, advances in protein

production, gene insertion, and embryo transfer will play a major role in increasing efficiencies in animal production.

Production of Protein.—One major thrust of

biotechnology in animals is the mass production in micro-organisms of protein-like pharmaceuticals, including a number of hormones, enzymes, activating factors, amino acids, and feed

supplements. Previously, these biological products could be obtained only from animal and

human organs and were either unavailable in

sufficient amounts or were too costly.

Some of these biological products can be used

for detection, prevention, and treatment of infectious and genetic diseases; some can be used

to increase animal production efficiency, One

of the applications of these new pharmaceuticals is the injection of growth hormones into

animals to increase production efficiency. For

example, several firms are developing a genetically engineered bovine growth hormone to

stimulate lactation in cows. Trial results indicate that cows treated with the hormone increase milk production by 20 to 30 percent, with

only a modest increase in feed intake. Commercial introduction of the new hormone awaits

approval by the U.S. Food and Drug Administration, which is expected to approve the hormone within the next 3 years.

In the area of disease prevention and treatment, an immunological product currently exists on the market that prevents “scours” in

calves. In addition, vaccines produced by rDNA

methods are currently being tested for foot-andmouth disease, swine dysentery and, most recently, coccidiosis in poultry.

Gene Insertion.—A new technique arising

from the convergence of gene and embryo manipulations promises to permit genes for new

traits to be inserted into the reproductive cells

of livestock and poultry, providing major opportunities to improve animal health and productivity. Unlike the genetically engineered hormones discussed above, which cannot affect

future generations, gene insertion will allow future animals to be endowed permanently with

traits of other animals. In this technique, genes

for a desired trait, such as disease resistance

or growth, are injected directly into either of

the two pronuclei of a fertilized egg. on fusion

of the pronuclei, the guest genes become part

of all the cells of the developing animal, and the

traits they determine are transmitted to succeeding generations.

Embryo Transfer.—Embryo transfer, which

is closely related to gene insertion, involves artificially inseminating a super-ovulated donor

animal 1 and removing the resulting embryos

nonsurgically for implantation in surrogate

mothers which then carry them to term. Prior

to implantation, the embryos can be treated in

a number of special ways. They can be sexed,

split (generally to make twins), fused with embryos of other animal species (to make chimeric

animals or to permit the heterologous species

to carry the embryo to term), or frozen in liquid

nitrogen for storage. Freezing is of great practical importance because it allows embryos to

be stored until the estrus of the intended farm

animal is in synchrony with that of the donor.

Embryos used for gene insertions must be in

the single-cell stage, having pronuclei that can

be injected with cloned foreign genes, The genes

likely to be inserted into cattle maybe those for

growth hormones, prolactins (lactation stimulators), digestive enzymes, and interferon,

thereby providing both growth and enhanced

resistance to diseases.

Even though less than 1 percent of U.S. cattle

are involved in embryo transfers, the obvious

1An animal that has been injected with a hormone to stimulate

the production of more than the normal number of eggs per ovulation.
6 ● Technology, Public Policy, and the Changing Structure of American Agriculture

benefits of this technology will push this percentage upward rapidly, particularly as the costs

of the procedure decrease. Recently, a genetically superior Holstein cow and her 14 embryos

were purchased for $1.3 million.

Plant Agriculture

The application of biotechnologies in plant

agriculture could modify crops so that they

would make more nutritious protein, resist insects and disease, grow in harsh environments,

and provide their own nitrogen fertilizer. While

the immediate impacts will be greater for animal agriculture, the long-term impacts of biotechnology may be substantially greater for

plant agriculture. The potential applications of

biotechnology on plant agriculture include microbial inoculums, plant propagation, and genetic modification.

Microbial Inocula.-Rhizobium seed inocula

already are used widely to improve the nitrogen fixation of certain legumes. Extensive study

of the structure and regulation of the genes involved in bacterial nitrogen fixation will likely

lead to development of improved inocula. Moreover, research on other plant-colonizing microbes has led to a clearer understanding of the

role of these microbes in plant nutrition, growth

stimulation, and disease prevention, and the

possibility exists for the modification and use

of these microbes as seed inocula.

Monsanto has announced plans to field test

genetically engineered soil bacteria that produce a naturally occurring insecticide potentially capable of protecting plant roots against

soil-dwelling insects. The company developed

a genetic engineering technique that inserts into

soil bacteria a gene from a micro-organism

known Bacillus thuringiensis, a micro-organism that has been registered as an insecticide

for more than two decades. Plant seeds could

be coated with these bacteria before planting.

As the plants grow, the bacteria would remain

in the soil near the plant roots, generating an

insect toxin that protects the plants.

Plant Propagation.—Cell culture methods for

regeneration of intact plants from single cells

or tissue explants are now used routinely for

propagation of several vegetable, ornamental,

and tree species. These methods can provide

large numbers of genetically identical, diseasefree plants that often exhibit superior growth

and more uniformity over plants conventionally seed-grown. Such technology holds promise for breeding in important forest species

whose long sexual cycles reduce the impact of

traditional breeding approaches. Somatic embryos’ produced in large quantities by cell culture methods can be encapsulated to create artificial seeds that may enhance propagation of

certain crop species.

Genetic Modification.—Plant genetic engineering is the least established of the various

biotechnologies used in crop improvement, but

the most likely to have a major impact. Using

gene transfer techniques, it is possible to introduce DNA from one plant into another plant,

regardless of normal species and sexual barriers.

For example, it is possible to introduce storageprotein genes from French bean plants into

tobacco plants and to introduce genes that encode photosynthetic proteins in pea plants into

petunia plants.

Transformation technology also allows introduction of DNA coding sequences from virtually any source into plants, providing those sequences are engineered with the appropriate

plant-gene regulatory signals. Several bacterial

genes have now been modified and shown to

function in plants. By eliminating sexual barriers to gene transfer, genetic engineering will

greatly increase a plant’s genetic diversity.

Information TechnoIogy

Animal Agriculture.

Information technology is the use of computer- and electronic-based technologies for the

automated collection, manipulation, and processing of information for control and management of agricultural production and marketing.

The most significant changes in future livestock

production resulting from information technology will come from the integration of computers

2

Embryos produced from body cells rather than reproductive

cells.
Ch. 1—Summary ● 7

and electronics into modern livestock production systems that will help make the farmer a

better manager. Animal identification, animal

reproduction, and disease control and prevention are some promising areas for information

technology in livestock production.

Electronic Animal Identification.—Positive

identification of animals is necessary in all

facets of management, including recordkeeping, individualized feed control, genetic improvement, and disease control. Research on

identification systems for animals has been in

progress for some years. Soon, all farm animals

will be “tagged” shortly after birth by an electronic device, called a transponder, that lasts

the life of the animal. For example, some dairy

cows now wear a transponder in the ear or on

a neck chain. A feed-dispensing device identifies the animal by the transponder’s signal and

provides an appropriate amount of feed for the

animal.

Reproduction. —The largest potential use of

electronic devices in livestock production will

be in the area of reproduction and genetic improvement. An inexpensive estrus detection device will allow: 1) animals to be rebred faster

after weaning; 2) animals that did not breed to

be culled from the herd, saving on feeding and

breeding space; 3) time to be saved because

breeding can be done faster; and 4) easier embryo transplants because of improved estrus detection.

Disease Control and Prevention.—Herd recordkeeping systems for animal health are already being developed and refined in the dairy,

swine, and poultry industries. These recordkeeping systems will eventually be linked with

the animal identification systems discussed

above. Examples of the types of information that

can be recorded for each animal include production records, feed consumption, vaccination

profiles, breeding records, conception dates,

number of offspring, listing and dates of diseases, and costs of medicines for treatment or

prevention of disease. Bringing all this information together will allow the veterinarian and

a manager of the livestock enterprise to analyze

quickly a health profile for each animal and to

plan for improved efficiency in disease control

programs.

Plant Agriculture

Pest Management.—Information technology

is already being used in plant agriculture for

the management of insects and mites. Design

improvements and availability of computer

hardware and software will produce marked

changes in insect and mite management.

Availability at the farm level of microcomputers, equipped with appropriate software and

having access to larger centralized databases,

will accelerate transfer of information and facilitate pest management decisionmaking. The

advantages, simply in terms of information storage and retrieval, will be of major importance.

The ready storage of and access to current and

historical information on pest biology, incidence, and abundance; pesticide use; cropping

histories; weather; and the like at the regional,

farm, and even field level will facilitate selection of the appropriate management unit and

the design and implementation of pest management strategies for that unit.

Current software has already greatly improved

the efficiency and accuracy with which pest

management decisions can be made and implemented. Much effort is being devoted to the

development of new software and the improvement of existing software. The resultant products, in conjunction with the rapid advances

being made in computer hardware, will provide

a powerful force that will lead to dramatic

changes in the implementation of integrated

pest management (1PM) and to increases in the

level of sophistication of 1PM.

Irrigation Control Systems .—Because irrigation decisions are complex and require relatively large amounts of information, a microcomputer-based irrigation monitoring and control

system is especially useful in areas with soils

having variable percolation and retention rates,

where rainfall is especially variable, or where

the salinity of irrigation water changes unpredictably. In this system, a network of sensors,

with radio links to the central processor, is
8 • Technology, Public Policy, and the Changing Structure of American Agriculture

buried in irrigated fields. Additional sensors

may include weather station sensors to estimate

crop stress and evaporation rates, salinity sensors, and runoff sensors. The central processor uses such information to allocate water automatically according to crop needs in each field,

subject to considerations of cost, leaching requirements, and availability of water.

Radar, Sensors, and Computers.—Through

the use of radar, sensors, and computers the correct amount of fertilizer, pesticides, and plant

growth regulators can be applied to plants by

integrating tractor slippage and chemical flow.

The correct rate of application of most agricultural chemicals is usually within a narrow range

for a given crop and field. However, application rates are often variable from area to area

within a field, owing to changes in the flow rate

of chemical slurries and to changes in tractor

wheel slip, grading, and drawbar tension. Economic and environmental costs are associated

with applications of too little or too much chemicals. Control of application rate depends on the

ability to estimate rate of flow through the chemical sprayer and on the vehicle’s speed over the

field. The speed indicated by sensors in the tractor drivetrain is usually greater than the actual

speed over the ground, owing to slippage of the

drive wheels. The amount of slippage can be

monitored by a doppler radar device that compares actual speed to indicated speed in the

drivetrain. When all this information is available, a computer can then adjust the spray line

pressure to deliver the correct amount of chemicals at varying speeds and amounts of wheel

slip.

THE CHANGING STRUCTURE OF AGRICULTURE

Agriculture is entering a new technological

era at a time when the character of agriculture

is changing rapidly. Emerging biotechnologies

and information technologies will be introduced

within a socioeconomic structure that has undergone considerable change in the last 50 years

and that promises to continue to change throughout the remainder of this century.

one of the best ways to look at changes in the

economic structure of U.S. agriculture is in

terms of value of production as measured by

gross sales per year. In this way farms can be

Table 1-1 .—Distribution of Farm Sizes, Percent of

Farm and Off-Farm Income per

usefully classified into five categories of gross

sales, as shown in table 1-1.

Small and part-time farms generally do not

provide a significant source of income to their

operators. Most of these farmers obtain their

primary net income from off-farm sources.

However, this segment is highly diverse. This

class of farms is operated either by subsistence

farmers or by individuals who use the farm as

either a tax shelter or a source of recreation.

Moderate-size farms cover the lower end of

the range in which the farm is large enough to

Cash Receipts, Percent of Farm Income, and

Farm by Sales Class, 1982

Percent Percent of Percent of Average Average Average

Value of farm Number of all total cash net farm net farm off-farm total

Sales class products sold of farms farms receipts income income income income

Small . . . . . . . . . <$20,000 1,355,344 60.6 5.5 –3.8 (615) 20,505 19,890

Part-time . . . . . . $20,000-$99,000 581,576 25.9 21.8 5.4 13,220 14,218

Moderate. . . . . . $100,000-$199,000 180,689 8.1 19.1 14.6 17,810 11,428 29,236

Large . . . . . . . . . $200,000”$499,000 93,891 4.2 21.0 20.4 48,095 12,634 60,929

Very large . . . . . > $500,000 27,800 1.2 32.5 63.5 504,632 24,317 529,149

All farms . . . . . 2,239,300 100 100 100 $9,976 $17,601 $27,578

SOURCE: Compiled from Economic Indicators of the Farm Sector: Income and Balance Sheet Stat/st/cs, 19S3, USDA Economic Research Service, 19S4, table 59, using

farm number and cash receipts distribution from the 1992 Census of Agr/cu/ture, U.S. Department of Commerce, Bureau of the Census, 19S4.
Ch. 1—Summary ● 9

be the primary source of income. However, most

families with farms in this range also rely on

off-farm income.

Large and very large farms include a diverse

range of farms. The great majority of these farms

are family owned and operated. Most require

one or more full-time operators, and many depend on hired labor full time. The degree of contracting (monitoring and controlling production

to produce a specified quantity of homogeneous products for a buyer) and vertical integration is much higher in this class.

To appreciate how agriculture has changed

just between 1969 and 1982, consider the following:

●

●

●

The number of small farms declined 39 percent, while the number of very large farms

increased by 100 percent.

The share of cash receipts from very large

farms increased slightly, from 29 to 33 percent, while cash receipts declined from 40

to 25 percent for small and part-time farms.

The share of net farm income declined significantly (from 36 to 5 percent) for small

and part-time farms, and increased from

36 to 64 percent for very large farms.

These trends indicate that small and part-time

farms no longer can depend on the farm to provide an adequate income. Large-scale farms

dominate agriculture. Moderate-size farms have

a small share of the market and a stagnant share

of net farm income. The agricultural sector can

be described as a bipolar, or dual sector: As the

moderate-size farm disappears, it leaves small

and part-time farms clustered at one end of the

farming spectrum and large farms clustered at

the other, in terms of their importance to agriculture.

If present trends continue to the end of this

century, the total number of farms will continue

to decline from 2.2 million in 1982 to 1.2 million in 2000 (table 1-2). The number of small and

part-time farms will continue to decline, but will

still make up about 80 percent of total farms.

The large and very large farms will increase substantially in number. Approximately 50,000 of

these largest farms will account for 75 percent

of the agricultural production by year 2000. The

trend toward concentration of agricultural resources into fewer but larger farms will continue, although the degree of concentration will

vary by region and commodity.

Moderate-size farms will decline in number

and in proportion of total farms, have a small

share of the market and a declining share of net

farm income. These farms comprise most of the

farms that depend on agriculture for the majority of their income. Traditionally, the moderate-size farm has been viewed as the backbone

of American agriculture. These farms are failing in their efforts to compete for their historical share of farm income.

Table 1-2.—Most Likely Projection of Total Number of U.S. Farms

in Year 2000, by Sales Class

1982 2000

Number Number

of farms Percent of of farms Percent of

Sales class (thousands) all farms (thousands) all farms

Small and part-time. . . . . . . . . 1,936.9 86.0 1,000.2 80.0

Moderate. . . . . . . . . . . . . . . . . . 180.7 10.0 75.0 6.0

Large and very large . . . . . . . . 121.7 4.0 175.0 14.0

Total . . . . . . . . . . . . . . . . . . . 2,239.3 100.0 1,250.2 100.0

SOURCE: Office of Technology Assessment.
10 . Technology, Public Policy, and the Changing structure of American Agriculture

MAJOR FINDINGS

Emerging Technologies and Future

Agricultural Production

Like the eras that preceded it, the biotechnology and information technology era will bring

technologies that can significantly increase agricultural yields. The immediate impacts of these

technologies will be felt first in animal production. Through embryo transfers, gene insertion,

growth hormones, and other genetic engineering techniques, dairy cows will produce more

milk per cow, and cattle, swine, sheep, and poultry will produce more meat per pound of feed.

Impacts on plant production will take longer,

almost the remainder of the century. By that

time, however, technical advances will allow

some major crops to be altered genetically for

disease and insect resistance, higher production of protein, and self-production of fertilizer

and herbicide.

In both plant and animal production, information technologies will be widely used on farms

to increase management efficiency. Introducing to the marketplace these and the rest of the

150 emerging technologies forecasted in this

study raises questions about the effects these

technologies will have on crop yield, livestock

feed efficiency, reproductive efficiency, and future food production.

Many people are concerned that the trends

of major crop yields are leveling off and that

the world may not be able to continue to produce enough food to meet the demand of a growing population. OTA analyses indicate that the

emerging technologies, if fully adopted, will produce significant beneficial impacts on the performance of plant and animal agriculture. The

most dramatic impacts will be felt first in the

dairy industry, where new genetically engineered pharmaceuticals (such as bovine growth

hormone and feed additives) and information

management systems will soon be introduced

commercially. New technologies adopted by the

dairy industry will increase milk production far

beyond the 2.6-percent annual growth rate of

the past 20 years (table 1-3). Under OTA’s most

Table 1=3.—lmpact of Emerging Technology on Animal

Production Efficiency in Year 2000

Most Annual

Actual likely growth ratea

1982 2000 (percent)

Beef:

Pounds meat per lb feed . . . . 0.07 0.072 0.2

Calves per cow . . . . . . . . . . . . . 0.88 1.000 0.7

Dairy:

Pounds milk per lb feed . . . . . 0.99 1.03 0.2

Milk per cow per year

(1,000 lb). . . . . . . . . . . . . . . . . 12.30 24.70 3.9

Poultry:

Pounds meat per lb feed . . . . 0.40 0.57 2.0

Eggs per layer per year. . . . . . 243.00 275.00 0.7

Swine:

Pounds meat per lb feed . . . . 0.157 0.176 0.6

Pigs per sow per year . . . . . . . 14.400 17.400 1.1

asorne of tflese figures differ from those in table 2-2 of the firSt rePOrf from this

study, because actual 1982 figures were preliminary.

SOURCE: Office of Technology Assessment.

likely conditions, milk production per cow is

expected to increase from the 12,000 pounds

in 1982 to at least 24,000 pounds by 2000, an

annual growth rate of 3.9 percent. Applications

of new technologies also will increase the feed

and reproductive efficiency of other farm animals.

Because development of biotechnology for

plant agriculture is lagging behind that for

animal agriculture, equally significant impacts

from biotechnology will not be felt in plant agriculture before the turn of the century. Development and adoption of the new technologies under the most likely conditions will, in the short

run, increase the rates of growth of major crop

yields at about the level of historical rates of

growth (table 1-4). However, the impacts of these

technologies will be substantially greater for

plant agriculture after 2000.

Any conclusion about the balance of global

supply and demand requires many assumptions

about the quantity and quality of resources available to agriculture in the future. Land, water,

and technology will be the limiting factors as

far as agriculture’s future productivity is concerned.

Agricultural land that does not require irrigation is becoming an increasingly limited re-
Ch. 1—Surnrnary ● 7 1

Table l-4.—lmpact of Emerging Technology on

Crop Yields in Year 2000

Annual

Actual Most likely growth ratea

1982 2000 (percent)

Corn—bu/acre . . . . . . . 113 139 1.2

Cotton—lb/acre . . . . . . 481 554 0.7

Rice—bu/acre. . . . . . . . 105 124 0.9

Soy bean—bu/acre . . . . 30 37 1.2

Wheat—bu/acre . . . . . . 36 45 1,3

asome of these figures differ from those in table 2.2 of the first rOPod from this

study, because actual 1982 figures were preliminary.

SOURCE: Office of Technology Assessment

source. In the next 20 years, out of a predicted

1.8 percent annual increase in production to

meet world demand, only 0.3 percent will come

from an increase in the quantity of land used

in production. The other 1.5 percent will have

to come from increases in yields-mainly from

new technology. Thus, to a very large extent,

research that produces new technologies will

determine the future world supply/demand balance and the amount of pressure placed on the

world’s limited resources.

Table 1-5 shows the projections to year 2000

of increased production for some of the major

U.S. commodities, based on the above yield projections, land availability, world demand, public

policy, and other factors. OTA analyses indicate that with continuous inflow of new technologies into the agricultural production system, U.S. agriculture will be able not only to

meet domestic demand, but also to contribute

significantly to meeting world demand in the

next 20 years. This does not necessarily mean

that the United States will be competitive or have

the economic incentive to produce. It means

only that the United States will have the technology available to provide the production increases needed to export products for the rest

of this century.

Under the most likely environments the aggregate growth rate in production of these commodities, which includes inputs of additional

land resources and new technology, will be adequate to meet the 1.8 percent growth rate needed

to balance world supply and demand in 2000.

Under the more-new-technology environment,4

production could increase at 2 percent per year,

which would be more than enough to meet world

demand. This increased production could, however, point to a future of surplus production.

On the other hand, under the less-new-technology environments the production of major crops

in 2000 would drop to 1.6 percent per year, a

growth rate that would not allow the United

States to meet world demand.

3Assumes to year ZOOO: I ) a real rate of growth in research and

extension expenditures of 2 percent per year, and 2) the continuation of all other forces that have shaped past development and

adoption of technology.

4Assumes to year 2000: 1) a real rate of growth in research and

extension expenditures of 4 percent, and 2) aIl other factors more

favorable than those of the most likely environment.

‘Assumes to year 2000: 1) no real rate of growth in research

and extension expenditures, and 2) all other factors less favorable than those of the most likely environment,

Table 1.5.—Projections of Major Crop Production

2000

No-new-technology Most likely More-new-technology

Crop Unit 1984 environment environment environment

Corn:

Production . . . . . . . Billion bu 7.7 8.6 9.3 9.7

Growth rate. . . . . . . Percent 0.7 1,2 1.5

Soybean:

Production . . . . . . . Billion bu 1.9 3.0 3.2 3.3

Growth rate. . . . . . . Percent 3.1 3.4 3.6

Wheat:

Production . . . . . . . Billion bu 2.6 3.3 3.5 3.5

Growth rate. . . . . . . Percent 1.5 1.9 2.0

aThO projections shown in this table differ from those in table 2-3 of the first report from this study, because the Previous

figures were preliminary,

SOURCE: Office of Technology Assessment,
12 ● Technology, Public Policy, and the Changing Structure of American Agriculture

Emerging Technologies and the

Future Structure of Agriculture

New technologies have historically had significant impacts on structural change. New disease control technologies gave poultry and livestock farmers unprecedented opportunities to

specialize and vertically integrate. Improvements in farm machinery fostered large-scale,

specialized farm units.

Like their predecessors, the emerging technologies examined in this study will make a considerable impact on farm structure, especially

by 2000. Biotechnologies will have the greatest

impact because they will enable agricultural

production to become more centralized and vertically integrated. Although in the long run the

use of new technologies will not increase the

farmer’s overall need for capital, there will be

trade-offs: biotechnology will require less capital; information technology will require more.

The new technologies will allow increased

control over end-product characteristics, for example less fat per unit of lean in meat animals

or a specific color characteristic in corn. This

implies that increased homogeneity within an

agricultural product may result and that there

will be a growing number of end products with

engineered characteristics. This would require

less sorting or grading to achieve increased

homogeneity and a shift toward having more

control over the production process so as to

achieve homogeneity during production.

An anticipated economic consequence of this

increased control over production is an increase

in the practice of contracting. Contracting allows husbandry and cultural practices to be

monitored and controlled closely during the production process. This greater process control

leads to uniform product differentiation.

Biotechnologies will have relatively more important effects on resource concentration than

will other technological developments. Even

though mechanical technologies will continue

to be important, they are not expected to have

as important an impact on future structure. In

particular, biotechnologies are expected to encourage closer coordination and greater process control in livestock production, permitting

more contract livestock production. One example is the potential from these technologies for

modifying milk at the farm rather than at the

processing plant. This technology holds promise for producing more highly unsaturated fats

in milk. If adopted, it would entail close coordination at the producer/first-handler markets and

additional process control at the production

level.

The biological technologies will encourage

coordination in crop production, as well. However, the magnitude of change in this area is Z

expected to be relatively less for crops than livestock. Part of the reason is that biotechnologies

for livestock production are further advanced.

The biotechnology era is expected to encourage

closer vertical coordination, with a slight reduction in market access as a consequence. This

situation would subsequently lead to fewer but

larger farms.

The information technologies are expected

to reduce barriers to entry and to increase market access without any significant change in vertical coordination or control at the producer/

first-handler level-especially for crop agriculture. Information technologies hold the potential for significantly increasing the amount of

information across markets. This impact would

be attributable to improved communication of

buyers’ needs to production-level managers,

which should result in more equality between

buyers and sellers.

The largest farms are expected to adopt the

greatest amount of the new technologies. Generally, 70 percent or more of the largest farms

are expected to adopt some of the biotechnologies and information technologies. This contrasts with only 40 percent for moderate-size

farms and about 10 percent for the small farms.

The economic advantages from the technologies

are expected to accrue to early adopters, a large

proportion of which will probably be operators

of large farms.

Impacts of Agricultural

Finance and Credit

The severe financial stress of a large proportion of farmers and the recent regulatory and
Ch. 1—Summary ● 1 3

competitive changes in financial markets have

combined to change significantly the financial

framework of farming. The farm of the future

will be treated financially like any other business—it will have to demonstrate profitability

before a bank will finance its operation. Managing a farm efficiently and profitably, which

will necessitate keeping up-to-date technologically, will be the key to access to credit.

The cost of credit, however, will be higher and

more volatile. Interest on loans may be variable rather than fixed. Moreover, given the concentration in the banking industry, decisions

about extending credit more likely will be made

at large, centralized banking headquarters far

removed from a loan applicant’s farm. Loan decisions will thus be less influenced by the considerations of neighborly good will that frequently shaded decisions of local farm banks.

Congress will have to consider all these factors because the availability of capital will continue to be an important factor in agricultural

production in general and in the adoption of

agricultural technologies in particular. Readily available capital at reasonable rates and

terms, plus technologies that aid profitability,

provide a favorable environment for technology adoption. Emerging technologies, for the

most part, will pass the test for economic feasibility.

The financing consequences of new technologies in agricultural production will probably

depend on the relationships between three important factors: 1) the financing characteristics

of the new technologies, 2) the creditworthiness

of individual borrowers, and 3) the changing

forces in financial markets that affect the cost

and availability of financial capital. The financing characteristics suggest that most of the new

technologies should be financed largely with

short- and intermediate-term loans that are part

of the normal financing procedures for agricultural businesses. However, the technical characteristics of the technologies, together with the

factors constituting the creditworthiness of individual borrowers, suggest that increased emphasis in credit evaluations will be placed on

the farmers’ management capacity, on their ability to demonstrate appropriate technical competence in using the new technologies, and on

building human capital, where appropriate. In

some cases—particularly for Farmers Home Administration borrowers—significant investments inhuman capital, with related financing

requirements, may accompany new technology

adoption. This is consistent with the more conservative responses by lenders to the agricultural stress conditions of the early 1980s. Lending institutions themselves, in turn, must have

sufficient technical knowledge and expertise to

evaluate these management and credit factors

along with other sources of business and financial risks in agriculture. Finally, some forms of

new technology involving large investments and

having long-run uncertain returns will probably

rely more on equity capital for financing.

The changing regulatory and competitive

forces in financial markets, including the preference for greater privatization of some credit institutions, means that the cost of borrowing for

agricultural producers will likely remain higher

and more volatile than before 1980 times and

will follow market interest rates much more

closely. Similarly, the continued geographic liberalization of banking and the emergence of

more complex financial systems mean that the

functions of marketing financial services, loan

servicing, and credit decisions will become

more distinct, with an increasing proportion of

credit control and loan authority occurring subregionally and with regional money centers being located away from the rural areas. This will

continue to fragment and dichotomize the farmcredit market so that commercial-scale agricultural borrowers will be treated as part of a financial institution’s commercial lending activities and small, part-time farmers will be treated

as part of consumer lending programs.

The competitive pressures on financial institutions and the risks involved will bring more

emphasis on analyzing the profitability y of various banking functions, including loan performance at the department level and individual customer level. Innovative lenders will strive more

vigorously to differentiate their loan products

and financial services, especially for more profitable borrowers, and will tailor financing programs more precisely to the specific needs of
14 . Technology, Public Policy, and the Changing Structure of American Agriculture

creditworthy borrowers. In turn, however, to

compete for credit services these agricultural

borrowers must be highly skilled in the technical aspects of agricultural production and marketing as well as in financial accounting, financial management, and risk analysis.

In general, most forms of new technology in

agricultural production should meet the tests

of both economic and financial feasibility, although the structural characteristics of the

adopting farm units will continue to evolve in

response to managerial, economic, and market

factors. The structural consequences of these

factors are severalfold:

1.

2.

3.

4.

5.

6.

a continuing push toward larger commercial-scale farm businesses, with greater skills

in all aspects of business management;

continuing evolution in the methods of entry into agriculture by young or new farmers, with greater emphasis on management

skills and resource control and less emphasis on land ownership;

the continuing development of a marketing systems approach toward financing

agriculture, with more sophisticated skills

in marketing analysis by farmers and higher

degrees of coordination with commodity

and resource markets;

more formal management of financial leverage and credit by farmers, with greater

diversity of funding sources by farmers and

better developed markets for obtaining outside equity capital;

further development in financial leasing

and greater stability in leasing arrangements for real estate and other assets; and

more complex business arrangements in

production agriculture that accommodate

various ways to package effectively debt

and equity financing, leasing, management,

accounting, and legal services for the future farm business.

Emerging Technologies, Policy a n d

Survival of Various Size Farms

The size and, therefore, the survival of farms

is affected by several factors. Clearly, there are

economies of size in many commodity areas

covered by farm policy. These economies motivate further concentration of resources. In addition, present farm policy, more than any other

policy tool, makes major impacts on farm size

and survival. Although very large farms can survive without these programs, moderate-size

farms depend on them for their survival.

This study finds that substantial economies

of size exist for several major commodities (table

1-6). The commodities include dairy, corn, cotton, wheat, and soybeans. With the exception

of corn, economies of size do not exist uniformly

in all the production areas studied for these commodities. Table 1-6 shows the areas in which

economies of size do exist. It should be noted

that the analysis considered only technical economies of size. If it had also included pecuniary

economies, additional production areas would

have been found to have economies of size.

Table 1-6 also shows commodities in which

there will be significant gains in yield based on

emerging technologies. All of the commodity

areas except rice will experience substantial

gains in yield as well as significant economies

of size. (No economies of size were found for

Table 1.6.—Comparison of Commodities With Current

Economies of Size and Future Technological Gains

Greatest yield increases

Current economies of size for the future

(in descending order) (in descending order)

Dairy Dairy

Arizona

California Wheat

New Mexico

Corn Soybeans

Illinois

Indiana Corn

Iowa

Nebraska Rice

Cotton

Alabama Cotton

Texas

Wheat

Kansas

Montana

Soybeans

Iowa

SOURCE Office of Technology Assessment,
Ch. 1—Summary Ž 15

rice.) Dairy, in particular, leads all commodities in economies of size and production increases from new technologies. These forces

will combine to shift overtime the comparative

advantage in dairy production from the smaller

dairies in the Great Lake States and Northeast

to the larger dairies in the Southwest and West.

Overall, the combination of future yield increases from new technology and current economies of size in these commodities means that

there will be substantial incentives for farms

to grow in size. These powerful forces will continue, and may even speed up resource concentration in U.S. agriculture.

This study finds that farm programs, which

include Commodity Credit Corporation (CCC)

purchases and price and income supports, have

major impacts on rates of growth in farm size,

wealth, and incomes of commercial farmers.

Large farms increase their net worth significantly more than moderate-size farms under

current farm programs and large farms account

for a significantly large share of farm program

payments. In particular, price supports provide

most of the wealth and growth benefits to large

farms.

Removing farm programs reduces the probability of survival more for moderate-size farms

than for large farms. OTA’s analyses find that

large farms can survive and prosper without

farm programs. And, because these farms account for the vast majority of farm program benefits, significant savings in Government expenditures could be realized if large farms were

ineligible to receive program payments.

On the other hand, this study finds that moderate farms need farm programs to survive and

be successful. Income supports, in particular,

provide significant benefits to moderate farms,

and the targeting of income supports to moderate farms is an effective policy tool for prolonging these farms’ survival.

Those changes in tax policy that would be

more restrictive have little impact on farm survival. Increasing the Federal tax burden on

farmers reduces the average annual rate of

growth in farm size uniformly for all farm sizes.

Currently the financial position of many

farmers is under severe stress. The situation is

serious and may not improve for some time.

Two alternatives most discussed by policymakers are interest subsidy and debt restructuring programs. OTA finds that restructuring

debt for highly leveraged farms does not appreciably increase their probability for survival.

The interest rate subsidy substantially increases

average net income more than debt restructuring. It is the more effective strategy to ease financial stress. In addition, large farms with high

debts are not as dependent on these financial

programs for survival as moderate farms are.

impacts on the Environment and

Natural Resources

In general, with a few notable exceptions,

most emerging technologies are expected to reduce substantially the land and water requirements for meeting future agricultural needs.

Consequently, these technologies are expected

to reduce certain environmental problems associated with the use of land and water. The technologies are thought to have beneficial effects

relative to soil erosion, to reduce threats to wildlife habitat, and to reduce dangers associated

with the use of agricultural chemicals. New tillage technologies, however, may reduce erosion

and threats to wildlife while increasing the

dangers from the use of agricultural chemicals.

The new technologies are most likely to receive first adoption by farmers who are well

financed and are capable of providing the sophisticated management required to make profitable use of the technologies. Most of these

farmers will be associated with relatively large

operations. Hence, the technologies will tend

to give additional economic advantages to large

farm firms relative to moderate and smaller

farms, accentuating the trend toward a dual

farm structure in the United States.

In addition, since many of the new technologies tend to be environmentally enhancing,

public interest exists in research and education

that can lead to the rapid development and widespread adoption of the technologies. That con-
16 • Technology, Public Policy, and the Changing Structure of American Agriculture

elusion becomes even stronger if public policy

is aimed at maintenance of the moderate-size

farm. Larger farms, with their own access to

research results and scientific expertise, may

be able to advance the new technologies with

relatively little publicly sponsored research. But

moderate and small farms will have to depend

on publicly sponsored research and extension

education to gain access to the new technologies

and to adapt them to their individual needs.

The new technologies will entail more stringent environmental regulations and stronger enforcement of regulations than at present. The

complexities of some of the emerging technologies will pose significant challenges for those

promulgating wise environmental regulations.

The economic benefits of the technologies will

be inviting, but users may have little incentive

to use the technologies in ways that avoid unnecessary, adverse, third-party effects. Economic incentives or disincentives, including the

use of excise taxes to discourage overuse of

potentially threatening materials, represent a

promising approach to the protection of environmental values than do direct regulation. Additional efforts to enforce existing regulations

would hasten the adoption of the new technologies that seem less environmentally threatening. New regulations will be required, however,

for dealing with some aspects of the emerging

technologies.

Perhaps the most revolutionary of the new

technologies are those associated with rDNA.

While the specific applications of such technologies appear likely to reduce resource needs and

threats to the environment that arise from agricultural activities, dangers may accompany the

deliberate release of genetically altered microorganisms. The revolutionary nature of the new

biotechnologies and the lack of a scientifically

accepted predictive ecology prevent specific

evaluation of resource/environmental impacts

associated with the deliberate release of new

forms of life at this time.

Many scientists see little danger in the applications of rDNA technology in laboratory experiments. The proponents of biotechnology argue that genetic engineering has been used in

plant breeding and animal husbandry for centuries and that genetically engineered microorganisms are no more dangerous than microorganisms already in commercial use or that

might be used in nature. However, the opponents of deliberate release argue that the new

products of genetic engineering are different

from the old ones. Scientists do not know how

these new micro-organisms will behave in the

environment and fear adverse consequences to

the ecosystem. Both sides agree that more research should be conducted to assess the potential benefits and risks. Recently, the Environmental Protection Agency approved the first

two field tests of genetically altered organisms.

Impacts on Rural communities

The impacts of technological and structural

change in agriculture do not end with the individuals who live and work on farms. A variety

of additional consequences are expected at the

level of rural communities, consequences that

directly or indirectly affect farms and farmers.

As with individual farmers, some communities

are likely to benefit from change, while others

are likely to be affected adversely. Much depends on the type of overall labor force in the

community and on the opportunities for labor

to move to other employment areas.

Hard-hit communities may need technical

assistance to attract new businesses to their

areas, to develop labor retraining programs, and

to alter community infrastructure to attract new

inhabitants. To accomplish these goals, Federal

policy will have to be complemented by regional

and local policies.

Those rural communities that benefit from

changes in agricultural technology and structure may do so in several ways. For example,

as agriculture becomes more concentrated,

some communities will emerge as areawide

centers for the provision of new, high-value technical services and products. Likewise, some

communities will emerge as centers for highvolume food packaging, processing, and distribution. In both cases, the economic base of these

communities is likely to expand. However, un-
Ch. l—Summary . 17

less total demand for agricultural commodities

increases substantially, centralization of services, marketing, and processing will be like a

zero-sum game in many areas. The market centers will benefit at the expense of other communities. Many of the communities that are bypassed will decline as a result of the process of

centralization.

Communities also may benefit in those parts

of the country in which the number of small and

part-time farms is increasing. This phenomenon results in an increase in population in many

rural areas and an increase in total income and

spending in some of these areas. The increase

in small farms may sustain additional retail establishments than would otherwise be the case,

since purchases by small farmers may tend to

be more from local sources than those by larger

farmers. The operators of these farms in many

cases subsidize their own production from offfarm income.

A wide range of diversity is evident in the

character, agricultural structure, patterns of

change, and patterns of impact on rural communities in the five different regions of the

United States studied for this report:

1. the CATF (California, Arizona, Texas, and

Florida) region;

2. the South;

3. the Northeast;

4. the Midwest; and

5. the Great Plains and the West.

A clear picture of adverse relationships between agricultural structure and the welfare of

rural communities is evident in the industrialagricultural counties of the CATF region. Largescale and very large-scale industrialized agriculture in these communities is strongly associated with high rates of poverty, substandard

housing, and exploitative labor practices in the

rural communities that provide hired labor for

these farms. Very large-scale agriculture has

been a strong source of employment in the CATF

region for many years, although at very low

wage rates. Emerging technologies may reduce

the labor requirements throughout much of the

CATF region by 2000. Increased unemployment

will greatly increase the strain on these communities. A potential exists for the CATF region to increase its share of national agricultural

production, which would mitigate the trend

toward increasing unemployment. However, increased agricultural production in this region

will tend to be constrained by the cost of irrigation water and the need to control environmental impacts.

The coastal zone of the South also has a substantial potential for structural change similar

to that of the CATF region. Topography and climate favor large-scale, labor-intensive production of fruits, vegetables, and dairy products.

The area also has a segmented, relatively unskilled labor force that could provide a source

of low-cost labor similar to that of the CATF

region. It is difficult to generalize about the rest

of the South, owing to the diversity of agricultural structure and production. Evidence exists

of a relatively strong association between rates

of unemployment and agricultural structure.

Unemployment rates tend to be lowest in counties with a predominance of moderate farms.

In the Northeast, dairy products are the single

most important agricultural commodity group.

Because dairy farms are likely to experience

widespread failure as a consequence of the combination of technological change and public policies, the structure of agriculture in the Northeast is likely to change substantially during the

next 10 to 15 years. However, rural communities in the Northeast have a low overall dependence on income from agriculture. Most productive agricultural counties in the Northeast

are adjacent to metropolitan areas where greater

employment opportunities and services are

available. The most rural counties sometimes

are not the most agricultural. Therefore, rural

communities in the Northeast generally are not

likely to experience adverse consequences from

structural change, with the exception of a few

localities with especially high dependence on

dairy production.

No clear-cut evidence exists that rural communities in the Midwest were adversely affected

by structural change during the 1970s. In general, alternative sources of employment in the

manufacturing and service sectors were rela-
18 • Technology, public Po/icy, and the Changing Structure of American Agriculture

tively prevalent and are expected to continue

to be relatively good in the Midwest. Indicators

of social welfare, in general, tended to improve

as farm structure moved from small and parttime farms toward moderate to large farms during the 1970s. However, there was a tendency

for population to decline in counties where the

share of part-ownership of farms increased. As

with the Northeast region, there is a reasonable

expectation that technological change in the

dairy industry will result in a mass exodus of

small to moderate dairy farms during the next

5 to 15 years. Rural communities in dairy counties may not be adversely affected because offfarm employment is quite high in these counties. Those mixed agricultural counties on the

western edge of the Midwest that are relatively

dependent on agriculture are the most likely to

suffer adverse consequences from structural

change. If the percent of part-ownership increases as agriculture becomes more concentrated, population, median income, and retail

sales may decline in these counties.

Strong potential exists for development of a

high concentration of agricultural production

in the Great Plains and the West, especially in

terms of farm size, if not gross sales per farm.

In turn, the number and percent of hired managers in this region is likely to increase. Unlike

the South, there is a low potential for development of an industrialized agriculture with large

numbers of hired field workers. The most likely

adverse impact will be the loss of population

and small retail firms in the region. In general,

fewer alternate employment options will be

likely in manufacturing and the service industries in this region than in the other regions of

the country.

This study shows clearly that policies designed to prevent or ameliorate adverse impacts

and promote beneficial impacts need to be crafted

with consideration for regional structural/technological differences. Generalizing about the

impacts of changing agricultural technology

and structure on rural communities across regions of the United States is difficult.

Impacts on Agricultural Research

and Extension

U.S. agriculture has been very successful to

an important extent because of technological

advances. However, agriculture’s adoption of biotechnology and information technology raises

several questions about the impact of technical

advances on the performance of the research

and extension system and about how that performance will ultimately affect the structure of

agriculture.

Public research in the past was the driving

force for agricultural production. Now, with the

private sector becoming more involved in certain aspects of applied research, the public sector is emphasizing increased basic research.

This situation leaves open the question of who

will do applied research in the public sector.

Although the public sector has allocated resources to research in biotechnology and information technology, extension has done little

to make information about these technologies

available to farmers. The extension service must

thus decide what its mission will be, for extension policy will determine how effective moderate farm operators will be in gaining access

to new technology. Without such access moderate-size farms will disappear even faster.

Consideration of specific changes in research

and extension policy may be justified. The following areas have been identified as meriting

consideration for policy changes:

●

●

The social contract on which the agricultural research and extension system was created needs reevaluation. This issue should

not be left for resolution by the courts. Specific guidelines must be developed that allow the system to compete while protecting the public interest and investment in

the agricultural research and extension

functions. Both Congress and the U.S. Department of Agriculture (USDA) should

have a voice in this type of policy development.

Some experts believe that increased private

sector support for agricultural research sig-
Ch. l—Summary ● 1 9

nals less need for public support. Even

though private sector support complements

public support, basic biotechnology and

information technology research is very

costly. A reduced role for public research

and extension would result in a slower rate

of technological progress and a lower level

of protection for the public. In addition, the

public has a strong interest in maintaining

an agricultural research component in each

State to serve the problem-solving needs of

that State’s agriculture.

● Many agricultural problems are local or regional in scope. The applied nature of the

system, having an agricultural experiment

station and extension service in each State,

has provided a unique capacity to identify

and solve local or regional problems. Reality suggests that only certain universities

have sufficient resources to compete for private sector support in biotechnology and

information technology. The result is a confluence of forces that is creating a dichotomy of “have” and “have not” universities.

There is, however, still an important role

for even the smallest, poorest funded landgrant university. It plays an important part

in a national system designed to deal with

thousands of agro-ecosystems and to the

existence of a decentralized system with

nationwide capability. Because of these inequalities, there is concern that the traditional extension-research interaction and

feedback mechanisms could break down,

particularly in States that are not in a position to command a major biotechnology

component.

● The role of extension is even more important than it has been in the past. New, more

complex products require evaluation and

explanation. In States where experiment

stations have attracted substantial private

sector support, the product testing function

can be most objectively performed by extension. The recently passed 1985 farm bill

gives explicit authority for extension to engage in applied research functions such as

product testing and evaluation.

● While most agricultural research is not in-

●

●

herently biased toward large-scale farms,

lags in adoption by small and moderate

farms have the effect of such a bias. Unless

special attention is given to technology generation and transfer to moderate farms, major structural changes could result, leading

to the eventual demise of a decentralized

structure that includes moderate farms. To

the extent that preservation of these farms

is a policy objective, special funding for and

emphasis on the problems of technology

generation and the transfer of that technology to moderate farms is warranted.

Although the agricultural research system

has received the benefits of increased funding from both private and public sources,

extension funding has not materially increased. As a result, extension staff at the

county and specialist levels are being caught

up in a whirlwind of technological change.

The result is a need for the injection of substantial staff development funding into the

extension system.

Basic organizational issues must be addressed by the Extension Service. The premise on which extension was developed was

that of research scientists conveying the

knowledge of discoveries to the extension

specialist who, in turn, supplied information to the county agent who then taught

the farmer. Over time, this concept has

gradually but persistently broken down as

agricultural technology has become more

complex and insufficient resources have

been devoted to staff development. Consequently, more emphasis has been placed

on direct specialist-to-farmer education.

More specialists have been placed in the

field to be closer to their clientele, but at

the cost of less contact with research scientists. As these changes have occurred, the

role of the county agent has become increasingly unclear. Appreciation for and use of

county agents as educators and technology

transfer agents has declined. As a result of

these changes, a basic structural reevaluation of the organization of the extension

function of the agricultural research system is needed.
20 ● Technology, Public Policy, and the Changing Structure of American Agriculture

IMPLICATIONS AND POLICY OPTIONS FOR AGRICULTURE

The Issue of Farm Structure

This study indicates that the process of structural change in agriculture has already begun.

Based on a continuation of current policies, past

trends, and future technological expectations,

the net result of this structural change could be

the development of a farm structure composed

of three agricultural classes:

1.

2.

The large-scale farm segment would be

composed of a relatively small number of

farms that produce the bulk of U.S. production. By year 2000 there could be as few as

50,000 large-scale farms producing as much

as three-fourths of the agricultural production. This large-scale farm segment would

be highly efficient in the performance of

production, marketing, financial, and business management functions. Such farms

would be run by full-time, highly educated

business managers. Barring unforeseen

acts of nature, farm operators would be able

to predict their chances of making a profit

before planting or breeding.

The struggling moderate-size farm segment

would be trying to find a niche in the market and survive in an industrialized agricultural setting. The potential for the moderate farm finding that niche is rapidly

becoming the center of the farm policy debate. Traditionally highly productive, efficient, moderate-size, full-time farms have

been the backbone of American agriculture.

It is still true that a moderate, technologically up-to-date, and well-managed farm

with good yields is highly resilient. One key

to the success of these farms clearly lies in

the management factor. But more often

than not, management has to be willing to

accept a relatively low return on invested

capital, time, and effort. With ever-increasing educational requirements associated

with farming, there will likely be less willingness by successful managers of moderate farms to accept a lower return for their

services and for invested capital. Another

key to the survival of moderate farms lies

in access to state-of-the-art technologies at

competitive prices. Cooperatives traditionally have performed that role. But cooperatives by and large are not conducting or

funding basic or applied research in biotechnology and information technology.

Also, like their predominantly moderatesize farmer members, cooperatives, too,

have encountered financial difficulty.

3. The small, predominantly part-time farm

segment tends to obtain most of its net income from off-farm sources. However, this

segment is highly diverse. It includes

wealthy urban investors and professionals

who use agriculture primarily as a tax shelter and/or country home. It also includes

would-be moderate farm operators who are

attempting to use off-farm income as a

means of entering agriculture on a full-time

basis. Finally, this segment includes a number of poor, essentially subsistence, farmers

who are vestiges of the war on poverty in

the 1960s. Such farmers remain a significant social concern that must be dealt with

from a policy perspective, although traditional farm price and income policy hold

no hope for solving their problems.

Contemporary farm programs have fostered

this trend toward three farm-size classes. payments to farmers on a per-unit-of-production

basis concentrate most of the benefits in large

farms that produce most of the output. Large

farms have been in the best position to take

advantage of new technologies arising out of

the public sector agricultural research system.

Without substantial changes in the nature and

objectives of farm policy, the three classes of

farms will soon become two-the moderate-size

farm will largely be eliminated as a viable force

in American agriculture. In addition, the problems of the small subsistence farm will continue

to fester as an unaddressed social concern.

This section sets forth the policy changes that

would be required if it were decided by Congress that overt steps should be taken to foster

a diverse, decentralized structure of farming

where all sizes of farms had an opportunity to
Ch. l—Summary . 21

compete and survive in a time of rapidly changing technology. The objective of giving every

farm the opportunity to compete and survive

does not imply an unchanging and stagnant

farm structure. It does imply a political and social sensitivity y both to the impact of current farm

programs on farm structure and to the different needs of large, moderate, and small farms

for Government assistance. It can be expected

that regardless of what Government does fewer

commercial farms will exist in year 2000. However, Government can do much to ease the pain

of adjustment.

Required Policy Adjustments

Substantive changes in policy direction are

needed to address the structure issue. Specifically, separate policies and programs must be

pursued with respect to each of the three farm

segments—large farms, moderate farms, and

small farms. The choice of any one set of policies to the exclusion of the other policy sets

would imply that Congress desired to selectively

enhance the status of one farm segment.

Policy for all farmers implies two basic policy

●

●

goals:

All farmers need to operate in a relatively

stable economic environment where they

have an opportunity to sell what they

produce.

All farmers need a base of public research

and extension support whereby they can

maintain their competitiveness in the markets in which they deal.

The needs of large farms can be met by addressing just these goals. The needs of moderate and small farms are more complex, however. Policy to address the needs of moderate

and small farms must include the elements of

large farm policy as well as additional elements,

Policy for Large Commercial Farms

A basic conclusion of this study is that largescale farmers do not need direct Government

payments and/or subsidies to compete and survive. However, this does not preclude the need

for a commercial farm policy.

The criteria for determining what constitutes

a large-scale farm is important but also somewhat arbitrary. The dividing line developed

from this study is about $250,000 in sales for

a crop or dairy farm unit under single ownership or control. This level of sales is generally

required to achieve most of the economies of

size found to exist in agricultural production.6

Over time, this optimum size has had, and will

continue to have, a tendency to increase. As this

occurs, the farm size criteria for limiting program benefits would likewise have to increase.

Creating a Stable Economic Environment.—

The policy goal of creating a relatively stable

economic environment where farmers have an

opportunity to sell what they Produce implies

the following major farm program initiatives:

●

●

●

●

Direct Government payments to all farms

having over $250,000 in sales would be

eliminated. This implies the elimination of

the target-price concept for this sales class.

Elimination of payments to those farms

would significantly reduce Government expenditures in agriculture.

The nonrecourse loan would be converted

to a recourse loan. The nonrecourse feature has resulted in the accumulation of

large Government commodity stocks. The

recourse feature would provide a continuing base of support for the orderly marketing of farm products.

Aside from the recourse price support loan,

Government credit to farms having over

$250,000 in sales would not be available.

An expanded international development

assistance program would be established.

Such a program would have to include an

optimum balance of commodity aid and

economic development aid. Its primary

objective would be to help developing countries improve economic growth, thus becoming better future customers of American agriculture.

A balanced macroeconomic policy that

facilitates growth of export markets and

8The $250,000 figure is based on census data and the economies

of size analysis discussed previously.
22 . Technology, Public Policy, and the Changing Structure of American Agriculture

maintains a relatively low real rate of interest would have to be maintained.

Maintaining Technological Competitiveness.

—The technological competitiveness of American farmers would be aided by continuing a

policy that encourages public and private investment in agricultural research. The major

thrust of the research and extension programs

as they affect larger scale commercial farms

would be as follows:

●

●

●

The trend toward increased public sector

emphasis on basic research would be continued. Increased reliance would be placed

on the private sector for applied research

in the development of new products.

Even though public sector research would

be aimed more toward basic research, an important problem-solving component would

be maintained to adopt new technologies

to various agro-ecosystems and to maintain

newly achieved productivity from the evolution of pests and disease, decline in soil

fertility, and other factors.

Extension’s role in direct education of,

or consultation with, large-scale farmers

would be deemphasized. Private consultants could play an increased role in technology transfer to the large-scale farm

segment.

Policy for Moderate-Size Farms

Policy for moderate farms includes the aforementioned options as well as additional options

tailored specifically to the needs of moderate

farms. OTA finds, for example, that moderate

farms having $100,000 to $250,000 in gross sales

face major problems of competing and surviving in the biotechnology and information technology era. Some moderate farms will survive

and some will not. This latter group should be

assisted in their move to other occupations.

Policy for moderate farms requires the same

stable economic environment and base of support for agricultural research and extension as

for large farms. But, in addition, the following

specific policy goals for moderate farms can be

specified:

• The risk of moderate farmers operating in

an open market environment would be

reduced.

● New technologies that have the potential for

adoption would be available to moderate

farmers.

● Opportunities for employment outside agriculture would be created for those farmers

who are unable to compete.

Diligent enforcement would be needed to assure that the benefits of programs established

to favor moderate farms are limited to those

farmers for whom they are intended.

Reducing Risks to Moderate-Size Farms.-The

most difficult obstacle to survival facing the

moderate farm is that of managing risk. Three

options, that are not necessarily mutually exclusive, could reduce the risks confronting moderate farms.

1.

2.

3.

Income protection could be provided through

either a continuation of the current targetprice concept for moderate farms only or

through a device known as the marketing

loan. Like the current nonrecourse loan, the

marketing loan is a loan from the Government on commodities in storage. If the commodity is sold for less than the loan value,

the farmer pays back only those receipts

to the Government in full payment of the

loan. The marketing loan, in essence, becomes a guaranteed price to the producer.

The level of the marketing loan should be

no greater than the average cost of production for moderate farmers.

The nonrecourse loan concept could be

continued for moderate farms. However,

the nonrecourse loan level should not be

set any higher than the recourse loan suggested previously for large farms, or else

the Government could end up acquiring

most of the production from moderate

farms.

Sharply increased assistance could be provided by the public sector to reduce the risk

to moderate farms. Such assistance could

be in the form of educational programs for

example, on risk management, futures markets, contracting, and cooperative marketing.
Ch. l—Summary ● 2 3

Technology Availability and Transfer to Moderate-size Farms.—OTA finds that agricultural

research, as a general rule, is not inherently biased against moderate farms. Rather, moderate farms may be seriously disadvantaged either by lags in adoption or by lack of access to

competitive markets for the products produced

by new technology. The following initiatives

could help curtail such problems of technology

availability and transfer.

● Extension’s evaluation of the increasing

number of new products entering the market would be intensified. This increased effort would play the dual role of: 1) providing a check on the efficacy and efficiency

of new products in biotechnology and information technology, and 2) eliminating

the costs associated with individual farmer

experimentation with those new products.

● Extension technology transfer services

would be aimed specifically at moderatesize farms. The primary goal of such programs would be to ensure the same schedule of adoption of technologies for

moderate-size as well as large farms.

• The development of cooperatives that emphasize technology supply and transfer

services to moderate farms would have to

be undertaken.

● Ample credit would have to be made available to moderate-size farms that have the

potential to survive and grow. Government

credit in concert with cooperative credit

could be aimed specifically toward filling

the needs of moderate-size farms. Emphasis should be placed on credit required to

keep moderate farms technologically upto-date.

Transition Policy to Other Agricultural Enterprises or Nonfarm Employment.—Regardless

of the effectiveness of the initiatives discussed

above, an accelerated need exists to assist farm

families to either move to other agricultural enterprises or out of agriculture into other occupations. The need arises, therefore, for specific

public action to facilitate the farmer’s transition from the current farm operation into gainful, productive employment elsewhere, Specific

initiatives to ease this process include the following:

●

●

●

●

New opportunities for employment of displaced farmers need to be explored and developed within agriculture as the industry

continues to evolve.

To facilitate the transition to nonfarm jobs,

special skills training programs aimed at

those areas where significant employment

opportunities exist must be considered.

Jobs in rapidly growing service, health care,

or care-for-the-aged industries provide contemporary examples.

Financial assistance, similar to the famous

G.I. bill, might be established to assist displaced farmers or rural residents during the

period of transition while skills training is

being received.

In areas of severe financial stress, assistance may be provided in the form of Government purchase of land or production

rights from displaced farmers at its “longterm fair market value. ” The returns from

the land could be used by the displaced

farmer for relocation and retraining. The

Government could retain the land in conservation reserve status until it is needed

for future production,

Policy for Small/Part-Time Farms

Policy for small/part-time farms includes several elements in addition to those mentioned

under large farm policy.

With few exceptions, small farms, those having less than $100,000 in sales, are not viable

economic entities in the mainstream of commercial agriculture—nor can they be made so, However, even a small increase in their farm income

could have a significant multiplier effect on the

local economy because of the large number of

small farms. These farms survive because their

operators have substantial outside income (parttime farmers), or because they have found themselves a niche in marketing a unique product

with special services attached (often direct to

consumers), and/or because they are willing to

accept a very low return on resources contributed to the farming operation.

For the small farmers who have substantial

outside income or who have found a niche in

the market, Government’s role would be severe-
24 . Technology, Public Policy, and the Changing Structure of American Agriculture

ly restricted. They are as much able to take care

of themselves as owners of large farms.

However, small subsistence farmers who have

limited resources, and often limited revealed

abilities, represent a genuine problem for which

public concern is warranted—these indeed are

the rural people left behind. Price and income

support programs have done and can do little

to solve their problems. These impoverished individuals are a social and economic problem.

The following suggestions are made for dealing

●

●

●

●

●

with the problems of subsistence farmers:

Initiate a special study to identify those individuals and their specific statuses and

needs. Develop social programs to meet

those needs.

USDA and the land-grant university bear

a special burden of responsibility y for serving the needs of these subsistence farmers.

This responsibility has not generally been

realized and, therefore has not been fulfilled. In the South, this responsibility falls

particularly heavily on the 1890 land-grant

universities in concert with the statewide

extension education programs and the 1862

land-grant universities. In the North, the

responsibility for serving the agricultural

educational and research needs of subsistence farmers falls exclusively on the 1862

land-grant universities.

USDA and these land-grant universities

could be directed to develop jointly a plan

for serving the agricultural research and

educational needs of these farmers. Such

a plan could include the delivery of farming, credit, and marketing systems designed

to maximize the small farm’s agricultural

production and earning capacity.

Specific farming systems must be developed to serve specifically the needs of small

subsistence farms. Such systems should, to

the extent practicable, encompass the use

of new technologies.

Credit delivery systems for small subsistence farmers could be developed specifically by USDA through the Farmers Home

Administration. Such systems should consider the unique capital and cash flow-limiting factors associated with subsistence

●

farmers who are often not in a position to

take advantage of other farm programs such

as price and income supports.

Marketing programs geared to subsistence

agriculture-are essential for providing hope

for this farm segment. The difficulty lies

in the inability of these farmers to obtain

access to the mass markets through which

most agricultural production moves.

Policy for Rural Communitie s

The impact of adjustment in agriculture to

changing technology will by no means be limited to the farm sector. Rural communities will

beat least equally affected by increasing farm

size, integration, and moderate farm displacement. Although, these effects will be felt initially

by implement dealers, farm supply and marketing firms, or bankers, the reverberations will

extend throughout the community in terms

of employment levels, tax receipts, and required

services. Rural communities should assess these

impacts and prepare to make needed adjustments. To ease the pain of adjustment the following actions are suggested:

●

●

●

●

Comprehensive programs for community

redevelopment and change need to be initiated throughout rural America. Such development plans should be fostered and

facilitated by Federal and State government

agencies.

Increased employment opportunities in rural areas could be fostered by aggressively

attracting new business activities in rural

communities. Particular emphasis would

be placed on attracting those businesses

that develop technologies and serve the

needs of high-technology agriculture in rural areas.

Rural communities could be assisted in developing and modernizing the infrastructure needed to be a socially and economically attractive place to live. Some rural

communities can serve as an attractive retirement residence for an aging population.

But this would require that a higher level

of social services be developed.

Rural communities need to play a vital role

in skills training for displaced farmers and
Ch. l—Summary ● 2 5

rural community employees. School and

university outreach programs could be

modified to serve this important role.

Policy for Technology and

Environmental Resource Adjustment

One of the major reasons that American agriculture has been so productive is because technological change has been fostered by the public sector and nurtured by a profit-seeking

private sector. As a result, American consumers

have enjoyed a plentiful supply of low-cost food

and natural fiber. In addition, agricultural exports have made a major contribution to the

overall development of export markets, to the

benefit of the general economy. Biotechnology

and information technology promise to offer

more of the same, with the added bonus of less

chemicals used in the production of food—

whether for the control of pests, disease, and

weeds, or for the production of commercial fertilizer.

Maintaining the productivity and competitiveness of U.S. agriculture in the public interest requires a balance between public and private sector support for technological change.

Yet it would be wrong to imply that there are

no risks. The conferring of property rights on

discoveries of the agricultural research system

has shifted the agricultural research balance between the public and private sectors toward the

private sector. While the effects of this shift appear to be positive, concerns exist that a substantial portion of the benefits of even public

research could be captured by private firm interests. Distribution of these benefits maybe so

unequally distributed that competitive performance is impaired. In addition, no scientifically

acceptable methodology exists for weighing the

risks or hazards of biotechnology research. To

deal with such issues, the following policy suggestions are made:

Steps should be taken to secure the public

interest on which the USDA and land-grant

university agricultural research system has

been based. Assurance must be provided

that the benefits of publicly supported research and extension are not captured in

the form of excess profits by the private sector based on research property rights and

increased private sector funding of public

research. The effect would be to stifle the

process of discovery and the dissemination

of new knowledge.

Major investments must be made to foster

the development of human capital that is

in a position to cope with the process of

rapidly changing agricultural technology.

This need extends from the training and development of the most basic biological research scientists, through the extension specialist and county agent, to the farmer who

adopts the new technology and the banker

who supplies the loan for its purchase.

Little is known about the adverse impacts

of potential biotechnology developments

on the ecosystem. These risks must be carefully assessed, monitored, and where necessary, regulated. Care must be taken, however, not to overregulate and thereby stifle

the potential competitiveness and productivity of U.S. agriculture.

SUMMARY CONCLUSIONS

The biotechnology and information technol- tential adjustment problems in the agricultural

ogy revolution in agricultural production has sector and in rural communities. Those costs

the potential for creating a larger, safer, less ex- can be minimized by careful analysis, planning,

pensive, more stable, and more nutritious food and implementation, This study is only the first

supply. Yet it will exact substantial costs in po- step in that direction,
Part I
Chapter 2

Emerging Technologies

for Agriculture
Contents

Page

Biotechnology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31

Animal Agriculture . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31

Plant Agriculture . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32

Information Technology. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32

Survey of Emerging Technologies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33

Animal Genetic Engineering . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33

Animal Reproduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37

Regulation of Livestock Growth and Development . . . . . . . . . . . . . . . . . . . . 38

Animal Nutrition . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39

Animal Disease Control . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40

Livestock Pest Control.. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41

Environment and Animal Behavior . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42

Crop Residues and Animal Wastes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42

Plant Genetic Engineering . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44

Enhancement of Photosynthetic Efficiency . . . . . . . . . . . . . . . . . . . . . . . . . . 47

Plant Growth Regulators . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48

Plant Disease and Nematode Control . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49

Management of Insects and Mites . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 50

Biological Nitrogen Fixation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 52

Water and Soil-Water-Plant Relations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53

Land Management . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54

Soil Erosion, Productivity, and Tillage . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 56

Multiple Cropping ......, . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 58

Weed Control . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 59

Commercial Fertilizers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 60

Organic Farming . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 61

Communication and Information Mangement . . . . . . . . . . . . . . . . . . . . . . . 62

Monitoring and Control Technology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63

Telecommunications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 65

Labor-Saving Technology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 67

Engines and Fuels ...,... . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 68

Crop Separation, Cleaning, and Processing Technology . . . . . . . . . . . . . . . 68

Chapter preferences . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 70

Table

Table No. Page

2-1. Emerging Agricultural Production Technology Areas . . . . . . . . . . . . . . . 34

Figures

Figure No. Page

2-1. General Configuration of Information Technologies in Production

Agriculture . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33

2-2. Recombinant DNA Procedure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34

2-3

2-4

2-5

2-6

2-7

Monoclinal Antibody Production . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ., 36

Schematic Presentation of Cow Embryo Transfer Procedures . . . . . . . . . 37

Plant Propagation—From Single Cells to Whole Plants . . . . . . . . . . . . . . . 45

Gene Modification-Insertion of a Desired Gene Into the

Host Plant Through Vectors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46

Configuration of Monitoring and Control Technologies in Agriculture . 65
Chapter 2

Emerging Technologies for Agriculture

American agriculture is on the threshold of

the biotechnology and information technology

era. Like the eras that preceded it—the mechanical era of 1930-50 and the chemical era of 1950-

70—this era will bring technologies that can significantly increase agricultural yields.

The immediate impacts of the biotechnologies

will be felt first in animal production. Through

embryo transfers, gene insertion, growth hormones, and other genetic engineering techniques, dairy cows will produce more milk per

cow; cattle, swine, sheep, and poultry will produce more meat per pound of feed. Impacts in

plant production will take longer to occur,

almost the remainder of the century. By that

time, however, technical advances will allow

major crops to be altered genetically for disease

and insect resistance, higher production of protein, and self-production of fertilizer and herbicide. Until then, crop yields will increase

through the use of traditional technologies, but

at less than past rates.

Both plant production and animal production

will benefit from advances in information technology. Computers, telecommunications, monitoring and control technology, and information management will be widely used on farms

to increase management efficiency.

Some of these new technologies will emerge

unexpectedly; however, most will undergo a

long process of development, from initiation of

ideas to commercial introduction. Since the

development of a new technology takes years,

often decades, it is often possible to forecast future technologies while they are still in the laboratory. One method is to obtain collective judgments from experts who have direct access to

the latest available information, a method OTA

chose. OTA collected information from three

rounds of a mailed survey to about 300 leading

public and private scientists and research administrators who had broad, cross-cutting perspectives about future technologies (Lu, 1983).

Based on these surveys and on subsequent interviews with scientists in various disciplines

around the country, OTA thus identified the 28

areas of emerging technologies that are likely

(with at least a 50-50 chance) to emerge before

2000 and to have major impacts on the agricultural sector. Many of the technologies examined

for this study, such as growth hormones, monoclonal antibodies, superovulation, and embryo

transfers, are already in the marketplace, while

others are still in the laboratory and will not become available for commercial introduction until 2000.

This chapter presents an overview of the major advances in biotechnology and information

technology and then describes in more detail

the 28 areas of technologies that were assessed

for this study. It should be noted that some of

the emerging technologies assessed will be in

neither the biotechnology nor information technology categories.

● OT8CHNOLOOY

Biotechnology, broadly defined, includes any the processes of living things. Such knowledge

technique that uses living organisms to make and skills will give scientists much greater conor modify products, to improve plants or ani- trol over biological systems, leading to signifimals, or to develop micro-organisms for specific cant improvements in the production of plants

uses. It focuses on two powerful molecular ge- and animals.

netic techniques, recombinant deoxyribonucleic acid (rDNA) and cell fusion technologies. Animal Agriculture

With these techniques scientists can visualize

the gene—to isolate, clone, and study the struc- One of the major thrusts of biotechnology in

ture of the gene and the gene’s relationships to animal agriculture is the mass production in

31
32 . Technology, Public Policy, and the Changing Structure of American Agriculture

micro-organisms of proteinaceous pharmaceuticals, l including a number of hormones, enzymes, activating factors, amino acids, and feed

supplements (Bachrach, 1985). Previously obtained only from animal and human organs,

these biological either were unavailable in practical amounts or were in short supply and costly.

Some of these biological can be used for the

detection, prevention, and treatment of infectious and genetic diseases; some can be used

to increase production efficiency.

Another technique, embryo transfer in cows,

involves artificially inseminating a superovulated donor animal2 and removing the resulting embryos nonsurgically for implantation in

and carrying to term by surrogate mothers. Prior

to implantation, the embryos can be treated in

a number of ways. They can be sexed, split (generally to make twins), fused with embryos of

‘Pharmaceuticals that are proteins.

‘An animal that has been injected with a hormone to stimulate

the production of more than the normal number of eggs per ovulation,

other animal species (to make chimeric animals

or to permit the heterologous species to carry

the embryo to term), or frozen in liquid nitrogen.

These and other genetic engineering techniques are explained more fully under “Animal

Genetic Engineering,” later in this chapter.

Plant Agriculture

The application of biotechnologies in plant

agriculture could modify crops so that they

would make more nutritious protein, resist insects and disease, grow in harsh environments,

and provide their own nitrogen fertilizer. While

the immediate impacts of biotechnology will be

greater for animal agriculture, the long-term

impacts may be substantially greater for plant

agriculture. The potential applications of biotechnology on plant agriculture include microbial inocula, plant propagation, and genetic

modification (Fraley, 1985). All are explained

later in this chapter under “Plant Genetic Engineering. ”

INFORMATION TECHNOLOGY

Agricultural information technologies can be

classified as: 1) communication and information management, 2) monitoring and control

technologies, or 3) telecommunications. The

relationships of these classifications are shown

in figure 2-1.

Communication and information management consists of onfarm digital communication

systems, known generically as local area networks (LANs), combined with the microcomputer-based information processing technologies used by the farm operator as the central

information processing and management system. This central computer system may include

remote terminals with keyboards, display

screens, and printers used for onsite data entry

and readout by the farm operator. The computer

terminals are indicated on figure 2-1 by the small

boxes labeled “T.”

Monitoring and control technologies automatically monitor and control certain aspects

of a wide variety of production processes. These

technologies, generally considered to be subsystems, are located at the site of production

activities, such as livestock confinement systems, storage facilities, and irrigation pumping

and control stations, and on mobile equipment

such as tractors and combines. Monitoring and

control systems can function autonomously, although they are increasingly being connected

to the central onfarm information processing

system through fixed links and low-power radio links to the onfarm LAN. The LAN connections between the central information management system and the onsite monitoring and

control technologies are indicated by the boxes

on figure 2-1 labeled “N,” for network node. Several different kinds of local configurations of

the LAN and the components of the onfarm

computer system are possible. The arrangement

shown here is just one of many possibilities.

Telecommunication technologies comprise

the hardware and software that connect the onfarm systems with the rest of the world so that
Ch. 2—Emerging Technologies for Agriculture ● 3 3

the farmer can communicate with people and Three types of telecommunication technologies

with computer systems in other firms and in- are shown on figure 2-1: satellite ground stastitutions. Telecommunication systems may tions, low-power radio links, and telephone

combine both voice and data communications. lines. -

Figure 2-1 .—General Configuration of Information Technologies in Production Agriculture

Tractor, combine Radio Onfarm Satellite

and implement link weather ground

system N station station

1

Livestock Radio

-

identification links

and automatic C ‘

--tral computer system

feeding

micro processors,

network controllers

Irrigation pump — displays

and flow controls — mass storage devices

— printer J

— software packages Communication

Livestock environment interface(s)

and waste monitoring

and control Communication

software

Crop, feed, storage

control and

processing

Monitoring and Onfarm communication and Telecommunication

control technologies information management technologies technologies

I I

N - network node T - Computer terminal

SOURCE Off Ice of Technology Assessment

SURVEY OF EMERGING TECHNOLOGIES

The 28 areas of technologies are shown in ta- Animal Genetic Engineering

ble 2-1. OTA commissioned papers by leading

scientists in each of these technological areas.

A summary of each paper is presented in this

section. 3

3The papers prepared by those scientists are referenced at the

end of this chapter and are available in Technology, Public Policy, and the Changing Structure of American Agriculture, VoIume 11—Z3ackground Papers through the National Technical Information Service, U.S. Department of Commerce.

Genetic engineering includes a number of procedures by which genes can be manipulated for

improving the health and productivity of plants,

animals, and humans (Bachrach, 1985). Three

important genetic engineering procedures are:

1) recombinant DNA (rDNA) techniques, also

called gene splicing; 2) monoclinal antibody

production; and 3) embryo transfer.

Recombinant DNA Techniques

Because of its power to alter life forms, rDNA

technology is considered to be one of the great-
34 ● Technology, Public Policy, and the Changing Structure of American Agriculture

Table 2-1.—Emerging Agricultural Production Technology Areas

Animal Plant, soil, and water

Animal genetic engineering Plant genetic engineering

Animal reproduction Enhancement of photosynthetic efficiency

Regulation of growth and development Plant growth regulators

Animal nutrition Plant disease and nematode control

Disease control Management of insects and mites

Pest control Weed control

Environment of animal behavior Biological nitrogen fixation

Crop residues and animal wastes use Chemical fertilizers

Monitoring and control in animals Water and soil-water-plant relations

Communication and information management Soil erosion, productivity, and tillage

Telecommunications a Multiple cropping

Labor savinga Organic farming

Monitoring and control in plants

Engine and fuels

Land management

Crop separation, cleaning, and processing

a

These technologies also apply to plant, SoiI, and water

SOURCE: Office of Technology Assessment.

est achievements of biological science. Through

this technology DNA fragments from two different species can be fused together to form new

units called recombinant plasmids (figure z-2).

Such rDNA molecules might contain, for example, a gene from human insulin fused with

DNA that regulates the reproduction of bacteria. When such molecules are inserted into bacteria, they instruct that bacteria to manufacture

human insulin. Molecules of rDNA can now be

inserted into a variety of bacteria, yeasts, and

animal cells, where they replicate and produce

many useful proteins, such as insulin, growth

hormones, prolactin, prolaxin, enzymes, toxins,

blood proteins, subunit protein vaccines, immunity enhancers (such as interferon and interleukins), and nutrients like amino acids and

single-cell protein feed supplements. Recombinant DNA technology also produces DNA sequences for use as probes in detecting bacterial

poisoning of foods and for diagnosing and treating infectious and genetic diseases.

Figure 2=2.—Recombinant DNA Procedure

Animal DNA

Enzyme cuts

vector open

Sticky ends

spliced by

Iigase

product

Insert into

 bacterium

~ /

Expression

I

Replication

. -—-—— — of plasmid

rDNA plasmid

Bacterium with

functional animal gene

Bacterial DNA plasmid

(vector)

An animal gene is spliced into a carrier DNA (called a vector) for insertion into a micro-organism (a bacterium is shown) or alternate animal

host cell, and is made to replicate and express its protein product.

SOURCE: Off Ice of Technology Assessment
Ch. 2—Emerging Technologies for Agriculture . 35

One of the applications of the new pharmaceuticals is the manufacture of growth hormones that can be injected into animals to increase production efficiency. Monsanto, Eli

Lilly, and other firms are developing genetically

engineered bovine growth hormone (bGH) to

stimulate lactation in cows. This hormone,

produced naturally by a cow’s pituitary gland,

was synthesized by Genentech for Monsanto.

It has been reported that daily injections of bGH

into dairy cows at the rate of 44 milligrams per

cow per day have resulted in an increase of 10

to 40 percent in milk yield. The response to injections is rapid (2 to 3 days) and persists as long

as treatment is continued (Kalter, et al., 1984).

More recently, it was reported that the bGH

treatments have increased milk yield 25 to 30

percent in the laboratory and could increase

milk yield 20 percent on the farm (Kalter, 1985).

The new hormone now awaits approval by the

U.S. Food and Drug Administration and is expected to be introduced commercially in 1988

(Bachrach, 1985; Hansel, 1985; Chem. and Eng.

News, 1984).

Another new technique arising from the convergence of gene and embryo manipulations

promises to permit genes for new traits to be

inserted into the reproductive cells of livestock

and poultry, opening a new world of improvement in animal health and production efficiency. Unlike the genetically engineered

growth hormone, which increases an animal’s

milk production or body weight but does not

affect future generations, this technique will allow future animals to be permanently endowed

with traits of other animals and humans, and

probably also of plants. In this technique, genes

for a desired trait, such as disease resistance

and growth, are injected directly into either of

the two pronuclei of a fertilized ovum (egg).

Upon fusion of the pronuclei, the guest genes

become a part of all of the cells of the developing animal, and the traits they determine are

transmitted to succeeding generations.

In 1983, scientists at the University of Pennsylvania and University of Washington successfully inserted a human growth hormone gene,

a gene that produces growth hormone in human

beings, into the embryo of a mouse to produce

a supermouse that was more than twice the size

of a normal mouse (Palmiter, et al., 1983). In

another experiment, scientists at Ohio University inserted rabbit genes into the embryos of

mice. The genetically engineered mice were 2.5

times larger than normal mice (Wagner, 1985).

Encouraged by the success of the supermouse

experiments, U.S. Department of Agriculture

(USDA) scientists at the Beltsville Agricultural

Research Center and the University of Pennsylvania are conducting experiments to produce

better sheep and pigs by injecting the human

growth hormone gene into the reproductive

cells of sheep and pigs (Hammer, 1985). USDA

scientists provide scientists at the University

of Pennsylvania with fertilized embryos from

sheep and pigs at their Beltsville farms. After

being injected with the human growth hormone

genes, the embryos are returned to Beltsville for

insertion into surrogate mothers.

The experiments of crossing the genetic materials of different species in general and of

using the human growth hormone in particular have prompted lawsuits from two scientific

watchdog groups: the Foundation of Economic

Trends and the Humane Society of the United

States. Both groups charge that such experiments are a violation of “the moral and ethical

canons of civilization, ” and have sought to halt

the experiments. The researchers argued that

they are continuing the experiments cautiously

and countered that the potential scientific and

practical benefits far outweigh the theoretical

problems raised by the critics. While the lawsuit is pending, the experiments are continuing.

Monoclonal Antibody Techniques

Antibodies are proteins produced by white

blood cells in response to the presence of a foreign substance in the body, such as viruses and

bacteria. Each antibody can bind to and inactivate a cell of the foreign substance but will not

harm other kinds of cells. Until recently, the primary source of antibodies used for immunization and other purposes was blood serum from

many animal species. However, such serum also

contains antibodies to hundreds of other substances, and each antibody type was limited in

quantity.
36 • Technology, Public Policy, and the Changing Structure of American Agriculture

To produce large quantities of a single antibody, scientists now use a technique called monoclonal antibody production (figure 2-3). By fusing a myeloma cell4 with a cell that produces

an antibody, scientists create a hybridoma,

which produces (theoretically in perpetuity)

large quantities of identical (i.e., monoclinal)

antibodies in a pure, highly concentrated form.

An array of monoclinal antibodies can now be

produced to fight major virus, bacteria, fungi,

and parasites and to diagnose the presence of

a specific agent in body fluid. The many important uses of monoclinal antibodies in agriculture include: the purification of proteins made

by rDNA; the passive immunization of calves

against scours; the detection of food poisoning;

substitutions for vaccines, antitoxins, and antivenoms; sexing of livestock embryos; post-coital

contraception and pregnancy testing; the imaging, targeting, and killing of cancer cells; the

monitoring of levels of hormones and drugs; and

the prevention of rejection of organ transplants.

Embryo Transfer

Embryo transfer is used for the rapid upgrading of the quality and productive efficiency of

livestock, particularly cattle, In the process a

superovulated donor animal is artificially inseminated, and the resulting embryos are removed nonsurgically for implantation in and

carrying to term by surrogate mothers (figure

2-4). Before implantation, the embryos can be

sexed with monoclinal antibody, split to make

twins, fused with embryos of other animal species, or frozen in liquid nitrogen for storage until

the estrus of the surrogate mother is in synchrony with that of the donor.

For gene insertions, the embryo must be in

the single-cell stage, having pronuclei that can

be injected with cloned foreign genes. The genes

likely to be inserted into cattle maybe those for

growth hormones, prolactins (lactation stimulator), digestive enzymes, and interferon, collectively providing both growth and enhanced

resistance to disease.

4Myelomas are cancerous, antibody-producing cells.

Figure 2-3.—Monocionai Antibody Production

—

Spleen cells Myeloma cell s

Fusion

v

Testing and

selection

Growth clones

.

. . . . . . 

. . 0 . . . . . ,

1151

. . . ::.: ’.” .. .”:” .: ...

. .. .. . . . “.,: ..:..

,.. ‘.’. 6 .,. . . . . . . . .

:

Induce and collect ●

fluid-containing

Freeze

hybridomas

antibodies

To produce monoclinal antibodies, spleen cells from a mouse immunized against a specific disease are fused with mouse tumor (myeIoma) cells to create hybrid cells (hybridoma) that grow in culture. The

hybridoma cells are then screened for the production of antibodies.

Hybridomas that test positive are injected into a mouse, and the

mouse becomes a living factory for the production of antibodies

against the same disease. Other positive hybridomas are frozen for

future use.

SOURCE: U.S. Department of Agriculture, Agricultural Research Service.

While less than 1 percent of U.S. cattle are

involved in embryo transfers, the obvious benefits will cause this percentage to increase rapidly, particularly as the costs of the procedure

decrease (Brotman, 1983). One company, Genetic

Engineering Inc., already markets frozen cattle embryos domestically and abroad and provides an embryo sexing service for cattle breeders (Genetic Engineering News, 1983).
Ch. 2—Emerging Technologies for Agriculture . 37

Figure 2-4.—Schematic Presentation of Cow Embryo Transfer Procedures

\

uporovuletlon f donor rtlfklal rrsemb?atlon (5 daya

with onadotrotrbm fter Mtlath-ig superovulation)

Nonsurgical recovery of embryos (0 to

8 days after artlfkiel haemlnatlon)

./7 ‘ \ \

Foley catheter for olation and claaalf~

recovery of embryoa cation of ambryos

Storage of embryoa Indeflnltely

h Ikauld rhfrooen or at 37C

or room temperature for 1 day

Pregnancy diagnosla by palpation

Tranafer of ambryoo to recbkrrta through the rectal wafl 1 to 3 Bkth (9 montha after

eumka~ or nonaurgkalty montha after embryo transfer embryo tranafer)

SOURCE: AdarXed from G.E. Seidel, Jr., “Suoer Ovulation and Embryo Transfer in Cattle, ” Sc/errce, vol. 211,

Jan: 23, 1981, p. 353.

Because of intense competition between hundreds of firms in the United States and abroad,

a great many useful genetically engineered products and processes will be introduced during

the 1980s.

Animal Reproduction

The field of animal reproduction is undergoing a scientific revolution that could scarcely

have been visualized a decade ago (Hansel,

1985). Indeed, if all of the technology now available were used, a new kind of animal breeding

system could be put into operation within 10

years.

By year 2000, artificial insemination maybe

replaced by a system best characterized as “artificial embryonation.” In this system highly

trained technicians will place embryos into the

uteri of groups of outstanding female animals

whose estrous cycles have been regulated by

artificial means, such as hormone injections,

ear implants, or intravaginal devices. The ova

from this “superovulation” will be culled surgically or nonsurgically (by flushing) and then

fertilized in the laboratory by spermatozoa from

outstanding males. The fertilized ova can then

be cultured, frozen, and stored until needed.

Finally, the embryos will be placed in foster

mothers nonsurgically.

Ultimately, it maybe possible to sex the embryos by separating the X- and Y-bearing spermatozoa or by identifying the male embryos by

immunological techniques so that recipient beef

cows will receive primarily male embryos and

dairy cows will receive primarily female embryos. Techniques for reducing early embryonic

deaths, the major cause of infertility in all farm
38 • Technology, Public Policy, and the Changing Structure of American Agriculture

animals, are also likely to be developed within

this time frame.

Achieving these goals will entail the funding

of research in three major areas: 1) the development of improved estrous cycle regulation

techniques; 2) the development of improved

techniques for superovulation and embryo collection, storage, sexing, and transfer; and 3) the

development of methods for reducing embryo

mortality and improving fertility in all classes

of farm animals.

Vigorous pursuit of research in these areas

could result, by year 2000, in the marketing of

large numbers of genetically engineered embryos containing genes that will improve fertility and fecundity and will result in improved

rates of gain, improved carcass characteristics,

increased milk production, and increased resistance to diseases in offspring. Despite recent

spectacular breakthroughs in introducing human genes into laboratory animals, a great deal

remains to be learned about the factors that control chromosomal integration of foreign DNA,

the retention of that DNA during embryonic development, and ultimately the expression of

DNA, without disruption of the formation and

development of the embryo. These developments

will affect the major drug companies, genetic

engineering companies, equipment manufacturers, veterinarians, inseminators, and extension workers, as well as the Nation’s farmers.

The ultimate goal of this research is to increase

the efficiency of production so that fewer animals, and less input of labor will be needed to

produce the needed animal products.

Regulation of Livestock

Growth and Development

The rate and composition of growth is a critical factor in determining the cost of producing

livestock products (Allen, 1985). While much

is known about genetic and nutritional variables that influence animal growth, much less

is known about the hormonal, cellular, and

metabolic mechanisms that determine how and

at what rate nutrients are partitioned into the

growth of muscle, fat, bone, and the tissues of

major concern. An understanding of these fundamental mechanisms is needed to provide a

foundation for applying new technologies to the

development of products to improve the rate,

efficiency, and composition of animal growth.

The potential applications of genetic engineering, cloning, and immunology for the improvement of growth in food-producing animals

are many. For example, recombinant DNA technology is responsible for providing sufficient

quantities of bovine and porcine growth hormone so that scientists can now determine their

role, mode of action, and potential use when

administered to animals used for producing

meat and milk. In the future, this kind of research may also lower the cost of beef production by permitting small cows, which have lower

maintenance costs, to produce large market cattle of desirable composition. It also seems likely
Ch. 2—Emerging Technologies for Agriculture • 39

that biotechnology will give rise to new products that can alter the inherent mechanisms of

muscle protein and adipose (fat) tissue accretion so that the efficiency of meat production

will be improved by the conversion of more nutrients into lean meat and less nutrients into fat.

Such a development would be in keeping with

the consumer demand for lean, but highly palatable, meat at a reasonable cost, and with the

medical recommendations that the U.S. consumer reduce the intake of calories from dietary fat.

Other opportunities for advances involve the

physical sciences. These include the need for

more rapid, accurate, and economical ways of

maintaining the identity of animals through the

time of slaughter, and for determining the composition of the living animal and its carcass. Improved methods of identifying mammalian meat

animals would be a basis for a national record

system. This system would benefit producers,

packers, regulatory agencies, and consumers,

since it could provide information concerned

with marketing, carcass merit, disease, and residue-monitoring programs.

A quick and accurate assessment of body composition not only would improve livestock production data and marketing procedures, but

would be an example of new technology that

could also be used to address human concerns

about body weight and obesity. Current procedures used for determining body composition

in livestock are too slow, inaccurate, or expensive for adoption by the industry. As a result,

the real value differences between animals of

low and high carcass merit, as affected by fat

content, are normally not fully realized in the

market when animals are sold alive.

The implications of applying these kinds of

technologies for improving the production efficiency, composition, and consumer cost of animal products are numerous. They include the

more efficient use of livestock feeds, possible

changes in crop production priorities, improved

composition of animal food products, improved

production practices from more complete animal records, and implications related to human

health. The application of these technologies

will depend on understanding the fundamental

principles or mechanisms involved in each major research area.

Animal Nutrition

The U.S. food animal industry is immense.

Food animals provide 70 percent of the protein,

35 percent of the energy, 80 percent of the calcium, 60 percent of the phosphorus, and significant proportions of the vitamins and mineral

elements in the average human diet in the United

States (Pond, 1985).

The future of this industry will depend not

only on profitability, but also on the industry’s

adoption of new technology and on the industry’s response to consumer concerns about cost,

esthetics, convenience, and health. Areas of nutrition research that may result in major advances in animal food production and use in

the next 20 years include: 1) the relation of animal product consumption to human health, 2)

alimentary tract microbiology and digestive

physiology, 3) voluntary feed intake control, 4)

maternal nutrition and progeny development,

and 5) aquiculture.

Many consumers are concerned about the effect on human health of consuming animal food

products because of the amount and composition of fat in those products as well as the

amount of sodium, nitrates, and potentially

harmful bacteria or chemical residues. Studies

have suggested strong links between some of

these factors and human cancer, osteoporosis,

and cardiovascular disease. Research on-line

is addressing these concerns by applying nutritional and genetic principles to the improvement of animal food products. For example,

changes in animal fatty acid composition will

be possible by using “protective” feed additives

in specific animal diets. Changes in total animal fat content will probably occur through

energy restriction, nutrient partitioning, and

genetic selection. Sodium content of animal

products can be reduced at the processing stage.

The direct impact of advances in this area will

be animal food products that are safer for human health. The indirect impacts may be great-
40 . Technology, Public Policy, and the Changing Structure of American Agriculture

er, however: to produce such products, producers may have to switch to more pasture, forage,

and nonconventional feed resources. Such adjustments could change the total profile of agriculture.

Research into factors controlling voluntary

feed intake and nutrient partitioning will result

in the diversion of the use of nutrients from body

maintenance to lean tissue growth and other

productive functions. Such methods will save

feed and provide opportunities for alternative

uses of feed resources.

More complete knowledge of maternal nutrition in relation to fetal survival and prenatal and

postnatal development may lead to significant

increases in the amount of edible product per

breeding unit. This outcome will be translated

into savings in labor and resource use.

Finally, aquiculture has emerged as an important new field of animal agriculture in the

United States. Research into specific nutrient

requirements for different species of fish during all phases of the life cycle, and interactions

between nutritional requirements and water

environment, will provide new technology that

will make the industry more competitive in animal agriculture. Future growth of private aquaculture will provide an additional supply of edible fish and shellfish for consumption by the

U.S. population, whose per capita appetite for

animal products may be saturated.

Diseases of livestock are the greatest single

deterrent to the efficiency of animal production

(Osburn, 1985). Together, animal health-related

problems and the resulting inefficiencies in reproduction limit the productive capacity of livestock enterprises to 65 to 70 percent of their potential. Although major epidemic diseases such

as foot-and-mouth disease and tuberculosis have

been eradicated or controlled, an estimated $17

billion or more annually is lost in production

because of a variety of infectious diseases, parasites, toxins, and metabolic disorders.

Some of these losses result from a lack of understanding of animal health problems, such as

reproductive inefficiency, neonatal death losses,

or mastitis. Other losses relate to the change in

structure of livestock enterprises to a system

that has both fewer farms and a greater concentration of animals per farm. For example, dairy

operations of up to 5,000 milking cows, and

poultry operations of 100,000 or more birds, are

now relatively common. In these large production units the introduction of an infectious disease can have devastating consequences.

The technologies that show the greatest promise for improving management schemes and

controlling disease are: 1) data management and

systems analysis, 2) rapid diagnostic tests, 3)

selection for disease-resistant strains of livestock, 4) genetic engineering of micro-organisms and embryos, and 5) immunobiology.

Computers and computer programs already

allow the farm manager to assess the well-being

of each animal in large production units. Data

on feed consumption, vaccination records, and

conception dates, for instance, can be stored

in the computer and retrieved quickly by the

manager or veterinarian. Such systems can be

coordinated with radiotransmitters used to

identify each animal. Within 5 to 10 years such

systems will be widely used by progressive animal producers.

Advances in biotechnology will include further development of animal-side test kits for

rapid assessment of animal health. One of these

tests, the enzyme-linked immunosorbent assay,

can test for hormones (to determine pregnancy),

detect drug residues in milk or feed, and diagnose disease (through antibody detection). If

economical tests can be developed, their use will

be widespread and immediate (5 to 10 years).

For certain intractable health problems, like

parasites and mastitis, efforts are being made

to breed disease-resistant strains of livestock.

Advances in embryo transfer, gene insertion

into embryos, and amplification of gene products will increase the number of more desirable offspring by year 2000.

Recombinant DNA technology is already being used to alter vaccines genetically so that

pathogens in the vaccines cannot replicate in

the inoculant and cause a mild infection that
Ch. 2—Emerging Technologies for Agriculture . 41

could spread to other animals. The development

of vaccines for several viral diseases, such as

bluetongue, should be possible in the next 15

years.

Finally, knowledge gained in the past two decades is being used to improve that system’s efficiency. Ingredients (adjuvants) in vaccines are

being used to pace the release of antigens into

the body or to manipulate or favor certain immune responses. In addition, monoclinal antibodies are being used to detect and prevent disease. The major constraints to the use of these

technologies include: 1) funding of field studies,

2) commercialization of products by the biological and pharmaceutical industries, and 3) cumbersome and expensive processes for assuring

quality. The benefits of controlling disease will

be a decrease in the cost of production for the

farm operator and a decrease in food cost for

the consumer.

Livestock Pest Control

Major insect pests cause losses to livestock

and poultry of more than $2.5 billion (Campbell, 1985). Some insects, primarily the blood

feeders, are pests of all warm-blooded animals.

Others are host-specific, although related species may prey on several classes of livestock.

Losses maybe direct, in terms of decreased livestock products; or indirect, in the form of insecttransmitted disease, secondary infections, predisposition to other diseases, irritation that

causes unthriftiness, and costs of insect control.

New technology, particularly for livestock insects that are difficult to control, will be more

expensive and will have a lower cost-benefit ratio than that of current technology. Progress in

new technology in the science of veterinary entomology is relatively slow for the same reason

that adaptation of existing technology is slow—

there are few scientists (60) doing research. Several technologies show promise for controlling

insect pests of livestock, however.

Although animal producers will continue to

use insecticides for the immediate future, progress is being made in such areas as habitat management (pasture rotation and brush control for

ticks); integrated pest management (biocontrol,

sanitation, and waste management for fly control at feedlots and dairies); and use of pestresistant breeds in cross-breeding programs (Indian crossed with European cattle).

For blood-feeding insects research is directed

at developing slow-release technology, whereby

a chemical ingredient is formulated into a matrix that slowly erodes or vaporizes to release

insecticide. For example, insecticide boluses are

used in the stomach of animals, where they

slowly release insecticide that destroys manuredeveloping fly larvae. Insecticide can also be

implanted in an animal’s body. Eartags impregnated with a slow-release insecticide have been

very effective for horn fly control and have improved face fly control in cattle. As the insecticide vaporizes, it spreads over the haircoat of

the animal, destroying insects that rest or feed

on the animal. (However, horn fly resistance

to the pyrethroid insecticides used in eartags

has become widespread.) The newest of these

technologies are implants that directly release

insecticide into the bloodstream, destroying

blood-feeding insects. However, implants and

boluses will have a limited effect for migratory,

blood-feeding insects unless many producers

join the control effort.

Recombinant DNA technologies will be used

for the molecular cloning of desired antigens,

toxins, enzymes, or other biologically important molecules for use as research tools or in

the development of vaccines for bluetongue,

anaplasmosis, and other diseases for which insects are vectors. In addition, this technology

will enhance the study of molecular genetics

and metabolic control in Bacillus thuringiensis, a bacterium pathogenic to some insects.

Advances in genetics will allow scientists to

manipulate the reproductive capabilities of pest

species. These advances include the sterile insect release method and chromosomal translocation, among others.

If technology already available were used on

a wider scale, livestock losses from insects could

be reduced by one-third ($700 million). This outcome would entail at least a doubling of current extension efforts in livestock entomology.

The new methodology discussed might reduce
42 ● Technology, Public Policy, and the Changing Structure of American Agriculture

losses by another 15 to 25 percent, but at a lower

cost-benefit ratio.

Environment and AnimaI Behavior

The effects of environment on animal wellbeing have become ever more important because of the trend toward production systems

that confine a large number of animals together

in a more artificial environment (Curtis, 1985).

Confinement simplifies the environment, reducing an animal’s opportunities to alter its surroundings to advantage. While such intensive

systems increase production per unit of labor

input or space, they can be detrimental to animal function and performance.

The advent of intensive production systems

changed the relative importance of various environmental factors as well as the strategies for

improving animal production through the application of technology. New technologies likely

to emerge by 2000 as a result of current research

lie in the areas of energy conservation, optimization of total stress, stress-altered disease resistance, and photoregulation of physiological

phenomena.

Feed and fuel—sources of energy—account

for much of the cost of animal production. Although the trade-offs between feed and fuel have

been quantified for most species, the integration of additional research will result in further

energy savings. For example, environmental

temperature management schemes developed

in an era of cheaper fuel are too luxurious today.

Animal producers tend to maintain constant

environmental temperatures for their stock,

even though the animals evolved in the cyclical thermal environment of nature. In one experiment, when young pigs were allowed to regulate their own environmental temperatures,

they inserted a daily 200 F fluctuation of warm

afternoons and cool nights, resulting in unchanged pig performance but a 50-percent reduction in fuel use during cold weather. Lowering

thermostat settings to parallel age-dependent

changes in thermal requirements has also been

found to save fuel. In some cases cooler surroundings spur appetites, so performance actually increases. Cost-effective, low-maintenance designs of heat exchangers and solar heating systems will affect further energy savings.

Either too much or too little environmental

stimulation can have deleterious effects on the

performance, health, and well-being of agricultural animals. To optimize total stress, more

must be learned about how stress acts on and

is perceived by animals. Devices that animals

can use to regulate certain environmental factors are already being recommended to farmers.

Computerized sensing devices and control

equipment will make biofeedback-linked automation of environmental regulation a reality in

animal agriculture.

Researchers are also investigating how the

environment influences specific mechanisms

of immunity to disease. A variety of common

environmental stressors-temperature, crowding, mixing, weaning, limit-feeding, noise, and

movement restraint—are known to alter animals’ defenses against infectious agents. New

techniques in basic science, coupled with more

traditional neurobiological, endocrinological,

and immunological approaches, can yield abetter understanding of how stressors influence

regulatory signals among lymphoid cell subpopulations.

The regulation of light is of particular interest in animal production. The advent of photoperiod management revolutionized the poultry

industry 40 years ago. Light is managed in poultry confinement operations so that it stimulates

poultry growth. In the last two decades the effects of photoperiod management have also

been characterized for sheep reproduction. Although the results of similar studies on cattle

and swine have been less definitive, some results have been encouraging: under controlled

lighting, sows weaned heavier piglets, cows

yielded more milk, and lambs grew faster. Experiments now in progress will produce information immediately applicable to animal production.

Crop Residues and Animal Wastes

Improved use of crop residues and animal

wastes represents a tremendous potential for

more efficient use of resources (Fischer, 1985).
Ch. 2—Emerging Technologies for Agriculture ● 4 3

Livestock on U.S. farms produce about 55 million tons of recoverable manure. Approximately

363 million tons of crop residues are produced

annually in the United States. Several technologies and major lines of research and development exist in this area: 1) energy from manure,

2) animal feed from manure, 3) chemicals from

crop residues, and 4) animal feeds from crop

residues.

The high volume of manure production that

occurs at many large feedlots and dairies is an

opportunity in disguise. Manure has value both

as a soil additive and as a source of energy for

heat and electricity. Traditionally, manure has

been either applied to the soil surface in an unprocessed form or disposed of in a sewage lagoon. Application of manure to the soil surface

creates environmental problems in many areas

and results in a loss of up to 90 percent of the

useful nitrogen value of the manure. Technology is available to inject the manure below the

soil surface, resulting in only a 5-percent loss

of nitrogen (Suttan, et al., 1975) 0

Large farms may benefit from installing anaerobic digesters to produce methane from manure, for use as a heating fuel or as a substitute

for propane in electric generators. The slurry

that remains after digestion contains most of

the original nutrient value and may be applied

to cropland as fertilizer. Injection of the slurry

is preferred, since most of the nitrogen after

digestion is in the form of ammonia.

In many farm operations, it is profitable to

process manure and use it as a source of nonprotein nitrogen and fiber in cattle and dairy

cow rations. Manure is a low-cost source of nutrients, and reusing it as feed reduces the volume of animal wastes that must be processed

or disposed of. If used for cattle feed, manure

must first be concentrated, then processed by

heat treatment or by ensiling.

Using crop residues as a source of chemical

feedstocks and animal feed involves some complex trade-offs in most areas because crop residues are becoming widely valued for their ability to reduce soil erosion in combination with

conservation tillage practices. Even when crop

residues are completely tilled into the soil, they

have significant value in maintaining soil structure and nutrient content. However, useful

amounts of residues maybe removed from fields

in many parts of the United States where

cropland slopes are gentle and residue density

is high. The cost of transporting bulky crop

residues generally constrains the area over

which collection is economically feasible.

Several technologies under development have

promise in areas where residue collection is economically feasible. Residues may be broken

down into their component parts by mechanical, chemical, or biological processing, or a combination of all three. The principal components

of crop residues are lignin, hemicellulose, and

cellulose. Lignin can be used to produce solvents

such as benzene, toluene, and xylene. Hemicellulose is readily converted into furfural, which

is, in turn, a feedstock for the production of numerous chemicals. Plastic films and fibers and

the simple sugar, glucose, can be produced from

cellulose. Production of these chemicals is likely

to require moderately large-scale technology

based on industrial processes and equipment.

Transportation costs reduce the likelihood that

crop residues will be used as feedstock for industrial processing. Some farms may adopt direct combustion of crop residues for use as a

source of heat for grain drying.

In the near term, the most likely process for

conversion of crop residues is biological: ruminant animals. Most crop residues can be fed

directly to ruminant animals as a source of

roughage. A substantial potential exists for developing technologies to increase the palatability and digestibility of crop residues. Numerous efforts have been made to develop simple

mechanical and chemical pretreatments, with

some success. The problem is difficult, owing

to the degree with which the digestible hemicellulose and cellulose are bound to the nondigestible lignin component in the residues of mature

cash grain crops. Additional research and development leading to economic and effective

pretreatments would have substantial benefits

because the size of this resource is so large.
44 . Technology, Public Policy, and the Changing Structure of American Agriculture

Plant Genetic Engineering 

Biotechnology is not new to plant agriculture

(Fraley, 1985). Plant breeding, agrichemicals,

and microbial seed inocula have made major

contributions to the remarkable development

of American agriculture. Within the last decade, major advancements have been made in

the understanding of gene function and architecture, and powerful methods have been developed for identifying, isolating, and modifying specific DNA segments.

The further application of biotechnologies in

plant agriculture could modify crops so that they

would make more nutritious protein, resist insects and disease, grow in harsh environments,

and provide their own nitrogen fertilizer. While

Photo credit: US. Department of Agriculture, Agricultural Research Service

Plant geneticist is determining the structure of a soybean

DNA segment that resembles the movable genetic

elements first discovered in corn. Each band represents

a “letter” or nucleotide, in the genetic code.

the immediate impacts of biotechnology will be

greater for animal agriculture, the long-term impacts may be substantially greater for plant

agriculture. The potential applications of biotechnology on plant agriculture will include

microbial inocula, in vitro plant propagation

methods, and genetic modification.

Microbial Inocula

Research on plant-colonizing microbes has

led to a much clearer understanding of their role

in plant nutrition, growth stimulation, and disease prevention, and the possibility exists for

their modification and use as seed inocula. Rhizobium seed inocula are already widely used

to improve nitrogen fixation by certain plants

(legumes). Extensive study of the structure and

regulation of the genes involved in bacterial nitrogen fixation will likely lead to the development of more efficient inocula.

Two years ago, scientists at the University of

California, Berkeley, genetically engineered icenucleation bacteria that inhibit frost formation

in potato plants. To form ice, there must be nucleation sites around which the water molecules

can form the regular ice structure. In the ecosphere, this role is performed by specialized bacteria called Pseudomonas syringae, which contain specific proteins that act as the nucleation

centers for the growth of ice crystals. B y colonizing plants in the manner of epiphytes,5 these

bacteria induce ice formation and thus cause

frost damage to plants as the temperature drops

below freezing (Feldberg, 1985).

Scientists constructed a new strain of bacteria in which the nucleation protein is absent or

altered so that the bacteria can no longer play

the role of nucleation centers. Having successfully constructed a new strain of bacterium,

these researchers were ready to field test this

new organism to see if it would outcompete the

normal strains. If so, the new bacterium would

protect crops from frost damage, and millions

of dollars in lost crops would be saved. As the

Plants that derive their moisture and nutrients from the air

and rain and that usually grow on another plant. Spanish moss

is an epiphyte.
Ch. Z—Emerging Technologies for Agriculture . 45

novel bacteria were scheduled for release to the

field, a coalition of public interest groups filed

a lawsuit to postpone the field trials (see chapter 10 for more detailed discussion about this

controversy).

Recently, Monsanto announced plans to field

test genetically engineered soil bacteria that produce naturally occurring insecticide capable of

protecting plant roots against soil-dwelling insects (House Committee on Science and Technology, 1985). The company developed a genetic

engineering technique that inserts into soil bacteria a gene from a micro-organism known as

Bacillus thuringiensis, which has been registered as an insecticide for more than two decades. Plant seeds can be coated with these bacteria before planting. As the plants from these

buds grow, the bacteria remain in the soil near

the plant roots, generating insecticide that protects the plants.

Plant Propagation

Cell culture methods for regenerating intact

plants from single cells or tissue explants are

being used routinely for the propagation of several vegetable, ornamental, and tree species

(Murashige, 1974; Vasil, et al., 1979). These

methods have been used to provide large numbers of genetically identical, disease-free plants

that often exhibit superior growth and more uniformity over plants conventionally seed-grown

(figure 2-5). Such technology holds promise for

important forest species whose long sexual cycles reduce the impact of traditional breeding

approaches. Somatic embryos6 produced in

large quantities by cell culture methods can be

encapsulated to create artificial seeds that may

enhance propagation of certain crop species.

‘Embryos reproduced asexually from body cells.

Figure 2-5.-Plant Propagation— From Single Cells to Whole Plants

The process of plant regeneration from single cells in culture

Cell multiplication Cell wall

— removal

Tissue

Leaf

Virus-free

Field performance tests

Exposure to

selection pressure, ● OO

e.g., high salt . ● °

concentrate ion ● O .

Roots and

shoots

. @

Surviving cells

go on to form callus

+ Root-promoting

hormones

SOURCE: Off Ice of Technology Assessment.
6 ● Technology, Public Policy, and the Changing Structure of American Agriculture

Genetic Modification

Three major biotechnological approaches—

cell culture selection, plant breeding, and genetic engineering—are likely to have a major

impact on the production of new plant varieties.

The targets of crop improvement via biotechnology manipulations are essentially the same

as those of traditional breeding approaches: increased yield, improved qualitative traits, and

reduced labor and production costs. However,

the newer technology offers the potential to

accelerate the rate and type of improvements

beyond that possible by traditional breeding.

Of the various biotechnological methods that

are being used in crop improvement, plant genetic engineering is the least established but the

most likely to have a major impact. Using gene

transfer techniques, it is possible to introduce

DNA from one living organism into another,

regardless of normal species and sexual barriers

(figure 2-6). For example, it has been possible

to introduce storage protein genes from French

bean plants into tobacco plants (Murai, et al.,

1983) and to introduce genes encoding photosynthetic proteins from pea plants into petunia

plants (Broglie, et al., 1984).

Transformation technology also allows the introduction of DNA coding sequences from virtually any source into plants, providing those

sequences are engineered with the appropriate

plant gene regulatory signals. Several bacterial

genes have now been modified and shown to

function in plants (Fraley, et al., 1983; HerreraEstrella, et al., 1983). By eliminating sexual barriers to gene transfer, genetic engineering will

greatly increase the genetic diversity of plants.

This technology will have a major impact on

the seed and plant production industries as well

Figure 2-6.—Gene Modification-insertion of a Desired Gene into the Host Plant Through Vectors

(or gene taxis)

Designed gene

Bacteria

DNA plasmid

(vector)

\

plasmid

“

Infect host plant

SOURCE: Office of Technology Assessment
Ch. 2—Emerging Technologies for Agriculture Ž 47

as on the chemical, food processing, and pharmaceutical industries.

The commercialization of plant biotechnology will require breakthroughs in several technical areas, including increased understanding

of plant cell culture, plant transformation systems, plant gene structure and function, the

identification of agronomically useful genes,

and plant breeding. Increased research funding is needed in these specific areas and generally in the basic plant sciences and in molecularbiology to accelerate technical development.

Commercialization of plant biotechnology will

also depend on other factors, including environmental regulation, university-industry relations, economic incentives, and consumer acceptance.

Improved plants produced by gene transfer

methods should be commercially available in

7 to 10 years. The introduction of plants

produced and selected using cell culture manipulations and certain biotechnology-derived microbial seed inocula or products could occur

earlier.

Plant genetic engineering methods will initially emphasize the same targets for crop improvement (increased yield, improved qualitative traits, and reduced labor and production

costs) as traditional breeding programs do. Ultimately, the technology will lead to improvements not even imagined in American agriculture.

Enhancement of

Photosynthetic Efficiency

Photosynthesis is the fundamental basis for

plant growth (Berry, 1985). Through photosynthesis, energy from sunlight is absorbed by

chlorophyll-containing tissues of the plant and

used to assimilate carbon dioxide into organic

molecules. The photochemical reactions in the

process are intrinsically very efficient. However, several factors inhibit photosynthetic efficiency in plants: 1) certain mechanisms of photosynthesis itself, 2) the efficiency of water and

nutrient use, and 3) environmental stress. Research is ongoing in each of these areas.

Plants vary in their efficiency of photosynthesis. Higher plants have an enzyme (RuBP carboxylase) that causes oxygen to react in a side

reaction during photosynthesis, diverting energy that would otherwise be used to fixate carbon dioxide. This oxygenase reaction, which

appears to result from a metabolic defect in

plants, is encouraged by the high-oxygen, lowcarbon dioxide concentration of normal air. Artificially increasing the content of carbon dioxide in the air partially suppresses this mechanism and generally results in increased crop

yields. This suggests that improvements in the

mechanism of photosynthesis could result in

increased yields, all else being equal.

Plants known as C4 plants have developed a

biological and morphological modification that

reduces the impact of the oxygenase reaction.

As a result, they waste less energy during photosynthesis. C4 plants include corn, sorghum,

sugarcane, and millet. Plants that cannot suppress the oxygenase reaction are called C3

plants. They include wheat, soybeans, cotton,

and rice.

C4 plants have an advantage over C3 plants

when leaf temperatures are high and a disadvantage when they are low. Moreover, C4 plants

use nitrogen and water more efficiently in photosynthesis. Thus water use efficiency could be

increased in warm, arid regions if more C4

plants could be used.

Along-term prospect for improving photosynthetic efficiency lies in research to understand

the basis for the oxygenase reaction and efforts

to inhibit the reaction chemically or to modify

the enzyme by using rDNA technology. Success

will depend on many breakthroughs in understanding the chemistry and molecular biology

of chloroplasts and in manipulating chloroplast

genes.

Molecular biology has already yielded the ability to modify the sequence of amino acids in

RuBP carboxylase to produce modified versions

of the protein. This provides experimental tools

of unimagined power for investigating the

mechanisms of enzyme-catalyzed reactions.

Other research is being directed at improving

the efficiency of use of water and nutrients
48 ● Technology, Public Policy, and the Changing Structure of American Agriculture

through: 1) better management techniques that

use microcomputer-based plant growth models,

and 2) new instrumentation to monitor cropperformance. Improved weather forecasting will

also be important. Breeding plants for efficient

water and nutrient use and for stress resistance

is possible and has already had some impact.

These technologies have the greatest immediate prospect for improving the efficiency of photosynthesis in the next decade, although a strong

research effort is needed to realize these potentials.

Plant Growth Regulators

Plant growth regulators are natural or synthetic compounds that are applied (usually directly) to a plant to alter its life processes or structure in order to improve quality, increase yields,

facilitate harvesting, or any combination of

these (Nickell, 1985). Used commercially since

the 1920s, plant growth regulators have had a

variety of impacts. One of their earliest was in

rooting powders and solutions for the propagation of cuttings. Another was the use of maleic

hydrazide to prevent sprouting in potatoes and

onions during storage.

The biggest boost to plant growth regulation

came with the discovery that phenoxyacetic

acids kill broadleaved plants (such as weeds)

but not grasses, Using such chemicals in herbicides has out distanced economically all other

uses of plant regulators and, until recently,

dwarfed their general importance.

Overall research on plant growth regulation

is currently multipronged. Industrial research

is particularly directed at two major U.S. crops—

corn and soybeans.

An increasingly important research effort is

that for antidotes to herbicides. Called protectants, or safeners, such compounds can be applied to the crop, usually to the seed, to make

it resistant to an herbicide. When the herbicide

is applied to the crop row, it kills only the weeds.

USDA has used plant growth regulators so

successfully in the guayule bush that it maybe

theoretically possible to have a rubber industry within the boundaries of the United States.

Photo cradlt: John Gardner, Brigham Young UniverWy

Scanning electromicrograph of a developing wheat head

reveals vertebrae-like spikelets branching from its axis.

By unlocking the hormonal secrets locked in the tissue

of the spikelets, researchers hope to increase the number

of spikelets per head, and the number of kernel-producing

florets on each spikelet—thus increasing yield.

Ethephon, which is used to prevent coagulation

of latex flow in rubber trees, eliminates the need

to tap the tree daily. Plant growth regulators of

the triethylamine type are used to increase the

total rubber content of the guayule bush. A similar use of growth regulators is the use of para-
Ch. 2—Emerging Technologies for Agriculture ● 4 9

quat on pine trees. The result is a significant

increase in oleoresin content and the possibility that the naval store industry may take on new

life in the Southeast United States.

The success in the sugarcane industry in the

control of flowering, in the use of gibberellic

acid to increase the tonnage of both cane fiber

and sugar, and in the use of ripeners to enhance

sugar yields allows industry to turn its attention to developing dessicants for use as harvest

aids.

In the grape industry the successful use of gibberellins on grapes is stimulating studies on the

control of abscission (the shredding or separating of plant organs such as fruit or leaves) and

the use of ripeners to increase sugar content.

Abscission agents have been used successfully

on cotton, oranges, cherries, and olives, where

it reduces the tenacity of the fruit sufficiently

to allow easy harvest by hand-picking, mechanical harvest, or shaking. Abscission agents have

also been used to thin apple blossoms, changing the yield pattern from alternating lightfruiting and heavy-fruiting years to annual, successfully bearing years.

Plant growth regulators can reduce harvesting costs by changing the shape of the whole

plant or just its fruit to allow easier mechanical

harvesting. Apples, grapes, and wheat are examples. Gibberellic acid is used with grapes,

for instance, to lengthen the pedicel to each

berry. This reduces the rotting that normally

occurs because grapes grow too close together.

The size and shape of both apples and grapes

can be changed by cytokinins and gibberellic

acid.

Regulators can also be used to speed or delay

the maturation of fruit. Success has already been

notable with navel oranges and with pineapple,

peppers, cherries, coffee, tomatoes, and tobacco. In addition, the tremendous losses of food

crops following harvest almost guarantees an

increase in research to develop preharvest and

postharvest preservation through plant growth

regulators.

Finally, preliminary indications with Cycocel

and other chemicals suggest that overcoming

environmental limitations via plant growth regulators should be a fertile field for investigation.

A substantial number of new products or new

uses for existing products can be expected in

the 1990s. Because of the difficulty in registering new compounds, many of the advances will

be extensions of uses of existing products. Since

so much of the chemistry, evaluation, and expensive toxicology has already been done on

existing products, finding new uses for those

products might well have a greater impact than

researching new compounds.

Plant diseases are caused by viruses, fungi,

bacteria, nematodes, and other micro-organisms (Browning, 1985). Collectively, these

organisms cause considerable losses before and

after harvest, an estimated $18.6 billion annually. Only a few of the thousands of species of

pathogens and insects cause concern, however;

the rest are controlled by natural immunity.

Many organisms that do cause loss may theoretically be controlled by managing more wisely

the mechanism of host-plant resistance. This

area is a major one for research.

Some beneficial micro-organisms help protect plants from disease. In addition to their

nutritional benefits, modulating bacterial and

mycorrhizal (root-extending) fungi render some

plants more disease resistant. Micro-organisms

also provide a vast gene pool for improving

plants and other micro-organisms through rDNA

technology. “That technology is already available for synthesizing microbes of naturally occurring products for use as pesticides. Such genetic engineering should lead to new biocontrol

agents; for example, modified plant viruses that

wilI give cross protection. One success story is

that of crown gall, a serious bacterial disease

of many woody and herbaceous plants. Crown

gall is now controlled biologically by the K84

strain of bacterium that is a close relative of the

bacterium that causes the disease. Inoculating

a seed or transplant with K84 produces a bacteriocin that protects against crown gall.
50 Ž Technology, Public Policy, and the Changing Structure of American Agriculture

quently been disappointing. Thus researchers

have turned to minor-effect genes, which are

more difficult to work with but are the most successful way of controlling disease in the homogeneous cultivars demanded by mechanized

Western agriculture. Major-effect genes show

promise for controlling disease in heterogeneous cultivars, as occurred with multiline

oat and wheat cultivars developed in Iowa and

Washington, Even highly epidemic foliar pathogens can be controlled in this manner. A major

line of research may result in using resistance

genes to obtain diversity without sacrificing

bona fide needs (as opposed to merely cosmetic

needs) for uniformity. This may be one of the

fastest ways simultaneously to control certain

highly epidemic diseases and to reap the tremendous potential benefits from plant genetic engineering.

Additional work is needed at all levels of pesticide development, but is especially needed for

completing the development of systemic pesticides that have two sites of activity on the molecule, thereby extending the pesticide’s effective

life. Research is also needed on more effective

delivery systems for systemic pesticides.

Other research will be directed to developing naturally occurring chemicals that will stimulate the plant’s defense mechanisms or enhance activity by biocontrol agents. Ultra-lowvolume delivery systems will be needed for these

and regular pesticides that are active at very low

dosages.

A final important area for research is that of

crop loss assessment. Although it is possible to

assess plant loss from single pathogens, weeds,

and arthropods (and a few combinations of

these), such assessments are less precise when

made for larger areas, several cultivars, and a

wide variety of plant stresses. Research to improve crop loss assessment will help set research

priorities and aid in making management decisions.

Management of Insects and Mites

Insects and mites are humankind’s greatest

competition for food and fiber (Kennedy, 1985).
Ch. 2—Emerging Technologies for Agriculture • 51

Although less than 1 percent of all insect and

mite species are considered agricultural pests,

those pests cause average annual losses to agricultural production of 5 to 15 percent, despite

the expenditure of millions of dollars each year

for agricultural pest control. Thus, protecting

crops from such losses will continue to be an

important component of agricultural production.

Research on this problem is being conducted

in the broad areas of: 1) chemical controls for

insects and mites, 2) genetic manipulation of

plants and insects and their natural enemies,

and 3) information processing.

Because they are highly effective, economical, and fast acting, chemical insecticides and

acaricides (for mites) are widely used for reducing insect and mite populations to subeconomic

levels. Advances in insect physiology, toxicology, and analytical chemistry are leading to the

discovery of new compounds that disrupt the

normal growth and development processes of

insects. Compounds with juvenile hormone activity that prevent an insect from molting to the

adult stage, those with anti juvenile hormone

activity that cause insects to molt prematurely

to the adult stage, and those that interfere with

the normal synthesis and deposition of exoskeleton all hold promise for the future. Similarly,

advances in the chemistry of natural products

and the study of plant defenses against insects

and mites are leading to the identification of

naturally occurring, insecticidal and acaricidal

compounds with novel modes of action. Many

such compounds are likely to be suitable for

large-scale production via fermentation processes with genetically engineered micro-organisms.

With existing application technology only 25

to 50 percent of a pesticide is actually deposited on plant surfaces, and less than 1 percent

actually reaches the plant. In addition to being

wasteful, this situation greatly exacerbates undesirable effects to the environment. One factor is the incorrect mixing and calibration by

pesticide applicators. Efforts are thus being

made to design equipment that injects pesticides

at the proper rate directly into the lines carrying water to the nozzle, eliminating the need

for tank mixing. Other research will ensure

more uniform droplet size, will control spray

drift, and will improve adherence of the spray

to the plant.

Advances in genetic engineering greatly increase the likelihood of new classes of insecticides and acaricides. Insect pathogens, including bacteria, fungi, protozoa, and viruses, are

likely candidates for genetic engineering to enhance their utility as microbial insecticides. The

pathogenic bacterium Bacillus thuringiensis is

already commercially available and widely used

to control caterpillars on certain crops. Genetic

engineering holds great promise for expanding

the spectrum of pests controlled by this bacterium.

Crop varieties resistant to insect pests have

been used to manage insects with success in a
52 ● Technology, Public Policy, and the Changing Structure of American Agriculture

number of important crops. Use of genetic engineering to transfer genes from resistant wild

plants to crop cultivars holds great potential for

insect and mite management, but requires very

specific knowledge of the biochemical bases of

the resistance crop to be transferred. In most

cases, the requisite knowledge is not yet available.

Improvements in the design and availability

of computer hardware and software will produce tremendous changes in insect and mite

management at the research, extension, and

farm levels. To contribute to crop profitability,

insect and mite management entails the processing of tremendous amounts of information

on the condition and the phonological stage of

the crop, the status of insects and mites and their

enemies in the crop, incidence of plant diseases

and weeds and measures used in their control,

weather conditions, crop production inputs,

and insect and mite management options. Computers at the farm level, with access to centralized databases, will allow farm operators to design and implement pest management strategies

for their farms. Some software systems are already in place and are continually being improved. In general, however, improvements in

databases are awaiting advances in knowledge

about pest dynamics and crop pest interactions.

Biological Nitrogen Fixation

Nitrogen is a critical nutrient for crop production (Alexander, 1985). Although abundantly

available-either as atmospheric nitrogen (N2)

or in organic complexes in the soil—nitrogen

in these forms cannot be used directly by plants.

It must first be changed to ammonia (NH3) or

nitrate (NO3). Thus the large supply of nitrogen

needed to grow crops is most commonly provided by nitrogen fertilizers. However, such fertilizers are expensive, and their production consumes a nonrenewable resource, hydrocarbons.

Nitrogen can also be provided through biological nitrogen fixation, a process by which certain bacteria and blue-green algae use an enzyme, nitrogenase, to convert N2 to NH3

. The

most important of these bacteria agriculturally

belong to the genus Rhizobium. These bacteria

Photo credit: Howard Berg, University of Florida

A scanning electron micrograph of a root tip from a

sorghum (Sorghum bicolor) plant with kidney bean

shaped bacteria (Azospirillum brasilense) on its surface.

Such nitrogen-fixing bacteria may live on the root surface

or in the surrounding soil. The white, threadlike

projections are root hairs.

enter the roots of legumes and form nodules in

which they “fix,” or convert, nitrogen in the air

to forms used by plants. A legume may receive

all of its nitrogen needs this way, given the right

Rhizobium. In turn, the rhizobia are somewhat

protected from microbial competition and predation and from other detrimental effects in the

soil environment.

Other kinds of nitrogen-fixing bacteria live

near cereal crops and grasses, possibly providing small, beneficial amounts of nitrogen to the

plants and receiving needed organic compounds

but no protection from detrimental effects in
Ch. Z—Emerging Technologies for Agriculture . 53

return. This relationship is known as associative fixation.

If its magnitude can be increased, the process of biological nitrogen fixation offers an attractive way to supply the large nitrogen demand of crops without the extensive use of

nitrogen fertilizers. To this end, considerable

research has been done in the last decade on

the biochemistry and genetics associated with

the process, and much useful information has

been gleaned from this basic research. Research

is also under way to determine the possibility

of developing cereal crops that fix their own

nitrogen, and recent studies have provided

needed approximations of the amount of nitrogen provided by associative fixation.

To provide enough nitrogen biologically to

sustain high crop yields, however, the stresses

affecting legumes and rhizobia must be better

understood, and improved bacterial strains and

other ways to overcome these constraints must

be found. These developments will come from

a combination of well-established techniques

and agronomic practices as well as new technologies. For example, conventional strain selection and genetic manipulation may be used

to produce strains of rhizobia that can compete

with soil micro-organisms or that can resist abiotic stresses such as pesticides, drought, and

high temperatures. Plant breeding will be used

to develop legumes that are better acclimated

to soil conditions, have greater photosynthetic

activity and less photorespiration, can resist

modulation by less effective soil rhizobia in favor of inoculated rhizobia, and can prolong the

duration of fixation. Less likely to come to fruition in this century, but of great importance,

will be the development of cereals that can fix

their own nitrogen in their tissues or root zones.

If funding is adequate, greater nitrogen fixation from legume-bacterial symbiosis will be

realized in the next 10 years, and that from the

associative fixation of cereal roots will be realized in 15 years. The benefits of these and future improvements will be the reduced use of

hydrocarbons for fertilizer production, an increase in the availability of fertilizer worldwide,

and less contamination of ground water.

water and Soil-Water-Plant

Relations

The distribution of vegetation over the Earth’s

surface is controlled more by the availability

of water than by any other factor (Boersma,

1985). In the United States, agriculture accounts

for over 80 percent of the water consumed;

about 98 percent of that water is used for irrigation of crops, particularly in the more arid

Western States. Several factors complicate the

availability of water for irrigation: 1) cities, industry, and farming are in fierce competition

for the water available; 2) ground-water sources

are gradually being depleted; 3) the costs of

pumping and distributing surface water are

gradually increasing; and 4) many surface and

groundwaters are being contaminated by a variety of pollutants. Thus techniques to conserve

adequate supplies of fresh water have become

important.

Many important contributions have been

made by studying water requirements of crops.

Although this information has helped in planning reservoir and canal sizes, the hope for

breeding plants with lower requirements for

water has not been realized, and no technologies

have been advanced that would help realize this

goal in the next 15 years. Nearly all improvements in water use efficiency have come from

improved irrigation techniques, especially the

timely application of the amount of water

needed and application in a manner that minimizes evaporation. (At present, nearly all the

water taken up by the plant is immediately

passed through and evaporated at the leaf surfaces. Only a very small fraction becomes part

of the plant’s permanent structure.)

Progress in improving the water use efficiency

of crops will hinge on gathering the information needed to develop a theoretical framework

of the mechanisms that influence uptake, use,

and loss of water—in humid regions as well as

arid and semiarid regions. Dramatic progress

in the development of instrumentation now permits researchers to measure many plant physiological responses in real time. It also allows

the recent measurements of plant hormones and
54 Ž Technology, Public Policy, and the Changing Structure of American Agriculture

obtained that tolerate 2 percent sodium chloride, a salt concentration lethal to nonselected

cells.

For the near term, however, traditional methods of plant breeding must be relied on, even

though there is increasing evidence that for

many crops the limits to improvement by this

method are being approached. To breakthrough

this yield plateau, the breeder must work with

the physiologist and biochemist to understand

the stress response hierarchy and eventually to

control enzymes, membrane characteristics,

and mechanisms for communication in the

plant.

The technologies available for immediate application are those that prevent losses in transport, particularly those for farm distribution of

irrigation water. These include drip irrigation,

below-ground distribution of water, deficit irrigation, water harvesting, time and frequency

of application, and the forecasting of time and

frequency of application.7

Land Management

Land is one agricultural resource that cannot

be replaced. Thus a variety of methods and technologies have been developed to conserve soil

while increasing yields. These land management technologies include conservation tillage,

controlled traffic farming, custom-prescribed

tillage, multicropping systems, and organic

farming.

Conservation tillage is a tillage and planting

system that leaves 30 percent of the crop residue on the soil after planting. The use of the

various forms of this system has increased at

over 13 percent annually from 1972 to 1982. The

specific system used depends on local crops,

soil type, moisture levels, and pest infestation,

among other factors. Most conservation tillage

methods eliminate the use of the moldboard

plow, using instead chisel plows or heavy disks

in conjunction with heavy-duty planting equipment to cut through soil residues. Mulch-till

7

For more information on this area see the OTA study WaterRelated Technologies for Sustainable Agriculture in U.S. Arid/

Semiarid Lands, 1983.
Ch. 2—Emerging Technologies for Agriculture ● 5 5
56 . Technology, Public Policy, and the Changing Structure of American Agriculture

system. The specific machines to be used, as

well as the sequence and time of their use, is

defined in the prescription.

Multicropping is the practice of planting more

than one crop on a field during the same growing season. Such crops can be grown sequentially (double cropping) or simultaneously (intercropping). For example, corn and soybeans can

be grown in the same field in strips, reducing

soil erosion, using nutrients more efficiently,

and increasing crop yield. Currently available

machinery and practices are used to perform

the field operations needed in multicropping.

Organic farming reduces or eliminates chemical inputs in favor of more “natural,” and supposedly safer, inputs. The products from this

method are sold to markets willing to pay a

premium for the assurance that chemical fertilizers and pesticides have not been used in production. Organic farmers generally prefer to use

fewer technological inputs than do conventional

farmers, including lower levels of mechanization. They also derive their nitrogen requirements from planting leguminous crops in rotation with nonleguminous crops and sometimes

by adding animal manure. If this system were

adopted on a large scale in the United States,

the need for more mechanization technologies

would be reduced, with the exception of the area

of waste handling systems for livestock.

No new or unique machinery is needed to further implement conservation tillage, multicropping, or organic farming, However, considerable interdisciplinary research will be needed

to implement controlled traffic farming and

custom-prescribed tillage commercially. While

these concepts have many perceived economic

benefits, their true cost-benefit relationships

must be evaluated for the wide variety of crops,

terrain, soil types, and climate existing across

the United States.

Soil Erosion, Productivity ,

and Tillage

The quantity and quality of harvested crops

depend on the amount of land, the suitability

of its soil for growing crops, the biology of crops,

and the environment (Foster, 1985). Most crops

are grown on clean, tilled soil, leaving the soil

exposed and unprotected. Severe erosion can

result, and over time so much soil is lost that

crop yields decrease and some land may be

forced from agricultural production. Excessive

soil erosion is estimated to occur on about 30

percent of U.S. cropland, but its effects on

productivity are thought to have been masked

by new technological inputs like hybrids, fertilizers, and chemicals.

Soil erosion is the detachment of soil particles by the erosive effects of rain, surface runoff, and wind. When erosion removes soil more

rapidly than it can be formed, soil becomes thinner with less rooting depth for crops. When the

topsoil becomes thinner than the tillage depth,

subsoil becomes mixed with topsoil during tillage, degrading the soil. Erosion also removes

the fine silt, clay, and organic particles most important for good soil quality. The resultant increase in sand content of the soil reduces the

soil’s productive potential. Sediment from erosion can create off-site problems through deposits inroad ditches, reservoirs, and river channels. Sediment or the chemicals it transports

can also pollute off-site air and water.

Four major lines of research on erosion control are proposed: 1) improved conservation

farming systems, 2) improved methods for assessing erosion’s impacts, 3) evaluation of the

potential for restoring productivity to severely

eroded soils, and 4) improved understanding

of how to use public policy to encourage soil

conservation.

Of all factors affecting erosion, crop residue

left on the soil surface is most effective in reducing erosion. Research on improved conservation systems will thus emphasize conservation tillage, including reduced tillage, minimum

tillage, and no-tillage. These types of conservation tillage differ only in the amount of soil disturbance and in the amount of crop residue left

on the soil. When matched to soil conditions,

conservation tillage can potentially provide

greater economic return and often equal or

greater yield than that of conventional tillage.

For example, no-tillage works well on well-
Ch. 2—Emerging Technologies for Agriculture . 57

drained, sloping soils but not on cool, poorly

drained soils in the Corn Belt. Although conservation tillage has the fewest drawbacks of

all erosion control practices, considerable development of the method is still required.

The degree of erosion’s impact is a major issue that needs a conclusive answer. The principal tool used to estimate erosion by water is

the Universal Soil Loss Equation. The tool for

estimating wind erosion is the Wind Erosion

Equation. Recent developments in erosion theory and the availability of powerful, portable

computers make possible new methods that are

more detailed and more accurate for estimating erosion over a varied landscape, erosion

from individual storms, and average annual erosion. Remote sensing technology and special

image processing equipment will aid in the collection of data. New field studies have been initiated and several mathematical modeling techniques have been developed to evaluate the

effect of erosion on crop yield.

If eroded soils can reasonably be reclaimed,

the problems of erosion maybe less serious than

presently thought. Current research in the Piedmont region shows that conservation tillage and

multiple cropping (explained later) can be used

to restore productivity. Much research must still

be done in this area.

Although several practices are available for

controlling erosion, many have drawbacks that
58 ● Technology, Public Policy, and the Changing Structure of American Agriculture

hamper adoption by farmers. As a result, various policy alternatives are used and have been

suggested to provide incentives to farmers to

implement soil conservation. Improving the use

of public policy will entail the incorporation of

major analytical tools into an integrated package compatible with affordable computer resources. Such tools will include models for climate, erosion, water quality, crop yield, pests,

and economics.

The major potential impact of this technology on agriculture will be significantly improved erosion control with little loss, if not

gain, in crop yield, improved water quality, improved formability, and increased profit. It is

hoped that this technology will provide farming systems with enough positive benefits that

erosion control becomes a side benefit.

Multiple cropping

Multiple cropping is the intensive cultivation

of more than one cropper year on the same land

so as to use land, water, light, and nutrients efficiently (Francis, 1985). Double cropping, or

the sequential planting of two crops, such as

wheat in the winter and soybeans in the summer, is the only pattern commonly used in the

United States. Intercropping, the simultaneous

culture of two or more crops in the same field

at the same time, is popular with low-resource

farmers.

Although widely used in the lesser developed

countries by farmers with limited land and resources, multiple cropping systems have not

been extensively explored for their applications

in this country. Yet, in addition to their efficient

use of resources, intensive cropping systems

offer several other benefits: vegetative cover

through much of the year, which prevents erosion; the need for less fertilizer, owing to the

contributions of legumes in these systems; and

moderate to high potential yields that are sustainable over time.

Relatively little research attention has been

paid to these systems in temperate agricultural

regions. If such systems are to be widely adopted

in the United States, major new technological

advances may be necessary in four areas: breeding crops for intensive planting systems, understanding competition by plant species for growth

factors, improving plant nutrition through fertilizers and microbiology, and developing mechanization for multiple cropping.

Crop breeding for multiple cropping systems

can lead to the development of crops that can

endure the stress conditions found in multiplespecies crop combinations. Varieties and hybrids already exist that are well adapted to double cropping and reasonably well suited to relay cropping, the planting of two or more crops

with an overlap of the significant part of the life

cycle of each crop. Further refinement is needed

in developing new hybrids and in further selecting for adaptation. Results could be available in 15 years.

The competition for growth factors by crops

that are grown together or sequentially is not

well understood. Such competition includes

that between two plants of the same species,

between two crops of different species, and between crops and weeds. Competition has been

studied in grass/legume mixtures for pasture

systems, and basic work on crop/weed competition gives insight on species interactions. Some

of the results and much of the methodology can

be applied to intercropping. Since existing varieties can be used for most preliminary work,

results could be available in 6 to 10 years.

Multiple cropping entails a greater input of

nutrients or an alternative approach to plant nutrition. Low-resource alternative cropping systems include rotations, minimum-tillage methods, and use of low levels of fertilizers that do

not disturb the biological balance in the soil.

Research on nitrogen fixation is an active area

at present, but a basic understanding of plant

nutrition could take 10 to 15 years to develop.

Machines already available can be used for

planting and for most other cultural operations.

Through modifications of existing tillage, planting, and cultivating equipment, the farmer can

accomplish multiple cropping. However, the development of a combine that can harvest two

crops simultaneously is necessary for intercropping to have widespread applications. This

short-term objective could be achieved within
Ch. 2—Emerging Technologies for Agriculture . 59

5 years, using expertise from the commercial

sector.

The principal impacts from multiple cropping

will be reduced production costs and increased

output per year from a given unit of land. The

greater sustainability of production and the reduction in energy use would lead to a more stable agricultural sector.

Weed Control

The cost of weeds to agricultural production

is one of the most expensive factors in crop production, amounting to more than $20.2 billion

annually (McWhorter and Shaw, 1985). Losses

caused by weeds include not only direct competition of weeds to reduce crop yields, but also

reduced quality of produce; livestock losses;

weed control costs; and increased costs of fertilizer, irrigation, harvesting, grain drying,

transportation, and storage.

Weeds can be defined as plants growing

where they are not wanted. They range from

trees and shrubs to grasses and even cultivated

crop species. Volunteer corn, for example, is

becoming an increasing problem in soybean

production as more conservation tillage practices are being adopted.

In modern agriculture, weeds are controlled

through integration of crop competition, crop

rotation, hand labor, and biological, mechanical, and chemical methods into integrated weed

management systems (IWMS). Since 1950, the

use of mechanical power for weed control has

increased 30 percent, and herbicide use has increased sevenfold. However, manual labor has

decreased 40 percent. As a result of modern

weed control technology, farming is now less

physical and more technological.

Although significant progress has been made

in developing new weed control technology,

weeds continue to cause severe reductions in

yield and quality. Weeds often limit expanded

use of conservation tillage and multicropping.

New difficult-to-control weed problems develop

through ecological shifts and because more

established weeds develop increased tolerance

to herbicides.

New weed control technologies needed include: 1) improved chemical and biological

methods, 2) allelopathic chemicals to bioregulate weeds, 3) crop cultivars with improved tolerance to herbicides and the discovery of the

nature of weed resistance to herbicides, and 4)

the development of improved IWMS for conservation tillage and for annual multicrop production.

Development of selective herbicides has spearheaded the advances in weed control technology during the last 30 years and will continue

to be important in the foreseeable future. Major breakthroughs needed in this area include

a nonselective chemical to control vegetation

in falIow fields, more selective chemicals for

control of broadleaved weeds in dicotyledonous crops (e. g., cotton, soybeans), and a chemical that can be applied postemergence for effective contol of perennial weeds. There is also

interest in control of weeds by bioagents, particularly with native pathogens like fungi.

Photo credit U S Department of Agrfcu/fure Agricultural Research Service

Seed-killing methyl isothiocyanate kept crabgrass seeds

(Digitaria sanguinalis) in flask on right from germinating.

One week after the seeds were placed in flasks the

untreated crabgrass seeds in flask on left have germinated,

The chemical degrades rapidly in the soil,

usually within a few days.
60 ● Technology, Public Policy, and the Changing Structure of American Agriculture

The effectiveness of many herbicides is limited by soil activity; for example, some microbial

populations rapidly degrade certain herbicides,

limiting the residual effects of the herbicides.

Advances in controlled-release technology

could aid in this and other problems by reducing volatility and rates of application, reducing

herbicide movement through the soil profile,

increasing crop selectivity, and reducing environmental exposure. Also helpful is a class of

chemical protestant that slows the action of soil

micro-organisms, permitting more cost-effective control.

Crops can be protected against the toxicity

of certain herbicides through chemical antidotes

called safeners, another class of plant protectant. When applied to seeds or soil, these chemicals make an otherwise susceptible plant species tolerant to an herbicide without affecting

the weed control aspect of the herbicide.

Plants themselves release secondary chemicals during metabolism that can be toxic to other

plants. Such allelopathic chemicals are being

studied for their potential use in weed control.

Developments in genetic engineering may allow the availability of herbicide-tolerant crop

cultivars in agronomic crops in the next 10 to

15 years. Many weeds have evolved a tolerance

to herbicides. The availability of herbicidetolerant crop cultivars would permit the use of

herbicides at higher rates to reduce the evolution of tolerance and would permit the use of

herbicides that were previously nonselective.

Finally, research efforts need to be increased

to develop more effective IWMS. Basic ecological research is needed to understand weed population dynamics, weed threshold levels, and

shifts in weed populations caused by control

technology. Research is also needed on how to

use rotational tillage to aid in controlling the

weeds that develop through several years of conservation tillage. Perennial weeds become particularly troublesome after only 2 or 3 years and

have forced many farmers to return to conventional tillage.

Improved weed control technology will result in a slow but steady decrease in production costs and an estimated 10 percent increase

in the cost of weed control. Increased use of conservation tillage will necessitate increased herbicide use in the next two decades.

Commercial Fertilizers

The substantial use of commercial fertilizers—

about 50 million tons per year—is generally credited with 30 to 50 percent of the cost of U.S. agricultural production (Davis, 1985). Corn and

wheat are the most heavily fertilized crops.

Commercial fertilizers supply crops with one

or more of the primary plant nutrients (nitrogen,

phosphorus, and potassium) in forms usable by

crops. Nitrogen and phosphorus are produced

in the United States; most (about three-fourths)

potassium must be imported from Canada. Nitrogen, phosphate, and potassium intermediates

are produced in large plants and then shipped

to small plants for combination into final products.

Although expenditures for research and development (R&D) in fertilizer technology are less

than 10 percent of that for the entire chemical

industry (as a percent of sales), the R&D that

exists is aimed at maximizing fertilizer effectiveness, minimizing costs, and protecting the environment.

At present, one-half of the nitrogen applied

to the soil is lost to the plants through a variety

of inefficiencies, some of which are still not

understood. Several new types of nitrogen products under development might improve efficiency of use. They include products with inhibitors to decrease undesirable transformations

in the soil (vitrification and urease inhibitors),

products coated for controlled release (e.g., the

sulfur-coated urea sold for turf and horticultural

uses), and acidified products that decrease the

volatilization of ammonia (the reaction of urea

with mineral acids in the soil). In addition, the

use of urea phosphate, urea-nitric phosphate,

and sulfur-coated urea may allow closer placement of fertilizer to seed without inhibiting germination. Urea phosphate may also aid in recovering phosphorus, 80 percent of which is

unused by the plant and remains fixed in the

soil in insoluble forms.
Ch. 2—Emerging Technologies for Agriculture Ž 61

Other research is under way to decrease the

energy required to produce, transport, and apply fertilizers. The escalation in oil prices following the oil embargo spurred efforts to design new energy-efficient plants and to retrofit

existing plants. In addition, several new urea

processes that have been announced will decrease production energy requirements by 25

to 50 percent. New phosphoric acid technology

also promises energy savings. To avoid dependence on oil or natural gas (the raw material

for ammonia for nitrogen fertilizers), technology for the production of ammonia from coal

is in advanced stages of development. Finally,

efforts are being made to increase the nutrient

content of fertilizers so that the energy expended

in transporting and handling will be decreased.

Another area under development is that of

phosphate fertilizer production. Because reserves of high-quality phosphate ore are being

depleted, researchers are attempting to use

lower quality phosphate ore in fertilizers. Their

efforts focus on removing the carbonate impurities in such ore and on determining what effect such impurities would have on the efficacy

of phosphate fertilizers.

In reduced tillage agriculture, R&D efforts are

directed at developing urea-nitric phosphate,

urea with urease inhibitors, and urea phosphate

and urea sulfate, all of which have the potential to decrease ammonia loss from surfaceapplied urea. Also, new types of equipment are

being designed for precision placement of fertilizer and for simultaneous application of fertilizer with seed.

New developments in the industry evolve

rather slowly because of the low level of R&D.

Therefore, any new technology that is likely to

be introduced by 1990-2000 would have to be

under development now. No revolutionary or

radically new products or processes appear to

be near commercialization.

The future direction of energy prices will

probably be the major factor affecting the commercialization of new technology, Because the

production of nitrogen, particularly ammonia

production, is the most energy-intensive operation for the industry, new nitrogen technology is especially geared to energy prices. The

high cost of new facilities is also a deterrent to

the adoption of technology. In many situations

the industry will prefer to debottleneck or add

to existing plants to conserve capital.

Organic Farming

Organic farming uses many conventional

farming technologies but avoids, where possible, the use of synthetically compounded fertilizers, pesticides, growth regulators, and animal feed additives (Liebhardt and Harwood,

1985). It relies on crop rotations, crop residues,

animal and green manures, legumes, off-farm

organic wastes, mechanical cultivations, mineral-bearing rocks, and biological pest control.

Organic farmers tend to integrate their farming techniques to a greater extent than conventional farmers do.

In the last 6 to 8 years, several studies have

compared organic farming with conventional

farming. Although final conclusions must await

more rigorous studies and a wider sample of

farms, preliminary conclusions show some interesting benefits of organic farming: first, yields

per acre are generally equal to or only slightly

less than those from conventional farming.

Some organic farms have significantly higherthan-average yields. Second, production costs

are lower by a high of 30 percent and an average of 12 percent, while energy inputs per unit

produced are lower by 50 to 63 percent. Few

or no insecticides, fungicides, and herbicides

are used. Third, soil erosion is significantly reduced through various cultivation practices. Although organic farming maintains soil quality

better and reduces contamination of air, water,

soil, and the final food products, much research

is needed to determine just why organic practices have this effect and to determine how to

maximize the integration of organic practices.

One of the most significant factors in reducing production costs and energy inputs in organic farming is nitrogen self-reliance. Many

organic farmers increase nitrogen fixation in

their crops by seeding legumes between rows

of grain crops during the growing season or after harvest. Research is under way to breed
62 . Technology, Public Policy, and the Changing Structure of American Agricultur e

plants that fix nitrogen more efficiently or that

fix nitrogen longer in the season. By 1990, research already on-line in this area should be well

developed.

For weed control, cover crops are used in rotation; for example, sorghum crops are used to

suppress nutsedge. In addition, crop residues

are used in conservation tillage to suppress sensitive weed species. Much information about

weed control should be available by 1990; however, the technology for wide application of crop

rotations will not be available for at least 5 to

10 years.

Organic farms appear to cycle nutrients more

efficiently than conventional systems do. One

reason is the reduction in erosion that occurs,

which allows better soil tillage and better maintenance of productivity. Furthermore, some organic practices enhance the soil’s ability to suppress disease, Scientists hope to identify the

helpful bacteria and bacterial byproducts involved in disease resistance and to harness them

as biocontrol agents.

Biocontrol agents are also used to control insects. One example is the tansy, an insect-repellent plant that shows potential for controlling

the Colorado potato beetle. Another example

is the use of an antijuvenile hormone, extracted

from a common bedding plant, that induces premature metamorphosis in insects, shortening

their immature stages and rendering the adult

females sterile. In 10 to 20 years, biological pest

repellents will probably dominate the market

because of their safety for users, consumers, and

the environment.

Converting from conventional to organic

farming takes about 3 to 5 years, during which

yield may initially be reduced. Some of this problem relates to the nitrogen content of the soil

and to weed pressure.8 However, detailed studies of holistic systems are needed to understand

better the extremely complex changes in nutrient flow in soil during organic and conventional

farming. The potential impact of such studies

on U.S. agriculture in the next 10 years could

‘This can be minimized by selecting the correct crops and structuring the production system to avoid nutrient deficiency or weed

problems,

be considerable. If farmers shifted to organic

production, farms would be more diverse biologically and economically, and the small farm

could remain economically competitive and

ensure diverse, competitive food production

systems.

Communication and Information

Management

Technology for communication and information management helps farm operators collect,

process, store, and retrieve information that will

enable them to manage their farm so as to minimize costs, maintain and improve product quality, and maximize returns. There are three basic

components to such technology: 1) microcomputer-based hardware systems for information

processing, storage, and retrieval; 2) high-speed

LANs for onfarm communication of digital information; and 3) applications software. The

computer allows farm operators to keep track

of more detailed information, apply complex

problem-solving techniques to this information,

and thereby make better, more timely, decisions.

Microcomputers appropriate for onfarm use

cover the range of business-class computers.

Larger and more complex farm operations will

generally benefit from larger, more complicated

computer systems. Onfarm computers are likely

to be subject to more adverse operating environments than those found in typical nonfarm

businesses. Thus some additional equipment

and adaptations are needed for onfarm operations (Battelle, 1985).

While LAN technology is rapidly becoming

more mature and standardized, onfarm installations are likely to be more expensive per node

than the typical business system. Farm nodes

are generally much farther apart than nodes of

the average office system. Farm installations

placed among several separate buildings are

also more susceptible to lightning-induced electrical problems. Photoelectric isolators at every

node will enable use of copper wiring between

nodes. Alternately, use of LANs with fiber optic cabling will eliminate problems from electromagnetic interference.
Ch. 2—Emerging Technologies for Agriculture . 63

Photo credit: Dr. S.L Spahr, University of Illinois

Example of microcomputer-based system for onfarm use.

This system collects, processes, stores, and retrieves

information to control computer feeders and

electronic milk flow meters.

Many software packages sold for use on farm

computers are general-purpose packages that

are identical to those used in other businesses,

Spreadsheet programs and database management systems fall into this category. Other packages have only minor modifications and upgrades. The most expensive class of software

is generally that written for specialized applications. Few farms are large enough to afford

custom programming for their own operations.

The range of specialized applications programs

that have been developed and are being developed by extension personnel at land grant colleges is quite large, Agricultural software from

commercial sources and the land grant institutions is generally task-specific.

Another promising software concept is that

of a fully integrated system that would allow

the farm operator to simulate the outcome of

small and large changes in production practices,

The software could generate distributions of

prices and weather impacts and simulated biological growth functions, It could produce detailed listings showing expected costs, returns,

production schedules, cash flows, and net income streams, working within the constraints

of those assets and productive potentials that

the operator chooses to consider fixed. Such

software would give operators much greater

ability to maximize income and flexibility in

planning for growth and in

changes in the economic and

ronment.

Monitoring and Control

Many processes in plant and

responding to

technical enviTechnology

animal production may be monitored and controlled by new

and emerging electronic technologies. In some

cases these devices are designed simply to detect

certain conditions and report the information

to the farm operator. In other cases, the technology operates essentially autonomously, without operator attention. Devices of this nature

are usually programmable, can operate continuously, can be designed to be very sensitive to

changes in target variables, and can respond

very quickly. These devices, therefore, offer improvements in speed, reliability, flexibility, and

accuracy of control, and sometimes reduce

Photo credit: Dr. S.L. Spahr, University of IllnoIs

Electronic animal identification unit around cow’s neck

with automatic dispensing grain stall in background. Cow

goes into stall, is identified electronically, and has grain

dispensed to her automatically. Using computer controls,

the feed dispensed is individualized to provide each

cow a different amount of feed and a different protein

percentage based on her nutrient needs
64 ● Technology, Public Policy, and the Changing Structure of American Agriculture

labor requirements (Battelle, 1985). Some applications of this technology include irrigation

control, pest monitoring and control, and the

automatic animal identification and feeding system in livestock operations.

Positive identification of animals is necessary

in all facets of management, including recordkeeping, individualized feed control, genetic improvement, and disease control. All animals

could be identified soon after birth with a device that would last the life of the animal. The

device would be readable with accuracy and

speed from 5 to 10 feet for animals in confinement and at much greater distances for animals

in feedlots or on pasture. Research on identification systems for animals has been in progress

for some years, especially for dairy cows. For

example, an electronic device now used on dairy

cows is a low-power radio transponder that is

worn in the ear or on a neck chain. A feed-dispensing device identifies the animal by its transponder and feeds the animal for maximum efficiency, according to the lactation cycle and the

life cycle of that animal. This technology also

permits animals in different stages of production to be penned together yet still be fed

properly.

The largest potential use of electronic devices

in livestock production will be in the area of

reproduction and genetic improvement. Estrus

in dairy cows can be detected automatically by

using sensors that remotely detect small changes

in the body temperature of the cows. Such an

estrus detection device could prove profitable

in several ways:

●

●

●

●

Animals could be rebred faster after weaning and could increase the number of litters per year.

Animals that did not breed could be culled

from the herd, saving on feeding and breeding space.

Time would be saved because breeding

would be done faster.

Embryo transplants would be easier because of better estrus detection.

Environmental control of livestock facilities

is another area where monitoring and control
Ch. 2—Emerging Technologies for Agriculture ● 6 5

Figure 2-7.—Configuration of Monitoring and

Control Technologies in Agriculture

Example 1. Positive feedback for irrigation control

Field Controller

(+) Bends on-off

signal ●

Additional Actuator

water water pump

on

flow valve signal

Example 2. Negative feedback for temperature control in livestock

confinement

Building Thermostat

, “

Temperature signal

Thermo- -. - - - . - -

meter

( - )

Removes

hot Exhaust

air fan

SOURCE. Off Ice of Technology Assessment

evaluated for use on a regional scale by a USDA

Animal and Plant Health Inspection Service regional program, Such systems will provide rapid

analysis, summarization and access to general

crop summaries, observer reports, pesticide and

field management information, reports of new

or unknown pests, general pest survey information, and specified field locations with pest

severities.

Other software systems designed to facilitate

directly the implementation of pest management programs are in use and are continually

being improved, The Prediction Extension Timing Estimator model (Welch, et al., 1978) is a

generalized model for the prediction of arthropod phonological events but is sufficiently flexible to be used for management in many agricultural and nonagricultural systems. For example,

it is used as a part of the broader biological monitoring scheduling system developed in Michigan by Gage and others (1982) for a large number of pests on a wide variety of crops (Croft

and Knight, 1983).

An irrigation control system is another example of using monitoring and control technology

(figure z-z). Since irrigation decisions are complex and require relatively large amounts of

information, microcomputer-based irrigation

monitoring and control systems are especially

useful in areas where soils have variable percolation and retention rates, where rainfall is

especially variable, or where the salinity of irrigation water changes unpredictably, In this

system, a network of sensors is buried in the

irrigated fields, with radio links to the central

processor. Additional sensors may include

weather station sensors to estimate crop stress

and evaporation rates, as well as salinity sensors and runoff sensors. The central processor

can then automatically allocate water to each

field according to the needs of the crops in each

field, subject to considerations of cost, leaching requirements, and availability of water.

Telecommuications

Telecommunications technology provides

links for voice communications and the transmission of digital data between farms and other

firms and institutions. Through such technology, farms, firms, and institutions can be joined

together in a large number of formal and informal networks. These networks enable farmers

to have relatively rapid, inexpensive, and reliable access to central databases, centralized software packages, and information on weather,

markets, and other subjects of interest. Virtually the same technology will be applied to both

animal and plant agriculture. Telecommunications include high speed, low speed, and radio

telecommunications, satellite base communications, and remote sensing technology (Battelle,

1985).

High-speed or high-bandwidth communications allow the farmer to send and receive much

larger amounts of data at lower costs per bit of

information. This capability is needed for videotext services, teleconferencing, and, in many

cases, satisfactory real-time use of remote computer facilities,

High-speed telecommunications is still undergoing substantial amounts of development. New
66 ● Technology, Public Policy, and the Changing Structure of American Agriculture

transmission capabilities or new technologies

are needed for bringing high-speed telecommunications to most rural areas, High-bandwidth

telecommunications can be provided by technologies that range from conventional highcapacity, coaxial cable, microwave relay systems to fiber optics systems and high-bandwidth direct transmit/broadcast satellite systems. High-bandwidth send-and-receive service for the average farm operation is not likely

to be available for some time.

The existing telephone system is capable of

handling the demand for slow-speed telecommunications services in many rural areas, The

latest generation of microcomputers, modems,

and communications software is capable of

automatically accessing remote databases and

quickly downloading and uploading information

at regular intervals without operator attention,

Rural areas that install fiber optic telecommunication systems will have enormous information capacity that will easily support very high

data rates. In fact, the perennial dream of lowcost, two-way videoconferencing, education,

and entertainment may well become a reality

in these rural areas by 1990 or 2000,

A number of emerging radio telecommunication technologies will provide improved service in rural areas without the need to rewire the

local telephone networks. These technologies

can be put into two groups: ground-based, lowpower radio repeater systems, such as cellular

mobile phone systems; and satellite-based communication systems. In principle, the cellular

radio technology being installed in major cities can be expanded to smaller cities, towns,

and rural areas at higher power levels for use

in voice and data communications. For applications where data transmissions are sufficient

and instantaneous communications are not necessary, technology for packet radio messages

may provide substantial savings. packet radio

systems use ground-based repeater stations to

funnel messages with a standard, or “packaged, ” format from distributed users to one

another or to a point where the messages can

be inserted into a national telecommunication

network, Messages are entered at each user station, then converted into encoded “packets”

complete with addresses and distribution instructions. Each user station then transmits to

the local repeater station when the transmission channel is free. This technology may enable

cellular radio repeater technology to be extended to especially remote and sparsely populated areas and to areas where the basic telephone system is inadequate and is unlikely to

be upgraded.

Satellite-based communication technologies

may provide very high-capacity telecommunication channels for rural areas. These systems

may be the only feasible high-capacity link for

some especially isolated rural areas. Large farms

may opt to establish their own ground stations

for satellite-based telecommunication, but new

generations of communication satellites may

have the power to serve many small individual

subscribers in remote rural locations.

Almost all commercial satellite communication systems employ satellites in geosynchronous orbit.9 Alternately, the feasibility y of using

low-cost, low-Earth orbit satellites for the collection, storage, and rebroadcasting of message

packets has been demonstrated by amateur radio groups. Commercial satellites using this design could enter service by 1990.

Remote sensing is a collection of technological

systems used to detect, process, and analyze

reflected and emitted electromagnetic radiation

at a distance. This includes the National Oceanic and Atmospheric Administration weather

satellites, land and ocean resource mapping satellites (the Landsat series), airborne camera and

electronic sensor systems, and ground-based

photogrametric and radiometric sensors. Information from remote sensing technology is used

for a wide range of applications. Some examples are weather reporting and forecasting, land

use planning, environmental monitoring, crop

production estimates, soil mapping, range and

forest management, mineral exploration, and

watershed management.

Remote sensing technology in the form of

weather forecasting has already made a great

‘Traveling in orbit around the Equator at the same speed as

the Earth rotates,
Ch. 2—Emerging Technologies for Agriculture ● 6 7

impact on agricultural production. Weather

reports and forecasts help farmers decide when

to plant and when to harvest, Fruit growers depend on local weather forecasts to help make

frost protection decisions.

Farmers can also use remotely sensed information to make other management decisions,

Soil moisture levels can be estimated accurately

for large northern plains wheat farms that depend on stored soil moisture. Selection of fields

for rotation, seeding, and fertilizer rates could

then be planned for the available moisture on

different parts of the farm to optimize net income.

Remote sensing technologies provide crucial

and timely information for the process of estimating global crop production. These crop estimates can have large impacts on price levels

and price variability. Estimates of crop production in different countries are an important factor in the administration of commodity and export policies.

Labor-Saving TechnoIogy

Labor-saving technologies have made a significant dent in the cost of labor for animal production and, to a lesser extent, for field crops.

The change to large-scale confinement operations of livestock and poultry has dramatically

reduced labor costs through the automation of

feeding, waste disposal, and egg collecting, For

field crops, reductions have come from using

larger tractors, combines, and tillage equipment.

Opportunities still exist, however, for reducing labor costs, particularly through: 1) mechanization of fruit and vegetable operations, and 2)

robotic farming. Researchers and growers are

exploring ways to use these technologies with

other technologies to change cultivars and cultural practices, rearrange work patterns, develop labor-aid equipment (e. g., conveyors and

hoists), improve human relations, and develop

labor replacement equipment (Battelle, 1985).

Mechanical harvesting is most applicable for

fruits and vegetables that are to be processed

or dehydrated, because such products will not

show the effects of mechanical handling. Most

fruits and vegetables targeted for the fresh market must still be harvested by hand.

The most economically important of the process vegetables are the potato and the tomato,

both of which are mechanically harvested. The

development of mechanized tomato harvesting

is a particularly good example of technological

success: the concurrent development of a mechanical tomato harvester and anew, high-yield

process tomato, shaped for easy mechanical harvesting, gave California a production increase

of 300 percent with only a 50-percent increase

inland. Many other process vegetables are harvested mechanically, and research is still under way to automate the harvesting of cauliflower, lettuce, okra, and asparagus.

Of the fruit crops, citrus crops are the largest

in total value. Although oranges would seem

to be ideally suited to mechanical harvesting

(80 percent of the crop is processed), the “bag

and ladder” method of hand picking remains

the most economical and widely used method.

For mass removal of some crops, mechanical

or oscillating-air tree shakers, usually in conjunction with abscission chemicals, are used.

(Mechanical shakers are also used to harvest

process grapes and process deciduous fruits,

such as apples, pears, and peaches,) Technology trends in citrus production point to higher

density plantings and the maintenance of trees

at a height of 5 meters or less. If high fruit yields

result, there is good potential for development

of over-the-row equipment for production and

harvesting.

The use of robotics in agriculture is likely to

be centered on high-value, labor-intensive crops

like oranges. Research is also being done on apple harvesters that will use ultrasonic sensors

to detect tree trunks and steer around the trees.

It is conceivable that by 1990, reductions in cost

and increases in the speed of operation will

make such robotic technology economically attractive. Robotics may also have applications

in animal agriculture—for example, in checking calving and farrowing, identifying estrus,

managing feeding, and handling manure.

Future labor replacement in agriculture will

likely involve some aspect of electronics tech-
68 ● Technology, Public Policy, and the Changing Structure of American Agriculture

nology, much of which will be adapted from offshoots of military and aerospace technology.

Such technology will have to be adapted to withstand the variety in agricultural environments

and will have to have better cost-benefit ratios

for widespread adoption. Many new electronics

technologies may affect the quality more than

the quantity of labor. People with higher level

skills will be needed to operate and maintain

the new, more complex equipment.

Engines and Fuels

Continued improvements in engines and fuels

can be expected in the energy efficiency, durability, and adaptability of self-propelled farm

equipment. These improvements are likely to

come from R&D in a number of areas: 1) adiabatic and turbocompound engines, 2) electronic

engine controls, and 3) onboard monitoring and

control devices (Battelle, 1985).

Expenditures by farms on liquid fuels were

$10 billion in 1982. Even modest improvements

in energy efficiency in farm production will

have a significant impact on the total cost of

production in agriculture. However, these technologies will not be adopted unless they also

deliver significant increases in productivity to

individual farms. Farms are continuing to improve their energy efficiency by converting from

gasoline-powered equipment to diesel-powered

equipment at a rapid rate. Diesel fuel has more

energy per dollar, and diesel engines extract

more useful work from each calorie of fuel than

do gasoline engines.

All conventional internal combustion engines, including diesel engines, are thermally

inefficient because they must dispose of large

amounts of heat by means of cooling systems.

If engines can be constructed of special ceramics to withstand high operating temperatures, they would not need cooling systems and

would be much more efficient. Engines of this

type are called adiabatic engines.

Turbochargers are being widely used to increase the performance of gasoline and diesel

engines by putting some of the exhaust gas

energy to use. Even more work can be extracted

from the exhaust gases by means of a device

called a turbocompound unit. This device captures exhaust gas energy and applies it directly

to the drivetrain of the vehicle instead of using

the energy solely to compress intake air, as in

the conventional turbocharger. Turbocompound

units will be especially useful when installed

on adiabatic diesels, owing to the high energy

content of the exhaust gases from these engines.

Electronic engine controls are being introduced by some manufacturers in an effort to

improve the efficiency of the fuel injection system on diesel engines. As with similar systems

developed for automotive applications, this

technology automatically works to optimize fuel

delivery under changing conditions, based on

information from engine sensors, implement

sensors, and operator inputs. Minimization of

tractor wheel slip by means of onboard monitoring and control technology also improves fuel

efficiency. Other applications of this technology to onboard control of field operations is described in the section on monitoring and control technologies.

Considerable research has been conducted on

the use of alternate fuels for agricultural applications. Much of this research was motivated

by the oil embargo crisis and rapidly rising liquid fuel prices of the 1970s. None of the alternate fuels hold much promise to increase the

fuel efficiency of conventional engines, This research has revolved around the use of onfarm

production of ethanol for use primarily in gasoline-powered equipment and the onfarm pressing and refining of sunflower oil for use in dieselpowered equipment. Neither fuel is economically competitive with purchased liquid fuels

in the absence of substantial subsidies. Moreover, both fuels are more difficult to use than

fossil fuels, Ethanol-based fuels tend to absorb

moisture and to separate in storage. Vegetable

oil-based diesel fuels require special processing, which changes their chemical and physical characteristics, before they can be used reliably in unmodified diesel engines.

Crop Separation, Cleaning, and

Processing Technology

New technologies being developed to separate, clean, and process crops offer many ben-
Ch. 2—Emerging Technologies for Agriculture ● 6 9

efits in increased yield, quality, and value of

crops. There are two major lines of research in

this area: 1) improvements in separating and

cleaning grain, and 2) in-field or onfarm processing of forages and oilseeds (Battelle, 1985).

The mechanization of grain harvesting and

separation has been one of the most important

factors in reducing the labor cost of grain production. Even small improvements in labor or

capital efficiency have significant impacts on

grain production because of the large total cost

of producing the U.S. cash grain crop.

The basic methods of grain separation used

in all combines are mechanical beating, aeration, and screening. While these methods have

been continually refined, the same basic techniques have remained unchanged since antiquity.

Grain harvesting productivity has been improved over the past three decades by increasing the size and power of combines. Combines

separate grain by beating and rubbing the grain

stalk between a stationary surface and a cylinder rotating at high speed. The chaff and other

debris are cleaned from the grain by blowing

a large amount of air through the grain/chaff

mix, The difference in the ability of the two materials to float on the airstream effects their separation.

Constraints on the total size and weight of

combine equipment that can be transported

over public roads limit the increases in general

harvest productivity. Within this constraint,

however, continued improvements in microprocessor-based monitoring and control technologies incorporated into grain combines will

permit significant increases in capital and labor productivity. New electronic sensors will

detect grain loss more accurately, allowing the

operator to make adjustments quickly. Moreover, if enough of the internal monitoring and

control of the grain separation process can be

automated, and if grain losses are minimized,

combine operators will be able to devote all of

their attention to guiding the combine and can

proceed at higher speeds. At present, the rate

of travel must be held to 5 to 7 acres per hour

so that the combine operator can monitor several functions of the combine.

Improvements in cleaning grain will result

in a higher quality of grain and a reduction of

dockage at the point of distribution or sale. New

technologies will detect contaminants and remove them on the combine or as the grain is

transferred into farm storage. Further improvements in grain cleaning will necessitate the use

of automated aeration and screening processes.

Another way to increase the value of a crop

is to do some of the processing in the field or

on the farm. A good example of in-field processing is the extraction of leaf protein juice from

alfalfa for use as high-value feed for pigs and

poultry and as a food additive for humans. The

residue of the process can be used as roughage

for livestock.

Onfarm extraction of oil from oilseed crops

such as soybeans and sunflowers has technical

merit as a way for farms to produce a diesel fuel

substitute or extender for tractors, combines,

and other equipment. Onfarm production of

vegetable oil fuel is more efficient than the conversion of grain to ethanol fuels. Moreover, the

oilseed meal and glycerol byproducts from oilseed processing have substantial value as animal feed and chemical feedstocks. However, the

principal technology employed uses highly volatile solvents and has a large requirement for capital, prohibiting its practical use on the farm,

Moreover, present vegetable oil prices are approximately double the price of diesel fuel.

The adoption of onfarm processing of forages

and oilseed is contingent on many domestic

and international economic, political, and institutional factors that currently override technical considerations. On the other hand, most

technologies to improve combine performance

should be achieved by the end of the decade,

at costs that will not significantly add to the total costs of today’s combines.
70 . Technology, Public Policy, and the Changing Structure of American Agriculture

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Chapter 3

Impacts of Emerging Technologies

on Agricultural Production
Contents

Page

Technology Adoption and Primary Impacts . . . . . . . . ., . . . . . . . ... , . . . . . .. .75

The Timing of Commercial Introduction. . . . . . . . . . . . . . . . . . . . . . . .., ....76

Primary Impacts. ,. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .., ...76

Adoption Profiles ......,. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . , . . . . . . . . .78

Projection of Per-Unit Crop Yields and Livestock Feed Efficiencies . .......79

Projections of Aggregate Crop and Livestock Production . . . . . . . . . . .......82

Crop Production. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .......82

Livestock and Milk Production . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ., ..,.,.83

Summary and Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ., . . . . . ...85

Chapter 3 References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .......85

Tables

Table No. Page

3-1. Estimated Percentage Change in Crop Yield.. . . . . . . . . . . . . . . . . . . . . . . 77

3-2. Estimated Percentage Change in Animal Feed and

Reproductive Efficiency . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 78

3-3. Estimates of Crop Yield and Animal Production Efficiency . . . . . . . . . . . 80

3-4, Historical and Projected Rates of Annual Growth in Crop Yield.. . . . . . 80

3-5. Projections of Crop Production . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 82

3-6. Projections of Animal Production. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 84

Figure

Figure No. Page

3-1. Logistic Adoption Curves for Corn, Package A . . . . . . . . . . . . . . . . . . . . . 79
Chapter 3

Impacts of Emerging Technologies

on Agricultural Production

Introducing to the marketplace the 150 emerging technologies forecasted in this study raises

questions about the effects these technologies

will have on crop yield, livestock feed efficiency,

and future food production. Many people are

concerned that the trends of major crop yields

are leveling off and that the world may not be

able to continue to produce enough food to meet

the demands of its growing population. However, OTA’s analysis indicates that the United

States can continue to meet foreign and domestic demand for agricultural products if agricultural research is adequately supported and if

economic and political environments are favorable. What this conclusion means in practice

is the subject of this chapter.

OTA commissioned leading scientists, specialists in the 28 technological areas, to prepare

state-of-the-art papers. Each paper: 1) defined

and delineated the scope of a technology area,

2) identified four or five major lines of research

where significant technologies were likely to

emerge by 2000, 3) discussed the current state

of technology development, 4) identified major

breakthroughs in other science and technology

areas that would be necessary for successful development of the technology in question, 5) discussed the institutional arrangements necessary

for the research of the technology to be conducted or supported, 6) estimated the time in

which a particular line of research would likely

be completed and the resulting technology introduced commercially, and 7) estimated the potential primary impacts of each technology on

crop and livestock production. These papers

provided the basis for discussion in two technology workshops conducted by OTA.

The workshops—one for animal technology

and the other for plant, soil, and water technolOTA study participants arrived at this conclusion by first projecting where and under what

economic and political conditions the various

emerging technologies would be adopted and

what the primary impacts of those technologies

would be on net increases in production. Based

on this information OTA projected the impacts

of technology adoption on agricultural production on a per-unit basis (e. g., bushels of corn per

acre) and then on an aggregate basis (e. g., million bushels of corn produced in the entire

country).

AND PRIMARY IMPACTS

ogy—were conducted to assess the impacts of

emerging technologies on agricultural production. Workshop participants, carefully selected

to include those with expertise in different

stages of technological innovation, comprised

physical and biological scientists, engineers,

economists, extension specialists, commodity

specialists, agribusiness representatives, and experienced farmers.

The participants provided data on: 1) the timing of commercial introduction of each technology area; 2) primary impacts, or net yield

increases (by commodity), expected from each

package of technologies; and 3) the number of

years needed to reach various adoption percentages (by commodity).

The Delphi technique was used to obtain collective judgments from the workshop participants on the development and adoption of the

emerging technologies.1 To facilitate the procIThe Delphi technique is a systematic procedure for eliciting

and collating informed judgments from a panel of experts. It has

75
76 . Technology, Public Policy, and the Changing Structure of American Agriculture

ess of obtaining consensus, an electronic Consensor was used to help tabulate the ratings assigned by each expert. A detailed discussion of

the methodology and workshop procedures is

presented in appendix A.

The Timing of Commercial Introduction

Since the impact of a new technology on agriculture at a given time depends in part on when

the technology is available for commercial introduction, workshop participants were asked

to estimate the probable year of commercial introduction of each technology under three alternative environments:

1. Most likely environment—assumes to year

2000: a) a real rate of growth in research and

extension expenditures of 2 percent per year,

and b) the continuation of all other forces that

have shaped past development adoption of technology.

2. More-new-technology environment (relative to the most likely environment)—assumes

to year 2000: a) a real rate of growth in research

and extension expenditures of 4 percent, and

b) all other factors more favorable than those

of the most likely environment.

3. Less-new-technology environment (relative

to the most likely environment)—assumes to

year 2000: a) no real rate of growth in research

and extension expenditures, and b) all other factors less favorable than those of the most likely

environment.

4. No-new-technology environment—assumes

to year 2000: a) none of the emerging technologies identified in the study will be available

for commercial introduction, andb) all the other

factors are the same as those under the lessnew-technology environment.

two distinct characteristics: feedback and anonymity. During the

Delphi process, responses are collated and then referred to the

experts for review. Each expert reevaluates his or her original

answers after examining the summary of the group’s responses.

The iterative process of evaluation, feedback, and reevaluation

continues until a consensus is reached. Since this is not a random sampling, the results obtained through the Delphi process

depend heavily on the experts selected.

Table A-1 in appendix A shows in more detail

the sets of assumptions made under the alternative technology environments.

The year of commercial introduction ranged

from now—for genetically engineered pharmaceutical products; control of infectious disease

in animals; superovulation, embryo transfer,

and embryo manipulation of cows; and controlling plant growth and development—to 2000

and beyond, for genetic engineering techniques

for farm animals and cereal crops. Of the 57

potentially available animal technologies, it was

estimated that 27 would be available for commercial introduction before 1990, and the other

30 between 1990 and 2000, under the most likely

environment. In plant agriculture, 50 out of 90

technologies examined were projected to be

available for commercial introduction by 1990,

and the other 40 technologies between 1990 and

2000. The major categories of animal and plant

technologies are listed in appendix A, tables

A-2 and A-3.

When a given package of technologies is

adopted by a farmer and put into agricultural

production, its immediate impact on plant agriculture is increased yields and/or increased

percentage of planted acreage harvested.2 To

determine immediate impacts on animal agriculture, OTA considered feed efficiency for all

animals and reproductive efficiency for beef

cattle and swine, milk production per cow for

‘It is often stated that U.S. agriculture needs cost-saving technology, not yield-increasing technology. Technologies can be classified into two general types according to their impact: 1) those

that reduce the cost of production directly, and 2) those that increase productivity through yield increases, The first type of technology, such as nitrogen fixation and new crop varieties resistant to pest, disease, and environmental stress, saves costs of

purchasing agricultural chemicals, at little additional expense.

The second type of technology, such as pesticides, herbicides,

plant-growth regulators, irrigation, and fertilizer, typically increase yields, but at additional expense. Regardless of the type

of technology, all technologies reduce average costs if they are

worth adopting. For example, a new variety of corn increases

yields from 100 to 140 bushels per acre. Assuming no additional

increase in the cost of purchasing the new variety of seeds, the

total cost of production using the new variety will be shared by

140 bushels rather than 100 bushels. Thus, the new variety reduces the average cost 29 percent.
Ch. 3—impacts of Emerging Technologies on Agricultural Production Ž 77

dairy cows, and the number of eggs per layer

(producing hen) for poultry.

To estimate the net impact of emerging technologies on agricultural production, workshop

participants were first asked to project the performance measures of crop and livestock production, such as crop yields and livestock feed

efficiency, to 1990 and 2000 under the no-newtechnology environment. Historical trend lines

of the performance measures of crop and livestock production were provided to the participants as a basis for their projections. Through

the Delphi process, participants collectively projected the performance measures for each of

nine commodities for 1990 and 2000 (app. A,

table A-5). The nine commodities included corn,

cotton, rice, soybeans, wheat, beef cattle, dairy

cattle, poultry, and swine.

Based on those estimates and on the information obtained from the presentations and from

discussions with the authors of the commissioned papers, participants then jointly projected the net increases in crop yields, animal

feed efficiencies, and other performance measures that could be expected if specific packages

of technologies were commercially available

and fully adopted by farmers. Generally, the

28 areas of technologies were grouped in “packages” according to their probable impacts on

a commodity. Each package was further categorized as a 1990 version of the package or a 2000

version of the package, thus delineating those

technologies that are expected to be introduced

by 1990 and 2000, respectively. The packages

of technologies are described further in appendix A.

Through the Delphi process, OTA obtained

estimates for each package of technologies on

each of the nine commodities under the three

alternative environments. The results are shown

in tables 3-1 and 3-2. In soybean production, for

example, if technology package 1990A—which

includes genetic engineering, enhancement of

photosynthetic efficiency, plant growth regulators, plant disease and nematode control, and

multiple cropping—is adopted by soybean producers, yields are predicted to increase 2.2 percent under the most likely environment, 15.2

percent under the more-new-technology environment, and only 1.2 percent under the lessnew-technology environment. If package 2000A

is adopted, soybean yields are predicted to increase 22.1 percent under the most likely environment, 23.9 percent under the more-newtechnology environment, and 14.9 percent under

the less-new-technology environment. Package

2000A increases soybean yields substantially

more than package 1990A because it includes

such major technologies as genetically engineered soybean plants, photosynthetic molecular biology and genetics, and genetically engineered pest-resistant plants, all of which would

Table 3-1 .—Estimated Percentage Change in Crop Yield

Technology environments

Technology Less-new-technology Most likely More-new-technology

Crop package 2000 2000 2000

Corn . . . . . . . . . . . . Package A

B

c

Cotton. . . . . . . . . . . Package A

B

c

Rice. . . . . . . . . . . . . Package A

B

Soybean . . . . . . . . . Package A

B

c

Wheat . . . . . . . . . . . Package A

B

c

15.6°\o

8.8

–31.2

5.4

2.3

0

8.4

8.8

14.9

4.9

3.7

24.0

1.5

5.0

21 .5%0

14.4

–28.8

9.0

2.8

0

12.4

14.4

22.1

7.2

4.6

24.0

1.5

5.0

28.50/o

20.8

–28. 0

12.0

3.1

0

15.6

18.6

23.9

7.5

5.5

24.0

1.5

5.0

SOURCE: Office of Technology Assessment.
78 • Technology, Public Policy, and the Changing Structure of American Agriculture

Table 3-2.—Estimated Percentage Change in Animal Feed and Reproductive Efficiency

Technology environments

Technology Less-new-technology Most likely More-new-technology

Animal package Efficiency measure 2000 2000 2000

Beef . . . . . . . . . . . . . . . Package A Pounds meat per lb feed o 22.4% 30.4%

Calves per cow o 0 28.4

B Pounds meat per lb feed 5.8% 10.4 12.4

Calves per cow 1.2 5.2 6.4

C Pounds meat per lb feed 1.8 4.5 5.8

Calves per cow 1.2 2.0 3.2

D Pounds meat per lb feed 0.1 1.2 1.7

Calves per cow o 0.3 0.9

E Pounds meat per lb feed 1.4 2.8 3.3

Calves per cow 2.3 5.3 6.6

F Pounds meat per lb feed o 1.1 1.5

Calves per cow o 0 0

Dairy. . . . . . . . . . . . . . . Package A Pounds milk per lb feed 5.8 13.2 15.2

Pounds milk per cow 6.8 12.2 15.2

B Pounds milk per lb feed 7,6 11.0 13.0

Pounds milk per cow 9.4 12.2 14.6

C Pounds milk per lb feed 7.8 12.4 15.2

Pounds milk per cow 15.0 21.3 24.3

D Pounds milk per lb feed 25.6 25.6

Pounds milk per cow 25.6 25.6

Poultry . . . . . . . . . . . . . Package A Pounds meat per lb feed 7.3 9.2 11.3

Eggs per layer per year 4.6 5.8 7.1

B Pounds meat per lb feed 2.5 3.1 3.9

Eggs per layer per year 4.0 5.0 6.2

C Pounds meat per lb feed 1.3 1.6 2.0

Eggs per layer per year 1.6 2.0 2.5

Swine . . . . . . . . . . . . . . Package A Pounds meat per lb feed 4.8 12.6 15.0

Pigs per sow per year 14.4 27.6 50.0

B Pounds meat per lb feed 2.8 4.0

Pigs per sow per year 14.4 20.8

C Pounds meat per lb feed 2.1 2.1

Pigs per sow per year 0.8 2.4

SOURCE’ Office of Technology Assessment.

not be ready for commercial adoption until after

1990.

Note that technology package C for corn production, which consists of only organic farming, received very low marks from the Delphi

panel. If fully adopted, this technology will result in yield reductions ranging from 23 to 28

percent. Some organic farming specialists feel

that the panel overestimated the negative impact. Harwood (1985) indicates that the best estimate from the published reports is about a 10-

percent reduction. Since the cost of organic

farming is lower, the economic efficiency for

organic farming may be higher than that for conventional farming.

Adoption Profiles

The primary impacts estimated above assume

that the technologies will be fully adopted by

farmers and put into agricultural production.

But when a new technology is introduced for

commercial adoption, only a small number of

farms, mostly the large and innovative ones, will

adopt the technology initially because the possible payoff of the new technology is uncertain

and because the potential adopters need time

to learn how to use the new technology and to

evaluate its worth. As early adopters benefit

from using a new technology, more and more

farmers will be attracted to it, increasing the

speed of adoption exponentially. Eventually, as
Ch. 3—impacts of Emerging Technologies on Agricultural/ Production . 79

most potential adopters adopt a new technology, the percentage of adoption will level off

and approach a maximum; thus, the adoption

profile follows an S-shaped curve (Lu, 1983).

To derive an adoption profile of each package of technologies for each commodity under

different economic environments, participants

were divided into commodity groups according to their expertise in a particular commodity.

There were four groups in the animal technology workshop (beef, swine, dairy, and poultry)

and five in the plant, soil, and water technology workshop (wheat, corn, cotton, soybean,

and rice). The participants were then asked the

question, “If a specific package of technologies

is introduced in the market today, how long will

it take for farmers to have it adopted?” Based

on their collective experience, the participants

estimated the following for each package of technologies:

1.

2.

3.

The maximum percentage of adoption.

The number of years it would take to reach

20)-percent adoption.

The number of years it would take to reach

50-percent adoption.

Based on information from the commodity

groups, a logistic curve was fitted for each package of technologies applied to each of the nine

commodities under different scenarios. Figure

3-1 shows the estimated adoption curves for

package A corn technologies, which consist of

plant genetic engineering, plant disease and

nematode control, management of insects and

mites, water and soil-water-plant relations,

Figure 3-1 .—Logistic Adoption Curves for Corn,

Package A

 . 

0 2 4 6-8 10 12 14 16 18 20 22 24

Years from introduction date

— More-new-technology .—— Less-new-technology

environment environment

—— Most likely environment

Source. Office of Technology Assessment

communication and information management,

monitoring and control, and telecommunications. The participants estimated that it would

take 8 years to reach 20-percent adoption under the most likely environment, while it would

take only 6 years to reach it under the morenew-technology environment, where the economic environment is more favorable for technology adoption. To reach 50-percent adoption,

it would take 11 years under the most likely environment and 10 years under the more-new-technology environment. The maximum adoption

rate projected is 80 percent under both environments.

PROJECTION OF PER-UNIT CROP YIELDS AND

LIVESTOCK FEED EFFICENCIES

Based on the information obtained from the ronments. The results are presented in tables

workshops on: 1) the years of commercial intro- 3-3 and 3-4.3

duction, 2) the primary impacts, and 3)the adop- Under the most likely environment, feed effition profiles, OTA computed the efficiency ciency in animal agriculture will increase at a

measurements for all animals and the average 3

For ease of presentation, the less-new-technology environment yield and percentage of planted acreage for all is not presented. Its estimates fall between the no-new-technology

crops in 1990 and 2000 under alternative envi- and most likely environments.
80 • Technology, Public Policy, and the Changing Structure of American Agriculture

Table 3-3.—Estimates of Crop Yield and Animal Production Efficiency

No-new-technology Most likely More-new-technology

environment environment environment

Actual 1962 2000 2000 2000

Corn—bu per acre . . . . . . . . . . . . . . . . . . 113 124 139 150

Cotton—lb per acre . . . . . . . . . . . . . . . . . 481a511 554 571

Rice—bu per acre. . . . . . . . . . . . . . . . . . . 105 109 124 134

Soybeans—bu per acre . . . . . . . . . . . . . . 30a35 37 37

Wheat—bu per acre . . . . . . . . . . . . . . . . . 36 41 45 46

Beef:

Pounds meat per lb feed. . . . . . . . . . . . . 0.070 0,066 0.072 0.073

Calves per cow . . . . . . . . . . . . . . . . . . . . . 0.88 0.96 1.0 1.04

Dairy:

Pounds milk per lb feed . . . . . . . . . . . . . 0.99 0.95 1.03 1.11

Milk per cow per yearb (1,000 lb) . . . . . . 12.3 15.7 24.7 26.1

Poultry:

Pounds meat per lb feed. . . . . . . . . . . . . 0.40 0.53 0.57 0.58

Eggs per layer per year . . . . . . . . . . . . . . 243 260 275 281

Swine:

Pounds meat per lb feed. . . . . . . . . . . . . 0.157 0.17 0.176 0.18

Pigs per sow per year . . . . . . . . . . . . . . . 14.4 15.7 17.4 17.8

aNot actua[—baged on estimate from trend line.

bThe~e e~timate~ differ from those in table 2.2 of the first rep~rf from this study because of changes made at a later date by workshop p’rticlp’IltS irl the adoption

rate of some of the dairy technology packages.

SOURCE: Office of Technology Assessment.

Table 3-4.-Historical and Projected Rates of Annual Growth in Crop Yield

1982-2000

No-new-technology Most likely More-new-technology

1960-82 environment environment environment

Corn . . . . . . . . . . . 2.60/o 0.5% 1.2% 1.60/0

Cotton . . . . . . . . . 0.1 0.3 0.7 1.0

Rice . . . . . . . . . . . 1.2 0.2 0.9 1.4

Soybean. . . . . . . . 1.2 0.8 1,2 1.2

Wheat . . . . . . . . . 1.6 047 1.2 1.4

SOURCE: Office of Technology Assessment.

rate of from 0.2 percent per year for beef to 1.4

percent for poultry. In addition, reproduction

efficiency will also increase, at an annual rate

ranging from 0.6 percent, for beef cattle, to 1.1

percent, for swine. Milk production per cow

per year will increase at 3.9 percent per year,

from 12,300 pounds to 24,730 pounds per cow,

in the period 1982-2000.

Major crop yields are estimated to increase

from 1982 until 2000 at a rate ranging from 0.7

percent per year, for cotton, to 1.2 percent per

year, for wheat and soybeans. Wheat yield, for

example, is projected to increase at the rate of

0.7 percent per year, from 36 bushels per acre

in 1982 to 41 bushels per acre in 2000, assuming no new technologies will become available

before 2000. Under the most likely environment,

wheat yields will increase at the rate of 1.2 percent per year to 45 bushels per acre. The difference in wheat yield between the two environments, 4 bushels per acre, represents the impact

of new technologies under the most likely environment.

How do these rates of increase compare with

historical trends? Will emerging technologies

significantly change the trends? By far the most

drastic increases in productivity will be in milk

production, primarily because the products of
Ch. 3—impacts of Emerging Technologies on Agricultural Production • 81

genetic engineering will soon be available for

commercial adoption by the dairy industry. One

of the proteinaceous pharmaceuticals, bovine

growth hormone, is alone expected to increase

milk yields between 20 to 40 percent almost

overnight via daily injections of the hormone

into cattle.

From 1960 to 1982 milk production increased

2.6 percent per year, from 7,029 pounds per cow

per year to 12,316 pounds. If no new technology is available from now until 2000, this rate

of increase would not be maintained. Under

such an environment milk production per cow

per year is expected to increase at only 1.4 percent per year, from 12,316 pounds in 1982 to

15,700 pounds in 2000. However, if new technologies are adopted, the rate of increase in milk

production would far surpass the historical rate,

under the remaining technology environments.

Under the more-new-technology environment,

milk production is expected to reach 26,080

pounds in 2000, at an annual rate of 4.2 percent.

Application and adoption of new technologies

will also increase the feed efficiency of other

animals. Poultry feed efficiency has been increasing at 1.2 percent per year for the last 15

years, Under the most likely environment, feed

efficiency will increase at 1.4 percent per year

through 2000.

The feed efficiencies for beef and swine have

not increased for the last 15 years. Beef feed efficiency declined from 0.093 pounds of beef per

pound of feed in 1965 to 0.065 pounds in 1973

and then maintained at about 0.070 pounds in

recent years, The introduction of new technologies will increase feed efficiencies. Under the

most likely environment, the feed efficiency is

projected to increase at an annual rate of 0.2

percent, reaching 0.072 pounds of beef per

pound of feed in 2000. Swine feed efficiency

has declined steadily from 0.19 pounds of pork

per pound of feed in 1974 to 0.15 pounds in 1980.

Under the most likely environment, feed efficiency will increase to 0.18 pounds of pork per

pound of feed in 2000, at the rate of 0.4 percent

per year,

Efficiencies in crop production will be less

dramatic than those in animal production, primarily because development of biotechnology

for plants is far behind that for animals, Most

of the major plant biotechnologies will not be

commercially available before 2000. Therefore,

it will be difficult to maintain historical trends

without infusion of new technologies. As shown

in table 3-4, all major crops included in this

study, except for cotton, have experienced phenomenal growth during the past 20 years. The

average annual rates of growth range from 1.2

percent, for rice and soybeans (and 1.6 percent

for wheat), to 2.6 percent for corn. Without new

technologies, these trends cannot continue. Under the no-new-technology environment, the

yields of major crops are expected to grow only

at 0.2 percent per year for rice, to 0.8 percent,

for soybeans. Even under the most likely environment, corn and wheat yields still could not

keep up with past growth. Under the more-newtechnology environment, the annual rates of

growth of all major crops, except for corn and

wheat, are expected to equal or exceed historical rates of growth. The growth rate of corn

yields under the most favorable environment

is expected to be 1.6 percent, which is far short

of the historical rate of 2.6 percent per year.

New technologies could have a significant impact on cotton and rice yields. Cotton yields have

not increased much during the last two decades.

Instead, they have been fluctuating around the

trend line, which has increased at the rate of

only 0.1 percent per year from 1960 to 1982.

Adoption of new technologies could shift the

trend upward. Under the most likely environment, cotton yields are projected to increase at

0.7 percent per year, and under the more-newtechnology environment, 1.0 percent per year.

Although rice yields have increased at an average of 1.2 percent per year since 1960, the yield

curve has been flattened since 1967. During the

1960-67 period, rice yields increased at 4.1 percent per year, but the rate of growth has declined

to only 0.2 percent per year since 1967. Introduction of new technologies into rice production could turn the yield curve upward. Under

the most likely environment, rice yields are expected to increase 0.9 percent per year, and under the more-new-technology environment, 1.4

percent. This is the highest rate of growth estimated among all major crops.
82 ● Technology, Public Policy, and the Changing Structure of American Agriculture

PROJECTIONS OF AGGREGATE CROP AND LIVESTOCK PRODUCTION

OTA used the projected crop yields and percent of planted acres harvested for major crops,

and the projected feed and reproductive efficiencies of livestock, to assess the collected impacts of the 28 areas of emerging technologies

on the total production of various crop and livestock products. The primary tool used in the

analysis was an econometric model which is

an annual, partial equilibrium model consisting of a crop sector, a livestock sector, and a

financial sector.4 The model is a partial equilibrium model in that a general equilibrium solution is solved within the agricultural sector

while a specified set of conditions are assumed

to exist within the rest of the economy, such

as population growth, income growth, export

demand, and interest rates. The model was used

in a 20-year simulation projecting the effects

of technological change on the various crop and

livestock commodities previously discussed.

The results appear below.

IThe model used was the Iowa State University econometric

model developed by Earl Heady.

crop Production

Applications of new technologies will increase aggregate crop production throughout

the projection period—from 1981 to 2000. Table

3-5 shows projections to year 2000 of increased

production for five major crops. Total U.S. crop

production was determined by average crop

yields and acres of crops harvested. Crop yields

were projected to 2000 under the three technology environments from the results of the technology workshop. The projections took into account the timing, adoption profiles, and primary

impacts of emerging technologies. Acres of

crops harvested were determined by the model,

based on expected returns from crop production, diversion payments, and other cropspecific considerations.

Although there will be a drop in the number

of acres of corn planted, projected yield increases and increases in the proportion of

planted acres actually harvested will cause corn

production to increase over time under each

environment. The increase will be greatest under the more-new-technology environment, a

Table 3-5.—Projections of Crop Production

2000

No-new-technology Most likely More-new-technology

crop Unit 1984 environment environment environment

Corn?

Production . . . . . Billion bu 7.7

Growth rate. . . . . Percent

Cotton:

Production . . . . . Billion lb 6.2

Growth rate. . . . . Percent

Rice:

Production . . . . . Million cwt 137.0

Growth rate. . . . . Percent

Soybean. a

Production . . . . . Billion bu 1.9

Growth rate. . . . . Percent

Wheat?

Production . . . . . Billion bu 2.6

Growth rate. . . . . Percent

8.6

0.7

6.4

0.1

153.6

0.7

3.0

3.1

3.3

1.5

9.3

1.2

6.9

0.7

163.4

1.1

3.2

3.4

3.5

1.9

9.7

1.5

7.2

0.9

169.2

1.3

3.3

3.6

3.5

2.0

apro~ections shown for this commodity differ from those in table 2-3 of the first report from this study because the Previous

figures were prelimina~.

SOURCE: Office of Technology Assessment.
Ch. 3—impacts of Emerging Technologies on Agricultural Production ● 8 3

situation that is also true for the other crops

analyzed.

Unlike planted acres of corn, planted acres

of soybeans will increase during the projection

period. Increases in yields and increases in harvested acres will cause total U.S. soybean production to increase significantly over the 1982

through 2000 projection period, Because yields,

planted acres, and proportion of planted acres

harvested vary little across different environments, production increases do not vary much

across environments. The rate of increase ranges

from 3.1 to 3.6 percent per year for the no-newtechnology and more-new-technology environments, respectively.

Planted acres of wheat are projected to increase under the no-new-technology environment but to decrease under the most likely and

more-new-technology environments, Increases

in average wheat yields will cause wheat production to increase over the projection period.

As shown in table 3-4, cotton yields are projected to increase relatively less than corn, soybean, and wheat yields. Planted acres of cotton

are projected to increase under each of the technology environments, with only slight differences across environments. Increases in both

yields and harvested acres will cause total U.S.

cotton production to increase,

Planted acres of rice are also projected to increase under each technology environment. As

shown in table 3-4, rice yields are projected to

increase over time for each environment. Increasing yields and increasing harvested acres

will cause total rice production to increase over

time.

Livestock and Milk Production

Technology impacts are felt in the livestock

sector through calving rate changes for beef and

through feed input price differentials for beef

and other livestock. Higher feed efficiencies and

crop production levels under the more-newtechnology compared with the no-new-technology environments result in lower corn, soybean

meal, and wheat prices, The lower prices of

these feed inputs cause livestock production to

increase generally. The higher calving rates

under the more-new-technology environment

also tend to increase beef production. Increased

production tends to depress livestock and meat

prices if demand for livestock and meat does

not increase proportionately.

The production of prime beef is determined

by the number of feeder cattle slaughtered, the

average fed cattle weight at slaughter, and the

conversion ratio of live weight to carcass weight

(dressing percentages).

As shown in table 3-6, prime beef production

decreases over time for all technology environments. Due to higher calving rates and lower

feed costs, beef production is highest under the

more-new-technology environment. Under the

most likely environment, beef production is projected to decline from 1984 to 2000 based on

a weakness in consumer demand caused by

changes in income levels, shifts in taste, and

concern over potential health problems associated with the consumption of red meat, among

other factors.

The impacts of technology on pork production are reflected only through differences in

feed input prices. Differences in farrowing rates

are not accounted for across environments, As

shown in table 3-6, pork production is projected

downward for all technology environments,

The downward trend is attributed to higher feed

input prices and higher retail pork prices resulting from lower production. Pork production

under the most likely environment is projected

to drop 15 percent from 1984 to 2000.

Chicken production is projected to increase

over time for all technology environments, and

the differences across the various environments

are minimal.

Total milk production is determined by multiplying milk yield times milk cow numbers.

Milk yield, as indicated earlier, is projected to

increase through 2000, owing in large part to

the anticipated emergence and adoption of biotechnologies in the dairy industry, Cow numbers are determined in the model as a positive

function of the ratio of the blend price of Grade

A and Grade B milk over the average ration cost
84 ● Technology, Public Policy, and the Changing Structure of American Agriculture

Table 3.6.—Projections of Animal Production

2000

No-new-technology Most likely More-new-technology

Livestock Unit 1984 environment environment environment

Prime beef:

Production . . . . . Billion lb 16.0 12.5 14.1 15.7

Growth rate. . . . . Percent – 1.5 –0.8 –0.2

Poultry:

Production . . . . . Billion lb 13.5 16.8 16.7 16.7

Growth rate. . . . . Percent 1.4 1.3 1.3

Pork:

Production . . . . . Billion lb 13.8 10.7 11.7 13.0

Growth rate. . . . . Percent – 1.6 – 1.0 –0.4

Milk:

Production . . . . . Billion lb 135.4 126.1 192.1 201.8

Growth rate. . . . . Percent –0.4 2.2 2.5

SOURCE: Office of Technology Assessment

and a negative function of the cull price of dairy

cows. The blend price falls slightly for each environment over the projection period. The average ration cost and cull cow price are exogenously projected to increase over the 1983-2000

period. As a result, cow numbers are projected

to decline by at least 30 percent over the period,

with only small differences across the environments.

Given the increases in milk productivity and

the decreases in cow numbers, what will happen to total milk production over time? As

shown in table 3-6, under the no-new-technology

environment, milk production will fall at 0,4 percent per year from 1982 through 2000 because

reductions in cow numbers more than offset increases in milk yield. Under the other two environments, milk production will increase despite

the reductions in numbers of cows. The largest

increases are projected to occur before 1990.

In the world agricultural marketplace, available information points to a periodic series of

surpluses and deficits over the next two decades (Mellor, 1983; Resources for the Future,

1983). A Resources for the Future (RFF) study

indicates that the global balance between cereal

production and population will remain quite

close until year 2000, indicating vulnerability

to annual shortfalls resulting from weather,

wars, or mistakes in policy, Over the next 20

years the world will become even more dependent on trade, There will be increasing competition for U.S. farmers in international markets.

Much of this increased competition will come

from developing countries selling farm commodities as a source of exchange to pay for imports such as oil. Despite this increased competition, exports of grain from North America

are projected nearly to double by year 2000.

On the other hand, there is another school of

thought that believes current studies such as that

by RFF have not properly assessed the magnitude and impact of emerging technologies on

farm production. Technologies such as genetic

engineering and electronic information technology that are available now in various forms

could mean rapid increases in yields and productivity. While such changes may improve the

competitive position of American agriculture,

they might create surpluses and major structural change—favoring, for example, larger,

more industrialized farms.

Any conclusion regarding the balance of global supply and demand requires many assumptions about the quantity and quality of resources

available to agriculture in the future. Land,

water, and technology will be the limiting factors to agriculture’s future productivity.

Agricultural land that does not require irrigation is becoming an increasingly limited resource. In the next 20 years, out of a predicted

1.8-percent annual increase in production to

meet world demand, only 0.3 percent will come

from an increase in quantity of land used in pro-
Ch. 3—Impacts of Emerging Technologies on Agricultural Production • 85

duction (RFF, 1983). The other 1.5 percent will

have to come from increases in yields–mainly

from new technology. Thus, to a very large extent, research that produces new technologies

will determine the future world supply/demand

balance and the amount of pressure placed on

the world’s limited resources.

The OTA results indicate that with continuous inflow of new technologies into the agricultural production system, U.S. agriculture will

SUMMARY AND

OTA finds that emerging agricultural technologies, if fully adopted, will produce significant impacts on the performance of plant and

animal agriculture. The most dramatic impacts

will first be felt in the dairy industry, where new

genetically engineered pharmaceuticals (such as

bovine growth hormones and feed additives)

and information management systems will soon

be introduced commercially. New technologies

adopted by the dairy industry will increase milk

production far beyond the 2.6-percent annual

rate of growth of the past 20 years. Under the

most likely environment, milk production per

cow is expected to increase at an annual rate

of 3.9 percent. Applications of new technologies

will also increase the feed efficiency and reproductive efficiency of other agricultural animals.

Because development of biotechnology for

plant agriculture is lagging behind that for aniCHAPTER 3

Harwood, Richard, Program Officer, International

Agricultural Development Service, personal communication, July 30, 1985,

Lu, Yao-chi, “Forecasting Emerging Technologies

in Agricultural Production, ” in Yao-chi Lu (cd.),

Emerging Technologies in Agricultural Production, Cooperative State Research Service, U.S. Department of Agriculture, 1983.

be able not only to meet domestic demand but

also to contribute significantly to meeting world

demand in the next 20 years. This does not necessarily mean that the United States will be competitive or have the economic incentive to produce. It means only that the United States will

have the technology and resources available to

provide the production increases needed to export for the rest of this century.

CONCLUSIONS

mal agriculture, significant impacts from such

technology will not be felt in plant agriculture

before the turn of the century. The development

and adoption of the new technologies under the

most favorable environment will, in the short

run, increase the rates of growth of major crop

yields, except for corn, at about the level of the

historical rates of growth. However, the impacts

of these technologies will be substantially

greater for plant agriculture after 20000

The OTA study indicates that, with a continued flow of new technologies into the agricultural production system, major crop yields will

continue to grow and U.S. agriculture will continue to provide enough food to meet domestic

and foreign demand as long as agricultural research is adequately supported and economic

and political environments are favorable.

EFERENCES

Mellor, John W., “Food Prospects for the Developing Countries, ” American Economic Review,

May 1983, pp. 239-243.

Resources for the Future, “Meeting Future Needs

for United States Food, Fiber and Forest Products, ” report prepared for the Joint Council on

Food and Agricultural Sciences (Washington, DC:

December 1983),
—

Part II

The Changing Structure

of American Agriculture
Chapter 4

Dynamic Structure

of Agriculture
Contents

Page

Present Structure of Agriculture......,., . . . . . . . . . . . . . . . . . . . . . . . . . . . . 91

Changes in the Structure of U.S. Agriculture. . . . . . . . . . . . . . . . . . . . . . . . , 92

Changes in Farm Size and Number . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 92

Changes in the Distribution of Sales and Income . . . . . . . . . . . . . . . . . . . . 93

Changes in the Sources of Income . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 94

Projections of Structural Change in U.S. Agriculture to Year 2000 . . . . . . . 96

Structure of U.S. Agriculture by Major Commodity Groups . . . . . . . . . . . . . 97

Cash Grain Subsector . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 98

Cotton Subsector . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 98

Dairy Subsector . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 99

Poultry Subsector ..., . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 99

Cattle and Calf Subsector . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 99

Pork Subsector . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 100

Regional Structure. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 100

Comparison Between Regions and Commodities . . . . . . . . . . . . . . . . . . . . . 102

Distribution of Sales Within Regions and Among Regions . . . . . . . . . 103

Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ., . . . . . . . . . . . . . . . . . 105

Table

4-1.

4-2

4-34

4-4.

4-5

4-6.

4-7.

4-8.

4-9.

4-10,

Tables

No. Page

Sales Classes of Farms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 92

Number of Farms and Percent of Farms by Sales Class, 1969-82 ..., 93

Gross Farm Income and Percent of Gross Farm Income

by Sales Class, 1969-82 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 93

Net Farm Income and Percent of Net Farm Income

by Sales Class, 1969-82 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 94

Total Farm Income and Percent of Total Farm Income

by Sales Class, 1969-82 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 94

Average Gross Farm Income, Net Farm Income, Off-Farm Income,

and Total Income of Farms, 1969-82 . . . . . . . . . . . . . . . . . . . . . . . . . . . . 95

Most Likely Projection of Total Number of U.S. Farms

in l990 and 2000, by Sales Class, . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 96

Percent of Total U.S. Sales of Al] Commodities

by Commodity Group and Region, 1982 . . . . . . . . . . . . . . . . . . . . . . . . . . 102

Percent of Total U.S. Sales of Each Commodity by Region, 1982 . . . 102

Percent of Total Regional Sales by Commodity, 1982 . . . . . . . . . . . . . . 103

Figures

Figure No. Page

4-1.

4-2,

4-3.

4-4.

4-5,

4-6.

4-7.

Cash Grain Sales by Sales Class, 1969-82 ........, . . . . . . . . . . . . . . . . . . 98

Cotton Sales by Sales Class, 1969-82, . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 98

Dairy Sales by Sales Class, 1969-82 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 99

Poultry Sales by Sales Class, 1969-82 . . . . . . . . . . . . . . . . . . . . . . . . . . . . 99

Cattle Sales by Sales Class, 1969-82 . . . . . . . . . . . . . . . . . . . . . . . . ......,100

Hog and Pig Sales by Sales Class, 1969-82 . ........................100

Regions and Divisions of the United States. . . . . . . . . . . . . . . . . . . . . .,.,101
Chapter 4

Dynamic Structure of Agriculture

Who will use a technology is as important a

consideration as which technology will be

adopted, for the distribution of technology affects both agricultural production and the socioeconomic structure of the entire agricultural

sector.

The trend toward concentration of agricultural resources in fewer but larger farms will

continue, although the degree of concentration

will vary by region and by commodity. Indeed,

in the future, 75 percent of the food and fiber

in this country will probably be produced by

only 50,000 of the 1 million farms in existence.

Further concentration of resources will be most

likely in those industries already highly concentrated, for example, the broiler, fruit and vegetable, and dairy industries.

Several factors contribute to the changing

character of the agricultural sector: policies,

institutions, economies of size, and new technologies themselves. This chapter provides a

perspective for analyzing technology’s distributional impacts on agricultural structure by surveying the characteristics of that structure and

the factors that affect it.

PRESENT STRUCTURE OF AGRICULTURE

The heart of agriculture—the farm—is officially defined as a place that produces and sells,

or normally would have sold, at least $1,000

worth of agricultural products per year. So defined, there were about 2.2 million farms in

1982. Farms in that year had an average net income from farming of $9,976 and an average

off-farm income of $17,601, for a total of $27,577.

Perhaps the best known characteristic of U.S.

agriculture is the trend toward larger but fewer

farms. Currently, about 1 billion acres of land

are in farms, resulting in an average farm size

of about 400 acres. However, this average size

has little meaning, since fewer than 25 percent

of all farms fall within the range of 180 to 500

acres. Almost 30 percent of all U.S. farms have

less than 50 acres, whereas 7 percent have more

than 1,000 acres.

The number of farms reached a peak of about

6.8 million farms in 1935 and is now approximately 2.2 million. The rate of decline has

slowed since the late 1960s, with a loss of about

100,000 farms since 1974.

Employment in farming began a pronounced

decline after World War II, when a major technological revolution occurred in agriculture.

The replacement of draft animals by the tractor began in the 1930s and was virtually complete by 1960, releasing about 20 percent of the

cropland, which had been used to grow feed

for draft animals.

The increased mechanization of farming permitted the amount of land cultivated per farm

worker to increase fivefold from 1930 to 1980.

The amount of capital used per worker increased more than 15 times in this period. Total productivity (production per unit of total inputs) more than doubled because of the adoption

of new technologies such as hybrid seeds and

improved livestock feeding and disease prevention. The use of both agricultural chemicals and

fuel also grew very rapidly in the postwar period. Agricultural production began to rely heavily on the nonfarm sector for machinery, fuel,

fertilizer, and other chemicals. These, not more

land or labor, produced the growth in farm production. The resultant changes have greatly increased the capital investment necessary to

enter farming and have generated new requirements for operating credit during the growing

cycle.

One of the best ways to look at changes in the

economic structure of U.S. agriculture is in

terms of value of production as measured by

91
92 ● Technology, Public Policy, and the Changing Structure of American Agriculture

gross sales per year. Farms can be usefully classified into the five categories of gross sales

shown in table 4-I.

Small farms generally do not provide a significant source of income to their operators.

This class of farms is operated by people living

in poverty and by people who use the farm as

a source of recreation.

Part-time farms may produce significant net

income but in general are operated by people

who depend on off-farm employment for their

primary source of income.

Table 4-1.—Sales Classes of Farms

Amount of gross

Class sales per year

Small. . . . . . . . . . . . . . . . . . . . . . . . . . < $20,000

Part-time. . . . . . . . . . . . . . . . . . . . . . . $20,000 to $99,999

Moderate . . . . . . . . . . . . . . . . . . . . . . $100! 000 to $199,000

Large. . . . . . . . . . . . . . . . . . . . . . . . . . $200,000 to $499,999

Very large. . . . . . . . . . . . . . . . . . . . . . $500,000

SOURCE: Office of Technology Assessment.

Moderate-size commercial farms cover the

lower end of the range in which the farm is large

enough to be the primary source of income for

an individual or family. Most families with

farms in this range also rely on off-farm income.

In general, farms in this range require labor and

management from at least one operator on more

than a part-time basis.

Large and very large commercial farms include a range of diverse farms. The great majority of these are family owned and operated.

Most farms in these classes require one or more

full-time operators, and many depend on hired

labor on a full-time basis. Five percent of these

farms are owned by nonfamily-owned corporations, a much higher percentage than in the

other three classes. In general, the degree of contracting and vertical integration is much higher

in these classes.

CHANGES IN THE STRUCTURE OF U.S. AGRICULTURE

In tables 4-2 to 4-5 changes in the structure

of U.S. agriculture between 1969 and 1982 are

presented in terms of four basic attributes: numbers of farms, gross income of farms, net farm

income, and off-farm income. The information

in each table has been adjusted to account for

the impact of inflation and is presented in terms

of constant 1982 dollars. Inflation in commodity

prices over the 13 years between 1969 and 1982

has tended to move many farms from lower sales

classes into higher sales classes. Farm numbers,

sales, and income values have accordingly been

redistributed to correct for this.1

Changes in Farm Size and Number

Major changes in the structure of U.S. agriculture can be seen in the changes in the number of farms shown in table 4-2. Even after the

IThe redistribution to correct for inflation in terms of 1982 dollars has the effect of moving farm numbers, sales, and income

from lower sales classes into higher sales classes in the years prior

to 1982.

number of farms was redistributed toward the

larger sales classes in the years prior to 1982,

the real number of small farms declined by about

39 percent—a dramatic decline. Recent reports

that the number of small farms has actually increased since 1978 refer primarily to farms that

have less than 50 acres, not to farms with less

than $20,000 per year in sales. The number of

part-time farms has increased by about 57 percent. The number of moderate-size farms has

increased greatly, by 111 percent. The numbers

of large and very large farms have also increased

very dramatically, by about 130 and 101 percent, respectively. The substantial increase in

the real number of moderate-size farms appears

to contradict many claims that the moderatesize farm is disappearing from the structure of

American agriculture. However, as will be shown

in the next two sections and in later chapters,

changes in the number of farms is not, by itself,

a good indicator of economic health or the abilit y of different classes of farms to survive financially.
Ch. 4—Dynamic Structure of Agriculture . 93

Table 4-2.—Number of Farms and Percent of Farms by Sales Class, 1969-82 (1982 dollars)

Value of farm Number of farms Percent of farms

Sales class products sold 1969 1974 1978 1982 1969 1974 1978 1982

Small . . . . . . . . . . . . . . . . . . . . . c $20,000 2,216,851 1,926,875 1,617,385 1,355,344 81.30/o 70.90/0 66.00/0 60.50/0

Part-time . . . . . . . . . . . . . . . . . . $20,000-$99,999 371,180 559,076 573,976 581,576 13.6 20.6 23.4 26.0

Moderate . . . . . . . . . . . . . . . . . . $100,000-$199,999 65,569 146,089 160,289 180,689 3.1 5.4 6.5 8.1

Large . . . . . . . . . . . . . . . . . . . . . $200,000-$499,999 40,691 67,091 75,891 93,891 2.5 3.1 4.2

Very large . . . . . . . . . . . . . . . . . $500,000 13,800 19,200 21,500 27,600 0.5 0.7 0.9 1.2

All farms. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2,728,111 2,718,331 2,449,041 2,239,300 100.0% 100.0% 100.0% 1OO.OO/.

SOURCE: Office of Technology Assessment, Compiled from data in Economic Indicators of the Farm Sector: Income and Balance Sheet Statistics, 1983. USDA, Economic Research Service, 1984. Data adjustment for inflation baaed on redistribution of farm numbers in the Census of Agriculture, 1989, 1974, 1978, 1982,

Bureau of the Census, U.S. Department of Commerce. Price indices in Agricultural Statistics, 1983, USDA.

Changes in the Distribution

Of Sales and Income

Changes in the number of farms do not give

the whole picture. Changes in the distribution

of sales and income are more important to the

economic structure of U.S. agriculture and more

clearly show the direction in which U.S. agriculture is heading.

Changes in the distribution of gross farm income between 1969 and 1982 are shown in table

4-3.2 As can be seen, the real value Of total gross

farm income increased significantly in the period 1969-78, then declined somewhat by 1982.

The gross farm income of small farms decreased

significantly between 1969 and 1978, then decreased greatly between 1978 and 1982, resulting in an overall reduction in the share of gross

income, from 17 percent in 1969 to 6 percent

in 1982. Gross income of part-time farms remained roughly the same over the period. Gross

farm income of moderate-size farms increased

‘Gross farm income includes cash receipts; net Commodity

Credit Corporation loans; income from recreational, machine

hire, and custom work; the value of home consumption of products produced onfarm, and gross rental value of farm dwellings.

from 15 to 19 percent. In the same period, the

percent of sales from large and very large farms

combined increased from 45 to 54 percent. Overall, the majority of market share shifted from

the combined shares of the small, part-time, and

moderate-size farms in 1969 to the combined

shares of the large and very large farms in 1982.

The most telling changes of all have occurred

in the distribution of net farm income, as shown

in table 4-4. The large and very large farms not

only have captured the majority of gross farm

income, but also have controlled or substantially

reduced their costs of production. As a result,

their combined share of net income has increased from 51 percent in 1969 to 84 percent

in 1982, after adjustment for inflation. Very large

farms have been responsible for the majority

of this growth in net income. This class of farms,

which currently accounts for only 1.2 percent

of U.S. farms, increased its share of net farm

income from 36 percent in 1969 to 64 percent

in 1982.

Examination of the amounts of net farm income in real terms shows that the total amount

of net farm income for all farms increased

greatly from 1969 to 1974, and then declined.

Table 4.3.–Gross Farm Income and Percent of Gross Farm Income by Sales Class, 1989-82 (1982 dollars)

Value of farm Gross farm income Percent of gross farm income

Sales class products sold 1969 1974 1978 1982 1969 1974 1978 1962

Small . . . . . . . . . < $20,000 $21,791,756 $16,160,371 $ 17,694,223 $ 7,260,143 17.2% 12.70/o 12.1 0/0 5.5%

Part-time . . . . . . $20,000-$99,999 28,012,247 30,844,011 35,623,571 28,763,908 22.1 24.3 24.3 21.9

Moderate . . . . . $100,000-$199,999 19,477,342 22,930,645 26,794,096 25,100,815 15.4 18.1 18.3 19.1

Large. . . . . . . . . $200,000-$499,999 19,566,095 22,233,997 26,180,305 27,680,560 15.4 17.5 17.9 21.0

Very large. . . . . $500,000 37,635,967 34,704,598 40,311,553 42,764,189 29.9 27.4 27.5 32.5

All farms . . . . . . . . . . . . . . . . . . . . . . $126,683,408 $126,873,622 $146,603,748 $131,589,615 100.0% 100.0% 100.0% 100.0%

SOURCE: Office of Technology Assessment. Compiled from data in Economic Indicators of the Farm Sector: Income and Balance Sheet Statistics, 1983. USDA, Economic Research Service, 1984. Data adjustment for inflation based on redistribution of farm numbers In the Census of Agriculture, 1989, 1974, 1978, 1982,

Bureau of the Census, U.S. Department of Commerce. Price indices in Agriculture/ Statistics, 1983, USDA.
94 . Tecnology, Public Policy, and the Changing Structure of American Agriculture

Table 44.—Net Farm Income and Percent of Net Farm Income by Sales Class, 1969-82 (1982 dollars)

Value of farm Net farm income Percent of net farm income

Sales class products sold 1989 1974 1978 1982 1989 1974 1978 1982

Small . . . . . . . . . . . . c $20,000 $3,791,609 $ 1,802,327 $ (675,036) $ (847,409) 10.3% 3.2% 1.70/o –3.80/o

Part-time . . . . . . . . . $20,000-$99,999 9,026,790 13,033,232 8,010,487 1,186,510 24.5 23.2 20.2 5.4

Moderate. . . . . . . . . $100,000-$199,999 5,400,579 11,384,523 7,720,282 3,218,012 14.6 20.3 19.4 14.6

Large . . . . . . . . . . . . $200,000-$499,999 5,474,381 11,887,994 8,149,347 4,515,675 14.8 21.2 20.5 20.4

Very large . . . . . . . . $500,000 13,210,919 18,091,384 16,511,511 14,034,343 35.8 32.2 41.6 63.5

All farms . . . . . . . . . . . . . . . . . . . . . . . . . . $38,904,279 $58,199,461 $39,716,592 $22,107,132 100.0% 100.0% 100.0% 100.0%

SOURCE: Office of Technology Assessment. Compiled from data In Ecorromic Imficatora of the farm Sector: Irrcorrre and Balance Sheaf SfaMks, 1983. USDA, Economic Research Service, 1984. Data adjustment for Inflation baaed on redistribution of farm numbers in the Census of AgrhWture, 1989, 1974, 1978, 1982,

Bureau of the Census, U.S. Department of Commerce. Price indices in Agr/cu/tura/ StatMcs, 1983, USDA.

Table 4&-Total Farm Income and Percent of Total Farm Income by Sales Class, 1969-82 (1982 dollars)

Value of farm Off-farm income Percent of off-farm income

Sales class products sold 1969 1974 1978 1982 1989 1974 1978 1982

Small . . . . . . . . . . . . <$20,000 $37,936,097 $46,908,672 $33,712,998 $24,266,444 87.80/, 85.50/0 76.70/o 71.80/,

Part-time . . . . . . . . . $20,000-$99,999 2,898,500 4,852,067 6,697,884 5,593,893 6.7 15.2 16.5

Moderate. . . . . . . . . $100,000-$199,999 1,268,407 1,842,151 1,872,481 1,998,753 2.9 3.4 4.3 5.9

Large . . . . . . . . . . . . $200,000-$499,999 802,790 981,677 1,103,743 1,256,672 1.9 1.8 2.5 3.7

Very large . . . . . . . . >$500,000 285,377 282,039 575,800 687,778 0.7 0.5 1.3 2.0

All farms . . . . . . . . . . . . . . . . . . . . . . . . . . $43,191,171 $54,864,605 $43,962,685 $33,801 ,541 100.0% 100.0% 100.0% 100.0%

SOURCE: Office of Technology Assessment. Compiled from data in Economic Indicators of the Farm Sector: Income and Balance Sheet Statistical IW. USDA, Economic Research Service, 1984. Data adjustment for inflation based on redistribution of farm numbers in the census of Agriculture, 1989, 1974, 1978, 1982,

Bureau of the Census, U.S. Department of Canmerce. Price indices in Agricu/tura/ Statistics, 1983, USDA.

Changes in net farm income by sales class generally reflect this rise and fall in total net income.

However, real net farm income has declined the

least for very large farms, while all other classes

of farms have had substantial declines in real

net income. Moderate-size farms had an increase in percent of net farm income between

1969 and 1974. Since then their share of net income has declined. Farms in the large sales class

increased their percentage of net income, from

16 to 20 percent in 1974, and basically held this

share in 1978 and 1982. Moderate-size farms

clearly have not been as successful as large and

very large farms in controlling or reducing their

costs of production.

Table 4-6 shows the average gross farm income, net farm income, off-farm income, and

total income by sales class. As can be seen, the

average net farm income of all classes of farms

has declined substantially in real terms since

the highly profitable years in the 1970s. But the

comparison between 1969 and 1982 is even

more telling. The average net farm income has

declined. The average real net farm income of

part-time and moderate-size farms has declined

by a factor of 12 and 3.5, respectively. The net

income of large farms has declined by a factor

of 3, while the net farm income of very large

farms has declined by a factor of 2.3 In 1969 the

average farm in the part-time sales class produced enough income to support a family. A

farm that in 1982 is classed as moderate clearly

had a substantial income in 1969. By 1982, the

average part-time farm was extremely dependent on off-farm income, while even moderatesize farms required off-farm income to make

ends meet.

Changes in the Sources of Income

Employment and the sources of income of

U.S. farmers changed greatly in the 20th century, continuing at a rapid rate in the 1970s. The

largest single source of change has been the

tremendous increase in labor productivity made

sTable 4-6 must be interpreted in terms of 1982 dollars. Consequently, the values of earlier years are adjusted upward so that

they are equivalent to the values in 1982. The sales class intervals

are not adjusted. Therefore, the sales class names—small, parttime, moderate, large, and very large should be understood in

terms of income-generating potential in 1982. For example, a farm

in the part-time sales class in 1969 is roughly equivalent to a farm

in the “moderate” sales class in 1982 in terms of income.
Ch. 4—Dynamic Structure of Agriculture • 95

Table 4-6.—Average Gross Farm Income, Net Farm Income, Off-Farm Income, and Total Income of Farms,

1989-82 (1982 dollars)

Value of farm Average gross farm income Average off-farm income

Sales class products sold 1989 1974 1978 1982 1969 1974 1978 1982

Small . . . . . . . . . . < $20,000 $ 9,830 $ 8,387 $ 10,940 $ 5,357 $17,113 $24,343 $20,844 $20,499

Part-time . . . . . . . $20,000-$99,999 75,488 55,170 62,085 49,493 7,809 8,679 11,669 13,216

Moderate. . . . . . . $100,000-$199,999 227,568 156,984 167,161 138,917 14,820 12,610 11,682 11,428

Large . . . . . . . . . . $200,000-$499,999 480,848 331,401 344,972 294,816 19,729 14,632 14,544 12,834

Very large . . . . . . $500,000 2,741,737 1,807,531 1,674,958 1,538,280 20,679 14,690 26,761 24,317

All farms. . . . . . . . . . . . . . . . . . . . . . . . $ 46,436 $ 46,673 $ 59,862 $ 58,764 $15,832 $20,183 $17,951 $17,601

Average net farm income Average total income of farms

Small . . . . . . . . . . < $20,000 $ 1,710 $ 935 ($417) (.$825) $ 16,823 $25,279 $20,427 $ 19,874

Part-time . . . . . . . $20,000-$99,999 24,319 23,312 13,956 2,040 32,126 31,991 25,625 15,258

Moderate. . . . . . . $100,000-$199,999 63,099 77,929 48,165 17,810 77,919 90,538 59,847 29,238

Large . . . . . . . . . . $200,000-$499,999 134,535 177,192 107,382 48,095 154,264 191,824 121,926 60,929

Very large . . . . . . z $500,000 957,313 942,260 767,977 504,832 977,992 958,949 794,759 529,149

All farms. . . . . . . . . . . . . . . . . . . . . . . . $ 13,527 $ 20,674 $ 16,217 $ 9,872 $29,359 $40,857 $34,168 $ 27,474

SOURCE: Office of Technology Assessment. Compiled from data in Econornlc Indicators of tfre Farm Sector: Income and Balance Sfreef Statistics, 1983. USDA, Economic Fksearch Service, 1984. Data adjustmmt for inflation based on redistribution of farm numbers in the Census of Agriculture, 19S9, 1974, 1978, 1982,

Bureau of the Census, US. Department of bmmerce. Price indices in Agrlcu/tura/ Statlstlcs, 19S3, USDA.

possible by technological changes, resulting in

a sharp drop in the demand for agricultural labor. During the 1930s the disposable farm income per capita was less than 40 percent of that

in the rest of the economy. This income differential resulted in the large migration of the farm

labor force out of agriculture and rural areas.

This outmigration accelerated after the Great

Depression of the 1930s as employment and per

capita income opportunities increased greatly

outside of agriculture. In general, the marginal

productivity of labor was higher outside the agricultural sector from the 1930s to the early 1970s.

Therefore, the migration of labor from farming

to the nonfarm sector has contributed to national economic growth.

In the 1970s, the average income differential between farm and nonfarm households narrowed to about 88 percent, owing both to rapid

increases in farm prices and a substantial increase in the number of farm jobs available from

growth in rural industries. These two factors

resulted in a slowing of the rate of outmigration.

Changes in off-farm income by sales class are

shown in table 4-6. In 1982 the average income

of farm and nonfarm households was quite

close, at $27,578 and $28,638, respectively.

However, two-thirds of the income of farm

households comes from off-farm sources. The

majority of farm operators today have some offfarm employment.

The average income statistics mask economic

problems that exist for part-time and moderatesize farms. Farms in the part-time class are in

serious trouble. There were about 580,000 farms

in this class in 1982, at an average total income

of about $15,000. The average net income from

such farming is only $2,040. These farms are

not large enough to generate much net farm income, and at the same time these farms have

lower-than-average off-farm incomes. Moreover, the amount of off-farm income earned by

part-time farmers decreased substantially between 1978 and 1982. Thus, part-time farms

have a smaller share of total off-farm income

now than in 1969. In contrast, households that

operate farms with sales of less than $20,000

have substantial off-farm incomes and low or

negative net farm income. Small farms have the

largest share of off-farm income, and their share

has increased the most since 1969. However,

it should be noted that the socioeconomic structure of the small farm subsector is nonhomogeneous. This subsector contains a large number of subsistence-type farms whose operators

live at or below the poverty level as well as a

large number of affluent families to whom the

farm is more a form of recreation than a source

of income. So, while the average off-farm income of these households enables them to maintain this way of life, there are probably many

small farms that may leave agriculture for economic reasons.
96 ● Technology, Public Policy, and the Changing Structure of American Agriculture

Moderate-size farms have sufficient off-farm

income to maintain a household. However, this

group maybe under the most stress. To provide

an adequate total income, moderate-size farms

must earn almost as much off-farm as onfarm

income. The total amount of off-farm income

earned by moderate-size farms has declined in

real terms since 1969. Since the number of these

farms has increased in the same period, the average off-farm income of moderate-size farms has

declined from $14,800 in 1969 (1982 dollars) to

$11,400 in 1982. Farms with sales in excess of

$200,000 have moderate off-farm incomes and

moderate-to-very large net farm incomes. As a

group, the households that own and operate

these farms are well-off.

PROJECTIONS OF STRUCTURAL CHANGE IN

U.S. AGRICULTURE TO YEAR 2000

The dramatic changes in the structure of agriculture that have occurred between 1969 and

1982 raise a new set of questions: if these trends

continue what will the structure of agriculture

be in 1990 and the year 2000? It is risky to extrapolate very far into the future on the basis

of changes in the past, especially in a sector as

dynamic as that of agriculture. However, the

structural changes in agriculture are generally

strong and consistent and warrant some extrapolation.

The most likely projection of farm numbers,

based on a Markov chain projection using a 1969

through 1982 base, suggest that farm numbers

are likely to decline from 2.2 million in 1982

to 1.8 million in 1990 and 1.2 million in 2000.

The projections indicate that farm numbers will

follow a bimodal or bipolar distribution—a large

proportion of small and part-time farms, an increasing proportion of large farms, and a declining number of moderate farms (table 4-7). Small

farms are projected to account for approximately 51 percent of all farms—down from 61

percent in 1982. In contrast, large and very large

farms are projected to account for about 15 percent of all farms, three times their proportion

in 1982. The number and proportion of moderate-size farms is likely to begin declining by the

end of the century.

The projected decline in the number of small

farms is dramatic but plausible, given the strong

trend in this direction and the persistently negative farm income in this class. However, a substantial number of farms in the small size class

are horse farms, small orchards, and vineyards

that are primarily recreation or “hobby” type

Table 4.7.—Most Likely Projection of Total Number of U.S. Farms in 1990 and 2000, by Sales Classa

1982

Sales class (actual) 1990 2000

Small . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . <$20,000 1,355,344 991,609 637,597

Part-time . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . $20,000-$99,000 581,576 486,790 362,555

Moderate . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .$100,000-$199,000 180,689 126,205 75,011

Large . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ... . .$200,000-$199,000 93,891 144,234 125,019

Very large . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . $500,000 27,800 54,087 50,008

Total . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2,239,300 1,802,925 1,250,190

Percent of total farm numbers:

Small . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . <$20,000 61 55 51

Part-time . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . $20,000-$99,000 26 27 29

Moderate . . . . . . . . . . . . . . . . . . . . , . . . . . . . . . . . . . . . . . . . . .$100,000-$ 199,000 8 7 6

Large . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ... . .$200,000-$499,000 4 8 10

Very large . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2$500,000 1 3 4

Total. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 100 100 100

aBa9ed on a MarkOV chain projection Using a 1969S2 base.

SOURCE: Office of Technology Assessment.
Ch. 4—Dynamic Structure of Agriculture Ž 97

farms. The proportion of recreational farms is

not known, but such farms may help stabilize

the precipitous decline in the number of small

farms.

The projections for the number and proportion of moderate-size farms show a decline, indicating that a farm of that size may not be economically viable. In the past there has been a

steady increase in the number of these farms

in real dollar terms, however, the outlook for

financial survival of many of the moderate-size

farms is not very good. In 1982, the average net

farm income of $17,810 for moderate-size farms

was less as compared with $63,099 in 1969 and

$77,929 in 1974 (measured in 1982 dollars). During this period a large proportion of the growth

of moderate-size farms was due to expansion

of production by small farms and part-time

farms into moderate-size farms. Survival of

moderate-size farms will depend on the operator’s ability to increase farm income or to provide sufficient off-farm income to compensate

for low farm income.

An important implication of the projections

is the further concentration of agricultural production in terms of total net farm income and

total farm cash receipts. The share of total farm

income by large farms has grown steadily from

51 percent in 1969 to 84 percent in 1982. If this

trend continues, over 90 percent of net farm income will be earned by farms with sales over

$200,000 by year 2000.

About 35 percent of total farm cash receipts

were received by farms with sales over $100,000

in 1969. About 30 percent of the total farm production was produced by the largest 50,000

farms (2 percent of the total farms) and 50 percent by the largest 200,000 farms. This pattern

will likely continue to the year 2000 when approximately 95 percent of total production is

projected to come from farms with sales over

$100,000. The 50,000 largest farms (sales over

$500,000) will probably produce 75 percent of

all farm products.

In general, if these trends continue, small

farms are likely to disappear to the extent that

the operators of these farms depend on them

for income. The number of small recreational,

or hobby, farms may increase or hold steady.

Part-time farms could increase in number if the

families that live on these farms are willing and

able to earn the bulk of their income from offfarm sources. The number of moderate-size

farms are likely to decrease and such farms will

have a small share of total gross farm income

and a declining share of net farm income. Large

and very large farms will dominate agriculture.

Moderate-size farms comprise most of the

farms whose owners depend on agriculture for

the majority of their income. Traditionally, the

moderate-size farm has been viewed as the backbone of American agriculture. As the numbers

and economic importance of small and parttime farms decline, moderate, large, and very

large farms all have an opportunity to increase

their shares of farm income. However, large and

very large farms are maintaining or increasing

their shares of farm income, whereas the net

income of moderate-size farms is decreasing

both in absolute terms and in terms of their share

of total farm income.

STRUCTURE Of U.S. AGRICULTURE BY MAJOR COMMODITY GROUPS

The preceding sections have provided a picture of the overall structure of agriculture for

all commodities. This section provides a set of

pictures of the structure of U.S. agriculture in

terms of six major agricultural commodity

groups: cash grains (primarily corn, wheat, and

soybeans), cotton, dairy, poultry and eggs, cattle and calves, and pork. In particular, changes

in the pattern of concentration of production,

as measured by sales, will be described.
98 • Technology, Public Policy, and the Changing Structure of American Agriculture

Figures 4-1 through 4-6 show the percent of

commodity group sales in 1969 dollars by sales

class for 1969 to 1982.4 The general pattern is:

the percent of sales by the lower sales classes

declines, while the percent of sales of the upper

sales classes increases.

Figure 4-1 shows the percent of cash grain

sales in real terms by sales class for 1969 to 1982.

This figure clearly shows the dramatic decline

in cash grain sales by farms with sales less than

$100,000 and the great increase in sales by farms

with sales over $100,000. The increase in market share from farms with sales in the $200,000

fThe discussion of national aggregate farm structure in the

preceding section was presented in terms of constant 1982 dollars. In this section, the percents of sales and sales classes are

presented in terms of 1969 dollars, This means that the sales class

intervals used in the tables, figures, and text represent different

real values. For example, farms with sales in the $20,000 to $99,000

interval in 1969 dollars as presented in this section would have

sales in approximately the $45,000 to $225,000 range in 1982 dollars, Therefore, results for a given sales class in this section cannot be directly compared with results from a sales class in the

previous section on national aggregate statistics. Since the sales

class names used in the previous section—small, part-time, moderate, large, and very large—are defined in terms of the average

income of farms in these classes in 1982 dollars, these names

would be misleading if used in this section. Consequently, the

sales classes in 1969 dollars are referred to in terms of the sales

class interval alone.

Figure 4-1 .—Cash Grain Sales by Sales Class,

1969-82 (1969 dollars)

1969 1974 1978 1982

Year

OSmall ~ Part-time _ Moderate m Large ~ Very

large

SOURCE: Office of Technology Assessment.

to $499,999 class is also especially striking.

These farms increased their percent of total

sales from 2 percent in 1969 to 34 percent in

1982. In the same period, farms with $100,000

to $199,000 in sales increased their share of gross

sales from 6 to 18 percent. The combined sales

of the top two sales classes of cash grain farms

had increased to 50 percent of the total sales

in 1982. Concentration of sales from farms with

more than $500,000 in sales was lower than for

most of the other commodity groups. However,

the rate of growth of the market share of the

top sales class was relatively high.

There is evidence that the structure of cash

grain farms is bimodal in terms of sales by sales

class. In both 1978 and 1982, farms in the $20,000

to $99,999 and $200,000 to $499,999 classes had

more sales than farms in the middle class ($100,000

to $199,999 in sales).

cotton Subsector

The cotton subsector includes all sales of cotton and cottonseed. Figure 4-2 shows the percent of cotton and cottonseed sales in 1969 dollars by sales class for 1969 to 1982. The growth

in sales by cotton farms with more than $500,000

in sales has been very dramatic. The market

share of these farms has increased from less than

Figure 4.2.—Cotton Sales by Sales Class,

1969=82 (1969 dollars)

1969 1974 1978 1982

Year

O Small ~ Part-time _ Moderate ~ Large _ Very

large

SOURCE: Office of Technology Assessment.
Ch. 4—Dynamic Structure of Agriculture ● 9 9

7 percent in 1969 to 48 percent in 1982. In the

same period, sales from farms in the $20,000

to $99,999 class declined from 56 to 14 percent

of the total. It is interesting to note that between

1974 and 1978 there was an upswing in the percent of sales from farms in the middle of the

range ($20,000 to $499,999) and then a subsequent decline from 1978 to 1982. If the trend

of the period 1978-82 continues, sales of cotton

and cottonseed are likely to become even more

heavily concentrated in the top sales class.

Dairy Subsector

Figure 4-3 shows the percent of dairy sales

in real terms by sales class for 1969 to 1982.

Farms in the $20,000 to $99,999 sales class completely dominated the production of dairy products in 1969 with about 66 percent of sales. By

1982 their share had declined to 41 percent. During the same period, dairy farms with sales in

excess of $100,000 increased their share of production substantially. The most dramatic single change occurred in the period 1978-82, when

dairy farms in the $200,000 to $499,999 class

increased their market share threefold, from less

than 5 to 14 percent. As with the other commodity groups, the trend in structural change

is unambiguously in the direction of greater concentration of sales in the top sales classes. It is

Figure 4-3.—Dairy Sales by Sales Class,

1969-82 (1969 dollars)

1969 1974 1978 1982

Year

DSmall ~Part-time -Moderate D Large = Very

large

SOURCE: Office of Technology Assessment.

also clear that the subsector is likely to pass

through a transition period in which there will

be a bimodal distribution of sales among the five

classes shown on figure 4-3. That is, dairy farms

with sales less than $100,000 and more than

$200,000 in 1969 dollars will both have greater

shares of the market than farms in the $100,000

to $199,999 class.

Poultry Subsector

Figure 4-4 shows the percent of poultry and

poultry products sales in real terms by sales class

for 1969 to 1982. As with dairy farms, poultry

farms in the $20,000 to $99,999 class dominated

the structure of the subsector in 1969 with 61

percent of sales. Since 1969, the percent of sales

from poultry farms with sales greater than

$500,000 has increased at a very rapid rate, while

the percent of sales from the $20,000 to $99,999

class has declined greatly. In 1982, poultry farms

with sales in excess of $200,000 accounted for

77 percent of sales, compared with less than 25

percent of sales in 1969.

Figure 4-5 shows the percent of cattle and calf

sales in 1969 dollars by sales class for the years

1969, 1974, 1978, and 1982. Sales from cattle

Figure 4.4.—Poultry Sales by Sales Class,

1969-82 (1969 dollars)

60 I

1969 1974 1978 1982

Year

D Small N Part-time - Moderate m Large ■ Very

large

SOURCE: Office of Technology Assessment,
100 • Technology, Public Policy, and the Changing Structure of American Agriculture

Figure 4=5.-Cattle Sales by Sales Class,

1969=82 (1969 dollars)

1969 1974 1978 1982

Year

O small ~ Part-time 9 Moderat e

~Large ■ Ver y

large

SOURCE: Office of Technology Assessment.

feedlots were excluded. The inversion of structure that has taken place over this time period

is striking. In 1969, cattle operations in the

$20,000 to $99,999 class had 56 percent of sales,

and operations with sales in excess of $500,000

had 22 percent of total sales. The ranking of

these two classes reversed in the 4 years between

1969 and 1974. By 1982, cattle operations with

sales in excess of $500,000 had about 62 percent of sales, whereas the operations in the

$20,000 to $99,999 range had fallen to 12 percent of total sales. Nationwide, cattle operations

with sales greater than $200,000 per year had

77 percent of sales. This is remarkable in light

of the broad distribution of cattle farms and the

large numbers of cattle farms nationwide.

This subsector also clearly has a bimodal

structure. While sales are skewed towards the

largest cattle farms, both of the lower sales

classes have a larger percentage of sales than

the middle range ($100,000 to $199,999).

There is a common perception that U.S. agriculture has become increasingly homogeneous

from one part of the country to another. This

is true in terms of many aspects of agricultural

technology: machinery, crop varieties, livestock

Pork Subsector

Figure 4-6 shows the percent of hog and pig

sales in 1969 dollars by sales class for 1969 to

1982. As of 1982 this subsector did not yet have

the same degree of concentration of sales in the

upper sales classes that was apparent in the

other commodity groups. As of 1982 there was

a relatively high degree of equality of market

share among the different sales classes. However, there have been tremendous structural

changes in this subsector, and the direction of

change is clear. Sales from farms in the $20,000

to $100,000 class have declined from 61 percent

in 1969 to 28 percent in 1982. As a group, hog

and pig farms with sales in excess of $100,000

in 1969 dollars had a majority of sales in 1982.

Farms with sales in excess of $200,000 are gaining market share at the fastest rate. It is likely

that these largest hog farms will soon have a

majority of sales, if this has not already occurred.

Figure 4-6.—Hog and Pig Sales by Sales Class,

1969.82 (1969 dollars)

70

60

 50

1969 1974 1978 1982

Year

n Small M Part-time _ Moderat e ~Large ● Ver y

large

SOURCE” Office of Technology Assessment.

STRUCTURE

breeds, chemicals, and cultural practices have

become standardized in many ways. However,

there are still major differences in the structure

of agriculture in the United States. These differences are seen in the predominance of certain
Ch. 4–Dynamic Structure of Agriculture . 101

commodities in different parts of the country,

the extent to which the production of some commodities is concentrated in different regions,

and the pattern of concentration of sales within

regions. The basic intent of this section is to

show how the structure of agriculture differs

between the four major agricultural regions of

the United States. The data on which this section is based came from the 1982 Census of Agriculture.5 The basic units of analysis are the four

SThis data is the most current that is available on the regional

structure of agriculture. Since the general trend has been toward

increasing concentration of production in the large and very large

sales classes of farms, it is likely that the distributions of sales

by sales class described in this section underestimate the true

structure of agriculture in 1985.

regions of the United States shown in figure 4-7:

the Northeast, South, North Central, and West.

Alaska and Hawaii are included in the Western region.

Attention is concentrated on eight different

commodity groups. These groups include the

six commodity groups whose structure was considered in the national context in the previous

section: cash grains (corn, wheat, soybeans, and

other specialty grains); cotton and cottonseed;

cattle and calves (except sales from feedlots);

hogs and pigs; poultry and poultry products; and

dairy products. Two additional commodities are

included in this section on regional structure:

1) fruit and tree nuts, and 2) vegetables (including potatoes and melons).

Figure 4-7.—Regions and Divisions of the United States

west North Central Northeast

N Dak

West

S Dak North

Central

Wyo

Nebr

Mountain

Utah Kans

Colo

N Mex I 

SOURCE. U S. Department of Commerce, Bureau of the Census
102 ● Technology, Public Policy, and the Changing Structure of American Agriculture

The first part of this section presents summary

statistics on sales by region and commodity

group. The second provides a more detailed look

at the differences in the structure within and

among the four regions in sales classes of farms.

The use of sales classes as the unit of structural

analysis is more useful than size of farm, since

some commodities require much more land per

dollar of sales than other commodities and since

land values vary greatly from one part of the

country to another. Information in this subsection is organized both in terms of sales by sales

class and farm numbers by sales class.

Comparison Botwoon Regions

and Commodities

Table 4-8 shows the percent of combined total U.S. sales of the eight major commodity

groups by group and region in 1982. Ranked

in order of total sales, cash grains come first,

and cotton is the least valuable commodity. The

North Central region accounted for the largest

share of sales of the combined commodity groups,

at 47 percent. The Northeast region had only

5.2 percent of total sales in the United States

in 1982.

Table 4-9 shows the distribution of total U.S.

sales of each commodity among the four regions

in 1982. The North Central region stands out

as the predominant agricultural region of the

United States. This region had the most sales

in four of the eight commodity groups. It also

had 80 percent of hog sales, the highest proportion of any region in any commodity. The West

dominated the fruit and tree nut sales and

vegetable and melon sales, with 65 and 58 percent, respectively.

A measure of the dependence on particular

commodity groups by the agricultural sectors

of the different regions can be seen on table 4-

10, which shows the percent of each region’s

total sales of the eight commodities by commodity in 1982. The New England region had

more sales from a single commodity group than

Table 48.–Percent of Total U.S. Sales of All Commodities by Commodity Group and Region, 1982

Northeast Southern North Central Western Total

Commodity groups I region region region region United States

Cash grains . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 0.4 ”/0 6.50/o 20.2 ”/0 3.4%0 30.4 ”/0

Cattle and calves . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 0.3 7.4 12.0 6.5 26.3

Dairy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.9 2.7 6.0 3.0 14.5

Poultry and eggs. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 0.8 5.7 1.5 1.1 9.1

Hogs and pigs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 0.2 1.2 6.4 0.2 8.0

Fruit and tree nuts . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 0.4 1.2 0.3 3.3 5.1

Vegetables, melon, and potatoes. . . . . . . . . . . . . . . . . . . . . . . 0.3 1.0 0.3 2.1 3.7

Cotton. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 0.0 1.5 .0 1.2 2.8

Total . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 27 47 21 100

NOTE: Totals may not add due to rounding.

SOURCE: Office of Technology Assessment. Compiled from regional data provided by the U.S. Department of Commerce, Bureau of the Census, Agriculture Division,

1982 Census of Agriculture.

Table 49.–Percent of Total U.S. Sales of Each Commodity by Region, 1982

Northeast Southern North Central Western Total

Commodity groups region region region region United States

Cash grains . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

Cattle and calves . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

Dairy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

Poultry and eggs. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

Hogs and pigs. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

Fruit and tree nuts . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

Vegetables, melon, and potatoes. . . . . . . . . . . . . . . . . . . . . . .

Cotton. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

1 .3%0

1.3

19.9

8.6

2.0

6.9

7.6

0.0

21 .4%0

28.2

18.4

62.2

15.3

23,4

25.8

54.9

66.20/o

45.6

41.2

17.0

80.1

5.2

9.0

1.4

11 .O%

24.9

20.5

12.2

64.5

57.6

43.7

100.070

100.0

100.0

100.0

100.0

100.0

100.0

100.0

NOTE: Totals may not add due to rounding.

SOURCE: Office of Technology Assessment. Compiled from regional data provided by the U.S. Department of Commerce, Bureau of the Census, Agriculture Division,

1982 Census of Agriculture.
Ch. 4—Dynamic Structure of Agriculture . 103

Table 4-10.–Percent of Total Regional Sales by Commodity, 1982

Northeast Southern North Central Western Total

Commodity groups region region region region United States

Cash grains . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.7 ”/0 24.00/o 43.1 ‘/0 16.1 0/0 30%

Cattle and calves . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.6 27.2 25.6 31.3 26

Dairy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 55.4 9.8 12.8 14.3 15

Poultry and eggs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14.9 20.8 3.3 5.3 9

Hogs and pigs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.1 4.5 13.7 1.0 8

Fruit and tree nuts..... . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.8 4.4 0.6 15.8 5

Vegetables, melon, and potatoes . . . . . . . . . . . . . . . . . . . . . . . 5.4 3.5 0.7 10.3 4

Cotton . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 0.0 5.7 0.1 5.9 3

Total . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 100.0 100.0 100.0 100.0 100.0

NOTE: Totals may not add due to rounding.

SOURCE:Office of Technology Assessment Compiled from regional data provided by the US. Department of Commerce, Bureau of the Census, Agriculture Division,

lW2Census ofAg;icu/ture.

did any other region, with 57 percent of sales

coming from dairy products alone. The North

Central region ranked second, with 43 percent

of sales in the cash grain group alone. TheSouthern and Western regions had relatively diversified agricultural sectors. However, both regions

were more dependent on cattle production than

on any other commodity. It is interesting to note

that the West accounted for only l percent of

national hog sales. This seems to be anomalous

in light of the relatively large production of the

other seven commodity groups in the West.

Distribution of Sales Within Regions

and Among Regions

The data for this section is contained in appendix B, which shows the amount of sales of

each commodity by sales class and region for

1982. Sales are expressed as a percent of the

total regional sales of each commodity and as

a percent of the national sales total for each commodity. Examination of these tables provides

useful information on the distribution of production within regions and among regions. The

extent to which agricultural production is concentrated in the large and very large sales classes

is of particular interest because this information can contribute to an assessment of the rate

of technology adoption and the impacts from

technology adoption. However, in many cases

the degree of concentration should also be considered in the context of the proportion of total

national sales. Production of some commodities is highly concentrated in certain regions,

but this production amounts to only a small percentage of the national sales of these commodities.

Cash Grains

Cash grain production was the least concentrated of the eight commodity groups within

each region in 1982. With the exception of the

West, sales of cash grains were concentrated

in the part-time, moderate, and large sales

classes. The West differed from the other regions in that its cash grain production was relatively skewed toward the larger farms. In the

other regions, the moderate-size farm had the

largest share of sales. Moderate-size farms in

the North Central region had a relatively large

share of national cash grain sales, 25 percent

of the total. With the exception of the Western

region, large farms also had higher sales than

very large farms. The North Central region had

69 percent of the total number of cash grain

farms in the United States, with small and parttime farms accounting for 57 percent of the total.

Cattle and Calves

The South had 159,000 small farms that raised

cattle in 1982. These small farms accounted for

91 percent of the number of cattle farms in the

region and 54 percent of the national total. However, these farms accounted for only 3.1 percent of national cattle sales. The other regions

also had a disproportionate number of cattle

farms in the small farm class. In general, these

farms were either subsistence farms, whose

owners had low incomes, or they were hobby

farms, whose owners had sufficient income
104 . Technology, Public Policy, and the Changing Structure of American Agriculture

from other sources to subsidize this type of production.

The pattern of cattle and calf sales in the

Northeast stands out in comparison with the

other regions. The other regions had a high concentration of sales in the large farm class and

fairly even distributions in the other classes. The

Northeast had more sales in the small farm class

(less than $20,000) than in any other class, and

the sales from the very large farms and large

farms were lower than those from the small,

part-time, and moderate-size farms. However,

the sales of cattle and calves from Northeast

farms accounted for only 1.3 percent of the national total, whereas 24 percent of the Nation’s

cattle and calf sales were made by the very large

farms in the North Central region.

Dairy

Three different distributions of dairy production are evident among the four regions. The

Northeast and North Central regions had high

concentrations of dairy production in part-time

and moderate farms and very little production

in very large farms. It is striking that the largest

single national share of dairy sales were made

by part-time farms in the North Central region.

There were about 60,000 part-time dairy farms

in 1982, 36 percent of all dairy farms in the

United States. This large group of farms is especially at risk from rapid changes in the technology and cost structure of the dairy industry.

In contrast, the West had a moderately high

concentration of production, 64 percent, in very

large farms and relatively little production from

part-time and moderate-size farms. The very

large dairy farms of the West accounted for 13

percent of the Nation’s dairy sales in 1982. This

share is expected to increase rapidly.

The South falls between these two patterns,

with 46 percent of production in large and very

large farms combined and 38 percent in moderate-size farms. None of the regions had more

than 2 percent of dairy production in small

farms.

Poultry and Eggs

The South had the largest number of poultry

and egg farms in the United States, with 28,000

operations in 1982. Twenty-five percent of all

poultry and egg farms in the United States were

moderate-size farms in the South.

Poultry and egg production was the most concentrated of the eight commodity groups in

1982. In all four regions, very large farms had

the highest percentage of sales. The West had

the highest degree of concentration, with 85 percent of sales from very large farms. The South

had the least amount of concentration, with 39

percent of sales from very large farms and 54

percent of sales from moderate and large farms

combined. However, the very large poultry and

egg operations in the South had the largest single share of national sales, at 24 percent in 1982.

Hogs and Pigs

The North Central region had the largest number of hog farms in the country in 1982. Thirty

percent of the Nation’s hog farms were in the

moderate size class in this region.

Next to the cash grains, hogs and pigs showed

the least amount of concentration. The West was

the most highly concentrated region, with 37

percent of sales from very large farms. However, the West had only 3 percent of the national

sales of hogs. The North Central region had a

low degree of concentration in the very large

class, with only 17 percent of sales from these

farms. Thirty-eight percent of sales came from

moderate-size farms. However, since the North

Central region accounted for 80 percent of national hog sales, the moderate-size farms in this

region had the largest single share of national

sales, 30 percent. Concentration of hog sales in

the South was close to that of the West, 21 percent from moderate-size farms and 33 percent

from very large farms. However, the very large

farms in the South had only 0.9 percent of the

national sales in 1982.
Ch. 4—Dynamic Structure of Agriculture ● 105

Fruit and Tree Nuts

The South had the most concentrated sales

of fruits and nut crops; 65 percent of sales were

from very large farms, Part-time, moderate, and

large farms in the South all had nearly equal

shares of 10 to 11 percent. It is interesting to

note that 15 percent of the U.S. fruit and nut

tree sales come from farms in the part-time sales

class in the West. There are 4,462 fruit and nut

farms in the part-time class in the West as compared with 7,247 in all 5 classes in the Northeast. The North Central region had the lowest

concentration of sales; only 23 percent of sales

were from very large farms. Twenty-four percent of sales in the North Central region came

from moderate-size farms; however, these

moderate-size farms accounted for only 1.3 percent of national sales in 1982.

Vegetables and Melons

The West has a high concentration of national

and regional sales of vegetables and melons in

the very large class of farms. In 1982 these farms

had 83 percent of regional sales and 48 percent

of national sales. Vegetable production is popularly associated with small and part-time farmers. The Northeast came the closest to meeting

this concept, with 21 percent of sales from the

part-time class of farms. However, none of the

regions has more than 3 percent of national sales

from small and part-time farms combined.

Cotton

The South produced 55 percent of the national

cotton sales in 1982, and the West had 44 percent of sales. The Northeast does not produce

any cotton, and the North Central region accounted for only 1.4 percent of national production in 1982. Very large farms in the West had

33 percent of total national sales of cotton in

1982, Cotton sales are highly concentrated in

the West, with 76 percent of regional sales from

very large farms. In contrast, most of the sales

in the South came from moderate and large

farms (with combined sales of 62 percent),

accounting for 34 percent of the national total.

SUMMARY

Overall the trend toward concentration of

agricultural resources in fewer but larger farms

will continue but will differ by commodity and

region. Farm numbers will continue to decline

from 2.2 million in 1982 to approximately 1.2

million in 2000. They will follow a distribution

of a large proportion of small and part-time

farms, an increasing proportion of large farms

and a declining number of moderate-size farms.

Small farms will account for 50 percent of all

farms–a decline from 60 percent in 1982. In

contrast, large and very large farms will account

for 15 percent of all farms, three times their

proportion in 1982. The number and proportion of moderate-size farms will begin to decline

by the end of the century.

An important implication of these projections

is the further concentration of agricultural production. Over 90 percent of total net farm income will be earned by operators of large and

very large farms by year 2000, And the 50,000

largest farms will produce 75 percent of all farm

products sold.

However, the increased concentration of resources will differ by commodity and region.

The four major agricultural regions differ in

their total contribution to U.S. agriculture as

a whole and in their contribution to the production of specific commodities. Major differences

in structure are apparent when each region is

considered in terms of the distribution of sales

by sales class for each of the eight major commodity groups. Some regions, such as the West,

have a high concentration of sales for several

commodities in the large and very large sales

classes and a low concentration of sales for other

commodities. The North Central region is characterized by very large shares of regional and

national production concentrated in moderatesize farms, especially in hogs, dairy, and cash

grain sales. The Northeast stands out as a region that has little concentration of sales in the
106 • Technology, Public Policy, and the Changing Structure of American Agriculture

large and very large farms in any commodity, units. Differences in agricultural structure beincluding dairy products, its largest single com- come even more extreme when the United

modity. States is considered at the subdivision and State

level. As a consequence, agricultural policies

In general, the Nation’s agriculture cannot be that may be appropriate for one part of the counconsidered structurally homogeneous even when try run the risk of being inappropriate when apexamined on the basis of large geographical plied to another.
Chapter 5

Factors Contributing to

Structural Change

in Agriculture
Contents

The Economic Perspective. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

Concentration. . .. ... ... ... .... . . ... ... ..... . . . . . . . . . . . . . . . .

Vertical Integration . . . . . . . . . . . . . . . .. ... ... ..... .. ... ... ..... .

The Sociological Perspective . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

Causes of Structural Change . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

Technological Forces . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ........ . . . . . .

Institutional Forces . ............ . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

Economic and Political Forces . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

The Dynamics of Structural Change . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

Summary .. ... ... ... .... . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

Chapter preferences . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

Table

Table No.

5-1. Histor

Farms

Figure No.

cal and Projected Percentages of Cropland Harvested by

With Sales in Excess of $200,000 . . . . . . . . . . . . . . . . . . . . . . . . . .

Figure

5-1.Factors Influencing the Structure of Agriculture. . . . . . . . . . . . . . . .112
Chapter 5

Factors Contributing to

Structural Change in Agriculture

Traditionally, American agriculture has been

dominated by farms in which the operators and

their families provided most of the labor, made

the management decisions, owned part of the

resources, accepted most of the production and

price risks, bought and sold in the open market, and depended on the farm as their major

source of family income. Such farms have been

revered since the days when Thomas Jefferson

argued for national policies of public land distribution that favored small, independent landholders. In recent years, the dispersed, independent-farm, open-market system has become

less dominant in American agriculture. Major

questions are whether this system can compete

for world markets and whether society should

take steps to halt present trends that are gradually diminishing this system’s prominence. Answering these questions entails viewing the

causes of structural change—that is, how farm

resources are organized and controlled—through

economic and noneconomic perspectives.

THE ECONOMIC PERSPECTIVE

An economic perspective encompasses concentration and vertical integration in agriculture.

Concentration refers to the proportion of production controlled by the largest firms. It is an

important aspect to consider because the more

highly concentrated the market, the greater the

potential impact of a firm or group of firms on

price.

Concentration of total production in agriculture compared with that in many of the other

economic sectors is generally low. As discussed

in chapter 4, concentration has occurred to the

point where in 1982 about 28,000 very large commercial farms—1.2 percent of all farms—produced a third of the total value of U.S. farm products and accounted for over 60 percent of U.S.

farm net income.

However, concentration inland resources is

also occurring in agriculture.1 Trends in the dis-

*Land resources in the agricultural sector can be viewed in the

general category of “land in farms, ” as defined by the Bureau

of the Census, or in the “harvested cropland” category. The acreage of cropland harvested is a more accurate measure of productive agricultural resources than is the general category of land

in farms.

tribution of harvested cropland according to

sales class show that these productive acres are

rapidly becoming concentrated in the farms in

the large and very large sales classes. Table 5-1

shows the percentage of total cropland harvested by the top two sales classes of farms for

the census years 1969 and 1982 and projects

them linearly to 1990 and 2000. If present trends

continue, almost half of all cropland will be harvested by farms in these sales classes by 2000.

The degree of concentration varies by commodity. For example, beef cattle operators with

Table 5.1 .—Historical and Projected Percentages of

Cropland Harvested by Farms With Sales

in Excess of $200,000

Year

Sales class 1969 1982 1990 2000

$200,000-499,000 . ............12.0 25.3 27.0 32.0

. . . . . . . . . . . . . . . . . . . 6.0 11.2 12.0 14.0

Total . . . . . . . . . . . . . . . . . . . . .. 18.0 36.5 39.0 46.0

Projection assumptions:

1, Growth in total harvested acres is linear, resulting in an increase of 2.4 roll.

lion acres per year.

2. Growth follows the linear trend for the two sales classes and results in an

increase of 2.7 million acres per year for the farms in the $200,000-$499,000

class and of 1 million acres per year for the >$500,000 class.

3. The linear projections are based on the acres harvested by sales classes,

adjusted for inflation. Inflation in commodity prices tends to move acres

from lower to upper sales classes. Since inflation in commodity prices is

likely to continue, nominal growth in acreage harvested by these sales

classes may be greater than projected.

SOURCE: Office of Technology Assessment

109
110 . Technology, Public Policy, and the Changing Structure of American Agriculture

sales over $500,000 per year in 1982 represented

only 0.5 percent of all beef cattle operations and

accounted for 55 percent of the total value of

cattle sales. The 69 largest of these feedlots

produced 21 percent of the fed cattle in 1980

(USDA, 1981). The largest cattle feeders were

also some of the largest feed manufacturers and

grain companies.

Higher levels of concentration exist for broilers (chickens). In 1977 the 16 largest broiler

producers and contractors controlled about 50

percent of the production (Brooke, 1980). In

vegetable crops, such as lettuce and celery, concentration is comparably high (Brooke, 1980).

On the other hand, concentration is still very

low for most crop agriculture. Relative to other

American industries, where the market share

of the four largest manufacturers frequently exceeds 50 percent, concentration in agriculture,

even for cattle feeding, broilers, lettuce, and celery, is low. However, attention is drawn to agriculture because of the rapidity with which certain industries, such as broilers and feed cattle,

have gone from a diffused to a concentrated and

integrated agriculture (Knutson, et al., 1983).

Concern exists that if extended over a period

of time, the increasing concentration of agricultural production could lead to higher food

prices (Breimeyer and Barr, 1972). This would

result from increased merchandising and marketing costs, potential unionization of agricultural workers, and the lack of effective competition (Rhodes and Kyle, 1973).

Vertical Integration

Firms are vertically integrated when they control two or more levels of the productionmarketing system for a product. Such control

may be exercised by contract or by ownership.

Contract integration exists when a firm establishes a legal commitment that binds a producer

to certain production or marketing practices.

At a minimum, contract integration requires

that the producer sell the product to the buyer.

Additional commitments may bind the farmer

to specified production practices and sources

of inputs. While all forms of contract integration have created concern, the greatest controversy exists with contracts that control both

production and marketing decisions of farmers.

In addition, from a legal perspective, the producer may not even own the product being

grown (Knutson, et al., 1983).

The extent of contract integration is not well

documented. Ronald Knutson estimates that all

forms of contract integration represented 32

percent of farm sales in 1981 (Knutson, et al.,

1983). He makes the following observations on

the extent of contracting:

1. Contracting used to be limited to perishable

products; now it has expanded to virtually

all commodities.

2. Production contracting appears to be associated with commodities where breeding and

control of genetic factors play an important

role in either productivity determination or

quality control.

Ownermhip integration is a single ownership

interest extended to two or more levels of the

production-marketing system. It may involve

either cooperatives or proprietary agribusiness

firms. Knutson estimates that proprietary ownership integration accounts for about 6 percent

of farm sales. Some proprietary agribusiness

firms—such as Cargill (beef); Superior Oil (fruits,

vegetables, and nuts); Coca-Cola (oranges and

grapefruit); Tysons (broilers and hogs); Tenneco

(fruits, vegetables, and nuts); and Ralston Purina (mushrooms)-have made substantial investments in agricultural production. In products

such as broilers, eggs, cotton, vegetables, and

citrus fruits, ownership integration is over 10

percent of total U.S. production (Knutson, et

al., 1983).

Cooperative ownership integration is much

more prevalent than proprietary ownership integration, accounting overall for 34 percent of

farm sales. However, in only 13 percent of cooperative integration is there a legal commitment by farmers to market their commodities

or to purchase inputs from the cooperative.

The economic implications and concern for

structural change of vertical integration are debated. A principal problem in agriculture has

been the difficulty of coordinating production
Ch. 5—Factors Contributing to Structural Change in Agriculture ● 111

with market needs. Vertical integration can

make a substantial contribution to satisfying this

need. For example, in broilers and turkeys, vertical integration has contributed to the uniform

size and quality of poultry sold. It has also contributed to increased efficiency and reduced

costs (Schrader and Rogers, 1978).

On the other hand, there are potentially adverse consequences of vertical integration. Contract integration with corporations, and sometimes cooperatives, radically changes the role

of the traditional, independent farmer. More

often than not, the farmer loses control of, if

not legal title to, the commodities grown under

a production-integrated arrangement. Payment

to the grower is largely on a per-unit or piecewage basis, and not necessarily related to product value.

It has been argued that in the long run, market power in integrated agriculture will become

sufficiently highly concentrated that the consumer will pay higher prices for food. However,

no definitive conclusion can be made. The above

argument fails to take into account efficiency

gains from integration. The extent to which

these gains could be realized without the development of a vertically integrated system is

open to question.

THE SOCIOLOGICAL PERSPECTIVE

Many concerns relating to structural change

are of a sociological nature. They revolve around

the impact of concentration and integration on

the institution of the family farm, on rural communities, and on rural institutions.

Concern has been expressed that continuously increasing the concentration and integration

will lead to the demise of the family farm as an

institution. The term family farm has been associated with the existence of an independent business and social entity that shares responsibilities

of ownership, management, labor, and financing. The family farm system leads to dispersion

of economic power and has been associated

with the perpetuation of basic American values

and of the family as an institution. Increased

concentration and integration tend to destroy

the family farm institution. Very large farms lose

many of the characteristics of the traditional

family farm because their business and hired

labor aspects clearly predominate. Most of the

management functions traditionally associated

with the family farm institution are removed

by integration. With integration the farmer takes

on more of the characteristics of a businessman.

Another concern is that concentration and

ownership integration reduce the number of

farms and make the integrator less dependent

on the local community. As a consequence,

small rural towns and their social institutions

decline or vanish. Recent research conducted

in California provides some evidence to substantiate such a relationship. Dean MacCannell

(1983) has found that rural communities where

a few large and integrated farms dominate are

associated with few services, lower quality education, and less community spirit. (This is discussed in greater detail in chapter 11.)

Concerns are also expressed about the impact

of structural change on the nature of the U.S.

political system. Thomas Jefferson visualized

the merits of a decentralized political system

where power was highly diffused and where

every individual had the opportunist y to participate in public decisions. His philosophy placed

a high value on independent farmers and landowners as a means of maintaining a democratic

system of government.

Already there has been a marked departure

from the decentralized power structure ideal

visualized by Jefferson. The question is whether

agriculture is basically unique and different

from other sectors of U.S. society, as has long

been maintained. Are there unique social, cultural, and traditional values in having landownership widely dispersed, or should agriculture join
112 ● Technology, Public Policy, and the Changing Structure of American Agriculture

the mainstream where the other economic sec- of the nonfarm sector. Some people will intertors have long been? As U.S. agriculture con- pret this trend as progress; others will interpret

tinues along the trends laid out in this report, it as a step backward.

it will increasingly take on the characteristics

CAUSES OF STRUCTURAL CHANGE

A number of factors have been identified by

researchers as causes of structural change.

However, there has been no delineation of the

relative importance of each factor. One of the

objectives of this study is such a delineation.

Before moving to that analysis in the following

chapters, however, it is important to understand

why each of these factors is considered important to structural change.

Most observers of structural change cite three

main determinants:

1. technology and associated economies of size,

specialization, and capital requirements;

2. institutional forces; and

3. economic and political forces (figure 5-1).

Technological Forces

Certain farmers have a strong incentive to

adopt new technology rapidly. The early innovator achieves lower per-unit costs and increased profits, at least for a short time, before

other farmers follow his lead. For example, in

Figure 5-l.— Factors Influencing the Structure of Agriculture

Economic environment

● Growth of demand

● Consumer tastes and preferences

Institutional factors (agriculture specific)

● Credit institutions (public and private)

● Agribusiness firms (cooperative and proprietary)

● Research and development (public and private)

Technical factors affecting farming

• New technologies available

● Distribution of costs of production

Structure of agriculture

. Number and size of farms

● Contractual arrangements

● Control of management decisions

● Ownership of farmland

SOURCE: Office of Technology Assessment
Ch. 5—Factors Contributing to Structural Change in Agriculture ● 113

Washington State a winter wheat farmer with

2,500 acres can reduce average machinery costs

by 9 percent per acre by replacing a conventional crawler tractor with a four-wheel-drive

tractor. If he also expands the size of his farm

to 3,900 acres, he can reduce costs by an additional 18 percent (Rodewald and Folwell, 1977).

This nearly 60-percent increase in farm size can

be made without additional labor. Once the innovative wheat farmer adopts the technology,

other crop farmers generally have two options:

purchase a four-wheel-drive tractor and expand

the size of their farm, or accept a lower net income as market prices for their crops fall. In

short, new technology can play an important

role in determining acreage and capital requirements. Different farmers have different costs

because they use different combinations of inputs, have different management skills, or have

a different scale of operation.

Economies of size

The relationship of scale of operation to cost

is of particular significance to structure. If costs

are relatively the same for all farm sizes, one

would expect all farm sizes to have relatively

little incentive to increase in size. In addition,

with relatively even costs, consumers would

clearly not benefit from increases in farm size.

If, on the other hand, costs decline sharply as

farm size increases, not only would there be

strong incentives for farms to grow in size, but

consumers would potentially realize lower

prices for food. Of at least equal importance to

policy makers, if costs decline sharply as farm

size increases, efforts to prevent this change

from occurring—e.g., to preserve the family

farm–would not only be difficult, but could be

counterproductive from a consumer perspective. Smaller farm operators could exist in a costdeclining environment only if they were willing to accept lower returns to contributed labor, capital, and management, and/or had an

off-farm job.

Past studies of the relationship between average production costs and farm size support two

major conclusions: First, most economies of size

are apparently captured by moderate-size farms.

Second, while the lowest average cost of production may be attainable on a moderate-size

farm, average cost tends to remain relatively

constant over a wide range of farm sizes. Thus,

farmers have a strong incentive to expand the

sizes of their farms in order to increase total

profits. (This phenomena is explored in detail

in chapters 8 and 9.)

Earlier studies on economies of size have several limitations. External economies gained

from buying and selling in large volumes and

from access to credit have usually been ignored.

Common ownership of related farm and nonfarm activities has not been considered. There

is some evidence that inclusion of such pecuniary economies would lower the average production costs for large farm units and would

shift the conclusion about the size of the most

competitive farm (Smith, et al., 1984).

Specialization

Technology has also influenced specialization

and regional production patterns. Cotton production has moved westward, for example, into

areas of broad, flat fields where larger machinery can be used to optimum advantage. Specialization in crop production is also due in part to

technology, Farmers who once relied on crop

rotation and diversification to conserve soil fertility, prevent soil erosion, and control pests

have replaced these practices by chemical fertilizers, insecticides, and herbicides, with questionable long-run effects. Such farmers can thus

grow one crop exclusively year after year, specializing in commodities that are the most

profitable. Similarly, the development of new

disease control techniques has given poultry and

livestock farmers unprecedented opportunities

to specialize. The vertically integrated broiler

industry of today would have been impossible

without scientific advances in breeding, feeding, housing, and medicine, which have reduced

the real cost of broilers by as much as 50 percent over the past 30 years.

These scientific breakthroughs have generally enabled both small and large farmers to specialize more. However, improvements in farm

machinery have perhaps been most important

in fostering large-scale, specialized operations.
114 • Technology, Public Policy, and the Changing Structure of American Agriculture

A decision to invest in a specialized piece of

equipment means that an operator will emphasize production of the commodity for which the

machine is intended, quite likely at the expense

of some other commodity. And insofar as a machine is most economical on a particular size

of operation, expansion to that size is encouraged. Thus specialization and farm growth occur simultaneously.

Capital Requirements

Agriculture is one of the most capital-intensive industries in the American economy. As

a result, the requirements for credit to finance

new capital investments, production, or storage are high. Technology has made barriers to

entry more formidable. The cost of machinery

raises capital requirements for beginning farmers. Technologies that allow individuals to farm

increasingly larger acreages have added to the

competition for land, resulting in high land

prices, the single greatest expense in farming

today. The average investment in 1980 in a farming operation with gross sales between $40,000

and $60,000 ranged from $350,000, for fruit and

nut farms, to over $800,000, for livestock ranches.

InstitutionaI Forces

Institutional factors have their primary influence on the costs of inputs used in production,

the prices of products, and the generation of

new technology for agriculture. These institutions may be either in the private or the public

sector.

The costs of inputs are primarily a function

of competition between private sector agribusiness firms. Input costs do not have to be the

same for all farmers. Input suppliers may offer

farmers discounts for larger volume purchases

of fertilizer or chemicals. Likewise, larger scale

farmers may receive higher prices for products

marketed through the use of crop contracts or

futures markets.

Research and Extension

New technologies are generated in both the

public and private sector. Basic agricultural research is primarily a public sector function performed by the U.S. Department of Agriculture

(USDA) and the land-grant universities. Applied

research functions are shared between the public and private sector, with the private sector

dominating development activities. Extension

activities assist in evaluating and transferring

technological innovations into practice. An integral part of the agricultural research and extension policies involve the generation of higher

levels of training and expertise embodied in human capital. The result is more skilled farmers,

agribusinessmen, scientists, and agricultural

policymakers.

Research and extension have had different

impacts on farms, farmworkers, rural communities, and even entire regions, depending on

the characteristics and type of technology developed. Some technological innovations, particularly mechanical innovations, have favored

and hence fostered larger farms. Technological innovations that could be applied on farms

of any size are often first adopted by larger farms

(Paarlberg, 1981; Perrin and Winkelman, 1976).

By being the first to adopt new technologies,

larger farms receive greater benefits than those

not adopting the technologies (typically, smaller

farms).

A major effort of extension is to disseminate

timely information through public meetings.

The topics covered in publications and public

meetings are heavily influenced by current research results. Any bias toward larger farms that

is embodied in research results would most

likely be carried over into meetings and publications.

Even though extension personnel make information available to all farmers, those farmers

that make the most use of the research results

and extension information can generally be

characterized as the more innovative, more aggressive, and better managers, usually of larger

farms (Paarlberg, 1981). Such farmers are also

generally more vocal, providing feedback to research and extension personnel on the usefulness of the information received. Even though

no overt effort is made to exclude particular

groups, such as operators of small farms, the

net result is that many research and extension

programs become more oriented toward those
Ch. 5—Factors Contributing to Structural Change in Ariculture Ž 115

select groups that generally avail themselves of

the information (Paarlberg, 1981).

This lack of structural neutrality was recognized in 1979 by Secretary of Agriculture Bergland when he questioned the use of Federal

funds for research projects having the objective

of producing large-scale, labor-saving technology and set up a special task force to investigate

the impact of research and extension on structure. At the same time, Congress earmarked research and extension funds for increased work

with small farms and for projects involving direct marketing from farmers and consumers.

However, no special programs were developed

for moderate-size farms.

The Bergland initiative on research was deemphasized with the change in administration

in 1981. It has, however, been rekindled by the

announcement of joint initiatives in biotechnology research between private sector companies and universities. Questions have arisen

as to whether the primary beneficiaries of the

initiatives will be the private sector firms or the

initial adopters of the resulting new technology.

Public Policy

Many public policies affect the structure of

agriculture by influencing resource use, capital requirements, technology development and

adoption, freedom of decisionmaking, exchange

arrangements, risks, and costs and profits. Some

policies are oriented specifically to the farm sector, such as price and income policy (commodity

programs). Others affect agriculture directly but

are more broadly oriented, such as tax policy.

Still others are general—e.g., national macroeconomic policy—and affect agriculture indirectly,

Public policies offer viable ways to maintain

or alter the structure of the agricultural sector.

In this section, areas of public policy involvement that affect the structure of agriculture are

briefly examined.

Commodity Programs.—Beginning with the

Agricultural Adjustment Act of 1933, a series

of commodity programs have evolved to deal

with price and income problems in farming.

These programs have covered such commodities as wheat, feed grains, cotton, wool, sugar,

rice, peanuts, tobacco, and dairy products. To

stabilize and increase farm prices and incomes,

a variety of program tools has been used: price

supports, direct payments, acreage allotments,

set-asides, conservation reserves, surplus disposal, and stock accumulation.

There is widespread agreement that these programs, in the short run, held farm incomes above

the long-run income effects. Price stability from

these programs has enabled farmers to adopt

new and improved technologies. And logic suggests that the higher the level at which prices

are stabilized, the more rapid and widespread

will be technological adoption in farming. But

it does not follow that high and stable prices

necessarily speed resource concentration in

farming. The high and stable prices may help

the weak and inefficient stay in business. Little

is known about how different levels of price and

income support affect the rate of resource concentration in farming (Cochrane, 1983). Thus,

the question becomes whether policy makers

who want to change the rate at which productive resources in farming are concentrated in

fewer and fewer hands should support and stabilize product prices at low levels, at high levels,

or somewhere in between—or whether they

should do something different instead. (The effect of commodity programs on resource concentration is analyzed in chapters 8 and 9.)

Tax Policy.—Tax laws and provisions are

widely recognized as being a determinant of

agricultural structure. There is, however, no

agreement about the relative importance of tax

policy because of its interactions with other

structural determinants. Some tax laws and provisions can be directly related to structure (i. e.,

estate and corporate tax law), while others (i.e.,

investment tax credits, depreciation provisions,

capital gains, and cash accounting) are indirectly related and often interact with credit and commodity policies.

In animal agriculture, tax factors such as cash

accounting, current deductibility of costs of raising livestock, and capital gains treatment for

sales of breeding livestock, together with invest-
116 ● Technology, Public Policy, and the Changing Structure of American Agriculture

ment tax credits and accelerated depreciation,

influence livestock investments and can affect

structure. Tax policy issues in animal agriculture include tax shelter and nonfarm investments, tax provisions as a factor in economies

of size, and the legal structure of agriculture.

The cattle sector provides one example.

The income tax advantages of cattle feeding

were packaged as limited partnership syndicates in the late 1960s and early 1970s and sold

to nonfarm investors. The growth of nonfarm

investment in cattle feeding was closely associated with the movement of cattle feeding out

of the Midwest and with the growth of largescale feedlots in the High Plains area. Other factors also played a role, but limited empirical evidence suggests that tax-induced investment in

cattle feeding through limited partnerships was

related to structural change (Carman, 1983).

For mechanical technology, current tax laws

favor the substitution of capital for labor and

may speed the adoption of mechanical systems.

Two tax factors are at work: payroll taxes, which

increase the cost of labor, and provisions for

investment tax credit and accelerated depreciation, which decrease the cost of machinery

(Carman, 1983).

It is conventional wisdom that tax provisions

are an important consideration in the adoption

of capital-intensive innovations, since investment tax credit and accelerated depreciation

do have a significant impact on after-tax costs.

Such innovations include large four-wheel-drive

tractors, circle irrigation systems, minimum tillage systems, and large-scale and improved harvesters.

An important implication can be drawn about

structural change from the above discussion.

Small farms and very large farms have more offfarm interests against which to offset farm losses

than do moderate-size farms. This could be a

significant factor in accounting for the decline

of the moderate farm. (The effect of tax policy

on structural change in agriculture is examined

in chapters 8 and 9.)

Agricultural Credit Policy.—Public policy

directly influences the supply of capital to

farmers through the Farmers Home Administration (FmHA) of USDA and the Farm Credit

System, which includes the Federal Land Bank,

the Production Credit Association, and the Bank

for Cooperatives. The original capital for the

Farm Credit System was supplied by the Federal Government, but the system is now wholly

owned by its borrowers. However, the Farm

Credit System is still accorded agency status

whereby interest costs on its bonds and discount

notes are lowered. The FmHA is a Government

agency that has a mandate from Congress to

make low interest loans to family farmers who

cannot obtain credit elsewhere. The FmHA and

the Farm Credit System together account for

approximately 40 percent of the total farm debt

outstanding (8 and 33 percent, respectively)

(Barry, 1983).

The general intent of farm credit policies has

been to ensure appropriate capital availability

for agriculture. Policies established by these

agencies and their attendant programs are

thought to have influenced the structure of the

farm sector, although the extent of their impact

has not been studied thoroughly. (Chapter 7 explores the relationship between credit policy

and structural change in agriculture.)

Economic and Political Forces

Agriculture operates in a broader overall economic and political environment. This environment determines the rate of interest, the rate

of inflation, and the value of the dollar—all of

which influence the costs and prices of farm

products. The increased importance of these

effects has made macroeconomic policies that

influence the overall economic environment

within which agriculture operates more important to farmers.
Ch. 5—Factors Contributing to Structural Change in Ariculture Ž 117

THE DYNAMICS OF

A study of this type cannot possibly analyze

all of the technical, economic, and institutional

factors that influence the structure of agriculture. This study therefore concentrates on those

factors that appear to be the most critical in

affecting structure and that also relate to current farm policy decisions. These factors

include:

●

●

●

the technical factors influencing the costs

of production as related to farm size;

the major farm program elements; and

the institutions that lead to the development

and assimilation of new technology.

These factors interact in a dynamic fashion

to influence the structure of farming. New technology continuously infused into agriculture is

adopted by the most progressive farmers. While

the initial adopters assume increased risk in applying a new technology, they generally also

gain substantially higher returns. Farm programs that reduce price risk help assure higher

returns.

As more farmers realize the advantages of

new technology, the adoption process becomes

STRUCTURAL CHANGE

more general. As this happens, supplies increase, with the tendency to force down market prices. If Government policies prevent market prices from falling, surpluses build up, as

they have in the dairy industry or did before the

payment-in-kind (PIK) program. If market prices

fall, Government payments rise.

Wider adoption of technologies also changes

the nature of costs as farm size increases. If

larger farms are the first adopters, their costs

are substantially lower. The laggers in adoption

realize much higher costs. By not adopting, they

become, in effect, left behind–eventually being either forced off the farm altogether or

forced to take an off-farm job.

These consequences often lead to suggestions

of turning off the technological wheels of progress. Such a strategy, however, would have a

devastating impact on the competitive position

of American farmers in world markets. Instead

of just some people being left behind, the whole

American farm system would be left behind.

This chapter has viewed structural change

from both an economic and sociological perspective and identified the major forces of this

change. These include technological, institutional, economic, and political factors. Technology and associated economies of size, specialization, and capital requirements have had

an important influence on structural change in

agriculture. Likewise, private and public institutional factors, which include research and extension, credit institutions, farm programs, and

tax policies, have played a significant role. And

the economic and political environment including the value of the dollar, rate of inflation,

growth in demand, and consumer tastes and

preferences are becoming even more important

factors.

There has been a marked departure from the

decentralized power structure ideal visualized

by Thomas Jefferson that causes many people

to be concerned for a variety of reasons. The

question is whether agriculture is basically

unique and different from other sectors of U.S.

society. Are there unique social, cultural, and

traditional values in having landownership

widely dispersed, or should agriculture join the

mainstream where other U.S. economic sectors

have long been? As American agriculture continues along the trends laid out in this report,

it will increasingly take on the characteristics

of the nonfarm sector, Some people will interpret this as progress; others will interpret it as

a step backward.
118 . Technology, Public Policy, and the Changing Structure of American Agriculture

To help policymakers better understand how

and why agricultural structure is changing, this

study focuses on the major technical, economic,

and institutional factors which influence structural change. They are: 1) technology, 2) major

farm program and tax elements, 3) financial institutions, and 4) institutions that lead to the development and assimilation of new technology.

In the chapters to follow the impact of each of

these factors, as well as the dynamic interactions between them, will be studied.

CHAPTER 5 REFERENCES

Barry, Peter J., “Credit Policy and Structural Issues

in Agriculture,” paper prepared for the Policy,

Technology, and Structure of Agriculture Workshop, Office of Technology Assessment, Washington, DC, March 1983.

Breimeyer, Harold F., and Barr, Wallace, “Issues

in Concentration Versus Dispersion, ” Who Will

Control U.S. Agriculture? North Central Regional

Extension Publication 32 (Urbana, IL: University

of Illinois, August 1972), pp. 13-27.

Brooke, Donald L., “Changing Structure of Florida

Vegetable Farms 1945-1974,” Farm Structure—

A Historical Perspective on Changes in the Number and Size of Farms, U.S. Senate, Committee

on Agriculture, Nutrition, and Forestry, April

1980, pp. 363-379.

Carman, Hoy F., “Tax policy, Technology, and the

Structure of Agriculture,” paper prepared for the

Policy, Technology, and Structure of Agriculture

Workshop, Office of Technology Assessment,

Washington, DC, March 1983.

Cochrane, Willard W., “Farm Commodity Policy,

Technological Advance and Structural Change,”

paper prepared for the Policy, Technology, and

Structure of Agriculture Workshop, Office of

Technology Assessment, Washington, DC, March

1983.

Knutson, Ronald D., et al., Agricultural and Food

Policy (Englewood Cliffs, NJ: Prentice Hall, Inc.,

January 1983).

MacCannell, Dean, “Agribusiness and the Small

Community,” paper prepared for the Policy, Technology, and Structure of Agriculture Workshop,

Office of Technology Assessment, Washington,

DC, March 1983.

Paarlberg, Don, “The Land Grant Colleges and the

Structure Issues,” American Journal of Agricultural Economics 63:129-134, 1981.

Perrin, Richard, and Winkelman, Don, “Impediments to Technical Progress on Small vs. Large

Farms,” American Journal of Agricultural Economics 58:888-894, 1976.

Rhodes, James, and Kyle, Leonard R., “A Corporate

Agriculture,” Who Will Control U.S. Agriculture?

North Central Regional Extension Publication 32-

3 (Urbana, IL: University of Illinois, March 1973).

Rodewald, Gordon E., and Folwell, Raymond J.,

“Farm Size and Tractor Technology,” Agricultural Economics Research 29:89, July 1977.

Schrader, Lee F., and Rogers, George B., “Vertical

Organization and Coordination in the Broiler and

Egg Subsectors,” Vertical Organization and Coordination in Selected Commodity Subsectors,

North Central Regional Project No. 117, W.P. 20

(Madison, WI: University of Wisconsin, August

1978).

Smith, Edward, et al., Cost and Pecuniary Economies in Cotton production and Marketing: A

Study of Texas Southern High Plains Cotton Producers, Texas A&M University, Texas Agricultural Experiment Station Bulletin B-1475, August

1984.

U.S. Department of Agriculture, Cattle on Feed,

MTAN 2-16 (Washington, DC: Economic, Statistics and Cooperative Service, January 1981).
Part III

Analyses of Technology,

Public Policy, and
Chapter 6

Emerging Technologies and

Agricultural Structure
Contents

Page

Methodology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .......................123

Technology Groupings . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ...............124

Structural Elements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .............124

Impacts of Technology on Structural Elements . .....................,..125

Capital and Labor . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .....,......125

Vertical Coordination and Control . . . . . . . . . . . . . . . . . , ................127

Market Access . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ...................129

Barriers to Entry . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ..................129

Relative Adoption Rates by Size... . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. ...130

Relative Effectiveness of Policy in Achieving Structure . . . . . . .. ... ... ....131

Policies Considered . . . . . . . . . . . . . . . . . . . . . . . ........................131

Relative Effectiveness . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ...........,..131

Structural Impact on Technology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ..132

Summary and Conclusions . . . . . . . . . . . . . . . . . . . . . . . ...................133

Capita and Labor . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ...........,133

Other Structural Elements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .........133

Relative Adoption Rates by Size . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ., .,.134

Policies Effective in Achieving a Structure . . . . . . ..............,......134

Tables

Table No. Page

6-1.

6-2.

6-3.

6-4.

6-5,

6-6.

Groups of Technologies Analyzed, by Animal and Plant . ............124

Potential Directional Impact of Technology Groups on Structural

Elements at the Producer Level, by Animal and Plant . ..,.....,.....126

Potential Impact of Technology Groups on Capital and Labor

at the Producer Level, Assuming Adoption, by Animal and Plant .. ...126

Percent Adoption Rate of at Least One Technology Within

a Technology Group by Year 2000, by Size of Farm . . . . . . . . . . . . . . ...131

Relative Effectiveness of Types of Policy in Attaining a Desired

Specific Structure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ..........131

Potential Directional Impact of Farm Structure on Development

and Adoption of Technology Groups, by Animal and Plant . . . . .......132
Chapter 6

Emerging Technologies and

Agricultural Structure

New technologies have, historically, had significant impacts on structural change. New disease control techniques gave poultry and livestock farmers unprecedented opportunities to

specialize and vertically integrate. Improvements in farm machinery fostered large-scale,

specialized farm units.

Like their predecessors, the emerging technologies examined for this study will make a

considerable impact on farm structure, especially by year 2000. Biotechnologies will have

the greatest impact because they will enable

agricultural production to become more centralized and vertically integrated. Although in the

long run the use of new technologies will not

increase the farmer’s overall need for capital,

there will be trade-offs: biotechnology will entail less capital; information technology will entail more. This chapter discusses how the emerging technologies are expected to affect these and

other elements of agricultural structure.

The chapter evaluates the new technologies’

impacts on agricultural structure. It covers: 1)

the methodology and assumptions for evaluating the impact of technologies, 2) the analysis

of technology’s impact on structure, 3) relative

adoption rates by size of farm, and 4) relative

effectiveness of policy in achieving a structure.

METHODOLOGY

To assess the relationship between technology and structure, OTA conducted a 2-day workshop with a panel of 14 experts.1 This workshop

will be referred to as the Agriculture Structure

Group (ASG). The panel members represented

abroad range of backgrounds and regions within the United States. (The names, affiliations,

and disciplinary specialty of each workshop participant are included in app. C.) A major portion of the first day was devoted to briefings by

experts on the 28 previously defined technologies. The initial discussions of the ASG involved the potential impact of these technologies

on capital and labor at the farm level and on

the structural elements of vertical coordination,

market access, and barriers to entry. The second day’s discussions were concerned with the

distributional impacts of the technologies and

the broad categories of public policies that might

achieve a predetermined structural changeover

time.

*This chapter is based on the results of the workshop as analyzed by Thomas Sporleder in the OTA paper “Agricultural Structure Impacts of Emerging Technologies in American Agriculture” and reviewed by the workshop participants.

The assessment of distributional impacts was

by broad category of technologies. The panel

members considered both rate of adoption and

change in physical output by farm size, focusing on the flow of impacts from various technologies to structure.

As a final item, the ASG addressed the reverse

flow-from structure to technology development and adoption. Thus the potential causality between various farm structures and the development and adoption of technology groups

was also addressed by ASG.

Group discussion was unstructured except for

materials prepared for the ASG that provided

an agenda of discussion topics. The Delphi procedure was used to help the group reach consensus about the potential impacts of technology on selected economic variables.

The assumptions made by the work group that

applied throughout the assessment of technological impacts to the year 2000 were:

1. Economic variables such as tax policy, in123
124 . Technology, Public Policy, and the Changing Structure of American Agriculture

terest rates, inflation rates, and prices are

held constant at 1984 levels.

Z. Impacts of technology groups are assessed

separately, assuming that only one technology group exists and is 100 percent adopted

in 1984. Thus, potential interaction effects

among the technology groups were not assessed.

3. Capital and labor were defined broadly so

that they were the only factor inputs in the

production process. Capital was defined as

TECHNOLOGY

The ASG judged the 28 study technologies to

be sufficiently similar in their impacts on market structure to permit assessment of them within groups rather than individually. Thus the

technologies were grouped into three broad categories for animal agriculture and four broad

categories for plant agriculture (table 6-l).

The biological group essentially consists of

technologies that use living organisms or their

isolated components for manipulating plant or

animal production. The mechanical group encompasses technology development in machinery used to produce and/or harvest the results

of crop or animal production. The information

group includes the technologies of sensors, controllers, and actuators, along with broad developments in computer technology applicable to

the collection and analysis of information for

producer-level management decisions.

Whereas the other categories apply to both

crop and livestock production, the technologies

within the management techniques group apply

only to crop production. This group includes

all nonlabor, nonmanagement resources,

including land, while labor was defined to

encompass both management and labor.

4. The focus was production agriculture—

farms and ranches. An important element

of structure was defined as the number and

size of farm firms. Although the group recognized the importance of other levels in

the commodity marketing and food distribution channel, the focus remained on the

farm level for reasons of manageability.

Table 6-1.—Groups of Technologies Analyzed,

by Animal and Plant

Technology group/technologies included

Animal:

Biological.—Genetic engineering, Animal reproduction,

Regulation of growth and development, Animal nutrition,

Disease and pest control

Mechanical.-Environment of animals, Animal behavior, Crop

residues, Animal waste use, Robotics

Information. -Monitoring and control, Communication and

information, Telecommunications

Plant:

Biological.—Genetic engineering, Enhancement of photosynthetic efficiency, Plant growth regulators, Plant disease

and nematode control, Management of insects and mites,

Weed control, Biological nitrogen fixation

Mechanical.—Robotics, Engines and fuels,

Crop separation—cleaning—processing

Information.—Monitoring and control, Communication and

information, Telecommunications

Management techniques.—Water and soil-water-plant relations, Soil erosion, Soil productivity and tillage, Multiple

cropping, Organic farming, Land management

SOURCE: Office of Technology Assessment.

technologies that assist in a more optimal, longterm combination of inputs at the producer

level, and each involves cultural or management

practices.

STRUCTURAL ELEMENTS

The structural dimensions assessed for tech- barriers to entry, and regional impacts. These

nological impacts were capital and labor, ver- elements are not necessarily the only relevant

tical coordination and control, market access, ones for judging the impact of various technol-
Ch. 6—Emerging Technologies and Agricultural Structure ● 125

ogies. However, in the interest of manageability of scope and time, these were judged most

important.

Capital is viewed broadly as all nonlabor and

nonmanagement inputs, including land, while

labor was viewed broadly as both management

and labor. Vertical coordination is defined as

coordination of quality, quantity, and timing

across producer/first handler markets. Control

is primarily the ability of producers to exercise

authority over production and marketing decisions. Market access refers to whether producers have access to most or all buyers at a particular stage of the marketing channel. Barriers

to entry are defined as the inability, for whatever reason, of new firms to enter a particular

industry.

These structural elements are the common

ones normally viewed as important in agricultural commodity markets. For any of the structural elements, it is difficult to judge or measure precisely the magnitude of impact from

various technologies. Often, the direction of impact (positive or negative) is easier to judge. The

procedure used by workshop participants was

to discuss direction of impact, then use the Delphi process to judge magnitude of impact within

some predetermined range (e.g., O to 10 percent,

10 to 20 percent).

IMPACTS OF TECHNOLOGY ON STRUCTURAL ELEMENTS

The structural elements provide some indication of the potential for a technology group to

induce a change in farm size and number over

time. The directional impact of the technology

groups on each selected structural element is

summarized in tables 6-2 and 6-3. The tables apply to both animal and plant agriculture across

all technologies.

Capital and Labor

Technology can affect the capital and labor

used in production of either animals or crops.

The absolute and relative change induced by

technology in capital and labor was addressed

and is depicted in table 6-3.

Several primary effects of technologies in the

biological group on capital were identified. It

was assumed that reproduction and genetic engineering technology would be adopted by farmers via contracting for a specialized service. The

adoption would slightly reduce ( <5 percent)

both capital and labor necessary for a given size

herd. An example is the expected dramatic savings in time that will result from hiring a specialized service to check a dairy herd for estrus.

Growth hormone technology is also expected

to decrease both capital and labor needs slightly

in animal production. Animal nutrition and disease control technologies are expected to decrease slightly or have no impact on capital and

labor in the long run.

Because both capital and labor are expected

to decrease slightly as a result of these biological technologies, no significant change (i.e., <5

percent) is expected in the capital/labor ratio.

Capital is viewed as decreasing slightly less than

labor, on the average, but this difference is not

viewed as significant.

The biological group of technologies is viewed

as having a long-run neutral impact on capital

input at the farm level, primarily because the

majority of these technologies have become

available as new plants or seeds. Thus the technology is imbedded into the factor input without a separate purchase of it. Potential price increases in plants or seeds were viewed as being

offset by productivity gains.

A slight decrease in labor input is expected

from the technologies in this group for plant

agriculture. This was expected mostly from the

potential for weed, insect, and mite control technologies reducing some labor input to the pro-
126 ● Technology, Public Policy, and the Changing Structure of American Agriculture

Table 6.2.—Potential Directional Impact of Technology Groups on Structural Elements

at the Producer Level, by Animal and Plant

Potential additional direction of impact

induced by technology group by year 2000

Vertical coordination

Area and technology group and control Market access Barriers to entry

Animal:

Biological group. . . . . . . . . . . . . . . . . . . . . . . . . . Closer coordination Slight reduction No significant change

encouraged

Mechanical group . . . . . . . . . . . . . . . . . . . . . . . . No significant change No significant change No significant change

Information group . . . . . . . . . . . . . . . . . . . . . . . . No significant change Slight increase Slight-to-definite

reduction

Plant:

Biological group. . . . . . . . . . . . . . . . . . . . . . . . . .Slight encouragement No significant change No significant change

of closer coordination

Mechanical group . . . . . . . . . . . . . . . . . . . . . . . . No significant change No significant change Slight increase

Information group . . . . . . . . . . . . . . . . . . . . . . . . No significant change Increase No significant change

Management techniques group . . . . . . . . . . . . . No significant change No significant change Slight-to-moderate

SOURCE: Office of Technology Assessment

Table 6-3.—Potential Impact of Technology Groups on Capital and Labor at the Producer Level,

Assuming Adoption, by Animal and Plant

Potential additional change induced by technology

group by year 2000

Area and technology group Capital Labor Capital/labor ratio

Animal:

Biological group . . . . . . . . . . . . . . . . . .Slight decrease (<5°/0) Slight

Mechanical group . . . . . . . . . . . . . . . . . Moderate increase (5-10°/0) Slight

Information group . . . . . . . . . . . . . . . . . Moderate increase (5-10°/0) Slight

Plant:

Biological group . . . . . . . . . . . . . . . . . . No significant change Slight

Mechanical group . . . . . . . . . . . . . . . . . Moderate increase (5-10°/0) Slight

Information group . . . . . . . . . . . . . . . . . Moderate increase (5-10°/0) Slight

decrease (<5%) No significant change

decrease (<5%) Moderate increase (5-10°/0)

increase ( <5°/0) Moderate increase (5-10°/0)

increase ( < 5°/0) No significant change

increase ( <5°/0) Moderate increase (5-10°/0)

increase ( <5°/0) Moderate increase (5-10°/0)

Management techniques group. . . . . .Slight increase (<5°/0) ‘ Moderate increase (5-10°/0) No significant change ‘

SOURCE: Office of Technology Assessment.

duction process. This is also the potential impact of the plant disease and nematode control

technologies.

The capital/labor ratio is not expected to

change significantly as a result of the technologies in the biological group for crops. The decrease in labor anticipated from some technologies within the biological group is not expected

to be important enough to change the capital/

labor ratio significantly in the long run.

Mechanical Group

The mechanical group for animals is viewed

primarily as housing and lighting control for

animals that might influence breeding or growth.

The other technologies within this group are

viewed primarily as improvements in mechanical methods for crop residue or animal waste

processing.

Almost by definition, this technology group

is composed of technologies that require capital equipment expenditures if adopted, resulting in a moderate increase (5 to 10 percent) in

the amount of capital. Another expected consequence of these mechanical technologies is a

slight decrease in labor, primarily because of

the potential of these technologies to reduce

stress on livestock, which in turn may reduce

management input. The decrease in labor for

livestock operations, however, is anticipated to

be less than 5 percent in the long run. It is further expected that the moderate increase in capital and the slight decrease in labor will increase

the capital/labor ratio moderately (5 to 10 percent) for livestock producers.
.——

Ch. 6—Emerging Technologies and Agricu/tura/ Structure . 127

The adoption of the mechanical technologies

group for plant agriculture, as with this technology group for animal production, is expected

to result in a moderate increase in capital input.

The cost of engines is expected to rise, and engines are expected to have a higher horsepower

for the same size. The major technologies in this

group are expected to be capital-intensive.

The potential for labor reduction from the

technologies for crops is expected to be similar

to that for livestock. The expectation is for a

slight decrease ( <5 percent) in labor input attributable to this technology group. The capital/labor ratio is expected to increase moderately (5 to 10 percent), owing primarily to

increased engine costs and small labor reductions. The expectation for the capital/labor ratio for crop production is similar to that for livestock production.

WormaS#ua OThe technologies in the information group are

viewed as capital-intensive and are expected to

have impacts that are similar for both crop and

livestock producers. Actuators, sensors, and

controllers would require additional capital expenditures for production units such as feedlots, dairy barns, or crop fields. The consensus

is that these technologies would require a moderate increase (5 to 10 percent) in capital for either crop or livestock operations.

Because the total amount of data and information available to managers would increase

as a result of this technology, a slight increase

(<5 percent) in managerial time is expected.

The managerial input results from an increase

in information that would be generated by the

technology. Such information would require

more analysis time from managers. This situation is similar for either livestock or crop operations,

The capital/labor ratio is viewed as increasing moderately (5 to 10 percent) as the net result of these technologies. No significant differences are anticipated between livestock and

crop production as a result of information technologies. The capital/labor ratio is expected to

increase primarily from an increase in capital

equipment items used in production of either

crops or livestock.

Management Techniques Group

The management techniques group represents various management regimes useful in

crop production.

The organic farming technology within this

group is viewed essentially as a substitution of

mechanical factor inputs (more cultivation) for

chemical factor inputs (such as fertilizer or insecticides). This substitution would lead to some

capital expenditure decreases and some labor

expenditure increases in crop production in the

long run.

Other technologies in this group are viewed

as relatively more capital-intensive—such as soil

erosion, tillage, and general land management.

As a result, the net impact of this technology

category on capital used in crop production is

a slight increase ( <5 percent).

Many of the technologies within this group

are expected to require additional management

input. Almost by definition, the adoption of technologies in this group is expected to demand

greater management in the production process.

As a result, labor (encompassing management)

is expected to increase moderately (5 to 10

percent).

The capital/labor ratio attributable to this technology group is not expected to change significantly. The increase in capital and the increase

in labor are expected to be sufficiently similar

that no significant change is induced in the capital/labor ratio for crop production in the long

term.

Vertical Coordination and Control

The consideration of vertical coordination

and control is that if technology induces tighter

vertical coordination by an integrator, it may

simultaneously induce a shift in control over

production from the farmer to the integrator.

This, in turn, could affect the number and size

of farms in the long run. The poultry industry

is an example of a commodity marketing chan-
128 . Technology, Public Policy, and the Changing Structure of American Agriculture

nel that exhibits relatively tight vertical coordination, loss of producer control, and a consequent shift to fewer but larger production units.

In general, the emerging technologies are expected to allow more control over end-product

characteristics. Examples include less fat per

unit of lean meat in animals or a specific color

characteristic in corn, with the implication that

more homogeneity within a type of product may

result but that more end products will have engineered characteristics. This situation entails

some shift away from sorting or grading as a

way to achieve greater homogeneity y and a shift

toward more control over the production process.

An anticipated economic consequence of this

increased control over production practices will

be in the area of contracting. Contracting allows

husbandry practices or cultural practices to be

monitored and controlled closely during the production process, resulting in productsthat adhere

to uniform specifications. Controlled diversity

would result from this arrangement. That is, greater process control would lead to uniform product differentiation.

The biological group of technologies is expected to encourage closer coordination in livestock production compared with the situation

in 1984. The technologies in this group would

encourage greater process control, which would

be manifested in more contracted livestock production. One example is swine producers who

contract with meat packers to produce pork of

uniform specifications and are paid for their

labor and facilities at a predetermined fixed rate.

Another example is the potential from these

technologies for modifying milk at the cow

rather than at the processing plant. This technology group holds promise for producing more

highly unsaturated fats in milk. If such technology is adopted, it would require close coordination at producer/first handler markets and additional process control at the production level.

The expectation for this technology group is

that it will encourage closer coordination in

crop production, as well. However, even though

the direction of impact was viewed similarly

between livestock and crop production, the

magnitude is expected to be relatively less for

crops compared with livestock. Part of the reason is that relatively more process control in

livestock than in crops is expected from adopting the technologies.

Mechanical Group

No significant change in vertical coordination

is expected from the technologies in the mechanical group for either crop or livestock production. The technology for this group is essentially

embodied in capital equipment items such as

tractors and other machinery. As a consequence, the adoption and use of the technology

is a decision made by individual production

units, with no real implication for vertical coordination or control.

Information Group

The technologies within the information

group are not expected to have any significant

impact on vertical coordination or control for

either crop or livestock production. The situation for this technology group is similar to that

for the mechanical group. Adoption and use of

these technologies are decisions of individual

farm and ranch managers.

One possible impact from the information

technologies on vertical coordination is the

potential for the technology to encourage more

open markets for commodities rather than contractual arrangements. The technology is viewed

as having some potential for coordination across

markets without integration. This impact would

be attributable to better communication of buyers’ needs to production-level managers. This

potential is slightly more important for livestock

production than for crops.

Management Techniques Group

The technologies in this group are expected

to be neutral in impact on vertical coordination

and control, again mostly due to individual

managers’ decisions on adoption and use. Also,

this technology group primarily reflects production decisions that are more likely to change

quantity of output rather than quality of output.
Ch. 6—Emerging Technologies and Agricultural Structure • 129

Market Access

Market access as a structural element of agricultural marketing channels refers to the ability of sellers or potential sellers to gain access

to buyers or potential buyers. The extent to

which producers have a number of alternative

buyers or marketing arrangements for their

commodities is the essence of market access.

If alternatives are few, market access is low.

Foreclosure to participation is not necessary for

market access to be low, but it maybe one reason for lack of market access.

Biological Group

The biological group is expected to reduce

market access slightly for livestock producers

in the long run. This impact is expected because

the biological technologies will allow targeting

of certain product characteristics to specialized

end-use markets, narrowing the range of alternatives for producers adopting the technology.

Thus foreclosure to other market segments is

one impact expected from the technology.

The impact of the biological group on market

access for crop production is expected to be neutral. Even though some potential for market segmentation by end-use characteristics is possible from this technology group, no significant

change is expected.

With today’s technology of production, there

are a number of possibilities for producing specialized crops or for sorting commodities for

particular end-use markets. One example is sorting and grading soybeans on the basis of oil

yield. For storable commodities, especially, the

sorting may well occur after production rather

than through exercising process control during

production. To the extent this happens, the technology group would have a negligible impact

on market access over time.

Mechanical Group

Mechanical group technologies are not expected to have any significant long-term impact

for either crop or livestock production. The technologies in this group are viewed as neutral on

market access for reasons similar to those for

vertical coordination and control.

Information Group

Some differences are expected between plant

and animal agriculture from information group

technologies. The direction of impact from this

technology group is the same for both areas—

to increase market access. The magnitude, however, is expected to be relatively more for crop

producers.

This technology group encompasses increased

information available to managers for both production decisions and marketing decisions. The

marketing information component is expected

to be important to all farm managers, but relatively more important to crop producers. As

marketing information increases among buyers

and sellers, improvement in market access is

expected. If market information is asymmetrically held by buyers and sellers, the technology

should result in more equality among buyers

and sellers. The potential significance of this

improvement is expected to be slightly greater

for crops than for livestock.

Management Techniques Group

No significant change in market access is expected as a result of the technologies within the

management techniques group for crops. Again,

reasons are similar to those provided under vertical coordination and control.

Barriers to Entry

A variety of barriers restrict the ability of new

firms to enter an industry. For example, use of

specialized capital-intensive and managerially

sophisticated technologies for production within an industry can represent a barrier to new

entrants in the long run. Unequal access to information or significant amounts of proprietary

information within an industry are also conventionally regarded as discouraging to new entrants.

Biological Group

No significant impact on barriers to entry is

expected from the biological technology group

for either crop or livestock production. This

technology group may increase the level of so-
130 ● Technology, Public Policy, and the Changing Structure of American Agriculture

phistication and/or specialized knowledge necessary for production. However, the expertise

necessary would be available to nearly all production units. This expertise is seen as being

available through firms that specialize in providing the necessary expertise that may be required for successful adoption and use of the

biological technologies. Thus, impact on barriers to entry was viewed as negligible.

Mechanical Group

The impact of mechanical technologies on

barriers to entry in the long term is expected

to be a slight increase for crop production but

no significant increase for livestock production.

This difference is attributable to the expected

differences in the relative importance of mechanical technologies in the two areas.

Mechanical technologies for crops are viewed

as relatively more important and capital-intensive per dollar of output than are mechanical

technologies for livestock. The capital-intensive

nature of this technology group for crops is expected to impose slightly increased barriers to

entry in long-run production.

Information Group

Information group technologies have different impacts on barriers to entry for crop and

animal production. This technology group is not

expected to change barriers to entry significantly in crop production. However, slight-todefinite reductions in barriers to entry in livestock production are expected from information technologies.

This group holds the potential for significantly

increasing the amount of information on markets available to livestock producers without entailing large increases in capital expenditures

for adopting the technology. In addition, monitoring and control devices are expected to be

relatively more cost-effective for livestock producers than for crop producers. This implies

the potential for increased productivity with,

perhaps, a lower quality of management. Both

the production and marketing impacts from information technologies combine to encourage

new entrants into livestock production—or reduce barriers to entry in the long run.

Management Techniques Group

Some portions of the management techniques

group are expected to be capital-intensive. For

example, land management strategies and multiple cropping are considered to be technologies

that will require expanded capital expenditures

for adoption. The potential to create an additional barrier to entry from this technology

group stems from the capital-intensive nature

of the technology. If multiple cropping or land

management practices were the norm for crop

producers, they would discourage new entrants

into crop production in the long run.

RELATIVE ADOPTION RATES BY SIZE

The rate of adoption by size of firm for each expected to be significantly higher by year 2000

technology group was addressed. Adoption than that for any of the other technologies. This

rates were estimated for the year 2000, assum- is especially true in the smallest size category

ing that firms would adopt at least one of the of farm. About 40 to 50 percent of the smallest

technologies within the group in some signifi- crop production units are expected to adopt at

cant way (table 6-4). Size categories used for least one technology, whereas 10 to 20 percent

farms were annual sales in 1984 dollars: of the smallest animal production units are expected to adopt.

1. less than $20,000;

2.$20,000 to $99,999; This perceived difference in adoption rates

3.$100,000 to 499,999; and between crop and livestock producers is attrib4.$500,000 and over. utable to the form in which the technology will

be available for adoption. Many biological techRelative adoption rates of biological technol- nologies available to crop producers are expected

ogies for both plant and animal agriculture are to be embodied in seeds, fertilizers, or other in-
Ch. 6—Emerging Technologies and Agricultural Structure . 131

Table 6-4.—Percent Adoption Rate of at Least One

Technology Within a Technology Group by Year 2000,

by Size of Farm

Adoption rate range (percent),

by sales category (1984 constant dollars)

Area and

technology $20,000-$100,000-

group <$20,000 $99,999 $499,999 >$500,000

Animal:

Biological . . . . . . 10-20 30-40 60-70 80-90

Mechanical . . . . . 0-1o 10-20 40-50 70-80

Information . . . . . 0-1o 10-20 55-65 80-90

Plant:

Biological . . . . . . 40-50 60-70 85-9 0 90-100

Mechanical . . . . . 0-1o 10-20 40-50 70-80

Information . . . . . 0-1o 15-25 55-85 75-85

Management

techniques . . . 10-20 30-40 55-65 70-80

SOURCE: Off Ice of Technology Assessment.

put items that normally would be purchased by

crop producers. Many crop producers are expected to adopt some simple techniques and information technologies as a normal practice.

This accounts for the perception that these will

be relatively more widely adopted.

Relative adoption by size is expected to be

greatest for larger farms. Generally, 70 percent

or more of the largest farms are expected to

adopt some technologies from each technology

group. This contrasts with only 40 percent for

the second largest and about 10 percent for the

smallest two categories. The economic advantages from the technologies are expected to accrue to early adopters, and a larger proportion

of large farms are anticipated to be the early

adopters.

Among the largest production units, biological technologies would be adopted by a relatively higher proportion of producers than

would mechanical technologies. Adoption of

mechanical technology entails more capital expenditures, whereas the biological technologies

were anticipated to be available from a service

company on a fee basis. Thus large and small

firms may have these biological technologies

more readily available to them.

Policies Considered

The potential for achieving a particular distribution of farm size through various broad

types of policy was assessed, Eight types of policy were defined regarding their potential use

in changing relative size distributions of farms

in the future: commodity, tax, credit, research

and extension, trade, monetary, fiscal, environmental, and regulatory. The discussion focused

on which of these policies might be most useful

in achieving some public policy-determined

farm structure distribution (number and size of

firms). As before, a Delphi procedure was used

to rank the relative effectiveness of each of the

major policy categories. The relative effectiveness of these policy categories was then assessed

by the panel (table 6-5).

Relative Effectiveness

Commodity policy and tax policy are expected

to be most effective in achieving a particular

Table 6-5.—Relative Effectiveness of Types of Policy

In Attaining a Desired Specific Structure -

Policy type by rank order, most to least effective

1. Commodity

2. Tax

3. Credit

4, Macro policy—monetary and fiscal

5. Regulatory

6. Trade

7. Research and extension

8. Environmental

SOURCE: Off Ice of Technology Assessment.

size distribution in the future. That is, specific

policies under these two broad types could be

designed that would either significantly decrease or increase the trend toward fewer but

larger production units.

Credit and macroeconomic policy are also expected to be effective in changing structure.

Environmental policy and policies involving research and extension are expected to be the least

effective in changing the size distribution of production units.
132 ● Technology, Public Policy, and the Changing Structure of American Agriculture

Commodity policy is viewed as the single most

effective policy category for achieving some predetermined level of size and number of farm

firms. For example, if a commodity policy were

put in place that had a $10,000 limit on payments, the effect would be to slow or stop the

move toward larger farms. Similarly, adopting

a commodity policy that has no payment limitation would encourage the trend toward larger

size firms. Also, even though commodity policies primarily affect dairy and major crop

producers, aspects of commodity policy could

be designed to encourage participation. Such

items include differential support rates by size

of farm. Smaller firms would have higher support levels, thereby encouraging broader participation across all farm sizes.

Tax policy was considered the second most

effective policy category for changing the number and size of farm firms to some desired level.

The major items discussed were investment tax

credits or other tax items that encourage capital to flow into or out of agriculture for tax shelter considerations. Treatment of capital gains,

conversion of capital gains, and value-added

taxes were considered to be critical items that

could influence size distributions.

In the credit policies category, several specific items were considered to be a direct influence on the movement over time to larger size

firms. They include interest rates, availability

of credit for agricultural production, capital rationing, and subsidized credit.

STRUCTURAL IMPACT ON TECHNOLOGY

The majority of this analysis is directed toward

technological impacts on structure. Another

area considered by ASG is the potential causality of a particular farm structure on the direction and magnitude of development and adoption of technology. The notion is that structure

and technology are simultaneously related

through time. Not only will technology influence structure, but structure will influence technology. As a final item, the panel considered

the potential relationship from structure to the

various technology groups.

The assessment of structure on technology

considered each previously defined technology

group separately for both animal and plant agriculture. The question addressed by ASG is the

direction and magnitude of impact that a bimodal farm structure would have on the development and adoption of the technology groups.

The impact is relative to the structure that currently exists. The bimodal distribution assumed

to exist for this portion of the assessment was

a total of 1.1 million farms, 528,000 in an annual

sales category of less than $20,000; 143,000 in

a category between $20,000 and $99,999; 120,000

in a category between $100,000 and $499,999;

and 309,000 in a category above $500,000. This

bimodal distribution has 48 percent of all farms

in the smallest category and 28 percent of all

farms in the largest category. Thus these two

categories account for 76 percent of all farms.

As before, the Delphi technique was employed

to gain a consensus on the influence of structure. Overall, the influence of structure on the

development and adoption of technology is substantial (table 6-6). A bimodal structure is viewed

as having moderate or large increases on development and adoption for each of the technologies for both animals and plants. This is primarily because of the proportion of total farms

Table 6=6.—Potential Directional impact of Farm

Structure on Development and Adoption of

Technology Groups, by Animal and Plant

Potential direction of impact

Area and induced by bimodal structure

technology group by year 2000

Animal:

Biological group . . . . . . . . . Moderate increase

Mechanical group . . . . . . . . Moderate to large increase

Information group . . . . . . . . Moderate to large increase

Plant:

Biological group . . . . . . . . . Moderate increase

Mechanical group . . . . . . . . Moderate increase

Information group . . . . . . . . Moderate to large increase

Management techniques . . Large increase

SOURCE: Office of Technology Assessment.
Ch. 6—Emerging Technologies and Agricultural Structure • 133

in the largest size category (28 percent in the

bimodal distribution compared with less than

2 percent actual in 1982).

Large farmers have relatively greater adoption rates, and a significant proportion of large

farms would also encourage development of

various technologies beyond what would otherwise be developed. Differences among technology groups are slight. The greatest impact of

structure on development and adoption is the

management techniques group for crop producers. Larger crop farms would tend to adopt technologies such as multiple cropping, soil erosion,

or tillage practices; or soil-water-plant management techniques considerably quicker than

would smaller farms.

SUMMARY AND CONCLUSI0NS

All technology groups are expected to have

considerable economic impact on farm structure by 2000, Biological technologies will have

a more important impact than that of the other

technology groups. A historical perspective on

mechanical technologies is that they have been

vitally important in shaping the livestock and

plant production structure that existed in 1984.

However, they are not expected to have as important an impact on future structure.

Captal and Labor

For animal production, both the information

and mechanical groups are expected to increase

capital moderately, with a slight decrease in capital induced by the biological group. Both biological and mechanical groups are expected to

generate slight decreases in labor, with a slight

increase from information technologies. No significant change is expected in relative capital

and labor from the biological group. Moderate

increases in capital intensity (the increase in

the capital/labor ratio) are expected from both

the mechanical and information groups.

For plant production, moderate increases in

absolute capital are expected from both the mechanical and information groups, a slight increase from the management techniques group,

and no significant change from the biological

group. In terms of absolute labor and management, all but the biological group are expected

to increase labor slightly to moderately. The only

labor-decreasing technology is expected to be

in the biological group. No significant change

in the relative amounts of capital and labor are

expected from the biological or management

techniques groups. The mechanical and information groups are expected to induce a moderate increase in capital relative to labor. None

of the technologies is expected to induce a decrease in the capital/labor ratio.

Comparison of the technology groups for

plant and animal production reveals that, in general, the technologies are expected to be similar in terms of impact on capital and labor. Neither the biological nor management techniques

groups are expected to induce any significant

change in the capital/labor ratio for plant production. The biological group is not expected

to change the capital/labor ratio, whereas all

other techniques are expected to increase this

ratio moderately in the long term.

The potential direction of the marginal impact induced by the technology groups on vertical coordination and control, market access,

and barriers to entry was also assessed. The biological group is expected to encourage closer

vertical coordination (i.e., more contracting),

with a slight reduction in market access as a

consequence. This would subsequently encourage the trend toward fewer but larger farms.

In the opposite direction, the information

group is expected actually to reduce barriers

to entry and to increase market access without

any significant change on vertical coordination

or control at the producer/first handler level.

The mechanical group is expected to be neutral on all structural elements analyzed.
134 ● Technology, Public Policy, and the Changing Structure of American Agriculture

Expected impactson these structural elements

for plant production is similar to that expected

for animal production. However, the impact of

the biological group on crop agriculture is expected to be less than that on animal agriculture. Less impact on vertical coordination and

market access is expected from biological technologies for crops. The management techniques

group, a group unique to crops, is not expected

to change vertical coordination or market access, but is expected to increase barriers to entry slightly to moderately in crop production

in the long run.

The potential for regional shifts from technology was seen as most likely from the livestock

biological technology group, particularly as it

affects beef production. Biological technologies

that increase the efficiency of beef cattle forage utilization may have an important regional

dimension. The potential for increasing pasture

conversion of beef cattle is likely to favor shifts

in production away from the higher opportunity cost of agricultural lands in the Midwest

to those in the South and West.

Relative Adoption Rates by Size

Relative adoption rates of biological technologies for both plant and animal agriculture are

expected to be considerably higher by year 2000

than they are for any other technology group.

This is especially true for small farms.

Relative adoption rates of all technology

groups are expected to be greatest for larger

farms. Generally, 70 percent or more of the

largest farms are expected to adopt some technologies from each technology group. This contrasts with only 40 percent for the middle-size

farm units and about 10 percent for the smallest

farms. The economic advantages from the technologies are expected to accrue to early adopters; a large proportion of large farms are anticipated to be early adopters.

Policies Effective in

Achieving a Structure

Commodity policy and tax policy are the two

broad categories of policy that are expected to

be most effective in achieving a particular size

distribution of farms. Specific policies under

these two policy categories could be designed

either to enhance or to slow the historical trend

toward fewer but larger production units.
Chapter 7

Impacts of Agricultural

Finance and Credit
Contents

Page

Present Financial Situation in Agriculture . . . . . . . . . . . . . . . . . . . . . . . . . . . . 137

Impacts of Monetary and Fiscal Policy. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 138

Monetary Policy. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 138

Fiscal Policy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 139

Capital Sources for Agriculture . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 140

Debt Capital Sources for Agriculture. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 141

The Farm Credit System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 141

Commercial Banks... . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 142

Insurance Companies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 142

Government Lending Agencies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 143

Merchants and Dealers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .,,.,. 143

Market Shares of Farm Debt . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 144

Equity Capital Sources for Agriculture . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 145

Net Farm Income and Capital Gains . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 145

Off-Farm Income . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 147

New Equity Investment Capital . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 147

The Role of Credit. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 147

Credit Determinants and Relatioships to New Technologies . . . . . . . . . . . 147

Credit Policy and Structural Issues in Agriculture. . . . . . . . . . . . . . . . . . . . 148

Regulatory Issues . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 150

Regulatory and Performance Issues Affecting Depository Institutions . . . 150

Regulatory and Performance Issues Affecting the Farm Credit System. . 152

Public Credit Programs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 154

Future Role of State Credit Programs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 156

Future Role of FmHA. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 156

Implications for Technology Adoption and Structural

Change in Agriculture . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 157

Chapter 7 References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 159

Tables

Table No. Page

7-1. Distribution of Farms by Debt-to-Asset Ratio and Sales Class,

January 1984.... . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 138

7-2, Balance Sheet of the Farming Sector, Jan, 1, 1985 . . . . . . . . . . . . . . . . . . 140

7-3. Income and Capital Gain Returns for the Farming Sector . . . . . . . . . . . 146

Figures

Figure No, Page

7-1. Real Interest Rate on 3-Month Treasury Bills . ......................139

7-2. Market Shares of Farm Real Estate Debt . . . . . . . . . . . . ...............144

7-3. Market Shares of Non-Real Estate Farm Debt. . . . . . . ................145
Chapter 7

Impacts of Agricultural

Finance and Credit

The severe financial stress of a large proportion of farmers and the recent regulatory and

competitive changes in financial markets have

combined to change forever the financial framework of farming. The farm of the future will be

treated financially like any other business—it

will have to demonstrate profitability before a

bank will finance its operation. Managing a farm

efficiently and profitably, which will necessitate keeping technologically up-to-date, will be

the key to access credit.

The cost of credit, however, will be higher and

more volatile. Interest on loans may be variable rather than fixed. Moreover, given the concentration in the banking industry, decisions

about extending credit will more likely be made

at large, centralized banking headquarters far

removed from a loan applicant’s farm. Loan decisions will thus be less influenced by the considerations of neighborly goodwill that frequently shaded the decisions of the more local banks.

Congress will have to consider all of these factors because the availability of capital will continue to be an important factor in agricultural

production in general and in the adoption of

agricultural technologies in particular. Readily available capital at reasonable rates and

terms, plus technologies that aid profitability,

provides a favorable environment for technology adoption. For the most part, the emerging

technologies will pass the test for economic feasibility.

This chapter considers the relationships between technology adoption, financing consequences, and the structure of agriculture. The

major financing focus is on the credit component of financial capital, and on how the regulatory and competitive changes in U.S. financial markets during the 1980s will influence

structural change as well as the cost, availability, and other terms of credit for agricultural

producers. In the following sections, some background information on capital and credit markets and institutions is reviewed, and an analytical framework is established for understanding

the relationships between credit, technological

change, and agricultural structure. Then, various changes in the regulatory environment affecting farm lenders are reviewed, and implications are given for technology adoption and

structural change.

Before considering the long-run impacts of

technological change and of financing consequences, it is important to consider the present

deteriorating financial situation in agriculture.

Financial conditions of many farmers and farm

lenders have deteriorated significantly over the

past 4 years. Large supplies and weak export

demand have squeezed farm income and reduced the net worth of farmers. Many farmers

face insufficient cash flow, declining asset

values, problems of access to credit, and forced

liquidation, foreclosure, and bankruptcy.

A substantial proportion of the U.S. farm sector is under severe financial stress, which can

be measured by use of the debt-to-asset ratio.

Approximately 11 percent of all farms (243,000

farms) have debt-to-asset ratios of 40 to 70 percent. These farms are “highly leveraged,” tend

to have serious cash shortfalls, and together owe

one-third of all farm debt. Another 143,000

farms have debt-to-asset ratios above 70 percent,

These “very highly leveraged” farms make up

about 7 percent of all farms, but they owe almost

25 percent of all farm debt (table 7-1).

137
138 . Technology, Public Policy, and the Changing Structure of American Agriculture

Table 7-1 .–Distribution of Farms by Debt-to-Asset Ratio and Sales Class, January 1984

Highly leveraged Very highly leveraged

(debt-to-asset ratios of 40 to 70°/0) (debt-to-asset ratios over 70°/0)

Percent Number of Percent Percent Number of Percent

Sales class of class farms of debt of class farms of debt

>$500,000 . . . . . . . . . . . . . . . . . . . . . . . . . . 17.4 5,200 4.8 15.3 4,500 4.9

$250,000-$499,999 . . . . . . . . . . . . . . . . . . . . 19.0 . 17,600 5.1 12.6 11,000 4.2

$100,000-$249,999 . . . . . . . . . . . . . . . . . . . . 18.1 52,800 10.5 9.2 26,400 5.9

$50,000-$99,999 . . . . . . . . . . . . . . . . . . . . . . 14.7 44,000 6.2 8.7 26,400 3.9

<$50,000 . . . . . . . . . . . . . . . . . . . . . . . . . . . 8.3 123,200 5.8 5.0 74,800 4.8

All farms . . . . . . . . . . . . . . . . . . . . . . . . . 11.1 242,600 32.5 6.6 143,100 23.7

SOURCE: U.S. Department of Agriculture, The Current financial Condition of farmers and Farm Lendera, Economic Research Service Bulletin No. 4Wr, March 19S5,

Many short-run programs are being consid- ments needed to solve the problem adequately.

ered to alleviate this current financial situation. In chapters 8 and 9, alternative short-term polHowever, as discussed later in this chapter, icies are analyzed along with other policy

these programs will not allow for the adjust-

—

changes.

IMPACTS OF MONETARY

The agricultural sector is closely linked to national and international economies. Thus the

public sector policies and programs that influence these economies also influence technology adoption and structural change in agriculture. The potential influences of monetary and

fiscal policies on agriculture are identified in

the following sections.1

Monetary Policy

The amount of money and credit in the economy, and its rate of change, are the primary concerns of monetary policy. The Federal Reserve

System (FRS) is the primary regulatory authority that determines the direction of monetary

policy in the United States. The objectives of

FRS are to promote domestic economic growth,

avoid excessive inflationary or recessionary

pressures, maintain a sound U.S. balance of payments, and promote full employment. The simultaneous achievement of these goals is extremely difficult, and FRS is often faced with

selecting which policy objective has highest

priority.

‘This section and the next are based on a paper by David A.

Lins, “Overview of Capital and Credit Markets Serving Agriculture: Their Impact on Technology Adoption and Structural

Change,” prepared for the Office of Technology Assessment,

Washington, DC, March 1985.

AND FISCAL POLICY

FRS influences the amount of money and

credit in the economy through a variety of instruments. Discussion of these instruments in

detail is beyond the scope of this chapter. To

determine its success in controlling the amount

of money and credit in the economy, FRS uses

indicators, the most commonly used being: 1)

interest rates, and 2) the rate of growth in the

money supply (this is also used as an instrument

by FRS).

For many years FRS used the level of interest

rates as a key indicator of the success of monetary policy. Nominal interest rates were controlled within a fairly narrow range. However,

during the 1970s the inflation rate began to rise,

while interest rates were controlled by FRS actions. The net effect was a fall in real interest

rates. Figure 7-1 identifies the estimated real interest rate on 3-month Treasury bills from 1962

through 1984.

From 1962 through 1972 the real interest rate

was generally positive, in the range of 1 to 2 percent. From 1972 through 1979, real interest rates

were usually negative, suggesting that investors

in Treasury bills lost money in real terms. FRS

actions to control interest rates in the face of

rising inflation were primarily responsible for

this outcome.
Ch. 7—impacts of Agricultural Finance and Credit ● 139

Figure 7-1 .—Real Interest Rate on

3-Month Treasury Bills

1964 1968 1972 1976 1980 1984

Year

SOURCE: Office of Technology Assessment.

Recognizing that savings are strongly discouraged by negative real interest rates, FRS in 1979

shifted from a policy of controlling interest rates

to a policy of controlling the rate of growth in

the money supply. The result was a rapid increase in the real interest rate as well as increased variability in nominal interest rates.2

Since 1983, the real interest rate on 3-month

Treasury bills has generally been in the range

of a positive 5 to 7 percent. The level of real interest rates is likely to be a major determinant

of investment in agricultural assets, particularly

for nonfarm investors. With high real interest

rates there is less incentive to borrow money

to invest in new technologies. Consequently, actions taken in the pursuit of monetary policies

have a major impact on the agricultural sector.

The strength of the U.S. dollar also has a major impact on the agricultural sector. The level

of interest rates in the United States compared

with those in other countries is a major determinant of the strength of the dollar. Since monetary policies have a direct influence on the level

of interest rates, they also have a direct impact

on the strength of the dollar. In 1984 the U.S.

dollar reached a 12-year high against many major foreign currencies. A strong dollar decreases

the level of agricultural exports, thereby reducing incomes of producers of export-dependent

Interest rates not adjusted for inflation.

products. While the incomes of other producers

may actually increase in such a situation, the

overall level of income for the agricultural sector would probably decline. As incomes of agricultural producers decline, less capital is available for investment in new technologies, and

credit may be used more to overcome shortfalls

in income than to finance new investments or

transfer resources.

Many of the emerging agricultural technologies appear to be those that will require expenditures on operating inputs such as genetically

enhanced seeds, chemicals, embryo transplants,

and other products normally financed with the

farm operator’s capital or short-term credit. Although the decisions on the purchase of these

inputs is affected by the level and variability of

interest rates (which in turn are influenced by

monetary policy), it seems more likely that the

decisions to adopt these new technologies will

be more strongly influenced by the expected

returns from adoption. Some technologies may

be so profitable to adopt, at least in the short

run, that the level of interest rates has little impact on the decision process.

Fiscal Policy

Fiscal policy involves the taxation and spending policies of the Federal Government. The

objectives of fiscal policy are to carry on the

functions of Government, promote economic

growth and full employment, and maintain

price stability. Fiscal policy instruments used

to achieve these objectives include both automatic and discretionary taxation and spending

alternatives.

Automatic taxation instruments include the

progressive income tax structure, which raises

taxes as incomes increase and decreases taxes

when incomes fall, even if Congress has made

no explicit changes in tax rates. One automatic

spending instrument is unemployment compensation, which automatically changes Government expenditures when unemployment

changes.

Discretionary items include those taxation

and spending patterns that require specific congressional action to change. In the area of taxa-
140 ● Technology, Public Policy, and the Changing Structure of American Agriculture

tion, for example, depreciation rates and investment credit change only as the result of

legislative changes. Likewise, numerous spending programs require legislative action before

the level of expenditure is changed. A major

problem in meeting the objectives of fiscal policy is that much Government spending falls into

the category of “entitlements,” leaving little that

legislators can do to change the total level of

Government expenditures.

Federal budget deficits, the excess of Government spending over tax revenues, are frequently

cited as a major determinant of interest rates.

Some argue that large budget deficits create

such a strong demand for credit that Government borrowing will “crowd out” the demands

of the private sector for credit if the deficit is

not funded by expanding the money supply.

Others suggest that budget deficits occur primarily as a result of high unemployment and

recessions, which reduce tax revenues and create more expenditures on income transfer programs. If true, this latter view suggests that Federal deficits have little impact on interest rates.

In fact, statistical studies show a very low correlation between budget deficits and the level of

interest rates.

To the extent that fiscal policies affect the level

and variability y of interest rates, they also affect

credit availability and the adoption of new technologies in agriculture. Again, the impact on

the adoption of new technologies may depend

on whether the new technologies are capitalintensive. Fiscal policies that result in large

budget deficits will have a more deleterious effect on capital-intensive technologies than on

technologies that require little capital investment and that reduce costs of production.

CAPITAL SOURCES FOR AGRICULTURE

It is useful to separate capital used for agriculture into two broad categories—debt capital and equity capital. Debt capital is defined

as funds that are borrowed and must be repaid

with interest. In contrast, equity capital represents an ownership interest in the business. Net

income and capital gains reflect the returns to

equity capital,

As shown in table 7-2, approximately 20 percent of the total capital used in agriculture is

in the form of debt capital. Debt capital as a percent of total capital in agriculture increased

from about 10 percent in 1950 to 20.7 percent

by 1985. To the extent that new technologies

require the use of borrowed funds for adoption,

lenders as well as farm operators must be convinced of the value of new, and perhaps untested, technologies. Educational efforts to acquaint lenders with new technologies will

become increasingly important if such technologies are to be financed with debt capital.

Equity capital accounts for the majority of

funds used in agriculture and may come from

a variety of sources, including initial investment

Table 7-2.—Balance Sheet of the Farming Sector,

Jan. 1, 1985

Item (billions of dollars)

Assets:

Physical assets:

Real estate . . . . . . . . . . . . . . . . . . . . . . . . .

Non-real estate:

Livestock and poultry . . . . . . . . . . . . . .

Machinery and motor vehicles . . . . . . .

Crops stored onfarm and off-farm . . . .

Household equipment and

furnishings . . . . . . . . . . . . . . . . . . . . .

Financial assets:

Deposits and currency . . . . . . . . . . . . . . .

Savings bonds . . . . . . . . . . . . . . . . . . . . . .

Investments in co-ops . . . . . . . . . . . . . . . .

$ 749.2

50.4

106.5

38.2

26.0

18.7

3.7

29.7

Total assets. . . . . . . . . . . . . . . . . . . . . ... , $1,022.4

Claims:

Liabilities:

Real estate debt . . . . . . . . . . . . . . . . . . . . ,

Non-real estate debt to:

ccc . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

Others . . . . . . . . . . . . . . . . . . . . . . . . . . .

Total liabilities . . . . . . . . . . . . . . . . . . . . . .

Proprietors’ equity . . . . . . . . . . . . . . . . . . . . .

Total claims . . . . . . . . . . . . . . . . . . . . , . . .

Debt-to-asset ratio ... , . . . . . . . . . . . . . . . . .

aPreliminary.

$ 110.4

8.3

93.0

211,7

810.7

$1,022.4

20.7

SOURCE: U.S. Department of Agriculture, Economic Research Service, Agricu/.

tura/ Finance: Situatlorr arrd Out/ook, AFO-25, December 19S4,
Ch. 7—Impacts of Agricultural Finance and Credit ● 141

and retained earnings of farm owners and operators. Importantly, much of the equity capital

in agriculture is the result of asset appreciation.

Equity capital is also provided by investors who

are not farmers but do have an ownership interest through shares of stock, partnership interests, or other forms of equity investment.

Much of the equity capital in agriculture is

invested in farm real estate, although not necessarily by farm operators. For example, a significant portion of this equity capital, some 42

percent nationwide, is rented (but substantial

regional variation exists). In addition, a growing

amount of machinery and equipment is leased

to take advantage of tax regulations. Overall,

the significant amount of leasing of agricultural

assets suggests that a considerable amount of

equity capital in agriculture is controlled by individuals or institutions that may not be actively

engaged in farming operations.

DEBT CAPITAL SOURCES FOR AGRICULTURE

American agricultural producers borrow from

a wide variety of lending sources. The financial institutions that serve agriculture are in a

constant state of change, in part because of

changes in the regulatory environment under

which they operate. Indeed, recent changes in

the regulatory environment have altered the nature and operating characteristics of these financial institutions. Savings and loan associations, as well as Sears, American Express, and

other nontraditional sources, are more likely

now to provide financial services to farmers.

The financial institutions that serve agriculture,

and the changes within those institutions, are

described below.

The Farm Credit System

The Farm Credit System (FCS) is a cooperative

that is owned and controlled by member borrowers. The system began in 1916, when the

Federal Land Banks were established to help

farmers and ranchers gain access to long-term

farm loans under more favorable rates and terms

than were available from other sources. In 1923

the Federal Intermediate Credit Banks were

formed to provide discounting services for

short- and intermediate-term loans. Production

Credit Associations (PCA) and the Banks for

Cooperatives were started in 1933. The cooperative nature of this system is the central focus of its organization and operation.

Originally, the system was partially capitalized by the Federal Government; however, all

Government capital has since been repaid. Although the system is directed by the borrowers

and their elected representatives, it is supervised

by the Farm Credit Administration, an independent agency in the executive branch of the

Federal Government. Unlike most other private

lenders serving the farm sector, FCS is restricted

to making loans only to farmers, fishermen, agricultural cooperatives, and rural residents who

meet eligibility standards set by law. Commercial banks and life insurance companies, in contrast, face no such restrictions.

FCS acquires funds to lend through the sale

of bonds and discount notes in the national

money market. The system has agency status

in selling its bonds and discount notes. (Agency

status has been shown to reduce the cost of issuing bonds.) In recent years, agency status for

FCS has come under attack as an unfair competitive advantage, and will be discussed in a

later section of this chapter.

Today, virtually all loans from FCS are on a

variable interest rate. As a result, interest rate

risks have been passed onto borrowers. Despite

the charging of variable rates, the system has

achieved a fairly stable pattern of rates. However, some farmers have experienced interest

rate increases so high that anticipated profitability was not achieved. In recent years some

parts of the system have begun to offer fixedrate financing alternatives through financial

leasing of machinery and 5-year, fixed-rate loans

on real estate. Such fixed-rate alternatives may

help risk-averse farmers finance the purchase

of new technologies not previously available

through variable-rate loans.
142 . Technology, Public Policy, and the Changing Structure of American Agriculture

At present, there appears to be a move within

the system to consolidate administrative units

and to offer a broader range of financial services to farmers. These actions may have little

direct impact on credit availability or on the

adoption of new technologies. However, they

may make the system more efficient and costeffective, thereby reducing the cost of credit.

Such a reduction in cost would likely provide

some small impetus to technology adoption.

Commercial Banks

Commercial banks area major source of both

real estate and non-real estate loan funds for

agriculture. Historically, they have been the

largest institutional source of non-real estate

farm loans. While not all commercial banks are

actively involved in making long-term farm

mortgage loans, most provide referral services

that help farm operators obtain farm mortgage

funds from other lenders.

In general, most of the loans to farmers and

ranchers made by commercial banks come from

small and intermediate-size banks serving a relatively small geographic area. While large banks

also lend directly, they serve agriculture through

correspondent services through smaller rural

banks and via loans to agribusiness firms.

Each State regulates the extent to which both

State-chartered and nationally chartered commercial banks can branch within a State. The

alternatives include: 1) unit banking, 2) limited

branching, and 3) statewide branching. Twentythree States now allow statewide branching, 16

allow limited branching, and 11 are unit banking States. Some States also allow multibank

holding companies, whereas others do not.

A fundamental change in the structure of commercial banks appears to be taking place. Many

small and intermediate-size banks are being acquired by larger banks or bank holding companies. As a result, the number of banks is expected

to decline and the average bank size is expected

to increase. It remains to be seen how this situation will affect credit availability, technological adoption, or structural change in agriculture. However, one change appears to be the

growing aggressiveness of banks in seeking

farm real estate loans, suggesting that larger

banking units may more aggressively seek new

lending opportunities, including those for technology adoption. However, metropolitan banks

that acquire smaller rural banks may be reluctant to finance new technologies if they are not

familiar with agricultural lending. The movement of the decisionmaking process from the

local scene to a more metropolitan center, and

the need for specialists in agricultural lending,

may make lender education on new technologies more important.

Insurance Companies

Several major life insurance companies have

been actively engaged in farm mortgage lending for many years. Five companies (Equitable,

John Hancock, Prudential, Travelers, and Metropolitan) have accounted for over 75 percent

of the total farm mortgage lending by insurance

companies. Insurance companies have tended

to focus on real estate loans for owners of larger

farms. Since these farmers may have been early

adopters of new technology, insurance companies may have had a greater role in the adoption of land-intensive technologies than their

market share of farm debt would indicate. However, the market share of insurance companies

as a whole has diminished over time.

Insurance companies offer a variety of loan

terms and financing plans. Fixed-rate loans with

a relatively long amortization period, but with

balloon payments after 10 or 20 years, used to

be common. However, the inflationary environment of the late 1970s and early 1980s caused

insurance companies to shorten substantially

the period before which interest rates could be

renegotiated.

Some insurance companies have also experimented with shared appreciation mortgages

(SAMs). A SAM works in the following manner. In exchange for a fixed interest rate at below market rates, the lender shares in a designated portion of capital gains. At the end of a

designated period, normally 5 or 10 years, the

land is either sold or reappraised, with the

lender’s share of the gain due. The amount due

the lender can be handled either as a lump sum
Ch. 7—Impacts of Agricultural finance and Credit ● 143

payment or, more likely, as an increase in the

loan balance.

The insurance industry has undergone substantial changes in its sources of funds and in

the products and services that it offers to agriculture. Equity participations appear likely to

flourish in the future, either in the form of direct investment or in the form of shared-appreciation mortgages. Thus insurance companies

may become more actively engaged in equity

financing than in debt financing.

Government Lending Agencies

The Federal Government provides loan funds

to agriculture primarily through the Farmers

Home Administration (FmHA) and the Commodity Credit Corporation (CCC). The Small

Business Administration (SBA) no longer lends

to farm firms.

The FmHA offers insured and guaranteed

loans. Insured loans are made and serviced by

FmHA personnel and represent about 80 to 90

percent of FmHA’s total loan volume. Guaranteed loans are made and serviced by other

lenders, but are guaranteed against default by

FmHA. To be eligible for these loans, farm

owners must demonstrate that they are unable

to obtain adequate loan funds at reasonable

terms from other lenders. As a result, FmHA

is usually considered a lender of last resort.

Congress controls the extent of FmHA lending programs in two major ways: appropriations

and lending authorization. Appropriations are

used to cover losses and administrative expenses. By controlling the level of appropriations, Congress also controls the extent to which

administrative expenses and loan losses can be

incurred.

Lending authorizations specify the maximum

amount that FmHA can lend out under various

programs. Set annually, lending authorizations

are designed to control the nature of the programs offered. For example, if Congress wishes

to encourage guaranteed loan programs over

insured programs, it can raise the lending authorizations for guaranteed loans while reducing the authorization for insured loans.

FmHA farm loan programs have focused on

farm operators with limited resources and on

those affected by disaster. The impact of these

programs has probably been to slow the concentration of resource ownership and control

in large farms by keeping smaller farms in agriculture. It is less clear what impact, if any,

FmHA programs have had on technological

adoption. However, to the extent that adoption

of new technologies is based on the ability of

farm operators to control larger units (e.g., by

using four-wheel drive tractors), FmHA loan

programs may have slowed the rate of adoption

by preventing additional land from coming onto

the market through foreclosure.

CCC is part of the U.S. Department of Agriculture (USDA). It provides financial assistance

to farm operators through four channels: 1) deficiency payments, 2) disaster payments (although

these have essentially been replaced by multipleperil crop insurance), 3) crop loans, and 4) storage facility loans. The programs offered by CCC

are part of the Government farm programs designed to improve and/or stabilize the incomes

of agricultural producers.

CCC loan programs and the associated farm

commodity programs have likely had a significant

influence on the structure of U.S. agriculture.

The stabilization and improvement of incomes

generated by such programs have probably reduced risks and encouraged the adoption of new

technologies.

Merchants and Dealers

The term “merchants and dealers” refers to

farm suppliers of feed, seed, chemicals, fertilizer, petroleum, machinery, and equipment.

These firms are an important source of non-real

estate loan funds for agriculture. For operating

inputs, such credit often takes the form of accounts payable. For capital inputs, credit may

be extended for a period of 3 to 5 years.

Dealer credit is often viewed as a method of

promoting sales. To some degree, merchantdealer credit programs have helped foster the

adoption of new technologies. This is particularly true for new technologies associated with
144 ● Technology, Public Policy, and the Changing Structure of American Agriculture

Figure 7-2.- Market Shares of Farm Real Estate Debt

Year

Federal Land Banks — —

— Life insurance companie s

Bank s —— - Farmers Home Administratio n

—

‘— Individuals and other s

SOURCE: Office of Technology Assessment.

high-cost capital items. Dealer credit programs

would appear to have less impact on the adoption of new technologies associated with operating inputs.

Market Shares of Farm Debt

The “market share of farm debt” refers to the

percentage of the total volume of lending by a

particular lender. It is useful to distinguish market shares of farm real estate debt from market

shares of non-real estate farm debt.

Trends in the market share of farm real estate debts are shown in figure 7-2. By 1978, the

Federal Land Banks had become the dominant

source of farm real estate loans, surpassing those

provided by individuals. In contrast, the market shares for individuals, life insurance companies, and commercial banks have decreased.

The market share of farm real estate debt for

FmHA has remained fairly constant.

The growing dominance of the Federal Land

Bank System has implications for the future

structure of agriculture. Policies adopted by the

system will tend to dictate how transfers of land

ownership will be financed. However, the changing market shares of farm real estate debt would

appear to have little direct impact on the adoption of new technologies.

Market shares of non-real estate farm debt are

illustrated in figure 7-3. The most notable feature of this graph is the rather sharp decline in

market share for commercial banks and the increase in market share for CCC and FmHA.

Market shares for PCAs and others (primarily

merchants and dealers) have remained fairly

constant.
Ch. 7—Impacts of Agricultural Finance and Credit ● 145

60

50

40

30

20

10

0

Figure 7-3.— Market Shares of Non-Real Estate Farm Debt

. .

\ \

- -

--- 

- —.—9—----

‘ 

, . 

.

. 

,

I

1971 1973 1975 1977 1979 1981 1983 1985

Year

Commercial banks Production Credit Association

Farmers Home Administratio n

——– Commodity Credit Corporatio n

—— - Individuals and other s

SOURCE Office of Technology Assessment

The increase in market shares for Government commercial banks have had in attracting deposlending institutions (FmHA and CCC) reflects its. Legislative constraints periodically prethe expansion of Government programs to sup- vented commercial banks from offering comport agricultural prices and to deal with eco- petitive rates to savers. These constraints are

nomic and natural disasters. The decline in mar- being phased out by tlie Monetary Control Act

ket shares of commercial banks resulted from of 1980,

many factors, including the problems that some

Equity capital, the dominant form of capital tal and on the structure of U.S. agriculture. Each

used by U.S. agriculture, arises from three pri- of the three sources of equity capital is described

mary sources: 1) net farm income and unreal- in more detail below.

ized capital gains, 2) off-farm income, and 3)

the infusion of new investment capital from per- Not Farm income and Capital Gains

sons or institutions not actively engaged in agriculture. Differences in the relative importance Table 7-3 identifies the USDA estimate of net

and access to these forms of equity capital have farm income and capital gains achieved by the

a direct impact on the availability of debt capi- farm sector since 1940. Net income can be bro-
146 . Technology, Public Policy, and the Changing Structure of American Agriculture

Table 7=3.—income and Capital Gain Returns for the Farming Sector

Return as a percentage

Imputed return to Residual income Real capital of equity value (percent) ·

operator’s labor and to equity gains a From residual From real

Year management (percent) (billions of dollars) income capital gains Total

1940-49 average . . . . . . . . 8.250/o $4.32 $ 1.67 6.900/0 3.360/0 10.260/o

1950-59 average . . . . . . . . 9.12 4.24 2.87 3.57 2.38 5.95

1960-69 average . . . . . . . . 7.17 6.06 5.19 3.44 3.06 6.50

1970-79 average . . . . . . . . 9.00 21.55 30.05 4.46 7.86 12.32

1980 . . . . . . . . . . . . . . . . . . 10.80 9.80 –4.50 1.30 –0.60 0.70

1981 . . . . . . . . . . . . . . . . . . 12.50 17.00 –75.30 2.10 –9.20 –7.10

1982 . . . . . . . . . . . . . . . . . . 10.70 9.90 –61 .30 1.30 –7.00 –6.50

1983 . . . . . . . . . . . . . . . . . . 9.40 4.00 –22.90 0.50 –3.10 –2.60

-he change in the real value of pf’tysical farm assets (after subtraction of real net Investment) plus the changes in the real values of currency, demand deposits, and

farm debts.

SOURCE: U.S. Department of Agriculture, Economic Research Service, Econm/c/ndlcatom of the F~ Sectoc/rwomeand8afanceSheetStati@/cs, EClF2-2, September 19S4.

ken down into two components—l) returns to

operators’ labor and management, and 2) residual income to equity in farm assets. The decades of the 1940s and 1950s marked a period

in which returns to operators’ labor and management nearly doubled the income return to

equity. During the 1960s, the two components

of net income were about equal. During the

1970s the income return to equity exceeded the

return to operator labor and management. This

pattern reflects the significant substitution of

capital for labor that occurred over the last four

decades.

Net income, whether in returns to operators’

labor and management or in returns on equity

capital, is in the form of cash. Estimates of the

amount of net income retained in the farming

sector are not readily available, but it does seem

likely that the majority of these funds would be

used for other purposes, especially for family

living.

Real capital gains reflect the return to equity

capital from an appreciation in asset values that

is greater than the rate of inflation in the general economy. In the decades of the 1940s and

1950s, real capital gains were on average positive and were 40 to 70 percent as large as the

residual income to equity. During the 1960s, real

capital gains nearly equaled the residual income

to equity; during the 1970s, real capital gains

on average far exceeded the residual income

to equity. Thus from 1940 to 1980, landowners

came to expect significant real capital gains

from the ownership of agricultural assets.

Since 1980, real capital gains have been negative every year. By 1984 real wealth of the sector was down by over $160 billion from what

it had been in 1979. This massive reduction in

the real wealth position of agriculture has had

a dramatic impact on the economic and psychological attitudes toward new investment. Purchases of capital assets such as machinery have

been postponed or delayed as long as possible

by many operators. Land sales languish from

an overabundance of parcels offered. The problems created by this massive reduction in real

wealth have been most strongly felt by farm

operators with heavy debt loads. While forced

sales have not yet reached substantial proportions, most observers believe that a major restructuring of asset ownership could occur as

a result of the economic conditions of the early

and mid-1980s. In particular, land will likely be

redistributed from the highly leveraged operators to those with a strong financial position

and low leverages.

Income returns as a percentage of equity value

were relatively high during the 1940s, but

dropped to an average of under 5 percent for

the next three decades. Since 1980, income

returns as a percent of equity value have been

extremely low. Total returns, measured as income and real capital gains, were relatively high

in the 1970s, but have been negative in recent

years.

Since unrealized capital gains can be monetized only through the sale of assets or by borrowing, the magnitude of such returns may have
Ch. 7—Impacts of Agricultural Finance and Credit • 147

limited direct impact on the purchases of operating inputs. However, the impact on the ability to borrow and the psychological impacts of

declining real asset values will likely adversely

affect the ability and desire to adopt new technologies that are costly or uncertain.

Off-farm income is a major source of income

for farm firms, as indicated in chapter 4. For

example, in 1983 off-farm income accounted for

nearly 60 percent of the total income per farm

firm. However, as farm size (measured by annual gross sales) decreases, the relative importance of off-farm income increases.

If technological adoption occurs first on the

very large-scale farms (over $500,000 in gross

sales), then the impact of off-farm income on

technological adoption may be low, since such

income is a relatively small component of total

income for the largest farms. In contrast, if technological adoption occurs first on small or moderate farms, off-farm income may bean important source of income for financing technology

adoption.

Credit Policy and Structural

Issues in Agriculture

The impacts of credit and credit policies on

structural change in agriculture can be viewed

from two vantage points: 1) a broad view of the

farm production sector as an aggregate unit

structured to achieve desired social objectives,

and 2) an intrasector view that considers changes

in the sector’s makeup overtimes Viewed from

the broad vantage point, credit arrangements

and policies of the past are believed to have contributed to maintaining a structure of the farm

production sector that, compared with many

tThis section and the next are based on a paper by Peter J, Barry,

“Regulatory and Performance Issues for Financial Institutions:

Their Effects on Technology Adoption and Structural Change

in Agriculture, ” prepared for the Office of Technology Assessment, Washington, DC, December 1984.

Now Equity Investment Capital

Capital from the sales of stocks in corporations or shares of partnership interest has been

a rather limited source of equity capital for agriculture. In the past, shares of partnership interest have generated significant amounts of

new equity capital for large cattle feeding and

poultry operations. These investments were motivated by favorable tax laws—laws that have

since been changed.

At present, farmers are considerably interested in the possible infusion of new equity capital to assist financially distressed operators with

large debt loads. Interest by investors, however, is rather limited. Nevertheless, significant

amounts of new equity capital have been raised

for investing in new technologies in agriculture.

For example, much of the equity investments

for embryo transplants in dairy cattle have come

from nonfarm investors. Thus, while new equity

capital may be a small component of the total

equity capital in agriculture, it maybe used to

finance some of the new technologies whose

risks and payoffs are expected to be high.

other sectors, largely has a small-scale, pluralistic, noncorporate, competitive market organization of ownership, management, and control. These characteristics presumably have

been consistent with social objectives for agriculture, including low-cost, abundant, and reliable supplies of food and fiber, although empirical verification of this situation needs further

testing. Some examples of these past credit arrangements and policies include:

●

●

●

creation and evolution of the Cooperative

Farm Credit System;

maintenance of a dual system of commercial banking (basically, large and small

banks) with some special provisions for

agricultural financing;

creation of government credit programs for

agriculture—FmHA and CCC at the Fed-
148 . Technology, Public Policy, and the Changing Structure of American Agriculture

●

●

●

eral level and various credit programs at

the State level;

actions and policies taken by Federal and

State governments to discourage or impede

the flow of outside equity capital into the

agricultural production sector;

laws to protect the interests of tenants and

thus encourage the traditional leasing arrangements for farm land; and

encouragement of seller financing of farm

land that keeps the financing function within local communities.

Given this broad view of the farm production

sector, credit arrangements and policies have

also facilitated various structural changes within the sector. Included, among others, are the

mechanization and modernization of farm units,

greater capital intensity, growth in farm size

(and reductions in farm numbers), greater leverage from debt and leasing, and greater market

coordination. Credit also plays an important

risk-bearing role through providing the liquidity

to cope with risk and through the various alternatives in debt management for restructuring

and rescheduling farmers’ financial obligations.

However, special credit programs and concessionary terms are also believed to have highly

sensitive, adverse effects on resource allocation,

asset values, and risk positions. That is, these

credit programs may, on occasion, tend to overfacilitate changes or to hamper long-term resource adjustment.

In general, then, a reasonable consensus of

past studies and observations on the relationships between credit and structural change in

agriculture is twofold (U.S. Department of Agriculture, 1980; Farm Credit Administration,

1980; Lins and Barry, 1980; Lee, et al., 1980).

First, the availability of credit is a necessary condition for undertaking the investments and other

activities (including adoption of new technology) that result in structural change. However,

credit availability y is not a sufficient condition—

basic economic incentives are needed as well.

Second, credit and credit policies can be facilitating instruments for structural change in

agriculture, although not very effective ones,

since the unintended negative effects may outweigh the intended positive effects. That is, the

special credit policies may sometimes result in

too much use of credit, too much risk-taking,

higher land values, and slower mobility of some

resources.

Credit Determinants and

Relationships to New Technologies

The availability and cost of credit to agricultural producers are based on a number of determinants that may change over time and that

may differ among financial institutions. Some

credit determinants originate in the financial

markets. These include both macro and micro

conditions. Macro conditions reflect monetary

and fiscal policies, inflation, savings rates, and

other forces, both domestic and international,

affecting interest rates, money supplies, and

credit use. Micro conditions reflect the responses of both financial institutions and borrowers to changes in the market and regulatory

environment.

Other credit determinants originate in agriculture through the macro effects of supplydemand conditions for commodities and resources, and through factors affecting the creditworthiness of individual borrowers. Creditworthiness is based on those fundamental factors

that lenders use to evaluate a borrower’s ability to meet his financial obligations.

In this chapter, the primary focus is on the

relationships between credit terms from the major farm lending institutions and changes over

time in these institutions’ regulatory and competitive environments. For the above credit determinants, this focus primarily involves the

micro conditions of these lending institutions,

although the interrelationships with various

macro financial forces are important, too. Also

important are the impacts of new technologies

on the creditworthiness of farm units with different structural characteristics, and the implications for the cost and availability of credit.

From a creditworthiness standpoint, most of

the new technologies projected by OTA for

adoption involve refinements in production

processes without requiring large capital outlays by agricultural producers. This is especially
Ch. 7—impacts of Agricultural Finance and Credit ● 149

true for technologies involving genetic engineering, diseases and pests, and fertility and nutrition. Such technologies should largely be embodied in the operating inputs used by crop and

livestock operations to carry out production.

Credit for acquiring most of these technologies

will probably come from short-term operating

loans, with loan repayment occurring from the

sale of products being produced. For crops, the

sales may occur at harvest or over a marketing

year as stored inventories are liquidated. For

livestock the sale time is based on the market

readiness of the animals and on the byproducts

involved. Most of these new technologies should

be financed by short-term, self-liquidating loans

that are highly preferred by most lenders. Moreover, as normally occurs in production loans,

lenders will take security interests in the products being produced (e.g., growing crops, marketing contracts, feeder livestock, milk products) in order to provide the necessary loan

collateral. In many cases, security interests will

also be taken in the borrower’s capital assets

(e.g., machinery, facilities, breeding livestock)

in order to provide a broader collateral base,

especially when the same lender finances both

operating inputs and intermediate-term capital assets.

For those new technologies involving fixed

capital, as with systems for environmental control, irrigation and water management, performance monitoring, and information and communication, the capital outlays will be greater and

the economic payoff periods will be longer.

Credit arrangements for these technologies will

likely involve intermediate or longer term loans,

with security interests in the capital assets serving as loan collateral. Important considerations

are the length of payoff period for these technologies and the time pattern of returns. Some

of the assets maybe highly specialized, with low

liquidity and high transactions costs in the event

liquidation must occur. Others will be more easily transportable, with lower transactions costs

and thus greater liquidity.

In general, then, according to evaluations of

creditworthiness based on repayment expectations and collateral alone, the new technologies

should not encounter financing limits or other

loan terms that differ much from those for other

types of agricultural assets. However, a more

important lender response will likely involve

the management skills and risks associated with

using these technologies. Clearly, more complex technological systems will demand greater

skills in both management and labor for their

effective use. In some cases, considerable investments inhuman capital by agricultural producers may be needed to provide the necessary management skills. Complementary investments in

computers and information processing technologies may also accompany the adoption and use

of new technologies. Both of these factors may

involve financial requirements and thus influence the borrower-lender relationship.

From the risk standpoint, considerable uncertainty may arise about the proper use and payoffs from these technologies, especially in the

early stages of adoption and use. Moreover, the

market values of some new technologies could

drop rapidly, owing to obsolescence or to lower

production costs as sales increase. Thus lenders

will place greater emphasis in credit evaluations

on the ability of agricultural producers to demonstrate rigorously that they have the necessary

resources and skills in management and labor

to use the new technologies effectively, and that

the risks are not excessive. Moreover, lenders

themselves must be able to understand the new

technology and to communicate clearly with

borrowers about its adoption, use, and financial consequences. These features will likely favor those lending institutions that have the size,

expertise, funding capacity, and other characteristics to make a substantial commitment to

agricultural finance.
150 • Technology, Public PolIcy, and the Changing Structure of American Agriculture

REGULATORY ISSUES

Regulatory and Performance hues

Affecting Depository Institutions

During the 1980s virtually all of the major farm

lenders have experienced significant changes in

their competitive environment, owing to the combined effects of numerous factors. Among these

factors are the following: 1) the high, volatile

inflation rates of the 1970s and early 1980s and

the related pressures on interest rates; 2) the

strong growth in competition for funds and

financial services from new entrants to the financial services industry (Sears, Merrill Lynch,

J.C. Penney, money market mutual funds, and

others); 3) the new technology in financial markets, involving electronic transfers of funds and

cash management services; 4) the financial

stresses affecting many borrowers; and 5) the

regulatory changes affecting financial institutions, with heavy emphasis on deregulation. The

regulatory changes are considered in the following sections.

At the beginning of the 1980s, the major areas

of regulatory change affecting commercial banks

and other depository institutions involved four

areas: 1) the decontrol of interest rate ceilings

on deposits and loans, 2) controls on ownership

forms and geographic scope–the branching

and holding company issues, 3) the range of

products and services these institutions can offer, and 4) the adoption of uniform Federal Reserve requirements for all depository institutions. The major pieces of legislation enacted

by 1984 included the Depository Institutions Deregulation and Monetary Control Act of 1980

and the Garn-St. Germain Depository Institutions Act of 1982. These acts focused primarily

on the decontrol of interest rates, changes in

reserve requirements, and aid for ailing thrift

institutions. In addition, several bills under consideration by the House and Senate in late 1985

could affect the range of products and geographic liberalization.

Interest Rate Regulation

The deregulation of interest rate ceilings on

bank deposits–called Regulation Q–was largely complete by 1984. It has made the pricing

environment more homogeneous among depository institutions and has greatly reduced the

historic insulation of rural banking markets

from national and even international forces. The

levels and volatilities of banks’ costs of funds

have increased, and virtually all of the funding

sources for banks have become rate-sensitive.

In response, banks of all types and sizes have

adopted more market-oriented pricing policies

for loans, funds acquisition, and services, and

have moved toward improved methods of managing assests and liabilities. Greater emphasis

has been placed on the use of such techniques

as floating rates, risk assessment and pricing,

spread and gap management, matching maturities, interest rate hedging, cost accounting,

loan documentation, and market analysis. The

traditional loan-deposit relationship at the customer level is changing too, with more emphasis on revenue generation from borrowers rather

than reliance on deposit balances and related

lending terms. Most of these new banking practices were initially undertaken by larger banks

and holding company systems, although their

use by smaller banks has increased as well.

In the early stages of interest rate deregulation, most small banks were able to maintain

strong profit performance. Banking data indicate, for example, that the average annual aftertax rate of return on equity capital for about

4,300 “agricultural” banks (banks with ratios

of farm loans to total 1oans of 0.25 or above) was

14 percent for the 1970s. This figure climbed

to 16 percent in 1980 and then declined, falling

to 11 percent in 1983. Most of the decline appears attributable to higher loan losses, including those on farm loans, rather than on narrower

margins between loan rates and cost of funds.

But the full story is probably not yet available

on banks’ profitability y responses to both financial stress in agriculture and financial deregulation. These two phenomena may be closely

related, since banks have responded to higher,

more volatile costs of funds by passing risks on

to borrowers through floating loan rates and

other loan repricing methods. This in turn has
Ch. 7—impacts of Agricultural Finance and Credit . 151

caused greater financial distress for many borrowers, which then reverts to the lender through

higher loan risks, more delinquencies, and greater loan losses. Moreover, the bank’s practice of

responding to these credit problems by spreading the increased lending risk to other borrowers

through higher risk premiums in loan rates has

likely widened the incidence of credit problems

in agriculture. This, of course, reflects the strong

market power of most banks in local credit markets. However, it also means that the profit position and lending capacity of many agricultural

banks could deteriorate further in the future as

lagged responses occur to farmers’ stress positions, and as the competitive pressures of financial deregulation become more intense.

An offsetting factor to these interest rate conditions for banks and borrowers is that interest

rate deregulation has relieved the disintermediation pressures of the past and reduced the likelihood of periodic credit crunches in which the

bank’s availability of loan funds is dried up.

Thus the past risk of swings in credit availability, and the attendant liquidity problems for

banks and borrowers, has shifted strongly to

swings in interest rates. This in turn gives

clearer signals about changes in financial markets and improved the efficiency of financial

markets.

Banks, Products, and Services

The second line of deregulation is the focus

on possible changes in the authority of banks

and other depository institutions to offer various products and services. Many banks are seeking greater authorities to offer insurance, real

estate brokerage, securities underwriting, equity

participations, and other nonlending activities.

In addition, some banks are becoming more active in adopting, using, and merchandising information processing activities that meet their

own needs for information (e.g., credit evaluations), while offering information services to

customers (e.g., accounting systems). The products and services area will receive careful scrutiny and much debate in the policy arena. Nonetheless, additional liberalization of banking

powers seems likely, given the thrust of competitive market forces. The effects on rural credit

may not appear significant, although indirect

effects may occur if new banking products have

favorable profit prospects relative to lending.

Geographic Structure Issues

The third major line of regulatory change involves the geographic scope of banking. A longstanding U.S. philosophy has been to let individual States determine branching and holding

company activities within their boundaries.

Various laws have prohibited national branching, given State branching authority to each

State, and prevented bank holding companies

from crossing State lines unless agreed to by

the States involved. The result has been a diverse set of State limitations on branching and

holding companies.

Considerable attention has focused on liberalizing these geographic restrictions. But except

for savings and loan associations and other thrift

institutions, Congress began to address these

issues only in 1984. The approach in the recent

past mostly involved letting individual States

initiate geographic liberalization using reciprocal authorities granted in existing legislation.

In addition, greater discretionary relaxation by

the various regulatory agencies has occurred.

This approach essentially allows the drift of

market forces to work, creating a climate in

which many banks and banking systems have

exhibited considerable aggressiveness. Examples of these movements have included the

development of regional banking markets, especially among States in the Northeast and Southeast, the creation of nonbank banks (banks that

do not simultaneously make commercial loans

and take deposits), and the rapid expansion of

multibank holding companies in States that have

eased restrictions on these activities.

Moderate deregulation should affect smaller

institutions more heavily than larger ones; thus,

the number of banking entities in the United

States should decline significantly–perhaps by

one-third by the mid-199@. However, public

pressures will likely continue to provide various types of protection for smaller community

banks that have been so prominent in States that

have prohibited branch banking. Moreover, the
152 . Technology, Public Policy, and the Changing Structure of American Agriculture

financial stresses being faced in many unit banking States during the 1980s may accelerate the

trend toward reciprocal banking agreements between States in order to broaden the market for

failed and stressed banks.

The surviving banks will be higher performing community banks that are well managed,

well capitalized, and strongly localized in their

services. They will serve portions of the financial markets that are not well suited to the scale

and technology of the larger banking systems.

These banks will give considerable attention to

the competitive pricing of products and services and to market segmentation, including specialization in activities like agricultural lending.

In general, geographic liberalization should

bring greater competition in all phases of banking. This will put downward pressure on bank

earnings, but will contribute positively to the

availability, cost, and usefulness of financial

services for customers. Banks may take on greater risks but have greater risk-carrying capacity

through increased diversity in loan portfolios,

larger resource bases, greater depth and breadth

in management, and the discipline exerted from

market factors rather than from regulations. For

agricultural finance, geographic liberalization

should enhance the availability of credit services, although more along the lines of commercial lending procedures for commercial-scale

farmers and consumer lending procedures for

small, part-time farmers.

A continued swing will occur toward greater

financing from larger, more sophisticated banking systems, with these larger systems seeking

the business of larger farm units and agribusinesses. Smaller, independent banks with strongly localized customer orientations will make

substantial use of funding and service relationships with larger banking systems. This arrangement will be similar to the correspondent

arrangements of the past, although the correspondent institutions themselves will be operating in larger markets. In the near term some

banks may seek to develop funding and loan participation arrangements further with various

units of the Farm Credit System, although over

the long term, bankers prefer a reliable, costeffective source of nonlocal funds within the

banking industry. The funding mechanism provided by MASI, Inc. (a division of Mid-America

Banking Service Co., MABSCO) is a step in this

direction. This mechanism will allow participating banks in more than a dozen States to discount acceptable farm loans with a funding

source in the national-international financial

markets. This future funding should also include

the ability to make long-term real estate loans

in a fashion that will not jeopardize bank liquidity or increase interest rate risks.

In light of these developments, the location

of credit control and loan decisions may continue to shift away from the local rural community; however, the availability of experienced,

well-trained farm lenders in rural areas should

maintain an emphasis on local servicing of farm

loans while still fostering greater uniformity in

loan documentation, risk assessment, and other

lending practices. This standardization should

benefit both the financial institutions and farm

borrowers.

Regulatory and Performance Issues

Affecting the Farm Credit System

The major legislative authority of FCS is the

Farm Credit Act of 1971 (as amended). In general, the system’s legislative authority defines

its mission as one of providing appropriate

credit and related services to eligible, creditworthy agricultural borrowers throughout the

United States during all phases of the economic

cycle in order to improve their income positions

and overall well-being. FCS is specialized in

financing agriculture. Thus local associations

and individual districts are vulnerable to the

problems affecting their agricultural borrowers.

Moreover, because the system is a cooperative

organization, much of its equity capital is owned

by farmers who in turn financed this equity contribution with funds borrowed from the system.

However, a number of factors at the systemwide

level help counter the risks associated with this

mandated specialization: 1) the system’s national structure of full-service agricultural lending;

2) diversification of loans across borrowers,

associations, districts, and farm types; 3) losssharing and participation agreements between

the various banks and associations; 4) a strong
Ch. 7–impacts of Agricultural Finance and Credit ● 1 5 3

financial position and excellent credit history;

5) efficient operations with low per-unit costs

of funds management, loan administration, and

the like; and 6) a systemwide emphasis on risk

management. These characteristics, along with

regulatory privileges in funding (see below),

have enabled FCS to grow significantly and to

become the largest farm lender in the 1980s,

especially in long-term lending.

Like other lenders, FCS has been significantly

affected by the financial stresses of agriculture

in the early 1980s. Most indications during the

early 1980s were that unless farm losses became

extremely heavy and widespread, the FCS should

come through the stress times in reasonably

good shape. Loan volume had declined for some

units, higher loss rates were occurring, some

borrowers were discontinued, more associations

were merging, and intra-system assistance packages were developed for some units. Moreover,

the FCS had taken several actions to strengthen

its liquidity and build its risk management. Some

of these actions involved continued restructuring of the system’s capital positions, operations,

and management through greater centralization

of these functions at the system, district, bank,

and association levels. In general, the overall

financial structure of FCS remained relatively

strong through the mid-1980s and the system’s

capacity to sell securities in financial markets

was not impaired. Nonetheless, policy makers,

regulators, and others continued to maintain

close surveillance of the system’s performance.

Then, in the fall of 1985, the governor of FCA

with subsequent agreement by the leadership

of FCS concluded and announced that substantial Federal assistance could be needed in the

next 18 to 24 months to keep the system solvent

if farm financial conditions continued to deteriorate. After much debate, including concerns

about the standing of the system’s securities in

the financial markets and equitable treatment

for other troubled farm lenders, Federal legislation was passed that strengthened the regulatory authority of the FCA, strengthened the system’s capacity for handling problem and loss

loans, and essentially provided a contingent line

of credit from the Federal Government if the

system’s own reserves proved inadequate to deal

with continuing financial problems in agriculture. While further regulatory changes likely will

occur in the future, these developments should

enable FCS to come through the stress times in

reasonably good condition.

Moreover, over the long term, FCS is clearly

taking actions to perform more effectively in

a more competitive, deregulated financial environment. One such action during 1983-85 has

been the initiation of a significant self-study

(called Project 1995) of the system’s future missions and directions in all phases of its activities [agricultural financing, financial markets,

government affairs, personnel, and management). Other actions have in general reflected

the emergence of FCS as a vigorous commercial entity seeking to achieve high performance

for its member borrowers. Among these actions

have been a stronger emphasis on the development and marketing of new products and services; the continuing trend toward centralization

and unification of territorial boundaries, management, service provisions, and other functions; the formalization of government affairs

activities through trade association arrangements; and a moderately paced expansion of

international activities.

From a policy perspective, FCS has also been

caught up in the swift and significant changes

in regulation and competition affecting the U.S.

financial system. The effects have been less direct than on depository institutions but, over

a longer term, basically involve the trade-offs

between: 1) the needs by the U.S. agricultural

sector for a specialized, reliable, nationally oriented credit system with special privileges in

the financial markets; and 2) the trend toward

greater openness in financial markets, with less

emphasis on regulatory preferences in funding

and mandated specialization in asset allocations.

These issues began to emerge during the debate preceding the passage of the Farm Credit

Act Amendments of 1980. Much concern arose

about the concept of a “level playing field” in

the regulatory environment for commercial

banks, FCS, and other types of lenders. Included

in the debate were differences between institutions in their access to financial markets (the
154 . Technology, Public Policy, and the Changing Structure of American Agriculture

agency status issue), geographic restrictions, tax

obligations, legal reserve requirements and

lending limits, stringency of regulation and supervision, and the range of financial services

and borrower clientele for these types of institutions. None of these issues affecting FCS were

fully resolved in the debate on the 1980 Act, although the legislation that was finally passed

did reflect responses to some of the concerns

raised by commercial bankers and others.

Since 1980, much attention in policy circles

has focused on the “agency status” of the securities that FCS sells in the financial markets.

While FCS is privately owned and operated, the

securities it sells still have some special regulatory privileges, giving rise to the “agency status” label. To some extent, agency status is a

vestige of earlier times when FCS had significant Government involvement and formal backing. However, the system’s securities have a set

of regulatory exemptions and preferences that

have continued since FCS reverted largely to

a private status in the late 1960s (Lins and Barry,

1984; Barry, 1984). This status helps the system

achieve a very large volume of security sales

at interest costs that are just above those of the

U.S. Government and below those of the largest,

most creditworthy corporate issuers.

Several groups have studied the possible effects of removing agency status. While these effects are difficult to measure precisely, the general consensus is that loss of agency status

would increase the interest cost on farm credit

securities to the interest rate levels of high-grade

corporate bonds or commercial paper. This

might bean increase of ().5 to 1 percent, or even

more. In addition, the volume of marketable

securities could decline significantly, since the

past volume of these sales far exceeds the annual volumes of the largest corporate issuers.

A contrary view, however, is that even without

agency status the financial markets are efficient

and deep enough and the Farm Credit securities have a favorable enough record that the entire funding needs of the system could still be

met, although at higher interest rates.

The agency status issue will eventually be resolved by the political process that, in the mid1980s, has favored continuation of agency privileges for FCS, especially in light of the financial stresses affecting agriculture. But it seems

likely that attempts to remove agency status will

continue, as has been the case for some of the

housing agencies whose securities also have

agency status. In its own self-study, FCS states

that Government-sponsored agencies can probably retain agency status in some form through

the mid-1990s. However, political pressures

toward privatization will continue and will

bring higher costs for the agencies involved, as

well as perhaps greater interest in broadening

their authorizations in funding methods and asset allocations as various agency attributes are

diminished. Indeed, having a reliable source of

funding is essential if FCS is to retain its mandate to provide credit in all regions of the United

States and through all phases of the economic

cycle. Thus, the agency status issue has important policy implications that affect the financial markets in general, the farm credit markets

in particular, and especially the costs and availability y of credit from FCS. In turn, these effects

will have important implications for the structure and performance of the agricultural sector.

PUBLIC CREDIT PROGRAMS

Public credit programs currently adminis- help foster a smaller scale, pluralistic structure

tered through FmHA and CCC at the Federal for the farm sector; they provide financing oplevel, and in numerous State governments as portunities for beginning and limited resource

well, have long been important in achieving so- farmers; they provide valuable liquidity for

cial objectives for the U.S. agricultural sector. emergency situations; and, in the case of CCC,

These programs help channel funds to selected they contribute valuable inventory financing to

geographic areas and types of borrowers; they promote orderly marketing of farm commodi-
Ch. 7—impacts of Agricultural Finance and Credit ● 155

ties. In addition, from a policymaker’s standpoint, credit programs are a popular, politically

expedient policy instrument. They are relatively

easy to administer; they are highly visible to constituents; they can be quickly developed for

responding to ad hoc crises; and they do not

directly influence commodity and resource markets, even though the secondary effects on asset value, income, and risk can be significant.

Moreover, the administrative and risk-bearing

costs of such programs are difficult to measure

and are effectively hidden from taxpayers.

The growth in FmHA lending has been substantial since the late 1970s, especially through

various emergency loan programs. This lending helped considerably in softening the impacts

of high interest rates and weak farm income on

some farmers and relieved commercial lenders

of many problem loans. But this liberal lending

may have helped worsen some farmers’ financial conditions. Some observers have suggested

that part of the financial stress of farmers is due

to excessive public sector lending and that more

credit will only worsen the conditions of highly

leveraged farmers and will needlessly delay the

departure of some farmers from the industry.

Similar observations over a longer term perspective suggest that strong Government lending

may have overfinanced the farm sector, accelerated the adoption of capital-intensive technology, shifted too much risk bearing to the Government, and capitalized the effects of easy

financing terms into higher values of land and

other assets.

Much concern has surfaced about the role of

special credit treatment in agriculture, the proper balance between private and public sectors,

which farmers are served, the level and form

of subsidies, and the resulting tax burden. These

are sensitive issues in the public arena. On the

one hand, the stresses of the early 1980s have

brought increasing pressure from farmers, farm

groups, and others to provide additional public assistance to solve these problems. Yet, at

the same time, the liberal, high-cost, public programs of the recent past have fostered growing

dissatisfaction and closer scrutiny by nonfarm

groups as well as by those farmers with stronger

financial positions and less indebtedness.

In terms of regulatory change, the public programs have not, of course, experienced the same

considerations of deregulation as those that affect lenders in the private sector. Nonetheless,

these public programs must still operate under

various regulations and practices affecting interest rates, lending limits, credit decisions,

eligibility of borrowers, disaster declaration authorities, and relationships with other lenders.

In general, the interest rates on public loan programs now reflect the level and frequency of

changes in the Government’s costs of funds.

Thus interest rates on public credit follow market interest rates much more closely, and while

rate levels are higher than in the past, they are

still more favorable than commercial loan rates.

An exception occurs in the case of various emergency loan programs in which significant concessions in interest rates may occur for the affected borrowers.

Lending limits on various loan programs in

general are still set by law rather than by individual credit factors. These limits provide controls on the magnitude of appropriations, and

impose an administered allocation of loan funds

among eligible borrowers. The limits tend toadjust upward over time to reflect the effects of

inflation and the costs of establishing and operating viable farm businesses. However, the adjustments occur at sporadic intervals with no

formal indexing to other measures. A continuing dilemma in setting loan limits involves the

choice between the levels of credit needed by

individual borrowers to move in an orderly way

toward eventual graduation to commercial financing versus the preference to spread an allocation of funds that is fixed in the short run

among the greatest possible number of borrowers.

Closely related to lending limits for individual borrowers are the issues associated with the

allocation of funds among various States and

regions and over the various loan programs. It

is not unusual for funds in some uses and locations to be fully allocated part way through a

budget year so that otherwise eligible latecomers

may find that loan funds are depleted. This process may then trigger the need for new appropriations, rechanneling of funds from other uses,
156 ● Technology, Public Policy, and the Changing Structure of American Agriculture

discretionary rationing, or other responses.

Thus, lending limits at the agency, program, and

borrower levels may introduce considerable uncertainty about the availability of credit.

Another administrative issue in FmHA lending involves the form of credit programs—that

is, the choice between direct (insured) loans and

guarantees of loans made by commercial lenders. To date, nearly all FmHA lending to farmers

has occurred through direct loans, even though

both programs are available. In concept, direct

loans and guaranteed loans have similar effects

in that the bulk of the credit risk is still carried

by the Government. However, the guarantee approach is considered to involve lower degrees

of subsidy and to involve more formally the commerical lender in the credit decision and loan

servicing. Thus loan guarantees can be a more

efficient method of program design that has less

disruptive effects on credit markets. Some of

these possible benefits have been offset, however, by the commercial lenders’ perception of

the costly process of using the guarantee program, and by the greater effectiveness of direct

loans in emergency situations. In response,

FmHA has sought to simplify procedures for

using guarantees through a “preferred lender”

program that expedites the private lenders’ use

of the program.

Another administered change has involved

the centralization of decision authority for

declaring disaster conditions in various geographic areas. In the past the location of these

authorities at the State level gave too much incentive to the parties involved to declare emergencies in their respective States in order to

qualify for low-cost emergency loans. It is believed that centralizing this decision authority

allows the allocation of emergency funds to be

more objective.

FUTURE ROLE OF STATE CREDIT PROGRAMS

At the State level, a number of States have developed farm credit programs with a heavy emphasis on financing the acquisition of farmland

and other capital assets by younger farmers

(Lowenberg-DeBoer and Boehlje, 1983). These

programs vary considerably, but tend, like

FmHA, to have a set of regulations affecting borrower eligibility, loan purposes, loan limits,

budget limits, interest rates, and so on. Heavy

emphasis in many of these programs has been

placed on lending financed by tax-exempt bonds

and on various types of tax incentives affecting land purchases and leasing by young farmers. The tax-exempt bond programs appear to

be less cost-effective compared with other program methods, since they essentially involve the

Federal Government in sharing the State program costs. Recently, the Federal authorizations

for States to offer tax-exempt bond programs

have been curtailed, with further limitations anticipated for the future. In the future, the general importance of State credit programs could

increase, especially if Federal credit programs

are cut back. However, the scope, missions, and

instruments used in these programs will likely

receive careful review and revisions to assure

that the programs are formulated in the best public interest of the States involved.

FUTURE ROLE OF FmHA

Central to the debate on FmHA’s future role characteristics of the average FmHA borrower,

in the process of technological change is the it would seem that several barriers would have

question of adoption constraints.4 Given the to be removed or diminished if this group is to

4This section on FmHAs role is based on a paper by David Trech- be a major beneficiary of the emerging technolter and Ronald Meekhof, “The Role of Federal Credit Assistance ogies. A number of options are available to

Programs in the Process of Technological Change,” prepared for FmHA if it undertakes the task of removing or

the Office of Technology Assessment, Washington, DC, August

1985. reducing these barriers.
Ch. 7—impacts of Agricultural Finance and Credit ● 157

Many of the technologies that will influence

agriculture in the coming period will not require

major capital investments in order to be adopted

by the majority of farmers. However, even today

many of FmHA’s clients control too few resources to compete effectively. Some of the technologies that will be developed between now

and year 2000 will only exacerbate this situation. One option for FmHA would be to change

the type of clients it serves. However, it makes

little political or economic sense to change the

focus of FmHA to the larger, more economically

viable farms.

A second option would be for FmHA to help

its clients attain a more economically viable size.

If FmHA increases its lending activities so that

a specific subsection of the farm population can

acquire anew technology and the resources that

go with it, serious equity considerations are

raised. Even in the best of times, the special benefits given FmHA farmers pose equity questions.

When times in farming are difficult, the rumblings of farmers who cannot or have not taken

advantage of FmHA loans grow louder. Selectively providing the means to acquire and use

new technologies, particularly when this is accompanied by significant increases in the assets controlled by FmHA farmers, would be expected to increase the controversy surrounding

the agency. In addition, providing FmHA’s

clients with more resources does not ensure success unless the management skills necessary to

use them fully are also available.

A third option for FmHA is to alter its operations in an attempt to fill an empty market

niche—the development of human capital.

FmHA and other lenders are presently operated

to facilitate the acquisition of physical assets.

Most lenders are very reluctant to provide credit

for the acquisition of human capital because

payoffs are typically long-term in nature, repayment risks are substantial, and little collateral

is available. The preference for financing physical capital acquisition is understandable from

the individual bank’s point of view but may result in suboptimal outcomes for society. For example, society might prefer that a farmer use

a loan to buy training in integrated pest management techniques rather than more lethal pesticides.

FmHA could play a particularly important

role in the acquisition of human capital, given

the nature of most of its clients. FmHA farmers

are relatively richly endowed with one resource

—labor. Since it is impractical to expand its

clients’ base of physical capital, a fruitful role

for FmHA could be in facilitating the acquisition

of human capital. One means of implementing

this would be to expand the training component

that is attached to existing FmHA loan activities. A hallmark of early FmHA operations was

a substantial farm management/advisory role

for loan officers. An increased emphasis on this

type of operation would entail a significant expansion of the number of personnel in FmHA,

greater coordination with public advisory services such as the extension service, or increased

use of private farm management firms. A second option would be to develop a loan program

to finance human capital acquisition. Such loans

could be used by the farmer to acquire training

directly or to purchase the services of farm financial managers.

Technologies that might be especially appropriate for this loan category are those that lack

congruence. Investments in human capital to

learn how to use these technologies could be

used by FmHA farmers to improve the management of their farms. In addition, these farmers

might be capitalized by selling their expertise

to other farms. Finally, these training investments would facilitate the transition out of agriculture for those who decide to leave the sector.

IMPLICATIONS FOR TECHNOLOGY ADOPTION AND

STRUCTURAL CHANGE IN AGRICULTURE

The discussion in the preceding sections has likely depend on the relationships between three

indicated that the financing consequences of important factors: 1) the financing characternew technologies in agricultural production will istics of the new technologies, 2) the credit-
158 Ž Technology, Public PoIicy, and the Changing Structure of American Agriculture

worthiness of individual borrowers, and 3) the

changing forces in financial markets that affect

the cost and availability of financial capital. To

review these factors briefly, the financing characteristics suggest that most of the new technologies should largely be financed with shortand intermediate-term loans that are part of the

normal financing procedures for agricultural

businesses. That is, the basic criteria of structuring loans to match loan maturities with anticipated payoff periods and to provide adequate

loan security should not change in any fundamental way, although the risks associated with

obsolescence and collateral values will need

careful consideration. However, the technical

characteristics of the technologies, together

with the factors constituting the creditworthiness of individual borrowers, suggest that much

greater emphasis in credit evaluations will be

placed on the management capacity of the agricultural production units, on the ability of farm

operators to demonstrate appropriate technical competence in using the new technologies,

and on building human capital, where appropriate. In some cases—particularly for FmHA

borrowers—significant investments in human

capital, with related financing requirements,

may accompany the adoption of new technologies. This is consistent with the more conservative responses by lenders to the agricultural

stress conditions of the early 1980s. In turn, the

lending institutions themselves must have sufficient technical knowledge and expertise to evaluate these management and credit factors, along

with the other sources of business and financial risks in agriculture. Finally, some forms of

new technology involving large investments and

having long-run uncertain returns will likely

rely more on equity capital for financing.

The changing regulatory and competitive

forces in financial markets, including the preference for greater privatization of some credit institutions, means that the cost of borrowing for

agricultural producers will likely remain higher

and more volatile than in pre-1980 times and

will follow market interest rates much more

closely. Similarly, the continued geographic

liberalization of banking and the emergence of

more complex financial systems mean that the

functions of marketing financial services, loan

servicing, and credit decisions will become

more distinct, with an increasing proportion of

credit control and loan authority occurring

subregionally and with regional money centers

that are located away from the rural areas. This

will continue to fragment and dichotomize the

farm credit market so that commercial-scale

agricultural borrowers are treated as part of a

financial institution’s commercial lending activities (although separate personnel for agricultural and commercial loans should still be prevalent) and so that smaller, part-time farmers are

treated as part of consumer lending programs.

The competitive pressures on financial institutions and the risks involved will bring more

emphasis on analyzing the profitability of various banking functions, including loan performance at the department level and individual customer level. Innovative lenders will strive more

vigorously to differentiate their loan products

and financial services, especially for more profitable borrowers, and will more precisely tailor financing programs to the specific needs of

creditworthy borrowers. In turn, however, these

agricultural borrowers must be highly skilled

in the technical aspects of agricultural production and marketing as well as in financial accounting, management, and risk analysis as they

compete for credit services.

In general, most forms of new technology in

agricultural production should meet the tests

of both economic and financial feasibility, although the structural characteristics of the

adopting farm units will continue to evolve in

response to managerial, economic, and market

factors. The structural consequences of these

factors are severalfold:

a continuing push toward larger sizes of

commercial-scale farm businesses, with

greater skills in all aspects of business management;

continuing evolution in the methods of entry into agriculture by young or new farmers, with greater emphasis on management

skills and resource control, and less emphasis on land ownership;

the continuing development of a marketing systems approach toward financing

agriculture, with more sophisticated skills
Ch. 7—impacts of Agricultural Finance and Credit ● 159

in marketing analysis by farmers and higher

degrees of coordination with commodity

and resource markets;

● more formal management of financial leverage and credit by farmers, with greater

diversity of funding sources by farmers and

better developed markets for obtaining outside equity capital;

● further development in financial leasing

and greater stability in leasing arrangements for real estate and non-real estate assets; and

● more complex business arrangements in

production agriculture that accommodate

various ways to package effectively debt

and equity financing, leasing, management,

accounting, and legal services for the farm

business of the future.

Given the above consequences, FmHA clientele face severe challenges. The farmers served

by FmHA have, with some notable exceptions,

been drawn from the lower end of the economic

spectrum. Given their resource endowments

and the nature of many of the technologies that

are emerging, these farmers are not the most

likely adopters of new technologies, given the

current institutional setting.

FmHA should consider a significant shift in

how it serves this clientele. Historically, FmHA

played an important role in human capital formation in agriculture. FmHA loan officers were

actively involved in the management of their

clients’ farms, particularly the management of

farm finances. Given the increasingly important role played by debt capital in agricultural

finance and the volatility of agricultural markets, sound financial management of the farm

business was never more important than it is

today. FmHA might provide more farm financial management services.

At a more ambitious level, FmHA might consider the development of a special class of loans

devoted to human capital formation. Loans used

by farmers to acquire the skills necessary to take

advantage of the emerging technologies that require major human capital development could

have two beneficial effects: First, skills would

be learned that would improve the management

of these smaller farms. Given that many of these

farms are at a competitive disadvantage in terms

of the amount of resources they control, the

management of their resources becomes of paramount importance. Second, the skills acquired

by these farmers would have wide applicability in the farm and nonfarm sectors. It is possible that these skills could be sold to other farmers

as a source of off-farm employment or could

be used in the broader economy if the individual decided to leave the farm or seek off-farm

employment. In short, human capital investments would be expected to increase the longterm economic viability of loan recipients,

whether they remain in farming or make the

transition to the nonfarm economy.

CHAPTER 7 REFERENCES

Barry, P. J., Impacts of Financial Stress and Regulatory Forces on Financial Markets for Agriculture:

Current Directions and Policy Issues, Food and

Agriculture Committee, National Planning Association, Washington, DC, 1984.

Farm Credit Administration, Risk and the Structure

of Agriculture, Washington, DC, 1980.

Lee, J., Gabriel, S., and Boehlje, M., “public Policy

Toward Agricultural Credit,” Future Sources of

Loanable Funds for Agricultural Banks, Federal

Reserve Bank of Kansas City, 1980.

Lins, D. A., and Barry, P. J., “Availability of Financial Capital as a Factor of Structural Changes in

the U.S. Farm Production Sector, ” Farm Structure, U.S. Senate, Committee on Agriculture, Nutrition, and Forestry, Washington, DC, 1980.

Lins, D. A., and Barry, P. J., “Agency Status for the

Farm Credit System, ” American Journal of Agricultural Economics, vol. 66, 1984,

Lowenberg-DeBoer, J., and Boehlje, M., “Evaluation

of State Legislative Programs To Assist Beginning

Farmers, ” Agricultural Finance Review, vol. 43,

1983, p. 9-20,

U.S. Department of Agriculture, Economic Research Service, A Time to Choose, Washington,

DC, 1980,
Chapter 8

Emerging TechnoIogies,

Public Policy, and Various

Size Crop Farms
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ize Economies nd Comparative c?vantage n rop Production n

Various reas of the ited ates*** **w$ * * # * * * s . * . . . . * ****..... 0 3

Corn * ● ● ● ● ● ● ● ● ● ● ● ’ ● ● ● , ● ● ● ● ● ● ● . . ● ● * ● ● ● * ● ● ● * ● ● ● * ● ● * . ● ● ● ● . * ● 164

Soybeans ..** *.** ?*. .*** *9, *.** ****, ..o. .*, ****, oc*, *a,*,@*****, M4

Wheat ***. .*** .,** ..** ***. ,*. *.*. *.*. *.. **** *.**, **use ,,** ** 166

Rice ... *.**   ** . .***   ..**  ****, .**e. *a u

* **, ******e,,******* ?$7

cotton ● *.*. ..** ***q   q** * *q**   ** e, O*, .*, O*,.* ● ******,8  168

summary and Collch.lsioris ● ** ** ***. . * * * * * * s * . * . * . * . . * * * . * *.,*.,.,* 169

Ecortotic Impacts of Emerging Technologies and Selected Pol%ies for

Various Size Crop Farms ● ****Q*********.*,.* ● * * * * * * * * * ******.*, 171

Crop Farms Arialyzed ● 4*4* **. .*** ub .0*,*****..*,,”,,*,***,**,**** 171

Farm l?o~cy$ Tax Policy, ~nd Technology Scenatio$. . . . . . . ., ., . . . ., . . 175

Summary and Conclusions *.*. *+*. *.** *,. *.. ..*. ***+ *b****..***,,, 182

Financial Stress and New Entrants Scenario .,. , , , , . . . . . . ., , , . . . , . . . 183

Summary and Conclusions ●   ** *. ***$ .*** .***, ,.*, *.** *.. ... ,.e* 185

Chapter #1 References ● . * .***,..,. ● .#***. ..**   ** *. *.....9,******* 185

T%bl~S

Table No. Page

8-1. Homogeneous Production Areas for the Varims Commodities . . . . . . 164

8-2. Summary of the Measures of Internal Economies of Size in the

Selected Corn, Soybean, and Wheat Producing Aeas, 1983 . . . . . . 165

8-3. Summary of the Measures of 1nterregional Comparative Advantage

Among Selected Corn, Soybean, and Wheat Producing Areas, 1983. . 166

8-4. Summary of the Measures of Internal Economies of Size in Selected

Rice and Cotton Producing Areas, 1979 and 1982 . . . . . . . . . . . . . . . . , 168

8-5. Summary of the Measures of Interregional Comparative Advantage

Among Selected Rice and Cotton Producing Areas, 1979 and 1982. , 168

8-6. Economies of Size and Structural Elasticities for Selected Commodities and Areas ● *.** .**. *,** **. .*** *. .., *,** u*, **, *****em***, 170

8-7. Financial characteristics of Three Representative Corn-soybean

Farms in East Central lllinois 9.. **u* *.. *** ”***, ... ,.*+...., 172

8-8. Financial Characteristics of Three Representative Irrigated Corn

Farms in South Central Nebraska ● * * . . . * * *, *, * . . . . . * . *, *.*.*,.** 172

8-9. Financial Characteristics of Three Representative Wheat Farms in

the Southern Plains ● **v **.4,*.**....*...,**.*,****,***,,***,*, 173

8-10. Financial Characteristics for Three Representative General Crops

Farms in the Delta of Mississippi ● 174

8-11. Financial Characteristics of Three Representative Cotton Farms in

the Texas Southern High Plains ● *,**..,,*** . ● . , * ● . ● ● ● . ● ● . ● ● ● 175

Figures

Figure No. Page

8-1. Firm Enterprise Data System Production Areas * ***.****,.*,.****,,* 165
Chapter 8

Emerging Technologiesr Public Policy,

and Various Size Crop Farms

The size, and therefore the survival, of farms

is affected by several factors. Clearly, there are

economies of size in many of the crop areas covered by farm policy, These economies motivate

further concentration of resources. In addition,

present farm policy, more than any other policy tool, makes major impacts on farm size and

survival. Although very large farms can survive

without these farm programs, moderate farms

are very dependent on them for their survival.

Given these realities, an important question

arises: What combination of policy and technology advances will encourage each size farm

to grow, or at least maintain itself? To answer

this question, this chapter and the next will

present findings of an analysis of selected regions in the United States that represent significant agricultural production regions for dairy,

corn, cotton, soybeans, rice, and wheat. The

analysis is presented in two parts. First, the results of an analysis of size economies is presented for each commodity. This is followed by

the findings from an analysis of the economic

impacts of emerging technologies and selected

public policies for crop farms of various sizes.

SIZE ECONOMIES AND COMPARATIVE ADVANTAGE IN

CROP PRODUCTION IN VARIOUS AREAS OF THE UNITED STATES

A major question asked throughout this study

is what is the impact of resource concentration

in U.S. agriculture. Very little is known about

the process of resource concentration. Willard

Cochrane states that:

. . . we are almost totally ignorant regarding the

shape of the long-run planning or cost curve

at very large volumes of output. Thus, we don’t

know whether we are working with one economic force that is so powerful that there is

little or no possibility of controlling it with public policies or whether the force has largely expended itself and with some intelligent policy

action we could slow the process of resource

concentration to a walk (Cochrane, 1983).

The intent of this section is to provide policymakers with an overview of variation in cost

of production by enterprise size and geographic

location for corn, soybeans, wheat, rice, and cotton. This information should improve understanding of the process of resource concentration in American field crops.1

I Information presented in this section is based on the OTA paper “Size Economies and Comparative Advantage in the ProducThe concept of economies of size is defined

as the relationship between enterprise size

(measured in acres) in combination with other

productive services, and the rate of output of

the enterprise. Three enterprise size categories

are used: very large, large, and moderate. If enterprises are arranged in increasing order by

planted acreage, very large enterprises are those

enterprises in the 90th percentile. Large enterprises comprise the 70th and 80th percentiles,

and moderate enterprises comprise the 40th

through the 60th percentiles. The evidence for

size economies is based on a weighted average

of measures outlined in appendix D. These

measures include production cost, use of harvesting machinery, and the Herfindahl indices.

In addition, a structural elasticity measure of

internal size economies is applied to the percentage increase in enterprise size in acres relative to a l-percent decrease in production costs.

The geographic locations of the selected protion of Corn, Soybeans, Wheat, Rice, and Cotton in Various Areas

of the United States, ” prepared by Stephen C. Cooke, 1985.

163
164 ● Technology, Public Policy, and the Changing Structure of American Agriculture

ducing areas for the five field crops are identified in table 8-1 and shown in figure 8-1. These

production areas have been designated by the

U.S. Department of Agriculture (USDA) on the

basis of soil types and/or levels of rainfall.

In corn production, very large enterprises of

about 1,000 acres have an n-percent cost advantage over 250-acre, medium-size enterprises

(table 8-2). The change in relative production

and resource concentration between 1978 and

1982 is positive for very large corn enterprises

and is increasing at a rate of about 10 percent

a year. The structural elasticity measure is — 15.

This indicates that each 15-percent increase in

enterprise acreage (between the 250- and 1,000-

acre range) results in a l-percent decrease in

corn production costs. There is strong evidence

to argue for the existence of increasing internal economies of size in corn production across

the selected producing areas.

Table 8-1 .- Homogeneous Production Areas for

the Various Commodities

Abbreviations and

Description FEDSa designation

Corn:

East Central Illinois . . . . . . . . . . . . . . . . IL 300

Central Indiana. . . . . . . . . . . . . . . . . . . . . IN 101

North Central Iowa . . . . . . . . . . . . . . . . . 1A 201

South Central Nebraska . . . . . . . . . . . . . NE 400

Soybeans:

East Central Illinois . . . . . . . . . . . . . . . . IL 300

North Central lowa . . . . . . . . . . . . . . . . . 1A 201

Mississippi Delta. . . . . . . . . . . . . . . . . . . MS 100

Western Ohio . . . . . . . . . . . . . . . . . . . . . . OH 101

Wheat:

Western Kansas. . . . . . . . . . . . . . . . . . . . KS 100

Northeast Montana . . . . . . . . . . . . . . . . . MT 200

Central North Dakota . . . . . . . . . . . . . . . ND 200

Eastern Washington . . . . . . . . . . . . . . . . WA 400

Wee:

North Central California. . . . . . . . . . . . . CA 400

Texas Upper Gulf Coast . . . . . . . . . . . . . TX 100

Mississippi and Arkansas Delta . . . . . . DLT 100 & 300

Northeast Arkansas. . . . . . . . . . . . . . . . . AR 200

Cotton:

Northern Alabama . . . . . . . . . . . . . . . . . . AL 600

South Central California. . . . . . . . . . . . . CA 500

Mississippi Delta. . . . . . . . . . . . . . . . . . . MS 100

Texas High Plains . . . . . . . . . . . . . . . . . . TX 200

aFirm Enterprise Data System.

SOURCE: Office of Technology Assessment

In general, the sources of internal size economies among the selected corn-producing areas

are as follows. Corn yields are from 3 to 10 percent higher on very large enterprises than on

medium-size enterprises. Very large corn enterprises spend less on fertilizer, fuel, lubrication,

repairs, and custom harvesting than do mediumsize enterprises. Finally, very large corn enterprises consistently use some combination of

fewer and/or more efficient machines and tractors that go over the field fewer times.

Of the areas studied, Iowa has a comparative

advantage in corn production (table 8-3), regardless of whether the return to land is assumed

to be 10 or 5 percent of its 1982 value. Iowa also

has the smallest average corn enterprise size,

the lowest level of resource concentration, and

the lowest percentage of change in resource concentration of the selected corn-producing areas

studied. The trends in relative yield and land

values indicate that the comparative advantage

in corn production in Iowa will continue in the

future, other things being equal. There are also

size economies that can be exploited. Therefore,

the data suggest that corn producers in Iowa

are not operating at the least-cost production

point. The quality of initial resource endowment

in Iowa provides producers with the ability to

remain highly competitive in corn production

without fully exploiting size economies.

The comparative disadvantage for Nebraska

increases from 8 to 29 percent relative to Iowa,

associated with a 10- and 5-percent return to

land, respectively. This 21-percent difference

results in Nebraska becoming a very marginal

corn-producing area in the presence of lower

interest rates. This is because Nebraska’s comparative advantage is largely associated with

inexpensive land relative to that of other areas.

A lower rate of return to land effectively discounts the source of comparative advantage in

corn production in this area.

Soybeans

In soybean production, very large enterprises

of about 800 acres have a l-percent cost advantage over 300-acre,” medium-size enterprises

(table 8-2). The change in relative production
Ch. 8—Emerging Technologies, Public Policy, and Various Size Crop Farms . 165

Figure 8-1.— Firm Enterprise Data System Production Areas

PACIFIC

(lo)

SOURCE: US. Department of Agriculture,

Table 8=2.—Summary of the Measures of Internal Economies of Size in Selected

Corn, Soybean, and Wheat Producing Areas, 1983

Commodity, State, Economies Structural Enterprise size (acres) Enterprise cost ($/unit)

and area of size elasticity (0

/0) Medium Very large Medium Very large

Corn:

IL 300 . . . . . . . . . . . . . . . . . . . . . Very large – 7 246 1,113 $119 $100

IN 101 . . . . . . . . . . . . . . . . . . . . . Very large –22 271 903 105 100

1A 201 . . . . . . . . . . . . . . . . . . . . . Very large –22 170 576 105 100

NE 400b . . . . . . . . . . . . . . . . . . .Very large –12 266 1,715 113 100

Overall . . . . . . . . . . . . . . . . . . Increasing –15 234 1,003 111 100

Soybeans:

IL 300 . . . . . . . . . . . . . . . . . . . . . None –44 270 684 102 100

1A 201 . . . . . . . . . . . . . . . . . . . . . Very large –14 210 707 108 100

MS 100 . . . . . . . . . . . . . . . . ... .None 45 795 1,262 99 100

OH 101 . . . . . . . . . . . . . . . . . ...None 8 244 897 86 100

Overall . . . . . . . . . . . . . . . . ..Constant –179 302 790 101 100

Wheat

KS 1OO . . . . . . . . . . . . . . . . . . ..Very large - 8 774 3,909 118 100

MT 200 . . . . . . . . . . . . . . . . . . ..Very large –12 421 577 110 100

ND 200 . . . . . . . . . . . . . . . . . ...None –38 338 1,283 103 100

WA 400 . . . . . . . . . .. ... ... ...None 6 753 2,388 85 100

Overall . . . . . . . . . . . . . . . . ..Constant –36 647 2,661 103 100

~he percentage change in enterprise slzein acres feral-percent change introduction costs relafive tothe medium and very large enterprise size range,

blrrigated.

SOURCE: Office of Technology Assessment.
166 ● Technology, Public Policy, and the Changing Structure of American Agriculture

Table 8-3.–Summary of the Measures of

Interregional Comparative Advantage

Among Selected Corn, Soybean, and

Wheat Producing Areas, 1983

Commodity, Comparative Comparative Enterprise

State, and Economies advantage Aa

advantage Bb

size

area of size (%) (%) (acres)

Corn:

IL 300 Very large 114 116 663

IN 101 . . . Very large 110 114 586

1A 201 ., Very large 100 100 335

NE 400C ... Very large 108 129 1,095

Total Increasing 109 113 610

Soybeans:

IL 300 . . . . . None 103 102 464

1A 201 . . . . . Very large 100 100 362

MS 100 None 124 138 1,126

OH 101, . . . None 105 109 570

Total Constant 105 106 521

Wheat:

KS 100. .., Very large 100 100 2,152

MT 200 ., Very large 103 112 1,352

ND 200 . . . None 109 133 893

WA 400 . . . . None 100 104 1,613

Total . . . Constant 102 109 1,647

aASSUrneS a lo-percent rate of return on 19e2 land values.

bASSumeS a S.percent rate of return on 19e2 lamJ values

Clrrigated.

SOURCE: Off Ice of Technology Assessment.

and resource concentration is positive for very

large enterprises and is increasing at a rate of

about 13 percent per year. The structural elasticity is – 179, indicating that a 179-percent increase in (or nearly tripling from 300 to 500

acres) an average soybean enterprise acreage

results in a l-percent decrease in production

cost. There is evidence to argue for the existence

of constant internal economies of size in soybean production across the selected producing

areas.

In general, internal size economies do not exist (except for Iowa) among the selected soybean

areas because soybean yields are only from 1

to 6 percent higher on very large enterprises relative to medium-size enterprises. Very large enterprises have higher expenditures on fertilizer,

herbicides, and insecticides in proportion to

yield. And very large enterprises use relatively

more efficient machines and tractors, particularly in land preparation, relative to mediumsize enterprises.

Iowa also has a comparative advantage in soybean production among the selected producing

areas (table 8-3), regardless of whether the return to land is assumed to be 5 or 10 percent

of its 1982 value. This area has the smallest average soybean enterprise size, the lowest level of

resource concentration, and one of the lowest

percentages of change in resource concentration of the selected producing areas. The trends

in relative yield and land prices indicate that

the comparative advantage of Iowa in soybean

production will decrease in the future, particularly in relation to Illinois. Iowa is unique among

the soybean areas studied in that it has substantial internal economies of size, particularly for

custom harvesting of very large enterprises, that

can be exploited. The data suggest that soybean

producers in Iowa are not operating at the least

cost of production. The quality of the initial resource endowment in Iowa provides producers

with the ability to remain highly competitive

in soybean production without having to exploit

size economies fully.

In Mississippi the comparative disadvantage

in soybean production increases from 24 to 38

percent relative to Iowa, associated with a 10-

and 5-percent return to land, respectively. This

14-percent difference results in Mississippi becoming an even more marginal soybean-producing area as interest rates decrease. This is because Mississippi’s “comparative advantage”

is associated with less expensive land. A lower

rate of return to land effectively discounts the

source of comparative advantage in soybean

production in this area.

Whea t

In wheat production, very large enterprises

of about 2,600 acres have a 3-percent cost advantage over 640-acre, medium-size enterprises.

The change in relative production and resource

concentration is positive for very large enterprises and increases at a rate of about 5 percent

per year. The structural elasticity is – 36, which

indicates that each 36-percent increase in an

average wheat enterprise acreage (between the

640- and 2,600-acre range) results in a l-percent

decrease in production cost. There is evidence ,

to argue for the existence of constant internal

economies of size in wheat production across

the selected producing areas overall.
Ch. 8—Emerging Technologies, Public Policy, and Various Size Crop Farms ● 167

In general, the absence of internal size economies in wheat production is the result of statistical averaging. Only North Dakota exhibits

constant returns to scale in wheat production.

Kansas and Montana both have increasing returns to scale, while Washington has a decreasing return to scale. In Kansas, economies associated with custom harvesting are an important

source of size economies for very large wheat

enterprises. In Montana, size economies for

very large wheat enterprises are the result of

the combination of slightly higher yield and the

economies associated with custom harvesting.

In North Dakota, the lack of internal economies

of size for very large wheat enterprises is the

result of slightly higher yields offset by higher

harvesting costs relative to those of mediumsize enterprises. In Washington, size economies

do not exist for very large wheat enterprises relative to that of medium enterprises because of

the substantial diseconomies related to yield and

the slightly higher capital ownership and harvesting costs.

Kansas has a comparative advantage in wheat

production among the selected areas, maintaining this advantage regardless of whether the return to land is assumed to be 5 or 10 percent

of its 1982 value. This area has the largest average enterprise size, the lowest resource concentration, and one of the lowest percentages of

change in resource concentration of the selected

wheat-producing areas studied. The trends in

relative wheat yield and land prices indicate that

the comparative advantage of this area may decrease slightly in the future. Kansas has substantial size economies that can be exploited. The

data suggest that wheat producers in Kansas

are not operating at the least cost of production.

The quality of the initial resource endowment

in Kansas provides producers with the ability

to remain highly competitive without having to

exploit size economies fully.

In North Dakota the comparative disadvantage in wheat production increases from 9 to

35 percent relative to Kansas, associated with

a 10- and 5-percent return to land, respectively.

This 24-percent difference results in North Dakota becoming a marginal wheat-producing

area as interest rates decrease. This is because

North Dakota’s comparative advantage is associated with less expensive land. A lower rate

of return effective y discounts the source of comparative advantage in wheat production in this

area.

Rice

In rice production, very large enterprises of

about 2,400 acres have a 4-percent cost disadvantage over 600-acre, medium-size enterprises

(table 8-4). The change in relative production

and resource concentration is positive for very

large enterprises and increases at a rate of 1 percent per year. The structural elasticity is 27, indicating that each 27-percent decrease in the

average acreage for a rice enterprise (between

600 and 2,400 acres) results in a l-percent decrease in production cost. There is evidence to

argue for the existence of decreasing internal

economies of size in rice production across the

selected producing areas.

In general, the presence of internal diseconomies of size among the selected rice-producing

areas is a result of the following: Rice yields are

from about O to 10 percent lower on very large

enterprises than that of medium-size enterprises.

Rice yield diseconomies are related to the ability to maintain a uniform distribution of flood

irrigation water on the field. The uniform distribution of water is affected by the extent to

which the fields have been leveled and the water

levels maintained. The data indicate that the extent of leveling is approximately 100 percent

across enterprise size among the rice-producing

areas. Therefore, yield diseconomies necessarily relate to the timeliness of water, fertilizer,

herbicide, insecticide, and fungicide application. Rice producers on medium enterprises are

better able to manage these applications than

those on very large enterprises. Finally, in Texas

there are diseconomies associated with purchased canal water used for irrigation.

California has a comparative advantage in rice

production among the selected rice-producing

areas (table 8-5), maintaining this advantage

regardless of whether the return to land is assumed to be 5 or 10 percent of its 1982 value.

California has the largest average rice enterprise
168 ● Technology, Public Policy, and the Changing Structure of American Agriculture

Table 8-4.—Summary of the Measures of Internal Economies of Size in Selected

Rice and Cotton Producing Areas, 1979 and 1982

Commodity, State, Economies Structural

and area of size elasticity (0

/0)—

Rice:

CA 400b . . . . . . . . . . . . . . . . . . . None 40

TX 1001b. . . . . . . . . . . . . . . . . . . None 33

DLT 100 & 300b . . . . . . . . . . . . . None 14

AR 200b . . . . . . . . . . . . . . . . . . . None 30

Overall . . . . . . . . . . . . . . . . . . Decreasing 27

Cotton:

AL 600 . . . . . . . . . . . . . . . . . . . .VL –14

CA 500b . . . . . . . . . . . . . . . . . . .None 64

MS 100 . . . . . . . . . . . . . . . . . . . . None Infinite

TX 200b 

. . . . . . . . . . . . . . . . . . . . None 118

TX 200. . . . . . . . . ... .., ... ...VL – 9

Overall . . . . . . . . . . . . . . . . ..Constant – 7 7

Enterprise size (acres) Enterprise cost ($/unit)

Medium Very large Medium Very large

850 3,575 $ 9 7 $100

691 2,068 97 100

509 1,904 92 100

377 1,619 96 100

625 2,397 96 100

568 1,842 108 100

614 2,833 98 100

754 2,868 100 100

436 1,707 99 100

972 5,920 117 100

653 3,040 102 100

aThe percentage change in enterprise size in acres fora l-percent change in production costs relative to the medium and very lar9e enterprise size ran9e.

‘Irrigated.

SOURCE: Office of Technology Assessment.

Table 8-5.-Summary of the Measures of

lnterregional Comparative Advantage Among

Selected Rice and Cotton Producing Areas,

1979 and 1982

Commodity, Comparative Comparative Enterprise

State, and Economies advantage Aa

advantage Bb

size

area of size (%) (%) (acres)

Wee:

GA 400C None 100 100 1,071

TX 100 1

C Non e 124 134 802

DLT 100

&300c

None 114 120 694

AR 200C None 108 114 45

Total Decreasing 100 115 776

Cotton:

AL 600.,... VL 123 125 1,225

CA 500C . . . . None 109 107 2,088

MS 100 None 100 101 1,787

TX 200 C ,... None 101 103 1,224

TX 200..,,, V L 104 100 3,283

Total Constant 106 105 2,020

aASSUrneSalo.perCeflI rateof returnon 19S21and values(1978values for rice)

bA5sumesa5.percent rate of returnon 19S21and values (197e values fordceh

Clrrigated.

SOURCE: Office of Technology Assessment

size and the second highest level of production

or resource concentration, after Mississippi, of

the selected rice-producing areas. Production

and resources are becoming less concentrated

in California, at a rate of — 2 percent per year.

The trends in relative yield and land prices indicate that comparative advantage in this area

will remain unchanged. Internal size economies

have been fully exploited in this area. The data

suggest that the minimum average cost point

in rice production is reached at the medium enterprise size. The data also suggest that the support price for rice is such that the average revenue curve is substantially above the minimum

point on the average cost curve. In this case

firms can achieve a higher profit by extending

output beyond the minimum point, even though

they experience higher average total costs than

smaller firms operating at a low point on the

average cost curve.

In Texas the comparative disadvantage in rice

production increases from 24 to 34 percent relative to that of California, associated with a 10-

to 5-percent return to land, respectively. This

lo-percent difference results in Texas becoming an even more marginal rice-producing area

as interest rates decrease, because Texas’ comparative advantage is associated with less expensive land. A lower rate of return effectively

discounts the source of comparative advantage

in rice production in this area.

Cotton

In cotton production very large enterprises

of about 3,000 acres have a 2-percent cost advantage over 650-acre, medium-size enterprises.

The change in relative production and resource

concentration is positive and increases at a rate

of 10 percent per year for very large cotton enterprises. The structural elasticity is –77, which
Ch. 8—Emerging Technologies, Public Policy, and Various Size Crop Farms • 169

indicates that each 77-percent increase in the

average acreage for a cotton enterprise (between

650 and 3,000 acres) results in a l-percent decrease in production cost. There is evidence to

argue for the existence of constant internal economies of size in cotton production across the

selected producing areas.

In general, the absence of internal size economies in cotton production is the result again

of statistical averaging. Only three of the five

selected cotton-producing areas have constant

returns to scale. These areas include California, Mississippi, and Texas (irrigated). On the

other hand, Alabama and Texas (dryland) both

have increasing returns to scale in cotton production. Size economies exist for Alabama’s

very large cotton enterprises because these enterprises incur lower machinery and tractorrelated expenses for a given field operation and

still manage to obtain a slightly higher yield. In

California, size diseconomies are primarily related to the diseconomies of purchased irrigation water for cotton production. In Mississippi

the lack of size economies in cotton production

for very large enterprises relates to similar preharvest and ownership costs without significantly higher yields. In Texas (irrigation), the

lack of size economies relates to the combination of size diseconomies in harvesting and cultivation, along with slightly higher yields enjoyed by the medium enterprise in this area. In

Texas (dryland), size economies for very large

enterprises relate to the substantial preharvest

and ownership cost advantage associated with

lower machinery and tractor expenses for a

given field operation without substantial loss

in yield.

In terms of comparative advantage, there is

a three-way tie for comparative advantage in

the production of cotton among the selected

areas. These three areas are Mississippi and

Texas (both irrigated and dryland). These three

areas maintain their comparative advantage

(within 2 to 3 percentage points), regardless of

whether returns to land are assumed to be 5 or

10 percent of its 1982 value. These three areas

have the lowest resource concentration and an

average enterprise size of between l, ZOO to 3,300

acres. The percentage of change in resource

concentration in these areas ranges from 2 to

20 percent per year. The trend in relative yield

and land prices indicates that Mississippi will

have a comparative advantage over Texas in cotton production. On the other hand, Mississippi

loses comparative advantage in cotton production to Texas as interest rates decrease. Even

though land prices are higher in Mississippi,

a lower rate of return on land works to the advantage of Texas because land there is a larger percentage of total cost. Also, in Texas more of the

cotton is planted in strip rows to conserve soil

moisture, and fewer herbicides or insecticides

are used than is the case in Mississippi. The data

also suggest that cotton producers in Texas

(dryland) are not operating at the least cost of

production. The quality of the initial resource

endowment in Texas (dryland) provides producers with the ability to remain competitive without having to exploit size economies fully. As

a result, Mississippi and Texas have nearly equal

comparative advantages in cotton production.

In Alabama the comparative disadvantage in

cotton production increases from 23 to 25 percent relative to that of Mississippi and Texas,

associated with a 10- and 5-percent return to

land, respectively. A decrease in the interest rate

does not substantially alter the marginal position of Alabama in cotton production. The comparative disadvantage in cotton production in

Alabama is due to low relative yields, combined

with high fertilizer, lime, insecticide, and harvesting costs. Aside from more fully exploiting

size economies, Alabama has few options for

increasing its comparative advantage in cotton

production in the future.

Summary and Conclusions

The data suggest the following conclusion regarding internal economies of size for the selected commodities (table 8-6): The evidence for

increasing returns to scale for corn and decreasing returns to scale for rice, given the configurations for the enterprises for these commodities in 1983 and 1979, respectively, are the

strongest. Each of the selected corn areas has

substantial internal economies as enterprise size

increases from medium to very large. On the
170 . Technology, Public Policy, and the Changing Structure of American Agriculture

Table 8-6.—Economies of Size and Structural Elasticities for Selected Commodities and Areas

Internal economies of size

Commodity Size economies Structural elasticity Increasing Constant Decreasing

Corn Increasing –15 IL, IN, 1A, NE

Wheat Constant –26 KS, MT ND WA

Cotton Constant –77 AL, TX C A

a

, MS, TXa

Soybeans Constant – 179 1A IL, MS OH

Rice Decreasing 27 C A

a

, TXa, DLTa, ARa

alrrlgated.

SOURCE: Of fIce of Technology Assessment.

other hand, each of the selected rice areas has

internal diseconomies as enterprise size increases from medium to very large.

The strongest case for constant returns to

scale can be made for cotton. Three of the five

selected cotton-producing areas have constant

size economies, given the configuration of cotton enterprises in 1982. Only Alabama and

Texas (dryland) have increasing economies of

size. Alabama is a marginal cotton-producing

area, however, as indicated by the data on comparative advantage.

Texas (dryland) is one of the most competitive cotton-producing areas studied. The average enterprise size in Texas is the largest of the

selected areas, while the percentage change in

resource concentration between 1978 and 1982

was the lowest. This seems to indicate that producers of dryland cotton in Texas are willing

to forego the potential gain associated with expanding enterprise size to avoid the additional

exposure to risk and uncertainty. The capital

investment in land and machinery required as

cotton enterprises in this area expand from

1,000 to 6,000 acres substantially increases indebtedness, which threatens survivability of the

firm. In particular, the uncertainty associated

with cotton yields in this area of Texas make

indebtedness unattractive.

The arguments for constant returns to scale

in the cases of wheat and soybeans are somewhat more ambiguous. Selected areas having

increasing, constant, and decreasing returns to

scale are present for both of these commodities.

In the case of wheat, Kansas has substantial size

economies as enterprises increase from 800 to

4,000 acres. This area has the largest average

wheat enterprise size of the selected areas and

the lowest change in resource concentration between 1978 and 1982. This seems to indicate

that producers of wheat in Kansas are willing

to forego the potential gains in order to avoid

additional exposure to risk and uncertainty associated with expanding enterprise size. A similar case can be made for wheat production in

Montana. In the case of soybeans, enterprises

in Iowa have size economies as they increase

from 200 to 700 acres: This area has the lowest

average enterprise size and the lowest percentage of change in resource concentration between 1978 and 1982 of the selected soybean

areas.

Therefore, size economies for corn and size

diseconomies for rice are constant and broadly

based results. Constant size economies for soybeans, wheat, and cotton exist in general, but

important exceptions exist for increasing (and

decreasing) economies of scale in each of these

commodities. Two of the important exceptions

of increasing returns are Texas dryland cotton

and Kansas wheat. The data suggest in both of

these areas for their respective commodities that

producers are willing to forego increasing return to scale to avoid additional risk and uncertainty associated with expanding enterprise

size, which potentially could also threaten the

survival of the firm.

It should be noted that the analysis focused

on technical efficiencies in determining economies of size. Pecuniary economies (e.g., discounts on input supplies or services purchased

in volume) that are important for very large

enterprises were not taken into account. If they

had been considered, pecuniary economies

would have provided more economies of size

for the very large operations. For wheat, soy-
Ch. 8—Emerging Technologies, Public Policy, and Various Size Crop Farms ● 171

beans, and cotton this could have changed the crops wiIl probably continue for some time.

above overall analysis from constant to increas- Powerful forces at work in the farm economy

ing economies of size. will lead to fewer and larger farms.

This information strongly suggests that resource concentration for most American field

ECONOMIC IMPACTS OF EMERGING TECHNOLOGIES AND

SELECTED POLICIES FOR VARIOUS SIZE CROP FARMS

The analysis of size economies did not take

into account various policies and their impacts

on resource concentration. This section will

present the findings of just such an analysis of

four selected regions in the United States that

represent significant agricultural production in

the commodities considered in farm policy:

corn, cotton, soybeans, rice, and wheat. Within

each production region, representative moderate, large, and very large commercial farms were

identified and analyzed.2 It was assumed for the

analysis that the technology development and

adoption conditions in existence would be those

of the most likely environment, outlined in chapter 2.

Two techniques were used to analyze the effects of selected policy provisions and technology on farms within each region. Information

was obtained on resource characteristics, acreage devoted to specific crops, and historic projected yields of crops eligible for farm program

provisions. These data were used to develop resource characteristics of the three different farm

sizes. Then a simulation model was used to analyze the economic viability and growth potential of each representative farm for selected policy and technology advance scenarios (appendix

E contains a detailed discussion of the model).

The following presents the representative

farms and major findings for the production

areas analyzed. Obviously, more areas could

have been analyzed, but neither time nor the

resources allocated to this study would permit

their inclusion. It is expected that the results

%mall and part-time farms were not included because these

farm operators in general depend on off-farm employment for

their primary source of income.

will apply in broad principle to the major production region of which each area is a part. It

is important to remember that the results of this

analysis are mainly illustrative. Thus the relative results for the several farm sizes and for

the several alternative policy and technology

scenarios are probably more important than any

specific numbers generated by the analysis.

Crop Farms Analyzed

Corn-Soybean Farms in the Corn Bolt

The North Central Region of the United States

produces approximately 50 percent of the U.S.

total production of corn and soybeans. Representative farms for this region are three farms

from the corn-soybean cash grain area of east

central Illinois and three farms from the irrigated row crop area of south central Nebraskans

The representative farm situations developed

and used in this analysis were constructed from

two basic data sources: 1) national cost-of-production surveys by USDA in 1978 and 1983, and

2) farm record data collected and analyzed by

the Universities of Illinois and Nebraska. The

size of the representative farms and the acreages of owned and rented cropland were developed from the size distributions in the USDA

cost-of-production surveys. The very large farms

approximate the largest 10 percent of farms in

the surveys; the large farms, the 70th to 90th

percentiles; and the moderate farms, the 40th

to 70th percentiles.

3These representative farms were developed and analyzed in

the OTA paper “Economic Impacts of Selected Farm Policies,

Income Tax Provisions, and Production Technology on the Economic Viability of Corn-Soybean Farms in East Central Illinois

and Irrigated Row Crop Farms in South Central Nebraska, ” prepared by W,B. Sundquist, 1985.
172 ● Technology, Public Policy, and the Changing Structure of American Agriculture

Financial status, as measured by net worth,

debt load (both intermediate-term and longterm), and leverage ratio, differs dramatically

from farmer to farmer. Data from the most recent USDA Agricultural Finance Survey were

used to depict the beginning financial characteristics for the six representative farms (tables

8-7 and 8-8).

All of the representative farms are wellmechanized production units ranging from 640

to 2,085 acres of cropland, and all farms include

a combination of owned and rented land. Of

the six representative farms, only the very large

units in each area employ full-time workers. The

other farms operate with a combination of family and part-time workers. The Illinois farms

have all of their cropland devoted to cash crop

production of corn and soybeans. The Nebraska

farms are cash crop operations that combine

both gravity and sprinkler technologies to irrigate corn and a small acreage of soybeans. In

addition, these farms produce a substantial acreage of grain sorghum under a nonirrigated (dryTable 8-7.—Financial Characteristics of Three

Representative Corn-Soybean Farms in

East Central Illinois

Farm size (acres)

Characteristics Moderate Large Very Iarge

land) regime. Production on this dryland acreage tends to be somewhat riskier than that for

the irrigated component of the farming operations, but irrigated farming still has some yearto-year yield variability, owing to weather. Although a number of these irrigated corn farms

also produce some wheat and/or corn silage,

those enterprises have not been included in the

analysis.

The crop mix for the Nebraska farms is identical for all three farm sizes: irrigated corn (58.3

percent of cropland acres), irrigated soybeans

(6 percent), dryland sorghum (35.7 percent). On

the Illinois farms, the proportion of corn to soybeans varies only slightly for the three representative farms, with corn planted on 52 to 55

percent of the cropland acreage and soybeans

on the balance.

For the Illinois farms, all cropland has the

same per-acre value, whereas the price of cropland on the Nebraska farms reflects the differentials for four categories of land: 1) gravity irTable 8-8.—Financiai Characteristics of Three

Representative irrigated Corn Farms in

South Central Nebraska’

Farm size (acres)

Characteristics Moderate Large Very Iarge

Cropland acres ... ... ., 640 982 1,630

Acres owned ... , . . 260 429 458

Acres leased ., 380 553 1,172

Value of owned real estate

($1,000 b

) . . . . . . . . . . . . . . 900.5 1,480.6 1,538.4

Value of machinery ($1,000) 92. 2 104.8 129.0

Long-term debt ($1,000) . . . 126.1 557.4 579.4

Intermediate-term debt ($1,000) . . . 55.3 62.9 83.8

Initial net worth ($l,000C

) 855.4 1,027.6 1,106.4

Leverage ratio (fraction ) 0.21 0.61 0.60

Long-term debt/asset ratio

(fraction) , ...........,,,, , 0.14 0.38 0.38

Intermediate-term debt/asset ratio

(fraction) ... .., ....,,.. . 0.60 0.60 0.65

Equity ratio (fraction) . . . . . . ., 0.82 0.62 0.63

Off-farm income ($1,000) ., 8.2 7.4 7,6

Minimum family living expenses

($1,000). . . . . . . . . . . . . . . . . . . . . 18.0 20.0 24.0

Maximum family living expenses

($1,000). . . . . . . . . . . . . . . . . . . . . 36.0 40.0 48.0

Marginal propensity to consume

(fraction) , ...,...,.....,, . 0.20 0.20 0.20

% family size of four persons was assumed for the purposes of estimating family labor supply and determining appropriate income tax rates.

blncludes land and building.

cMay include assets other than land, buildings, and rnachinew.

SOURCE: Office of Technology Assessment.

Cropland acres ., . . . . . 672 920 2,085

Acres owned . . 302 530 1,042

Acres leased . . . . . . . . . . 370 390 1,043

Value of owned real estate

($l,000 b

) . . . . . . 477.7 838.4 1,648.3

Value of machinery ($1,000) ., 102.7 112.1 183.9

Long-term debt ($1,000) . . . . . . . . 123.2 102.0 291.1

Intermediate-term debt ($1,000) , ... 40.1 53.7 98.0

Initial net worth ($l,000C

) ... 448,3 839.0 1,463.1

Leverage ratio (fraction) . . . . . . ., 0.39 0.20 0.27

Long-term debt/asset ratio

(fraction) . . . . . . . . . . . 0.26 0.12 0.18

Intermediate-term debt/asset ratio

(fraction) ., .., . ... . . . 0.39 0.48 0.53

Equity ratio (fraction) ., ... ., . . 0.72 0.84 0.79

Off-farm income ($1,000). . . . . . . . 8.2 8.2 9.7

Minimum family living expenses

($1,000) . . . . . . . . . . . . . . . . . . . . 18.0 18.0 24.0

Maximum family living expenses

($1,000) ..............,,,,,. 36. 0 36.0 48.0

Marginal propensity to consume

(fraction) .., ., ...... , ..,... 0.20 0.20 0.20

aA f~ity size of four persons was assumed for the purposes of estimating family labor supply and determining appropriate income tax rates,

blnclude land and building.

cMay include assets other than land, buildings, and machinery.

SOURCE: Office of Technology Assessment.
Ch. 8—Emerging Technologies, Public Policy, and Various Size Crop Farms Ž 173

rigated, 2) sprinkler irrigated, 3) dryland with

irrigation potential, and 4) dryland without irrigation potential. Each of the three Nebraska

farms, however, has the same proportions of

gravity irrigation, sprinkler irrigation, and

dryland acres.

Wheat Farms in the Southern Plains

Approximately 65 percent of U.S. wheat production is produced in the Great Plains. For the

analysis of representative wheat farms, farms

were selected from the Southern Plains region.

They are representative of wheat farms in western Kansas, eastern Colorado, and the Oklahoma and Texas Panhandle.4

The three farms selected for the analysis are

a typical moderate farm in the region (1,280

acres), a large farm (1,900 acres), and a very large

farm (3,200 acres). The initial financial characteristics for the three representative farms are

summarized in table 8-9. The proportion of cropland owned by each farm was obtained from

the most recent Agricultural Finance Survey,

summarized for wheat farmers in western Kansas, eastern Colorado, the Oklahoma Panhandle, and the Northern High Plains of Texas.

Average long- and intermediate-term debt-toasset ratios from the Agricultural Finance Survey were used to estimate initial values for longand intermediate-term debts. All three wheat

farms had about the same beginning equity

levels (75 percent) (table 8-9). Minimum family

living expenses were based on values obtained

from a Texas A&M survey that asked for the

minimum annual cash expenditure for family

living. The Agricultural Finance Survey was

used to obtain values of off-farm income for the

three representative farm operators.

A typical cropping pattern in the Southern

Plains is to irrigate 50 percent of all cropland

and to raise wheat on one-half of this irrigated

land. Grain sorghum is typically raised on the

tThese representative farms were developed and analyzed in

the OTA paper “Economic Impacts of Selected Policies and Technology on the Economic Viability of Three Representative Wheat

Farms in the High Plains, ” prepared by James W. Richardson,

1985.

Table 8-9.—Financiai Characteristics of Three

Representative Wheat Farms in the Southern Plains

Farm size (acres)

Characteristics Moderate Large Very large

Cropland acres. ... ., ., . .....,1,280

Acres owned, ... ., ., , . . 640

Acres leased, . . ., ., 640

Acres of pastureland owned . . 120

Value of owned cropland ($1,000) . . 296.0

Value of owned pastureland . . . . . . . 29.4

Value of machinery ($1,000), 241. 9

Value of off-farm investments

($1,000) . . . . . . 37.3

Beginning cash reserve ($1 ,000) ., ,.. 10.0

Long-term debt ($1,000), .“, ., ,., 60.2

Intermediate-term debt ($1,000). ., 83.2

Initial net worth ($1 ,000) ., ... 470.3

Equity ratio (fraction), ., ., . . . . 0.77

Leverage ratio (fraction) ., ., . . . . ., 0.31

Long-term debt/asset ratio

(fraction). , ,, ., ,, .,...,, ,. 0.19

Intermediate-term debt/asset ratio

(fraction). ,,. .,..,,.,,.,,,,, . 0.34

Off-farm income ($1,000) ., . 12.4

Minimum family living expenses

($1,000) . . . . . . . . . . . . . . . . . . . . 18.0

Maximum family living expenses

($1,000) . . . . . . . . . . . 40.0

Marginal propensity to consume

(fraction), . . . . . ., . . . . . . . . 0.25

1,920

840

1,080

220

388.5

53.9

352.2

49.0

12.0

86.3

126.5

642.3

0.75

0.33

0.20

0.36

9.8

20.0

50.0

0.25

3,200

1,400

1,800

360

647.5

88.2

477.2

53.5

20.0

143.5

171.3

970.7

0,75

0,33

0.20

0.36

9.0

23.0

50.0

0.25

SOURCE: Office of Technology Assessment

other half of the irrigated cropland. Wheat is

generally also raised on the portion of the cropland that is not irrigated. This cropping pattern

was assumed for all three farms.

Numerous crop share arrangements prevail

in the region for leased land. However, these

arrangements generally involve the producer

paying the landlord about 25 percent of the crop

and the landlord paying none of the production

and harvesting costs. This crop share arrangement was assumed for all leased cropland.

The Mississippi Delta is an excellent region

for analysis of general crop farms.5 Farms in

this area can produce a variety of crops not possible in other parts of the United States. The repBThese representative farms were developed and analyzed in

the OTA paper “Economic Effects of Selected Policies and Technology on the Economic Viability of General Crops Farms in the

Delta Region of Mississippi,” prepared by B.R. Eddleman, 1985.
174 • Technology, Public Policy, and the Changing Structure of American Agriculture

resentative farms in this region produce cotton,

rice, soybeans, and wheat (or other small grains).

The three representative farms developed for

this study are a moderate farm (1,443 acres), a

large farm (3,119 acres), and a very large farm

(6,184 acres). Table 8-10 provides a summary

of the financial and resource characteristics for

the three representative farms. The long-term

and intermediate-term debt-to-asset ratios for

the 1,443-acre farm and the 3,119-acre farm were

obtained from the Agricultural Finance Survey

and adjusted to reflect the equity levels as reported from a 1983 mail survey of farms in the

delta. These debt ratios are the average for partowner general crops farms in the Mississippi

Delta region that had debt on real estate in 1979.

Financial ratios for the largest farm were developed by extending the ratios on a per-acre

basis for a 3,457-acre farm, as reported in the

most recent Agricultural Finance Survey, and

Table 8-10.—Financial Characteristics of Three

Representative General Crops Farms

in the Delta of Mississippi

Farm size (acres)

Characteristics Moderate Large Very large

Cropland acres. . . .................1,443 3,119

Acres owned. ., ., 533 1,419

Acres leased. ,. . . . . . 910 1,700

Acreage of principal crops in 1983:

Cotton ... ., 395 1,088

Rice. ., . . ... . . . . . ., 305 574

Soybeans . . . . . . . . . . . . . . . 640 1,190

Wheat (or other small grains) 82 247

Value of owned cropland ($1,000) 799.5 2,128.5

Value of farm machinery ($1,000) . . . 378.9 786.7

Value of off-farm investments

($1,000) . . . . . . . . . . . . . . . . . . 129.1 210.3

Beginning cash reserve ($1 ,000) ., 31.9 71.1

Long-term debt (1,000), . . . . . 331.4 840.8

Intermediate-term debt ($1,000). 243.8 413.0

Net worth ($1,000) ., . . . . . . 748.6 1,921.5

Total equity to assets (fraction) ... 0.56 0.60

Long-term debt/asset ratio

(fraction). . . . . . . . 0.41 0.40

Intermediate-term debt/asset ratio

(fraction). .... ... ,, ,, . . . . . . ., 0.64 0.52

Off-farm income ($1 ,000) ... . . . . 18.3 18.2

Minimum family living expense

($1,000 ) . . . . . . . . . . . . .,,,.,,, 18.0 24.0

Maximum family living expense

($1,000 )... . . . . . . . . . . . . . . . . . . . . 27.0 36.0

Marginal propensity to consume

(fraction). . . . . . . . . . . . . . ., 0.25 0.25

SOURCE: Office of Technology Assessment.

6,184

3,064

3,120

2,250

871

2,539

180

4,596

1,209.8

358.7

141.6

1,640.8

574.7

4,047.5

0.64

0.36

0.48

36.0

30.0

45.0

0.25

were adjusted by the equity levels reported for

the largest farm-size group.

The mix of acreages planted in each crop

changed by farm size. In general, the acreage

planted in cotton and soybeans increased relative to the acreage planted in rice and wheat

as farm size increased. The moderate farm

planted 73 percent of tillable cropland in cotton and soybeans, while the large and the very

large farms planted 89 and 82 percent, respectively, of tillable cropland in cotton and soybeans. In the analysis, as the farm was allowed

to grow in size to the next largest farm size, the

proportion of cropland planted in each crop was

changed to reflect these differences in crop mix.

Cotton Farms in the Texas

Southern High Plains

Cotton is an important commodity in the

United States, and over one-half of the cotton

produced can be found in the Southern High

Plains of Texas. The three farms selected for

analysis are a typical moderate farm in the region (1,088 acres), a large farm (3,383 acres), and

a very large farm (5,570 acres).6 These size farms

accounted for 31 percent of the farms and 62

percent of the cotton lint produced in the Texas

Southern High Plains.

Table 8-11 provides a summary of the demographic and financial characteristics for the

three representative cotton farms used in the

present study. The long- and intermediate-term

debt-to-asset ratios for the moderate farm were

obtained from the Agricultural Finance Survey.

These debt ratios are the average for part-owner

cotton farmers in the Texas High Plains who

had debt on real estate in 1979. Financial ratios

reported by Smith (1982) for the two larger farms

were used because the Agricultural Finance Survey did not provide information for farms in

these categories.

A special survey of farmers identified average annual off-farm income and minimum fameThese representative farms were developed and analyzed in

the OTApaper’’Economic Impacts of Selected Policies and Technology on the Economic Viability of Three Representative Cotton Farms in the Texas Southern High Plains,” prepared by James

W. Richardson, 1985.
Ch. 8—Emerging Technologies, Public Policy, and Various Size Crop Farms ● 175

Table 8-11 .—Financial Characteristics of Three

Representative Cotton Farms in the

Texas Southern High Plains

Farm size (acres)

Characteristics Moderate Large Very large

Cropland acres 1,088 3,383 5,570

Acres owne d 381 1,048 3,453

Acres lease d 707 2,335 2,117

Value of owned real estate

($1 ,000 ) 222.4 611.7 2,015.4

Value of machinery ($1 ,000) 144.5 420.8 713.9

Value of off-farm investments

($1 ,000) 59.0 110.0 213.7

Beginning cash reserve ($1 ,000) 16.7 52.0 85.5

Long-term debt ($1 ,000) 61,1 120.9 4887

Intermediate-term debt ($1 ,000) 98.3 203.6 475,4

Initial net worth ($1,000c

) 275.0 854.8 2,032.3

Leverage ratio (fraction ) 0.62 0.72 0.67

Long-term debt/asset ratio

(fraction) 0.61 0.40 0 4 9

Intermediate-term debt/asset ratio

(fraction) 0.27 0.20 0.24

Equity ratio (fraction) 0.68 0.48 0.67

Off-farm income ($1 ,000) 16.0 0.0 0 0

Minimum family living expenses

($1,000) . . . 15.2 29.1 38.0

Maximum family living expenses

($1 ,000) 50.0 50.0 6 0 0

Marginal propensity to consume

(fraction) 0.25 0.25 0 2 5

SOURCE Office of Technology Assessment

ily living expenses by farm size (Smith, 1982).

Maximum annual family living expenses were

assumed to be $50,000 to $60,000, depending

on farm size. The model assumes the family will

use 25 cents of every additional dollar of disposable income, over and above the minimum

requirement, for family living. In no instance,

however, will family living expenses exceed the

maximum indicated in table 8-11.

Cotton production costs for the three farms

were estimated based on Smith’s (1982) study.

The two larger farms had a 13 percent lower

total cost of production, per pound of cotton

lint, than the moderate-size farm. The mix of

irrigated and nonirrigated cotton changed

across farm size. The moderate farm irrigated

32 percent of its available cotton acreage, while

the two larger farms irrigated only 23 percent.

In the simulation analysis, as the moderate farm

grew in size, its proportion of irrigated cropland

was decreased to 23 percent.

Farm Policy, Tax Policy, and

Technology Scenarios

The three representative farms for each production region were analyzed for the period

1983-92 under alternative policy scenarios.7 Six

farm policy scenarios (including a continuation

of the 1981 farm bill), an income tax provision

scenario, two financial stress scenarios, a technology option, and anew entrant scenario were

analyzed for each farm. All assumptions and

policy values associated with each scenario

were held constant across farm sizes to allow

direct comparison of their impacts on different size farms. Appendix E contains a summary

of the analysis for each farm size by region.

Current Policy

The current policy scenario involves continuing through 1992 the current income tax provisions and the price supports, income support,

and supply control programs of the 1981 farm

bill. In addition, it was assumed that annual

mean crop yields for the three representative

farms in each of the four production regions

will increase as new technologies are introduced and adopted by farmers in the most likely

technology environment. For this policy scenario it was assumed that the following farm

policies were in effect:

●

●

●

The Commodity Credit Corporation (CCC)

loan program is available to producers for

corn, cotton, rice, sorghum, soybeans, and

wheat.

A 3-year, indirect, farmer-owned reserve

(FOR) is available for feed grains and

wheat. 8

An acreage diversion/set-aside program is

in effect for 1983 to 1985, using the actual

acreage reduction levels and diversion payment rates specified for these years.

‘The current version of the Firm Level Income Tax and Farm

Policy Simulator (FLIPSIM V), developed by James W. Richardson and Clair J. Nixon, was used to simulate the three representative farms in each region.

*The 1977 farm bill established FOR as a 3-year extension of

the CCC loan after grain had been in the regular loan for 9 months.

Stocks remain in the farm operator’s control until the Secretary

of Agriculture authorizes release.
176 • Technology, Public Policy, and the Changing Structure of American Agriculture

●

●

●

A target price-deficiency payment program

is available for corn, cotton, rice, sorghum,

and wheat in all years.

The $50,000-payment limitation for deficiency and diversion payments is in effect

and is effective on the farm as specified.

Farms of all sizes are eligible to participate

in these farm program-provisions.

Values for loan rates, target prices, diversion

rates, and diversion payment rates for 1983 and

1984 are set at their actual values, expressed in

1982 dollars. Values for these variables for 1985

are set at their respective levels announced on

or before September 14, 1984, by Secretary of

Agriculture Block. Loan rates and target prices

for 1985 are held constant through 1992. No

acreage reduction program is assumed to be in

effect after 1985.

It was assumed that the following options for

depreciating machinery and calculating income

taxes are used for the current policy scenario:

●

●

●

●

●

●

●

●

Machinery, livestock, and buildings placed

in use prior to 1981 are depreciated using

the double declining balance method.

Machinery, livestock, and buildings placed

in use after 1980 are depreciated using the

accelerated cost recovery method.

The operator elects to claim first-year expensing for all depreciable items placed

into use after 1980.

The operator elects to take maximum investment tax credit (ITC) and thus reduces

the basis for all depreciable assets placed

into service after 1980.

The operator adjusts crop sales across tax

years to reduce current-year taxes.

The operator may use either the regular income tax computation or income averaging to calculate Federal income tax liabilities.

There is no maximum interest deduction

for calculating taxable income.

The actual self-employment tax rates and

maximum income levels subject to this tax

for 1983 and 1984 are used. Announced

values for these variables in 1985-86 are

used, and the 1986 values are held constant

through 1992.

● The operator elects to trade in old machinery on new replacements at the end of each

item’s economic life.

Results Expected

Since this policy includes price supports, income supports, and supply control programs

to maintain and stabilize prices and farm income at a reasonable level and reduce the price

and income risks, it is anticipated that all farms

under this program will have a higher probability of remaining solvent over the lo-year planning horizon, will have higher net farm incomes,

and will have stronger financial positions.

Results Obtained

●

●

●

●

Except for Texas cotton farms, all farms in

the other four regions had a 100-percent probability of remaining solvent over the lo-year

period. For Texas cotton farms, the probability y of survival ranged from 92 percent for the

moderate farms to 94 percent for very large

farms.

All farms in four of the five regions increased

their absolute net worth by the end of the

period, with very large farms increasing more

than the moderate farms. The two smaller

farms in Illinois experienced a loss in net

worth over the period, while the largest farm

experienced a 14.5-percent increase in real

net worth.

On average, all three farms were able to grow

by purchasing and leasing cropland. Moderate farms grew faster than the very large

farms. The moderate and large grain farms

grew at approximately the same rate.

Average annual net farm incomes for all

farms substantially benefited by the presence

of price and income supports in the current

policy. Removal of these program provisions

resulted in negative average annual net farm

incomes for farms in all regions except Illinois. (Illinois net farm incomes did not fall

below zero because a large portion of cropland was devoted to soybeans, which do not

receive a deficiency payment.)
Ch. 8—Emerging Technologies, Public Policy, and Various Size Crop Farms • 177

● Ratios of net farm income to total Government payments reveal that, across all regions,

the moderate farms were more dependent on

Government payments to maintain their incomes than were the very large farms.

Price Supports

The price supports program is designed to prevent prices from falling below a certain level

and to stabilize prices through the CCC nonrecourse loans at established loan rates to

farmers. Such loans, plus interest and storage

costs, can be repaid within 9 to 12 months when

the commodity is sold on the cash market. If

the market is not favorable for a farmer to sell

the commodity and repay his loan, CCC accepts

the commodity in full payment of the loan.

CCC releases its stock to the market when

prices are high and withdraws stocks from the

market when prices are low. Thus the program

also stabilizes prices.

Results Expected

● Since price supports stabilize prices and prevent prices from falling below the loan rate,

this program should increase farm income

and reduce the price risk for farmers.

● All farms should have a higher probability

of survival, greater net present value,9 and

higher net farm incomes than they would

have had without the program.

Results Obtained

● Price supports increased the probability of

survival for all three representative farms in

all regions.

● Net farm incomes for these farms also increased with the price supports program. In

‘The concept of present value is used to help measure the profit

potential of an investment decision. Simply put, a dollar today

is worth more than a dollar in the future because today’s dollar

can be invested and can accrue interest. Thus the present value

of a specified amount of money payable at a specified future date

is the amount of money that one would have to invest now in

order to have that future amount by that future date. In analyzing an investment over several periods, a positive present value

would indicate an economically attractive decision; a negative

present value would not.

all regions, the larger the farm, the greater

the increase in net farm income.

With increased farm incomes and reduced

price risk, all three farms in all regions experienced increases in real net worth.

Average ending farm sizes were not significantly different because of the price support

program.

Income Supports

Income supports are accomplished through

deficiency payments and the target price. Deficiency payments are paid to farmers to make

up the difference between a price determined

to achieve a politically acceptable income level

(target price) and the average market price. Deficiency payments are made on each farm’s base

acres and farm program yield. The farm program yield is based on each farm’s yield history.

Target prices were set initially to reflect an average cost of production.

Deficiency payments were initiated to raise

and stabilize farmer incomes to the level of the

nonfarm population while allowing farm prices

to be competitive in the export market. Total

annual Government payments (deficiency and

diversion) were limited to $50,000.

Results Expected

● The major impact of deficiency payments

should be to increase the income level of producers who participate in the farm program.

Since the payments are based on the quantity of eligible production, large-scale producers benefit more than small-scale producers,

up to the $50,000-payment limitation.

● Deficiency payments also reduce income risk

for producers, increase their ability to obtain

financing, and thus increase the probability

of all farms remaining solvent.

Results Obtained

● The deficiency payment program increased

the probability of survival more for moderate Texas cotton farms than for the very large

Texas farm. For farms of other regions, the
178 ● Technology, Public Policy, and the Changing Structure of American Agriculture

●

●

●

●

●

●

●

probability of survival was 100 percent, with

or without income supports.

Income supports increased net farm incomes

substantially for all farms, often moving net

farm incomes from negative to positive.

Income supports enhanced net farm incomes

of all farms more than the price support

program.

The presence of the $50,000-payment limitation causes the income support program to

benefit moderate farms relatively more than

very large farms. In contrast, the price support program results in a greater advantage

for large and very large farms.

With reduced income risk and greater farm

incomes under the income support program,

all farms improved real wealth, and average

after-tax net present value increased for all

farms.

Income supports increased the average ending farm size for all farms. Average ending

farm size increased at a faster rate for moderate farms than for very large farms.

Removal of the $50,000 limitation on deficiency payments benefited larger farms more

than smaller farms. Big winners of this program were big farms in Texas and Mississippi. In Texas, for example, when the $50,000-

payment limitation was removed, average

annual net farm income increased $3,600,

$50,000, and $104,000 for moderate, large,

and very large farms, respectively.

Increased farm income strengthened the financial positions of larger farms, increasing

their ability to obtain more financing. All

three representative farms, especially the very

large farms, had increased net worth at the

end of the lo-year period. For example, removal of the $50,000 limitation increased the

ending net worth of the moderate Texas cotton farm by $37,000, of the large Texas farm

by $441,000, and of the very large Texas farm

by $1,019,000.

supply Control Policy

(Acreage Reduction Program)

The objective of acreage reduction programs

is to reduce the quantity produced, and thus the

supply, of a given commodity. Acreage reduction consists of an acreage set-aside and/or acreage diversion that is generally voluntary. Acreage set-aside programs require participating

farmers to idle a percentage of their crop base

acres so that they are eligible for other program

benefits. Acreage diversion programs pay producers a given amount per acre to idle a percentage of their base acres. A farmer’s base acres

are determined by the production history of the

crop.

For this analysis the provisions of the current

policy were modified by adding a 15-percent

set-aside with a 5-percent diversion for corn,

cotton, rice, sorghum, and wheat in 1986-92.

Normal slippage10 (30 percent for corn and 70

percent for all other crops) and program participation rates were used to estimate the resulting real increase in mean prices for these crops

in 1986-92. All other provisions of the current

policy were used without change.

Results Expected

●

●

To the extent that acreage reduction programs reduce production, they reduce supply and stocks and increase prices domestically for those commodities. Higher prices

will result in higher total and net incomes for

all farm sizes. Farms that participate in diversion payments also benefit from the program

through increased cash receipts, up to the

$50,000 limit.

Slippage in the programs reduces the programs’ effectiveness, increases the farms’ net

present value, and increases farm size.

los]ippage is the difference between the percent of production

decrease and the percent of acreage reduced. These two percentages are different because farmers tend to set aside marginal lands

in Government programs or intensify the cultivation of remaining land ,
Ch. 8—Emerging Technologies, Public Policy, and Various Size Crop Farms Ž 179

●

●

●

Higher incomes lead to more disposable income for debt repayment and retained earnings for accelerating farm growth.

Farm operators’ average net present value

should increase.

Faster rates of growth should be experienced

by the farms because of increased cash accumulation, repayment capacity, and equity in

existing land assets.

Results Obtained

● Imposing a 20-percent acreage reduction program increased the average net present value

and ending net worth for all three farms in

all regions except for the large farm in Illinois.

● Imposing a 20-percent acreage reduction to

existing farm programs resulted in an increase of 20 to 300 percent in net farm income

for almost all farms.

● Average ending farm size for all three farm

sizes increased relative to the initial farm size.

● Imposing additional supply controls to existing farm programs does not substantially

change the rate of growth or ending farm size

of all farms. Moderate farms continued to

grow faster than larger farms.

● Eliminating slippage reduced the rate of

growth relative to that in the current policy

for all three farm sizes.

● The less slippage in an acreage reduction program, the smaller the increase in average net

present value for all three farm sizes.

No Farm Program

In the no-farm-program scenario, all farm programs outlined for the current policy were eliminated for all 10 years of the planning horizon.

In this essentially free market environment,

farm prices and income are very unstable because: 1) production varies, owing to weather

and biological factors; and 2) demand for farm

products changes. The inelastic nature of supply and demand for farm products makes farm

prices particularly unstable. The variability in

prices and incomes has both favorable and unfavorable aspects. From a favorable perspective,

the movement in prices reflects changes in supply and demand conditions and is a signal for

production regarding market needs. However,

when prices become highly unstable, the signals may be misinterpreted, and mistakes may

be made in production and marketing decisions.

The result frequently is misallocation of resources. In addition, variability in price and income increases the risk and uncertainty to the

farm business.

Results Expected

●

●

●

●

Average farm incomes will be less with no

loans or price supports because the floor on

prices received for these commodities has

been removed, allowing prices to fluctuate

freely.

Net present value will be lower and more unstable than with price and income supports.

Net worth of farms will decline because the

market value of cropland will be less, since

there are no benefits from the programs to

be capitalized into the land.

Farms will have less probability of survival

because of increased instability in prices for

crops. The impact will be more pronounced

for highly leveraged farms that cannot survive without price and/or income support and

for smaller farms that cannot survive with

high price risk.

Results Obtained

. Removing all farm programs reduced the

probability of survival for all three farm sizes

in cotton and wheat regions, relative to the

base policy. The probability of survival fell

more for the moderate farms in these regions

than for the very large farms. For example,

in cotton the moderate farm’s chance of remaining solvent for 10 years decreased from

92 to 42 percent. The chance for the solvency
180 Ž Technology, Public Policy, and the Changing Structure of American Agriculture

●

●

●

of very large farms decreased from 94 to 78

percent.

The probability y of having a positive after-tax

net present value declined significantly for

all farm sizes in all regions except for those

farms in the Mississippi Delta, For example,

in the Southern Plains the probability of a

positive net present value for the moderate

farm declined to about 10 percent. In most

cases the very large farms had a higher probability of positive net present value than did

the moderate farms. The probability of a positive net present value was 100 percent in the

Mississippi Delta without the farm program,

owing primarily to diversification of crop production and the reduced relative yield variability in the Delta compared with that of the

other regions.

Ending net worth declined for all three farm

sizes in all regions. In most regions the absolute decline in net worth was greater for the

large and very large farms than for the moderate farms. For example, the large and very

large Texas cotton farms experienced a decline of $743,000 and $1,100,800 in net worth,

respectively, from that of the current policy,

while the moderate farms’ net worth declined

$396,800. The ending net worth of the Mississippi Delta farms declined the least of all

regions because a significant portion of crop

acreage was devoted to soybeans.

In the absence of farm programs, all three

farm sizes continued to grow in all regions,

but at a much slower rate than under the current policy, For example, farms in the Southern Plains declined from the current policy

an average of about 20 percent in ending farm

size.

Target Farm Program Benefits

For the target farm program benefits scenario,

all farm program and income tax provisions of

the current policy were used except that large

farms were not eligible to participate in farm

program provisions. Farms producing more

than $300,000 worth of program commodities

(corn, cotton, rice, sorghum, soybeans, and rice)

valued at their localized loan rate were not permitted to participate directly in the program provisions (CCC loan, FOR, target price/deficiency

payments, and diversions/set-asides), Mean

prices and relative variability in prices were not

adjusted because it was assumed that a sufficient number of “small” farms participated in

the farm program for the price support actions

of the CCC loan and FOR to function normally.

Results Expected

●

●

●

Findings for moderate farms will be the same

as the findings for the current policy.

Large and very large farms exempted from

the programs will receive indirect benefits

from other farms participating in the programs,

Compared to the no-farm-program scenario,

the following should be observed for large and

very large farms:

—Net present value will be higher and more

stable.

—Net worth of these farms will be greater.

—Farms will have a greater probability of survival because of the increased stability in

prices.

—Farms will be larger because of increased

income and large repayment capacity.

Results Obtained

●

●

●

Moderate farms consistently producing less

than $300,000 in program crops exhibited the

same growth rates, net farm incomes, and

ending financial positions as they do under

the current policy,

Farms that grew beyond or were initially

larger than the $300,000 threshold level of

sales experienced lower average Government

payments, net farm incomes, average net

present values, and net worths than under

the current policy, owing to targeting program benefits.

The larger the farm, the greater the reduction in average ending acres from the current policy for farms in the Southern Plains,

Nebraska, and Illinois. Moderate grain farms

in these regions experienced no real change

in average ending farm size, because their

level of total sales was less than $300,000.
Ch. 8—Emerging Technologies, Public Policy, and Various Size Crop Farms Ž 181

● Growth rates for the very large farms in Texas

and the Delta were similar to those experienced under the no-farm-program option.

The moderate and large farms in the Delta

experienced reduced rates of growth relative

to that of the very large farms. A similar relationship was observed between the large and

very large cotton farms in Texas. The reason

for these different rates of growth is that the

very large farms in these regions depend less

on farm programs than smaller size farms do.

Reduced Income Tax Benefits and

Current Farm Program

The Federal income tax provisions in place

for the current policy were made more restrictive in the reduced income tax benefits and base

farm program scenario. All farm policy provisions of the current policy were left unchanged,

The more restrictive Federal income tax provisions included the following:

●

●

●

●

●

Machinery, livestock, and buildings were

depreciated using the straight-line cost recovery method.

First-year expensing provisions were eliminated for all depreciable items.

Maximum ITC provisions were eliminated.

The maximum annual interest expense that

could be used to reduce taxable income was

$15,600.

The operator was required to sell obsolete

machinery upon disposition rather than

trading it in on new replacements, thus

forcing recapture of excess depreciation

deductions.

Results Expected

● Making Federal income tax policies less

favorable tends to increase income tax payments by reducing tax deductions. Net cash

farm income is not affected directly in the

first 4 to 6 years. After that, interest income

usually becomes a factor, and higher tax payments in the first 4 years reduce cash available for interest income in later years.

● The farm operator will have lower tax deductions and tax credits when machinery is

replaced. The length of time machinery is

●

kept will not likely be shortened from the current policy because machinery was replaced

based on its normal economic life, not its

depreciation life.

Reducing tax deductions and tax credits will

mean greater annual income tax payments,

resulting in greater cash flow requirements

and reduced ending cash reserves. Net

present value will likely be reduced because

of lower retained earnings and the slower accumulation of wealth.

Results Obtained

Adoption of a more restrictive set of Federal

income tax provisions had little impact on

farm survival.

Increasing the Federal tax burden on farmers

reduced the average annual rate of growth

in farm size about the same for all sizes of

farms in each region. Average ending farm

size was about 8 percent less than that for

the current policy for large and very large

farms and about 4 percent less for moderate

farms.

The more restrictive income tax provisions

reduced the propensity to grow through purchasing cropland and increased the propensity to lease cropland for growth. For example, in the Mississippi Delta the growth rate

in owned cropland for the moderate farm was

reduced to 4 percent, and the growth rate in

leased cropland increased by 49 percent,

The changes in the tax provisions resulted

in reduced annual net farm incomes for all

sizes of farms in all regions. The reduction

in net farm income was greater for the very

large farm relative to the moderate farm because the very large farm had more depreciable items affected by changes in depreciation rules, ITC s, and capital gains treatment

of sales of used machinery.

Technology Scenarios

To determine the impact of technology on

structure, selected farm policy scenarios were

simulated, assuming increases in mean yields
182 . Technology, Public Policy, and the Changing Structure of American Agriculture

of crops only from the use of existing technologies. A comparison of these simulated results

with those of the previous farm policy scenarios,

which included increases in mean yields from

emerging technologies, indicates the impact of

new technology on structure. Three policy alternatives were analyzed under these conditions.

They were the base farm policy, which continues all provisions of the 1981 farm bill, the

elimination of income support provisions, and

the elimination of all farm program provisions.

Results Expected

●

●

The longer the technology is in use for each

farm, the greater should be the benefit to

wealth accumulation, net income, and rate

of growth in acres controlled.

The greater the increase in productivity, the

greater should be the increases in wealth,

net income, and rate of growth in acres controlled.

Results Obtained

●

●

●

●

●

●

●

Farm policies had more effect on the final

amount of acres controlled than did technology, across all sizes of farms in all regions.

Technology had a greater impact on the final

amount of acres controlled for the very large

farms in all regions (except Nebraska) than

for the moderate and large farms. Yieldenhancing benefits from emerging technologies increased average final farm size 1 to

2 percent in the Delta, Illinois, and Texas and

10 percent in the Southern Plains. The greatest increase in farm size occurred in the

Southern Plains, because these farms are

principally wheat producers. The greatest increases in yields were predicted by OTA to

occur for wheat.

Small increases in final farm size for the other

regions can be explained by the relatively

smaller increases in yields (based on the results of OTA workshops for corn, soybeans,

cotton, and rice).

●

●

●

●

Flows of new technology for all commodities in all regions were found to increase annual net farm incomes for each size of farm.

Net farm income was increased relatively

more for the very large farms than for the

moderate farms, across all farm policies

evaluated.

Summary and Conclusions

Farm programs have major impacts on rates

of growth in farm size, wealth, and incomes

of commercial farmers.

Most farm program benefits are capitalized

into land values and net worth. Very large

farms increase their net worth significantly

more than moderate farms under current

farm programs and account for a very large

share of the program payments.

Moderate farms depend more than very large

farms on farm programs to maintain their

incomes.

Income supports provide significantly greater benefits to moderate farms than to very

large farms. (In contrast, price supports provide more wealth and growth benefits to very

large farms.) Targeting of income supports

to moderate farms is an effective policy for

prolonging those farms’ survival.

Very large farms can survive without income

supports.

Adoption of a more restrictive set of Federal

income tax provisions had little impact on

farm survival.

Farm policies had more effect on the final

amount of acres controlled than did technology, across all sizes of farms in all regions.

However, in a relative context, technology

had a greater impact on the final amount of

acres controlled for the very large farm than

for the moderate and large farms.

Flow of new technology will increase annual

net farm income for all sizes of farms, How-
Ch. 8—Emerging Technologies, Public Policy, and Various Size Crop Farms ● 183

ever, net farm income increased more for the

very large farms than for the moderate farms.

Financial Stress and

New Entrants Scenarios

Financial Stress Scenario

The financial position of many farmers is currently under severe stress. The situation is serious and may not improve for some time. Policymakers are considering various solutions to this

problem. Two of the most discussed alternatives

are interest rate subsidy and debt restructuring.

An interest rate subsidy is a loan at belowmarket interest rates. For example, if the Government’s cost of money is 11 percent and the

Farmers Home Administration makes loans at

5 percent, there is a 6-percent direct interest rate

subsidy. To analyze the effects of such a credit

policy, the financial positions of the three representative farms in each of the five regions were

modified to depict highly leveraged farms. The

long-term debt-to-asset ratio for each farm was

increased to 55 percent, the intermediate-term

debt-to-asset ratios were set equal to 60 percent,

and annual interest rates on old loans were increased to their average values for 1980 to 1983.

The object of an interest rate subsidy is to reduce the cash expenses for interest costs, thus

increasing total net cash farm income. The total cash requirements are reduced, thereby benefiting all farms. The total saving is greater for

larger farms because of the total debt being

larger on these farms.

Debt restructuring refers to the rescheduling

of loan commitments. Debt maybe restructured

by rewriting short- or intermediate-term debt

to a long-term basis if the collateral justifies such

change. The amount paid per year is then reduced. Without sufficient additional long-term

collateral, debt restructuring is limited to rescheduling each class of loans—short-, intermediate-, and long-term—over a longer repayment period. Also, if the debt is on a fixed

interest rate basis and interest rates have declined, the debt might be rescheduled in part

to take advantage of lower interest rates and to

obtain a longer repayment period.

Restructuring debt has the same type of expected effects as interest rate subsidy; however,

the methods differ. Debt restructuring does not

reduce the annual interest payments in the initial period unless long-term interest rates are

less than intermediate-term interest rates. Annual principal payments are reduced, thus reducing cash flow needs of the farm operator.

Results Expected for Interest Rate

Subsidy and Restructuring Debt

●

●

●

Higher probability of survival.

Higher land values, net worth, and average

net present value.

An increase in the equity ratio because current debts are paid and longer-term debts are

reduced, allowing greater opportunity for the

farm to grow in size because of the increased

ability to leverage existing equity.

Results obtained From

Financial Stress Scenarios

●

●

●

Restructuring initial debt for highly leveraged

farms failed to increase appreciably the probability of survival for each size of farm in any

region except for moderate and large wheat

farms in the Southern Plains.

In all regions, the interest rate subsidy strategy substantially increased the average net

farm income more than did the restructuring of farms’ debts.

Both debt restructuring and interest rate subsidy policies resulted in increased growth in

farm size and real wealth (i.e., ending net

worth) on the very large farms in all regions.

In all regions but Texas, very large farms with

high debts are not as dependent on financial

bailout strategies for survival as moderate

farms are.
184 ● Technology, Public Policy, and the Changing Structure of American Agriculture

● Both alternatives increased growth in farm

size. Debt restructuring resulted in more

rapid rates of growth than did interest rate

subsidies.

Now Entrants Into Farming Scenario

All previous simulations of the effects from

the farm commodity policy alternatives were

based on representative farms operated by established farm producers. These simulations provide indications of the short-run effects of the

alternative farm commodity policy provisions

on economic survival and growth characteristics of established farm operations. They do not

provide information on the survivability and

economic viability of potentially new entrants

into farming. To gain some general notions of

the effects of selected farm commodity policies

on newly established farming operations, the

smallest farm in each region was simulated under the condition that the farm operator was

a new entrant.

In this scenario the entering farm operator

was allowed to have only minimum equity in

owned farmland (30 percent) and farm machinery (35 percent). All farm machinery was considered to have a new machinery cost, and

annual interest rates on long- and intermediateterm loans were equal to the 1980-83 averages.

The operator was not allowed to have any offfarm investments. Because the farm operator

was paying the full cost of all inputs (land, capital, machinery, and labor), these simulations

provide an indication of long-run survivability

and profitability of the representative farms.

Three policy alternatives were analyzed under

these conditions for the new entrant. They were

the base farm policy, which continues all provisions of the 1981 farm bill, the elimination of

the target price/deficiency payments provision

of the program (no income support provisions),

and the elimination of all farm program provisions.

Results Expected

● New entrants would be expected to face lower

probabilities of survival, slower rates of real

wealth accumulation, and slower rates of

growth in farm size than would current operators on the representative farms in each region under existing farm legislation. Because

both depreciation adjustments on machinery

and annual cash requirements for debt repayment on real estate and machinery loans are

based on new 1982 costs and current (1980-

83) interest rates, annual net farm incomes

will be lower for new entrants than for current operators, under existing policy.

● Elimination of income support provisions of

the 1981 farm bill will be expected to reduce

the probability of survival, rate of growth in

real net worth and farm size, and annual net

farm incomes of new entrants in each region.

The greatest impacts would be expected for

specialized crop farms producing commodities eligible for target prices and deficiency

payments. Elimination of all farm program

provisions would be expected to reduce further the rate of growth in real wealth and farm

size. Annual net farm incomes for new entrants would be expected to be even lower,

particularly on representative farms producing commodities eligible for set-asides and

paid diversion provisions.

Results Obtained

● New entrants exhibited considerably lower

probabilities of survival under the base farm

policy than did current operators for all specialized crop farms. The diversified crop

farms in Nebraska and the Mississippi Delta

exhibited relatively high probabilities of survival for new entrants.

● New entrants experienced much lower rates

of real wealth accumulation than did current

operators under current policy. In two of the

regions—High Plains wheat farm and Nebraska and Illinois crop farms–real net worth after 10 years was lower than initial net worth

on the farms, indicating that the new entrant

operator had to sell owned cropland to remain solvent. Net farm incomes were negative for all farms, with the High Plains wheat

farm experiencing the largest relative decline

in annual net income.
Ch. 8—Emerging Technologies, Public Policy, and Various Size Crop Farms ● 185

●

●

●

●

●

New entrant farm operators in the High

Plains wheat and Nebraska and Illinois crop

regions were unable to increase farm size over

the 10-year period under current farm policy,

The Texas cotton farm and the Mississippi

Delta crops farms experienced considerable

growth, 20 and 33 percent, respectively,

Eliminating the target price/deficiency payments provision of current legislation substantially decreased the probability of survival and ending net worth on all farms.

Only the Texas cotton farms exhibited any

appreciable growth in farm average (about

10 percent).

Under the policy alternative of no farm programs, none of the farms exhibited reasonable

potentials for remaining solvent over the 10

years, Farms in the Texas High Plains, Southern Plains, and Corn Belt had less than a 10-

percent probability of survival, Mississippi

Delta farms had only a 60-percent chance for

remaining solvent over the 10 years.

Under the current farm program only the Nebraska and Mississippi Delta crop farms had

sufficient returns for new farmers to enter

agriculture with a reasonable chance of remaining solvent and making a reasonable return on their investment,

Elimination of income support, price support,

and supply control provisions of current farm

CHAPTER 8

Cochrane, Willard W., “Farm Commodity Policy,

Technological Advance and Structural Change,”

paper prepared for the Policy, Technology, and

Structure of Agriculture Workshop, Office of

Technology Assessment, 1983.

Cooke, Stephen C., “Size Economies and Comparative Advantage in the production of Corn, Soybeans, Wheat, Rice, and Cotton in Various Areas

●

●

●

●

●

policy resulted in new entrant farmers in all

five regions facing little chance of surviving

and becoming an economically viable farming operation.

Other sources of income, economic assistance, or wealth accumulation will be required for these new entrants to survive economically in an open market farm policy

environment,

Summary and Conclusions

Restructuring of debt for highly leveraged

farms does not appreciably increase their

probability of survival.

Interest rate subsidy substantially increases

average net farm income more than debt restructuring. It is, therefore, a more effective

strategy for easing financial stress.

Very large farms with high debts do not depend on these financial programs for survival

as moderate farms do. Under these programs,

very 1arge farms will grow significantly in

farm size and real wealth.

New entrants into agriculture will not likely

survive even with current farm programs.

Other sources of income, economic assistance, or wealth accumulation will be required.

REFERENCES

of the United States, ” prepared for the Office of

Technology Assessment, Washington, DC, 1985.

Smith, E. G., “Economic Impact of Current and

Alternative Farm Programs on Farm Structure on

the Southern High Plains of Texas, ” unpublished

doctoral dissertation, Texas A&M University,

1982.
Chapter 9

Emerging Technologies,

Public Policy, and Various

Size Dairy Farms
Contents

Page

Size Economies and Comparative Advantage in Milk Production . ........190

Dairy Operations Analyzed . . . . . . . . . . . . . . . . . . . . ..................190

Technologies and Practices . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .........191

Costs and Returns . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .................192

Summary and Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ..........193

Economic Impacts of Emerging Technologies and Selected Policies

for Various Size Dairy Farms.... . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ...194

Farm Policy, Tax Policy, and Technology Scenarios . . . . . . . . ...........194

Summary and Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ..........202

Chapter preference . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .................202

Tables

Table No, Page

9-1. Total Producers and Size Distribution of Herds Selling Milk to Plants

Regulated by Federal Milk Marketing Orders, May 1983 . ............190

9-2. Representative Dairies Selected for Preparation of Whole Farm

Budgets, by Region and Herd Size . . . . . . . . . . . . . . . .. .. .. ... +.+ ...-.191

9-3. Very Large, Large, and Moderate Herd Sizes, by State.. . ............195

9-4. Year-to-Year Percentage Increase in Milk Production per Cow

for Three Technology Groups, by Herd Size, 1983-92................196

9-5. Milk Production per Cow for Most Likely Technology Scenario,

1982-92 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . + . . . . . . . . . . . . . ............196

9-6. Financial Characteristics Assumed for Eight Dairy Operations in

Four States . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .....................197

9-7. Comparison of Continuation of Present Policy (Base Scenario)

on Dairy Farms From Various Regions . . . . . . . . . . . . . . . . . . . . . .......198

9-8. Comparison of No Milk Price Support Program on Dairy Farms

From Various Regions. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ...........200

9-9. Comparison of Supply Control Program on Dairy Farms

From Various Regions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ....200

Figures

Figure No. Page

9-1. How the Dairying Picture Has Changed . ..........................189

9-2. Total Cost per Hundred weight of Milk by Herd Size and State, 1982 ..192

9-3. Long-Term Rate of Return to Investment by Herd Size and State. ... ..l93
—.

Chapter 9

Emerging Technologies, Public Policy,

and Various Size Dairy Farms

One of the most controversial areas involving agricultural technology, policy, and structural change in the United States is in the dairy

sector. In 1983 the large amount of surplus milk

production cost taxpayers approximately $2.6

billion. Emerging technologies promise to dramatically increase milk production per cow by

year 2000, from a national average of 12,000

pounds in 1982 to over 24,000 pounds in 2000.

As discussed in chapter 3 a reduction of approximately 30 percent in cow numbers will be

needed by year 2000 to counteract the effect of

the emerging technologies. and the static demand for milk and milk products. Thus the impact of these technologies and policy on the

dairy industry will be dramatic. This chapter

attempts to provide the foundation for understanding these changes and for analyzing various policies to cope with the dynamic interaction between policy and technological advance,

One of the changes will be a major regional

shift in milk production: the Midwest and the

Northeast will lose their comparative advantage

to the Southwest. During the 1970s milk production increased 41 percent in the Southwest

region of the United States, while U.S. milk production increased only 11 percent (figure 9-l).

Much of the increased production came from

dairies with more than 500 cows, with herds

of 1,500 to 2,000 cows being common. Although

303,710 farms in the United States reported having milk cows in 1983, all the milk that sold that

year could have been produced by less than

5,000 well-managed dairies with 1,500 cows

each.

This chapter examines important economic

factors that will affect the trend to fewer and

larger dairies and the regional shift in milk production. The first part of the chapter estimates

size economies and comparative advantage of

milk production for moderate, large, and very

large dairy operations in five major U.S. dairy

areas. These comparisons provide an indication of the most competitive farm sizes and regions. They are based on returns on investment

after all costs are paid, including the regional

SOURCE: U.S Department of Agriculture

189
190 ● Technology, Public Policy, and the Changing Structure of American Agriculture

replacement of depreciable assets needed to

maintain the long-term productive capacity and

viability of the farm.

The second part of the chapter develops a beginning financial situation for eight dairy operations in three regions. The ability of these operations to remain solvent and increase net worth

over a lo-year planning horizon is simulated

under conditions of risk and under alternative

policy and technology scenarios. These results

provide an indication of how alternative policies affect individual dairy farm operations.1

‘The representative farms were developed and analyzed in the

OTA paper “Economic, Policy, and Technology Factors Affecting Herd Size and Regional Location of U.S. Milk Production, ”

prepared by Boyd M. Buxton.

SIZE ECONOMIES AND COMPARATIVE ADVANTAGE

IN MILK PRODUCTION

Dairy Operations Analyzed

Herd size, technologies employed, and practices used in milk production vary considerably throughout the United States. In May 1983

the average herd size for 120,655 producers selling milk to plants regulated by Federal milk marketing orders was 63 cows per farm (table 9-1).

However, the average herd size in each State

varied from 49 cows in Pennsylvania, to 532

cows in Florida.

The variation in herd size within each State

was even more dramatic, Although the average

herd size in Florida was 532 cows, the average

herd size for the largest 10 percent of the herds

in that State was 1,861 cows (table 9-1). Similarly, the average herd size for the largest 10

percent of herds regionally was about 1,700

cows in the Southwest, but only 125 cows in

the Great Lake States region. Generally, dairy

herds are much larger in the Southwest, Southeast, and Northwest than in the Great Lake

States and the Northeast.

From the herd size information in table 9-I,

22 dairies were selected to represent existing

herd sizes in five major dairy areas (table 9-2).

Table 9-1.—Total Producers and Size Distribution of Herds Selling Milk to Plants Regulated by

Federal Milk Marketing Orders, May 1983a

Average herd size (milk cows)

Number of

Region (State) total producers All farms Largest 10°/0 70 to 89°/0 40 to 69°/0 Smallest 40°/0

Great Lake States:

Minnesota . . . . . . . . . . . . . . . . . . . . . . 9,968 53 116 74 49 30

Wisconsin . . . . . . . . . . . . . . . . . . . . . . 24,400 54 133 68 52 28

Northeast:

Pennsylvania. . . . . . . . . . . . . . . . . . . . 12,928 49 127 66 44 25

New York. . . . . . . . . . . . . . . . . . . . . . . 13,374 59 162 81 53 27

Southeast:

Georgia . . . . . . . . . . . . . . . . . . . . . . . . 962 127 343 181 117 54

Florida . . . . . . . . . . . . . . . . . . . . . . . . . 352 532 1,861 931 355 133

Southwest:

New Mexico . . . . . . . . . . . . . . . . . . . . 176 333 1,832 433 169 32

Arizona . . . . . . . . . . . . . . . . . . . . . . . . 160 510 1,733 714 433 160

California. . . . . . . . . . . . . . . . . . . . . . . 13 400 1,640 580 253 110

Northwest:

Idaho . . . . . . . . . . . . . . . . . . . . . . . . . . 574 135 607 169 90 34

Washington. . . . . . . . . . . . . . . . . . . . . 1,647 127 418 171 108 46

United States . . . . . . . . . . . . . . . . . . . . . 120,655 63 202 82 54 26

~he 120,655 farms accounted forebout 69 percent of all milk produced in May 1963, but excluded most farms in California and other States where there is no Faderal milk order,

SOURCE: Boyd M. Buxton and John P. Rourke, “Size Distribution of Dairy Farms Marketing Milk Under Federal Milk Orders, ”

Agriculture, Economic Research Service, April 1964,

unpublished report, U.S. Department of
Ch. 9—Emerging Technologies, Public Policy, and Various Size Dairy Farms Ž 191

Table 9-2.—Representative Dairies Selected for Preparation of Whole Farm Budgets, by Region and Herd Size

Herd Housing Silage Total

size Cropland facilities Sun Feed storage labor

Region/State (cows) (acres) (type) shades produced (type) (W/e)b

Great Lake States:

Minnesota . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 52

Minnesota . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 125

Northeast:

Pennsylvania . . . . . . . . . . . . . . . . . . . . . . . . . . . 52

Pennsylvania. . . . . . . . . . . . . . . . . . . . . . . . . . . 125

Pennsylvania. . . . . . . . . . . . . . . . . . . . . . . . . . . 200

New York . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 52

New York . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 200

New York . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 600

Southeast:

Georgia . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 200

Georgia . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 350

Florida . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 350

Florida . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 600

Florida . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1,436

Southwest:

New Mexico . . . . . . . . . . . . . . . . . . . . . . . . . . . 900

Arizona . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 359

Arizona . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 834

Arizona . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1,436

California . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 550

California . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1,436

Northwest:

Washington . . . . . . . . . . . . . . . . . . . . . . . . . . . . 140

Idaho . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 200

Idaho . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 550

188

449

156

375

600

156

600

1,800

400

700

0

0

0

0

0

0

0

0

0

51

400

0

Stanchion

Free stall

Stanchion

Free stall

Free stall

Stanchion

Free stall

Free stall

Free stall

Free stall

Open field

Open field

Open field

Corral

Corral

Corral

Corral

Corral

Corral

Free stall

Corral

Corral

No

No

No

No

No

No

No

No

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

No

No

—No.

Most

Most

Forage

Forage

Forage

Forage

Forage

Forage

Forage

Forage

None

None

None

None

None

None

None

None

None

Silage

Most

None

Upright

Upright

Trench

Trench

Trench

Trench

Trench

Trench

Trench

Trench

NA

NA

NA

NA

NA

NA

NA

NA

NA

Trench

Trench

NA

2.03

3.30

2.2

3.8

5.54

2.21

5.54

14.36

4.5

7.84

7

11

18

13

7

12

16

9

16

2.96

5.0

10.5

Housing types are”

● Stanchion — A conventional barn with locking stanchions in which cows are milked and fed

● Free sta// — A covered barn with individual stalls in which cows freely enter and exit.

● Open field — A field where cows are kept that is large enough to maintain plant cover.

● Corral — A drylot open pen where cows are kept an~ fed at ; fenceline feeder.

bLaborin worker equivalents of 2,500 hours annually.

NA—not applicable

SOURCEO fficeofTechnology Assessment

The 20()-cow Pennsylvania and 600-cow New

York dairies exceed the average size of the

largest 10 percent of dairies in those States.

However, such larger sized dairies exist in these

States and will become more prevalent in the

near future.

Technologies and Practices

The technologies and practices assumed for

each of the 22 dairy operations were based on

discussions with dairy producers, university

and Government employees, and equipment

representatives. The objective of these discussions was to describe efficiently organized dairy

operations that use proven technologies and

practices for each specified herd size. Therefore, the dairy operations in this analysis are

not the average of what now exists, but rather

approximate modern sizes and types of operations.

The 52-cow dairies in Minnesota, Pennsylvania, and NewYork use the conventional stanchion barns for housing and milkingcows(table9-2). For larger herds in the Great Lake States,

the Northeast, Washington, and Georgia, freestall housing and milking parlors are assumed.

Cows are kept in open corrals throughout the

Southwest and on larger Idaho dairies. Sun

shades in the corrals are assumed for farms in

New Mexico, Arizona, and California (Southwest), but not in Idaho. Cows are milked twice

a day in milking parlors and fed at fenceline

bunks from a feed wagon or truck.

Open fields with sun shades are assumed for

farms in Florida. One-half acre per cow is provialed, allowing fields to remain grass-covered
192 . Technology, Public Policy, and the Changing Structure of American Agriculture

to minimize mud problems. Cows are milked

twice a day in a milking parlor. After leaving

the milking parlor, they are fed concentrates

in a feed barn before being released back to the

field. Roughage is fed loose in the open fields.

The source of feed follows the common practice existing in the various States. For New Mexico, Arizona, California, and Florida, most feed

is purchased from off the dairy operation. The

same is assumed for the 550-cow Idaho dairy.

Dairy operations in Pennsylvania, New York,

and Georgia purchase most of the concentrates

but produce most of the forage used by their

dairy herds. All feed is assumed to be produced

on farm for the Minnesota and the 200-cow

Idaho dairies.

Costs and Returns

The specialized dairy operations considered

in this chapter receive all revenue from the dairy

enterprise. Milk sales are the single largest

source of revenue, but the sale of cull cows, bull

calves, and replacement heifers are also important. The prices received for milk delivered to

plants vary from one State to another, largely

reflecting the classified pricing policy of Federal and State milk orders and the proportion

of milk used as fluid in the various States.

Costs are divided into operating and ownership costs. Operating costs include purchased

feed and a wide range of expenses such as farm

repairs, hired and operator labor, utilities and

fuel for the dairy herd, and veterinary and breeding fees. Annual ownership costs include depreciation, property taxes, and insurance

premiums.

Based on the above, the estimated costs per

cow for assets required on the 22 dairies are illustrated in figure 9-2. These costs reflect an

amount sufficient to replace wornout assets

when needed and thus reflect an amount needed

to maintain the long-term viability of the

operation.

In calculating relative rate of return for these

dairies, milk prices received by dairy operators

were assumed to be those prices received in

Figure 9.2.–Total Cost per Hundredweight of Milk by Herd Size and State, 1982

18

17

16

15

14

13

12

11

10

0

0 200 400 600 800 1,000 1,200 1,400

Herd size (adult cows)

SOURCE. Office of Technology Assessment.
Ch. 9—Emerging Technologies, Public Policy, and Various Size Dairy Farms ● 193

1982. The price level varies from $12,70 per hundredweight (cwt) in Idaho to $16.40 in Florida.

The difference in price between States is due

in large part to pricing policies under Federal

and State milk marketing orders. States with

relatively high prices are areas where milk used

as a fluid beverage is priced relatively high and

is a relatively large share of total sales.

Given the above assumptions, costs and returns for the 22 operations were calculated (figure 9-3). The rate of return ranged from — 2.15

percent on the 52-cow New York dairy to about

15.72 percent for the 1,436-cow Florida and 900-

cow New Mexico dairies. z The differences are

due mostly to herd size. The differences between

New Mexico, Arizona, California, Idaho, and Washington costs

and returns were based on the current subsidized irrigation costs

for water to produce alfalfa hay. If the irrigated water were priced

to reflect actual costs more closely, which are about three times

the subsidized costs, the rate of return would be 2 to 3 percentage

points below the rates shown in figure 9-3 for these States. For

details of this analysis see Boyd M. Buxton, “Economic, Policy,

and Technology Factors Affecting Herd Size and Regional Location of U.S. Milk Production, ” paper prepared for the U.S. Congress, Office of Technology Assessment, 1985,

New Mexico, Arizona, and Florida (relatively

high return) and California (relatively low return) in part reflect differences in milk prices.

Note that the rate of return for the 600-cow

New York dairy was favorable compared with

that for herds of similar sizes in other States.

The differences between the 600-cow Florida,

600-cow New York, 550-cow California, and

550-cow Idaho dairies are in part related to

differences in milk prices.

●

●

●

Summary and Conclusions

Investment or replacement cost per cow is

less on larger farms.

For herd sizes that characterize dairy farming in each region, investment per cow is less

for the large dairy operations in the Southwest, Northwest, and Southwest than for the

Great Lake States and Northeast regions.

The larger dairies with 500 cows or more are

more profitable than smaller dairies. Dairies

in New Mexico, Arizona, and Florida are

Figure 9-3.—Long-Term Rate of Return to Investment by Herd Size and State

18

16

14

12

10

E

8

cl6

4

2

0

- 2

- Az

- 

[

I { I I I 1

0 200 400 600 800 1,000 1,200 1,400

Herd size (adult cows)

SOURCE Office of Technology Assessment
194 . Technology, Public Policy, and the Changing Structure of American Agriculture

●

●

more profitable than their counterparts in ●

Minnesota and the Northeast.

Although costs are highest in Florida, the relatively high price received for milk provides

a competitive return. The profitability of California and Idaho dairies is adversely affected

by lower milk prices than those of New Mexico, Arizona, and Florida dairies.

Strong economic pressure exists for herds to

The relatively favorable rate of return of largescale dairy farming in the Southwest, Southeast, and Northwest regions will likely result,

over the long term, in a continued shift in milk

production to those areas. Those areas will

likely increase their relative share of total U.S.

milk production, placing increased competitive pressure on the traditional Great Lake

States and Northeast dairy areas.

grow larger in all regions. This will continue

the trend to fewer and larger dairies.

ECONOMIC IMPACTS OF EMERGING TECHNOLOGIES AND

SELECTED POLICIES FOR VARIOUS SIZE DAIRY FARMS

The preceding section considered the longterm relative rates of return of different size

dairies in five regions. Implicit in the analysis

is that the real production costs will remain constant and the support price cannot be permanently above or below the price level that will

balance supply and demand. Under the present

purchase-type price support program, decisions

to set support prices above the long-term market clearing level would, in the long run, have

to be modified. Otherwise, the Government expenditures would grow to a level unacceptable

to policy makers. This means that alternative

support levels must reflect market conditions

in the long run.

This section considers the economic impacts

of selected policy decisions on dairy operations

over a 10-year period. Panelists at the OTA animal technology workshop discussed in chapter 2 identified likely new technologies that

would be available over the next 10 years, their

adoption rate by the industry, and their impact

on milk production per cow. They found that

the adoption of new technology would reduce

the real cost of producing milk. In turn, lower

real costs of production would be reflected in

lower milk prices. Eight of the 22 dairy operations analyzed in the previous section were

selected for analysis about the impact of alternative technologies and policies. Three of the

five regions are represented: Great Lake States,

Southeast, and Southwest.

A Firm Level Income Tax and Farm Policy

Simulator used in the previous crop farms analysis, and discussed in detail in appendix E, was

used to simulate the eight dairy operations for

selected policy and technology scenarios for 10

years, beginning in 1983. The planning horizon

was simulated 50 times (iterations) using a different set of: 1) random milk, cull cow, and replacement cow prices; 2) feed costs; and 3) milk production per cow for each iteration. At the end

of each iteration, values for present value of net

returns (revenue minus cost over lo-year period)

and ending net worth, long-term and intermediate-term debt, equity-to-asset ratio, internal

rate of return, and net farm income were calculated. The results of OTA’s analysis are discussed in further detail in appendix F.

Farm Policy, Tax Policy, and

Technology Scenarios

Base Scenario

The base scenario assumptions were those

considered most likely over the 10-year planning horizon and are summarized in the following sections.

Technology. —The impact of productivity

gains achieved through new technologies and

management practices are largely reflected in

increased milk production per cow and reductions in the real cost of producing milk. In the
Ch. 9—Emerging Technologies, Public Policy, and Various Size Dairy Farms Ž 195

longer run, lower production costs are largely

passed on to consumers through lower milk

prices. Dairy farmers who adopt technology and

achieve productivity gains are able to compete

and remain financially solvent, whereas those

farmers that cannot will likely become insolvent.

The pooled knowledge and judgment of the

above-mentioned panel at the OTA workshop

identified the most likely new technologies and

adoption rates and productivity gains over the

1983-92 period. Although milk production per

cow is expected to increase for all herd sizes

in all regions, the panel expected operators of

larger herds within each region to adopt new

technology more rapidly than operators of

smaller herds. While the 125-cow dairy is considered very large in Minnesota, it would be considered very small in California, Arizona, or

Florida. However, operators of the very large

125-cow Minnesota dairies are expected to

adopt new technology as rapidly as operators

of the very large 1,436-cow dairies in California and Florida (table 9-3).

Milk production per cow is expected to continue a long-term trend by increasing 1 percent

annually for all herd sizes (table 9-4). New technology likely to increase milk production above

this long-term trend was grouped into three

main categories: 1) information and nutrition,

including such technologies as computer management and feeding systems, communication

and information systems, improved environmental management, and feed additives;3

2) bovine growth hormone (bGH); and 3) other biotechnologies, including embryo transplants and

sexing, genetic engineering, and pest and disease control.

Information and nutrition technologies are

expected to increase milk production per cow

an additional 1.8 percent annually, starting in

1983 for very large dairies, in 1985 for large

dairies, and in 1987 for medium dairies (table

9-4).

31nformation and nutrition technologies were grouped together

because their availability y to dairy farmers will come at about the

same time, Information technologies will account for a 1,2-percent

annual increase and nutrition technologies 0.6-percent annual

increase for a total of a 1.8-percent annual increase.

Table 9-3.-Very Large, Large, and Moderate Herd Sizes,

by State

Herd size (milk Cows)a

State Very large Large Moderate

Minnesota. . . . . . . . . . . . . . . . 125 b 52

California . . . . . . . . . . . . . . . . 1,436 550

Arizona . . . . . . . . . . . . . . . . . . b

359

Florida , . . . . . . . . . . . . . . . . . . 1,436 600 350

asize groups based on market order data as found in Boyd M. Buxton and John

P Rourke, “Size Distribution of Dairy Farms Marketing Milk Under Federal Milk

Orders,” unpublished repod, U S Department of Agriculture, Economic Research

Service, April 1984

bDairies for these herd s!ze groups were not simulated

SOURCE Off Ice of Technology Assessment

Bovine growth hormone is expected to increase milk per cow 25.6 percent when adopted.

This jump in milk production is expected to continue after the adoption of bGH in 1987 by operators of very large dairies, in 1988 by operators

of large dairies, and in 1989 by operators of

medium dairies. The favorable economic incentives suggest a more rapid adoption than other

technology groups once bGH is available.

Increased feed costs per hundredweight of

milk due to bGH is estimated at $4.49 for California, Arizona, and Florida dairies and $3.59

for Minnesota dairies. About 90 percent of the

increased feed cost would be for concentrates,

and 10 percent for forage. The cost is less in

Minnesota because concentrate prices are lower

there than in other States (Kalter, 1984).

The other biotechnology group, which includes embryo transplants, genetic engineering, and pest and disease control, is expected

to increase milk production per cow an additional 0.5 percent annually, starting in 1987 for

very large dairies, in 1989 for large dairies, and

in 1991 for moderate dairies (table 9-4).

Table 9-5 summarizes the expected milk production per cow for various size herds in three

regions, given the above technology assumptions,

Milk Prices.—The base scenario assumes the

support price specified in the Dairy and Tobacco Production and Stabilization Act of 1983. The

1984 price likely will be 32 cents lower than the

1983 price, reflecting a 50-cent reduction in the

support price on December 1, 1983. The Government purchases of surplus dairy products
—

196 . Technology, Public Policy, and the Changing Structure of American Agriculture

Table 9-4.-Year-to-Year Percentage Increase in Milk Production per Cow

for Three Technology Groups, by Herd Size, 1983.92

Information and nutrition Bovine growth hormone Other biotechnologies

Very Very Very

Year Trend large Large Medium large Large Medium large Large Medium

1983 1.0% 1.80/0 o 0 0 0 0 0 0 0

1984 1.0 1.8 0 0 0 0 0 0 0 0

1985 1.0 1.8 1.80/0 o 0 0 0 0 0 0

1986 1.0 1.8 1.8 0 0 0 0 0 0

1987 1.0 1.8 1.8 1.80/0 25.60/o o 0 0.5% o 0

1988 1.0 1.8 1.8 1.8 0 25.60/o o 0.5 0 0

1989 1.0 1.8 1.8 1.8 0 0 25.60/o 0.5 0.5 ”/0 o

1990 1.0 1.8 1.8 1.8 0 0 0 0.5 0.5 0

1991 1.0 1.8 1.8 1.8 0 0 0 0.5 0.5 0.5 ”/0

1992 1.0 1.8 1.8 1.8 0 0 0 0.5 0.5 0.5

+ercentage lncrea9eS are for specified year and are maintained in all subsequent years. Percentage increases are above 1982 Production Per cow levels of 1471 cwt

in Minnesota, 165.7 cwt in California and Arizona, and 131.1 cwt in Florida.

SOURCE: Office of Technology Assessment, Animal Technology Workshop, Washington, DC, April 1964; and Robert J. Kalter, et al., 8iOteChn0/0f7y and the Dairy /ndustry: Production Costs and Cornrnercia/ Potentia/ of the Bovine Growth Hormone, AE Research 64-22 (Ithaca, NY Cornell University Department of Agricultural

Economics, December 1964).

Table 9-5.—Milk Production per Cow for Most Likely Technology Scenario, 1982-92 (hundredweight)

Minnesota California and Arizona Florida

Very

Year large Medium

1982 . . . . . . . . . . .

1983 . . . . . . . . . . .

1984 . . . . . . . . . . .

1985 . . . . . . . . . . .

1986 . . . . . . . . . . .

1987 ........, . .

1988 . . . . . . . . . . .

1989 . . . . . . . . . . .

1990 . . . . . . . . . . .

1991, . . . . . . . . . .

1992 . . . . . . . . . . .

147.1

151.2

155.5

159.9

164.3

169.0

225.4

232.9

240.7

248.6

256.8

147.1

148.6

150.0

151.5

153.1

157.2

161.7

166.4

221.9

229.3

237.1

Very

large

165.7

170.3

175.1

180.1

185.1

190.4

253.8

262.3

271.0

280.0

289.3

Large

165.7

167.3

169.0

173.0

178.6

183.6

188.7

252.0

260.0

269.1

278.0

Medium

165.7

167.3

169.0

170.0

172.0

177.1

182.1

187.4

260.0

258.3

267.1

Very

large

131.1

134.8

138.6

142.5

146.4

150.6

200.8

207.5

214.5

221.6

228.9

Large

131.1

132.4

133.7

137.5

141.3

145.3

149.3

199.4

206.1

212.9

219.9

Medium

131.1

132.4

133.7

135.0

136.5

140.1

144.1

148.3

197. 8

204. 4

— 211. 3

SOURCE Office of Technology Assessment.

are assumed to be high enough through 1985

and 1986 to trigger a 50-cent drop in support

price on April 1,1985, and again on July 1,1985,

as specified in the above-mentioned 1983 Act.

This price is projected through 1986.

From 1987 to 1992 the dairy support price is

expected to be reduced 50 cents per year as long

as the estimated variable milk production costs,

given assumed technological changes and associated declines in real costs, are less than market prices in the previous year. It is expected

that the 50-cent-per-hundredweight declines

will occur through 1992,4

Financial Characteristics.—The likelihood of

a particular dairy remaining solvent under alterqThese assumptions approximate the actual policy for dairy

as specified in the Food Security Act of 1985.

native policies is directly affected by its initial

financial characteristics. The characteristics of

most importance include the value of assets,

cash reserves, debt, net worth, equity, and family consumption needs. A policy change can

have quite different implications for the operator of a dairy with a high level of debt than one

with a low level of debt.

The average financial situation that exists on

the eight dairies of the size and location selected

are shown in table 9-6. The averages were approximated from a U.S. Department of Agriculture farm financial surveys Equity ranged from

69 to 76 percent of total assets. In contrast to

Summary of financial characteristics of dairy farms were estimated from farm financial summary data provided by Neil Peterson, Economic Research Service, USDA,
Ch. 9—Emerging Technologies, Public Policy, and Various Size Dairy Farms ● 197

Table 9-6.—Financial Characteristics Assumed for Eight Dairy Operations in Four States

Minnesota Arizona California Florida

Financial characteristics 52 125 359 550 1,436 350 600 1,436

Value of:

Cropland and farmstead

($1,000) . .................293.4 679.1

Buildings ($1,000) . . . . . . . . . . . 92.7 176.7

Farm machinery ($1,000). . ....104.1 159.0

All livestock ($1,000) . . . . . . . . . 77.9 181.4

Off-farm investments ($1,000) . . . 5.5 13.1

Beginning cash reserves

($1,000) . . . . . . . . . . . . . . . . . . . . 12,0 62.5

Debt ($1 ,000). . ................268.3 302.4

Initial net worth ($1 ,000) . .......417.1 969.4

Equity ratio (fraction) . . . . . . . . . . 0.71 0.76

Family living

Minimum ($1,000). . . . . . . . . . . . 20.0 25.0

Maximum ($1,000) . . . . . . . . . . . 32.0 35.0

Marginal propensity

(fraction) . . . . . . . . . . . . . . . . . 0.3 0.4

Off-farm income ($1,000) . . . . . . . 0 0

39.4

192.8

120.3

599.6

0

89.8

297.7

744.2

0.71

25

30

0.3

0

160.0

284.4

183.1

960.7

0

137.5

464.3

1,261.3

0.73

27

38

0.4

0

312.0

512.6

303.0

2,505.0

0

35.9

1,130.0

2,537.5

0.69

30

40

0.4

0

262.5

87.9

114.6

525.5

0

70.0

303.7

756.9

0.71

25

30

0.35

0

450.0

108,9

180.0

981.4

0

212.0

461.9

1,464.7

0.76

27

38

0.4

0

1,074.0

211.7

260.7

2,344.3

0

505.5

944.6

3,343.0

0.76

30

40

0.4

0

SOURCE Office of Technology Assessment

replacement values used in the previous section, the value of buildings and machinery are

market values; it was assumed that each asset

was about half depreciated.

Results Expected.—Under the base scenario,

it was expected that a well-managed dairy of

average size would about break even after paying expenses and farm overhead and making

withdrawals for family living. It was also expected that well-managed dairies in all regions

should be able to survive under a continuation

of the current program. Farms that were not

in a position to realize most of the economies

of size in dairying would be gradually forced

out of business. In other words, an extension

of current policy would force dairies to compete on the basis of cost and efficiency.

Results Obtained:

● All dairies except the 52-cow Minnesota

operation were able to increase their real

net worth over the lo-year planning horizon (table 9-7). The 52-cow dairy experienced a 42-percent reduction in net worth.

● The larger the dairy, the greater its financial

success. Dairies in Florida and the Southwest were more profitable than dairies in

Minnesota, The Florida dairy benefited

greatly from higher milk prices.

● The 52-cow dairy had the lowest probability of survival (74 percent), owing to having the highest unit cost of production. It

lost an average of $22,000 annually in net

farm income.

A Crop Acreage Reduction Program

The present feed grain program was assumed

through 1985. From 1986 to 1992 a 15-percent

set-aside with a 5-percent diversion for corn,

cotton, rice, sorghum, and wheat was assumed.

This program results in dairy feed prices being

9-percent higher than those under the base

scenario.

Results Expected.—Feed cost would represent

about 50 to 60 percent of total costs per cow.

A crop program that results in a 9-percent higher

feed cost is roughly equal to a 5-percent reduction in the price of milk. This would have an

adverse impact on a dairy’s ability to increase

net worth, reduce debts, and achieve as high

an internal rate of return as under current policy. In the short run, dairies that raise most of

their feed would be less directly affected. The

probability of survival would probably be reduced for dairies operating at or below the

break-even point under the current policy because they would be unable to absorb the higher

feed costs.
198 . Technology, Public Policy, and the Changing Structure of American Agriculture

Table 9-7.—Comparison of Continuation of Present Policy (Base Scenario) on Dairy Farms From Various Regions

Probability of Beginning Ending Average net Average

Dairy herds survival net worth net worth cash income net income

Minnesota:

52 COWS . . . . . . . . . . . 74 ”/0 $ 417,000 $ 240,000 $–7,000 $–22,000

125 COWS . . . . . . . . . . 100 969,000 1,120,000 49,000 20,000

California:

550 cows . . . . . . . . . . 96 1,261,000 2,055,000 101,000 10,000

1,436 COWS . . . . . . . . . 98 2,538,000 7,332,000 628,000 449,000

Arizona:

359 cows . . . . . . . . . . 96 744,000 1,296,000 77,000 14,000

Florida:

350 cows . . . . . . . . . . 96 757,000 1,004,000 41,000 –6,000

600 COWS . . . . . . . . . . 100 1,465,000 2,453,000 153,000 83,000

1,436 COWS . . . . . . . . 100 3,343,000 9,257,000 759,000 635,000

SOURCE Office of Technology Assessment.

Results Obtained:

The associated higher feed prices had the

greatest adverse financial impact on dairies

that purchased most of the feed from off

the farm. For example, compared with that

of the current policy, the average annual

net farm income of the 1,436-cow California

dairy declined 62 percent, from $449,000

to $171,000.

The probability of survival was reduced for

all dairies except the 1,436-cow Florida

dairy and the 125-cow Minnesota dairy.

There was relatively little impact on Minnesota dairies, where most feed is raised at

the dairies.

Crop Programs

There is much discussion of a desire to move

to more market-oriented crop programs. Removing all price supports and income supports

would increase the variability of feed prices,

subjecting the dairyman who purchases feed

to greater risk. For this scenario the Commodity

Credit Corporation (CCC) loan, farmer-owned

reserve (FOR), and target price provisions were

eliminated for all years in the planning horizon

(1983-92). This increased the variability in feed

costs facing dairy operations. The impact of this

variability was evaluated.

Results Expected.—Feed prices paid by dairies would be higher in some years but lower in

other years. Over time, high and low price years

would be expected to balance out, leaving a surviving dairy about as prosperous as it is under

the current policy. However, the cost associated with possible borrowing to tide a dairy over

periods of high feed costs might be expected

to affect somewhat adversely its ability to retire debt and increase net worth. Dairies under

tight financial conditions under current policy

would be expected to have a lower probability

of survival without crop programs because they

would be less able to absorb the effects of periods

of relatively high feed prices. This would be less

a problem for dairies in a relatively strong financial position under current policy because

they would be better able to absorb these shocks.

Results Obtained:

●

●

The increased variability in feed prices,

associated with eliminating all crop programs, had little financial impact on all

dairies compared with the results under the

current policy. Average net present value

declined less than 2 percent for all dairies.

Increased price risk did not reduce the

probability-of survival for any of the farms.

Fifty Cents Reduction in Price

All the assumptions of the current policy were

retained except that mean milk prices were assumed to be reduced 50 cents per hundredweight and the variability of milk prices was

assumed to have increased. This scenario was

included in the analysis because of the current

high level of Government stocks and program

costs.
Ch. 9—Emerging Technologies, Public Policy, and Various Size Dairy Farms . 199

Results Expected.— Lower support prices

would be expected to affect adversely the dairies’ net incomes as well as their survival and

growth. The dairies most adversely affected

would be those that were already in financial

difficulties under the base policy.

Results Obtained:

All farms, compared with results for the

current policy, were negatively affected. All

farms experienced losses in net farm income, net present value, and net worth

compared with results under current policy.

The largest dairies in each region experienced little reduction in the probability of

survival.

The greatest adverse impact was on the smallest Minnesota dairy, where the probability

of survival declined from 74 to 58 percent

and the probability of a positive net present

value declined from 26 to 18 percent. Other

dairies that were adversely affected included

the smaller Florida and California dairies,

Therefore, reduced price supports would

force many small dairies out of business.

Dairy Program

All assumptions of the base scenario were retained except that milk price variability was assumed to have increased, Milk price was expected to fall to the estimated average variable

cost for the most efficient dairies until 1990.

Price was then expected to recover in 1991 and

1992 until in 1992 the price would be equal to

the average total cost for the most efficient operations. However, with no price support program, the actual price may be either above or

below the average price. The model randomly

selects milk prices from a distribution that may

be as much as 20 percent above or 25 percent

below the mean price.6

Results Expected.—Without a dairy price support program there would be no guaranteed

5The variation of milk prices without a dairy price support program was developed from the following study: Cameron S. Thraen

and Jerome W. Hammond, Price Supports, Risk Aversion and

U.S. Dairy: An Alternative Perspective of the Long-Term Impacts,

Economic Report ER83-9, Department of Agricultural and Applied Economics, University of Minnesota, June 1983.

price floor. In some years milk prices would be

higher, while in other years they would be lower

than under current policy. However, they would

still fluctuate about the long-term equilibrium

price. Over time, favorable and unfavorable

prices should balance out, meaning that the ability of a dairy to increase net worth, repay debt,

and achieve a favorable internal rate of return

would not be seriously affected. However, the

probability of survival for dairies in tight financial situations would be adversely affected.

Results Obtained:

●

●

●

●

The probability of survival fell for all farms,

with the greatest reduction experienced by

the moderate farms analyzed. The lowest

probability of survival was 22 percent for

the 52-cow Minnesota dairy (table 9-8).

Ending net worth declined significantly on

all farms except for the very large farms

in California and Florida. For example, net

worth declined 73 percent for the 52-cow

Minnesota dairy and 37 percent for the 550-

cow California dairy.

Average net income was negative for all

farm sizes except for the very large dairies

in California and Florida.

Very large farms were the only farms abIe

to survive under no price support program.

Supply Control

All assumptions of the base current policy

were retained except that mandatory quotas

were assumed to be imposed on dairies. Quotas

equal to 96.5 percent of a producer’s normal production would, over time, be expected to maintain milk prices $1 above those under current

policy. Herd size would be reduced about 4 percent in order to reduce milk production 3.5 percent, assuming that poorer-than-average cows

would be culled in complying with the quota.

Results Expected. —The financial performance of all dairies would likely be improved as

a result of permanently higher milk prices, despite those dairies having to reduce total milk

produced within the designated quota, The probability of survival would increase along with

a greater ability to reduce debt and increase net

worth for dairies existing at the time the pro-
200 ● Technology, Public Policy, and the Changing Structure of American Agriculture

Table 9.8.—Comparison of No Milk Price Support Program on Dairy Farms From Various Regions

Probability of Beginning Ending Average net Average

Dairy herds survival net worth net worth cash income net income

Minnesota:

52 COWS . . . . . . . . . . . 22 ”/0 $ 417,000

125 COWS . . . . . . . . . . 98 969,000

California:

550 cows . . . . . . . . . . 62 1,261,000

1,436 COWS ... , . . . . . 96 2,538,000

Arizona:

359 cows . . . . . . . . . . 42 744,000

Florida:

350 cows . . . . . . . . . . 36 757,000

600 COWS . . . . . . . . . . 72 1,465,000

1,436 COWS . . . . . . . . . 100 3,343,000

SOURCE: Office of Technology Assessment.

gram is implemented. However, this economic

advantage could be capitalized into the quota

value, thereby eroding the advantage for new

entrants or producers that would have to purchase quotas to expand milk production.

Results Obtained:

●

●

Probability of survival was increased for

all farms of all regions (table 9-9). The 52-

cow Minnesota dairy experienced the largest increase in the probability of survival,

from 74 percent under the base scenario

to 92 percent,

Average net present value increased for all

dairy farms. The 52-cow Minnesota dairy

increased from –$61,000 to $13,000,

Ending net worth was increased for all

dairies, owing to retained earnings and

repayment of debt.

$ 114,000 $ – 19,000 $ –38,000

835,000 6,000 –21 ,000

800,000 – 72,000 – 166,000

4,418,000 187,000 7,000

276,000 – 55,000 – 121,000

317,000 – 49,000 – 97,000

1,268,000 –23,000 –97,000

6,625,000 366,000 242,000

● Net farm income for Minnesota dairies was

increased by at least $8,000 compared to

the base scenario. These dairies previously

had the lowest income.

Income Tax Changes

All assumptions of the base scenario were retained except that more restrictive Federal income tax provisions were included, such as the

following:

●

●

●

●

Machinery, livestock, and buildings were

depreciated using the straight-line cost recovery method.

First-year expensing provisions were eliminated for all depreciable items.

Maximum investment tax credit provisions

were eliminated.

The maximum annual interest expense that

Table 9.9.—Comparison of Supply Control Program on Dairy Farms From Various Regions

Probability of Beginning Ending Average net Average

Dairy herds survival net worth net worth cash income net income

Minnesota:

52 COWS . . . . . . . . . . . 92% $ 417,000 $ 310,000 $–2,000 $ – 14,000

125 COWS . . . . . . . . . . 100 969,000 1,190,000 59,000 33,000

California:

550 cows . . . . . . . . . . 96 1,261,000 2,349,000 161,000 76,000

1,436 COWS . . . . . . . . . 100 2,538,000 8,543,000 812,000 653,000

Arizona:

359 cows . . . . . . . . . . 96 744,000 1,486,000 112,000 54,000

Florida:

350 cows . . . . . . . . . . 98 757,000 1,164,000 67,000 25,000

600 COWS . . . . . . . . . . 100 1,465,000 2,681,000 201,000 137,000

1,436 COWS . . . . . . . . . 100 3,343,000 10,038,000 877,000 769,000

SOURCE Office of Technology Assessment
Ch. 9—Emerging Technologies, Public Policy, and Various Size Dairy Farms . 201

could be used to reduce taxable income was

$15,600 .

● The operator must sell obsolete machinery

on disposition rather than trading it in on

new replacements, thus forcing recapture

of excess depreciation deductions.

Results Expected.—These tax policy changes

would have an adverse impact on the ability of

a dairy to reduce debt, increase net worth, and,

if in a tight financial situation, reduce the probability of survival. All tax changes would increase the tax liability, reducing the net income

of the operation and leaving less for debt retirement and increases in net worth.

Results Obtained:

●

●

Eliminating the tax benefits increased tax

liabilities and reduced the net present value

and net worth for all farms, These reductions, however, were relatively small—in

the range of 1 to 10 percent.

The increased tax liabilities were not large

enough to reduce significantly the probability of survival.

Technology Scenarios

The milk price assumption of the base scenario was retained for the two technology scenarios discussed below. It should be recognized

that miIk prices would be expected to be higher

than the base scenario prices if productivity

gains from the designated technologies did not

materialize, Therefore, the adverse effect of

these technology scenarios is overstated,

No Information and Nutrition Technology.—

The 1.8-percent annual increase in production

per cow attributable to information and nutrition technology was excluded from the base assumption for this scenario. The financial performance of all dairies would be adversely

affected under this scenario, For the very large

farms the per-cow increase in 1982 milk production by 1992 was only two-thirds as much

as under the base scenario.

No Bovine Growth Hormone.—The 25.6-percent jump in milk production when bgh is adopted

was excluded from the base assumption for this

scenario. The financial performance of all dairies was expected to be adversely affected under

this scenario compared with the base scenario.

For the very large dairies, the increase in 1982

milk production per cow by 1992 was assumed

to be only 40 percent as much as under the base

scenario.

Results Expected. —The expected impact of

not adopting these technologies was to affect

significantly the financial performance of the

dairies. The probability of survival and all measures of financial performance would decline

compared with the base scenario.

Results Obtained.—Large decreases in net

farm income, net present value, and ending net

worth were experienced for all dairies compared with results from the base scenario.

Financial Stress Scenarios

The assumed beginning financial conditions

for four of the eight dairies were changed to reflect high-debt operators and new entrants. Debt

load was doubled to reflect high-debt situations,

For new entrants all equipment was assumed

to be new, which increased both the initial value

of the machinery and the total debt load.

Two policies were considered for high-debt

dairies. One was to subsidize interest rates on

all debt so that the effective rate for all loans

paid would be 8 percent rather than the higher

rates used in the current policy, The second was

to restructure the debt by converting a portion

of intermediate debt into long-term loans and/or

to extend the length of intermediate-term loans.

In the second case, interest rates, total debt

loads, and other assumptions of the high-debt dairies remained the same as in the base scenario.

The impact of higher feed costs and of eliminating the dairy price support program was

evaluated for new entrants with a high-debt position. The results obtained included the following:

● The probability of survival for any dairy

depends greatly on its initial financial position. Dairies with high debt and new

entrants with high debt had significantly

lower probabilities of surviving than dairies

with initial financial situations assumed

under current policy.
202 ● Technology, Public Policy, and the Changing Structure of American Agriculture

●

●

●

●

●

●

Neither interest subsidies nor opportunities for debt restructuring greatly improved

the chances of high-debt dairy farms remaining solvent.

The probability of survival for both Minnesota dairies was zero for all policy

scenarios. The implication is that high-debt

producers in this region cannot survive,

even under the current dairy policy,

Summary and Conclusions

Policies and technologies that are favorable

for dairy provide greater financial opportunities for large rather than small dairies.

Policies that adversely affect the dairy industry such as higher feed costs, fewer income

tax benefits, and no dairy price support program will negatively affect small dairies more

than larger dairies.

The major advantage enjoyed by larger dairies is more related to the efficiency of operation than to specific dairy policies.

There will be a continued trend to fewer and

larger dairies in all regions. Milk production

can be expected to continue to increase in

the lower cost regions of the Southwest and

West.

●

●

●

●

Traditional dairy regions will continue to experience increased competitive pressure from

larger scale, more efficient producers in other

parts of the United States. Substantial restructuring of dairies in the Great Lake States and

the Northeast will be required for those dairies

to compete.

Emerging technologies need to be transferred

to moderate-size dairy farms at a much earlier

time in the technology adoption process for

these farms to survive.

Dairy price supports must be sufficiently flexible to adjust to the increased production and

lower costs spurred by technological change.

This could be accomplished either by adjusting the price support level to changes in production costs per unit of output or by adjusting the level of CCC purchases.

Current geographic price alignment systems

in Federal milk marketing orders are becoming increasingly outdated. A comprehensive

study is needed of changes required to modernize the Federal order system in light of

technological changes.

CHAPTER 9 REFERENCE

Kalter, Robert J., et al., Biotechnology and the Dairy search 84-22 (Ithaca, NY: Cornell University DeIndustry: Production Costs and Commercial Po- partment of Agricultural Economics, December

tential of the Bovine Growth Hormone, A.E. Re- 1984).
Chapter 10

Impacts on the Environment

and Natural Resources
Contents

Page

Impacts of Emerging Technologies. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 205

Methodological Approach. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 205

Evaluation Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 206

Effects of Farm Structure on the Environment. . . . . . . . . . . . . . . . . . . . . . . . . 211

Environmental Concerns of Emerging Technologies . . . . . . . . . . . . . . . . . . . . 212

policy Implications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 215

Summary and Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 216

Chapter 10 References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 217

Tables

Table No. Page

l0-l. Impacts of Animal Technologies on the Environment

and Natural Resources . . . . . . . . . . . . . . . . . . , . . . . . . . . . . . . . . . . . . . . . 207

10-2. Impacts of Plant Technologies on the Environment

and Natural Resources . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 208
Chapter 10

Impacts on the Environment

and Natural Resources

Overall, the emerging technologies are expected to reduce land and water requirements

for agricultural use. They are also expected to

reduce certain adverse environmental impacts

associated with land and water use, such as soil

erosion, threats to wildlife, and pollution from

the use of farm chemicals. What impacts will

result from biotechnologies, however, are more

uncertain, for there are no good predictive ecological models or systems in existence that could

help evaluate the potential impacts of a release

of genetically altered organisms into the environment.

This chapter evaluates the implications for

the environmental and natural resource use of

emerging agricultural technologies. It is divided

into four parts: 1) impacts of technology, which

includes the methodology for identifying the

emerging technologies and evaluating their environmental/resource impacts, the evaluations

of the technologies, and some limitations associated with the evaluations; 2) the relationship

between the size and structure of farms and the

adoption of new technologies; 3) environmental

concerns of emerging technologies; and 4) policies for mitigating adverse consequences and

for enhancing the favorable aspects of the new

technologies.

IMPACTS OF EMERGING TECHNOLOGIES

Methodological Approach

One problem that arises in identifying the

technologies to be evaluated is the establishment

of a time horizon. Although the emerging technologies studied are expected to be adopted

before the end of the century, some of the technologies identified are not expected to be introduced for commercial adoption until very

late in the century. Even then, adoption may

be rather limited, and widespread adoption may

not occur until the first decade of the next century. Thus the environmental and resource impacts of such technologies may not manifest

themselves until early in the next century.

Since policy concerns focus on technologies

that should be discouraged or encouraged because of expected environmental/resource consequences, holding rigidly to the end of the century for evaluating the emerging technologies

is too limiting, Therefore, the convention used

in determining which technologies were to be

evaluated was that the technology be available

for adoption by the end of the century, even

though the environmental and resource impacts

from use of that technology might not occur until later.

The Delphi approach was used to facilitate

consensus in the evaluation of the impacts of

emerging technologies (Coates, in Teich, 1981;

Gordon and Ament, in Teich, 1981). While the

technique does not provide for a high level of

scientific rigor, the difficulties inherent in foreseeing the myriad possible consequences of a

new technology in a complex socioeconomic

system require subjective evaluation from a

well-informed, multidisciplinary team. The Delphi technique lends itself to identification of

consensus.

A team of 11 experts was assembled for a 2-

day workshop to perform the evaluation.’ These

IThis chapter is based largely on the results of the workshop

as analyzed by James Hite in the OTA paper “Environmental and

Natural Resource Impacts of Emerging Technologies in American Agriculture” and reviewed by the workshop participants.

205
206 ● Technology, Public Policy, and the Changing Structure of American Agriculture

experts represented a broad range of backgrounds and regions within the United States.

(The names, affiliations, and disciplinary specialty of each member of the team is included

in appendix C.) The first task of the team was

to group the technologies in a way that was

meaningful for evaluation of their environmental/resource impacts. The first division was

between animal and plant agriculture.

Four general types of technologies related to

animal agriculture were identified: 1) genetic

engineering, growth and development, reproduction, and nutrition; 2) animal disease, pest

control, environment, and behavior; 3) animal

waste and crop residues; and 4) aquiculture.

The nine general areas of technologies related

to plant agriculture included: 1) genetic engineering in plants; 2) photosynthesis; 3) nitrogen fixation; 4) plant growth regulators; 5) organic farming; 6) multiple cropping; 7) water

and soil-water-plant relationships; 8) soil erosion and land management; 9) disease, insect,

and weed control.

Each general area of technology was then

evaluated relative to eight types of impacts: 1)

water quality, 2) water quantity, 3) soil erosion,

4) soil productivity, 5) air quality, 6) wildlife,

7) solid waste, and 8) human health.

The evaluation was performed on a lo-point

scale. A technology with a strongly favorable

impact on the environment and/or natural resources would receive a rating of 10.0. A technology with a strongly adverse impact would

receive a rating of zero. If the impact were

judged to be neutral, the rating would be 5.0.

A computer-driven device, a Consensor, was

used to tabulate the ratings assigned by each

expert. In addition, the device allowed each expert to weight his or her rating according to the

degree of confidence he or she had in the rating. That level of confidence could be set at zero,

25, 50, 75, or 100 percent.

The Consensor provided an immediate video

screen readout of the rating distribution, the

weighted average rating, and the average degree of confidence. If the first vote showed a

very wide distribution of ratings, those experts

with outlying ratings were asked to explain their

reasons for their ratings. After additional discussion, another vote was taken. Since lack of

a consensus after such discussion is, in itself,

an indication of considerable uncertainty about

the impacts of new technology, no attempt was

made to force a consensus beyond a second vote.

The “with and without test” was adopted as

a basic guide in making the judgments necessary to assign a rating. Simply, the test involves

evaluating what the environmental/resource

situation would be with and without the technology. The rating, therefore, is based on an

assessment of the net effect of the emerging technology. A rating that suggests that a particular

technology will result in environmental improvement cannot be taken to mean that the

environment will be better after adoption of that

technology. Rather, such a rating means that

the group’s judgment was that the environment

will be better with the new technology than it

would be if the old technology were continued

into the future. The converse is also true.

Evaluation Results

Technology in Animal Agriculture

Genetic Engineering, Growth and Development,

Reproduction, and Nutrition.—Emerging technologies in the broad area of animal growth and

development center on recombinant deoxyribonucleic acid (rDNA), monoclinal antibodies,

estrous-cycle regulation, and embryo transfer.

All of these technologies are expected to lead

to production of increased output with fewer

animals and reduced input of feed. That means

that a given future demand can be met with

fewer animals, less land devoted to production

of feed grains and to pasture, and, in general,

less demand on natural resources than would

otherwise be the case.

On the other hand is the effect these new technologies might have on the structure of animal

agriculture. If the new technologies encourage

fewer but larger herds and greater geographic

concentrations of animal agriculture, localized

environmental problems might intensify. For

example, disposal of manure and increased use
Ch. 10—impacts on the Environment and Natural Resources • 207

of antibiotics, hormones, and other chemicals

could result.

The rating results indicate that only two of

the nine environmental impacts—water qualit y and human health—were judged relevant to

this group of technologies (table 10-1). Given

fewer animals, some marginal improvement in

water quality would result. The possibilities for

using genetic engineering techniques to reduce

unsaturated fats in red meats would have a marginally beneficial effect on human health.

Animal Disease, Pest Control, Environment, and

Behavior.—The emerging technologies in the

area of animal disease, pests, environment, and

behavior combined biotechnology and computer systems. In addition, it is expected that increased use will be made of existing technologies

for diagnostic testing, slow-release insecticides

and vaccines, and photoregulation.

Technologies in this area are viewed as similar in their effects on production to those associated with the previous area of animal growth

and development, reproduction, nutrition, and

genetic engineering, as shown in the rating. The

use of these technologies will result in increased

output of animal products with fewer inputs of

natural resources. The environmental/resource

consequences were also judged to be essentially

the same as those in the previous area. With

fewer animals needed to meet a given future

demand, less natural resources would be required for feed production. Thus reduced pressure would be exerted on the environment and

natural resources. However, as indicated above,

concentration of animals could cause environmental problems.

Animal Waste and Crop Residue.—The basic

features of emerging technologies in the handling of animal waste and crop residues center

on chemical and biological conversion, recycling, and fuel production. All of these technologies are already being used to varying degrees.

The new features involve increased adoption

and application of the technologies to specific

crops. One example is the use of corn cobs as

fuel in thermal gasifiers for drying.

In general, economic factors will prevent

widespread use of biomass for fuel or the conversion of animal waste to methane for the foreseeable future. Incorporating crop residues into

the soil is expensive and sometimes creates

disease and insect problems. Only if the field

burning of crop residues is banned bylaw, thus

raising the cost of conventional methods of managing these residues, would many new technologies in this area be widely adopted.

Assuming that the new technologies are

adopted, increased use of animal waste for

energy would reduce some water pollution and

would marginally improve water quality. If crop

residues were removed from the fields in large

quantities, some additional soil erosion and loss

of soil quality would result from the reduction

of humus, but it is thought that the new technologies would not have a large effect on residues left in the field. Any movement toward less

burning of crop residues, however, would produce a marginal improvement in air quality in

selected localities.

Aquiculture. —Aquacultural activities have

considerable potential for adverse impacts on

water quality and quantity. In some parts of the

country, aquacultural enterprises remove large

quantities of groundwater from aquifers. In

addition, some potential exists for wastewater

from such enterprises to pose a water quality

Table 10-1 .—Impacts of Animal Technologies on the Environment and Natural Resources

Water Water Soil Soil Air Solid Human

Technology group quality quantity erosion productivity quality Wildlife waste health

Genetic engineering, growth,

reproduction, and nutrition . . . . . . . . . . . . 5.9 NR NR NR NR NR NR 5.5

Animal disease, pest control,

environment, and behavior . . . . . . . . . . . . 5.9 NR NR NR NR NR NR 5.5

Animal waste and crop residues . . . . . . . . . 5.4 NR 5.0 NR 5.3 NR NR NR

Rating system 10 = strongly favorable impact; 5 = neutral; O = strongly adverse impact; and NR = not relevant.

SOURCE Off Ice of Technology Assessment
208 . Technology, Public Policy, and the Changing Structure of American Agriculture

problem. Yet there are important economic

questions about the potential growth in markets

for aquacultural products. Until these questions

are answered, it is not possible to make meaningful comments about potential environmental/resource impacts.

Technology in Plant Agriculture

Genetic Engineering.–While the technology

of genetic engineering in plants offers dramatic

possibilities for agriculture, the scientists working in the area believe that actual adoption of

new technologies on the farm is some years

away. Basic work in developing gene maps for

plants is somewhat behind that for animals.

The technology involves rDNA, cell culture,

cell fusion, and monoclinal antibodies. Much

of the effort will focus on moisture and drought

stress in plants, suggesting a reduced need for

irrigation water. There should also be reductions in the use of chemicals as resistances become engineered into plants, with favorable results for water quality. To produce a given level

of output, increased yields will allow retirement

of some marginal, erosion-prone land, increasing the amount of habitat available for wildlife.

Possibilities for using genetic engineering techniques to improve soil microbes was thought

especially promising for soil productivity. On

balance, therefore, these genetic engineering

techniques in plant agriculture would enhance

the environment rather strongly (see table 10-2).

Enhanced Photosynthesis. -Technologies that

enhance photosynthesis address the plant’s central productive process, increasing yields per

unit of land. With these technologies, marginal

lands could be retired, water needs could be held

down, and erosion would be reduced.

In general, the technologies would be environmentally helpful. One possible exception

concerns soil productivity. For example, with

enhanced photosynthesis, crops on land left in

production will draw out soil nutrients faster

than would otherwise be the case. Therefore,

the effect of the technology on those lands would

be to reduce the natural productivity of the soils.

However, with this technology there would be

less land in production. On those lands not in

production, soil productivity would be restored,

or at least maintained. On balance, the effect

of enhanced photosynthesis technologies on soil

productivity would be about neutral.

Nitrogen Fixation.–Legumes have the ability

to fix nitrogen from the atmosphere and transform it into plant food. However, cereal plants

generally lack this ability. Breeding cereal plants

with nitrogen-fixing abilities has long been the

Holy Grail of agricultural geneticists, but many

difficult problems have been encountered in its

pursuit. Advances in genetic engineering, however, have opened up new avenues for plant

breeders, and renewed hope exists of developing cereal plants with nitrogen-fixing capabilities. Even though the possibilities for significant

breakthroughs prior to the end of the century

are considered remote, some incremental advances are expected.

If such nitrogen-fixing technologies develop,

the environmental/resource implications would

be significant and positive. These technologies

Table 10=2.-lmpacts of Plant Technologies on the Environment and Natural Resources

Water

Technology group quality

Genetic engineering. . . . . . . . . . . . . . . . . . . . 6.4

Photosynthesis . . . . . . . . . . . . . . . . . . . . . . . . 6.2

Nitrogen fixation. . . . . . . . . . . . . . . . . . . . . . . 7.1

Plant growth regulators . . . . . . . . . . . . . . . . . 6.2

Organic farming . . . . . . . . . . . . . . . . . . . . . . . 5.7

Multiple cropping . . . . . . . . . . . . . . . . . . . . . . 6.4

Water and soil-water-plant relationships . . 6.2

Soil erosion and land management. . . . . . . 6.3

Disease, insect, and weed control. . . . . . . . 6.9

Water

quantity

6.9

6.2

6.2

5.1

4.8

7.5

6.6

5.3

Soil

erosion

6.5

6.3

NR

6.3

5.6

6.8

7.1

9.1

7.0

Soil

productivity

7.4

5.0

5.6

5.0

5.5

5.0

5.8

7.7

5.7

Air

quality Wildlife

5.9 6.3

NR 5.6

5.4 6.3

NR 5.6

NR 5.5

NR 4.8

NR 5.0

6.6 6.7

5.7 7.1

Solid

waste

5.4

NR

NR

5.5

NR

NR

NR

6.1

Human

health

6.1

NR

NR

5.9

NR

NR

5.1

7.4

Rating system: 10 = strongly favorable impact; 5 = neutral; O = strongly adverse impact; and NR = not relevant,

SOURCE: Office of Technology Assessment.
Ch. 10—Impacts on the Environment and Natural Resources . 209

would allow substantial reductions in the use

of nitrogen fertilizers, resulting in a decrease

in nitrogen runoff into surface waters and percolation into groundwater, with beneficial effects on water quality. With less nitrogen being

manufactured, fewer people would be exposed

to health risks in fertilizer plants and on the

farm. Improved air quality would result from

reduced fertilizer manufacturing, and wildlife,

especially aquatic life, would also benefit from

reduction of nitrogen runoff into surface waters.

Growth Regulation.—Plant growth regulators

are typically organic chemical compounds

sprayed on the surface of plants. They increase

yield by affecting the way the plant uses its nutrients, Chemical concentration in the sprays

is usually quite low. The compounds are rather

quickly metabolized by the soil and usually present few environmental problems because the

compounds themselves tend to break down

quickly. The environmental/resource impacts

of new technologies in plant growth regulation

are likely to be quite similar to those determined

for enhanced photosynthesis.

Organic Farming.—In some sense, organic

farming represents an old and traditional set

of technologies. However, in recent years, the

concept of organic farming has undergone some

changes. At the heart of organic farming are

technologies concerned with nutrient self-reliance and recycling and minimum use of, but

not necessarily total elimination of, chemicals.

New organic farming in particular could be expected to make use of advances flowing from

genetic engineering, enhanced photosynthesis,

simultaneous cropping, and several other technologies discussed in this chapter,

Assessing the environmental/resource implications of organic farming presented more problems than any other single set of technologies.

While there was general agreement that organic

farming approaches would require more land

to meet expected demand, there was skepticism

about the extent to which organic farming technologies would be adopted. If widely adopted,

organic farming would disperse animal agriculture geographically, since there would be a

greater need to keep animals on many farms

to produce manures. As a consequence, farm

energy consumption would be reduced. If energy prices rise substantially, organic farming

techniques might be adopted rather widely; but

barring such an increase, the panel thought it

unlikely that organic farming would account

for more than a small percentage of the Nation’s

farm output.

Organic farming could have adverse impacts

on environmental resources if it were widely

adopted. Increased pressures would be brought

on marginal and erodible lands, and widespread

use of animal manures could have some negative consequences for water and air quality.

While wildlife might be less threatened by the

use of fewer chemical compounds, wildlife habitats could be threatened by the need for more

land for crops.

Perhaps the strongest positive impact was

thought to be in the area of human health. Organic farming would reduce human exposure

to agricultural chemicals and could result in

food products that have higher nutritional value

and less chemical contamination,

Multiple Cropping.—The concept of multiple

cropping involves two separate types of practices. The first, called simultaneous cropping,

involves growing two or more crops in the same

field at about the same time, The second type

involves growing a second crop closely behind

the harvest of another in the same field in the

same year, The two types merge in some cases

where one crop is begun before the other is harvested.

The latter type of multiple cropping has been

increasing rather rapidly in the Southeast and

in California. Because the land is covered with

a crop for longer periods during the year, runoff and erosion are reduced, The result is improved water quality and soil conservation. In

certain instances, increased irrigation water is

required. Multiple cropping can be either beneficial or harmful to soil productivity, depending on the crops grown. In the wheat-soybean

systems of the Southeast, for instance, multiple cropping might improve soil productivity

marginally, but other systems would intensify

the removal of soil nutrients.
210 ● Technology, Public Policy, and the Changing Structure of American Agriculture

On balance, multiple cropping would probably have some adverse consequences for wildlife. Machinery would be in the fields more

often, disturbing nesting areas and wildlife generally. Also, there would be less stubble and

other crop residues available for cover and feed.

On the other hand, however, multiple cropping

has the potential to reduce the land needs of agriculture and, as a result, to protect habitat and

soil resources.

Water and Soil-Water-Plant Relationships.—

Technologies that affect water and soil-waterplant relationships include certain genetic engineering approaches. Improvements in plants’

capabilities to close leaf pore openings (stomata)

for longer periods to retain moisture are possibilities. The ratings, however, were assigned

primarily on perceptions of still-to-be-applied

irrigation technologies, particularly improvements in onfarm irrigation technologies.

Movement toward improved irrigation efficiency is considered environmentally benign.

Less water applied means less return flow and,

thus, less threat to water quality. It also means

substantial savings in water and reduced soil

erosion. The effects on soil productivity are

mixed, however. While improved technologies

will allow plants to make better use of existing

soil nutrients, they will also allow those nutrients to be used up faster. So, improved irrigation technologies would have a marginally beneficial effect on soil productivity. Similarly,

effects on wildlife are likely to be mixed. The

concentration of salts in runoff water might be

higher, and with less water, there might be fewer

reservoirs and other habitats. On the other hand,

with less water being drawn away from irrigation, more clean water might be available elsewhere. Thus the impact on wildlife would probably be neutral.

Soil Erosion/Land Management.—One major

technological change in agriculture in the 1970s

was the growth of what is called conservation

tillage. Conservation tillage implies limited tillage. It has several forms: in some cases, corn

and other grains are actually planted into grass

or stubble along with an herbicide applied in

a very narrow strip where the seed is injected.

A newer innovation is called prescribed tillage,

a practice that uses computer technology to

monitor soil conditions. This technique integrates such information with weather forecasts,

for example, to determine when and how to undertake tillage.

Conservation tillage has enormous possibilities for reducing soil erosion. The major environmental problem is the increased use of herbicides. Another problem is the reduced crop

yield that results from conservation tillage. To

meet given production demands additional

acreage must be cultivated. Although the health

impact on humans is likely to be negative because of an increased threat to groundwater

from agricultural chemicals, reduced tillage

could reduce mechanical energy consumption

and incidence of farm accidents associated

with tillage activities.

Disease, Insect, and Weed Control.–The

emerging technologies in plant disease, insect,

and weed control begin with integrated pest

management (1PM), an approach to pest control that does not eliminate use of pesticides but

does attempt to minimize those pesticides by

making maximum use of predators, by attempting to protect beneficial insects, and by applying pesticides in limited quantities only after

no other control mechanism is deemed feasible. 1PM has the potential to reduce pesticide

use by as much as 50 percent.

Integrated weed management is similar in

concept. In this practice changes in cultivation

practices are integrated into reduced use of herbicides.

These new technologies, combined with a

new generation of agricultural chemicals expected to appear on the market by late in the

decade, will tend to cause considerable environmental improvements over existing technologies, if properly applied. However, some concerns were expressed about application in the

field. Application machinery often is not precise, and knowing that, farmers sometimes deliberately use a greater application rate than that

called for in farm-chemical instructions. Unlike many of the other technologies examined,
Ch. 10—Impacts on the Environment and Natural Resources ● 211

these disease, insect, and weed control technol- of agricultural chemicals, which will clearly be

ogies will reduce the amount of land needed for environmentally beneficial to water quality, soil

crops. Most of the environmental improvements quality, human and animal health, air pollution,

are expected to be associated with reduced use and energy requirements.

EFFECTS OF FARM STRUCTURE ON THE ENVIRONMENT

To evaluate the impact of farm structure on

the environment and natural resources, three

scenarios related to three different structures

of production agriculture were postulated:

1. a continuation of current policy, which

could be expected to result, by the end of

the century, in a notably dual distribution

of farms by size—many small farms with

sales of less than $20,000 annually and

many large farms with annual sales of

$500,000 or more each year;

2. policies that accelerate the trend toward

a dual distribution and a significant reduction in the number of moderate farms; and

3. policies that would slow down the move

toward the bipolar distribution, maintaining the number of moderate farms at the

expense of larger farms.

The question posed was: what effect, if any,

would these structural scenarios have on the

environment and on natural resource use? The

scale used in assigning ratings was the same

as that used in evaluating the various sets of technologies, that is, a rating of 5.0 meant that the

scenario was expected to make no perceptible

difference. Ratings higher than 5.0 suggested

environmental improvement, all things being

equal, whereas ratings below 5.0 suggested

some environmental degradation.

Considerable evidence suggested that large

farms were more likely than small farms to adopt

new technologies. Small farms are constrained

by time limitations in the use of technologies

that require intensive management. They may

also be constrained by access to financing. In

this context, both considerations are especially

important, since many of the new technologies

are management-intensive and will require substantial front-end outlays.

On the other hand, it was noted that some large

farms may currently be more heavily leveraged

financially than the moderate farms. Moreover,

since almost all of the small farms are operated

by persons or families with some outside income, the small farms may be less constrained

financially to use technologies that save labor

and do not require enormous front-end outlays

that necessitate borrowing. Organic farming is

an example of a technology that may have more

appeal to small than to large farmers.

It is also important to note that many large

farms making use of hired labor may concentrate on minimizing labor costs. The new technologies, in the main, are not primarily laborsaving. The principal savings to be had from

these new technologies are in a reduction inland

and environmental degradation. Since the environmental effects are usually offsite and external to the farm firm’s accounts, there may be

only modest incentives for some of the larger

farms to adopt the new technologies unless the

farms are under strong regulatory pressures

from environmental agencies.

On balance, however, the technologies would

favor large-farm operators. Because the technologies, in general, tend to be environmentally

enhancing, it follows that movement toward a

greater concentration of production in the

hands of large operators would have beneficial

environmental effects. However, several panelists insisted on the caveat that the major factors

influencing adoption of the new technologies

are access to front-end capital and managerial

capability. Thus the technologies are not confined exclusively to large farms; many moderate farms are also in a position to adopt the technologies. In the areas of animal agriculture,

particularly, many of the new technologies are
212 ● Technology, Public Policy, and the Changing Structure of American Agriculture

being adopted first by seed-stock producers,

who tend to be moderate operators.

These reservations are important as background for the interpretation of the ratings. Scenario 1 represents a continuation of recent

trends and, given the “with and without” rule

used in evaluating the technologies, must be assigned a rating of 5.0 (environmentally neutral)

in all impact areas. Movement toward greater

concentration of production on large farms

would, in the panel’s judgment, have a general

tendency to enhance the environment because

the larger farms (as a class) will be more likely

to adopt the new technologies. The panel emphasizes, however, in strong terms, that movement toward greater concentration of large

farms is not a necessary condition for realization of environmental improvement flowing

from the new technologies. Public policies that

improve the access of small and moderate farms

to the new technologies would accomplish the

same end.

Scenario 3 represents public policy designed

to improve the survival rates of moderate farms.

Such policy, taken alone, was judged to have

unfavorable environmental consequences in

five of the nine impact areas addressed. If such

farms survive, but do not prosper, they will have

few resources available to use in adopting the

new technologies. Policies that improve the opportunities of moderate farms to prosper and

survive, however, would allow such farms to

avail themselves of new technologies that are

environmentally enhancing. Indeed, under such

conditions, moderate farms might well adopt

these new technologies more rapidly than the

larger farms for those reasons cited above.

ENVIRONMENTAL CONCERNS Of EMERGING TECHNOLOGIES

Looking at all the technologies assessed, the

environmental/resource impacts were believed

by the majority of the panel to be, at least marginally, environmentally enhancing. The panel

noted particularly the potential for new tillage

technologies to reduce soil erosion and improve

soil productivity, for new irrigation technologies

to conserve water, for nitrogen-fixing technologies to improve water quality by reducing nitrogen runoff, and for genetic engineering to

improve agricultural productivity. The technologies should reduce land needs and thereby reduce threats to wildlife habitats. They should

also reduce the use of chemicals and the resulting possible threats to human health.

In only a few cases were the new technologies

thought likely to have unfavorable environmental or resource impacts. Those concern the

impacts of multiple cropping on water quality

and on wildlife. In both these cases, however,

the unfavorable impacts were thought to be relatively mild. Concern also arose over possible

problems associated with human error or machine malfunction in the application of chemicals used in conservation tillage.

Perhaps the chief cause for concern was the

lack of knowledge about the potential effects

on the environment of the release of genetically

altered organisms. Most of the biotechnologies

applicable to agriculture that are expected to

be commercially adopted in the next few years

involve release of new organisms into the environment. The question of “deliberate release”

of engineered micro-organisms has already

arisen in agriculture, however, in connection

with the testing of genetically altered bacteria

in potato fields to prevent freeze damage. Other

cases are almost certain to arise. The potential

for these genetically altered micro-organisms

to interact with the environment in unpredictably harmful ways cannot be ignored. Considerable debate is occurring within the scientific

community and within the Federal bureaucracy

over proper controls on the testing and use of

genetically altered organisms prior to deliberate

release. The economic benefits from uses of biotechnology that require deliberate release of

modified organisms probably are substantial,

but the panel recognized the need to work out

suitable regulatory safeguards controlling such

releases (Doyle, 1985; Healy, 1985; Kendrick,

1985; Schatzow, 1985).

The safety issue of biotechnology was debated

when the first gene was about to be inserted into
Ch. 10—Impacts on the Environment and Natural Resources ● 213

a micro-organism. Concerned about potential

hazards of new rDNA techniques, a group of

the world’s leading scientists, headed by Paul

Berg, met 10 years ago for the second time at

the Asilomar Conference Center in Pacific Grove,

California. They agreed to strictly regulate those

experiments using rDNA techniques until more

data could be collected for assessing the potential hazards and until safety could be assured.

One day after the second Asilomar conference,

a National Institutes of Health (NIH) Recombinant DNA Advisory Committee, commonly

known as RAC, held its first meeting and began drafting a set of safety guidelines for the

rDNA experiments, guidelines that have governed rDNA research in the United States ever

since (Tangley, 1985).

As scientists learned more about rDNA techniques, the guidelines were periodically revised.

Each revision further relaxed the rules as scientists came to realize that the fears of hazards

from rDNA research, although not groundless,

were greatly overestimated a decade ago. During the last decade, hundreds of laboratories

around the world have been cutting and splicing DNA in a multitude of combinations.

As new products of rDNA research approach

field testing, clinical trials, and commercial introduction, safety and ethical issues have rekindled. Unlike 10 years ago, when concerns came

exclusively from scientists, concerns today

come from scientists, industry, social activists,

and the public, all for different reasons. Some

scientists, social activists, and members of the

public are concerned about possible adverse impacts on human health and the environment,

while some scientists and industry fear that public concerns may lead to overregulation.

One example illustrates the controversy over

rDNA research in agriculture. Two years ago,

Steven Lindow and Nicholas Panopoulos of the

University of California, Berkeley, successfully

constructed non-ice-nucleation bacteria that inhibit frost formation on potato plants. As these

researchers readied to field test the new organisms to see if they could protect crops from

frost damage, a coalition of public interest

groups filed a lawsuit to postpone the field trials

(Tangley, 1983). These groups believe that field

tests of genetically modified organisms should

not proceed until scientists develop a method

for establishing the safety of such releases. In

May 1984,9 days before the scheduled release

of the micro-organisms, a U.S. District Court

issued a temporary injunction halting the first

proposed release and prohibiting NIH from approving any more releases until the case was

fully resolved (Bioscience, 1984). 111 February

1985 a U.S. Court of Appeals upheld part of the

lower court’s decision, stating that NIH was required to prepare an environmental assessment

of the one field test in question but that the institute could go ahead and consider other release proposals. Recently, the Environmental

Protection Agency approved the first two field

tests of genetically altered organisms. In the first

experiment, Agracetus of Middletown, Wisconsin, would test the effects of their new products,

genetically modified disease resistant tobacco

plants, on the natural environment. In the second experiment, Advanced Genetic Sciences of

Oakland, California, would test bacteria that

have been genetically altered to prevent frost

formation on strawberry plants. This company

would spray the modified bacteria on 2,400 blossoming strawberry plants on a one-fifth acre plot

in Salinas Valley.

The central issue of these controversies is

whether genetically engineered micro-organisms will disrupt the ecosystem into which they

are released and will have adverse impacts on

human health and the environment. In the case

of non-ice-nucleation bacteria, scientists know

virtually nothing about the normal role these

bacteria play in the biosphere. Closely related

bacteria are apparently ubiquitous, and some

scientists suggest that they play a role in the

moisture nucleation in clouds, and consequently in rain or snowfall (Feldberg, 1985). What happens, however, if these new strains really are

effective in competing for the same ecological

niche as the natural strains? What if they allow

clouds to hold much more moisture before precipitation occurs?
214 . Technology, Public Policy, and the Changing Structure of American Agriculture

The major point the proponents of biotechnology, mainly the biotechnology industry and

some scientists, use to defend biotechnology is

that genetic engineering techniques have been

used in plant breeding and animal husbandry

for centuries. During the last several decades,

biotechnology has been used in chemical and

food processing industries (Fraley, 1985). For

example, antibiotics, amino acids, and other

supplements produced by fermentation technology are routinely added to feeds to stimulate animal growth and prevent disease. Microbial seed inoculums are commonly used to

increase crop yields. Immobilized cells and enzymes are being used extensively to catalyze biochemical conversions in the production of specialty chemicals and feedstock (Fraley, 1985).

Genetic engineering methods for manipulating

genes in micro-organisms such as bacteria and

yeast have also existed for several years.

Speaking for the biotechnology industry,

Hardy and Glass (1985) argue that genetically

engineered organisms are similar either to genetically engineered organisms already in commercial use or to naturally occurring organisms

indigenous to habitats where they would be introduced. The fact that similar organisms exist

or have been previously introduced into the

environment suggests that no adverse effects

would occur from the introduction of novel

micro-organisms. Hardy and Glass claim that

after nearly 10 years of close scrutiny, risk

assessment studies, and worldwide experience

with molecular and cellular genetic manipulations, there is no evidence of any significant hazards associated with this technology. The risks

remain only speculative. In fact, with the accumulation of knowledge, many of the fears

voiced in the early days of molecular genetics

have been shown to be unfounded. Therefore,

there is no reason to believe that cellular and

molecular genetic engineering should present

any greater hazard than that posed by the wholeorganism genetic manipulation that has been

practiced for centuries. With millions of dollars invested in biotechnology research and development, industry is concerned with overregulation that could stifle future growth of the

biotechnology industry and cause the U.S. industry to lose its competitive edge.

Opponents argue that the issue of deliberate

release of genetically engineered micro-organisms into the environment is quite different

from the genetic engineering methods, such as

production of antibiotics and amino acids by

fermentation technology, used in the past. McGarity (1985) points out several reasons why

risks of large-scale release of micro-organisms

are of much greater concern than the risks of

fermentation biotechnology; for example: 1) a

large-scale release of genetically engineered

micro-organisms into the environment significantly reduces the degree of human control over

the novel micro-organisms; 2) biological containment, by which strains of micro-organisms

are weakened so that they cannot survive outside the laboratory environment, can no longer

be used as a safeguard against potential hazards;

and 3) it is difficult to assess potential risks to

human health and the environment.

Alexander (1985) also expresses concerns that

there is not enough information to predict the

ecological consequences. The best model for

predicting ecological consequences is the exotic

species model, but this model has been criticized

as inappropriate for predicting the potential ecological consequences of the deliberate release

of novel micro-organisms because the model is

based on outdated ecological thinking (Regal,

1985). Because there is no adequate model, Alexander (1985) suggests that history be used as a

guide for the future. The history of the application of these emerging technologies provides

lessons for assessing new technologies. Alexander asserts that no technology was without

risk, and a risk-free technology probably does

not exist now. Regal (1985) also indicates that

there is no great power that is only good, that

has no dangers, and that cannot be misused. Although the risks from deliberate release appear

to be small, the consequences of an unlikely

event could be disastrous. It is the fear that new

micro-organisms or their genetic traits might

survive and multiply unchecked and thus have

adverse impacts on the ecosystem that make scientists worry about the release of the novel

micro-organisms (Robbins and Freeman, 1985).

Now rDNA technology is entering a crucial

stage in its development from laboratory to the
Ch. 10—impacts on the Environment and Natural Resources ● 215

marketplace. Goldberg (1985) demands that

there be a social responsibility to ensure that

rDNA technology is safe and suggests that all

sectors of society must participate in decisionmaking about new developments such as the

release of genetically modified organisms, In

light of public concerns, proponents of biotechnology realize that without public confidence

and support, this promising technology could

falter as it moves from the laboratory to the marketplace. Those scientists who believe the risk

of genetically modified micro-organisms is next

to nothing envision that public concerns about

such organisms must be addressed. Industry

also recognizes the need for some regulation

of the environmental applications of genetic

engineering and suggests that better risk assessment tools be developed (Hardy and Glass,

1985).

Some biotechnology companies that are or

will be introducing genetically engineered products believe that public perception translates

into public policy and that commercialization

of biotechnology may be in peril if ignorance

engenders fear of biotechnological research and

applications (Price, 1985). They call for public

education about biotechnology research and applications.

Since deliberate release of genetically engineered micro-organisms is such an important

and controversial issue, OTA and the National

Science Foundation cosponsored a workshop

late in 1985 to address this issue.

Although the discussion above addresses

some policy matters obliquely, the purpose of

this section is to discuss the way public policy

might enhance the positive environmental effects or mitigate the negative effects of the

emerging technologies,

The first point on which a consensus emerged

was the observation that the net movement of

new technologies is environmentally enhancing. Given the public pressure for environmental

improvement and the increased regulatory activity by Government in the environmental area,

that movement is consistent with economic theory. It follows, then, that increased Government

expenditures on research and education would

tend to have positive environmental effects. The

assessments presented here suggest that there

is a strong public interest in accelerating technological change in agriculture, and if that is

the objective, the action required is increased

research and education.

The second approach, which complements

the research and education effort, is to develop

more stringent environmental regulations and

provide stronger enforcement of regulations.

Such regulations tend to have the economic effect of raising the cost of using the environment

in agricultural production, Economic theory

suggests that as costs of particular inputs are

increased, economic agents will find ways to

reduce the use of the relatively higher priced

inputs. So, increasing the costs of environmental

inputs through stronger regulations and better

enforcement will accelerate the adoption of the

new technologies and give impetus to research

that has further environmental benefits.

Finally, policies that reward farmers who

adopt environmentally enhancing technologies

have some precedent. Cost-sharing programs

in the area of soil conservation are the best

known example. Targeted cost-sharing and

creative use of the relationship between environmentally enhancing farm practices and the

price support program, such as the so-called

cross-compliance proposal for using conservation cost-sharing funds, represents, at least in

a generic way, a policy option likely to enhance

the environmental benefits of the new technologies.

Turning to policies to mitigate the undesirable environmental impacts of new technologies, the panel moved quickly to the classic

prescription of exacting a user charge. If the

research base were available to determine the
216 . Technology, Public Policy, and the Changing Structure of American Agriculture

appropriate T value for soil erosion, a soil erosion tax could be levied on each ton of loss above

that level. Ideally, the tax would be equal to the

environmental damage caused by the erosion,

Unfortunately, the research base for setting the

T value is not sufficient. There would also be

very difficult enforcement problems with such

a tax. But the idea of making users of environmental inputs pay for them was considered fundamental to policy that mitigates undesirable

environmental consequences.

In general, with a few notable exceptions,

most of the emerging technologies are expected

to reduce substantially the land and water requirements for meeting future agricultural

needs. As a result, these technologies are also

expected to reduce environmental problems associated with the use of land and water. The

technologies were thought to have beneficial

effects relative to soil erosion, to reduce threats

to wildlife habitat, and to reduce dangers associated with the use of agricultural chemicals.

New tillage technologies, however, may reduce

erosion and threats to wildlife while increasing the dangers from the use of agricultural

chemicals.

The panel concluded that the new technologies were most likely to receive first adoption

by farmers who were well financed and were

capable of providing the sophisticated management required to make profitable uses of the

technologies. In the main, such farmers will tend

to be those with relatively large operations.

Hence, the technologies will tend to give additional economic advantages to large farm firms

relative to moderate and smaller farms, accentuating the trend toward a bipolar or dual farm

structure in the United States.

In addition, since the new technologies tend

to be, at the margin, environmentally enhancing, there is public interest in research and education that leads to their rapid development and

widespread adoption. That conclusion becomes

More practical applications of this concept

focus on raising the costs of agricultural chemicals by placing excise taxes on those chemicals. The more expensive the chemicals, the less

they will be used and the greater care the user

will take to be sure that the application rate is

not excessive. Similarly, policies to raise the

price of irrigation water might have some beneficial impacts on water quantity (although there

are studies suggesting that the price elasticity

on such water approximates unity),

CONCLUSIONS

even stronger if public policy is aimed at maintenance of the moderate farm. Larger farms,

with their own access to research results and

scientific expertise, may be able to advance the

new technologies with relatively little publicly

sponsored research. But moderate and small

farms will have to depend on publicly sponsored

research and extension education to obtain access to the new technologies and to adapt them

to their individual situations.

The new technologies will require more stringent environmental regulations and stronger enforcement of regulations. The complexities of

some of the emerging technologies will pose significant challenges for promulgation of wise

environmental regulations. The economic benefits from the technologies cannot be passed by,

but users may have little private incentive to

make use of the technologies in ways that avoid

unnecessary, adverse, third-party effects. The

panel considered that economic incentives or

disincentives, including the use of excise taxes

to discourage overuse of potentially threatening materials, represented a more intelligent approach to protection of environmental values

than did direct regulations. Yet the panel also

concluded that: 1) some additional effort to enforce existing regulations would hasten the

adoption of the new technologies that are, at

least potentially, less environmentally threatening; and 2) new regulations will be required

to deal with some aspects of the emerging technologies.
Ch. 10—Impacts on the Environment and Natural Resources ● 217

Perhaps the most revolutionary of the new

technologies are those associated with rDNA.

While specific applications of such technologies

that are currently apparent would appear to reduce resource needs and threats to the environment arising from agricultural activities, the

panel recognized possible dangers associated

with the deliberate release of genetically altered

micro-organisms. The very revolutionary nature

of the new biotechnologies and the lack of a

scientifically accepted predictive ecology prevented the panel from providing specific evaluations of resource/environmental impacts associated with the deliberate release of new forms

of life,

Ten years ago, scientists concerned about the

impact of rDNA agreed to regulate the rDNA

experiments. Now many scientists see little danger in the applications of the planned rDNA

technology. But as new products of rDNA research approach field testing and commercial

introduction, safety and ethical issues have

rekindled. Both sides of the issue agree that more

research should be conducted to assess the potential benefits and risks.

CHAPTER 10

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McGarity, Thomas O., “Regulating Biotechnology,”

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Price, Harvey S., “Biotechnology: The Need for

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p. 211.

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American Association for the Advancement of

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Schatzow, Steven, “Statement,” Testimony Before

the Subcommittee on Investigations and Oversight of the Committee on Science and Technology, U.S. House of Representatives, Washington,

DC, Apr. 17, 1985.

Tangley, Laura, “New Biology Enters a New Era, ”

Bioscience 35:5, May 1985, pp. 270-275.

Tangley, Laura, “Engineered Organisms in the Environment? Not Yet, ” Bioscience 33: 11, December 1983, pp. 681-682.

Teich, A.H. (cd.), Technology and Man Future,

Sd ed. (New York: St. Martin’s Press, 1981).
Chapter 11
Contents

Page

Structural Concentration and Industrial Agriculture. . . . . . . . . . . . . . . . . . . , 221

Regional Differences. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 222

The CATF Region . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 223

The Great Plains and the West Region.. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 227

The Northeast Region . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 229

The Midwest Region . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 232

The South Region . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 238

The Rural Development Policy Context.. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 242

Summary and Policy Implications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 244

Regional Rural Development Policy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 245

Roles and Capabilities of Government . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 246

Regional Policy Considerations. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 247

Chapter 11 References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 248

Tables

Table No. Page

11-1. Comparison of Selected Farm Characteristics, All Counties in

California, Arizona, Texas, and Florida, for 1969 and 1978 . . . . . . 224

11-2. Comparison of Selected Farm Characteristics, Agricultural Counties

in California, Arizona, Texas, and Florida, for 1969 and 1978 .. 225

11-3. Comparison of Northeast Regional Farms With Total U.S. Farms by

Selected Characteristics, 1974 and 1982.,. . . . . . . . . . . . . . . . . . . . . . . 231

11-4. Distribution of Rural Counties, by Varying Levels of Dependence

on Agriculture, for States in the Midwest . . . . . . . . . . . . . . . . . . . . . ,, 233

11-5. Characteristics of the Midwest Region, Rural Counties in the

Region, and the Rural Agricultural Counties. . . . . . . . . . . . . . . . . 234

11-6, Comparison of Midwestern Counties With Greater and Lesser Concentration of Sales on Farms With Sales of More Than $100,000 for

Counties Dominated by Dairy Production . . . . . . . . . . . . . . . . . . . . . . . 236

11-7. Comparison of Midwestern Counties by Concentration of Sales on

Farms With Sales of More Than $100,000 for Counties Dominated

by Grain Production . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 236

11-8. Comparison of Midwestern Counties by Concentration of Sales on

Farms With Sales of More than $l00,000 for Counties Dominated

by Mixed Agriculture . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ... . . 237

11-9. Comparison of Basic Farm Structure Statistics in Rural Counties of

the South With Those of Total United States . . . . . . . . . . . . . . . . . . . . 239

11-10. Quality of Life Variables in Rural Counties. . . . . . . . . . . . . . . . . . . . . . 240

Figure

Figure No. Page

11-1. Nonmetropolitan Counties With a Poverty Rate of

25 Percent or Higher . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .,.........243
Chapter 11

Impacts on Rural Communities

The impacts of technological and structural

change in agriculture do not end with the individuals who live and work on farms. A variety

of other consequences are to be expected at the

level of the rural communities that have direct

and indirect linkages to farms and farmers. As

with individual farmers, some communities are

likely to benefit from change, while others are

likely to be affected adversely. Much depends

on the type of overall labor force in the communit y and on the opportunities for labor to move

to other areas of employment.

Hard-hit communities may need technical assistance to attract new businesses to their areas,

to develop labor retraining programs, and to

alter community infrastructure to attract new

inhabitants. To accomplish these goals, Federal

policy will have to be complemented by regional

and local policies.

Those rural communities that benefit from

changes in agricultural technology and structure may do so in several ways. For example,

as agriculture becomes more concentrated,

some communities will emerge as areawide

centers for the provision of new, high-value technical services and products. Likewise, some

communities will emerge as centers for highI Rural communities are defined as places with less than 20,000

inhabitants in a nonmetropolitan county.

volume food packaging, processing, and distribution. In both cases, the economic base of these

communities is likely to expand. However, unless total demand for agricultural commodities

increases substantially, centralization of services, marketing, and processing will be like a

zero-sum game in many areas; the market centers will benefit at the expense of other communities. Many of the communities that are bypassed will decline as a result of the process of

centralization.

Communities may also benefit in those parts

of the country in which the number of small and

part-time farms is increasing. This phenomenon results in an increase in population in many

rural areas and in an increase in total income

and spending in some of these rural areas. The

increase in small farms may sustain more retail establishments than would otherwise be the

case, since purchases by small farmers may tend

to be more local than those by larger farmers.

The operators of these farms in many cases subsidize their own production from off-farm income.

This chapter assesses the impacts that emerging technologies and structural change have had

on rural communities in the 1970s in five regions of the United States, outlines several areas

of potentially adverse impacts, and provides a

policy framework for options that may help mitigate the adverse impacts.

A landmark study that addressed the relationships between increased concentration in agricultural production and community welfare

was done by Walter Goldschmidt in 1944. Goldschmidt found a series of negative social effects

associated with large-scale agriculture in the

central valley of California. His research was

based on a matched-pair comparison of a community of relatively large farms (Arvin) and a

community of relatively small farms (Dinuba).

He found higher median family income, lower poverty, better schools, more retail trade,

stronger institutions—including churches, more

recreational opportunity, and more newspaper

readership—associated with the set of small

farms surrounding Dinuba, Although numerous methodological and theoretical criticisms

have been made about this study, the thesis of

this study continues to frame the discussion of

structure and community relationships. Gold221
222 ● Technology, Public Policy, and the Changing Structure of American Agriculture

schmidt and his supporters argue that when

farming is practiced on a scale that exceeds a

family’s ability to provide the main source of

labor and management, it tends to acquire industrial relations of production in which ownership and management are separated from labor. As a result, this industrialized form of

agriculture tends to become disarticulate from

the surrounding communities, increasingly

relying on these communities as a source of lowcost hired labor. This, in turn, is thought to result in social inequality, poverty, and a range

of associated pathologies. The general hypothesis is that increases in structural concentration

of production, and especially industrial relations of production, are associated with decreases in social welfare.

There has been a long-run secular trend

toward an increased proportion of hired labor

and a decreased proportion of family labor in

agriculture. Large and very large farms tend to

rely more on hired labor than do moderate

farms. As total agricultural production becomes

more concentrated in the large and very large

sales classes, the proportion of hired labor in

U.S. agriculture is likely to continue to increase.

The available data on regional changes in proportion of hired labor is limited. Data is available on changes in number of hired workers by

the four regions of the United States defined

by the 1970 and 1980 Census of Agriculture.

Hired labor increased by 21 percent in the North

Central region during this period. In the West

hired labor stayed about the same. Hired labor

decreased by about 18 and 5 percent in the

Northeast and South, respectively (Pollack, et

al., 1983).

Increasing structural concentration in U.S.

agriculture is not necessarily synonymous with

agricultural industrialization. In many areas of

the United States large and very large farms are

owned and operated primarily with family labor. This is generally most true where the farming system is land-extensive and not labor-intensive, such as cash grain production in the

Midwest and ranching in the Great Plains and

the West. Increasing concentration is expected

to continue to take place in these regions without large increases in hired labor. However, adverse effects may also occur in regions where

continued concentration is likely to result in the

loss of a substantial proportion of the moderate farms. Adverse impacts may result simply

from the loss of a substantial proportion of the

local population if many farm families relocate

to other parts of the country and they are not

replaced by immigration. This in turn will result in a reduction in the population base that

supports civic activities and patronizes the small

businesses and services in the local rural communities.

Retail establishments in rural communities

that provide goods and services to these moderate farms may decline when the consolidated

farms choose to purchase goods and services

at greater distances. The operators of the large

and very large farms that emerge from the process of structural change are considered more

likely to purchase goods and services and to market their products over greater distances than

their more moderate predecessors were. The

argument is that they are able to receive volume discounts and premiums from more centralized purchases and sales.

The structure of agriculture and the characteristics of rural communities vary greatly

across the United States, owing to major differences in soils, climate, population density, pattern of land use, economic and social history,

availability of irrigation water, topography, availability of low-cost labor, and the level of education of the population. Following from this it

can be expected that changes in agricultural

structure will vary in different parts of the country and that the impacts of structural change

on rural communities may vary in different regions. To avoid overgeneralizing in its assessment of impacts of structural change on rural

communities, OTA analyzed five regions of the

United States: the Northeast; South; Midwest;
Ch. 1 I—Impacts on Rural Communities • 223

the Great Plains and the West; and the CATF

(those counties with the most industrialized agriculture in four Southern and Western States—

California, Arizona, Texas, and Florida). This

division of the United States differs in several

respects from the regional division used in the

U.S. Census of Agriculture. In particular, the

Great Plains and the West in this chapter include

most of the Western region of the Census of Agriculture and also have parts of the North Central and Southern regions from the census. The

CATF region in this chapter has no close counterpart in the Census of Agriculture.

Although the intended focus was on rural

communities, information on the welfare of individual communities and on linkages between

individual communities and surrounding farms

was not directly available on a regional basis

for most States. In general, it was necessary to

do the analysis in terms of rural agricultural

counties instead of individual communities.

This was a distinct disadvantage, since countylevel data tend to obscure the details of linkages

and impacts at the community level.2 With the

exception of the CATF region, the set of counties defined as rural and agricultural was drawn

from the set of nonmetropolitan counties. Metropolitan counties as defined by the Office of

Management and Budget are also known as

Standard Metropolitan Statistical Areas, with

the exception of the New England region. All

other counties may be considered to be rural

counties. Rural counties with a significant

proportion of total income from agricultural

sources were considered as candidates for inclusion in the set of rural agricultural counties.

The minimum proportion of agricultural income that was considered significant varied by

region and ranged from 5 to 20 percent.

ZHowever, if statistically significant relationships can be shown

between structural change in agriculture and changes in social

welfare when the unit of analysis is the county, then it is likely

that the associations are even stronger for a proportion of the

communities in the county. This is generally true because not

all communities are likely to be equally affected within a county.

The county level of analysis aggregates the different impacts on

the individual communities and tends to level out strong relationships with respect to particular communities. Therefore, statistically significant associations at the county level area strong

test of a hypothesis concerning associations at the community

level.

The CATF Region

The CATF region includes all of Florida and

the industrial-agricultural counties of California, Arizona, and Texas, s The counties studied

are the 98 counties that were either in the top

100 counties nationwide in sales or that had

$2,000 or more per year in per capita income

from agriculture. Twenty-six Texas cattle and

grain counties that met these criteria were

grouped with the region covered by the Great

Plains and the West, since they fit the land-use

intensity and farming system types of this region better than the CATF region. The CATF

regional set of counties4 differs considerably

from the nonmetropolitan counties in the rest

of the United States in that agricultural development is relatively recent in these counties.

Many CATF counties have not gone through

a period in which moderate farms dominated

agricultural structure. These counties are also

unique in the extent to which they depend on

subsidized irrigation water from State and Federal water projects.

CATF counties are of particular interest because agriculture in these counties has already

evolved into a highly concentrated structure,

The great majority of agricultural sales comes

from 1arge-scale, industrial-type farms. In 1982,

farms in the very large sales class had 66 percent of regional sales as compared with 25 percent for the rest of the United States. Moreover,

the share of sales from very large farms increased from 58 to 66 percent between 1978 and

1982. The number of farms in all acreage categories in CATF counties has recently declined,

with the exception of the very large sales class.

This reduction in the number of farms with

fewer than 2,000 acres increased the concentration of production by 17 percent at the same

time that total agricultural sales in the region

increased only 6.5 percent (see tables 11-1 and

11-2). The counties selected as the data set for

3“CATF counties,““CATF regional set of counties, ” and “CATF

region” are used synonymously.

qThe findings in this section are contained in Dean MacCannell and Edward Dolber-Smith, “Report on the Structure of Agricuhure and Impacts of New Technologies on Rural Communities in Arizona, California, Florida, and Texas, ” prepared for the

Office of Technology Assessment, Washington, DC, 1985.
224 ● Technology, Public Policy, and the Changing Structure of American Agriculture

Table 11-1.—Comparison of Selected Farm Characteristics, All Counties

in California, Arizona, Texas, and Florida, for 1969 and 1978

Change from

Attribute 1969 1978 1969 to 1978

Number of farms: . . . . . . . . . . . . . . . . . . 332,878 (856) 8

290,977 (746) – 9.88

Sales:

Sales categories:

>$100,000 . . . . . . . . . . . . . . . . . . . . . . 31,983 (82)

$40-90,999 . . . . . . . . . . . . . . . . . . . . . . . 29,693 (76)

> $40k . . . . . . . . . . . . . . . . . . . . . . . . . . 34,028 (88) 61,676 (158) 81.25

$20-39,999 . . . . . . . . . . . . . . . . . . . . . . . 27,672 (71) 28,536 (73) 3.12

$10-19,999 . . . . . . . . . . . . . . . . . . . . . . . 34,973 (90) 34,856

$ 5-9,999 . . . . . . . . . . . . . . . . . . . . . . . .

(90) – 0.33

43,733 (112) 42,960 (110) – 1.77

$2,500-4,999 . . . . . . . . . . . . . . . . . . . . . 46,947 (121) 45,679 (117) – 2.70

<$2,500 . . . . . . . . . . . . . . . . . . . . . . . . 145,476 (374) 76,903 (197) –47.14

Total sales per county . . . . . . . . . . . . . . $20,509,402 (1,000) $21,838,408 (1,000) 6.48

Average sales per farm (1980 dollars). , 16.85

Acreage:

Acreage categories:

>2,000 acres . . . . . . . . . . . . . . . . . . . . 14,826 (38) 14,869 (38) 0.29

1,000-1,999 acres . . . . . . . . . . . . . . . . . 16,969 (44) 16,468 (42) – 2.95

500-999 acres. . . . . . . . . . . . . . . . . . . . 32,419 (83) 27,772 (71) – 14,33

180-499 acres. . . . . . . . . . . . . . . . . . . . 70,513 (181) 56,773 (146) – 19.49

50-179 acres. . . . . . . . . . . . . . . . . . . . . 96,974 (249) 81,939 (210) – 15.50

10-49 acres. . . . . . . . . . . . . . . . . . . . . . 69,878 (180) 62,002 (159) –11 .27

<10 acres . . . . . . . . . . . . . . . . . . . . . . 31,319 (81) 31,154 (80) – 0.53

Average acreage per farm , . . . . . . . . . . 1,939 1,957 0.93

Type of ownership

Family ownership ., ... , . . . . . . . . . . . . 166,911 (429) 176,448 (452) 5.71

Partnership . . . . . . . . . . . . . . . . . . . . . . . 25,283 (65) 26,443 (68) 4.59

Corporate ownership . . . . . . . . . . . . . . . 5,209 (13) 9,529 (24) 82.93

Other type of ownership . . . . . . . . . . . . 1,866 (5) 1,287 (3) –31 .03

aFirS~ value IS Ifle Sum for all counties; the mean value per county IS in parentheses

SOURCE Off Ice of Technology Assessment

this analysis are shown in detail in a paper by

MacCannell and Dolber-Smith (1985).

Background on the CATF Region

Current agricultural patterns were not fully

established in the CATF area until after World

War II. Arizona was not even a State until 1912,

and many of the new agricultural regions of all

four States were not settled until this century.

Historically and nationally, the original pattern

of Spanish land grants and dependence on irrigation systems are the two main underlying

causes of present-day farming systems. Prior

to this century, agricultural landholdings were

enormous, and the main products were range

animals and nonirrigated grains. The first crude

irrigation systems were built at the beginning

of this century by land developers and speculators. The Imperial Valley of California went

through several successive cycles of irrigation,

land sales based on the promise of cheap and

plentiful water, irrigation system failure, farmer

bankruptcy, land repurchase or repossession

by the original speculators, irrigation system

overhaul, land resale, and so forth. These and

similar abuses eventually resulted in Federal intervention and public involvement in the construction and management of irrigation systems

throughout CATF. A number of these systems

are enormous and of very recent construction.

The San Luis Unit of the California Central Valley Project, completed in 1969, is one such system. This farm and rural community system in

the most productive area of California (West

Fresno County) has only a 15-year history.

Since World War II, the large agricultural

operators of the CATF region have exploited

their natural, historical, and political advantage

by combining new agricultural technologies,

modern irrigation techniques, Government sup-
Ch. 11—Impacts on Rural Communities ● 225

Table 11-2.—Comparison of Selected Farm Characteristics, Agricultural Counties

in California, Arizona, Texas, and Florida, for 1969 and 1978

Change from

Attribute 1969 1978 1989 to 1978

Number of farms: . . . . . . . . . . . . . . . . . . 97,557 (995) 84,951 (867) – 12.92

Sales:

Sales categories:

>$100,000 . . . . . . . . . . . . . . . . . . . . . . 16,184 (165)

$40-90,999 . . . . . . . . . . . . . . . . . . . . . . 13,703 (140)

> $40k . . . . . . . . . . . . . . . . . . . . . . . . . . 16,298 (166) 29,887 (305) 83.38

$20-39,999 . . . . . . . . . . . . . . . . . . . . . . 13,082 (133) 11,048 (113) –15.55

$10-19,999 . . . . . . . . . . . . . . . . . . . . . . 14,473 (148) 10,522 (107) –27.30

$5-9,999 . . . . . . . . . . . . . . . . . . . . . . . 14,240 (145) 9,942 (101) –30.18

$2,500 -4,999 . . . . . . . . . . . . . . . . . . . . . 11,696 (1 19) 8,766 (89) -25.05

<$2,500 . . . . . . . . . . . . . . . . . . . . . . . . 27,768 (283) 14,705 (150) –47.04

Total sales per county. . . . . . . . . . . . . . $10,307,860 (1,000) $11,855,632 (1,000) 15.02

Average sales per farm (1980 dollars) . $113,068 $153,750 27.14

Acreage:

Acreage categories:

>2,000 acres. . . . . . . . . . . . . . . . . . . . 5,418 (55) 5,744 (59) 6.02

1,000-1,999 acres . . . . . . . . . . . . . . . . 6,420 (66) 6,647 (68) 3.54

500-999 acres . . . . . . . . . . . . . . . . . . . 11,792 (120) 9,839 (100) –16.56

180-499 acres . . . . . . . . . . . . . . . . . . . 18,968 (194) 14,926 (152) –21.31

50-179 acres . . . . . . . . . . . . . . . . . . . . 21,507 (219) 18,303 (187) –14.90

10-49 acres . . . . . . . . . . . . . . . . . . . . . 24,757 (253) 21,047 (215) –14.99

<10 acres . . . . . . . . . . . . . . . . . . . . . . 8,702 (89) 8,445 (86) – 2.95

Average acreage per form . . . . . . . . . . . 2,074 2,192 5.69

Type of ownership:

Family ownership. . . . . . . . . . . . . . . . . . 60,503 (617) 55,639 (568) – 8.04

Partnership . . . . . . . . . . . . . . . . . . . . . . . 10015 (102) 10,019 (102) 0.04

Corporate ownership . . . . . . . . . . . . . . . 2,096 (21) 4,048 (41) 93,13

Other type of ownership . . . . . . . . . . . . 648 (7) 459 (5) –29.17

aFirSt value IS the sum for all counties; the mean value per county is in parentheses.

SOURCEO fficeofTechnology Assessment

port programs, and an abundant supply of

cheap, foreign labor. At the present time, CATF

counties occupy a preeminent position in the

national agricultural economy and international

trade. Half of the top 100 agricultural counties

nationwide are found in these four States. Agricultural products are the principal ’’industry”

and export of California, Arizona, and Texas.

In Florida, agriculture ranks behind tourism and

manufacture, but it still employs 77,000 workers and has an annual sales of $l.3 billion. In

Texas, the value added inagricultureis 1.3 times

that of all manufacturing. The economic position of agriculture within the CATF region is

all the more remarkable when the high level of

industrial development of these same States is

taken into consideration.

The agricultural commodity mix of the CATF

counties is extremely diverse. With the exception of Florida citrus, there is no statewide

monoculture or clear dominance of entire regions by a single crop or commodity. Leading

commodities of the four States are cotton, sorghum, beef, wheat, citrus, row-crop vegetables,

rice, sugarcane, sugar beets, grapes, melons,

avocados, strawberries, nuts, peanuts, and corn.

Over 9 million acres of cotton are grown in the

CATF region, amounting to 70 percent of the

U.S. total and about 30 percent of the world trade

in cotton. One hundred percent of U.S. citrus

and 55 percent of all noncitrus fruits are grown

in the CATF region.

The CATF counties have four dominant forms

of agricultural operations: 1) large-scale, familyowned corporations; 2) large-scale, corporate

farms and partnerships; 3) highly sophisticated

“part-time” operations owned by investors (usually urban-based professionals), which have

high gross sales from small acreages; and 4)

small-scale, unsophisticated, part-time farming
226 ● Technology, Public Policy, and the Changing Structure of American Agriculture

operations with low sales. There has never been

a widespread pattern of moderate-scale, familyowned farms in the CATF region.

Findings for the CATF Region

Examining the impact of structural change

that has already occurred on the rural communities among these counties may give insight

into the impacts of continuing concentration

in other regions of the country. The analysis of

these counties has benefited from the availability of data on poverty, unemployment, and

standard of living at the community level (census tract data) in contrast to the analyses of other

regions, which are based almost entirely on

county-level data.

The primary finding is that there is a strong

correlation between increased concentration

and substandard social and community welfare

in this regional set of counties. However, this

relationship is not strictly linear. As agricultural

scale increases from very small to moderate

farms, the quality of community life improves.

Then, as scale continues to increase beyond a

size that can be worked and managed by a family, the quality of community life begins to deteriorate. Increasing concentration in this region results in increasing poverty, substandard

living and working conditions, and a breakdown

of social linkages between the rural communities that provide labor and the farm operators.

In other words, the relationship of community

welfare to agricultural structure resembles an

inverted U curve (MacCannell and DolberSmith, 1985). This finding is a modification of

the basic Goldschmidt hypothesis that any increase in concentration is associated with a decrease in community well-being.

The most extreme poverty in CATF counties

is found in those counties with the most concentrated and productive agriculture. Up to 70

percent of the population of the most highly concentrated counties live in poverty. Up to 40 percent of the population live in houses without

plumbing in the same counties.5

‘The measure of agricultural concentration that is the best

predictor of change in median family income between the 19i’o

and 1980 census years is the proportion of farms in each county

It was found that the types of rural communities in the CATF counties could be usefully

placed in one of three categories:

1.

2.

3.

Communities in which the population living on farms is wealthy and the associated

rural communities are impoverished. This

pattern is found in the central valley of California and in parts of Texas.

Communities that are internally segregated;

the wealthy and the poor live in segregated

neighborhoods in the same community.

This pattern was found in and near Lubbock and Brownsville, Texas.

Communities that are externally segregated.

In this pattern, entire communities are

dominated by a single social class or ethnicity. The result of this pattern is a regional

set of counties within which some of the

towns are lower working class, farm worker, and transient communities, while other

towns nearby exhibit the classic pattern of

the rural trade center, and are the communities of choice for middle-class immigrants

and nonagricultural, industrial relocation.

In general, CATF communities that are surrounded by farms that are larger than can be

operated by a family unit have a bimodal income

distribution, with a few wealthy elites, a large

majority of poor laborers, and virtually no middle class. The absence of a middle class at the

community level has a serious negative effect

on both the quality and quantity of social and

commercial services, public education, and education. Rothman and others (1977) find that

hired agricultural laborers are always located

on the bottom of community status hierarchies,

are always transient to some degree (even if not

technically migratory), and are never treated

as full-fledged members of the rural community.

On the other hand, the large-scale farm owneroperators tend to bypass local public and commercial services and establishments, preferring

with greater than $40,000 in annual gross sales of farm products.

This variable is inversely related to the variation in median family income and accounts for 31 percent of the change in income.

That is, counties that had a high proportion of farms with sales

in excess of $40,000 in 1970 had a strong tendency toward low

growth in family income in the decade of the 1970s. This finding

lends strong support to the Goldschmidt hypothesis.
Ch. 1 I—Impacts on Rural Communities ● 227

to shop in distant cities. These same large-scale

farm owner-operators purchase education, police protection, recreation, and other public sector amenities from the private sector for their

own exclusive use. As a result, their needs and

desires are not translated into community wellbeing. The recent public involvements of the

largest farmers in CATF are not based in the

local community, but in lobbying and selling

at the State, Federal, and international levels.

In sum, CATF rural communities in the most

productive agricultural areas do not share in

economic or social benefits from increased production and sales. Instead, the rural community stagnates or declines in the context of increasing agricultural productivity. Under these

structural conditions, CATF agriculture is increasing its dependence on foreign labor. Continued importation of labor, operating within a

different value system than the rest of the United

States, is the only possible support for an agricultural economy that has become disarticulate from the local community. Increasingly,

the rural communities in CATF agribusiness

areas are not local in the sense of participating

in U.S. social and cultural traditions. Instead,

they resemble Honduran plantation communities more than their rural counterparts in other

areas of the United States. In effect, social and

economic relations from the developing world

have been adopted to maintain the world market position of CATF agriculture.

A major cause for concern in CATF counties

stems from the potential for substantial additional displacement of labor in the production

of certain fruit and vegetable crops and dairy

products. Historically, fruit and vegetable production in CATF has been a large, steady source

of employment, although low paying and often

seasonal in nature. However, one of the anticipated impacts of emerging technologies is a reduction in the labor required to prepare fields

and seedbeds, plant, cultivate, treat, harvest,

sort, and process fruits and vegetables. This situation could result in substantial increases in

unemployment in CATF counties among farm

laborers who have few employment alternatives. Since the rural-labor communities in this

region are already impoverished and alienated

from mainstream U.S. society, substantial increases in unemployment are likely to result in

increased unrest and discontent among the

farmworker population. This problem will be

offset to some extent if CATF counties succeed

in capturing additional production shares from

other regions of the country.

The Great Plains and

the West Region

Background on the Great Plains

and the West Region

The region of the Great Plains and the West

encompasses the 17 States—North Dakota,

South Dakota, Nebraska, Kansas, Oklahoma,

Texas, New Mexico, Colorado, Montana, Wyoming, Idaho, Utah, Arizona, Nevada, California, Oregon, and Washington—that potentially

had agricultural counties in which farming systems of grains, or livestock (excluding dairy),

or combined grains and livestock predominated.6 In these 17 States, 351 counties were classified as agricultural. The basic units of analysis are the 234 counties with at least 20 percent

of total proprietor and labor income from agriculture and counties with at least 25 percent

of their economically active population in agriculture in 1982. Each county in the region is

classified by its predominant type of agriculture, according to data from the 1974 and 1982

Census of Agriculture. The four classes of agriculture used are wheat farming (33 counties),

livestock (84 counties), wheat and livestock (72

counties), and mixed grains (corn, sorghum, and

wheat) and livestock (45 counties). Counties with

25 percent or more of production in other grains

and field crops, hogs, or dairy production are

eliminated from the analysis. Those counties

of California, Arizona, and Texas that are characterized by industrial agriculture are also excluded. The industrial-agricultural counties of

these States are considered separately as part

of the CATF region. The counties used to repeThe findings reported in this section are contained in: Jan L.

Flora and Cornelia Butler Flora, “Emerging Agricultural Technologies, Farm Size, Public Policy, and Rural Communities: The

Great Plains and the West, ” prepared for the Office of Technology Assessment, Washington, DC, 1985.
228 ● Technology, Public Policy, and the Changing Structure of American Agriculture

resent the Great Plains and the West are shown

in detail in the paper of Flora and Flora (1985).

Counties are further subdivided into those

with a predominance of moderate farms and

those with a predominance of large farms.

Farms in this region are generally much larger

per dollar of production than farms in other regions of the United States. Consequently, the

definition of large and moderate size is different than that in other regions. Farms with acreages in the range of 500 to 999 acres represent

moderate farms, whereas farms larger than

2,000 acres represent large farms.

The analysis was based on agricultural counties in the Great Plains and the West. The Great

Plains includes most of both the northern and

southern plains. Northeast New Mexico and the

eastern half of Colorado are included in the

Great Plains. The western half of South Dakota

and northwestern Nebraska, areas in which

grazing on federally owned land is common,

are considered part of the West. Annual precipitation in the Great Plains ranges from more

than 40 inches, in eastern Texas, to less than

20 inches, along the western border of the region. In the area of the West considered agricultural, annual precipitation ranges from 25

inches or less in most river valleys to 6 inches

or less in the intermountain basins and southern desert areas. Much of the land outside the

river valleys is owned by Federal or State governments that regulate access to the land, which

is used primarily for grazing cattle and sheep.

The frost-free period in this region ranges from

less than 100 days in North Dakota to over 300

days in southern Texas.

Wheat production (spring wheat in the northern plains and winter wheat in the southern

plains) dominates the Great Plains. Corn, an increasing proportion of which is irrigated from

west to east, is an important crop in southeastern South Dakota and eastern Nebraska and

Kansas. In those areas where integrated grainlivestock operations rotate corn and soybeans,

and are the dominant farming system, agriculture is very similar to that in the Corn Belt. Grain

sorghum is an important crop farther west in

Nebraska, Kansas, and Texas, where precipitation levels are less. Cotton dominates agriculture in parts of Oklahoma and Texas. However,

much of the land, especially in the western half

of the region, is in native range, whose productivity is limited by low precipitation.

Findings for the Great Plains and

the West Region

Growth in the number of large farms in each

type of county was associated with a decline

in the number of moderate farms. In general,

there was a kind of homogenization; more counties had a similar structure in 1980 than in 1970.

A number of significant findings parallel those

found in CATF counties, although they are not

as dramatic. Support for the Goldschmidt hypothesis was provided by two specific findings:

1. The decline in the number of retail services and retail sales was greatest in those

counties with the largest increase in very

large farms. Conversely, more retail services and sales were retained in counties in

which the number of moderate farms increased in the 1970s.

2. Counties with a predominance of moderate

farms in 1970 experienced a greater increase

in hired labor than large farm counties during the 1970s, where farm consolidation

occurred, and an even greater increase in

mechanization investment.

There is evidence that the growth in the number of very large farms was associated with moderate declines in rural, nonfarm, and total population. This suggests that concentration is

occurring at the same time that the total number of agricultural workers is declining. That

is, expansion of farm size in the Great Plains

and the West is taking place without a substantial increase in hired labor as has been the case

in industrial agriculture of the CATF counties.

Counties with growth in the number of moderate farms tended to retain rural nonfarm and

total population. Unlike CATF counties, there

was no significant association between change

in size of farms and change in measures of

income.
The counties with a predominance of moderate farms in 1969 had a much greater decline

in the numbers of commercial establishments

between 1969 and 1982 than did those counties

initially dominated by large farms. This was

largely because moderate farms in the 1970s declined in numbers during the process of land

concentration.

Median family income was generally positively associated with the proportion of land in

large farms and negatively associated with land

in moderate farms. This suggests that population has declined in large farm counties and that

the reduced number of large farms that remain

generally have higher net incomes than the

farms in counties where moderate farms still

predominate. During the decade of the 1970s,

the gap in median family incomes between large

and moderate farm counties generally tended

to close, especially for wheat and wheat/livestock counties,

A number of differences emerged between the

four different types of counties:

1. Livestock counties were the only counties

that gained population as a group. The livestock counties had the greatest increase in

rural nonfarm population and did not lose

as much of their farm population as the

other three types of counties. However, the

livestock counties had the lowest initial

population base.

2. Wheat counties that were dominated by

large farms in 1969 had a greater increase

in hired labor than wheat counties dominated by medium farms in 1969. This is the

opposite of what occurred in the other types

of counties.

3. The strongest correlation between increases

in median family income and increased

farm size occurred in wheat counties. This

was consistent with the relationship of poverty and farm size in wheat counties. Wheat

counties with a higher percentage of large

farms had a lower percent of poverty. Interestingly, wheat/livestock counties had

the opposite relationship between poverty

and farms size. Poverty was positively correlated with percent of large farms in the

Ch. 11—impacts on Rural Communities ● 229

wheat/livestock counties. Moreover, the

correlation between poverty and percent of

large farms became more strongly positive

during the 1970s. The dominant size class

of farms was unrelated to poverty for the

livestock and mixed crop/livestock counties.

The basic conclusion about the Great Plains

and the West is that there is some support for

the modified Goldschmidt hypothesis, but that

the outcome there is likely to be much different

than that in CATF counties. Incomes improved

as concentration increased, but there were declines in population and number of retail establishments. There is a strong potential for the

development of a high concentration of agricultural production in the Great Plains and the

West—especially in terms of farm size, if not

gross sales per farm. The most likely adverse

impact will be the loss of population and small

retail firms in the region. At the same time, there

is likely to be lower availability of alternate employment options in manufacturing and service

industries as compared with other regions of

the country. As a result, many small rural communities are expected to become substantially

less viable. As in the CATF region, the trend

is toward increasing sales and net income per

farm as farm concentration and consolidation

continue.

The region of the Great Plains and the West

is unlikely to develop the highly intensive, diversified agriculture with high dependence on lowcost, hired labor that characterizes the CATF

counties. The people that remain in the agricultural counties of the West are likely to have

higher median incomes as a result of concentration of production in this region. The type

of farming systems in the counties included in

the Great Plains and the West will simply not

have the labor requirements of the intensive

fruit, vegetable, and livestock production of

CATF industrial counties.

Background of the Northeast Region

The Northeast region comprises six New England States (Connecticut, Maine, Massachu-
230 Ž Technology, Public Policy, and the Changing Structure Of American Agriculture

setts, New Hampshire, Rhode Island, and Vermont) plus three Middle Atlantic States (New

Jersey, New York, and Pennsylvania).7 This region is characterized by a relatively uniform distribution of farm types and sizes and a relative

absence of large-scale or industrialized agriculture. Agriculture in this region is dominated by

the production of dairy products, followed by

fruit, vegetable, and poultry production for

nearby urban markets. There has been a longstanding decline in the amount of land in agriculture in this region; however, this decline was

attenuated in the early 1970s. The farm population as a percentage of the nonmetropolitan

population has been lower than that of the other

regions of the United States since the turn of

the century.

Urban economic and social influences have

a relatively dominant role over the well-being

of rural communities in this region. There are

only 105 nonmetropolitan counties in the whole

region, and only 30 counties in which 5 percent

or more of labor-proprietor income was derived

from agriculture. The most agriculturally productive counties in this region are not the most

rural, but are instead closely linked with major

urban centers. Inconsequence, it is reasonable

to expect that structural change in agriculture

in the Northeast is much less likely to be associated with adverse effects on rural communities

as it is in other regions of the United States, since

opportunities for off-farm employment are likely

to continue to be better for more rural residents

in this region than in many other parts of the

country.

The Northeast is also quite diverse. One

source of diversity is agroecological in nature.

The six New England States generally have lowquality soils and short growing seasons, with

a few exceptions, such as the Connecticut River

Valley. The Middle Atlantic States generally

have more favorable agricultural conditions.

The second source of diversity is socioeconomic

in nature and relates to the dramatic variations

‘The findings reported for the Northeast region are contained

in: Frederick H. Butte] and Mark Lancelle, “Emerging Agricultural Technologies, Farm Structural Change, Public Policy, and

Rural Communities in the Northeast,” prepared for the Office

of Technology Assessment, Washington, DC, 1985.

in urban-metropolitan influence in the region.

The contrasts are striking between the Boston

to Washington, DC, megalopolis, with its densely settled 35 million inhabitants, on the one

hand, and the State of Vermont, which is composed entirely of nonmetropolitan counties, on

the other.

The farm structure of the Northeast showed

increased strength in the small and part-time

farm component during the 1970s and early

1980s. The number of farm operators whose

principal occupation was not farming, or who

worked any days off-farm, or who worked 100

or more days off-farm increased more rapidly

in the Northeast than in the remainder of the

United States. The Northeast also exhibited a

larger increase in the number of individual or

family farms than in the rest of the United States,

primarily in the small and part-time classes of

farms. Table 11-3 shows a comparison of the

Northeast with the rest of the United States for

selected characteristics for 1974 and 1982.

Findings for the Northeast Region

The results of the analysis of relationships between measures of structural change and measures of community well-being support the expectation that structural change in agriculture

is not likely to have great impact on rural communities. This finding is in stark contrast with

the findings from CATF counties and also differs considerably from the findings for the Great

Plains and the West region. The analysis does

provide some useful insights of a more detailed

nature:

1,

2.

3.

4.

There was no strong pattern of social or

economic decline in rural counties between

1970 and 1980.

The rural population of the Northeast region has relatively high income levels and

access to services.

The rate of technical change as measured

by expenditures on machinery and chemicals was relatively low during the 1970s.

There was no significant relationship between: a) change in technology and farm

structure, and b) rural community welfare

changes during the 1970s.
Ch. 11—Impacts on Rural Communities . 231

Table 11-3.—Comparison of Northeast Regional Farms With Total U.S. Farms by Selected Characteristics,

1974 and 1982

Percent Percent

Northeast region change, Total United States change,

Farm structure characteristics 1974 1982 1974-82 1974 1982 1974-82

 - - Number of farms . . . . . . . . . . . . . . . . . . . 127,531

Land in farms (acres) . ..............23,359,889

Average size of farm (acres) . . . . . . . 183

Value of land and buildings:

Average per farm (dollars) . . . . . . . . . 121,227

Average per acre (dollars) . . . . . . . . . 662

Farms by size:

<10 acres. . . . . . . . . . . . . . . . . . . . . . . 7,689

10-49 acres . . . . . . . . . . . . . . . . . . . . . . 19,416

50-179 acres . . . . . . . . . . . . . . . . . . . . . 54,901

180-499 acres . . . . . . . . . . . . . . . . . . . . 37,864

500-999 acres . . . . . . . . . . . . . . . . . . . . 6,421

1,000-1,999 acres . . . . . . . . . . . . . . . . . 1,046

2,000 acres . . . . . . . . . . . . . . . . . . . . 194

Land use:

Total cropland (acres). . . . . . . . . . . . . 13,851,473

Woodland (acres) . . . . . . . . . . . . . . . . . 5,809,958

Agricultural products sold:

Market value ($1,000) . . . . . . . . . . . . . 4,291,380

Average per farm . . . . . . . . . . . . . . . 33,650

Crops . . . . . . . . . . . . . . . . . . . . . . . . . . . 1,440,397

Livestock . . . . . . . . . . . . . . . . . . . . . . . 2,216,436

Poultry . . . . . . . . . . . . . . . . . . . . . . . . . . 616,094

Farms by type of organization:

Individual or family . . . . . . . . . . . . . . . 82,142a

Corporation . . . . . . . . . . . . . . . . . . . . . 2,615a

Tenure of operator

Full owner . . . . . . . . . . . . . . . . . . . . . . 83,389

Part owner . . . . . . . . . . . . . . . . . . . . . . 36,112

Tenant . . . . . . . . . . . . . . . . . . . . . . . . . . 8,030

Principal occupation:

Farming . . . . . . . . . . . . . . . . . . . . . . . . . 78,144

Nonfarming . . . . . . . . . . . . . . . . . . . . . 46,390

Operators reporting: days of work off farm

Any days . . . . . . . . . . . . . . . . . . . . . . . . 56,670

100 days . . . . . . . . . . . . . . . . . . . . . . 46,691

Selected production expenses ($1,000)

Commercial fertilizer . . . . . . . . . . . . . 207,433

Other agricultural chemicals ....., . 74,225

Hired labor. . . . . . . . . . . . . . . . . . . . . . 401,846

Workers working >150 days:

farms . . . . . . . . . . . . . . . . . . . . . . . 21,775a

Numbers of workersb . . . . . . . . . . . . . 66,149

Machinery and equipment:

Estimated value ($1,000). . . . . . . . . . . 2,879,414

Average per form . . . . . . . . . . . . . . . . . 23,470

131,991

23,061,163

175

214,623

1,236

10,599

26,421

51,866

34,533

7,070

1,282

220

13,972,802

5,899,750

7,179,543

54,394

2,181,303

4,998,240

844,395

115,713

4,098

82,043

40,005

9,943

75,111

56,442

67,751

56,048

309,769

140,301

712,383

29,242

88,547

5,337,081

40,435

3.5

–1.3

–4.4

77.0

86.7

37.8

36.1

–5.5

–8.8

10.1

22.5

13.4

0.8

1.5

67.3

61.6

51.4

125.5

37.1

40.9

56.7

–1.6

10.8

23.8

–3.8

21.5

19.6

20.0

49.3

89.0

77.3

34.3

33.9

85.4

72.3

2,314,013

1,017,030,357

440

147,838

336

128,254

379,543

827,884

616,098

207,297

92,712

62,225

440,039,087

92,527,627

81,526,124

35,231

41,790,360

33,301,560

6,202,291

1,517,573a

28,656a

1,423,953

628,224

261,836

1,427,368

851,902

1,011,476

814,555

5,137,361

1,757,776

4,652,074

223,093a

712,715a

48,402,626

22.303

2,241,124

984,755,115

439

347,974

791

187,699

449,301

711,701

526,566

203,936

97,396

64,525

445,527,557

87,133,026

131,810,903

58,815

62,274,394

69,536,509

9,732,222

1,945,724

59,788

1,325,931

656,219

258,974

1,234,858

1,006,266

1,187,490

963,728

7,689,577

4,282,795

8,434,399

312,621

950,112

93,686,308

41.930

–0.3

–0.3

–0.1

135.2

135.4

46.3

18.3

–14.0

–14.5

–1.6

5.1

3.7

1.2

–5.8

61.6

66.9

49.0

108.8

56.9

28.2

108.6

–6.9

4.5

–1.1

–13.4

18,1

17.4

18.3

49.7

143.6

81.3

40.1

33.3

93.6

88.0

aAmong farms with sales ~$2,~

bcomputed from the preliminary reports for the nine Northeastern States

SOURCES Data for1974 1978 Census ofAgricu/ture FYelirninary l?eport Northeast region and United States (Washington, DC US Department of Commerce, Bureau

of the Census, 1980). Data for19B2 1992 Census ofAgrlculfum? Pre/irninarYRewrf, nine Northeastern States and United States (Washington, DC US Depart.

ment of Commerce, Bureau of the Census, 1983)
232 ● Technology, Public Policy, and the Changing Structure of American Agriculture

5.

6.

Farm population change, the proportion of

full-owner farms, and the proportion of

part-time farmers had a positive effect on

community well-being in rural counties, as

measured by the poverty rate and median

family income. That is, counties in which

farm population declined, the proportion

of full-owner farms declined, or the proportion of part-time farmers declined had a

moderate increase in the poverty rate. The

proportion of farms that were fully owned

had the strongest relationship to the poverty rate.

Counties in which the percent of farms

owned by corporations increased also had

increases in poverty rates and declines in

median family incomes.

The primary structural change that is likely

to occur in the Northeast is due to technological changes in the dairy industry. As discussed

in chapters 2 and 8, new dairy technologies, primarily bovine growth hormone and computerized feeding technologies, are expected to increase production greatly and lower production

costs substantially for those dairies that are able

to adopt them. The result will be greatly increased production at the currently administered milk price levels. This will in turn trigger

price support reductions and increased failure

rate among dairy farms. Over the next 10 years,

over half of the small-to-moderate dairies in the

Northeast may be forced to leave agriculture.

The production of milk will become concentrated in the larger dairies in the region, and

more milk will be shipped into the region. Unlike the CATF region, where the bulk of milk

production is expected to be concentrated in

very large-scale dairies, with thousands of cows

and with industrial relations of production,

dairy production in the Northeast is more likely

to remain concentrated in dairies with herds

in the 100-to 500-cow range and with relatively

few hired workers per dairy.

The Midwest Region

Background of the Midwest Study Area

The Midwestern region is composed of Illinois, Indiana, Iowa, Michigan, Wisconsin, Minnesota, Missouri, and Ohio.a The 565 nonmetropolitan counties served as the data set for the

analysis of this region. These counties were

segregated into three groups; those in which 10,

20, and 30 percent of total labor and proprietor

income came from agriculture. Table Ii-4

shows the distribution of the nonmetropolitan

counties according to the level of dependence

on agriculture by State. As can be seen, the counties most dependent on agriculture are located

in the western part of the region. The range of

dependence on agriculture among the seven

States varies considerably. For example, 29 percent of the counties with at least 30 percent of

income from agriculture are in Iowa alone,

whereas Ohio and Michigan have no counties

with 30 percent income from agriculture and

only one and two counties, respectively, with

at least 20 percent income.

With the exception of CATF counties, this region differs from the other regions of the United

States in the extent to which agriculture dominates its landscape. Nonetheless, this region also

has a large industrial base. Table 11-5 provides

aggregate statistics for the Midwest region as

well as for the nonmetropolitan counties and

agricultural counties of this region. The nonmetropolitan counties account for 30 percent

of the Midwest’s population and for over 75 percent of the farm population, farming acreage,

and farm sales. When the proportion of county

income derived from agriculture is taken into

account, it becomes apparent that the agricultural counties have only a modest percentage

of the regional population. Around 12 percent

of the region’s population live in counties with

at least 10 percent of income derived directly

from agriculture. The counties that depend most

on agriculture account for less than 2.5 percent

of the region’s total population and less than

10 percent of the nonmetropolitan population

(van Es, Chicoine, and Flotow, 1985).

Because change in agricultural technologies

will not have uniform impacts on agriculture—

aThe findings reported for the Midwest region are contained

in: J.C. van Es, David L. Chicoine, and Mark A. Flotow, “Agricultural Technologies, Farm Structure, and Rural Communities

in the Midwest: Policy Choices and implications for 2000, ” prepared for the Office of Technology Assessment, Washington, DC,

1985.
Ch. 11—Impacts on Rural Communities ● 233

Table ll-4.—Distribution of Rural Counties, by Varying Levels of Dependence on Agriculture,

for States in the Midwest

Dependence on agriculture

Rural counties At least 10°/0 At least 20°/0 At least 30°/0

Number Percent Number Percent Number Percent Number Percent

Ohio. . . . . . . . . . . . . . . . . . . .

Indiana . . . . . . . . . . . . . . . . .

Michigan . . . . . . . . . . . . . . . .

Wisconsin. . . . . . . . . . . . . . .

Illinois . . . . . . . . . . . . . . . . . .

Minnesota. . . . . . . . . . . . . . .

lowa. . . . . . . . . . . . . . . . . . . .

Missouri . . . . . . . . . . . . . . . .

Total . . . . . . . . . . . . . . . . .

49

62

56

57

80

71

91

99

565

8.67

10.97

9.91

10.09

14.16

12.57

16.11

17.52

100.00

9

35

50

58

76

62

334

4.19

8.98

2.69

10.48

14.97

17.37

22.70

18.56

100.00

1

15

2

16

30

35

56

37

0.52

7.81

1.04

8.33

15.63

18.23

29.17

19.27

3

17

26

29

20

5

-00

3.00

17.00

26.00

29,00

20.00

192 100.00 100 100.00

DePenden~e ~n~ri~”lture IS measured lntermsofthe proportion of laborand proprietary income derived from wflculture The figuresare based on theyears 1975-79.

SOURCE Office of Technology Assessment

for example, technological changes in cash

grain production will have little direct impact

on dairy farms and vice versa—a more accurate

analysis of technological impacts entailed the

subdivision of the nonmetropolitan agricultural

counties in the Midwest into grain counties,

dairy counties, and mixed agriculture counties.

The dominant type of agriculture in each county

(defined as 50 percent or more of sales) determined a county’s classification.

Dairy production in the Midwest accounts for

about 13 percent of the regional agriculture and

about 41 percent of dairy production nationwide. Dairy farms account for about 9 percent

of the farm population but only about 2 percent

of the regional population. There are more dairy

farms (about 60,000) in the part-time sales class

in this region than in any other sales class in

the other regions. Almost all of the dairy counties are concentrated in Wisconsin. In 1978

there were 28 counties in the Midwest in which

50 percent or more of total agricultural sales

were from dairy products. Only 3 dairy counties derived 30 percent or more of county income from agriculture, all in Wisconsin.

Grain counties realize more than 50 percent

of their agricultural sales from the sale of grain.

In the Midwest, grain sales include corn, wheat,

soybeans, and minor specialty grains. Within

the Midwest, grain farming is predominantly

carried out by family-based enterprises, with

a considerable input of outside capital. These

families rent the land but contribute much of

the labor and management. The grain counties

account for about 20 percent of the agricultural

agreage and sales and about 16 percent of the

farm population and number of farms of the

agricultural counties.

Once the counties characterized by the grain

and dairy industry have been separated, the remaining 181 counties are characterized as being mixed livestock, dairy, and grain counties.

The exact nature of these counties varies considerably. Most of the counties are in the western part of the Midwest and are characterized

by integrated grain and livestock farm enterprises. In a few cases, the counties contain

different types of enterprises that are primarily specialized, none of which account for more

than 50 percent of agricultural sales. The mixed

agricultural counties account for 6.5 percent of

the region’s total population but about 30 percent of the region’s farm population and sales,

The 53 mixed agricultural counties that are most

dependent on agriculture (30 percent or more

income from agriculture) have less regional significance than might be expected. These counties have only 1 percent of the regional population and less than 10 percent of its sales from

agriculture.

Findings for the Midwest Region

The impact of farm structure changes at the

community level are difficult to isolate from

other societal changes that may occur simultaneously. For example, agricultural counties
234 ● Technology, Public Policy, and the Changing Structure of American Agriculture

n l

) 

. - k

o -

4’-’ en l -

! -

.
Ch. 11—Impacts on Rural Communities ● 235

(those receiving at least 10 percent of their income from farming) in the rural Midwest experienced differential population change and

a sharp increase in unemployment during the

1970s; neither change can be attributed to structural changes in agriculture.

Comparisons within homogeneous groups

(grain, dairy, and mixed farming) of agricultural

counties show that those counties with larger

farms tend to be somewhat better off than those

counties where the process of concentration in

agriculture has not progressed so far.

The data available for this analysis does not

provide evidence of negative county-level consequences associated with the historical direction of change in farm structure in the Midwest

during the 1970s. Although the process of structural change continues in the Midwest, the major changes in population, agricultural structure, and impacts on rural communities appear

to have taken place before the 1970s.

The concentration of sales was used as the

principal indicator of structure and structural

change in the Midwest. Concentration of sales

is measured with reference to the mean percent

of farms with sales over $100,000 in 1980 for

each set of counties deriving 10, 20, and 30 percent of their income from agriculture. That is,

in each set, those counties whose percentage

of farms with sales over $100,000 was greater

than the median percentage were considered

to have greater concentration than those counties whose percentage was less. The degree of

concentration is measured by the difference between the median percent and the individual

percentage for each county. In terms of the sales

classes used elsewhere in this report, this measure of concentration groups counties into those

with small and part-time farms on the one hand

and those with more moderate to large and very

large farms on the other hand.

According to this basic dichotomy, dairy

counties with higher concentrations of moderate-to-large dairy farms consistently have higher

median family incomes and lower median percentages of poverty than dairy counties with

large percentages of small and part-time farms.

These findings are shown in table 11-6. This

measure does not provide any definite understanding of the association between increased

sales from large and very large farms and measures of welfare such as median family income

or poverty.9 It is surprising that in the dairy

counties, where the labor demands of the agricultural enterprise are high, there is a very large

amount of off-farm employment.

Table 11-7 indicates that in grain counties with

a higher concentration of sales of the moderate, large, and very large farms, it is more likely

that farms are fully rented and that more labor

will be hired. The farm operators are less likely

to work at least 100 days off the farm. These

factors are what would be expected in an agricultural setting characterized by moderate,

large, and very large farms. Table 11-7 also

shows that the grain counties characterized by

higher concentration tend to have larger populations and to be more urbanized. Median family incomes are higher and the occurrence of

poverty is less. In the counties with higher concentration, employment in manufacturing is

higher, and unemployment is lower. Overall,

it can be argued that grain counties with a higher

concentration of sales have a higher level of economic well-being. It should be noted again that

this definition of higher concentration includes

farms in the moderate, large, and very large sales

classes.

As shown in table 11-8, mixed agricultural

counties with a heavier dependence on agriculture are somewhat different from counties that

depend less on agriculture. Farms in the most

dependent counties are slightly more likely to

be rented fully and to hire labor. In these counties, the farm operator is less likely to work more

than 100 days off the farm. The most dependent

counties tend to contain fewer people, have a

higher percentage of farm population, have

lower median incomes, and have higher levels

of poverty. Their retail sales and manufacturing employment are lower. These kinds of differ-

‘Because of the small number of dairy counties, a comparison

of the different levels of agricultural dependence among the dairy

counties is not meaningful. Similarly, data on counties with increased concentration in sales among large and very large sales

class farms would probably not be statistically significant, since

the Midwest does not have many very large dairies.
236 • Technology, Public Policy, and the Changing Structure of American Agriculture

Table n-6.-Comparison of Midwestern Counties With Greater and Lesser Concentration of Sales on Farms

With Sales of More Than $100,000 for Counties Dominated by Dairy Production

Counties with sales concentration

10%0 20% 300/0

Below the Above the Below the Above the Below the Above the

regional regional regional regional regional regional

Mean county values (ca 1980) mean mean mean mean mean mean

Agriculture:

Percent of renters of farms . . . . . . . . . . . . . . . . . . . . . . . . 4.09

Hired labor, 150+ days per farm . . . . . . . . . . . . . . . . . . . . 0.29

Percent of farms hiring some labor. . . . . . . . . . . . . . . . . . 43.53

Percent of operators working 100+ days off farm . . . . . 35.20

Demographic:

Total population . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20,576.00

Percent of farm population . . . . . . . . . . . . . . . . . . . . . . . . . 18.93

Percent of urban population . . . . . . . . . . . . . . . . . . . . . . . . 19.07

Median family income ($) . . . . . . . . . . . . . . . . . . . . . . . . . . 15,790.00

Percent at poverty level . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10.60

Business and employment:

Retail sales per capita ($) . . . . . . . . . . . . . . . . . . . . . . . . . . 2,391.00

Percent employment: manufacturing . . . . . . . . . . . . . . . . 22.54

Percent employment: services . . . . . . . . . . . . . . . . . . . . . . 18.15

Percent of unemployed . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8.62

Number of counties . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13

5.63

0.43

47.27

30.39

31,950.00

16.34

27.27

16,996.00

8.60

2,687.00

23.40

18.53

7.47

15

6.54

0.32

45.15

27.46

20,280.00

26.19

15.60

15,703.00

11.00

2,379.00

20.00

17.20

7.40

5

4.50

0.46

48.17

30.71

21,929.00

19.45

22.83

16,262.00

9.17

2,154.00

21.17

18.50

8.00

6

8.69

0.21

55.56

31.25

25,642.00

31.55

14.00

15,703.00

10.60

1,807.00

16.00

19.00

6.00

1

6.45

0.54

48.91

24.76

14,309.00

27.33

18.00

16,996.00

8.60

1,893.00

16.00

18.00

7.00

1

SOURCE Office of Technology Assessment.

Table 11.7.—Comparison of Midwestern Counties by Concentration of Sales on Farms

With Sales of More Than S100,000 for Counties Dominated by Grain Production

Counties with sales concentration

10 ”/0 20% 30%

Below the Above the Below the Above the Below the Above the

regional regional regional regional regional regional

Mean county values (ca 1980) mean mean mean mean mean mean

Agriculture:

Percent of renters of farms . . . . . . . . . . . . . . . . . . . . . . . . 15.35 25.57 17.84 26.44 18.70 27.23

Hired labor 150+ days per farm . . . . . . . . . . . . . . . . . . . . 0.19 0.32 0.21 0.30 0.22 0.26

Percent of farms hiring some labor. . . . . . . . . . . . . . . . . . 38.18 46.85 40.63 47.67 42.80 48.00

Percent of operators working 100+ days off farm . . . . . 32.57 26.14 27.53 24.47 24.26 22.59

Demographic:

Total population . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18,074.00 21,022.00 14,258.00 18,507.00 13,269.00 14,829.00

Percent of farm population . . . . . . . . . . . . . . . . . . . . . . . . . 20.03 16.80 23.49 18.89 26.44 22.75

Percent of urban population . . . . . . . . . . . . . . . . . . . . . . . . 24.95 32.19 18.35 28.78 17.00 21.95

Median family income ($) . . . . . . . . . . . . . . . . . . . . . . . . . . 16,706.00 18,665.00 16,637.00 18,623.00 15,844.00 18,221.00

Percent at poverty level . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9.46 8.05 10.00 8.07 11.60 8.30

Business and employment:

Retail sales per capita ($) . . . . . . . . . . . . . . . . . . . . . . . . . . 2,521.00 2,711.00 2,370.00 2,627.00 2,519.00 2,446.00

Percent employment: manufacturing . . . . . . . . . . . . . . . . 20.93 22.04 18.15 21.80 14.78 19.36

Percent employment; services . . . . . . . . . . . . . . . . . . . . . . 18.72 18.38 19.10 17.53 18.95 18.00

Percent of unemployed . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.94 6.96 7.43 6.64 7.43 6.14

Number of counties . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 65 57 40 36 23 22

SOURCE Office of Technology Assessment.
Ch. 11—Impacts on Rural Communities . 237

Table 11-8.—Comparison of Midwestern Counties by Concentration of Sales on Farms

With Sales of More Than $100,000 for Counties Dominated by Mixed Agriculture

Counties with sales concentration

10 ”/0 200/0 30 ”/0

Below the Above the Below the Above the Below the Above the

regional regional regional regional regional regional

Mean county values (ca 1980) mean mean mean mean mean mean

Agriculture:

Percent of renters of farms . . . . . . . . . . . . . . . . . . . . . . . . 9.90 19,43 11.76 22.11 13.30 22.85

Hired labor 150+ days per farm . . . . . . . . . . . . . . . . . . . . 0.18 0.26 0.19 0.26 0.17 0.25

Percent of farms hiring some labor. . . . . . . . . . . . . . . . . . 36.85 43.87 37.67 45.87 38.35 47.03

Percent of operators working 100+ days off farm . . . . . 36.34 27.65 32.54 22.76 29.15 21.85

Demographic:

Total population . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16,776.00 21,874.00 13,174.00 16,794.00 10,213.00 13,290.00

Percent of farm population . . . . . . . . . . . . . . . . . . . . . . . . . 22.93 21.62 26.95 25.15 39.54 27.52

Percent of urban population . . . . . . . . . . . . . . . . . . . . . . . . 18.82 30.01 14.20 27.81 8.97 21.29

Median family income ($) . . . . . . . . . . . . . . . . . . . . . . . . . . 15,363.00 18,184.00 15,363.00 17,768.00 14,729.00 17,070.00

Percent at poverty level . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11,63 8.68 11.92 9.36 13.50 10.33

Business and employment:

Retail sales per capita ($) . . . . . . . . . . . . . . . . . . . . . . . . . . 2,300.00 2,699.00 2,162.00 2,616.00 2,197.00 2,398.00

Percent employment: manufacturing . . . . . . . . . . . . . . . . 20.16 19.72 17.18 16.95 13.97 15.81

Percent employment: services . . . . . . . . . . . . . . . . . . . . . . 17.95 18.40 17.87 18.63 18.21 18.10

Percent of unemployed . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.18 5.53 6.23 4.98 6.03 4.76

Number of counties . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 109 72 61 43 32 21

SOURCE: Office of Technology Assessment

ences were not expected in advance of the analysis. Counties that are most heavily dependent

on agriculture in general turnout to be less prosperous.

The general finding that measures of social

welfare improve as farm structure moves away

from a predominance of small to part-time farms

is consistent with the findings in other regions

of the country. However, negative associations

between farm scale and social welfare might

emerge if data were available to distinguish between counties with a predominance of moderate farms and those with a predominance of

large to very large farms. Also, the decade of

the 1970s was generally a very prosperous period for the Midwest. It is not easy to find adverse associations between social and economic

factors during periods of relatively little economic adversity.

An analysis of the relationships between factors as factors changed during the 1970s yields

results similar to those of the static, crosssectional analysis described above. As average

farm size increased in the direction of moderate to large farms, median levels of income increased in dairy and mixed agricultural counties. Associations between the change in poverty

rates and other factors did not yield consistent

or significant results. There was a negative association between population change and change

in the share of full ownership and part ownership of farmland. As the share of part ownership increased, county populations tended to

decrease. The percent of manufacturing and

service employment in 1970 was positively associated with population change during the 1970s.

The biotechnologies for animal agriculture

will be of less significance to technological

change in the Midwest because monoculture

cash grain farming characterizes much of the

agriculture in the region. Since the biotechnologies for plant agriculture are expected to bring

changes relatively more slowly to this subsector, past trends in the Midwest will characterize much of the farm structural change and related community effects of the rest of the century

(OTA, 1985).

The combined impacts of biotechnologies on

the mixed crop and animal counties of the Midwest is more difficult to discern. This is particularly true in much of Iowa and parts of Illinois and Missouri, where pork production is
238 ● Technology, Public Policy, and the Changing Structure of American Agriculture

the primary type of animal agriculture. As discussed in chapter 3, the rate of productivity increase in pork production is not significantly

different from past trends, even under the most

optimistic scenario of technological change.

The hog industry is already in the process of

restructuring in the direction of more concentration, vertical integration, and specialization

of production technology. The impact of new

technologies in the mixed crop and animal counties maybe simply to accelerate the changes that

are already taking place.

In general, the Midwest lies between the

Northeast and the Great Plains and the West

with respect to expected impacts on rural communities. At the regional level, the Midwest is

similar to the Northeast in that certain areas

have a concentration of dairy production-that

is likely to undergo considerable change. Also,

like the Northeast, employment opportunities

are likely for displaced farmers over large parts

of the region. Unlike the Northeast, the Midwest does have some areas—primarily the western counties of Iowa and Missouri—in which

agriculture dominates the economic base of the

rural communities. These areas are likely to be

more similar to the Great Plains and the West

in that a decline in population and number of

retail establishments will be associated with a

continuing concentration of agricultural resources. Like the Great Plains and the West, the

counties with at least 30 percent income from

agriculture are likely to have fewer opportunities for off-farm employment.

The South Region

In this study, the Southern region comprises

Alabama, Arkansas, Delaware, Georgia, Kentucky, Louisiana, Maryland, Mississippi, North

Carolina, South Carolina, Tennessee, Virginia,

and West Virginia.10 Florida is excluded from

this region and treated separately as part of the

CATF counties. The South is more difficult than

IOFindings repo~~ for the South are contained in: Jerry R. Skees

and Louis E. Swanson, “Examining Policy and Emerging Technologies Affecting Farm Structure in the South and the Interaction Between Farm Structure and Well-Being of Rural Communities, ” prepared for the Office of Technology Assessment

Washington, DC, 1985.

the other regions to characterize in terms of farm

structure, owing to the relative diversity and

complexity of areas within the South. For this

reason, the structure of agriculture for this region will be presented in more detail than it was

for the other regions.

Farm Structure in the South Region

The South has a high degree of diversity in

commodities and in structures of production.

It has the largest concentration of small, lowincome farms in the United States, particularly in the Appalachian and tobacco-producing

States. About 35 percent of the Nation’s farms

with sales less than $10,000 are located in the

South. In contrast, 21 percent of all farms in the

United States with sales in excess of $250,000

per year are also located in the South. In 1982,

farms with sales of less than $40,000 made up

82 percent of the farms in the South and 68 percent of the farms in the rest of the United States.

Commodities such as cotton and poultry are

highly concentrated, whereas other commodities such as cattle and tobacco have very low

concentrations. Hog and pig production is in

the middle of a transition period from widely

dispersed, small-scale production to concentrated, large-scale confinement operations.

Agriculture is more diversified in the South

than in the other major regions of the United

States. Cattle sales in the South account for the

largest percentage of total sales in the South.

However, at 27 percent in 1982, this share was

less that that of the top commodity in each of

the other four regions.

Different commodity groups have different

structures of production in the South. In the

deep South, where the plantation system was

strongest and both cotton and sugarcane are

still raised, there is a greater preponderance of

industrial-type farms and larger-than-family

farms. However, cotton and sugarcane have

never been raised by family farms. On the other

hand, both Kentucky and North Carolina are

centers for tobacco production and are characterized by a proportionately larger number

of small family farms. The South also has an

especially large number of farms that raise cattle—363,994 out of a national total of 618,270
Ch. 11—impacts on Rural Communities . 239

cattle farms in 1982. Fifty-three percent of all

U.S. farms that had sales or inventory of cattle

in 1982 were in the small farms class in the

South. In contrast, poultry and egg production

is relatively concentrated in the South in large

and very large farms, with 26 and 39 percent

of regional sales in 1982, respectively.

The South is also undergoing change at a faster rate than the rest of the United States. A comparison of basic farm structure statistics between nonmetropolitan counties in the South

and in the rest of the United States is shown

in table 11-9. The number of farms declined

faster in the South than in the rest of the United

States between 1969 and 1978. Average farm

sales increased by 87 percent in the same period

in the South, yet the increase was only 58,1 percent in the rest of the United States. Average

acres per farm increased 37 percent in the South

and actually decreased in the rest of the United

States. One of the most dramatic changes in the

structure of agriculture in the South involves

the percent of full owners: the rate of decline

was twice as great in the South as in the rest

of the United States. The South went from 71

percent full ownership in 1969 to 39 percent in

1978, a 33-percent decline. The rest of the United

States went from 56 to 39 percent, a 17-percent

decline.

Many rural areas of the South still have low

standards of living compared with other regions

of the United States. Table 11-10 shows several quality-of-life variables in nonmetropolitan

counties of the South and the rest of the United

States. Poverty in the South was greater in both

1970 and 1980. In 1980 the average rural county

poverty level was approximately 6 percent higher

in the South than in the rest of the country. Median family income was also significantly lower

than that in the rest of the United States. Because of the diversity of agriculture and the wide

dispersion of production scales, there is a strong

a priori expectation that definitive conclusions

may be difficult to achieve for the region as a

whole.

Findings for the South Region

The analysis of associations between structural change and rural community welfare is

Table 11.9.—Comparison of Basic Farm Structure

Statistics in Rural Counties of the South

With Those of Total United States

Rest of U.S. South

Variable county mean county mean

Farm numbers . . . . . . . . . . . . . . . . 860a

741

(-1 19)

Farm size (sales) ... ... ... ... .. $48,788

77,140

(28,352)

Farm size (acres) . . . . . . . . . . . . . . 1,232

1,220

( - 12)

Part-time farming ... , . . . . . . . . . . 36.1 0/0

38.2

(2.1)

Chemical and fertilizer

use per farm . . . . . . . . . . . . . . . . $2,697

5,713

(3,016)

Machinery value per farm ... ... .$27,584

54,710

(27,126)

Hired labor per farm . . . . . . . . . . . $4,781

4,638

(– 143)

Farms below 180 acres . . . . . . . . . 43.40/0

43.5

(0.1)

Percent full owners . . . . . . . . . . . . 55.80/o

38.9

( - 16.9)

Percent tenant operators . . . . . . . 13.9”/0

11.9

( -2.0)

Percent grain sales . . . . . . . . . . . . 18.40/o

25.9

(7.5)

Percent livestock sales . . . . . . . . . 38.00/o

36.9

(-1.1)

Percent unemployment . . . . . . . . . 4.5%

(2.0)

849

624

(–225)

$24,675

46,112

(21,437)

205

232

(27)

44.3

47.4

(3.1)

$2,646

5,517

(2,871)

$17,023

36,043

(19,020)

$3,049

3,936

(887)

71 .0 ”/0

67.2

(-3.8)

70.6%

38.0

(-32.6)

9.9 ”/0

(-1.9)

11 .0 ”/0

20.0

(9.0)

17.7%

20.6

(2.9)

4.9%

7.8

(2.9) . ,

a1987.70 values are listed first

1977-80 values are listed second

NOTE The change between the two time periods appears in parentheses

SOURCE Office of Technology Assessment

based on the complete set of 706 rural counties

in the region. Nonmetropolitan counties with

low proportions of income from agriculture

were not excluded from the set of counties.

The principal findings are as follows:

1. The Goldschmidt hypothesis is not confirmed at the regional level for the majority
240 • Technology, Public Policy, and the Changing Structure of American Agriculture

Table 1 l-10.—Quality of Life Variables

in Rural Counties

Rest of U.S. South

Variable county mean county mean

County population . . . . . . . . .

Percent families below

poverty . . . . . . . . . . . . . . . . .

Total year housing units . . . .

Property taxes per capita . . .

Retail establishments . . . . . .

Median family income . . . . . .

Percent unemployment . . . . .

Farm/rural population. . . . . . .

Percent employed in

manufacturing . . . . . . .

Percent employed in

services . . . . . . . . . . . . . . . .

al~T.TO values are Ilsted first

21,738

25,064

(3,326)

15.0”/0

11.8

(-3.2)

7,550

9,840

(2,290)

$346

396

(50)

247

248

(1)

$17,547

20,860

(3,313)

4.5 ”/0

(2.0)

27.1 0/0

18.8

(-8.3)

16.00/0

15.8

( -0.2)

7.0 ”/0

18.9

(11.9)

23,036

26,723

(3,687)

26.6°/

17.6

(-9.0)

7,500

9,907

(2,407)

$103

135

(32)

226

237

(11)

$14,055

18,112

(4,057)

4.9%

(2.9)

1 7.5%

(-8.4)

30.4%

29.3

(- 1.1)

7.7%

16.8

(9.1)

1977.80 values are listed second

NOTE The change between the two time pertods appears In parentheses

SOURCE Off Ice of Technology Assessment

of indicators for community welfare, although there is support for a modification

of the Goldschmidt hypothesis with respect

to levels of unemployment. The nonmetropolitan counties are more dependent on

manufacturing and service sector employment than on employment in agriculture.

The structure of the manufacturing and

service sector has a greater impact on social welfare than does agriculture in these

counties. Manufacturing industries are

associated with low levels of unemployment, but also with lower median family

incomes. Service industries in the South are

2.

3.

4.

5.

associated with both low levels of median

family incomes and high rates of poverty.

There is a strong association between average farm size and unemployment in southern agricultural counties in both 1970 and

1980. However, this association is not

strictly linear as is predicted by the Goldschmidt hypothesis, A pattern similar to an

inverted U emerges when the agricultural

counties are compared as a cross-section

in 1970 and 1980. In each year, unemployment decreases sharply over the range from

small farms to moderate farms. However,

unemployment is also strongly associated

with increasing average farm size over the

range from moderate to large-scale farms.

Other basic measures of social welfare,

such as percent of poverty and median family income, do not appear to follow the same

pattern. The basic conclusion is that the

lowest rate of unemployment is associated

with a farm structure dominated by moderate farms in the South. Unemployment

tends to be substantially higher when the

average farm size in a county is especially

small or large.

Counties in which the average farm size increased the most during the years between

1970 and 1980 were likely to have declining levels of unemployment but greater increases in poverty. This analysis of changes

over time provides some weak evidence in

support of the Goldschmidt hypothesis.

Counties that had a substantial decrease in

farm population have increased unemployment, poverty, and decreased median family incomes.

Levels of part-time farming are associated

with county well-being. Those counties

with high levels of part-time farming in 1969

and 1978 were more likely to have lower

levels of poverty and higher levels of median family income. Furthermore, counties

with the most rapid increase in the proportion of operators working 100 or more days

off of the farm were more likely to have had

a faster rate of decline in unemployment

and poverty, along with a faster rate of increase in median family income. It is likely

that the part-time farms have a welfare func-
Ch. 11—impacts on Rural Communities • 241

tion: they provide their operators with supplemental income and some security in the

context of employment variability.

Potential for Adverse Impacts o n

Rural Commnities in the South

The potential for structural change varies considerably in different parts of the South. Large

sections of the South are hilly and mountainous,

terrain more similar to that of the Northeastern

region of the United States. Like the Northeast

in general, there is relatively low potential for

the concentration of production in large and

very large farms in these areas. The geography

of these areas prevents the creation of large contiguous parcels of land on which large machinery can operate effectively. However, there is

cause for concern with respect to the potential

for developing highly concentrated industrialscale agriculture in the coastal areas of the

South. The topography and climate of this area

lends itself to the establishment of agricultural

structure with a pattern similar to that found

in the other Sunbelt States included in the CATF

region. This coastal area also has a labor force

with the same kind of characteristics as those

found in the CATF region–that is, relatively

poorly skilled, segmented, and impoverished.

Therefore, agriculture in the coastal plains has

the potential to develop a similar structure and

a similar set of adverse impacts on rural communities in this area as has already occurred

in CATF counties. This in turn may result in

substantial worsening of living standards and

community welfare in this area. Detailed research on this area would be necessary to assess the potential for adverse structural change

and the extent to which adverse impacts have

already occurred in rural communities in this

area,

In summary, the South is more similar to the

CATF region than to the other regions of the

United States. Unlike the CATF, the South has

a relatively high percentage of small farms and

rural poverty in areas that are not dominated

by industrial agriculture. The availability of

services, levels of education, and income levels

is substantially lower in the rural counties of

the South compared with those in the Northeast and Midwest. Unlike the Northeast, Midwest, and CATF regions, specific technologies

are not seen as having a clearly identifiable impact on rural communities in the South, since

production of particular commodities does not

predominate regionally or within a particular

State in the region. One moderate exception to

this lies in the soybean/rice rotations in Louisiana, which accounted for almost all of the cash

grains produced in this State in 1982. However,

cash grain production is already highly concentrated in Louisiana, and relative to other parts

of the South, the structure of agriculture is not

likely to change greatly in Louisiana. Public policies that pertain to tobacco production and cattle raising may have a detrimental impact on

small-scale farms that depend on these commodities.

The economic fortunes of the rural South are

tied to its position in the national and international economy. Given the relatively poor position of this region in the national economy it

is not reasonable to expect these areas to improve their social and economic conditions with

their own resources. Rural sociologists and agricultural economists have argued for a comprehensive rural development program for the

South. They argue that the rural areas are already experiencing extreme social and economic problems. The dire social consequences

of these depressed conditions and the persistence of social inequalities include “intolerably

high rates of infant mortality and homicide. . .

inadequate jobs and income, inadequate services” and a decline in effective grassroots, selfhelp initiatives (Wilkinson, 1984). Those communities that remain primarily dependent on

their farming hinterlands are thought to be the

most likely to experience a decline in their populations, quality of services, and retail establishments (Whiting, 1974).
242 ● Technology, Public Policy, and the Changing Structure of American Agriculture

THE RURAL DEVELOPMENT POLICY CONTEXT

Concern about the impact of changing agricultural technologies and structural change in

agriculture on rural communities should be

placed in the context of changes that have

occurred and are likely to continue to occur in

the general economic structure of rural areas.

For purposes of this discussion, all nonmetropolitan counties and areas of the United States

are aggregated together into the category of rural areas. Two basic trends have clearly been

operating in rural areas for several decades.

First, since about the time of World War I, there

has been along-run displacement of labor from

agriculture, primarily due to mechanization and

consolidation of agricultural production. Second, since about 1940 there has been a steady

growth in the number of manufacturing and

service sector jobs in rural areas. The rate of

industrialization of rural America increased

greatly in the 1970s, especially in the South. Reduction in the population of farm operators

slowed in the 1970s and reversed in some areas.

Overall, rural areas in the United States are

much less dependent on agriculture in 1980 than

they were in 1940. In 1983 the natural resource

sector, which includes all of agriculture as well

as forestry, fisheries, and mining, accounted for

only 11 percent of the wage and salary income

of nonmetropolitan areas (U.S. Department of

Commerce, 1984). Government accounted for

19.4 percent of income, and the service and manufacturing sector accounted for 70 percent. The

economy of rural areas has therefore become

more diversified and less sensitive overall to economic cycles in agricultural prices.

Along with the changes in population and economic structure in the 1970s, there were several trends of improvement in the welfare of rural areas and communities. The gap between

incomes of rural and urban workers narrowed,

to within 12 percentage points of each other,

availability of services has improved, and poverty rates in rural areas declined from 17 percent (in 1970) to 14 percent (in 1980). Even at

the historical low point in rural poverty, nearly

all of the poorest counties in the United States

were nonmetropolitan. There were only 8 metropolitan counties with poverty rates of 25 percent or more in 1980, whereas there were 339

nonmetropolitan counties with this rate of

poverty.

Improvement in the welfare of rural areas is

due to investment by both private and public

sectors. The private sector has invested in new

housing, manufacturing, and service facilities,

while the public sector invested primarily in the

basic infrastructure of rural areas: roads, water

and sewer systems, health and educational facilities, and so forth. Much of this development

has been interrelated; water projects such as

dams and reservoirs tend to attract investment

in retirement housing, which in turn provides

these rural areas with a relatively stable increase

in service sector jobs. In turn, increases in industrialization and retirement housing as well

as increases in land values in general tend to

increase the tax base, which provides for improvements in many rural services such as education, health facilities, water treatment plants,

and so forth.

The improvements in rural welfare have not

been evenly distributed. Some regions and subregions have not improved nearly as much as

other areas. Figure 11-1 shows the incidence

of nonmetropolitan counties with a poverty rate

of 25 percent or higher as determined by the

1980 Census of Population. Nonmetropolitan

counties with this high percentage of poverty

appear to occur in five groups. The largest group

is the southern “black belt” counties that run

across the States of North Carolina, South Carolina, Georgia, Alabama, Louisiana, Mississippi,

and Arkansas. Another group occurs in the cluster of Appalachian counties in eastern Kentucky

and northeastern Tennessee. Poverty is also

prevalent in a more diffuse pattern in rural counties along the Rio Grande Valley in Texas, and

among many of the counties dominated by Indian reservations in Arizona, New Mexico,

Utah, North Dakota, and South Dakota.

Moreover, the incidence of poverty falls disproportionately on minorities and women in

rural areas. In 1982, only 15 percent of non-
Ch. 11—impacts on Rural Communities ● 243

Figure 11-1 .—Nonmetropolitan Counties With a Poverty Rate of 25 Percent or Higher

metropolitan whites were poor, compared with

42 percent of nonmetropolitan blacks and 31

percent of nonmetropolitan Hispanics. In 1979,

34 percent of nonmetropolitan Indians had poverty-level incomes. The incidence of female

headed households in nonmetropolitan areas

increased by 25 percent in the 1970s. In 1980,

35 percent of these households were classified

as being at or below the poverty line (Skees and

Swanson, 1985).

Minorities have had some relative gains in

welfare in some areas. In the South, the poverty rates among nonmetropolitan blacks decreased by 10 percentage points from 1970 to

1982, while the poverty rates among nonmetropolitan whites increased somewhat, from 13 to

15 percent over the same period.

Trends toward improvements in rural welfare have taken a turn for the worse in the 1980s.

Rural poverty increased again in the recession

of 1980-81 to 18 percent in 1982. Many rural

service and manufacturing industries that relocated in rural areas in the 1960s and 1970s began to move overseas in search of still lower

labor rates. The tax base that supports many rural services such as schools and hospitals was

eroded as cropland values fell across the country. There is also evidence that the movement

of population from urban to rural areas in the

1970s has reversed in many areas. Throughout

the 1970s, population grew more rapidly in rural areas than in urban areas. The rate of population growth in rural areas fell rapidly in 1980

through 1982 and is now significantly lower

than that of urban areas.

In summary, rural poverty is still very prevalent at high levels compared with metropolitan

areas. Changes in the economic structure of rural areas have increased the economic base of

many communities and counties but have along

way to go before the welfare of rural areas is
244 • Technology, Public Policy, and the Changing Structure of American Agriculture

equivalent to that of urban areas. Moreover, the in previous decades, but the economic situation

economic and financial base of many rural of many rural counties is sufficiently precaricommunities is significantly less strong in 1985 ous that substantial changes in agriculture will

than in 1980. Overall, agriculture plays a much undoubtedly have an impact on the welfare of

smaller role in the rural economy than it did these areas.

SUMMARY AND POLICY IMPLICATIONS

A wide range of diversity is evident in the

character, agricultural structure, patterns of

change, and patterns of impact on rural communities in five different regions of the United

States. A clear picture of adverse relationships

between agricultural structure and the welfare

of rural communities is evident in the CATF

counties. Large-scale and very large-scale industrialized agriculture in these counties is

strongly associated with high rates of poverty,

substandard housing, and exploitive labor practices in the rural communities that provide hired

labor for these farms. Very large-scale agriculture has been a strong source of employment

in the CATF region for many years, although

at very low wage rates. Emerging technologies

may reduce the labor requirements throughout

much of the CATF region between now and year

2000. Increased unemployment will greatly increase the strain on these communities. There

is potential for CATF to increase its share of

national agricultural production, which would

mitigate the trend toward increasing unemployment. However, increased agricultural production in this region will tend to be constrained

by the cost of irrigation water and the need to

control environmental impacts.

There is a substantial potential for a pattern

similar to that of the CATF region to occur in

the coastal zone of the South. The topography

and climate favor large-scale, labor-intensive

production of fruits, vegetables, and dairy products. The South also has a segmented, relatively

unskilled labor force that could provide a source

of low-cost labor similar to that of the CATF

region. It is difficult to draw generalizations

about the rest of the South, owing to the diversity of agricultural structure and production.

There is evidence of a relatively strong association between rates of unemployment and agricultural structure. Unemployment rates tend

to be lowest in counties with a predominance

of moderate farms. Unemployment rates are

higher in counties with a predominance of small

or large farms.

Dairy products are the single most important

agricultural commodity group of the Northeast.

Dairy farms are likely to experience widespread

failure because of the combination of technological change and public policies. The structure of agriculture in the Northeast is therefore

likely to change substantially during the next

10 to 15 years. However, rural communities in

the Northeast have a low overall dependence

on income from agriculture. Almost all of the

most productive agricultural counties in the

Northeast are in metropolitan areas where employment opportunities and services are relatively available. The most rural counties are not

the most agricultural. Therefore, rural communities in the Northeast generally are not likely

to experience adverse consequences from structural change, with the exception of a few localities with especially high dependence on dairy

production.

There is no clear-cut evidence that rural communities in the Midwest were adversely affected

by structural change during the 1970s. In general, alternative sources of employment in the

manufacturing and service sectors have been

relatively prevalent and are expected to continue to be relatively good in the Midwest. In

general, indicators of social welfare tended to

improve as farm structure moved from small

and part-time farms toward moderate to large

farms during the 1970s. However, there was a

tendency for the population to decline in counties where the share of part-ownership of farms

increased. As with the Northeast region, there
Ch. 11—Impacts on Rural Communities ● 245

is a reasonable expectation that technological

change in the dairy industry will result in a mass

exodus of small to moderate dairy farms during the next 5 to 15 years. Rural communities

in dairy counties may not be adversely affected,

since off-farm employment is quite high in these

counties. Mixed agricultural counties on the

western edge of the Midwest that are relatively

dependent on agriculture are the most likely to

suffer adverse consequences from structural

change. If the percent of part-ownership increases as agriculture becomes more concentrated, population, median income, and retail

sales may decline in these counties.

There is a strong potential for the development of a high concentration of agricultural production in the Great Plains and the West, especially in terms of farm size, if not gross sales

per farm. In turn, the number and percent of

hired managers in this region is likely to increase. Unlike the South, there is low potential

for development of industrialized agriculture

with large numbers of hired field workers. The

most likely adverse impact will be the loss of

population and small retail firms in the region.

In general, fewer alternate employment options

will be likely in manufacturing and service industries in this region than in the other regions

of the country.

One of the most important findings is that it

is very difficult to generalize across regions of

the United States about the impacts of changing agricultural technology and structure on rural communities. As a consequence, policies designed to prevent or ameliorate adverse impacts

and promote beneficial impacts will run the risk

of being inappropriate unless they are crafted

with consideration for regional differences.

Regional Rural Development Policy

Rural development policies are carried out

at the national, State, and local levels. Over the

years, rural development policies have received

high priority and at other times, including the

early 1980s, they have received relatively little

attention in terms of leadership and resources.

This policy of “benign neglect” is based on the

view that rural communities have strong, cohesive social institutions and can help themselves

better than the Federal Government and States

can. There is strong evidence that many, if not

most, rural communities have suffered a decline

in their strength, cohesion, and capabilities and

are now much less able to help themselves. Urbanization has reduced the traditional bases of

cohesion in many rural communities (Wilkinson, Hobbs, and Christenson, 1983). Many of

the gains in social welfare that were achieved

in rural communities in the 1960s and 1970s are

in danger of being lost. Moreover, the examination of indicators -of social welfare of rural

communities in this study has shown that poverty, substandard housing, unemployment, and

lack of access to basic services continue to be

widespread problems in major regions of the

United States. There is strong potential for further declines in the welfare of rural communities in some areas. It follows that policy makers

who are concerned about the quality of life in

rural areas should give renewed consideration

to regional development policies. Regional development policies that address the quality of

life in rural areas will benefit from higher political priority and a new focus on the issues. It

will take the cooperation and coordination of

policy makers at all levels of Government to

achieve this increase in priority and this improvement in focus.

The national role is critical in providing leadership and in setting national standards for the

improvement of conditions in rural areas and

regions that cut across State lines. This responsibility is shared by the Federal Government and

national organizations. State governments and

State organizations have the responsibility for

selecting particular areas for assistance and for

assisting State and local organizations in the

delivery and use of services. Local governments

are responsible for direction and implementation of programs to meet local needs and in the

use of local capacities and resources (Bradshaw

and Blakely, 1983). In this section, the general

roles and capabilities of the three levels of Government are outlined, followed by a discussion

of policy considerations for each region.
246 ● Technology, Public Policy, and the Changing Structure of American Agriculture

Robs and Capabilities

of Government

The National Role in Regional

Rural Development

The Federal Government can promote regional rural development in a number of ways:

1.

2.

3.

4.

Develop a strong Federal rural policy that

would help coordinate the various activities of the several Federal agencies active

in rural development and set a clear direction for Government involvement.11

Develop rural human capital by targeting

resources for training and skills development to minorities and the poor. The Federal Government is in a better position to

do this than many State governments.

Integrate programs of economic development. The Federal Government can provide

incentives for establishing in rural areas

new industries that are integrated with the

need for human resource development in

those areas.

Directly provide resources for the most

needy rural areas (especially the South). The

Federal Government has the special ability to reallocate resources from areas and

regions of affluence to areas in which poverty and depressed conditions prevail. The

largest proportion of rural poverty in the

United States is in the South. As a consequence, actions by individual State governments in this region are not likely to be as

effective as national policies targeted at this

region. At the national level many of the

programs that have been most successful

in achieving improvements in social welfare in rural areas have not operated under

the label of rural development per se. Examples are the Interstate Highway System,

the Social Security System, Environmental

Protection Agency grants for pollution

abatement, Corps of Engineers’ waterway

development and flood control projects, the

Rural Electrification Administration, the

l~Federa] agricultural commodity policies, other income suPport policies, and Federal research and extension policies also

have an impact on regional rural development. These policies

are discussed in other chapters.

5.

6.

Farm Credit System, and the Farmer’s

Home Administration.

Create a context for improved assessment

and analysis of rural development problems

and policies. The U.S. Department of Agriculture is in a better position than single

State institutions to promote improved

scholarship on issues and policy options

for rural development. At the same time

there is a great need to sustain and improve

the Federal collection and dissemination

of information on rural communities. Only

the Federal Government can establish the

uniform national database and analytical

standards required for an adequate definition of the problems of rural communities

and rural development. Public policies are

best established on the basis of well-defined

problems. Public policies toward rural development in the past have been poorly

formulated, in part due to the lack of consistent definitions and data about rural

communities.

Provide certain governmental services with

indirect but potentially substantial impact

on regional rural development. For example, the welfare of many poor rural communities in the South and the CATF region

is affected by the rate of influx of immigrant

farmworkers. It will be very difficult to improve the incomes and housing standards

of hired labor in these two regions in the

face of uncontrolled competition from or

nonregulation of immigrant labor.

The State Role in Regional

Rural Development

Each State can play a pivotal role in many

respects in the process of regional rural development. While the Federal Government can provide leadership and funding, regional development policies will be carried out to a large extent

through State agencies and programs. To the

extent that the States increase their level of

responsibility and activity in rural development,

they will also have to increase their organizational capabilities. States also have the opportunity to organize themselves into regional

federations to coordinate programs and share
resources for regionwide development programs. The States have roles that the Federal

Government cannot perform:

1.

2.

3.

4.

5.

6.

Only individual States or regional groups

can adequately coordinate the different interest groups and opportunities within their

boundaries.

Strategies that are politically feasible can

only originate with the States; they cannot

be successfully imposed by the Federal Government.

The States are uniquely capable of improving the organizational capacity of rural development groups in those places

where the need for development is greatest.

States can exercise leadership in creating

multi jurisdictional organizations.

State responsibility for land use assessment

and zoning can bean effective way to minimize some of the disadvantages of growth

in rural areas.

Legislative and administrative actions by

State governments within the broad policy

guidelines of the Federal Government are

necessary to ensure that benefits from development programs reach the most needy

rural residents.

The Local Role in Regional

Rural DeveIopment

The basic economic development activities

that work to improve the quality of rural life are

conducted by jurisdictions that lie below the

level of the State government. These local efforts must work within national and State guidelines and priorities, but they must have a great

deal of flexibility to create programs appropriate to local conditions and resources. Local organizations working at the local level ultimately

have a great deal of responsibility to make sure

that the needs of disadvantaged rural residents

are met (Bradshaw and Blakely, 1983). Local

organizations have some strengths relative to

State and Federal agencies. Local governments

and agencies are capable of developing more

diversity in sources of funding and types of services that are delivered. Localities are better able

to identify and use particular local resources

in the process of development.

Ch. 11—Impacts on Rural Communities • 247

Regional Policy Considerations

The CATF Region

The social welfare of many rural communities

in CATF counties is already very poor. Public

policies aimed at rectifying the existing problems are needed in addition to policies to mitigate adverse impacts from continued concentration and technical change.

There are several essential elements of any

program directed toward correcting existing

problems in the CATF region:

1.

2.

3.

4.

5.

Community development, cooperative extension, and poverty programs might be focused on the specific needs of the small

communities and of displaced individuals.

Building codes could be enforced on rental

properties, and grants might be provided

to owner-occupants to bring their dwellings

up to code.

Safe and sanitary public housing could be

provided to migrant agricultural labor.

More rigorous monitoring and enforcement of water and air quality is needed in

rural communities. Specific controls could

be enacted on environmental problem areas

—burning of crop stubble, disposal of pesticide containers, and drainage of irrigation water.

The general issue of below-minimum wages

should be addressed:

a.

b.

c.

barriers to unionization of agricultural

labor could be removed;

benefit packages could be adapted for use

by migratory labor;

job costs (charges for transport to the

fields, lodging, and food) could be disallowed if they depress wages below the

minimum levels; and

d. professional standards and licensing

could be established for labor contractors.

The Great Plains and the West

The analysis of the Great Plains and the West

indicates that public policy rather than technology per se accounts for most of the recent shifts

in agricultural structure and will have the greatest impact in the foreseeable future. The prin-
248 • Technology, Public Policy, and the Changing Structure of American Agriculture

cipal impact of technology was the adoption of

larger machinery on moderate farms, which resulted in predominantly medium-sized farm

counties becoming more like large farm counties during the 1970s. Much of this change can

be attributed to Federal incentives for the substitution of capital for labor. While this has been

true since the 1940s, the process accelerated during the inflationary 1970s and received further

impetus through increases in investment tax

credits in 1981. The more recent reversal of

monetary policy has resulted in a great deceleration of capital investment, but has also greatly

decreased net farm income. Interest payment

write-offs have provided a major subsidy for

growth, especially in irrigated mixed crop and

livestock counties.

The structural change in dairy production will

have a substantial impact on the overall structure of agriculture in the Northeast because the

dairy farm is the predominant type of agriculture in this region. One possible way to mitigate this impact will be to convert dairy farms

to the production of fruits, vegetables, and poultry. These commodities are produced in large

quantities in the Northeast, the markets are well

developed, and demand is likely to be more elastic than the demand for dairy products.

As in the Northeast, there is a need to consider public policies for the Midwest that will

CHAPTER 1

Bradshaw, Ted K., and Blakely, Edward J., “National, State and Local Roles in Rural Policy Development,” The Rural Sociologist, vol. 3, No, 4,

July 1983.

Flora, Jan L., and Flora, Cornelia Butler, “Emerging Agricultural Technologies, Farm Size, Public

Policy, and Rural Communities: The Great Plains

and the West, ” prepared for the Office of Technology Assessment, Washington, DC, 1985,

Goldschmidt, Walter, As You Sow: Three Studies in

the Social Consequences of Agribusiness (Montclair, NJ: Allanheld, Osmun & Co., 1944),

address the major structural changes expected

in the dairy industry. Public policies that assist

dairy farmers to shift resources into alternate

types of production will benefit communities

in areas that are relatively dependent on income

from dairy production. Programs that enable

dairy farmers to retrain for employment in new

occupations and to leave agriculture maybe of

more benefit to farmers than to the communities in which they reside if these programs result in outmigration to other parts of the country.

The South

There seems to be a consensus among the

specialists in rural affairs about the character

of a national or regional rural development program. Such a program would require a “twofold attack, one that combines Federal initiatives with local initiatives—the former to increase resources, the latter to build a sense of

community” (Wilkinson, 1984). Four general

criteria are:

1.

2.

3.

4.

there must be a program aimed at the creation of jobs that generate a livable income;

basic rural services such as health care, education, water, sewer, and power must be provided or upgraded;

labor and civil rights laws must be strengthened and enforced; and

local participation must be included in any

rural development program,

REFERENCES

MacCannell, Dean, and Dolber-Smith, Edward, “Report on the Structure of Agriculture and Impacts

of New Technologies on Rural Communities in

Arizona, California, Florida, and Texas,” prepared for the Office of Technology Assessment,

Washington, DC, May 1985.

OTA, Office of Technology Assessment, Technology, Public Policy, and the Changing Structure

of American Agriculture: A Special Report for the

1985 Farm Bi]], OTA-F-272 (Washington, DC: U.S.

Government Printing Office, March 1985),

Pollack, Susan L., and Jackson, William R., Jr., The
Ch. 11—Impacts on Rural Communities ● 249

Hired Farm Working Force of 1981, U.S. Department of Agriculture, Economic Research Service,

Economic Development Division, Agricultural

Economic Report No. 507, 1983.

Rothman, Robert A., et al., “The Undulating Community: A Typology of Recurrent Migrations, ”

Rural Socio]ogy 42(1):93-100, 1977,

Skees, Jerry R., and Swanson, Louis E., “Examining Policy and Emerging Technologies Affecting

Farm Structure in the South and the Interaction

Between Farm Structure and Well-Being of Rural Communities, ” prepared for the Office of Technology Assessment, Washington, DC, 1985.

U.S. Department of Commerce, Bureau of the

Census, Current Population Survey, 1984.

van Es, J. C,, Chicoine, David L., and Flotow, Mark

A., “Agricultural Technologies, Farm Structure,

and Rural Communities in the Midwest: Policy

Choices and Implications for 2000,” paper prepared for the Office of Technology Assessment,

Washington, DC, 1985.

Whiting, Edward (cd.), Communities Left Behind:

Alternatives for Development (Ames, IA: Iowa

State University Press, 1974).

Wilkinson, Kenneth P., “Implementing a National

Strategy of Rural Development,” The RuraZSocioZogist 4(5):348-351, September 1984.

Wilkinson, Kenneth P., Hobbs, Darryl J., and

Christenson, James A., “An Analysis of the National Rural Development Strategy, ” The Rural

Sociologist 3(6):384-391, November 1983.
Chapter 12

Impacts on Agricultura l
Contents

Page

The Functions and Challenges of Research and Extension . . . . . . . . . . . . . . 254

Components of the Agricultural Research and Extension System . . . . . . . . 255

USDA. . . . . . . . . . . . . . . . . ..1...... . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 256

Other Federal Agencies . . . . . . . . . . . . . . . . . . . . . . . 6 . . . . . . . . . . . . . . . . . . . 259

Land-Grant Universities . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 259

Non-Land Grant Universities . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 264

Private Firms, Individuals, and Foundations. . . . . . . . . . . . . . . . . . . . . . . . . 264

Trends in Level of Support and Related Issues . . . . . . . . . . . . . . . . . . . . . . . . 266

Research-Extension Balance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 266

Federal-State Balance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 269

Research and Extension Professional Staff . . . . . . . . . . . . . . . . . . . . . . . . . . 269

Fund Allocation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ........ 270

Interactions Between Non-Land-Grant Universities and

Land-Grant Universities. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 272

Property Rights, Exclusivity, and the Land-Grant Universities’

Social Contract . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 272

Development of Discovery Property Rights . . . . . . . . . . . . . . . . . . . . . . . . . . 273

Land-Grant University Adjustments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 273

Impact on the Land-Grant Social Contract . . . . . . . . . . . . . . . . . . . . . . . . . . 275

Pricing Information Services . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 276

Distribution of Benefits and Structural Impacts. . . . . . . . . . . . . . . . . . . . . . . . 277

Summary and Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 278

Chapter 12 References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 279

Tables

Table No. Page

12-1. Trends in Agricultural Research and Extension Funding by Source

and User, Selected Years . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 267

12-2. Trends in Numbers of Extension Professional Staff

Selected Years . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 269

Figure

Figure No. Page

12-l, Agricultural Research and Extension Funding, 1982................256

12-2. Research and Extension Roles in the Technology Discovery and

Transfer Process. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. ....263
Chapter 12

Impacts on Agricultural

Research and Extension

Much of the success of American agriculture

is attributable to the creation of the Nation’s agricultural research and extension system (Ruttan,

1982; Cochrane, 1958). For well over a century

this system has contributed to a plentiful, lowcost supply of food and fiber, and to the positive U.S. balance of agricultural trade, through

the system’s research on new agricultural technologies and practices and through its transfer

of technology to farmers and other members of

the agricultural community. The technological

innovations brought about by agricultural research and extension increased agricultural output in 1945 through 1979 by 85 percent, with

no change in the level of agricultural inputs

(USDA, 1980).

The public has invested substantial sums of

money (currently about $3 billion annually) in

agricultural research and extension at Federal

and State levels. This investment has been no

accident. Several important events have helped

make the agricultural research and extension

system an integral and long-standing part of U.S.

agricultural policy—the first Federal appropriations to agricultural research in 1856, the establishment of the U.S. Department of Agriculture

(USDA] and land-grant university system in

1862, the funding of a State agricultural experiment station system in 1887, and the creation

of the Federal-State-local extension partnership

in 1914,

However, agriculture’s entrance into the era

of biotechnology and information technology

raises several questions about the impact of technical advances on the performance of the research and extension system and about how that

performance will ultimately affect the structure

of agriculture. For example, in the past, public

research was the driving force for agricultural

production. Now, with the private sector becoming more involved, the public sector is emphasizing more basic research while the private sector is focusing on certain areas of applied

research and development,

This situation leaves open the question of who

will do other aspects of applied research in the

public sector. Although the public sector has

allocated resources to research in biotechnology and information technology, extension has

done little to make information about these technologies available to farmers. Extension must

thus decide what its mission will be, for extension policy will determine how effective moderate farmers will be in gaining access to new

technology. without such access moderate

farms will disappear even faster.

The role of extension raises additional

questions:

●

●

●

●

●

●

who gains and who loses from the process

of technological change in agriculture?

Is agricultural research and extension

structurally neutral, or does it favor the

growth of large industrialized farms?

what are the roles of the various components of the agricultural research and extension system as they relate to technological change in the biotechnology and

information technology era?

What are the implications of increased private sector involvement in agricultural research?

what are the implications of patents being

conferred on biotechnology and information technology discoveries, that is, for the

social contract under which the agricultural research system was created?

How is a proper balance to be struck between public and private sector components of the agricultural research and extension system?

253
254 ● Technology, Public Policy, and the Changing Structure of American Agriculture

These major questions will be addressed in it relates to developments in biotechnology and

this chapter. The answers to the questions are information technology.1

based on previous OTA studies, on an extensive body of literature on the impact of technology on agriculture, and on papers commissioned by OTA regarding the status of the

1The OTA papers were prepared by George Hyatt, Roy Lovvorn, Ronald Knutson, and Fred White. The findings from these

agricultural research and extension system as papers were integrated into this chapter by Ronald Knutson.

THE FUNCTIONS AND CHALLENGES OF RESEARCH AND EXTENSION

Increasing demands are being placed on the

agricultural research and extension system.

These demands result largely from pressures

to increase food and fiber production in the face

of an ever-expanding world population, the goal

of eliminating hunger and malnutrition, higher

levels of consumer income, agriculture’s impact

on the environment and worker safety, policies

designed to expand exports, the desire for a safer

food supply, and reduced availability of water

for irrigation.

Technological change is necessary for solving each of the problems implied by these public concerns, The process of achieving technological change in agriculture involves three

basic steps, each a function of the research and

extension system:

1. basic research—discovery of new ideas,

concepts, and relationships;

2. applied and developmental research:

. development of ideas, concepts, and relationships into products (where a product is the output of technology);

● adaptation of new technologies to as

many agro-ecosystems as possible; and

. maintaining newly achieved productivity from evolving pests, disease, decline

in soil fertility, and other factors (sometimes referred to as maintenance research); and

3. adoption of products (transfer of technology).

Discovery is primarily the function of basic

research. Most basic research has traditionally

been done in the public sector. There appears

to be a general assumption that the private sector will not support sufficient amounts of highrisk basic agricultural research because that research is unlikely to yield a near-term payoff.

However, this assumption is now being challenged by large private sector investments in

biotechnology and information technology,

Developmental and applied research is conducted by both the public and private sectors.

The marked increase in the quantity of applied

private sector research has resulted in suggestions that public sector support for agricultural

research might logically be reduced, Such a suggestion, however, is overly simplistic. Research

policy decisions like this require an understanding of the relative payoffs from various types

of research, the interrelationships between basic

and applied research, and the types of research

undertaken by the public and private sectors

(White, 1984). Most of the applied research conducted by the private sector is development of

ideas, concepts, and relationships into products.

Very little private sector applied research is allocated to the adoption of new technologies to

a specific agro-ecosystem or to defense of newly

achieved productivity from enemies of the agroecosystem (maintenance research). This responsibility falls to the public sector.

The function of encouraging technology adoption has traditionally been shared by the public

and private sectors, In the public sector, extension educators at the Federal, State, and county

level work directly with farmers to test and demonstrate the usefulness of new products flowing

out of both sectors. Private firms tend to concentrate their adoption strategies on more conventional promotion and advertising strategies.

Over time, the effort and resources required

to achieve a technological breakthrough, as a
Ch. 12—impacts on Agricultural Research and Extension • 255

general rule, increase, This is true because the

simpler problems naturally tend to be solved

first. More difficult problems require more complex tools of analysis and thus a larger research

commitment in time, effort, and resources. The

entry of agriculture into the contemporary biotechnology era illustrates this increased complexity. For years, agriculture has depended on

chemicals to control pests, diseases, and weeds.

These chemicals have been applied without a

full knowledge of either precisely how they work

or how they affect the environment. This practice has increasingly been questioned as chemical residues have become more associated with

environmental contamination and safety concerns. Moreover, biotechnology research has

increased the understanding of the specific effects of chemicals, such as atrazine on weeds.

As a result of such research, it is becoming

possible to develop chemical control agents for

specific needs. Potentially, all agricultural

plants could, for example, be made resistant to

“Roundup” herbicide. With all cultivated plants

resistant to the herbicide and all undesirable

grasses susceptible to it, the potential exists for

nearly complete control of grassy weeds on a

farm. Higher output and/or reduced inputs

would result from improved weed control, In

addition, fewer and safer chemicals, and chemicals in smaller quantities, could be used. The

result could be a safer food supply and environment, less use of valuable resources, and a

higher level of output.

To achieve these benefits, large investments

must be made in basic research. Much of this

research uses techniques not common to agriculture. New scientists having modern biotechnology research skills must be trained for agricultural research, and existing scientists must

be retrained. Laboratories and related equipment will be more complex and expensive. The

educational levels of the producer clientele will

have to be improved to adopt and use effectively

the more complex new technologies,

Such needs will not be accomplished overnight. Research and education are, of necessity,

long-term processes, Interruptions in research

and education create gaps in the flow of technology into agriculture that are of a considerably longer duration than the interruption itself.

For example, if a line of research designed to

pinpoint molecular defects in genes that make

poultry and cattle vulnerable to leukosis (a form

of cancer) were interrupted, it could increase

the time required for discovery, development,

and adoption of leukosis control methods by several years.

Agricultural research and extension educational programs compete with other demands

for both public and private funds, In the private sector, support for research depends on

overall firm profitability and the potential for

near-term cost recovery and contribution to

profits. When firm profits fall, research funds

are traditionally among the first to be cut. This

variability yin private sector research investment

increases the need for stability of funding by

the public sector. It also increases the need for

policy makers to evaluate the comparative payoff from various forms of Government expenditures—recognizing that all requests for Government assistance cannot be satisfied. Weighing

the payoffs from the many alternative demands

on the public treasury may be the most complex task facing policy makers (Knutson, 1984).

COMPONENTS OF THE AGRICULTURAL RESEARCH

AND EXTENSION SYSTEM

The U.S. agricultural research and extension ● land-grant universities;

education system contains many research and ● non-land-grant universities; and

education agencies, grouped in the following • private firms, individuals, and foundations.

five categories: The agencies can be viewed both from the per-

. U.S. Department of Agriculture (USDA); spective of the sources of funds and from the

• other Federal agencies; perspective of the performers of research and
256 ● Technology, Public Policy, and the Changing Structure of American Agriculture

educational activities (users of funds) (figure 12-

1). Each of the components of the agricultural

research and extension system has its unique

role, although all components are interrelated

and tied to the central objective of technology

discovery and transfer for the benefit of farmers

and of society as a whole.

USDA

The 1977 farm bill designated USDA as the

lead Federal agency for research, extension education, and teaching in the food and agricultural

sciences. This action confirmed by law what

had been true since before the turn of the century. It did not, however, mean for USDA to provide a majority of the funds for these functions.

In fact, the proportion of funds provided by

USDA for agricultural research and extension

has declined from about 54 percent in 1966 to

47 percent in 1982 (CSRS, 1984).

Research

USDA provides funds both to its own research

agencies and to universities. Its own agencies

include mainly the Agricultural Research Service (ARS) and the Economic Research Service

(ERS); together they use about 75 percent of

USDA’s research funds. The remaining 25 percent goes almost entirely to universities. Most

of the university funds go to land-grant universities, established by law in 1862 and 1890.

USDA funds to non-land-grant universities, are

limited to a relatively few competitive grants

used to support high-priority research.

USDA’s agricultural research is carried out

at 148 locations across the United States. About

two-thirds of USDA’s agricultural research scientists are located in USDA laboratories, with

the remainder being located in the land-grant

universities’ agricultural experiment stations.

In contrast to its agricultural research, USDA’s

economic research tends to be heavily concentrated in Washington, DC. This concentration

is increasing with the recent policy decision to

eliminate the regular ERS field staff. In the future short-term detail to university sites will only

be possible. It remains to be seen how compatible this notion is with the kind of long-term commitment much research requires, -

Figure 12-1 .—Agricultural Research and Extension Funding (in million dollars]

Fund flows I k 1

SOURCE: Of Office of Technology Assessment
Ch. 12—impacts on Agricultural Research and Extension ● 257

—

Over time there has been considerable debate

regarding the role of USDA in both agricultural

and economic research. During the 1950s through

the 1970s, agricultural research had a tendency

to become increasingly decentralized, given the

proliferation of agricultural research facilities

located throughout the United States. Administration was also decentralized, with substantial authority for program development being

established at the regional level. As a result,

questions arose about the role of USDA and

about potential duplication of research functions between USDA and the land-grant universities (OTA, 1981). After a number of special

studies and recommendations, the issue of the

role of USDA in agricultural research came to

a head in the debate on the 1977 farm bill, when

Congress designated USDA as the lead agency

of the Federal Government for agricultural research and directed the Secretary of Agriculture to coordinate all agricultural research, extension, and teaching activities conducted or

financed by Federal funds.

The 1977 farm bill did not specifically address

the functions of USDA versus those of the landgrant universities, although it established the

Joint Council on Food and Agricultural Sciences

and the National Agricultural Research and

Extension Users Advisory Board to assist in

planning the research and extension program

agenda. The responsibilities of the Joint Council include a formidable list of tasks:

●

●

●

●

●

●

evaluating research, extension, and teaching program impacts;

identifying high-priority research;

developing memoranda of understanding

among the participants;

establishing priorities;

recommending responsibilities for research, extension, and teaching programs;

and

summarizing achievements.

In a recent comprehensive study, OTA concluded, “There is concern whether the functions

assigned the Joint Council are attainable” (OTA,

1981).

The Users Advisory hoard has a somewhat

less formidable task of determining the needs

and priorities for agricultural research and extension. Its major mandates include:

reviewing USDA’s policies, plans, and

goals for research and extension;

examining relationships between private

and public sector research and extension

programs;

recommending policies, priorities, and

strategies for research and extension; and

assessing distribution of resources and

allocation of funds for research and extension.

While it is generally agreed that the functions

of the Users Advisory Board are more attainable than those of the Joint Council, the impact

of the board in establishing research priorities

is unclear (OTA, 1981).

In addition, OTA concludes that there is still

no satisfactory long-term process for evaluating

existing research activities and potential research opportunities and for the development

of a new set of research priorities. At the same

time, OTA recognizes the potential for too much

planning and organization. Agricultural research is sufficiently complex that research administraters have difficulty evaluating the relative merits of particular projects. Therefore,

specific decisions on what research is to be undertaken are generally made by the research scientists. The administrator’s comparative advantage is in establishing policy, organizing to get

the job done, obtaining and allocating funds,

and coordinating to eliminate unnecessary duplication (OTA, 1981).

One of the most important contemporary issues that the Federal Government has to deal

with is that of establishing broad-priority research and extension needs and the roles of the

components of the research and extension system. The Joint Council and the Users Advisory

Board, if given time and sufficient encouragement to perform, have the potential for effectively dealing with the priorities issue. Positive

progress is indicated by the Joint Council’s

Needs Assessment for Food and Agricultural

Sciences .

The primary question about the roles issue

involves the line of demarcation between USDA
258 • Technology, Public Policy, and the Changing Structure of American Agriculture

and land-grant programs. This issue has been

treated quite differently by research and extension. The OTA study concludes that USDA research should concentrate on those agricultural

problems that are important to the Nation and

that no one State or private group has the resources, facilities, or incentive to solve (OTA,

1981). Such a role can logically be assigned to

both ARS and ERS, For ARS, however, a shift

in the focus of agricultural research to research

only on national and regional problems would

represent a marked departure from that agency’s increasing emphasis on research having

a State or local focus.

Available evidence suggests that the progress

of the agricultural research community in establishing priorities is more advanced than that of

the extension community. The agricultural research community has been extensively studied and critically evaluated in a series of projects

extending back to the mid-1960s. This series

of internal and external analyses has led to adjustments in the distribution of the research

system’s resources in recognition of potential

advances evolving in biotechnology and information technology (Knutson, 1984).

Extension

As the rate of technological change accelerates, access to information plays a more important role in agricultural productivity y and farm

survival. In the evolving biotechnology and information technology era the trend is to substitute information for time, capital, labor, land,

and energy throughout agriculture (Warner and

Christenson, 1984).

In the agricultural research system researchers have traditionally been the producers of new

technology, whereas extension personnel have

been the agents of technology transfer (through

their roles as adopters, evaluators, disseminators, and trouble shooters), An accelerating rate

of technological change thus places increased

demands on performance by the Extension

Service, making it more important that extension sort out its priorities.

The extension community has not made the

same progress in sorting out its priorities that

the research community has. Identified national

extension objectives play little role in program

development at the State and local level. (Most

extension planning takes place at the local level

through advisory committees and other forms

of direct contact with clientele [Marshall, et al.,

1985].) One major, congressionally mandated,

extension evaluation project culminated in a series of reports that concentrated more on past

benefits than on future needs, priorities, and

required adjustments (Extension Service, no

date). Moreover, there was relatively little reference to the functions or programs of extension

in the reports of either the Joint Council or the

Users Advisory Board.

Federal extension has dramatically deemphasized its direct educational role in the past 20

years (Hyatt, 1984). Although Federal extension

specialists were once generally viewed as having a vast subject matter base in their own right

and were frequently called onto engage in staff

training and to conduct educational programs,

they are now viewed more as program leaders,

coordinators, and facilitators, The technology

transfer and education function is thus left to

the State specialists and agents. These changes

were at least partially forced by reductions in

personnel ceilings and by limited appropriations. Nevertheless, this change in strategy has

not been beneficial to the overall national extension education program. In addition to the

lack of progress in national planning and needs

assessment, the quality of educational service

to the States has deteriorated.

As in research, there are issues of national

significance that the USDA Extension Service

is better able to cope with educationally than

are the States. While ultimately the States must

take the leadership in extending information to

farmers, USDA extension can play an important role in making the information and related

educational materials available on a timely basis. (For another perspective see Hyatt, 1984,

pp. 17-18.) This role is currently being played

on, at best, a spotty basis. The need is particularly critical for facilitating technology transfer between USDA research agencies and the

State extension services as well as facilitating

technology transfer between States. Facilitat-
Ch. 12—impacts on Agricultural Research and Extension ● 259

ing communication between the USDA and

State specialists should be a key mission of the

USDA Extension Service. Unless this function

is adequately performed, Federal research agencies such as ARS will be encouraged to develop

their own outreach programs, The need is for

increased integration of the research and extension function, not greater fragmentation.

With these needs in mind, if it is decided that

a portion of the USDA Extension Service staff

will be state-of-the-art national program leaders,

the following changes would be required:

● support for Federal extension would have

to be substantially increased;

● the designated leaders would have to be recognized as national extension program coordinators by the States and be provided

compensation consistent with that role; and

● the program leaders would have to have access to resources allowing them to coordinate with researchers and State specialists

to develop state-of-the-art educational materials that could be used in all States.

Other Federal Agencies

Although other Federal agencies have become

more important sources of funding for agricultural research in universities, they still provide

less than 3 percent of the total agricultural research funds. The main sources of these funds

are the National Institutes of Health, the Department of Defense, the Department of Energy, the

National Aeronautics and Space Administration, and the National Science Foundation. The

National Institutes of Health and the National

Science Foundation support basic university research, largely in the biotechnology area. Their

grants tend to go to leading scientists working

on the frontiers of promising new areas of basic

research.

Land-Grant Universities

Land-grant universities represent a joint Federal-State partnership in research, extension,

and teaching. Land-grant universities (1862 and

1890) perform the majority of total public sector

agricultural research. About 52 percent of their

funds are from State-appropriated sources—a

marked increase from the past. Fourteen percent were formula funds (explained later); 19

percent were other Federal funds; and 16 percent were funds from farm sales, private grants,

and contracts.

Research

Land-grant university research is performed

primarily in the academic departments (e.g., animal science, soil science, agronomy, agricultural economics, biochemistry) of the land-grant

universities. Land-grant universities combine

the training of future scientists (graduate and

undergraduate) with their research programs.

Having the research scientists teach in classrooms increases the relevance and timeliness

of those universities’ curricula. z

Research planning and priority setting is

much more decentralized in the land-grant

university system than it is in USDA. This decentralization results largely from the number

of research institutions involved, the orientation toward problems of the State, the increased

proportion of funding from individual States,

and the higher level of academic freedom afforded university scientists compared with that

of most Federal and private sector scientists.

Most land-grant universities now have or are

developing long-range research plans. These

plans are normally developed from the scientist up rather than from the administrator down.

Because of the increased complexity of projects,

experiment station directors and other highlevel research administrators are frequently not

in the best position to evaluate the relative merits

of particular projects. The more removed the

administrator’s training and expertise is from

that of the scientist, the more imperfect is his

or her level of knowledge in dealing with specific research problems. Academic heads of departments are thus generally in a better position

to judge the potential value of specific research

‘The same reasoning can be applied to split appointments involving extension and research or to extension and teaching. In

each instance, relevance and timeliness are fostered.
260 ● Technology, Public Policy, and the Changing Structure of American Agriculture

than are experiment station directors (Knutson,

et al., 1980).3 Administrators achieve their research priorities and goals through the funding, position description, and hiring processes.

On the other hand, some hold the view that

scientists are becoming more isolated in basic

research (Marshall, et al., 1985). At the same

time, administrators are being held more accountable for the performance of the system in

meeting public needs. They must develop a

sense of the broad needs of the public and build

the case for continued public support. A delicate balance must be struck between the needs

perceived by research administrators and the

needs of the scientists. In a system where communication is good, these needs should converge. In fact, communication and consensus

development is the key to performance, particularly in a system where one unit depends on

other units of the system for information and

coordinated action.

In this setting, the potential for unnecessary

duplication of research among universities and

between the State and Federal levels is reduced

by communication and by the reward system

within the scientific community. There is little

or no reward in the scientific community for

research that simply duplicates what has already

been discovered and confirmed. Failure to advance the frontiers of knowledge becomes the

basis for outright rejection of proposed scientific publications used as criteria for promotion

and tenure. Communication within professional

societies provides an important information

base on which future research decisions are

based. However, this is not to be confused with

the need for adaptive and maintenance research. Many technologies in agriculture need

to be modified to be successful in various agroecosystems. Likewise, once established, maintenance research is needed to prevent yield declines as a result of the evolution of pests and

pathogens, decline in soil fertility and structure,

and other factors. These areas of needed re3The same reasoning can also be applied to the administration

of extension programs— those closer to the work are better able

to etraluate it.

search are at times viewed as unnecessary duplication or replication of research. In fact, the time

may come when a relatively large share of the

public agricultural research effort will have to

be devoted to maintenance and adaptive applied

research, More communication on the need for

this research is warranted.

One avenue for research communication that

has been substantially curtailed by restricted

funding and the way funds are handled within

the system is regional research. Regional research allows scientists who have mutual interests in a problem area that concerns more

than a single State to work together. By bringing these scientists together, the critical mass

of knowledge, research skill, and resources can

be assembled to tackle a particular problem.

However, persistent problems have prevented

the fulfillment of the potential payoff from regional research, because even research funds

earmarked for regional research are generally

handled by universities in the same manner as

other funds. In most States, scientists or departments receive no additional support for engaging in regional research activities. As a result, scientists must conduct regional research,

which is often more costly, with the same funding base. When regional research funds were

relatively plentiful, regional research was frequently undertaken and completed because of

scientist initiative and the perceived administrative obligation to support regional research.

But as research budgets tightened, the interest

of both scientists and land-grant universities in

regional research declined.

Those who suffer the most from the declining interest in and commitment to regional research are the smaller, less well-financed landgrant universities. These universities frequently

do not have the critical mass of research talent

required to tackle larger research problems.

They can, however, get involved on a regional

basis. In contrast, the larger universities are

more likely to have that critical mass. As a result, in the absence of regional research, the

larger universities are in a position to compete

for the grants involving priority research on the

cutting edge of knowledge.
Ch. 12—impacts on Agricultural Research and Extension • 261

The superior ability of larger universities to

compete for grants, combined with the increasing complexity of agricultural research, has

from time to time led to proposals for establishing universities that are regional “centers of excellence” in either specific or broad areas of

agricultural research, The center concept was

expanded by Marshall in recommending the establishment of Centers of Research and Extension Excellence to methodically analyze, synthesize, and disseminate research findings and

to identify high-priority research needs (Kendrick, 1981). -

It can be argued that the marketplace, combined with contemporary public and private research funding policies, is already leading to

the development of such centers. Questions,

however, exist about whether the marketplace

will generate enough centers of excellence and

whether the result will be the creation of a set

of “have and have not” university research and

extension programs. Since the land-grant universities are public institutions, it would appear

appropriate that this be an overt public policy

decision rather than one left to the marketplace.

This does not mean that there would be no role

for even the smallest, poorest funded land-grant

university. It plays an important role in a national system designed to deal with thousands

of agro-ecosystems and is vital to the existence

of a decentralized system with nationwide capability.

Extension

Extension education of farmers is also an integral part of the land-grant universities’ functions. Extension receives about 63 percent of

its funds from State and county sources, with

the remainder provided by USDA, largely under

formula funds.

How to apply new research findings is seldom obvious. It cannot be assumed that once

research findings are available, they will be

quickly and effectively put to use.4 The process

of developing and using research is complex

4Th is analysis IS 1 i mited to the agricultural component of the

extension program, other functions include home economics,

4-H, an(l (,ommunit~’ detelopmt;nt.

and requires a close working relationship between the research and education functions. Extension plays a critical role in alerting farmers

to new discoveries and products, evaluating the

discoveries and products, and determining how

they can best be used in combination with existing products and techniques. This is particularly true for the vast majority of farmers (likely,

at least 95 percent of them) who do not have

direct access to research results and do require

extension interpretation of them.

Because of these complexities, extension activities go beyond a public information role. At

the State level, extension has technically trained

applied scientists (generally referred to as specialists) who are headquartered primarily at a

land-grant university. These scientists may also

have research and/or teaching responsibilities.

Their extension role is to develop educational

programs, prepare applied publications, conduct meetings, and provide technical assistance

at the request of county staff.

Extension is involved not only in educating

farmers but also in providing important feedback to research scientists about farmers’ problems and further needed research. The proximity of extension specialists to research scientists

is deemed critical for developing a working

knowledge of the scientific developments and

for closing the “feedback loop” between extension and research.

Available evidence suggests, however, that the

feedback loop concept is operating unsatisfactorily, Marshall (1985) and his colleagues found

that extension’s ability to influence what research was done in the agricultural research system was inadequate, His study projected that

more research coordination problems could be

anticipated with the expected increased orientation toward basic research. This finding appeared to be the main origin of Marshall’s

recommendation for the need for Centers of Research and Extension Excellence.

Because of their direct contact with agricultural producers and agribusiness clientele, extension programs tend to be more grassroots

oriented than research programs. In most States,

educational needs are determined predominant-
262 • Technology, Public Policy, and the Changing Structure of American Agriculture

ly by producer advisory committees. Programs

are then developed to address these needs using

county agent and State specialist expertise. In

addition, individuals from the private sector are

often called on b y extension to provide a working knowledge perspective on solving particular problems.

State extension specialists are normally highly

skilled scientists trained at the doctoral level in

specific agricultural disciplines such as agronomy, animal science, entomology, or agricultural economics. In addition, these scientists

develop skills in educational methodologies, including the ability to use computer and other

electronic technology as they become available,

to deliver research findings in an educational

context. With these interdisciplinary skills,

specialists develop educational programs designed to fill the needs of extension’s clientele.

They may prepare educational materials (including the development or adaptation of computer software), bulletins, press releases, and

radio or television tapes. Such educational materials may be used directly in farmer and rancher programs or in training county agents who

in turn work with farmers.

Of equal importance to extension programs

is extension’s use of the result demonstration,

The typical result demonstration involves the

planting of different crop varieties, the application of different fertilizer levels, or the application of different pest control methods to relatively small plots of land on an actual farm. The

result demonstration is open for inspection, and

field tours are periodically conducted for interested farmers to observe the progress of the crop.

Result demonstrations are not limited to products developed in university laboratories. As private sector-branded products enter the market,

they are also used in result demonstrations to

compare their effectiveness with that of established products and practices. Extension thereby serves as a public sector evaluation of new

products and practices. Without such evaluation individual farmers and ranchers would incur the costs of experimenting to determine the

optimum input combinations to use in production, These costs would be converted into reduced farm numbers (for those who used the

wrong input combinations), higher food costs,

and reduced competitiveness in international

commodity markets.

With renewed emphasis on basic agricultural

research, substantial concern arises over the potential for the development of an applied research gap (Christenson and Warner, 1985; Marshall, et al., 1985; and Feller, et al., 1984). This

gap could occur because applied scientists are

attracted to higher rewarded basic research,

leaving open the question of who will do the

applied research. The potential for such a gap

may be reduced by increased private sector interest in biotechnology research and development. However, as the private sector performs

a larger share of the applied research in the development of new products, extension has the

potential for becoming even more involved in

the evaluation of technologies and products

flowing out of the private sector.

Substantial challenge is involved in extension’s adjusting to this new role. Although in

some States extension is already deeply involved

in the evaluation of new products, in other

States product evaluation is primarily the function of experiment stations. In the future, experiment stations will likely be doing less of this

work, and extension’s responsibilities will correspondingly increase. This increased responsibility will entail a larger specialist staff with

modern scientific training.

Some States may be inclined to forego the

responsibility of getting involved in conflictoriented product evaluation programs. Some

probably already haves To the extent that this

occurs, the usefulness of extension to the farmer

clientele will decline. Leadership at the Federal

level will be required to assure that technology

transfer is facilitated in the farmer’s interest.

As agriculture becomes more complex, filling

the gap between research and extension will entail a larger role for extension in applied resThe problem of foregoing conflict-oriented product evaluation is by no means limited to extension. For example, private

firms supporting university research may place restrictions on

the university’s conducting and/or publishing evaluations of the

impacts or the economic feasibility of particular discoveries.
Ch. 12–impacts on Agricultural Research and Extension ● 263

search. This is already occurring. Marshall and

his colleagues found that 56 percent of extension agricultural specialists with 100-percent

extension appointments are involved in applied

research. Despite the need for extension involvement in applied research, the Smith Lever Act

provided no explicit authority for extension to

conduct research. However, an amendment in

the recently passed Food Security Act of 1985

(farm bill) gives extension explicit authority to

conduct applied research. The intent of this

amendment is to clarify extension’s role in the

process of technology transfer, not to duplicate

the mission of the experiment stations.

Extension has a regional counterpart to research, whereby specialists meet to develop

educational materials on a multi-State basis. As

in research, the funds committed to such activities (frequently referred to as “special need”

or “pilot project” funds) have been substantial y

curtailed. The decision to reduce these funds

occurred during the late 1970s when the Science and Education Administration was in control and when Federal funding was being substantially squeezed, As a result, communication

between extension specialists in different States

is more limited, and the quantity of educational

materials produced by regional committees has

been substantially reduced. Once again, this

occurrence has not had as much of an adverse

effect on the educational programs of the larger,

better funded universities as it has on the smaller

universities.

At current funding levels, one of the most difficult issues facing extension in their agricultural program is that of limiting its role and coverage to those functions for which it has the

greatest expertise (Feller, 1985). Without criteria

for limiting the role of extension, there is danger that extension activities in agriculture will

become so dispersed and out of focus that their

effectiveness will be impaired. Danger exists

that extension will be called on to solve any problem, whether related to agriculture or not, In

the process, the agriculture program of extension could become more of a social program

than an instrument of technology transfer.

This is not a new issue but a continuing and

progressively more complex one. It is made

more treacherous by the politics of funding and

the reality that once a new program is established it develops its own constituency and is

difficult to cutback (Feller, et al., 1984). It is not

possible for extension to be everything to everybody, particularly in times of limited resources.

Yet, additional functions are frequently dictated

by political realities at the Federal, State, and

local levels. (For further discussion of the difficulty in delimiting the clientele and roles of extension see Hyatt, 1984, pp. 14-19 and 33.)

The Joint Council has not given sufficient attention to the role of extension, As a starting

point for defining extension’s role, it must be

remembered that the root of extension is research. Similarly, extension is a primary outlet

for research, after an appropriate level of product development. Extension is, therefore, delimited by the scientific endeavors of the research components of the agricultural research

system, including both the public and private

sector components. This delimiting role is illustrated in figure 12-2.

Figure 12-2.— Research and Extension Roles in the

Technology Discovery and Transfer Process

SOURCE: University of Caiifornla, Cooperative Extension Long Range Planning

Statement, Berkeley, CA, August 1982, p viii.
264 ● Technology, Public Policy, and the Changing Structure of American Agriculture

Feller (1985) defines the mission of extension:

The core mission of extension is, therefore,

one of developing, extending, and bringing

about the use of research-based knowledge.

The core source of that knowledge is the agricultural experiment stations. Viewing extension in a broader context than this runs serious risk of reducing its overall effectiveness.

This is particularly the case when it is recognized that extension is likely to play an increasing role in filling a portion of the gap between

research and extension. Another dimension of

this role problem involves the tendency for experiment stations to become more involved in

extension-type education programs as a means

of gaining public recognition and support. Considerable care must be taken not to foster such

duplication of efforts.

Research done in the land-grant universities

is in direct proximity to extension specialists

and can therefore be directly channeled into the

State extension program. USDA research is

often done at locations distant from State extension programs, which sometimes creates an

incentive for USDA research agencies to reach

out and develop their own educational channels. Such initiatives generally amount to an unnecessary duplication of effort.

USDA research agencies that do not have direct channels of communication and cooperation with extension need to develop them. Perhaps the most important such communication

channels are the field staff, offices, and laboratories located on land-grant university campuses. Interestingly, ERS has attempted to move

most of its field staff into Washington—a strategy that runs counter to the need to improve

communication.

As indicated previously USDA extension can

also play a role in facilitating communication

between the USDA research agencies and the

State extension specialist. However, even maintenance (to say nothing of needed strengthening) of this role has been rendered impossible

by the previously discussed reemphasis of the

role of USDA extension’s staff in subject matter education.

Non-Land-Grant Universities

Non-land-grant universities include a broad

range of higher education institutions, ranging

from strictly private and autonomous State

universities having little or no direct relationship to agriculture, to State universities having

agriculture, forestry, and food-related programs

but not having land-grant status (1862 or 1890).

Some of these institutions have had significant

applied research programs in agriculture since

their founding. The major expertise of most,

however, lies in teaching and research in the

biological, physical, and social sciences. When

agriculture entered the biotechnology era, some

non-land-grant universities such as Stanford

were ahead of the land-grant universities in

numbers of discipline scientists (such as molecular biologists and biochemists) who were involved in basic biological research having potential application to agriculture.

The non-land-grant universities support their

research programs through State appropriations, Government grants, endowments, foundations, corporate grants, and contracts, Outstanding scientists in the non-land-grant universities

often received biological research support from

Government institutions such as the National

Science Foundation and the National Institutes

of Health. The 1977 farm bill opened up USDA

competitive grant research to proposals from

the non-land-grant universities,

Non-land-grant universities do relatively little in terms of extension-type adult education

programs. Involvement in such programs is

largely limited to “public service” conferences

and adult outreach programs held near these

universities or community colleges, Such services may be provided free as a public service,

on a cost basis, or under consulting arrangements with individual faculty members.

Private Firms, Individuals,

and Foundations

Private Sector Research

The land-grant university system was established largely because it was concluded that in

a decentralized competitive structure, the pri-
Ch. 12—Impacts on Agricultural Research and Extension ● 265

vate sector would not have the economic incentive to provide the level of funding needed to

maintain an efficient, viable agriculture. Despite

many changes in the structure of agriculture

since the founding of the land-grants system, this

premise went largely unchallenged until the

1970s. The presumption was that agricultural

firms would not undertake sufficient basic research and applied research to keep American

agriculture efficient, productive, and competitive.

Until recently, private sector research, therefore, has been limited largely to providing a

small number of grants for university research

and private sector developmental research associated with the introduction of new products.

As a result, private sector grants for agricultural

research have historically come primarily from

foundations such as Ford and Rockefeller. With

the advent of biotechnology, private firm interest in agricultural research increased sharply. While much of this interest appears to be a

spinoff from biomedical human research, substantially expanded resources have also been

committed to plant and animal reproduction designed to produce new varieties or to expand

the rate of genetic improvement. In addition,

increased interest is being shown in developing disease- and insect-resistant plants as well

as organic methods of pest control.

One of the major reasons for this expanded

private sector interest in agricultural research

has been the extension of patent rights to plant

varieties and other biological discoveries. The

potential for capturing the benefits of the resulting patented discoveries has spurred private

sector support of university research. Because

such arrangements hold the potential for substantially changing the basic public service nature of the land-grant system, a separate section of this report is devoted to the implications

of increased private sector involvement in biotechnology research. These implications are by

no means limited to research: they affect the

overall thrust of extension education and the

availability of new research knowledge to extension,

The current magnitude of private sector commitment to agricultural research is largely unknown, although studies suggest that it may approach $3 billion, particularly with the recent

increases in private funding (National Agricultural Research and Extension User’s Advisory

Board, 1983; and Agriculture Research Institute, 1985). That makes the private sector research commitment approximately equal to or

potentially larger than the public sector commitment and represents a major shift toward

private sector dominance of agricultural research.

Approximately half of the private sector research budget is spent on production agriculture and half on food production or postharvest

technology research. Private sector research resources are obviously devoted to those areas

having the highest short-run profit potential.

Also, despite recent large increases in private

sector agricultural research, questions remain

about the long-term willingness of private sector firms to invest large sums of money in agricultural research and about the breadth and stability of investment in such research. As noted

previously, private firms tend to cut back on research first in times of adversity.

Private Sector Promotional and

Educational Programs

The private sector is playing a more important role in education (Christenson and Warner,

1985). For most agribusiness firms, this role is

pursued in conjunction with their efforts to promote the products and services that they market. The educational value of these promotional

activities is more in terms of alerting farmers

to the availability of new products than in objectively evaluating the performance of those

products.

The burden of new product evaluation then

falls either on the farmer (through trial and error) or on the extension service (through result

demonstration). 6 While extension involvement

is more efficient, there is potential for increased

antagonism between private sector firms and

‘3A con siderable amount of new product testing IS also d ont~

by the university research community under contracts, grants,

or consulting arrangements. While product testing at one time

was an important component of experiment station research, it

is considerably less important toda}’.
—

266 ● Technology, Public Policy, and the Changing Structure of American Agriculture

extension. Extension testing will not be appreciated by firms found to produce products

having relatively lower levels of performance.

With a few important exceptions, such as integrated pest management (1PM) checkoff programs, private sector direct financial support

for agricultural extension programs has been

limited but appears to be growing. It might be

argued that limitations on private sector funding are essential for keeping extension education programs objective. There may be greater

dangers in increased private sector funding of

extension than of research. In both cases it is

critical that the objectivity and availability of

information flows be maintained.

Two of the most important private sector supporters of extension programs are the Farm

Foundation and the Kellogg Foundation.’ Both

of these institutions are maintained largely by

endowment grants. Each foundation has played

particularly important and unique roles during

the recent period of reduced funding for extension programs.

The Farm Foundation has played a particularly critical role in filling the void created by the

reduction in funds available for communication

and program development on a regional basis.

The foundation’s support of regional extension

committees in the areas of farm management,

—- — —.

7General farm organizations and commodity groups have been

important supporters of both research and extension programs

at the Federal and State levels. This support has, however, been

largely one of influencing Federal, State, and county government

appropriations. This important private sector role is frequently

not recognized.

marketing, policy education, and community

development has frequently been the only support for contact and coordination among specialists in neighboring States. The pressure on

Farm Foundation funding has become increasingly intense as Federal extension decisions

not to fund meritorious projects become more

prevalent.

The Kellogg Foundation has periodically attempted to fill a portion of the void left by the

reduction of USDA Extension Service pilot project funds. While Kellogg continues to support

what it perceives to be the most innovative

proposals for educational program development, an increasing backlog of proposals has

developed with little hope of their being funded

on a timely basis,

Increased pressure on funding from public

and unbiased private sector sources discourages

new program development by extension specialists. Potential and existing extension employees are increasingly being attracted by research positions and/or the private sector. A

large infusion of new private support, without

a vested interest, to institutions such as that provided by the Kellogg and Farm Foundations appears unlikely. The 1PM checkoff concept may

hold promise for increased, direct producer

funding of specific educational programs. The

only remaining option then becomes the establishment of a new thrust for public support of

extension education. Such a thrust is needed

particularly at the specialist and program development level, which is a logical level for increased Federal support and leadership (Knutson, 1984).

TRENDS IN LEVEL OF SUPPORT AND RELATED ISSUES

In the 10 years from 1966 through 1975, the increased 87 percent in current dollars and 9

level of support for agricultural research and percent in constant dollars.

extension programs increased 215 percent in

current dollars and by 30 percent in terms of Research-Extension Balance

constant dollars (table 12-1). During this period,

research and extension resources increased at From 1966 through 1975, Federal support for

nearly the same rate. From 1975 through 1982, extension increased considerably more than

total expenditures on research and extension Federal support for research. However, since
Table 12-1 .—Trends in Agricultural Research and Extension Funding by Source and User, Selected Years

Extension

Research Total

State agricultural extension service

State agricultural experiment stations

research

Total State and county Total and

Year Federal State Private Total USDA research Federal appropriations extension extension

Millions of current dollars:

1966 . . . . . . . . . . . . . . . 79 118 9 206 153 359 75 126 201 560

1975 . . . . . . . . . . . . . . . 135 331 23 489 266 755 179 269 448 1,203

1982 . . . . . . . . . . . . . . . 268 563 94 925 469 1,394 315 539 854 2,248

Millions of constant dollars:

1966 . . . . . . . . . . . . . . . 80 119 9 208 155 363 76 128 204 567

1975 . . . . . . . . . . . . . . . 83 203 14 300 163 463 110 164 274 737

1982 . . . . . . . . . . . . . . . 95 201 34 330 167 497 112 192 304 801

SOURCE Cooperative State Research Service, Inventory of Agricultural Research FY 1982, vol. II (Washington, DC: U S Department of Agriculture, 1982), U S Congress, Office of Technology Assessment,

An Assessment of the Umted States Food and Agr@uHural Research System. OTA. F-155 (Washington, DC U S Government Prlntlng Off Ice, December 1981)
268 • Technology, Public Policy, and the Changing Structure of American Agriculture

1975, research expenditures have increased

somewhat more than extension expenditures.

Increased support for agricultural research relative to extension has been particularly unbalanced when the surge of private sector investment in agricultural research is considered. This

increased emphasis on research likely reflects

the

●

●

●

●

●

following:

a higher level of sensitivity y to the needs for

high-priority biotechnology research;

the potential for major breakthroughs in

productivity;

a reaction to concerns about the availability of an ample supply of food;

a desire to maintain competitiveness in international trade; and

the higher costs associated with conducting biotechnology research, which has been

used to justify higher appropriations.

In a time of tight budget constraints, policymakers (particularly at the Federal level) have

apparently made a decision that research has

a higher priority than extension. Longer run

questions, however, exist regarding the need

to maintain a balance between research and extension activities.8

Research and extension are part of a complex

agricultural system designed to discover, adopt,

evaluate, and (where favorable) facilitate technology transfer to farmers and ranchers. All

parts of the system are equally important for

accomplishing this mission.

The biotechnology and information technology era presents at least as many, and probably

more, challenges for extension as it does for research. Many of the technologies that are on

the horizon are exceedingly complex and foreign to many extension staff, In the foreseeable

future, embryo transplant technology may be

as important to the dairy industry as artificial

insemination has been over the past three decades, growth regulators will increasingly be applied in minute quantities to plants to increase

productivity, and new strains of genetically

engineered plants and animals will be entering

commercial production channels. Extensive

staff training and development will be required

at both the specialist and county levels for extension to play an effective role in technology

transfer of biotechnology and information technology. Without such training, extension will

play an increasingly less important role in production agriculture. Technology transfer will

occur less efficiently and with more structural

impacts—larger farms will benefit at the expense of smaller farms.

Another important effect of the researchextension imbalance in emphasis is to attract

the best scientists into research rather than extension. While the public sector agricultural research community is experiencing increased

difficulty competing with private sector firms

for the services of qualified scientists, extension

is having even more difficulty competing with

both interests.9 At the specialist level, extension

draws on the same pool of doctoral-level scientists as does research. Because it is receiving

increased emphasis, research is able to compete

more effective y for the services of the top scientists. 10 Over time, unless corrected, the result

will be a lower quality of extension staff, The

same principle applies at the county level, where

extension must likewise compete for its professional staff with both public and private sector

employment alternatives. With relatively less

extension support, the best county and area extension staff will be attracted to the private sector or to other better endowed agencies in the

public sector. These effects are already occurring, at a time when extension is being called

on to transfer a larger quantity of increasingly

complex technology,

‘It is interesting to note that the relative increase in emphasis

on research began during the Carter-Bergland Administration.

Previously, the Nixon-Ford-Butz Administration had put relatively greater emphasis on extension programs, while the

Kennedy-Johnson-Freeman Administration had favored research.

The impacts of these shifts in emphasis in terms of productivity

have not been adequately studied.

QThere is a concurrent concern that the best research and extension scientists are being attracted into pri~’ate sector managerial

jobs.

Ioone method by which extension migbt ad lust to this competition is to reduce tbe number of staff and concentrate more resources around a smaller number of higbly qualified staff. Extension has not, as a general rule, employed this strategy.
Ch. 12—impacts on Agricultural Research and Extension • 269

FederaI-State Balance

The States have been picking up a larger share

of the cost of the agricultural research and extension system. From 1966 through 1975, Federal support for research and extension declined

as a proportion of the total, from 55 to 48 percent. In 1982 the Federal share was 47 percent.

The historic commitment to a national system

of developmental institutions in agriculture is

fading.

This trend is consistent with the philosophy

of a reduced overall Federal role. However, it

is inconsistent with the role of U.S. agriculture

nationally in terms of maintaining stable prices,

contributing to a favorable balance of trade, and

meeting world food needs. These are important

national goals that require a higher level of Federal involvement and support,

During the period 1975 through 1982, most

of the relative reduction in Federal support has

been in extension appropriations. While Federal support for research increased by 7 percent

(constant dollars), extension support increased

by less than 2 percent (table 12-1). State and

count y support for extension, on the other hand,

increased by 17 percent. The Federal share of

extension support, thereby, fell from 40 percent,

where it had been since the early 1950s, to 37

percent. Appropriations for extension in 1984-

86 suggest a further drop in extension’s share

of Federal support.

The rationale for reduced Federal support for

extension relative to research is unclear. Although education has traditionally been viewed

as a State and local community function, extension was formed on the principle of a FederalState-county partnership. The ability and willingness of State and county governments to support extension adequately in the face of reduced

Federal support is questionable.” Clearly, if the

biotechnology and information technology era

justifies higher levels of support for agricultural

research, it also justifies higher levels of support for agricultural extension—particularly because of the increased private sector commitment to agricultural research.

Research and Extension

Professional Staff

Despite increases in real appropriations for

agricultural research, the number of professional research staff has declined. This decline

results from the continuously increasing cost

of supporting a research scientist with research

equipment and materials. Greater cost increases

can be anticipated in the future as agricultural

research progresses into the biotechnology era

and as the demand of the private sector for

newly trained scientists continues to accelerate.

Extension experienced an n-percent increase

in the numbers of professional staff from 1966

to 1975, and a subsequent 6-percent decline

through 1984 (table 12-2). Nearly all of this decline was in the specialist staff, which experienced a 15-percent decline in numbers. This reduction in number of specialists is particularly

alarming since the specialist staff has the highest level of training and is the best equipped to

educate both county agents and farmers on

evolving agricultural technologies.

The disproportionate reduction in the number of specialist staff is probably best explained

by budget considerations and the lack of direct

State control over county staff. As budgets tighten, considerably more funds are made available

to the State director when a specialist position

is eliminated or not filled. In addition, competition for specialist staff has become increasingly

keen, For extension program administrators,

the avenue of least resistance compared with

the option of reducing the number of county

Table 12.2.—Trends in Numbers of Extension

Professional Staff, Selected Years

III nterestlng]y, the 1977 farm bill contained authorization for

(JSIIA to be the lead agency in universit} education programs

related to agriculture. While there was a transfer of staff and offices

from the Department of Education to LJSDA, this initiative has

receit”ed ~ery limited b’SL)A support and is essentially’ dead.

Year Specialist County Total

1966 . . . . . . . . . . . . . . . . . . 3,641 10,451 14,092

1975 . . . . . . . . . . . . . . . . . . 4,224 11,357 15,581

1984 . . . . . . . . . . . . . . . . . . 3,581 11,140 14,721

SOURCE Extension Serwce, U S Department of Agriculture
270 ● Technology, Public Policy, and the Changing Structure of American Agriculture

staff is reduction in force at the specialist level—

precisely what has occurred. Therefore, while

the need is for an increased emphasis on specialist staff, just the opposite is occurring. 1n

fact, one can forcefully argue that in the biotechnology and information technology era,

without substantially increased emphasis on

county agent development, the specialist will

have an ever-increasing and comparative advantage in educating farmers. The model for cooperative education could shift from county agentto-farmer education to specialist-to-farmer education. This is probably already happening and

has the potential for substantially changing the

structure and role of extension. Without substantially increased State or Federal support for

extension, counties will have to pickup a larger

proportion of extension’s costs, or the counties’

impact and effectiveness in education will gradually erode.

An alternative strategy for extension would

involve an intense, continuing program of staff

development at the county level designed to provide count y agents with state-of-the-art research

findings and related information. A decision to

emphasize this strategy is based on the premise that the strength of extension lies in the

county agent. Historically, the county agent has

been one of the best educated persons in the

county. Questions increasingly arise as to whether

this era is gone.

Christenson and Warner (1985) put the issue

in the following very cogent terms:

If county staff are not providing relevant and

timely information, if they do not have access

to innovative ideas, if they are not seen as outstanding educators in the county, they will not

have the trust and respect of the people. County

staff who are seen as just another information

disseminator who hands out pamphlets, gives

advice on fertilizing lawns and gardens, and

holds meetings for “expert” speakers from the

State university, may not survive in an information society.

This is not to contend that all county agents

are out-of-date. Many continue to carry out stateof-the-art programs. However, as the rate of

technological change accelerates, research results will become more complex and difficult

to comprehend. County agents will find it increasingly difficult to keep up.

Such observations are not limited to county

agents. Many researchers will also find their

knowledge level bypassed (antiquated) by biotechnology; specialists will thus also need to update their knowledge. However, the cost of retraining specialists will be less than that of

retraining county agents because specialists are

fewer and have closer day-to-day contact with

research.

The funds that land-grant universities receive

from the Federal Government can be allocated

either on the basis of competitive grants or formulas. Historically, about two-thirds of the

funds have been allocated to the States by formulas. While there are formula differences between research and extension, the principal factor in both formulas is rural population and farm

numbers. As a result, States having a larger rural population and greater farm numbers receive

more formula funds.

The specifics of the formula have been the subject of considerable debate. Large rural populations and farm numbers in the Southeast do

not correspond with the quantity or value of production. Midwestern and Western States feel

that formula funds ought to be allocated on the

basis of the value of commodities produced. Senator Lugar (Indiana) has become a champion

of debate to change the formula (GAO, 1983).

Since agricultural research deals more with

the products of agriculture, not population, Midwest advocates suggest that inequities result

from research funds being allocated on the basis of population. Current formula funding procedures have tended to promote regional crops

such as cotton, tobacco, and peanuts as opposed

to wheat, corn, soybeans, milk, beef, and hogs.

Yet, those States that produce the majority of

the wheat, corn, soybeans, milk, beef, hogs,

fruits, and vegetables have been more competitive in achieving competitive research grants,

In some instances, strong State research support has compensated for less Federal support.
Ch. 12—impacts on Agricultural Research and Extension ● 271

A change to a product value-based formula

would accelerate the trend toward increased

centralization of research in the major agricultural States such as California, Iowa, and Indiana. It can also be argued that the effect would

be to shift the allocation of research resources

in the direction of moderate-size and larger scale

farms. However, questions exist regarding

whether the size distribution of the clientele being served by research and extension is an y different in those Southern States that receive a

larger proportion of formula funds just because

of a larger number of smaller farmers. Maybe

the needs of small farmers are not being served

in the South any better than in the rest of the

country.

The case for a population-based formula appears to be stronger for extension than for research. Education deals more with people than

with the value of products, However, even here

the urgency of education can be argued to be

product value-based. That is, education is more

urgent where more products are produced. In

addition, as in research, questions arise as to

whether extension is effectively serving the

educational needs of farmers having smaller

scale operations.

Competitive grants are a much discussed

method of allocating USDA agricultural research funds. Prior to 1970, Federal contracts

and grants generally represented about 10 percent of the USDA funds going to the State agricultural experiment stations and about 2 percent of total experiment station funds. However,

the world food crisis and advances in biotechnology created greatly increased interest in

grants. By 1982, contracts and grants had increased to 16 percent of experiment station

funding.

In 1977 Congress authorized a special competitive research grants program primarily to

support basic research in food and agricultural

science. The competitive grants program was

available to any research institution, land grant

or not. In 1982 experiment stations received

only 38 percent of competitive grant appropriations, accounting for less than 1 percent of experiment station funds. The land-grant universities accepted the grants concept only on the

condition that grants not displace formula fund

appropriations. At least partially because of

land-grant resistance to formula fund reductions, the competitive grants program has received a low level of appropriations.

Although competitive grants are made on the

basis of a peer review system, basic research

scientists complain that the grants are generally so small that they cannot sustain even a

middle-size biotechnology research project. In

1982 the average size of a grant was approximately $70,000 (CSRS, 1984). The program is

frequently referred to by researchers as the

“small grants program. ” The underlying reason for the small size of these grants probably

lies in political pressure on USDA research administrators to distribute the grants geographically among the States,

For many years, extension has used savings

from Federal administration funds, plus approximately $500,000 in so-called special needs

funds, for allocation to the States in the form

of competitive grants to support, among other

priorities, the development and testing of innovative concepts of extension education. This

important, highly successful (albeit, informal)

counterpart of the competitive research grants

program has been severely restricted since

1978—ironically starting about the same time

as the research grants program was initiated.

The reasons for this restriction lie in the interaction of such factors as reductions in Federal

administration funds, the subversion of extension funds to support a vast experiment to coordinate research and extension at the Federal

level, a congressionally mandated evaluation

project, and the subsequent emphasis on increased ongoing evaluation, which had to be

absorbed out of existing funds.

One of the unique features of the extension

special grants program was that the projects

supported by it were frequently regional or national in scope, thus facilitating the production

of educational programs that could be replicated

and applied on a multistate basis. Since the restriction of this program, innovative extension

program development has been severely curtailed, particularly for programs having a regional or national focus. Individual States have
272 ● Technology, Public Policy, and the Changing Structure of American Agriculture

not, and probably cannot, fill this void. Several

recent attempts to provide funds for these purposes as a designated item in the Federal extension budget have been unsuccessful,

Interaction Between

Non-Land-Grant Universities

and Land-Grant Universities

The world food crisis and the biotechnology

era have fostered increased non-land-grant interest in agricultural research. This interest was

further heightened by the establishment of the

competitive grants program in the 1977 farm

bill. Experience indicates that the non-landgrant universities are fully competitive with the

land-grants in receiving these funds. However,

competitive grants have not been expanded sufficiently to augment significantly most nonland-grant agricultural research programs.

Increased funding for human research in the

biotechnology area holds the potential for rapid

technology transfer of medical discoveries to

agriculture at a relatively low cost. Potential also

exists for fortifying existing non-land-grant

basic research in photosynthesis, plant embryology, genetics, and animal physiology. This will,

however, require significant increases in funding beyond current levels as well as a movement

away from the “small grants” philosophy discussed previously.

One of the factors hindering the contribution

of the non-land-grant universities to discoveries

in agriculture is the traditional competition

within States between land-grant and non-landgrant universities. Because of increasingly limited funding, competition for the allocation of

appropriated funds and the establishment of

new educational programs has become increasingly intense, Over time, substantial conflicts

have developed over the favored position of

land-grant universities in having access to formula funds, Such conflicts even exist within

land-grant universities between experiment

station-related agricultural departments and

academic departments having no ties to the experiment stations, such as biology departments.

Such conflicts are difficult to overcome. Danger exists that in attempting to “force” cooperation, policy makers could destroy productive

elements of the existing system that have served

agriculture well. Yet constant pressure to obtain a higher level of cooperation would appear

to be warranted. Perhaps the most effective

means of applying such pressure would involve

the development of programs that provide financial rewards for cooperative land-grant/non-landgrant research programs. However, if funding

levels remain low little progress is likely.

PROPERTY RIGHTS, EXCLUSIVITY, AND THE LAND-GRANT

UNIVERSITIES’ SOCIAL CONTRACT

Land-grant universities were created to serve others could occur very rapidly. In other words,

the public. The agricultural component of land- changes in the rules may have also changed the

grant universities has unique responsibilities very concept of the land-grant system (Knutson,

to conduct research and extend the results of 1984),

agricultural research for the public benefit.

Traditionally, those research results have been Questions of how the land-grant universities

readily and freely available to the public, inas- might adjust to the new concept of research

much as the results have no private property property rights and the related opportunities

or exclusivity rights attached to them. policy for increased private sector funding have been

changes that have occurred over the past 15 the subject of extensive study. However, the imyears, however, hold the potential for substan- pact of this concept on the unique nature or “sotially changing this traditionally ready-and-free- cial contract” of the land-grant system has reaccess concept of land-grant university re- ceived little attention. A discussion of both

search. Some changes have already occurred; dimensions follows. This discussion is impor-
Ch.

tant because it has a potentially profound effect on the land-grant system and its relationship to the public.

The Development of Discovery

Property Rights

Policy changes regarding property rights in

agricultural research had their origin in the

enactment of the Plant Variety Protection Act

of 1970. Previously, patent protection in plants

was limited to asexually reproduced material—

mainly orchard fruits and ornamental flowers.

The Plant Variety Protection Act provided that

a breeder of a new, stable, and uniform variety

of sexually reproduced plants could restrain

other seedsmen from reproducing and selling

that variety for 17 years.

Of possibly greater significance was the 1980

landmark U.S. Supreme Court decision, Diamond v. Chakrabarty, which held that the inventor of a new micro-organism, whose invention otherwise met the legal requirements for

obtaining a patent, could not be denied a patent solely because the invention was alive. This

decision opened the door for patenting potentially all new products of the biotechnology era.

Since the passage of the Plant Variety Protection Act and the Chakrabarty decision, private

sector interest in agricultural research has

mushroomed. OTA, for example, found that in

1983 there were 61 companies pursuing applications of biotechnology in animal agriculture

and 52 companies applying biotechnology to

plants, The companies involved ranged from

established agricultural chemical suppliers such

as Monsanto, DuPont, Dow, Eli Lilly, and American Cyanamid to new biotechnology firms such

as Genentech, Biotechnical International, MGI,

and Genex (OTA, 1984).

Most of these firms have developed their own

in-house research capability by employing molecular biologists, biochemists, geneticists, plant

breeders, and veterinarians. whereas past emphasis in plant and animal science was on selecting and breeding for specific, desired traits,

the emphasis has changed to understanding the

factors that control the genetic traits and overtly

12—Impacts on Agricultural Research and Extension ● 273

changing them. Progress is already being made

with growth hormones, vaccines, and herbicideresistant varieties of plants. Several genetically

engineered products are very close to being marketed commercially.

Relationships are also developing between

many of these firms and universities. For example, Monsanto has a 5-year, $23.5 million

contract with Washington University under

which individual research projects are conducted. At Stanford University, five corporate

sponsors (General Foods, Koopers Co., Inc.,

Bendix Corp., Mead Corp., and McLoren Power

& Paper Co.) contributed $2.5 million to form

the for-profit Engenics and the not-for-profit

Center for Biotechnology Research.

Such relationships are not limited to private

universities. Michigan State University (a landgrant university) created Neogen to seek venture capital for limited partnerships to develop

and market innovations arising from research.

The formation of Neogen points up a significant problem being encountered by the universities. Neogen was formed, in part, to retain

faculty members who were getting offers from

biotechnology companies. In Neogen, faculty

members are allowed to develop their entrepreneurial talent and reap the associated financial

rewards while remaining at the university.

The establishment of biotechnology property

rights has substantially heightened scientists’

interest in private sector employment opportunities, In the process, questions have arisen

over who maintains the property right—the

university, the private firm, or the scientists?

In the Washington University-Monsanto case,

the University retains the patent rights while

Monsanto has exclusive licensing rights. In Engenics, Stanford likewise gets the patent rights

while the center and the six corporate sponsors

receive the licenses and pay royalties. Neogen

will buy patent rights from Michigan State University, while the inventor will get a 15-percent

royalty or a stock option in Neogen.

Land-Grant University Adjustments

The potentially profound implications of such

developments on the land-grant university sys-
274 . Technology, Public Policy, and the Changing Structure of American Agriculture

tern seem clear. Such private sector arrangements integrate business into the university fabric, raising questions about the control of the

university research agenda, the allegiance of

scientists to their university employer, the willingness of scientists to discuss research discoveries that have a potentially patentable products

associated with them, and potential favoritism

shown particular companies by the university

because of their research ties.

This controversy has caused the land-grant

Agricultural Experiment Station Committee on

Policy (ESCOP) to express its concerns publicly

and to develop guidelines to deal with these biotechnology issues. The statement of ESCOP concerns includes the following:

●

●

●

●

As publicly supported institutions, the

SAES (State Agricultural Experiment Stations) will need to assure that industrial relationships generate an end result in the interest of the general public. This end result

should reward the industrial investor but

avoid placing such an investor in an unwarranted position of financial advantage

through privileged use of information or

technology partly derived from research

using public funds; neither should a curtailment of new information to the public

occur,

The SAES are greatly concerned about the

curtailment of communication on early research results and about the constraints on

sharing of germplasm emerging due to concerns on the part of scientists and institutions for protecting potentially patentable

research results. Industry sponsorship of

this kind of research tends to exaggerate

this problem.

There is general concern in the academic

community about the drain of scientific

manpower from the universities to industry. The ability to continue to conduct basic

research in an academic environment and

the concurrent interdependent ability to

continue educating scientists are key issues.

There is concern that individual scientists

may place themselves in positions of compromise or conflict of interest as they establish personal relationships with industry

●

as contractors, consultants, or institutional

officers.

There is concern on the part of both scientists and the SAES that through industrial

sponsorship of research, there may be introduced an undesirable level of direction

of effort by industry (ESCOP, 1981).

Out of these concerns ESCOP developed the

following interim policy guidelines:

●

●

●

●

●

Maintain SAES management control of research: Consensus: SAES should retain the

ability to manage research programs, and

control the direction of new investigations,

regardless of the source of support, including situations in which one or several

firms may sponsor research at several institutions.

Strong basic research and graduate education capability: Consensus: SAES should

maintain and expand the basic research capability in genetic engineering and related

areas within the domain of publicly supported institutions.

Faculty-industry relationships: Consensus:

Scientists should maintain close communication with institutional administrators in

the development of relationships and commitments with the commercial sector. Institutional guidelines should be developed

that assist the scientists in avoiding institutional or personal conflicts of interest.

Publication and communication; Consensus: The ability to publish and exchange

information is essential and must be secured in agreements. In some instances,

publications or information exchange may

need to be temporarily delayed to allow

time for an institution or sponsor to assure

adequate patent protection. The final decision to defer or modify a publication

should reside with the public institution.

Trade secrets and confidential information:

Consensus: Protection of “trade secrets” or

“confidential information” for more than

a very limited period should be avoided by

public institutions. Advance review by a

private sponsor, to avoid premature release

of information, maybe advisable but should

not become a mechanism to “shelve” use-
Ch. 12—impacts on Agricultural Research and Extension • 275

ful information or unpatentable technology.

. Patent rights and premature disclosure:

Consensus: SAES should retain the right

to participate in the decisions related to the

disposition of intellectual and real property

and patent rights resulting from research.

Retained ownership of patents by the SAES

is preferred, In any agreement, the SAES

should retain the right to use discoveries

and inventions from SAES research to extend and enhance public research and education. The need of private sponsors to

obtain a return on investment must be recognized, and agreements may provide for

special licenses for patents originating from

sponsored research.

● Biosafety of recombinant DNA: Consensus:

SAES must retain responsibility for review

and decisions in the release or distribution

of laboratory research products, although

some research may be supported by outside sponsors.

● Grants and income earnings: Consensus:

Extending knowledge and developing new

technology while serving the public interest should be the prime motivations in

agreements between SAES and the private

sector. Royalty income from discoveries

originating under such agreements should

be recognized as a secondary consideration.

● Licensing responsibilities and performance

expectations: Consensus: SAES should assure that “due diligence” clauses are included in contracts to assure that new technology is not shelved and the public interest

is served while private investment in commercialization is respected. Assignments,

rights, or licensing of patents for commercial use should be considered separately

from contractual definition of research to

be conducted. Initial or developmental

processes and pervasive technology ultimately leading to improved biological materials generally should not be assigned for

sole use by a sponsoring firm.

● Tax code implications: Consensus: When

sponsored research is motivated by certain

interpretations of Tax Code Section 1235,

exclusive licensing or co-ownership of patent rights is a preferred alternative for the

institution, since the institution maintains

a vested interest and some ownership of

patent rights involving the scientist, the institution, and the firm may require unique

documentation. Careful attention to these

rights and relinquishments is suggested

(ESCOP, 1981),

Impact on the Land-Grant

Social Contract

Potential basic changes in the relationship between land-grant universities and the public are

implied by the preceding adjustments, although

not explicitly discussed. The land-grant university system was established on a public service

basis different from that of other universities.

Its tradition has implied a social contract that

makes its discoveries freely available to the public.

The advent of patent rights, exclusive licensing, and private sector investment in public sector research has the potential for changing the

distribution of benefits from land-grant research

discoveries.” These changes warrant direct

public discussion and consideration by policymakers. They occur for at least five reasons:

1. By exclusive licensing or transfer of patent rights to private firms, the right to use

discoveries is no longer freely available–

even if information on the discovery itself

is freely available,

2. Certain individuals or firms are conferred

the benefits of specific land-grant research

to the potential detriment of others. Prior

to the transfer of discovery rights, the benefits were available to anyone who adapted

a land-grant discovery to commercial usage.

3. The costs of the resulting discoveries are

internalized in the price of the resulting

product. The price the public pays for the

product also includes any monopoly rents

associated with the conferral of the rights.

IZS i ~1 i la r i m ~)1 i(; a t 10 ns n] a \ a ] s() ex I St I or A KS r~?s~a r(; h to the

extent that patent rights a n(l e~[ Iuslie I i(:ensi ng arrangements

are created by ARS.
276 . Technology, Public Policy, and the Changing Structure of American Agriculture

Society thus pays twice: once for the cost

of the research and then again for its benefits. without the conferral of property

rights, rents are minimized by competition. 2.

4. Private sector-public sector inequities are

virtually assured in any granting of research property rights to an individual firm.

This occurs because with a relatively small

private sector investment there is access

to a much broader range of current and

prior research.

5. The existence of patent rights, trade secrets, 3.

and confidential information has as many

potential}’ adverse implications for extension in terms of the increased burden for

product testing, the potential lags in information, and the absence of research information that would have previously been

readily available.

The argument does not, however, flow exclusively against the conferral of private sector If

riety Protection Act was enacted into law.

Over 1,088 patent-like certificates were

granted by February 1, 1983.

without land-grant university involvement

in private sector-funded research, the universities may not be able to retain the topquality scientists needed to conduct agricultural research that is on the frontier of

knowledge, In the process, agricultural research, extension, and teaching programs

would suffer.

Patent monopoly rights may be necessary

to attract the capital investment needed to

translate land-grant university scientific

advances into commercial reality. without

such proprietary protection, new discoveries may not be able to compete for resources to develop marketable products or

technologies. The public availability of such

products could thereby be affected.

policy makers do not want land-grant uniproperty rights by the land-grant universities. versities to confer property rights, policy makers

There are three main counterbalancing argu- must provide the level of funding necessary for

. . .

-merits: competing with other non-land-grant universities that confer such rights. This decision is a

1. With the conferral of private property rights basic public policy decision–maybe the most

and the associated private sector invest- basic decision since the land-grant system was

ment, the quantity of research discoveries created. Once the land-grant system begins acmay increase. Evenson (1983), for example, tively competing for private sector grants and

found a sharp acceleration in private plant conferring licensing rights, there will be no turnbreeding programs after the 1970 Plant Va- ing back,

PRICING INFORMATION SERVICES

Although seldom recognized as such, one of

the most critical aspects of U.S. agricultural policy is that of information policy. Much of what

USDA does is provide information. Until the

1970s, most agricultural information available

to farmers had its origin in USDA. The department gathered the information, interpreted it,

and published it. Extension Service personnel

at State and Federal levels and private sector

media made the information freely available to

the public. The information covered a very

broad range—technology developments, public

policy changes, statistical data, economic trends,

and price forecasts. USDA was respected for

having the best information system in the world.

In many respects, USDA had a monopoly on

information that was freely available to anyone—small farmer or large agribusiness firm.

The information policy of USDA began to

change in the 1970s. Tight Federal budgets resulted in cutbacks in the quantity and quality

of information at a time when, because of

greater instability y, more information and information of better quality were needed. New methods of communication made timely transfer of

information to the producer possible. Such communication could be accomplished in closed,

often computerized, systems where the benefits could be captured by the supplier. Larger
Ch. 12—impacts on Agricultural Research and Extension . 277

farm units required information of a more specific nature, tailored to their operations. Information had captured a value, yielding private

sector profit opportunities.

without substantially increased appropriations, neither USDA nor the land-grant system

could adequately respond to these new demands, Perhaps more significantly, private sector firms, seeing increased profit opportunities,

did not encourage increased funding for information, In the process, they indirectly (some

might argue, directly) discouraged increased

funding, Their philosophy was basically one of

“give us (the private firms) the raw data and we

will interpret it. ”

At a time when policy makers sought opportunities to transfer functions from the public

to the private sector, it seemed quite logical to

cut back on public sources of information. Since

the information that was being collected by

USDA had acquired greater value, it also seemed

logical to begin charging for all (or nearly all)

USDA publications.

Increasing quantities of information are now

available only to those farmers and agribusiness

firms who can afford to pay for it, Those who

can afford to pay for it are the larger farm operations and agribusiness firms. Those who cannot afford to pay for it are the moderate-size and

small farms as well as the moderate-size and

small agribusiness firms. Since information is

a lifeline for success in today’s agriculture, its

absence accelerates the trend toward a more

highly concentrated agriculture.

For many moderate and smaller farmers the

Extension Service was the only continuing reliable and consistent source of information. But

even that source was curtailed by a USDA policy requiring State extension staff to pay for

USDA publications they had the responsibility

for distributing. Many States did not have the

funds to obtain reports that were vital to timely

producer decisions. Such policy changes are difficult to justify or excuse.

This problem is by no means limited to the

Federal Government. Many States have also

been forced by budget constraints to charge for

publications as well as for many of their educational programs. Such policies aggravate the

comparative disadvantage of moderate farms

competing in agriculture,

DISTRIBUTION OF BENEFITS AND STRUCTURAL IMPACTS

Technology is one of the driving forces be- Two important lessons arise from this descriphind structural change in agriculture, This point tion of the process of technological change:

has perhaps been most clearly argued by Wil- 1

lard Cochrane (1958), who points out that the

first adopters of new technology are the immediate beneficiaries in that their costs per unit

of production are lowered and their profits thus

rise. The profits of those firms supplying the

products of new technology also rise. In addition to reducing costs per unit of production,

technology generally expands output. Also,

higher profits encourage the adopting farmers

to expand output—even to the extent of increasing the scale of their farm operations. But as

output expands, prices decline. Later technology adopters thus realize less profit. In fact,

those farmers who are the last to adopt new technologies may actually be forced either to adopt

or to get out of agriculture.

Those farmers who are most aggressive in

effectively adopting and applying new technologies are the most likely to survive. Their

size or scale of operation thereby influences

the structure of agriculture. Likewise, to the

extent that research discoveries or extension programs favor certain size farmers,

structure is affected. White (1984) finds that

this impact is less than has sometimes been

asserted (Hightower, 1973). These findings

do not, however, negate the concern about

the neutrality issue. The importance of

technology in fostering structural change

makes constant awareness and consideration of technology’s potential impacts important in designing research and extension programs.
278 ● Technology, Public Policy, and the Changing Structure of American Agriculture

2. The ultimate beneficiary of agricultural research and extension has and will likely

continue to be the consumer. Larger supplies, lower food prices, and better quality

have almost invariably been the main end

result of research. This does not mean that

research operates contrary to the interest

of farmers. Research directly benefits the

more progressive farmers. Research is critical to expanding markets for farm products and to maintaining the competitiveness of U.S. agriculture internationally.

Research overcomes the constant threat of

new disease and other vagaries of nature

that threaten the increased productivity created by science and its application.

These lessons present a difficult problem for

policy makers and land-grant university administrators. While the returns on investment in

agricultural research and extension programs

are high, their benefits are by no means uniformly distributed. Although farmers and agribusiness firms are frequently described as the

main clientele of the agricultural research and

extension system, they are not the long-term

beneficiaries. The benefits enjoyed by farmers

and agribusiness firms are not uniformly distributed. The adverse effects of technology on

farmers who fail to adopt, agribusiness firms

that fail to obtain the property rights, or on farm

laborers who are displaced may be dismissed

as one of the costs of progress. They are, however, accentuated by policies that:

●

●

●

fail to provide sufficient resources and incentives to serve the research and extension needs of the full range of farmer and

agribusiness clientele regardless of their

ability to pay for those services;

fail to provide alternative retraining and

employment opportunities for those who

are displaced by the effects of technological change; and

fail to take into consideration the unique

nature of the social contract under which

the land-grant university system was formed

in designing a system of pro pert y rights for

its discoveries.

In other words, the trend toward industrialization may continue—but the scales should not

be tilted by public policy to speed up the process or assure the final conclusion. Indeed, public policy should work to keep options open for

conscious public decision.

SUMMARY AND CONCLUSIONS

To an important extent, U.S. agriculture has ●

been very successful because of technological

advance. Yet, consideration of specific changes

in research and extension policy may be justified. The following areas have been identified

as meriting consideration for policy changes:

● The social contract on which the agricultural research and extension system was

created needs to be reevaluated. This issue

should not be left for resolution by the

courts. Specific guidelines must be developed that, while allowing the system to

compete, protect the public interest and

investment in the agricultural research and

extension functions. Both Congress and ●

USDA should have an input in this type of

policy development.

It is sometimes suggested that increased

private sector support for agricultural research signals less need for public support.

While private sector support complements

public support, basic biotechnology and information technology research is very costly, A reduced role for public research and

extension would provide a slower rate of

technological progress and a lower level of

protection for the public health and welfare. In addition, there is a strong public

interest in maintaining an agricultural research component in each State to serve

the problem-solving needs of State agriculture,

Many agricultural problems are local or regional in scope. The applied nature of the

system, having an agricultural experiment
Ch. 12—impacts on Agricultural Research and Extension ● 279

●

●

station and extension service in each State,

has provided a unique capacity to identify

and solve local or regional problems. Reality suggests that only certain universities

have sufficient resources to compete for private sector support in biotechnology and

information technology. The result is a confluence of forces that is creating a dichotomy of “have and have not” universities.

In the process, traditional extension-research interaction and feedback mechanisms could break down, particularly in

States that are not in a position to command

a major biotechnology component.

The role of extension is even more important than it has been in the past. New, more

complex products require evaluation and

explanation. In States where experiment

stations have attracted substantial private

sector support, the product testing function

can most objectively be performed by extension. The recently passed 1985 farm bill

gives extension explicit authority to engage

in applied research functions such as product testing and evaluation.

While agricultural research is not inherently biased toward large-scale farms, lags

in adoption by smaller and moderate farms

have the same effect. Unless special attention is given to technology generation and

transfer to moderate farms, major structural changes could result—leading to the

eventual demise of a decentralized, moderate farm structure. To the extent that preservation of these farms is a policy objective, special funding for and emphasis on

the problems of technology generation and

transfer of technology to moderate farms

is warranted.

●

●

While the agricultural research system

has received the benefits of increased funding from both private and public sources,

extension funding has not materially increased. As a result, extension staff at the

county and specialist levels are being

caught up in a whirlwind of technological

change. The result is a need for the injection of substantial staff development funding into the extension system.

Basic organizational issues must be addressed by the Extension Service. The premise on which extension was developed was

that of research scientists conveying the

knowledge of discoveries to the extension

specialist who, in turn, supplied information to the county agent who taught the

farmer. Over time, this concept has gradually but persistently broken down as agricultural technology has become more complex, and insufficient resources have been

devoted to staff development. As a result,

more emphasis has been placed on direct

specialist-to-farmer education. More specialists have been placed in the field to be

closer to their clientele, but at the cost of

less contact with research scientists. As

these changes have occurred, the role of

the county agent has become increasingly

uncertain. Appreciation for and use of

county agents as educators and technology

transfer agents has declined. As a result of

these changes, a basic structural reevaluation of the organization of the extension

function of the agricultural research system is needed. Such a reevaluation will inevitably have to tackle the politically sensitive issues of the role of the county, State,

and Federal components of the Extension

Service.

CHAPTER 12 REFERENCES

Agriculture Research

Agricultural Research by Private Industry III, BeInstitute, A Survey of U.S.

thesda, MD, July 1985.

Christenson, James A., and Warner, Paul D., “Extension Client Impact in Knowledge Transfer Processes” (Los Angeles, CA: American Association

for Advancement of Science, May 28, 1985).

Cochrane, Willard W., ~arrn Prices: WIyth and Reality (Minneapolis: University of Minnesota Press,

1958).

CSRS, Cooperative State Research Service, Inventory of Agricultural Research FY 1982, vol. 11

(Washington, DC: U.S. Department of Agriculture,

April 1984),
280 ● Technology, Public Policy, and the Changing Structure of American Agriculture

Evenson, Robert E., “Intellectual Property Rights

and Agribusiness Research and Development: Implications for the Public Agricultural Research

System,” Amer. J. Agr. Econ. 65:967-975, 1983,

ESCOP, Experiment Station Committee on Policy,

Genetic Engineering Policy for the State Agricultural Experiment Stations, Washington, DC, Nov.

11, 1981.

Extension Service, Extension in the ’80s (Washington, DC: U.S. Department of Agriculture, no date).

Feller, Irwin, “Organizational Innovation in Innovating Organization: The Case of Agricultural Extension” (Los Angeles, CA: American Association

for the Advancement of Science, May 28, 1985),

Feller, Irwin, et al., Agricultural Technology Delivery System: Executive Summary, Institute for Policy Research and Evaluation (University Park, PA:

Pennsylvania State University, December 1984).

GAO, General Accounting Office, Federal Agricultural Research Funding: Issues and Concerns,

GAO/RCEI)-84-20 (Washington, DC: General Accounting Office, Oct. 20, 1983).

Hightower, Jim, Hard Tomatoes, Hard Times (Cambridge: Schenkman Publishing Co., 1973).

Hyatt, George, “Agricultural Extension Policy: Its

Evolution and Current Issues,” paper prepared

for the Office of Technology Assessment, Washington, DC, 1984.

Joint Council on Food and Agricultural Sciences,

Needs Assessment for the Food and Agricultural

Sciences (Washington, DC: U.S Department of

Agriculture, January 1984),

Kendrick, James B., Jr., “Revitalize U.S. Agricultural

Food Research” (editorial), California Agriculture,

Berkeley, CA, January 1981.

Knutson, Ronald D,, Paarlberg, Don, and McCalla,

Alex, “Forces Affecting Food and Agricultural

Research Decisions, ” paper prepared for the Office of Technology Assessment, Washington, DC,

1980.

Knutson, Ronald D., “Agricultural Research and Extension Policy: Contemporary Issues,” paper prepared for the Office of Technology Assessment,

Washington, DC, 1984.

Lovvorn, Roy L., “Agricultural Research Policy: Its

Evolution and Current Issues” paper prepared for

the Office of Technology Assessment, Washington, DC, 1984.

Marshall, H. Peter, et al., Strengthening the Research

Base for Extension: Final Report (Washington,

DC: U.S. Department of Agriculture Extension

Service, March 1985).

National Agricultural Research and Extension User’s

Advisory Board, Agricultural Research, Extension, and Education Recommendations (Washington, DC: U.S. Department of Agriculture, July

1983).

OTA, Office of Technology Assessment, An Assessment of the United States Food and Agricultural

Research System, OTA-F-272 (Washington, DC:

U.S. Government Printing Office, December

1981).

OTA, Office of Technology Assessment, Commercial Biotechnology: An International Analysis,

OTA-F3A-218 (Washington, DC: U.S. Government

Printing Office, January 1984).

Ruttan, Vernon, Agricultural Research Policy (Minneapolis: University of Minnesota Press, 1982).

University of California, Cooperative Extension

Long Range Planning Statement, Berkeley, CA,

August 1982.

U.S. Department of Agriculture, Developments in

Marketing Spreads for Food Production in 1979,

AER 449 (Washington, DC: U.S. Department of

Agriculture, 1980).

Warner, Paul D., and Christenson, James A., The

Cooperative Extension Service: A National Assessment (Boulder, CO: Westview Press, 1984),

White, Fred C., “Economic Impact of Agricultural

Research and Extension,” paper prepared for the

Office of Technology Assessment, Washington,

DC, 1984.
Part IV

Implications and PoIicy Options

for Agriculture
Chapter 13

Implications and Policy Options
Contents

Page

Three Farm Classes . ............... . . . . . . . . . . . . . . . . .......... 285

Consequences of Continuing Current Policies and Programs

Versus No Programs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ........ ● 286

Required Policy Adjustments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 287

Policy for Large Commercial Farms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 288

Policy for Moderate-Size Farms + . . . . . . . . . . . . . . . . . . . . . . ........ . . . 289

Policy for Small Part-Time Farms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 291

Policy for Rural Communities . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 292

Policy for Technology and Environmental Resource Adjustment . . . . . . . 293

Conclusions . . . . . . . . . . . . . . . . . . . . ....... . .... 294
Chapter 13

Implications and Policy Options

for Agriculture

The biotechnology and information technol- food supply, more agricultural exports, imogy revolution has been fostered by substantially proved food quality, and reduced adverse enviexpanded private sector investment in agricul- ronmental impacts. However, if current agritural research, an investment complemented by cultural policies continue, this era also holds

increased public sector emphasis on basic re- the potential for marked changes in the strucsearch, The output of this revolution is in its ture of agriculture and rural communities, ininfancy today but can be expected to blossom eluding the demise of many small and moderateover at least the next 30 years. size farms, increased centralization and integrate potential payoffs of this era include in- tion of farm production, and the degradation

creased food production for domestic and ex- of many rural communities.

port demand, a lower cost and more nutritious

THREE FARM

Major structural change in agriculture has already begun. Based on a continuation of current policies, past trends, and future technological expectations, the likely net result of this

structural change would be the development of

a farm structure composed of three predominant agricultural classes:

1. The large-scale farm segment will be composed of a relatively small number of farms

that will produce the bulk of the production. By year 2000 there could be as few as

50,000 large-scale farms producing as much

as three-fourths of the agricultural production. Some of these large-scale farms will

be owned by agribusiness corporations and

some will not. This large-scale farm segment will be highly efficient in production,

marketing, financial, and business management functions. The farms will be run by

full-time, highly educated business managers. Most of their land maybe rented. These

managers will probably know their chances

of making a profit even before planting or

breeding.

2. The struggling moderate-size farm segment

will strive to find a niche in the market and

to survive in an industrialized agricultural

CLASSES

setting. The difficulty for the moderate-size

farm to find that niche is rapidly becoming the center of the farm policy debate.

Traditionally highly productive, efficient,

moderate-size, full-time farms have been

referred to as the “backbone” of American

agriculture, It is still true that a moderate,

technologically up-to-date, and well-managed farm with good yields is highly resilient. One key to their success clearly lies

in the management factor. But more often

than not, management has to be willing to

accept a relatively low return on invested

capital, time, and effort. With ever-increasing educational requirements in farming,

there will likely be less willingness by successful managers of moderate-size farms

to accept a lower return for their services

and for invested capital, Another key to

their survival lies in access to state-of-theart technologies at competitive prices. Cooperatives have traditionally performed

that role. But today, cooperatives are generally not conducting or funding basic or

applied research in biotechnology and information technology. Like their predominantly moderate-size farmer members, cooperatives, too, have encountered financial

difficulty.

285
286 . Technology, Public Policy, and the Changing Structure of American Agriculture

3. The small, predominantly part-time farm segment tends to obtain most of its net income

from off-farm sources. However, this segment is highly diverse.1 It includes wealthy

urban investors and professionals who use

agriculture primarily as a tax shelter and/or

country home. It includes the would-be

moderate farmers who are attempting to

INo analysis exists that accurately measures the diversity of

this farm segment.

use off-farm income as a means of entering agriculture on a full-time basis. This

modern version of the old farmhand-to-tenant-to-owner agricultural ladder is also fading. Finally, this segment also includes a

number of poor, essentially subsistence,

farmers, vestiges of the “war on poverty”

from the 1960s. These farmers remain a significant social concern that needs to be

dealt with from a policy perspective, although

traditional farm price and income policy

hold no hope for solving their problems.

CONSEQENCES OF CONTINUING CURRENT POLICIES

AND PROGRAMS VERSUS NO PR0GRAMS

Today’s farm structure is partly the product

of past policies and programs and partly the

product of technology. Since the 1930s, farm

program benefits have been allocated on the basis of cost of production. In the late 1960s the

conversion of farm programs from supporting

farm prices to supporting farm income resulted

in the imposition of limits on the amount of payments a person involved in farming could receive. These payment limits proved largely ineffective at stemming the flow of benefits to

large farms. Likewise, large farms have benefited disproportionately from other programs

such as economic emergency credit and soil

conservation. Large farms have been in the best

position to take advantage of new technologies

derived from the public sector agricultural research and extension system. If current farm

policies and programs continue, the number of

large farms will continue to grow and reap the

majority of program benefits.

Without substantial changes in the nature and

objectives of farm policy, the three classes of

farms will soon become two—the moderate-size

farm largely will be eliminated as a viable force

in American agriculture. In addition, the problems of the small subsistence farm will continue

to fester as an unaddressed social concern.

As this structural change occurs, the face of

rural America will change. Large farms naturally tend to concentrate their activities in larger

communities. Moderate-size and small rural

communities inevitably die as the business conducted by farm implement, fertilizer, and chemical dealers as well as agricultural bankers declines. As a consequence, the rural-community

tax base is eroded as business activity, employment, and property values decline. Children are

bused longer distances to schools. The economic and social fabric of rural America erodes.

It is still unclear as to what the consequences

of this change maybe, because the vast majority

of Americans have little or nothing to do with

agriculture other than consume its products.

Clearly, an increase in rural unemployment results in an increase in costly Government social programs. The uncertainty, however, arises

over food production efficiencies and costs.

This study shows that large farms can indeed

produce at lower cost than smaller farms. The

question is whether the only way this lower cost

can be achieved is through scale of operation.

Can a moderate farm with adequate educational

advice and assistance from existing institutions

achieve the same low production cost without

creating the adverse rural community economic

and social consequences that are a result of current farm policies and trends? The answer is

not clear.

In the much longer run an agriculture dominated by a few corporate giants may not be

desirable from a general public, taxpayer, or
Ch. 13—implications and Policy Options for Agriculture ● 287

consumer perspective. A progressive, decentralized, competitive structure would be preferable. The task facing policy makers is to foster

such a structure.

The results of this study clearly demonstrate

that these adjustment problems would not be

solved by a quick transition to no Government

involvement in agriculture. In fact, the adjustment problems at the farm and rural-community

level would be aggravated further by additional

farm and rural agribusiness financial failures.

While reduced Government involvement in

agriculture may be a desirable long-term goal,

longer term transition policies and programs

are clearly required. Indeed, every industrialized nation manages their agricultural sector

to some degree—none are free of Government

intervention. In fact, the U.S. agriculture economy is managed less than most other industrialized nations.

The remainder of this chapter sets forth the

policy changes that would be required if Congress and the related body politic decided that

overt steps should be taken to foster a diverse,

decentralized structure of farming where all

sizes of farms had an opportunity to compete

and to survive in a time of rapidly changing technology. It should be noted that the objective of

giving every farm the opportunity to compete

and survive does not imply an unchanging and

stagnant farm structure. It does imply a political and social sensitivity to both the impact of

current farm programs on farm structure and

to the different needs of large, moderate, and

small farms for Government assistance. It can

be expected that regardless of what Government

does, fewer commercial farms will exist in year

2000 than today. But at a minimum, Government

can do much to ease the pain of adjustment.2

‘The policy options presented are not all inclusive; e.g., international trade dimensions of agricultural policy are not covered

in detail.

REQUIRED POLICY ADJUSTMENTS

Previous attempts to deal with the agricultural

structure issue have been limited to actions such

as limiting direct income support payments to

some fixed amount per farmer, like the $50,000

cap in present programs. Such marginal policy

changes, though thought to be beneficial, are

not discrete enough to separate or distinguish

between the different farm segments effectively.

More substantive changes in policy direction

are required for addressing the structure issue.

Specifically, separate policies and programs

need to be pursued with respect to each of the

three farm segments—large farms, moderate

farms, and small farms. The choice of any one

set of policies would imply that Congress

desired to selectively enhance the status of one

farm segment.

Policy for all farmers implies two basic policy goals:

1.

2.

All farmers need to operate in a relatively

stable economic environment where they

have an opportunity to sell what they produce. Restrictive trade policies or misguided macroeconomic policies impede

this basic goal.

All farmers need a base of public research

and extension support whereby they can

maintain their competitiveness in the markets in which they deal. A loss of U.S. comparative advantage in the world agricultural product market would be a serious

blow to the American economy. Similarly,

a loss of consumer confidence in the abilit y of the food system to produce a safe and

nutritious food supply efficiently would undermine public support for all of agriculture.

Policy for large farms need address only these

two goals. Policies for moderate and small farms
288 ● Technology, Public Policy, and the Changing Structure of American Agriculture

—.

must address these same goals plus additional

problems now facing these farm segments.

Policy for Large Commercial Farms

A basic conclusion of this study is that largescale farmers do not need direct Government

payments and/or subsidies to compete and survive. However, there is still a need for a commercial farm policy.

Criteria for determining what constitutes a

large-scale farm are important but somewhat

arbitrary. The dividing line developed from this

study is about $250,000 in sales for a crop or

dairy farm unit under single ownership or control. This level of sales is generally required to

achieve most economies of sizes Overtime, this

optimum size has had, and will continue to have,

a tendency to increase. As this occurs, criteria

for limiting program benefits according to farm

size will likewise have to increase.

Creating a Stable

Economic Environment

The policy goal of creating a relatively stable

economic environment where farmers have an

opportunity to sell what they produce implies

the following major farm program initiatives:

●

●

Direct Government payments would be

eliminated to all farms having over $250,000

in annual sales. This implies the elimination of the target-price concept, at least for

this sales class. Elimination of payments

to these farms would significantly reduce

Government expenditures in agriculture.

The nonrecourse loan would be converted

to a recourse loan. The nonrecourse feature has resulted in the accumulation of

large Government commodity stocks. The

recourse feature would provide a continuing base of support for the orderly marketing of farm products. It would encourage

year-long producer marketing inasmuch

as farmers could not avoid interest payments by forfeiting commodities to the Government.

sThe $250,000 figure is based on census data and the economies of size analysis discussed in previous chapters.

●

●

●

Government credit to farms having over

$250,000 in sales would not be available,

except for the recourse price support loan.

An expanded international development

assistance program would be established.

Such a program would have to include an

optimum balance of commodity aid and

economic development aid. Its primary objective would be to help developing countries reach the takeoff phase of economic

growth, and thus become better future customers of American agriculture.

A balanced macroeconomic policy that

facilitates growth of export markets and

maintains a relatively low real rate of interest would have to be maintained. Reduced deficits, combined with more expansionary monetary policies, would have the

effect of expanding the growth of agricultural export markets and would result in

reduced interest payments on the record

agricultural debt.

Maintaining

Technological Competitiveness

The technological competitiveness of American farmers would be assured by continuing

a policy that encourages public and private investment in agricultural research. The major

thrust of the research and extension programs

as they affect large farms would be as follows:

●

●

●

The trend toward increased public sector

emphasis on basic research would be continued. Increased reliance would be placed

on the private sector for applied research

in the development of new products.

While the public sector would emphasize

basic research, an important problem-solving component would be maintained to

adapt new technologies to various agro-ecosystems and to maintain newly achieved

productivity from pests and disease, decline in soil fertility, and other factors.

Extension’s role in the direct education of,

or consultation with, large farmers would

be deemphasized. Private consultants would

play an increasing role in technology transfer to the large farm segment.
Ch 13–lmplications and Policy Options for Agriculture ● 2 8 9

Policy for Moderate-Size Farms

Policy for moderate-size farms must include

not only the elements of policy postulated for

large farms, but also additional elements that

are specific to the more complex needs of this

farm segment. For example, OTA finds that

moderate farms having $100,000 to $250,000 in

gross sales face major problems of competing

and surviving in the biotechnology and information technology era. Some moderate farms

will survive and some will not. This latter group

should be assisted in their move to other occupations.

The following are specific policy goals for

moderate-size farms:

●

●

●

The risk of moderate farms operating in

an open market environment needs to be

reduced.

New and easily adopted technologies

should be made available to moderate

farms.

Opportunities for employment outside agriculture should be created for those farmers

who are unable to compete.

Diligent enforcement would be needed to assure that the benefits of programs established

to assist moderate farms are limited just to those

farms.

Risks to Moderato-Size Farms

The most difficult obstacle to survival facing

the moderate farm is that of managing risk. The

initiation of market-oriented farm policies in

the early 1970s greatly increased the amount

of price and income risk facing the moderate

farm. Large farms are better able to manage risk

generally because of the higher level of their

management’s formal training and because of

their greater diversification. The potential advantages of diversification by moderate farms

commonly are offset by diseconomies associated with smaller scale, multiple enterprises.

Similarly, managers of moderate farms often

lack the skills associated with operating in the

futures market or understanding various forms

of contracting.

Three possible options exist for reducing the

risks confronting moderate farms, One involves

offering moderate farms a higher level of price

and/or income protection than would be available to large farms. It maybe argued that such

policies foster inefficiency, but this may be a

price that must be paid to maintain a decentralized agriculture. The three options are:

1.

2.

3.

Income protection could be provided

through either a continuation of the current target-price concept for moderate

farms only or through a device known as

the marketing loan, Like the current nonrecourse loan, the marketing loan is a loan

from the Government on commodities in

storage. If the commodity is sold for less

than the loan value, the farmer pays back

only those receipts to the Government in

full payment of the loan. The marketing

loan, in essence, becomes a guaranteed

price to the producer. The level of the marketing loan should be no greater than the

average cost of production for moderate

farmers.

The nonrecourse loan concept could be

continued for moderate farms. However,

the level of the nonrecourse loan should not

be set any higher than the recourse loan

suggested previously for large farms; otherwise, the Government could end up acquiring most of the production from moderate

farms.

The public sector could provide significantly increased assistance as a means of

reducing risk to moderate farms. Such assistance could be in the form of, for example, educational programs on risk management, futures markets, contracting, and

cooperative marketing, In addition, special

assistance could be provided for cooperatives that offer marketing and pooling programs designed to reduce risk. While such

programs might also benefit large farms,

cooperatives have tended to be institutions

used primarily by moderate and small

farms.
290 ● Technology, Public Policy, and the Changing Structure of American Agriculture

Technology Availability and

Transfer to Moderate Farms

OTA finds that agricultural research generally is not inherently biased against moderate

farms. Rather, moderate farms maybe seriously

disadvantaged either by lags in adoption or by

lack of access to competitive markets for the

products produced by new technology. The following initiatives could help minimize such

problems of technology availability and transfer:

●

●

●

Extension’s evaluation of the increasing

number of new products entering the market would be extended. This increased effort would play a dual role of providing a

check on the efficacy and the efficiency of

new products of biotechnology and information technology, and would eliminate

the costs associated with individual farmer

experimentation with them. These test results would be available to all farms, regardless of size.

Extension technology transfer services

would be specifically aimed at moderate

farms. The primary goal of such programs

would be to make technologies available

to moderate farms on the same schedule

as large farms. Farming systems encompassing new technologies would have to

be adapted specifically to moderate farm

needs and made available through extension programs. Where this requires special

research initiatives, the U.S. Department

of Agriculture (USDA) and the Experiment

Stations would provide the support. In

States where technological change threatens to displace large numbers of moderate

farms, such as in Midwest dairying, special initiatives by State and local governments to support research and extension

would also be warranted.

The development of cooperatives that emphasize technology supply and transfer

services to moderate farms would have to

be undertaken. Unlike private sector agribusiness firms, cooperatives do not appear

to conduct or fund any aspect of biotechnology and information technology research. Current financial stress in the cooperative sector suggests that this sector

●

may not be able to marshal the capital

needed for such research. At a minimum,

there seems to be a need for cooperatives

to have a strong applied and developmental program of research in biotechnology

and information technology buttressed by

land-grant university basic research. To

achieve such a research objective cooperatives should consider carefully the formation of a research agency in common (RAC).

USDA or land-grant university research

along with RAC could receive special public sector Federal and State appropriations

and support. Formal links might be encouraged between research, extension, and cooperative institutions to maximize the effectiveness of technology transfer to

cooperatives and their moderate-size farm

members.

Ample credit would have to be made available to moderate farms that have the potential to survive. Government credit, in concert with cooperative credit, should be

aimed specifically at filling the needs of

moderate farms. Emphasis should be placed

on credit required to keep moderate farms

technologically up-to-date.

Transition Policy to Other Agricultural

Enterprises or Nonfarm Employment

Regardless of the effectiveness of the initiatives discussed above, there will bean accelerated need to move farm families either to other

agricultural enterprises or out of agriculture into

new occupations. The need arises, therefore,

for specific public action to facilitate adjustment

of resources from the current farm operation

into gainful, productive employment elsewhere.

Adjustments in rural community business activity and social service will be directly affected

by such changes. (The specific nature of these

adjustments and potential public policy considerations are treated in greater detail later in this

chapter.) Specific initiatives to ease this structural adjustment process include the following:

● As a continuously evolving industry, new

opportunities for employment of displaced

farmers need to be explored and developed

within agriculture. Aquiculture, for exam-
Ch. 13—lmplications and Policy Options for Agriculture ● 291

pie, is becoming an important and viable

agricultural industry. A more urbanized society has resulted in the growth of a large

horticulture and nursery industry. Changing population demographics, particularly

in terms of aging, suggest marked increases

in the demand for fruits and vegetables.

Land-grant universities and the Extension

Service bear an important responsibility for

fostering the growth of these industries

through education and training. Displaced

farmers, having prior experience in agriculture, are logical clientele for such education and training activities.

● Special skills training programs aimed at

those areas where significant employment

opportunities exist need to be designed to

assist with the transition to nonfarm jobs.

Jobs in rapidly growing service, health care,

or care-for-the-aged industries provide contemporary examples.

● Financial assistance from Federal, State,

and local governments, similar to the famous G.I. bill, might be established to assist displaced farmers or rural residents

during the period of transition while they

receive skills training. For example, the

Federal Job Training partnership Act Title

III program is a federally funded, Stateadministered program that assists displaced

workers in obtaining vocational retraining

and counseling. Such a program could be

made available to displaced farmers.

● In areas of severe financial stress, assistance may be provided in the form of Government purchase of land or production

rights from displaced farmers at its “longterm fair market value. ” The returns from

the land could be used by the displaced

farmer for relocation and retraining. The

Government could retain the land in conservation reserve status until it might be

needed for future production.

● An alternative program to ease the transition for farmers leaving agriculture is a selffinanced agricultural transition loan. Its objective would be to allow a farmer to leave

agriculture without having to worry about

generating the funds needed to live on while

seeking new employment. Such a program

could involve the following: 1) farmer terminates the farm operation and becomes

eligible for a Federal or State guaranteed

living loan, 2) farmer liquidates the farm

business over time and ultimately finds

other employment, and 3) farmer uses the

net proceeds from liquidation and earnings

from new job to repay the loan.

Policy for Small/Part-Time Farms

policy for small/part-time farms includes the

elements of policy for large farms plus additional elements.

With few exceptions, small farms having less

than $100,000 in sales are not viable economic

entities in the mainstream of commercial agriculture—nor can they be made so. However,

even a small increase in their farm income could

have a significant multiplier effect on the local

economy because of the large number of small

farms. These farms survive because their operators have substantial outside income (part-time

farmers), or because they have found themselves

a niche in marketing a unique product with special services attached (often direct to consumers), and/or because they are willing to accept

a very low return on resources contributed to

the farming operation.

The Government’s role would be severely restricted for the small farms who either have substantial outside income or who have found a

niche in the market. They are as much able to

take care of themselves as large farms are.

However, subsistence farmers who have

limited resources, and often limited technical

abilities, represent a genuine problem for which

public concern is warranted—these indeed are

the rural people left behind. Commercial farm

programs have done and can do little to solve

their problems. These impoverished individuals

are a social and economic problem for which

only social programs can help. However, while

programs such as food stamps, social security,

and aid to families with dependent children are

important to many subsistence farmers, these

programs do not serve the farmers’ unique agricultural and related needs. The following sug-
292 ● Technology, Public Policy, and the Changing Structure of American Agriculture

gestions are made for dealing with the problems

of subsistence farmers:

●

●

●

●

●

●

Initiate a special study to identify these

individuals and their specific status and

needs. Develop social programs to meet

those needs.

USDA and the land-grant universities bear

a special burden of responsibility y for serving the needs of these subsistence farmers.

This responsibility has not generally been

realized and, therefore, has not been fulfilled. In the South, this responsibility falls

particularly on the 1890 land-grant universities along with the statewide extension

education programs and the 1862 landgrant universities. In the North, the responsibility for serving the agricultural educational and research needs of subsistence

farmers falls exclusively on the 1862 landgrant universities.

USDA and the land-grant universities could

be directed to develop a joint plan for serving the agricultural research and educational needs of these farmers. Such a plan

should include the delivery of farming,

credit, and marketing systems designed to

maximize the small farms’ agricultural production and earning capacity.

Farming systems must be developed specifically to serve the needs of small subsistence farms. Such systems should, to the

extent practicable, encompass the use of

new technologies. Special USDA and landgrant research program components must

be designed specifically to develop and/or

modify technology for use by small subsistence farms.

Credit delivery systems for small subsistence farmers must be specifically developed by USDA through the Farmers Home

Administration. Such systems should consider the unique capital and cash flow limiting factors associated with subsistence

farmers who commonly are not in a position to take advantage of other farm programs such as price and income supports.

Marketing programs geared to subsistence

agriculture are essential for providing hope

for this farm segment. The difficulty lies

in the inability of these farmers to obtain

access to the mass markets through which

most agricultural production moves. Cooperatives and direct marketing to consumers

offer two potentially viable alternatives.

USDA and the Extension Service should

play a critical role in assisting in the establishment of such markets.

Policy for Rural Communities

The impact of adjustment in agriculture to

changing technology will by no means be

limited to the farm sector. Rural communities

will be at least equally affected by increasing

farm size, integration, and moderate-size farm

displacement. Although these effects will initially be felt by implement dealers, farm supply

and marketing firms, or bankers, the reverberations will extend throughout the community

in terms of employment levels, tax receipts, and

required services. Rural communities should

be assessing these impacts and preparing to

make needed adjustments. To ease the pain of

adjustment the following actions are suggested:

●

●

●

Comprehensive programs for community

redevelopment and change need to be initiated throughout rural America. Such development plans should be fostered and

facilitated by both Federal and State government agencies. Rural community development research and extension programs

must be revitalized to serve the needs of

communities in transition.

Increased employment opportunities in rural areas should be fostered b y aggressively

attracting new business activities to rural

communities. Particular emphasis would

be placed on attracting those businesses

that develop technologies and serve the

needs of high-technology agriculture in rural areas.

Rural communities should be assisted in

developing and modernizing the infrastructure needed to be a socially and economically attractive place to live. Some rural

communities can serve as an attractive

retirement residence for an aging popula-
tion. But a higher level of social services

would clearly be required,

● To attract new industry to these areas, rural communities need to play a vital role

in skills training for displaced farmers and

rural community employees. School and

university outreach programs can be modified to serve this important role.

Policy for Technology and

Environmental Resource Adjustment

Technological change inherently creates a disruption or imbalance in the allocation of resources. Much of this study has been devoted

to analyzing these effects. Some may question

whether this degree of change is either necessary or desirable.

One of the major reasons that American agriculture has been so productive is because technological change has been fostered by the public sector and nurtured by a profit-seeking

private sector. Consequently, American consumers have enjoyed a plentiful supply of lowcost food and natural fiber. In addition, agricultural exports have made a major contribution to the overall development of export markets, to the benefit of the general economy.

Biotechnology and information technology offer more of the same, with the added bonus of

using less chemicals in the production of food—

whether for the control of pests, disease, and

weeds or for the production of commercial fertilizer.

Maintaining the productivity and competitiveness of U.S. agriculture in the public interest requires a delicate balance between public

and private sector support for technological

change. Yet it would be wrong to imply that

there are no risks, The conferring of property

rights on discoveries of the agricultural research

system has shifted the agricultural research balance to the private sector. While the effects of

this shift appear to be positive, concerns exist

that a substantial portion of the benefits of even

public research could be captured by private

firm interests, In addition, no scientifically

Ch. 13—Implications and Policy Options for Agriculture ● 293

acceptable methodology exists for weighing the

risks or hazards of biotechnology research. To

deal with such issues, the following policy suggestions are made:

●

●

●

●

Steps should be taken to secure the public

interest social contract on which the USDA

and land-grant university agricultural research system has been based. Assurance

needs to be provided that the benefits of

publicly supported research and extension

are not inappropriately captured in the

form of private monopoly rents. The effect

would be to stifle the process of discovery

and the dissemination of new knowledge,

Major investments need to be made to foster the development of human capital that

is in a position to cope with the process of

rapidly changing agricultural technology,

This need extends from the training and

development of the most basic biological

research scientists, through the extension

specialist and county agent, to the farmer

who adopts the new technology and the

banker who supplies the loan for its purchase. At a time when agriculture is in a

low-income crisis state, there maybe a tendency not to make such investments in the

future, Such a strategy would clearly be

counterproductive,

Biotechnology is not likely to replace land

and water as vital agricultural resources.

In recent years, soil conservation has taken

aback seat from a policy perspective to fullproduction policies. Such a strategy would

appear to be very short-sighted. Likewise,

the inability of policy makers to establish

a national water policy runs counter to

maintaining the competitive edge of U.S.

agriculture internationally,

Little is known about the adverse impacts

of potential biotechnology developments

on the ecosystem. These risks must be carefully assessed, monitored, and, where necessary, regulated. Care must be taken, as

well, not to overregulate and thereby stifle

the potential competitiveness and productivity of U.S. agriculture.
294 ● Technology, Public Policy, and the Changing Structure of American Agriculture

CONCLUSIONS

While the biotechnology and information tech- costs of these improvements to farming and runology revolution will create many adjustment ral communities can be minimized by careful

problems, it has the potential for creating ben- policy analysis, planning, and implementation.

efits in a safer, less expensive, more stable, and This study is only the first step in that direction.

more nutritious food supply. The substantial
Appendixes
Appendix A

Animal and Plant Technology

Workshop Methodology and Procedures

To assess the impacts of emerging agricultural

production technologies, two workshops—one for

animal technology and the other for plant, soil, anti

water technology–-were conducted in April 1984.

The objective of the workshops was to obtain information about the development and adoption of

emerging technologies so that the information

could be used to analyze the economic, social, and

environmental impacts of technology adoption,

Since the information needed spanned a wide

range on the spectrum of the process of technological innovation—from successful completion of research to widespread commercialization of t he

technology—participants of the workshops were

carefully selected to include expertise in different

stages of technological innovation. Participants

comprised physical and biological scientists, engineers, economists, extension specialists, agribusiness representatives, and experienced farmers.

The Delphi technique was used to obtain collective judgments from the workshop participants. To

facilitate the process of obtaining consensus, an

electronic Consensor was employed to tabulate the

ratings assigned by each expert, In addition to registering the ratings, the device allowed each expert

to weight his rating according to the degree of confidence or expertise he had in his rating. That level

of confidence or expertise could be set at zero, 25,

50, 75, or 100 percent,

The Consensor provided an immediate video

screen readout of the rating distribution, the

weighted average rating, and the average degree of

confidence, If the first vote showed a very wide distribution on ratings, those experts with ratings that

were outliers were asked to explain their reasons

for the ratings assigned. After additional discussion,

another vote was taken. Since lack of a consensus

after such discussion was, in itself, an indication

of considerable uncertainty about the impacts of

new tech no log y, no attempt was made to force a

consensus beyond a second vote.

The principal tasks accomplished at the workshops were:

a. Estimation of the year that each technology’

was likely to be introduced for commercial

adoption,

b.

c.

d.

e.

f.

g.

Estimation of the yield trends for each commodity in 1990 and 2000 under the no-newtechnology environmental

Packaging of technologies that are likely to be

introduced in the production of each commodity in 1990 and 2000,

Estimation of the increases in crop and livestock performance measures when the package of technologies is fully adopted by farmers.

Estimation of the adoption profile (i.e., the

number of years it takes to reach a certain percentage of adoption and the maximum percentage of adoption) of each package of technologies applied to a particular commodity}’,

Discussion of major barriers to the adoption of

a particular package of technologies to the production of each commodity.

Identification of public policy options that

could remove the barriers or facilitate adoption

of the packages of technologies.

Information obtained from this workshop was

used to assess the economic, social, and environmental impacts of these technologies. Iowa State’s

CARD econometric and hybrid models were used

to simulate the impacts of these emerging technologies on plant and animal production, inventory,

demand, supply, prices, gross farm income, production expenses, and net farm income in 1990 and

2000 under alternative technology environments.

Alternative Environments for the

Development and Adoption

of Technology

Since the information to be obtained at the workshops depended on the environment under which

a new technology would be developed and adopted,

it was necessary to make certain assumptions about

future environments, or scenarios. Four technology

environments were developed and used in the workshops: most likely, more-new-technology, less-newtechnology, and no-new-technology environments.

I.I\lternat ii e IW tlnology en~ Ironrments WIII1 he d iscussed In t hc nt’~t WI(

t]on

297
298 ● Technology, Public Policy, and the Changing Structure of American Agriculture

The most likely environment is bordered by the

more-new-technology and the less-new-technology

environments; both deviate from the position of the

most likely environment. It is assumed in the most

likely environment that the historical trends will continue into the future. Forces, such as gross national

product (GNP), population growth, export demand

for U.S. agricultural commodities, trade policy, inflation rates, energy prices, and research and extension expenditures, that have shaped the past would

continue to evolve as they had in the past decades.

Assumptions made for various economic variables

under the three environments are shown in table A-1.

Factors underlying the more-new-technology environment are generally more favorable for development and adoption of new technologies than those

under the most likely environment, and factors

underlying the less-new-technology environment

are less favorable than those under the most likely

environment.

The assumptions under the no-new-technology

environment are the same as that of the less-newtechnology environment except for new technologies. It is assumed that all emerging technologies

discussed in the two workshops will not be available for commercial introduction before year 2000.

Existing technologies will continue to be used.

Through education and extension, farmers will learn

to use the existing technologies better to increase

productivity. Productivity is likely to continue to increase at a decreasing rate and will eventually level

off.

Presentation and Discussion

Of Technologies

In a plenary session, each author of a technology

paper made a lo-minute presentation to give the participants essential information about a technology

area so that they would be able to make intelligent

projections about the development and adoption of

that area. The authors’ presentations focused on the

following:

a. When would a significant technology emerge

from each major line of research?

b. What is the output of the new technology? IS

it a new product (e.g., a new vaccine for a particular disease) or a new process to produce the

same product (e.g., no till)?

c. How will each technology be used by farmers?

Can it be used alone or in combination with

other technologies? If it has to be combined with

other technologies, how will they be packaged?

d. What will it take for farmers to adopt it? Do they

e.

f.

g.

have to make a capital investment, such as the

purchase of new chemicals, instruments, equipment, or machinery?

What specific crops or livestock would be affected by adoption of a specific package of technologies?

How would the package of technologies affect

the performance of crop and livestock production?

How would each package of technologies affect

the quality of the environment and resource use?

Table A-1.—Alternative Technology Environments

More new Less new

Factors technology Most likely technology

Population growth rate:

United States . . . . . . . . . . . . . . . 1 .0% 0.7% 0.5%

World . . . . . . . . . . . . . . . . . . . . . . 1.8 1.6 1,3

GNP growth rate:

United States . . . . . . . . . . . . . . . 4.0 3.4 3.0

World . . . . . . . . . . . . . . . . . . . . . . 5.0 3.5 2.0

Trade policy . . . . . . . . . . . . . . . . . . Less protectionist, Continuation of More protectionist,

more favorable present trends less favorable

terms of trade terms of trade

Rate of growth of export demand:

Grain . . . . . . . . . . . . . . . . . . . . . . 1.80/0 1.4% 0,80/0

Oilseeds. . . . . . . . . . . . . . . . . . . . 2.3 1.8 1.2

Red meat . . . . . . . . . . . . . . . . . . . 2.0 1.0 0.0

Energy price growth rate

(constant dollars) . . . . . . . . . . . . 5.0 3.0 1.0

Growth rate of research and

extension expenditures

(constant dollars) . . . . . . . . . . . . 4.0 2.0 0.0

Inflation rate . . . . . . . . . . . . . . . . . . 8.0 5.0 3.0

SOURCE Office of Technology Assessment
App. A—Animal and Plant Technology Workshop Methodology and Procedures ● 299

A checklist was given on the above information

to the participants. Based on information obtained

from the presentation and on interactions with the

authors, the participants collectively packaged the

technologies and estimated the impacts and the

adoption profile for each package of technologies

(see section “Packaging of Technologies”).

Timing of Commercial Introduction

Since the impact of a new technology on agriculture at a given time depends on when the technology is introduced for commercial adoption, each

author at the workshops was asked to make an initial estimate on the probable year of commercial int reduction for each technology. Following each presentation, the entire group evaluated and discussed

the author’s initial estimate. The entire group then

collectively estimated the year of commercial introduction of each technology under the three technology environments. Table A-2 shows the probable

years of commercial introduction of animal technologies, and table A-3 shows the same for plant technologies under the three alternative environments.

The years of commercial introduction estimated

ranged from the present or possibly earlier—for genetically engineered pharmaceutical products; control of infectious disease in animals; superovulation,

embryo transfer, and embryo manipulation of cows;

and control of plant growth and development—to

2000 and beyond for genetic engineering techniques

for farm animals and cereal crops. Of the 57 potentially available animal technologies, 27 were estimated to be available for commercial introduction

before 1990, and the other 30 between 1990 and 2000,

under the most likely environment. In plant agriculture, 50 of the 90 technologies examined were projected to be available for commercial introduction

by 1990, and the other 40 technologies between 1990

and 2000.

Packaging of Technologies

Since in practice most technologies would be used

in combination with other technologies, the 28 areas

of technologies were grouped into packages according to their probable impacts on particular commodities under different technology environments. Table

A-4 shows different packages of technologies used

in producing different commodities under the three

alternative environments. In beef production, for example, 12 animal technology areas were grouped

into six packages. Since more new technologies

would be available for commercial adoption in later

years than earliers years, each package of technologies was further categorized as a 1990 package and

a 2000 package. For example, package 1990A would

include all genetic engineering technologies introduced commercially by 1990, and package 2000A

would include all genetic engineering technologies

introduced commercially by 2000, including all

package 1990A technologies. Thus, there are really

a total of 12 packages of technologies for beef production.

Performance Estimates Under the

No-New-Technology Environment

To estimate the net impact of emerging technologies on agricultural production, the participants

of the workshops were first asked to project the

performance measures of crop and livestock production, such as crop yields and livestock feed efficiency, to 1990 and 2000 under the no-new-technology economic environment. Historical trend lines

of performance measures of crop and livestock production were provided to the participants as a basis

for their discussion, Through the Delphi process,

participants collectively projected the performance

measures for each of the nine commodities for 1990

and 2000 assuming that all emerging technologies

identified and discussed in this study would not be

available for commercial adoption by 2000. The performance

follows:

Wheat:

Corn:

Soybeans:

Cotton:

Rice:

Beef:

Swine:

Dairy:

Poultry:

measures used in this study were as

bushels per acre, percent of planted acreage harvested.

bushels per acre, percent of planted acreage harvested.

bushels per acre, percent of planted acreage harvested.

pounds per acre, percent of planted acreage harvested.

bushels per acre, percent of planted acreage harvested.

pounds of meat produced per pound of

feed, calves per cow per year.

pounds of meat produced per pound of

feed, pigs per sow per year.

pounds of milk produced per pound of

feed, pounds of milk produced per cow

per year.

pounds of poultry produced per pound

of feed, eggs per layer per year, -

The results of the estimates are shown in table A5. If all the new technologies identified in this study

do not become available for commercial adoption
300 . Technology, Public Policy, and the Changing Structure of American Agriculture

Table A-2.—Timing of Commercial Introduction of Animal Technologies

Technology environments

More new Most Less new

Technology technology likely technology

Genetic engineering:

Production of pharmaceuticals 1982

Control of infectious diseases 1983

Improvements in animal

production 1990

Detection and treatment of

genetic abnormalities:

Detection 1990

Treatment 1990

Control of cancer and Ieukemia 1990

Animal production:

Cycle regulation 1985

Superovulation, embryo transfer,

and embryo manipulations 1983

Improvement of fertility 1990

Genetic engineering techniques

for farm animals 1995

Regulation of growth and development:

Muscle and adipose tissue

accretion

Hormone, serum, and tissue

factors important to growth

Immunological attraction of

animals

Measuring body composition and

animal identification

Animal nutrition:

Animal product consumption and

human health

Alimentary tract microbiology and

digestive physiology

Voluntary feed retake and

efficiency of animal

production

Maternal nutrition and progeny

development

Aquiculture

Livestock pest control:

Slow-release Insecticides

Vaccines

Integrated systems

Modification of insect habitat

Insect-resistant animals

Utilizing Immunity systems

Disease control:

Data managment and systems

analysis

1987

1995

1990

1990

1995

1989

1989

1984

1984

1984

1986

1987

2000

2000

1990

1980

1982

1983

2000

1995

2000

1990

1989

1983

1995

2000

1992

2000

1995

1995

2000

2000

1995

1984

1984

1984

1986

1989

2000

2000

1990

1980

1982

1983

> 2000”

>2000

>2000

>2000

1995

1983

1995

>2000

>2000

>2000

>2000

>2000

>2000

>2000

>2000

1984

1984

1984

1991

1994

2000

2000

1995

1980

“ > = Atter

SOURCE OttIce of Technology Assessment

by 2000, the performance of crops and livestock

could continue to improve through 2000 (but at

slower rates), primarily because of better applications of existing technologies through education and

extension. For example, corn yields are projected

to increase from 115 bushels per acre in 1982 to 117

bushels per acre in 1990 and 124 bushels per acres

.—

Technology environments

More new Most Less new

Technology technology likely technology

Diagnostic methodologies 1986

Selection for disease resistance 1994

Genetic engineering of microorganisms and embryos

Embryos 1995

Micro-organism 1988

Immunobiology 1983

Environment and animal behavior:

Energy conservation

Non-Integrated system 1985

Integrated system 1995

Optimizing total stress 1995

Stress and immunity 1995

Photoregulation of physiological

phenomena 1990

Utilization of crop residues and animal wastes:

Energy from manure 1985

Chemicals from crop residues 1990

Animal feed from crop residue 1990

Animal feed from manure 1990

Monitoring and control technologies:

Sensors 1985

Controller s 1985

Displayers 1985

Actuators 1985

Communication and information management:

Local communication networks 1985

Data terminal s 1985

Software and database systems 1985

Manufacturing management

system s 1987

Expert system s 1992

Telecommunications:

Digital communication 1990

Fiber optics 1990

Personal computers 1985

Videotex and teletext 1985

Value-added networks 1985

Integrated services digital

network 1987

Remote sensing 1985

Labor-saving technologies:

Robotic farming 1995

—

1986

1999

1999

1989

1983

1990

2000

2000

2000

1990

1985

1990

1990

1995

1985

1985

1985

1985

1985

1985

1985

1990

1995

2000

2000

1985

1985

1985

1990

1985

2000

1988

>2000

>2000

1999

1983

2000

>2000

>2000

>2000

>2000

1985

>2000

>2000

>2000

1985

1985

1985

1985

1985

1985

1985

2000

2000

>2000

>2000

1985

1985

1985

2000

1985

>2000

in 2000. Wheat yields are projected to increase from

36 bushels per acre in 1982 to 38 bushels per acre

in 1990 and 41 bushels per acre in 2000. And milk

production could increase from 12,300 pounds per

cow per year in 1982 to 13,700 pounds in 1990 and

15,700 pounds in 2000.
App. A—Animal and Plant Technology Workshop Methodology and Procedures • 301

Table A-3.—Timing of Commercial Introduction of Plant Technologies

Technology environments

,

More new

Technology technology —

Genetic engineering:

microbial inoculums 1990

Plant propagation 1983-90

Genetically engineered plants

Vegetable 1990

Soybeans/cotton 1990

Cereals 1995

Enhancement of photosynthetic efficiency:

Basic process of photosynthesis

Photosynthetic control by Internal

and external factors

Photosynthetic molecular biology

and genetics

Estimation of photosynthesis and

project management

needs

Mechanisms of response and

adaptation to stress

Plant growth regulators:

Controlling growth and

development

Resistance 10 disease and Insect

pests

Overcoming environmental

stresses

Postharvest preservation

1983

1983-90

1990-2000

1983-90

1990

1984

1986

1986

1985

Plant disease and nematode control:

Breed cultivators 1984

Genetic engineering

Bacteriocides fungicides and

nematicides

Biocontrol agents

Crop loss assessment

Management of insects and mites,

Chemical controls

New chemicals

Application technology

Genetic engineering

Pathogen/c chemicals

Plants

Information processing

Weed control:

Bioregulation through chemical

and biological technology

Allelopathic chemicals as

bioregulators

Crop tolerance and susceptibility

to control agents

IWMS for conservation tillage and

annual multicrop production

Biological nitrogen fixation:

Improved strains of rhizobia

Stress-tolerant rhizobia

Legumes more active m nitrogen

fixation (plant breeding)

Root zone of cereals

Nitrogen-fixmg cereals

Chemical fertilizers:

Increasing efficiency of nitrogen

use

Decreasing energy required

Processing of lower quality

phosphate rock into

fertilizers

Ammonia from coal

2000

1988

1985

1985

>1995

1984

1988

1995

2000

1984

1984-2000

1990

1992

1984-2000

1984

1987

1990-95

>2000

>2000

1990

1980

1990

1995

MOST

likely

Less new

technology

1990

1983-90

1990

1995

2000

1983

1983-90

1990-2000

1983-90

1983-95

1984

1988

1988

1986

1984

2000

1990

1990

1990

2000

1984

1990

2000

2005

1984

1984-2000

1995

1998

1984-2000

1984

1990-95

1990-95

>2000

>2000

1995

1980

1990

2000

Never

>1 990’

1995

2000

2010

1983

1983-2000

1990-2000

1983-90

2000

1985

1990

1990

1990

1984

2025

2000

2010

2000

>2000

1984

1995

2005

2010

1984

1984-2000

2000

>2000

1984-2000

1984

1995-2000

1990-95

>2000

>2000

2000

1980

1990

2000

—

Technology environments ———-—

More new

Technology technology

Water and soil-water-plant relations:

Understanding drought

resistance/tolerance

Plant breeding

Biotechnology” recombinant DNA

Water use efficiency

Water management

Photovoltaic systems

Soil erosion, productivity, and tillage:

Conservation farming systems

Assessing erosion and

its impact

Reclaiming lands

Use of public for soil

conservation projects

Multiple cropping:

Breeding crops for intensive

planting systems

Double croppmg/intercropplng

Competition by plant species for

growth factors

Plant nutrition through fertilizers

and microbiology

Mechanization for multiple

cropping

Organic farming:

Reduced use of inputs

Biocides

Reduced soil erosion

Self-sufficiency for nutrients

Minimum tillage with minimal

biocide use

Rotations

Use

Knowledge

Labor-saving technologies:

Mechanized fruit and vegetable

operations

Robotic farming

Fruit and vegetable

Grains

2000

1984

2010

1984

1995

1995

1995

1995

1995

1985

1990

1990

1995

1987

1984

1984

1984

1990

1984

1990

1985

1995

1995

Crop separation, cleaning, and processing:

New methods for separating and

cleaning grain

Infield or onfarm processing of

forages and oilseeds:

Vegetable

Forage

Oilseed

Engine and fuels:

Adiabatic compression ignition

engines with

turbocompounding

Electronic engine controls

Alternative fuels

Grams

Cellulose

Land management:

Conservation tillage

Controlled traffic farming

Customed-prescribed tillage

Multicropplng

Organic farming

1995

1984

1990

1984

1990

1985-86

1984

1995

1984

1987

2000

1984

Most

Iikely —

2020

1984

2030

1984

1995

1995

1995

1995

1995

1985

1985

1995

2000

1990

1984

1984

1984

1990-95

1984

1990-95

1985

2000

2000

1995

1984

1990

1984

1990

1986

1984

2000

1984

1990

2005

1984

Less new

technology

2050

1984

2050

1984

2010

1995

2000

>2000

1995

1985

1985

1990**

>2000

1987’”

1984

1984

1984

2000

1984

2000

1985

2010

2010

1995

1984

2000

1984

1990

1986

1984

2010

1984

1995

2020

1984
302 . Technology, Public Policy, and the Changing Structure of American Agriculture

Table A-3.—Timing of Commercial Introduction of Plant Technologies—Continued

Technology environments Technology environments

More new Most Less new More new Most Less new

Technology technology likely technology Technology technology likely technology — —. —

Communication and information management:

Local communication networks 1985

Data terminals 1985

Software and database systems 1985

Manufacturing management

systems 1987

Expert systems 1990

Monitoring and control:

Sensors 1984

Telecommunications:

1985 1985 Digital communication 1995 2000 2010

1985 1985 Fiber optics 1990 2000 2010

1985 1985 Personal computers 1985 1985 1985

Videotex and teletext 1985 1985 1985

1990 2000 Value-added networks 1985 1985 1985

1992 1997 Integrated services digital

network 1990 1990 >2000

1984 1984 Remote sensing 1985 1985 1985

Controllers 1984 1984 1984

Displayer s 1984 1984 1984

Actuators 1984 1984 1984

-— —.- . - . . ..- . . —- — —

● > = After

● *May actually accelerate development m this area if there is increased Interest m resource

efficient/sustainable cropping systems

SOURCE Off Ice of Technology Assessment
App. A—Animal and Plant Technology Workshop Methodology and Procedures ● 303

Table A-4.—Packages of Technologies

Beef:

Package A:

Package B:

Package C:

Package D:

Package E:

Package F:

Swine:

Package A:

Package B:

Package C:

Package D:

Dairy:

Package A:

Package B:

Package C:

Package D:

Poultry:

Package A:

Package B:

Package C:

Genetic engineering

Animal reproduction

Regulation of growth and development

Animal nutrition

Crop residue and animal waste

Pest control

Disease control

Environment and animal behavior

Monitoring and control

Communication and information

management

Telecommunications

Labor saving

Genetic engineering

Animal reproduction

Regulation of growth and development

Animal nutrition

Pest control

Disease control

Environment and animal behavior

Monitoring and control

Communication and information

management

Telecommunications

Crop residue and animal waste

Labor saving

Genetic engineering

Animal reproduction

Pest control

Disease control

Regulation of growth and development

Animal nutrition

Environment and animal behavior

Crop residue and animal waste

Monitoring and control

Communication and information

management

Telecommunications

Labor saving

Bovine growth hormone

Genetic engineering

Animal reproduction

Regulation of growth and development

Animal nutrition

Pest control

Disease control

Environment and animal behavior

Crop residue and animal waste

Monitoring and control

Communication and information

management

Telecommunications

Labor saving

Wheat:

Package A:

Package B:

Package C:

Corn:

Package A:

Package B:

Package C:

Soybean:

Package A:

Package B:

Package C:

Rice:

Package A:

Package B:

Plant growth regulators

Plant disease and nematode control

Management of insects and mites

Weed control

Chemical fertilizers

Water and soil-water-plant relations

Soil erosion, productivity, and tillage

Multiple cropping

Organic farming

Land management

Labor saving

Crop separation, cleaning, and processing

Engines and fuels

Communication and information

management

Monitoring and control

Telecommunications

Genetic engineering

Plant disease and nematode control

Management of insects and mites

Water and soil-water-plant relations

Communication and information

management

Monitoring and control

Telecommunications

Weed control

Chemical fertilizers

Soil erosion, productivity, and tillage

Multiple cropping

Land management

Organic farming

Genetic engineering

Enhancement of photosynthetic efficiency

Plant growth regulators

Plant disease and nematode control

Multiple cropping

Management of insects and mites

Weed control

Biological nitrogen fixation

Chemical fertilizers

Water and soil-water-plant relations

Soil erosion, productivity, and tillage

Organic farming

Labor saving

Crop separation, cleaning, and processing

Communication and information

management

Monitoring and control

Telecommunications

Genetic engineering

Enhancement of photosynthetic efficiency

Plant growth regulators

Plant disease and nematode control

Management of insects and mites

Weed control
304 ● Technology, Public Policy, and the Changing Structure of American Agriculture

Table A-4.—Packages of Technologies—Continued

Cotton:

Package A:

Chemical fertilizers

Water and soil-water-plant relations

Multiple cropping

Crop separation, cleaning, and procesing

Communication and information

management

Monitoring and control

Telecommunications Package B:

Genetic engineering

Enhancement of photosynthetic efficiency

Plant growth regulators

Plant disease and nematode control

Package C:

Management of insects and mites

Weed control

Chemical fertilizers

Water and soil-water-plant relations

Soil erosion, productivity, and tillage

Multiple cropping

Labor saving

Engines and fuels

Land management

Communication and information

management

Monitoring and control

Telecommunications

Biological nitrogen fixation

Organic farming

Crop separation, cleaning, and processing

SOURCE: Office of Technology Assessment

Table A.5.—Performance Projections Under

No. New-Technology Environment

Commodity Unit

Beef . . . . . . . . . lb. meat per lb. feed

calves per cow

Dairy. . . . . . . . . lb. milk per lb. feed

milk per cow per year

(thousand lb)

Poultry , . . . . . . lb. meat per lb. feed

eggs per layer per year

Swine . . . . . . . . lb. meat per lb. feed

pigs per sow per year

Corn . . . . . . . . . bushels/acre

Cotton . . . . . . . pounds/acre

Rice . . . . . . . . . bushels/acre

Soybean. . . . . . bushels/acre

Wheat . . . . . . bushels/acre

1990 2000

0.071

0.940

0.938

13.7

0.52

255.0

0.167

14.8

116.5

502.0

108.6

32.2

37.8

0.066

0.950

0.952

15.7

0.53

260.0

0.17

15.7

123.5

511.0

111,9

34.8

40.8

SOURCE: Office of Technology Assessment
Appendix B

U.S. Regional Agricultural Sales

by Sales Class and Commodity

Table B-1 .–Sales of Cash Grains by Sales Class and Region, 1982

Northeast North Central Southern Western

Sales class definition region region region region U.S. total

Small <$19.9K . . . . . . . . . . . . . $ 58,622 $ 768,433 $ 516,810 $ 92,769 $ 1,436,634

Part-time $20 to 99.9K . . . . . . . 127,304 6,935,115 1,556,510 572,129 9,191,058

Moderate $100 to 249.9K . . . . . 128,851 8,222,262 2,086,754 1,017,181 11,455,048

Large $250 to 500K. . . . . . . . . . 71,517 3,774,781 1,563,810 727,328 6,137,436

Very large >$500K . . . . . . . . . . 46,052 1,899,146 1,269,735 1,180,086 4,395,019

Total . . . . . . . . . . . . . . . . . . . . 432,346 21,599,737 6,993,619 3,589,493 32,615,195 —

Percentage of total regional sales of cash grains, 1982

Small <$19.9K . . . . . . . . . . . .

Part-time $20 to 99.9K . . . . . . .

Moderate $100 to 249.9K . . . . .

Large $250 to 500K. . . . . . . . .

Very large >$500K . . . . . . . . . .

Total . . . . . . . . . . . . . . . . . . . .

13.60/o 3.60/o 7.4% 2.60/o

29.4 32.1 22.3 15.9

29.8 38.1 29.8 28.3

16,5 17.5 22.4 20.3

10.7 8.8 18.2 32.9

100.0 100.0 100.0 100.0

Percentage of total national sales of cash grains, 1982

Small <$19.9K . . . . . . . . . . . -

0.2 ”/0 2.40/o 1.6 0/0 0.3% -4.4% -

Part-time $20 to 99.9K . . . . . . . 0.4 21.3 4.8 1.8 28.2

Moderate $100 to 249.9K . . . . . 0.4 25.2 6.4 3.1 35.1

Large $250 to 500K. . . . . . . . . . 0.2 11.6 4.8 2.2 18.8

Very large >$500K . . . . . . . . . . 0.1 5.8 3.9 3.6 13.5

Total . . . . . . . . . . . . . . . . . . . . 1.3 66.2 21.4 11.0 100.0

NOTE Moderate and large sales class Intervals In fh(s table differ from Intervals used elsewhere tn the report

SOURCE Off Ice of Technology Assessment Compiled from regional data provided by Bureau of the Census Agriculture Dlvls!on

305
306 ● Technology, Public Policy, and the Changing Structure of American Agriculture

Table B-2.–Sales of Cattle and Calves by Sales Class and Region, 1982

Sales class definition

Small <$19.9K . . . . . . . . . . . . .

Part-time $20 to 99.9K . . . . . . .

Moderate $100 to 249.9K. . . . .

Large $250 to 500K. . . . . . . . . .

Very large >$500K . . . . . . . . . .

Total . . . . . . . . . . . . . . . . . . . .

Northeast North Central Southern Western

region region region region U.S. total

$95,569 $ 860,155 $1,491,739 $ 328,404 $2,775,867

84,425 1,450,508 1,089,760 719,211 3,343,904

74,117 2,022,486 726,592 745,354 3,568,549

57,267 1,825,647 577,156 836,720 3,296,790

58,996 6,677,929 4,048,095 4,372,600 15,157,620

370,374 12,836,725 7,933,342 7,002,289 28,142,730

Percentage of total regional sales of cattle and calves, 1982

Small <$19.9K . . . . . . . . . . . . . 25.80/o 6.70/o 18.8% 4.7%

Part-time- $20 to 99.9K . . . . . . . 22.8 11.3 13,7 10.3

Moderate $100 to 249.9K. . . . . 20.0 15.8 9.2 10.6

Large $250 to 500K. . . . . . . . . . 15.5 14.2 7.3 11.9

Very large >$500K . . . . . . . . . . 15.9 52.0 51.0 62.4

Total ... , . . . . . . . . . . . . . . . . 100.0 100.0 100.0 100.0

Percentage of total national sales of cattle and calves, 1982

Small <$19.9K . . . . . . . . . . . . . 0.3%0 3.1 % 5.3% 1.2% 9.9%

Part-time $20 to 99.9K . . . . . . . 0.3 5.2 3.9 2.6 11.9

Moderate $100 to 249.9K , . . . . 0.3 7.2 2.6 2.6 12.7

Large $250 to 500K. . . . . . . . . . 0.2 6.5 2.1 3.0 11.7

Very large >$500K . . . . . . . . . . 0.2 23.7 14.4 15.5 53.9

Total . . . . . . . . . . . . . . . . . . . . 1.3 45.6 28.2 24.9 100.0

NOTE: Moderate and large sales class intervals in this table differ from intervals used elsewhere in the report.

SOURCE: Office of Technology Assessment. Compiled from regional data provided by Bureau of the Census, Agriculture Division.

Table B-3.–Sales of Dairy Products by Sales Class and Region, 1982

Northeast North Central Southern Western

Sales class definition region region region region U.S. total

Small <$19.9K . . . . . . . . . . . . . $ 25,152 $ 109,070 $ 26,613 $ 11,508 $ 172,343

Part-time $20 to 99,9K . . . . . . . 1,040,132 2,760,985 437,924 134,948 4,373,989

Moderate $100 to 249.9K. . . . . 1,381,918 2,600,968 1,085,013 437,406 5,505,305

Large $250 to 500K, . . . . . . . . . 467,390 743,530 647,896 550,289 2,409,105

Very large >$500K . . . . . . . . . . 178,001 197,635 666,780 2,053,233 3,095,649

Total . . . . . . . . . . . . . . . . . . . . 3,092,593 6,412,188 2,864,226 3,187,384 15,556,391

Percentage of total regional sales of dairy products, 1982

Small <$19.9K . . . . . . . . . . . . . 0.80/0 1.7% 0.9% 0.4%0

Part-time $20 to 99.9K . . . . . . . 33.6 43.1 15.3 4.2

Moderate $100 to 249.9K. . . . . 44.7 40.6 37.9 13.7

Large $250 to 500K. . . . . . . . . . 15.1 11.6 22.6 17.3

Very large >$500K . . . . . . . . . . 5.8 3.1 23.3 64.4

Total . . . . . . . . . . . . . . . . . . . . 100.0 100.0 100.0 100.0

Percentage of total national sales of dairy products, 1982

Small <$19.9K . . . . . . . . . . . . . 0.2% 0.7% 0.2% 0.1% 1.1%

Part-time $20 to 99.9K . . . . . . . 6.7 17.7 2.8 0.9 28.1

Moderate $100 to 249.9K . . . . . 8.9 16.7 7.0 2.8 35.4

Large $250 to 500K. . . . . . . . . . 3.0 4.8 4.2 3.5 15.5

Very large >$500K . . . . . . . . . . 1.1 1.3 4.3 13.2 19.9

Total . . . . . . . . . . . . . . . . . . . . 19.9 41.2 18.4 20.5 100.0

NOTE: Moderate and large sales class intervals in this table differ from intervals used elsewhere in the report.

SOURCE: Office of Technology Assessment. Compiled from regional data provided by Bureau of the Census, Agriculture Division
App. B—U.S. Regional Agricultural Sales by Sales Class and Commodity ● 307

Table B-4.–Sales of Poultry and Eggs by Sales Class and Region, 1982

Northeast North Central Southern Western

Sales class definition region region region region U.S. total

Small <$19.9K . . . . . . . . . . . . . $ 7,081 $ 13,634 $ 23,616 $ 4,488 $ 48,819

Part-time $20 to 99.9K . . . . . . . 25,334 66,701 386,349 31,085 509,469

Moderate $100 to 249.9K . . . . . 85,870 241,939 1,719,082 52,012 2,098,903

Large $250 to 500K. . . . . . . . . . 153,000 490,911 1,559,160 96,664 2,299,735

Very large >$500K . . . . . . . . . . 562,178 845,410 2,373,048 1,009,005 4,789,641

Total . . . . . . . . . . . . . . . . . . . . 833,463 1,658,595 6,061,255 1,193,254 9,746,567

Percentage of total regional sales of poultry and eggs, 1982

Small <$19.9K . . . . . . . . . . . . . 0.80/0 0.80/0 0.4% 0.4%0

Part-time $20 to 99.9K . . . . . . . 3.0 4.0 6.4 2.6

Moderate $100 to 249.9K . . . . . 10.3 14.6 28.4 4.4

Large $250 to 500K. . . . . . . . . . 18.4 29.6 25.7 8.1

Very large >$500K . . . . . . . . . . 67.5 51.0 39.2 84.6

Total . . . . . . . . . . . . . . . . . . . . 100.0 100.0 100.0 100.0

Percentage of total national sales of poultry and eggs, 1982

Small <$19.9K . . . . . . . . . . . . . 0.1 % 0.1 % 0.2 ”/0 O.OO

/O 0.50/0

Part-time $20 to 99.9K . . . . . . . 0.3 0.7 4.0 0.3 5.2

Moderate $100 to 249.9K . . . . . 0.9 2.5 17.6 0.5 21.5

Large $250 to 500K. . . . . . . . . . 1.6 5.0 16.0 1.0 23.6

Very large >$500K . . . . . . . . . . 5.8 8.7 24.3 10.4 49.1

Total . . . . . . . . . . . . . . . . . . . . 8.6 17.0 62.2 12.2 100.0

NOTE Moderate and large sales class intervals In thls table differ from intervals used elsewhere in the report

SOURCE’ Off Ice of Technology Assessment Compiled from regional data provided by Bureau of the Census, Agriculture Division

Table B-5.–Sales of Hogs by Sales Class and Region, 1982

Northeas t North Centra l Souther n Wester n

Sales class definitio n regio n regio n regio n regio n U.S. total

Small c$19.9K . . . . . . . . . . . . . $ 18,399 $ 261,398 $ 139,914 $ 19,696 $ 439,407

Part-time $20 to 99.9K . . . . . . . 34,735 1,268,807 213,832 33,453 1,550,827

Moderate $100 to 249.9K ., . . . 49,804 2,596,083 273,900 50,072 2,969,859

Large $250 to 500K. . . . . . . . . . 35,215 1,595,321 252,456 35,143 1,918,135

Very large >$500K . . . . . . . . . . 36,848 1,158,830 436,821 80,313 1,712,812

Total . . . . . . . . . . . . . . . . . . . . 175,001 6,880,439 1,316,923 218,677 8,591,040

Percentage of total regional sales of hogs, 1982

Small <$19.9K . . . . . . . . . . . . . 10.5% 3.80/o 10.60/0 9.0 ”/0

Part-time $20 to 99.9K . . . . . . . 19.8 18.4 16.2 15.3

Moderate $100 to 249.9K . . . . . 28.5 37.7 20.8 22.9

Large $250 to 500K. . . . . . . . . . 20.1 23.2 19.2 16.1

Very large >$500K . . . . . . . . . . 21.1 16.8 33.2 36.7

Total . . . . . . . . . . . . . . . . . . . . 100.0 100.0 100.0 100.0

Percentage of total national sales of hogs, 1982

Small <$19.9K . . . . . . . . . . . . . 0.2% 3.0% 1.6% 0.2% 5.1 %

Part-time $20 to 99.9K . . . . . . . 0.4 14.8 2.5 0.4 18.1

Moderate $100 to 249.9K . . . . . 0.6 30.2 3.2 0.6 34.6

Large $250 to 500K. . . . . . . . . . 0.4 18,6 2.9 0.4 22.3

Very large >$500K . . . . . . . . . . 0.4 13.5 5.1 0.9 19.9

Total . . . . . . . . . . . . . . . . . . . . 2.0 80.1 15.3 2.5 100.0

NOTE: Moderate and large sales class Intervals in this table differ from intervals used elsewhere in the report

SOURCE Off Ice of Technology Assessment Compiled from regional data provided by Bureau of the Census, Agriculture Division
308 ● Technology, Public Policy, and the Changing Structure of American Agriculture

Table B-6.—Sales of Fruit and Nuts by Sales Class and Region, 1982

Northeast North Central Southern Western

Sales class definition region region region region U.S. total

Small <$19.9K . . . . . . . . . . . . . $25,913 $26,709 $ 49,595 $ 139,210 $ 241,427

Part-time $20 to 99.9K . . . . . . . 71,720 64,518 132,191 803,335 1,071,764

Moderate $100 to 249.9K . . . . . 75,058 69,900 135,530 456,103 736,591

Large $250 to 500K. . . . . . . . . . 93,410 58,372 137,860 643,407 933,049

Very large >$500K . . . . . . . . . . 112,430 66,268 826,993 1,490,301 2,495,992

Total . . . . . . . . . . . . . . . . . . . . 378,531 285,767 1,282,169 3,532,356 5,478,823

Percentage of total regional sales of fruit and nuts, 1982

Small <$19.9K . . . . . . . . . . . . . 6.8 ”/0 9.3% 3.9 ”/0 3.9 ”/0

Part-time $20 to 99.9K . . . . . . . 18.9 22.6 10.3 22.7

Moderate $100 to 249.9K . . . . . 19.8 24.5 10.6 12.9

Large $250 to 500K. . . . . . . . . . 24.7 20.4 10.8 18.2

Very large >$500K . . . . . . . . . . 29.7 23.2 64.5 42.2

Total . . . . . . . . . . . . . . . . . . . . 100.0 100.0 100.0 100.0

Percentage of total national sales of fruit and nuts, 1982

Small <$19.9K . . . . . . . . . . . . . 0.5 ”/0 0.5 ”/0 0.9%0 2.50/o 4.4 ”/0

Part-time $20 to 99.9K . . . . . . . 1.3 1.2 2.4 14.7 19.6

Moderate $100 to 249.9K . . . . . 1.4 1.3 2.5 8.3 13.4

Large $250 to 500K. . . . . . . . . . 1.7 1.1 2.5 11.7 17.0

Very large >$500K . . . . . . . . . . 2.1 1.2 15.1 27.2 45.6

Total . . . . . . . . . . . . . . . . . . . . 6.9 5.2 23.4 64.5 100.0

NOTE Moderate and large sales class intervals in this table differ from intervals used elsewhere in the report.

SOURCE Office of Technology Assessment Compiled from regional data provided by Bureau of the Census, Agriculture Division

Table B.7.—Sales of Vegetables and Melons by Sales Class and Region, 1982

Northeast North Central Southern Western

Sales class definition region region region region U.S. total

Small <$19.9K . . . . . . . . . . . . . $27,070 $34,743 $ 50,355 $ 19,196 $ 131,364

Part-time $20 to 99.9K . . . . . . . 61,918 61,181 64,749 69,592 257,440

Moderate $100 to 249.9K . . . . . 68,836 59,792 80,025 90,604 299,257

Large $250 to 500K. ., , , . . . . . 57,776 69,208 97,667 218,614 443,265

Very large >$500K . . . . . . . . . . 86,165 135,505 734,475 1,901,387 2,857,532

Total . . . . . . . . . . . . . . . . . . . . 301,765 360,429 1,027,271 2,299,393 3,988,858

Percentage of total regional sales of vegetables and melons, 1982

Small <$19.9K . . . . . . . . . . . . . 9.00/0 9.60/o 4.9 ”/0 0.80/0

Part-time $20 to 99.9K . . . . . . . 20.5 17.0 6.3 3.0

Moderate $100 to 249.9K . . . . . 22.8 16.6 7.8 3.9

Large $250 to 500K. . . . . . . . . . 19.1 19.2 9.5 9.5

Very large >$500K . . . . . . . . . . 28.6 37.6 71.5 82.7

Total . . . . . . . . . . . . . . . . . . . . 100.0 100.0 100.0 100.0

Percentage of total national sales of vegetables and melons, 1982

Small <$19.9K . . . . . . . . . . . . . 0.7% 0.9 ”/0 1 .3 ”/0 0.50/0 3.30/0

Part-time $20 to 99,9K . . . . . . . 1.6 1,5 1.6 1.7 6.5

Moderate $100 to 249.9K . . . . . 1.7 1.5 2.0 2.3 7.5

Large $250 to 500K, . . . . . . . . . 1.4 1.7 2.4 5.5 11.1

Very large >$500K . . . . . . . . . . 2.2 3.4 18.4 47.7 71.6

Total . . . . . . . . . . . . . . . . . . . . 7.6 9.0 25.8 57.6 100.0

NOTE Moderate and large sales class intervals in this table differ from intervals used elsewhere in the report,

SOURCE Office of Technology Assessment. Compiled from regional data provided by Bureau of the Census, Agriculture Division.
App. B—U.S. Regional Agricultural Sales by Sales Class and Commodity . 309

Table B-8.–Sales of Cotton by Sales Class and Region, 1982

Northeas t North Centra l Souther n Wester n

Sales class definitio n regio n regio n regio n regio n U.S. tota l

Small <$19.9K . . . . . . . . . . . . . 0.0 “ $ 729 $ 49,604 $ 4,279 $ 54,612

Part-time $20 to 99.9K . . . . . . . 0.0 5,967 276,144 32,822 314,933

Moderate $100 to 249.9K . . . . . 0.0 18,952 459,384 106,598 584,934

Large $250 to 500K. . . . . . . . . . 0.0 11,578 558,039 168,870 738,487

Very large >$500K . . . . . . . . . 0.0 5,085 311,511 1,003,784 1,320,380

Total . . . . . . . . . . . . . . . . . . . . 0 0 42,311 1,654,682 1,316,353 3,013,346

Percentage of total regional sales of cotton, 1982

Small <$19.9K . . . . . . . . . . . . . 0 0

Part-time $20 to 99.9K . . . . . . . 0 0

Moderate $100 to 249.9K . . . . . 0 0

Large $250 to 500K. . . . . . . . . . 0.0

Very large >$500K . . . . . . . . . . 0.0

Total . . . . . . . . . . . . . . . . . . 0.0

1 .7 ”/0 3.0 ”/0 0.3 ”/0

14.1 16.7 2.5

44.8 27.8 8.1

27.4 33.7 12.8

12.0 18.8 76.3

100.0 100.0 100.0

Percentage of total national sales of cotton, 1982

Small <$19.9K . . . . . . . . . . . . . 0 .0% 0.0% 1.6% 0.1% 1.80/0

Part-time $20 to 99.9K . . . . . . . 0.0 0.2 9.2 1.1 10.5

Moderate $100 to 249.9K . . . . . 0.0 0.6 15.2 3.5 19.4

Large $250 to 500K. . . . . . . . . . 0 0 0.4 18.5 5.6 24.5

Very large >$500K . . . . . . . . . . 0.0 0.2 10.3 33.3 43,8

Total . . . . . . . . . . . . . . . . . . . . 0 0 1.4 54.9 43.7 100.0

NOTE Moderate and large sales class intervals In ~hIs table mffer from Intervals used elsewhere In the report

SOURCE Office of Technology Assessment Compiled ‘rorn regional data provided by Bureau of the Census, Agriculture Dlvlslon
Appendix C

Participants in OTA workshops

Animal Technology

Michael Phillips (Co-leader)

Project Director

Office of Technology Assessment

U.S. Congress

Washington, DC

Yao-chi Lu (Co-leader)

Senior Analyst

Office of Technology Assessment

U.S. Congress

Washington, DC

C. Eugene Allen

Professor and Associate Dean

College of Agriculture

University of Minnesota

St. Paul, MN

Howard Bachrach

Consultant

Agricultural Research Service

Plum Island Disease Center

U.S. Department of Agriculture

Greenport, NY

Roger Breeze

Director of Research

College of Veterinary Medicine

Washington State University

Pullman, WA

Keith Brown

Professor

Department of Poultry Science

Ohio State University

Wooster, OH

John Campbell

Professor

North Platte Agricultural Experiment Station

North Platte, NE

Dennis Campion

Research Leader

Animal Physiology Research

Richard B. Russell Research Center

U.S. Department of Agriculture

Athens, GA

Workshop Participants

B. Joe Conlin

Extension Dairy Specialist

University of Minnesota

St. Paul, MN

Larry Corah

Extension Beef Cattle Specialist

Kansas State University

Manhattan, KS

Nicholas Cross

President

Cattle Code America

Schiller Park, IL

Stanley E. Curtis

Professor

Department of Animal Science

University of Illinois

Urbana, IL

James R. Fischer

Associate Director

Agricultural Experiment Station

Michigan State University

East Lansing, MI

Harold Hafs

Vice President

Agricultural Research

Merck Institute for Therapeutic Research

Rahway, NJ

William Hansel

Liberty Hyde Bailey Professor of Animal

Physiology

Department of Physiology

New York State College of Veterinary Medicine

Cornell University

Ithaca, NY

Maynard Hogberg (Swine group leader)

Extension Swine Specialist

Michigan State University

East Lansing, MI

310
App. C—Participants in OTA Workshops ● 311

Alan Holiday

Vice-President for Research and Development

Farmland Industries

Kansas City, MO

Keith Hera

Farmer

Riverside, IA

Ronald Knutson (Dairy group leader)

Professor

Department of Agricultural Economics

Texas A&M University

College Station, TX

Sid E. Kunz

Research Leader

U.S. Livestock Insects Laboratory

Agricultural Research Service

U.S. Department of Agriculture

Kerrville, TX

Juliette Linzer

Research Assistant

Office of Technology Assessment

U.S. Congress

Washington, DC

Charles McGinnis

Farmer

Kinards, SC

John McKnight, Jr.

Farmer

Searcy, AR

A.J. Muehling

Professor

Agricultural Engineering

Agricultural Engineering

University of Illinois

Urbana, IL

John Nye

Professor

Agricultural Engineering

Purdue University

West Lafayette, IN

P.A. Oltenacu

Extension Specialist

Department

Department

B.I. Osburn

Professor of Pathology

Associate Dean of Research

School of Veterinary Medicine

University of California

Davis, CA

John Paterson

Associate Professor

Animal Science Research Center

University of Missouri

Columbia, MO

Wilson G. Pond

Research Leader

Agricultural Research Service

Roman L. Hruska U.S. Meat Animal

Research Center

U.S. Department of Agriculture

Clay Center, NE

Rod Preston

Thornston Distinguished Professor

Department of Animal Sciences

Texas Tech University

Lubbock, TX

Hoyle B. Puckett

Research Leader

ARS-USDA

University of Illinois

Urbana, IL

Allan Rahn (Poultry group leader)

Professor

Department of Animal Science

Michigan State University

East Lansing, MI

Dave Reed

Director of Clinical Research and Immunology

Molecular Genetics, Inc.

Minnetonka, MN

Robert Reining

Analyst

Office of Technology Assessment

U.S. Congress

Washington, DC

Associate Professor of Systems Analysis

Department of Animal Science

Cornell University

Ithaca, NY
312 . Technology, Public Policy, and the Changing Structure of American Agriculture

Barry Richmond Tom Sporleder (Beef group leader)

Assistant Professor Professor

Dartmouth College Department of Agricultural Economics

Hanover, NH Texas A&M University

William Riddle

Research Leader

Food and Agricultural Economics

Battelle Columbus Laboratories

Columbus, OH

Marvin Rohlf

Vice President

Director of Research and Nutrition

Golden Sun Feeds

College Station, TX

R.H. Strickler

President

ROCCO Enterprises, Inc.

Harrisonburg, VA

Roy Van Arsdall

Economist

ERS-USDA

University of Illinois

Estherville, IA Urbana, IL

John V. Shutze Harold Wilcke

Head Consultant

Extension Poultry Science Ralston-Purina

University of Georgia St. Louis, MO

Athens, GA

Plant, Soil, and Water Technology Workshop

Michael Phillips (Co-leader)

Project Director

Office of Technology Assessment

U.S. Congress

Washington, DC

Yao-chi-Lu (Co-leader)

Senior Analyst

Office of Technology Assessment

U.S. Congress

Washington, DC

Delmar Akerlund

Farmer

Valley, NE

Martin Alexander

Professor of Soil Microbiology

Department of Agronomy

Cornell University

Ithaca, NY

Joseph A. Berry

Staff Member

Plant Biology Department

Carnegie Institution of Washington

Stanford, CA

Participants

Larry L. Boersma

Professor of Soil Physics

Department of Soil Science

Oregon State University

Corvallis, OR

J.A. Browning

Chairman

Department of Plant Sciences

Texas A&M University

College Station, TX

Bill Burrows

Manager

Product Systems Research

Deere & Co.

Moline, IL

John A. Chapman

Vice President

Engineering, Irrigation Division

Valmont Industries

Valley, NE
App. C—Participants in OTA Workshops ● 313

Louie J. Chapman

Head

Extension Agronomy

Auburn University

Auburn, AL

C.H. Davis

Director of Chemical Division

National Fertilizer Development Center

Tennessee Valley Authority

Muscle Shoals, AL

Lynn Forster (Corn group leader)

Associate Professor

Department of Agricultural Economics and

Rural Sociology

Ohio State University

Columbus, OH

George R. Foster

Hydraulic Engineer

National Soil Erosion Lab

ARS-USDA

Purdue University

West Lafayette, IN

Robert Fraley

Manager

Plant Molecular Biology Group

Monsanto Agriculture Products Co.

St. Louis, MO

Charles Francis

Professor

Department of Agronomy

University of Nebraska

Lincoln, NE

Nicholas M. Frey

Director, Department of Bio-Technology

Research

Pioneer Hi-Bred, Inc.

Johnston, IA

Arthur Gerlow (Rice group leader)

Economist

Texas A&M University

Bryan, TX

Don W. Graffis

Professor

Extension Agronomist

University of Illinois

Urbana, IL

W.R. Guthrie

Vice President

Agricultural Research

Butler Manufacturing Co.

Grandview, MO

Robert Hall

Assistant Professor

Extension Agronomist

Plant Science Department

Brookings, SD

Paul Hedin

Research Leader

Chemistry Research Unit

Boll Weevil Research Laboratory

ARS-USDA

Mississippi State, MS

Alan Holiday

Vice President for Research and Development

Farmland Industries

Kansas City, MO

Bobby A. Huey

Extension Agronomist

University of Arkansas

Stuttgart, AR

Robert Jolly (Soybeans group leader)

Associate Professor

Department of Economics

Iowa State University

Ames, IA

George Kennedy

Associate Professor

Department of Entomology

North Carolina State University

Raleigh, NC

John Knepler

Manager, Design Engineering

DICKEY-john, Corp.

Auburn, IL

Ron Lacewell (Cotton group leader)

Professor

Department of Agricultural Economics

Texas A&M University

College Station, TX
314 ● Technology, Public Policy, and the Changing Structure of American Agriculture

William Liebhardt

Assistant Director

Rodale Research Center

Kutztown, PA

Juliette Linzer

Research Assistant

Office of Technology Assessment

U.S. Congress

Washington, DC

Chester G. McWhorter

Chief, Southern Weed Science Laboratory

Agricultural Research Service

Delta State Research Center

U.S. Department of Agriculture

Stoneville, MS

Thomas A. Miller (Wheat group leader)

Research Leader

ERS-USDA

Colorado State University

Fort Collins, CO

Page W. Morgan

Professor of Plant Physiology

‘Department of Plant Sciences

Texas A&M University

College Station, TX

Louis G. Nickell

Vice President of Research and Development

Velsicol Chemical Co.

Chicago, IL

Paul Nixon

Agricultural Engineer

Soil and Water Conservation Research

Agricultural Research Service

U.S. Department of Agriculture

Weslaco, TX

Bob Nowatzki

Farmer

Langdon, ND

James H. Palmer

Extension Agronomist

Professor of Agronomy and Soils

Clemson University

Clemson, SC

Buford Perry

Farmer

Lake Providence, LA

Robert Reining

Analyst

Office of Technology Assessment

U.S. Congress

Washington, DC

Barry Richmond

Assistant Professor

Dartmouth College

Hanover, NH

William Riddle

Research Leader

Food and Agricultural Economics

Battelle Columbus Laboratories

Columbus, OH

Bill Robertson

Farmer

Holly Ridge, MS

Robert L. Schafer

Research Leader

Tillage Research

National Tillage Machinery Laboratory

U.S. Department of

Auburn, AL

Lyle Schertz

Senior Economist

Economic Research

U.S. Department of

Washington, DC

James W. Searcy

Research Manager

Agriculture

Service

Agriculture

E.I. du Pont de Nemours & Co.

Stine-Haskell Research Center

Newark, DE

B. Stout

Professor

Agricultural Engineering Department

Texas A&M University

College Station, TX

E.D. Threadgill

Associate Professor

Agricultural Engineering Department

Coastal Plain Experimental Station

University of Georgia

Tifton, GA

Larry Williams

Farmer

Hoopeston, IL
App. C—Participants in OTA Workshops ● 315

Agricultural

Resource Person s

Robert Fraley

Manager

Structure and Natural Environment

Workshop Participants

Plant Molecular Biology Group

Monsanto Agriculture Products Co.

St. Louis, MO

Charles Francis

Professor

Department of Agronomy

University of Nebraska

Lincoln, NE

William Hansel

Liberty Hyde Bailey Professor of Animal

Physiology

Department of Physiology

New York State College of Veterinary Medicine

Cornell University

Ithaca, NY

B.I. Osburn

Professor of Pathology

Associate Dean of Research

School of Veterinary Medicine

University of California

Davis, CA

William Riddle

Research Leader

Food and Agricultural Economics

Battelle Columbus Laboratories

Columbus, OH

Jeffrey Ruckman

Engineer

Food and Agricultural Economics

Battelle Columbus Laboratories

Columbus, OH

Agricultural Structure Group

Michael Phillips (Co-leader)

Project Director

Office of Technology Assessment

U.S. Congress

Washington, DC

Thomas Sporleder (Co-leader)

Professor

Department of Agricultural Economics

Texas A&M University

College Station, TX

Michael Boehlje

Professor and Head

and Resources

Department of Agricultural and

Applied Economics

University of Minnesota

St. Paul, MN

Boyd Buxton

Economist

ERS-USDA

University of Minnesota

St. Paul, MN

James Cothern

Extension Economist

Department of Agricultural Economics

University of California

Davis, CA

Lynn Forster

Associate Professor

Department of Agricultural Economics and

Rural Sociology

Ohio State University

Columbus, OH

Gene Futrell

Professor

Department of Economics

Iowa State University

Ames, IA

Harold Harris

Professor

Department of Agricultural Economics

Clemson University

Clemson, SC

Lester Myers

Branch Chief

Food Economics

Economic Research Service

U.S. Department of Agriculture

Washington, DC

George Mew

Industry Analyst

Farmland Industries

Kansas, City, MO

Allan Rahn

Professor

Department of Animal Science

Michigan State University

East Lansing, MI
316 • Technology, Public Policy, and the Changing Structure of American Agriculture

Lyle Schertz

Senior Economist

Economic Research Service

U.S. Department of Agriculture

Washington, DC

Lee Schrader

Professor

Department of Agricultural Economics

Purdue University

West Lafayette, IN

Ronald Ward

Professor

Department of Food and Resource Economics

University of Florida

Gainesville, FL

Natural Environment and

Resources Group

James Hite (Co-leader)

Professor

Department of Agricultural Economics

Clemson University

Clemson, SC

Yao-chi-Lu (Co-leader)

Senior Analyst

Office of Technology Assessment

U.S. Congress

Washington, DC

Robert Fraley

Manager

Plant Molecular Biology Group

Monsanto Agricultural Products Co.

St. Louis, MO

Charles Francis

Professor

Department of Agronomy

University of Nebraska

Lincoln, NE

Dellworth Gardner

Professor

Department of Agricultural Economics

University of California

Davis, CA

Clyde Kiker

Associate Professor

Department of Food and Resource Economics

University of Florida

Gainesville, FL

Randall Kramer

Associate Professor

Department of Agricultural Economics

Virginia Polytechnic Institute

Blacksburg, VA

James Moore

Associate Professor

Agricultural Engineering Department

Oregon State University

Corvallis, OR

Ford Runge

Assistant Professor

Department of Agricultural and Applied

Economics

University of Minnesota

St. Paul, MN

Ray Supalla

Associate Professor

Department of Agricultural Economics

University of Nebraska

Lincoln, NE

Robert Todd

Professor and Head

Department of Microbiology

South Dakota State University

Brookings, SD
Appendix D

Analysis of Size Economies and

Comparative Advantage in

Crop Production in Various

Areas of the United States

This appendix provides the detailed analysis of

size economies and comparative advantage by area

for crop production. A summary of this analysis

was presented in chapter 8. The analysis was conducted at the University of Minnesota by Steve

Cooke under the guidance of Burt Sundquist. Cooke’s

manuscript “Size Economies and Comparative Advantage in the Production of Corn, Soybean, Wheat,

Rice, and Cotton in Various Areas of the United

States” is published in a second volume to this OTA

report,

The following analysis is organized by commodity. Each section follows the same format. First,

there is a discussion of size economies by selected

producing areas. Second, there is a discussion of

comparative advantage, including the relationship

between comparative advantage and size economies. Each of these commodity sections includes

a summary of the size economy indices and “scoring table. ”

Corn

The corn-producing areas selected are:

1. Illinois area 300—Corn for grain

2. Indiana area 101—Corn for grain

3. Iowa area 201—Corn for grain

4. Nebraska area 400—Irrigated corn for grain

The four measures, or indicators, of size economies are estimated according to the procedures

outlined by Cooke (1985). These indicators include

production cost, use of harvesting equipment, and

static and dynamic Herfindahl production concentration indices.l A summary of these indices is presented in a table for each commodity by enterprise

size for each of the selected production areas (taI For simpllc.lty, II near production possibilities curves and homogeneous commodity price ratios were assumed in the analysls

ble D-l). There is an element of judgment required

in using these indices.

To clarify and facilitate the judgment used, a scoring table was set up and marked for each of the

measurement categories (table D-2). Each enterprise

within a category was given a plus or minus for the

presence or absence, respectively, of a “clear advantage” that enterprise size exhibited relative to

the others within a given production area. Each

measurement category was weighted equally so

that the overall or total measure of size economies

was expressed as the sum of the phases. The range

in scoring was from 0 to 4. Zero implies no advantage for that enterprise size. Four implies a clear

advantage for that enterprise size relative to one or

both of the other enterprise sizes within a production area. A table for each commodity presents the

results of the scoring procedure (table D-3).

In Illinois the very large corn enterprises have a

cost advantage both in cost per bushel and in capital ownership costs per acre (table D-l). Very large

corn enterprises in this area can fully use one to

five self-propelled, six-row harvesters. These harvesters are assumed to have an annual harvesting

capacity of about 450 acres per harvester, The static

Herfindahl index (which is the measure of relative

production concentration) in 1982 was greatest for

large and very large enterprises. The dynamic Herfindahl index (which is the change in relative concentration) from 1978 to 1982 was positive only for

the very large enterprise size. The scoring results

are 4, 1, and O for the very large, large, and moderate-size enterprises, respectively (table D-2). There

is strong evidence to argue for the existence of size

economies for very large enterprises relative to

large and moderate enterprises in corn production

in Illinois.

In Indiana large and very large enterprises have

nearly identical costs per bushel, In ownership cost

per acre, very large enterprises have a cost advantage. Very large enterprises can fully use two, and

large enterprises can fully use one, self-propelled,

317
318 ● Technology, Public Policy, and the Changing Structure of American Agriculture

Table D-l. -Indices Used to Determine Size Economies in Selected Corn. Producing Areas, 1983

Harvest machinery Herfindahl indices

State, area, and Total costa per Ownership costa per full utilization ‘ Static Dynamic

enterprise size bushel (percent) acre (percent) Maximum Minimum (percent) (percent)

IL 300

VL . . . . . . . . . . . . . . . . . . . . . . 100 100 5.4 0.9 100 35

L . . . . . . . . . . . . . . . . . . . . . . . 105 105 0.9 0.7 96 0

M . . . . . . . . . . . . . . . . . . . . . . . 119 110 0.6 0.5 43 –15

IN 101

VL . . . . . . . . . . . . . . . . . . . . . . 100 100 2.1 1.7 100 32

L . . . . . . . . . . . . . . . . . . . . . . . 99 105 1.4 0.9 95 21

M . . . . . . . . . . . . . . . . . . . . . . . 105 105 0.9 0.4 79 11

1A 201

VL . . . . . . . . . . . . . . . . . . . . . . 100 100 1.8 1.0 100 48

L 107 128 0.6 0.5 118 11

M:::::::::::::::::::::: : 105 155 0.5 0.3 85 –11

NE 400b

VL . . . . . . . . . . . . . . . . . . . . . . 100 100 5.2 2.6 100 42

L 107 100 2.1 0.9 62 11

M:::::::::::::::::::::: : 113 105 0.8 0.5 43 – 2

aExcluding land charge

blrngated

SOURCE Office of Technology Assessment

Table D.2.—Scoring Table Used to Determine Size Economies in Selected

Corn. Producing Areas, 1983

State, area, and Production Harvester Herfindahl indices

enterprise size cost utilization Static Dynamic Total

IL 300

VL . . . . . . . . . . . . . . + + + + 4

L — + — 1

M::::::::::::::: – — — — o

IN 101

VL . . . . . . . . . . . . . . -t + + + 4

L + + + + 4

M::::::::::::::: – — + + 2

1A 201

VL . . . . . . . . . . . . . . + + + + 4

L — — + + 2

M::::::::::::::: – — + — 1

NE 400a

VL . . . . . . . . . . . . . . + + + + 4

L — + — + 2

M::::::::::::::: – — — — o

alrrigated

SOURCE: Office of Technology Assessment
App. D—Analysis of Size Economies and Comparative Advantage in Crop Production in Various Areas of the United States • 319

Table D-3.—Production Costs and Yield by Enterprise Size

in Selected Corn. Producing Areas, 1983

State, area, and Total costa Yieldb Total costa

enterprise size $/bu. Percent Bu/acre Percent $/acre Percent

IL 300

VL . . . . . . . . . . . . . . . . .

L

M: :: : : : : : : : :: :: : : :

IN 101

VL . . . . . . . . . . . . . . . . .

L

M :::: ::: ::: :: :: :: :

1A 201

VL . . . . . . . . . . . . . . . . .

L

M:::::::::::::::: :

NE 400C

AL . . . . . . . . . . . . . . . . .

L

M:::::::::::::::: :

1.67

1.75

1.99

1.69

1.67

1.77

1.67

1.80

1.75

2.83

3.03

3.21

100

105

119

100

99

105

100

107

105

100

107

113

130.3

128.6

123.1

125.6

125.3

122.4

119.0

117.4

113.0

118.6

112.6

106.2

100

99

94

100

100

97

100

99

95

100

95

90

217

225

245

212

209

217

199

211

198

336

341

341

100

103

113

100

98

102

100

106

99

100

102

102

aExcluding land charges

b.state level ~ield~Perha~ested a~ref~rirdgated and nonlrrigatedin 1982.

Clrrigated,

SOURCEOffice of Technology Assessment

six-row harvesters. The relative production concentration in 1982 was nearly uniform across enterprise

sizes within 21 percent. The change in relative production concentration from 1978 to 1982 was positive and nearly uniform across enterprise sizes in

this area within 21 percent. The scoring results are

4,4, and 2 for the very large, large, and moderate

enterprises, respectively. There is evidence to argue that size economies exist for large and very large

enterprises relative to moderate enterprises in corn

production in Indiana.

In Iowa very large corn enterprises have a cost

advantage both in total cost per bushel and in capital ownership cost per acre. Very large enterprises

can fully use one to two self-propelled, six-row corn

harvesters. The relative production concentration

in 1982 was nearly uniform across enterprise size

within 33 percent. The change in relative production concentration from 1978 to 1982 was positive

for large and very large enterprises in this area. The

scoring results are 4, 2, and 1 for very large, large,

and moderate enterprises, respectively. There is evidence to argue that size economies exist for very

large enterprises relative to large and moderate enterprises in corn production in Iowa.

In Nebraska very large irrigated corn enterprises

have a production cost advantage in terms of total

cost per bushel. However, ownership costs per acre

are equal for large and very large enterprises and

less than those for medium enterprises. Very large

enterprises can fully use three to five, and large enterprises one to two, self-propelled, six-row harvesters. The relative production concentration from

1978 to 1982 was substantially higher for very large

enterprises relative to large and moderate enterprises. The change in relative production concentration from 1978 to 1982 was positive for large and

very large enterprises in this area. The scoring results are 4, 2, and O for very large, large, and moderate enterprises, respectively. There is clear evidence

to argue that size economies exist for very large enterprises in irrigated corn production in Nebraska.

The source of size economies can be found by examining the components of the production cost

measures (table D-3). Very large enterprises in general tend to have the lowest total cost per bushel.

Large and very large enterprises all have at least

slightly higher yields per acre relative to moderate

enterprises. Yield is a source of size economies in

corn production. Total cost per acre is relatively uniform across enterprises in which very large enterprises have a slight cost advantage.

In Illinois yield is a source of size economies. The

very large enterprises in this area have lower expenditures per acre for fertilizer, fuel lubrication,

repairs, and labor relative to large and moderate enterprises. In Indiana yield is also a source of size

economy. Preharvest and capital ownership costs

are not a source of size economies in this area. In

Iowa yield is again a source of size economy, as is
320 • Technology, Public Policy, and the Changing Structure of American Agriculture

custom harvesting. In Nebraska yield is a source of

size economies. Purchased irrigation water represents a potential for size diseconomies in Nebraska,

Very large enterprises in each of the selected producing areas consistently have slightly lower variable and ownership costs associated with machinery

and equipment. This implies that very large corn enterprises tend to use some combination of fewer

and/or smaller machines and tractors, and ones that

go over the field fewer times. This is a constant

source of size economies in the selected corn-producing areas.

Comparative Advantage in

Corn Production

The overall objective for including a discussion

on comparative advantage is to provide a context

within which to analyze size economies and to determine the source or explain the absence of comparative advantage between the selected production

areas.

In Illinois the total cost of corn production is about

14 percent higher than that in Iowa. There are size

economies for very large corn enterprises only. The

relative lack of comparative advantage is due to

higher expenditures on phosphate, potash, herbicide, and pesticide in conjunction with a 6-percent

lower yield compared with that in Iowa. The trends

in relative yield and land prices indicate that the

competitive position in corn production will decrease in this area. The absolute measure of production concentration in this area is low compared with

the other selected producing areas. In addition, corn

production is not particularly concentrated in any

one enterprise size category, which implies that producers are not beginning to exploit size economies

to increase their competitive position.

In Indiana the total cost of producing corn is about

10 percent more than in Iowa, and size economies

exist for large and very large enterprises relative to

moderate enterprises. The lack of comparative advantage is due to the relative price of nitrogen and

additional expenditures on pesticides compared

with those in Iowa. The absolute measure of production concentration in this area is high compared

with that of the other selected areas. This implies

that corn production is concentrated in one or more

enterprise size categories and that producers are

moving toward larger enterprise sizes to exploit size

economies in this area so as to increase their competitive position.

In Iowa the total cost of producing corn is the

lowest of the selected corn-producing areas. Size

economies exist in this area for very large enterprises

relative to large and moderate enterprises. Iowa’s

comparative advantage is related to higher yields

relative to fertilizer, herbicide, and pesticide use.

The absolute measure of production concentration

is also the lowest of the selected producing areas,

implying that corn production is not concentrated

in any one particular enterprise size in this area.

However, size economies exist for very large enterprises and can be exploited to improve Iowa’s comparative advantage.

In Nebraska the total cost of producing irrigated

corn is 8 percent higher than in Iowa, and size economies exist for very large enterprises relative to large

and moderate enterprises. The lack of comparative

advantage is due to the additional cost of irrigation

water pumped from wells. The trends in yield and

land prices indicate that the competitive position

of this area will substantially decrease. The absolute measure of production concentration in this

area is the highest of the selected corn-producing

areas. This implies that production is concentrated

in one or more enterprise size categories and that

producers are moving toward larger enterprise sizes

to exploit size economies and to enhance or maintain their competitive positions.

Soybeans

The soybean-producing areas are:

1. Illinois area 300

2. Iowa area 201

3. Mississippi area 100

4. Ohio area 101

Size Economies in Soybean Production

In Illinois very large soybean enterprises have

about a 5-percent cost disadvantage relative to large

enterprises in total cost per bushel (table D-4). Large

and very large enterprises in this area have nearly

equal capital ownership costs per acre. Large and

very large soybean enterprises in this area can fully

use one and two self-propelled, six-row harvesters,

respectively. This size harvester has an annual harvesting capacity of about 380 acres, The static

Herfindahl index (or measure of relative production

concentration) in 1982 was greatest for the large enterprises. Finally, the dynamic Herfindahl index (or

the measure of the change in relative production concentration) between 1978 and 1982 was positive only

for the large and very large enterprises in this production area. The scoring results are 2,4, and O for

the very large, large, and moderate enterprises, respectively (table D-5). Thus, there is evidence to argue that size economies exist for the large enterprises
App. D—Analysis of Size Economies and Comparative Advantage in Crop Production in Various Areas of the United States . 321

Table D.4.–lndices Used to Determine Size Economies in Selected Soybean-Producing Areas, 1983

Harvest machinery Herfindahl indices

State, area, and Total cost per Ownership cost per full utilization Static Dynamic

enterprise size bushel (percent) acre (percent) Maximum Minimum (percent) (percent)

IL 300

VL . . . . . . . . . . . . . . . . . . . . . . 100 100 2.4 1.4 100 65

L 95 102 1.3 0.9 142 16

M:::::::::::::::::::::: : 102 106 0.9 0.6 80 – 4

1A 201

VL . . . . . . . . . . . . . . . . . . . . . . 100 100 2.7 1.3 100 38

L 104 86 1.2 0.7 202 19

M:::::::::::::::::::::: : 108 90 0.7 0.4 198 – 5

MS 100

VL . . . . . . . . . . . . . . . . . . . . . . 100 100 2.1 1.2 100 9

L 116 98 1.2 1.1 24 11

M:::::::::::::::::::::: : 99 72 1.1 1.0 24 11

OH 101

VL . . . . . . . . . . . . . . . . . . . . . . 100 100 2.5 2.2 100 71

L 84 90 1.6 1.0 115 21

M:::::::::::::::::::::: : 86 90 0.9 0.3 73 – 3

SOURCE Office of Technology Assessment

Table D-5.—Scoring Table Used to Determine Size Economies in Selected

Soybean-Producing Areas, 1983

State, area, and Production Harvester Herfindahl indices

enterprise size cost utilization Static Dynamic Total

IL 300

VL . . . . . . . . . . . . . . – + — + 2

L + + + + 4

M::::::::::::::: – — — — o

1A 201

VL . . . . . . . . . . . . . . + + — + 3

L . . . . . . . . . . . . . . . – + + + 3

M . . . . . . . . . . . . . . . – — + — 1

MS 100

VL . . . . . . . . . . . . . . + + + — 3

L . . . . . . . . . . . . . . . – — — o

M . . . . . . . . . . . . . . . + + — — 2

OH 101

A L . . . . . . . . . . . . . . – — + + 2

L + + + + 4

M:::::::::::::: : + — — — 1

SOURCE Office of Technology Assessment

relative to very large and moderate enterprises in

soybean production in Illinois.

In Iowa very large soybean enterprises have about

a 4- to 8-percent cost advantage relative to large and

moderate enterprises in total cost per bushel. However, very large enterprises in this area have about

a 10-to 14-percent cost disadvantage relative to large

and moderate enterprises in ownership costs per

acre. Large and very large enterprises can fully use

one and two self-propelled, six-row harvesters, respectively. The relative production concentration

in 1982 was greatest for large and moderate enterprises. Finally, the change in relative production

concentration between 1978 and 1982 was positive

only for very large and large enterprises in this area.

The scoring results are 3, 3, and 1 for very large,

large, and moderate enterprises, respectively. There

is evidence to argue that size economies exist for

very large and large enterprises relative to moderate enterprises in soybean production in Iowa.

In Mississippi very large and moderate soybean

enterprises have about a 16-percent cost advantage

relative to large enterprises in total cost per bushel.

However, very large enterprises have a 26- to 28-
322 ● Technology, Public Policy, and the Changing Structure of American Agriculture

percent cost disadvantage in ownership costs per

acre in this area relative to large and moderate enterprises, respectively. Moderate and very large soybean enterprises can fully use one and two self-propelled, eight-row harvesters, respectively. This size

harvester has an annual harvesting capacity of about

800 acres, The relative production concentration in

1982 was greatest for very large enterprises. Finally,

the change in relative production concentration between 1978 and 1982 was relatively low and uniform

across enterprise sizes in this area. The scoring results are 3,0, and 2 for very large, large, and moderate enterprises, respectively. The evidence suggests

that there is no clear advantage for any enterprise

size relative to another in soybean production in Mississippi.

In Ohio very large soybean enterprises have between a 14-to 16-percent cost disadvantage relative

to large and moderate enterprises in total cost per

bushel. Similarly, very large enterprises have about

a lo-percent cost disadvantage in ownership cost

per acre relative to large and moderate enterprises

in this area. Large and very large soybean enterprises

can fully use one and two self-propelled, six-row harvesters, respectively. The relative production concentration in 1982 was greatest for very large and

large enterprises. Finally, the change in relative production concentration between 1978 and 1982 was

positive for very large and large enterprises in this

area. The scoring results are 2,4, and 1 for very large,

large, and moderate enterprises, respectively. Thus,

there is evidence to argue that size economies exist

for the large enterprises relative to very large and

moderate enterprises in soybean production in Ohio.

The source or absence of size economies can be

found by examining the components of the production cost measure. Total cost per unit of output is

equal to the total cost per acre divided by the yield

per acre. Table D-6 summarizes the production costs

and yield by enterprise size for the selected soybeanproducing areas, In Illinois soybean yields for large

and very large enterprises are equal and 2 percent

higher than those of moderate enterprises. In Iowa

soybean yields are nearly uniform across enterprise

sizes, with larger enterprises having 1 percent higher yields. In Mississippi soybean yields are 6 percent higher for very large enterprises relative to large

and moderate enterprises. In Ohio soybean yields

are nearly uniform across enterprise sizes, with

larger enterprises having 2 to 3 percent higher yields.

Yield is only a slight source of size economies in soybean production (table D-6).

In Illinois size diseconomies for very large enterprises are associated with the substantially higher

fertilizer, herbicide, and pesticide expenditures,

without corresponding higher yield, In Iowa the

modest size economies for very large and large enterprises relate to lower costs of owner-provided

relative to custom-provided durable services. In Mississippi the absence of size economies relate to the

diseconomies of additional horsepower used by very

large enterprises, In Ohio size diseconomies for very

Table D-6.-Production Costs and Yield by Enterprise Size

in Selected Soybean-Producing Areas, 1983

State, area, and Total costa Yieldb

Total costa

enterprise size $/bu. Percent Bu/acre Percent $/acre Percent

100

95

102

100

104

108

100

116

99

100

84

86

38.2

38.2

37.4

36,8

36.6

36.3

25.0

23.6

23.6

35,6

34.8

34.4

100

100

98

100

99

99

100

94

94

100

98

97

136

129

136

122

126

130

130

142

122

152

125

126

100

95

100

100

103

107

100

109

94

100

82

83

aEXc\udlng land charges.

bstate level Yielrjs per harvested acre for irrigated and nonirrigated h 19S2.

Cproductiorl year data fOr 1982.

SOURCE: Office of Technology Assessment.
App. D—Analysis of Size Economies and Comparative Advantage in Crop Production in Various Areas of the United States • 323

large enterprises relate to the diseconomies of additional horsepower used in land preparation and to

the substantially higher expenditure on fertilizer,

herbicide, and pesticide, without corresponding

higher yields.

Comparative Advantage in

Soybean Production

In Illinois total cost per bushel of soybeans is about

3 percent higher than in Iowa. Size economies exist

for large enterprises only. The slight lack of comparative advantage is due to higher expenditures on

herbicides, pesticides, and land. Trends in relative

yield and land value indicate that the competitive

position of Illinois will substantially improve. Soybean production is not concentrated in one or more

enterprise sizes; however, size economies exist for

large enterprises and can be exploited to improve

comparative advantage.

In Iowa total cost per bushel of soybeans is the

lowest of the selected soybean-producing areas. This

comparative advantage is related to the level of yield,

which is high relative to seed, herbicide, pesticide,

and fertilizer expenditures. Trends in relative yields

and land values indicate that Iowa’s comparative

position will decrease in the future. The measure

of production concentration in Iowa is the lowest

of the selected States, implying that soybean production is not concentrated in one or more enterprise sizes. Size economies exist for the large and

very large enterprises and can be exploited to improve comparative advantage in this area.

Total cost per bushel of soybeans in Mississippi

is about 24 percent higher than in Iowa. The substantial lack of comparative advantage relative to

Iowa is a result of low yields and high expenditures

on herbicides, pesticides, and ownership costs.

Trends in yield and land values indicate that the

competitive position of Mississippi will substantially

decrease in the future. Production is concentrated

in one or more enterprise sizes, and no size economies remain to be exploited in this area.

In Ohio total cost per bushel of soybeans is about

5 percent higher than in Iowa. The slight lack of comparative advantage is the result of lower yields and

higher expenditures on herbicides, pesticides, potash, and phosphate. Trends in yield and land values

indicate that the competitive position of Ohio will

improve slightly in the future. Production is concentrated in one or more enterprise size categories.

Size economies exist for large soybean enterprises

only and can be exploited to improve comparative

advantage in this area.

Wheat

For wheat the selected producing areas, type of

wheat grown, and cultural practices followed are:

1.

2.

3.

4.

Kansas area 100—Hard red winter wheat

following fallow

Montana area 200—Hard red spring wheat

following fallow

North Dakota area 200—Hard red spring

wheat following crop

Washington area 400—White wheat following fallow

Size Economies in Wheat Production

In Kansas very large wheat enterprises have a substantial cost advantage both in cost per unit of output and in ownership cost per acre (table D-7). Very

large, large, and moderate wheat producers in this

area can fully use 4 to 20, 2 to 3, and 1 to 2 selfpropelled, 20-foot-wide harvesters, respectively.

This size harvester has an annual capacity of harvesting about 500 acres. The static Herfindahl index (or measure of relative production concentration) in 1982 was greatest for the large and very large

producers. This difference in static concentration

was less pronounced in this area than in other wheatproducing areas in this study, however. Finally, the

dynamic Herfindahl index (or the change in relative concentration) from 1978 to 1982 was positive

for the large and very large enterprises. The scoring results are 4, 3, and 2 for very large, large, and

moderate enterprises, respectively (table D-8). There

is evidence to argue for the existence of size economies advantages for very large enterprises relative

to the large and moderate wheat enterprise in Kansas.

In Montana very large wheat enterprises have a

cost advantage both in cost per unit of output and

ownership cost per acre. The very large, large, and

moderate-size enterprises can fully use one to six

self-propelled, 20-foot-wide harvesters, The static

measure of relative production concentration in

1982 was greatest for the very large enterprises. Finally, the change in the relative concentration from

1978 to 1982 was positive only for the very large enterprise size. The scoring results are 4, 1, and 1 for

the very large, large, and moderate categories, respectively. There is strong evidence to argue for the

existence of size economies for very large enterprises relative to large and moderate sizes in the production of wheat in Montana.

In North Dakota large wheat enterprises have a

cost advantage when measured either in cost per

unit of output or ownership cost per acre. Large and
324 • Technology, Public Policy, and the Changing Structure of American Agriculture

Table D“7.–lndices Used to Determine Size Economies in Selected Wheat-Producing Areas, 1983

Harvest machinery Herfindahl indices

State, area, and Total cost per Ownership cost per full utilization Static Dynamic

enterprise size bushel (percent) acre (percent) Maximum Minimum (percent) (percent)

KS 100

VL . . . . . . . . . . . . . . . . . . . . . . 100

112

118

100

109

113

20.6

3.9

2.0

4.1

2.0

1.1

100

100

75

17

17

– 7

100

106

110

100

115

108

6.9

1.5

1.0

1.5

1.0

0.6

100

23

6

20

–12

–31

L

M:::::::::::::::::::::: :

ND 200

VL . . . . . . . . . . . . . . . . . . . . . .

L

M:::::::::::::::::::::: :

WA 400

VL . . . . . . . . . . . . . . . . . . . . . .

L

M:::::::::::::::::::::: :

100

95

103

100

90

98

3.5

1.6

0.9

1.6

0.9

0.4

100

72

36

100

118

85

100

117

91

6.2

2.7

1.9

3.7

1.9

1.3

100

100

31

3

3

– 2

SOURCE: Office of Technology Assessment.

Table D-8.—Scoring Table Used to Determine Size Economies in Selected

Wheat-Producing Areas, 1983

State, area, and Production Harvestor Herfindahl

enterprise size cost utilization Static Dynamic Total

KS 100

VL . . . . . . . . . . . . . . + +

+

+

+

+

+

+

+—

4

3

2

4

1

1

L

M:::::::::::::: :

MT 200

VL . . . . . . . . . . . . . . + +

+

+

+—

—

+—

—

L

M:::::::::::::: :

ND 200

VL . . . . . . . . . . . . . .

—

—

+

+—

+

+—

+

+—

3

4

0

—

+—

2

2

2

+

+

+

+

+—

—

—

—

—

—

L

M:::::::::::::: : +

SOURCE: Office of Technology Assessment

very large enterprises can fully use one and two to

three self-propelled, 20-foot-wide harvester(s), respectively. The static measure of relative production concentration in 1982 was greatest for large and

very large enterprises. Finally, the change in the relative concentration from 1978 to 1982 was positive

for both large and very large enterprises. The scoring results are 3, 4, and O for the very large, large,

and moderate categories, respectively. There is evidence to argue for the existence of size economies

for large and very large enterprises relative to moderate enterprises in the production of wheat in North

Dakota. The production data suggests size economies for large enterprises in particular.

In Washington moderate wheat enterprises have

a substantial cost advantage when measured in cost

per unit of output or ownership cost per acre. Moderate, large, and very large producers can fully use

one to two and four to six self-propelled, 19.4 -footwide harvesters, Washington wheat producers in

this area typically use a combination of “regular”

and “hillside” harvesters in approximately a 70:30
App. D—Analysis of Size Economies and Comparative Advantage in Crop Production in Various Areas of the United States ● 325

ratio in harvesting their crop. A “composite” harvester is assumed to have an annual harvesting capacity of 495 acres per harvester. The static measure of relative production concentration in 1982 was

greatest for large and very large enterprises. Finally,

the change in the relative production concentration

from 1978 to 1982 was positive for both large and

very large enterprises, but only by 3 percent. There

was virtually no change in production concentration between enterprise sizes from 1978 to 1982. The

scoring results are 2, 2, and 1 for very large, large,

and moderate enterprise categories, respectively,

There is no clear evidence on which to argue for

size economies in the production of wheat in Washington.

Yield is only a slight source of size economies in

wheat production (table D-9). Wheat yields in Kansas

are nearly uniform across enterprise size. In Montana and North Dakota the very large enterprise has

the greatest yield per acre, by about 3 to 6 percent.

In Washington large and very large enterprises have

the same yield. The moderate enterprises, however,

have substantially (20 percent) higher yields per acre

than do the large and very large enterprises in this

area. In fact, 1982 data reveals that small and very

small enterprises have substantially higher yields

than do moderate enterprises in this area. In Washington wheat yield is inversely related to enterprise

size. Otherwise, yield is only a slight source of size

economies in wheat production.

In Kansas the important factor for size economies

relates to economies associated with custom harvest

rates. In Montana size economies are the result of

the combination of slightly higher yields and lower

costs, again related to the use of custom harvesting.

In North Dakota size economies for large enterprises

relate to higher yield and lower ownership and harvest costs relative to those of moderate and very large

enterprises. In Washington size economies do not

exist for very large enterprises relative to moderate

enterprises because of the substantial diseconomies

associated with yield and the slightly higher ownership and harvesting costs. In Washington size economies for very large enterprises relative to large enterprises exist because of the substantially lower

ownership costs of the very large enterprises in this

area, which are related to the differences in horsepower tractors used particularly inland preparation.

Comparative Advantage in

Wheat Production

In Kansas and Washington the comparative advantages in producing wheat are nearly equal and

are the greatest of the areas studied. Kansas also has

the potential for increasing its comparative advantage relative to unexploited size economies that exist for large and very large enterprises. This area has

a relatively low level of production concentration.

Finally, there is little change (about 1 percent per

year) in production concentration in this area. The

relatively large average size of the wheat enterprise

in Kansas implies that enterprises are larger on average across size categories and that no one enterprise

size dominates production in the area. (A similar

set of characteristics exists for Iowa corn enterprises

Table D-9.-Production Costs and Yield by Enterprise Size

in Selected Wheat. Producing Areas, 1983

State, area, and Total cost Yield Total cost

enterprise size $/bu. Percent Bu/acre Percent $/acre Percent

KS 100

VL . . . . . . . . . . . . . . . . . 2.05 100 33.1 100 68 100

L 2.30 112 33.1 100 76 112

M : : : : : : : : : : : : : : : : : 2.41 118 33.2 100 80 118

MT 200

VL . . . . . . . . . . . . . . . . . 2.77 100 31.1 100 86 100

L 2.94 106 29.9 96 88 102

M :::: :: :: : :: :: : 3.05 110 29.2 94 89 103

ND 200

VL . . . . . . . . . . . . . . . . . 3.79 100 31.7 100 100 120

L 3.60 95 30.8 97 111 93

M : : : : : : : : : : : : : : : 3.91 103 29.7 94 116 97

WA 400

VL . . . . . . . . . . . . . . . . . 3.26 100 39.9 100 130 100

L 3.86 118 39.9 100 154 118

M : : : : : :: : : : : : : : : : 2.76 85 47.8 120 132 102

SOURCE: Office of Technology Assessment.
326 . Technology, Public Policy, and the Changing Structure of American Agriculture

except that the enterprises there tend to be smaller,

on average.)

It is only about 3 percent more costly to produce

a bushel of wheat in Montana than in Kansas. This

slight lack of comparative advantage is due to high

ownership costs associated with more and larger

machines being used on the land more times in land

preparation. Unlike those in Kansas, size economies

in Montana exist for very large enterprises only. This

implies that producers in Montana can exploit size

economies as a strategy to remain competitive.

It is about 9 percent more costly to produce a

bushel of wheat in North Dakota than in Kansas. The

increased costs are due to additional expenditures

on seed, fertilizer, and chemicals associated with

increasing relative yields, spring planting, and continuous cropping. Size economies exist for very large

and large enterprises in North Dakota. Production

concentration, though higher than in Kansas, is still

low, Thus, no one enterprise size dominates production in this area. Size economies can be exploited

to improve North Dakota’s comparative advantage.

Washington’s comparative advantage in wheat

production is nearly identical to that of Kansas. It

is less than 1 percent more costly to produce a bushel

of wheat in Washington than in Kansas. All size

economies within Washington are nearly fully exploited. The average enterprise size is quite large,

about 1,600 acres. The level of production concentration is the highest of any of the wheat-producing

areas studied. This implies that one or more enterprise sizes dominate production in this area.

Rice

For rice the selected producing areas and type of

rice grown include:

1.

2.

3.

4,

California area 400–medium-” and shortgrain rice

Texas area 1001—long-grain rice

Delta (Mississippi 100 and Arkansas 300–

long-grain rice

Arkansas area 200—long-grain rice

Size Economies in Rice Production

In California the total cost per hundredweight of

rice for all three enterprise sizes is nearly identical,

with moderate enterprises having a slight advantage

of about 3 percent (table D-10). On the basis of ownership cost per acre, the moderate enterprise has a cost

advantage of about 8 percent relative to very large

enterprises, Very large, large, and moderate enterprise sizes can fully use 5 to 11, 3 to 4, and 2 selfpropelled, 16-foot-wide harvesters, respectively.

This size harvester has an annual harvesting capacity of 465 acres. The static Herfindahl index (or measure of relative production concentration) in 1982 was

greatest for the large and very large producers, The

dynamic Herfindahl index (or the change in relative concentration) from 1978 to 1982 was negative

for all enterprise sizes in this rice-producing area.

Unfortunately, the data associated with the Herfindahl indices are not sufficiently disaggregated at the

large and very large rice enterprise sizes to allow

Table D.10.—lndices Used to Determine Size Economies in Selected Rice-Producing Areas, 1979

Harvest machinery Herfindahl indices

State, area, and Total cost per Ownership cost per full utilization Static Dynamic

enterprise size cwt a (percent) acre (percent) Maximum Minimum (Percent) (percent)

CA 400b

VL . . . . . . . . . . .

L

M::::::::::: :

TX 1001b

VL . . . . . . . . . . .

L

M::::::::::: :

DLT 100 and 300b

VL . . . . . . . . . . .

L

M::::::::::: :

AR 200b

VL . . . . . . . . . . .

L

M::::::::::: :

100

99

97

100

103

92

11.7

4.6

2.4

4.7

2.4

1.3

100

100

25

–4

–4

–19

100

98

97

100

98

95

6.2

2.8

1.8

3.7

1.8

1.1

100

80

61

28

24

20

100

94

92

100

109

113

6.5

3.3

1.5

2.2

1.0

0.6

100

85

58

–35

–19

11

100

100

98

100

105

102

6.3

2.1

1.0

2.1

1.0

0.6

100

84

67

21

–20

– 7

aHundredweight,

blrrigated.

SOURCE: Office of Technology Assessment.
App. D—Analysis of Size Economies and Comparative Advantage in Crop Production in Various Areas of the United States ● 327

for more detailed analysis. The scoring results are

2,2, and 2 for very large, large, and moderate enterprises, respectively (table D-II). There is evidence

to argue that no size economies exist in California

rice production, given the 1979 configuration of enterprise sizes.

In Texas large and moderate rice enterprises have

a slight cost advantage (3 to 4 percent), both in cost

per unit of output and ownership cost per acre. The

very large, large, and moderate enterprises can fully

use four to six, and one to two self-propelled, 16-

foot-wide harvesters. The static measure of relative

production concentration in 1982 was relatively uniform across enterprise sizes in this area. Finally, the

change in the relative concentration from 1978 to

1982 was also relatively uniform and positive across

enterprise sizes in this area. The scoring results are

2, 2, and 2 for the very large, large, and moderate

categories, respectively. There is evidence to argue

that no size economies exist in Texas rice production, given the 1979 configuration of enterprise sizes.

In the Delta large and moderate enterprises have

a cost advantage, when measured in cost per unit

of output, by about 6 to 8 percent. However, very

large enterprises have a cost advantage in capital

ownership cost per acre. Very large, large, and moderate enterprises in this area can fully use three to

six, two to three, and oneself-propelled, 17-foot-wide

harvesters, respectively. This size harvester has an

annual harvesting capacity of 495 acres per harvester. The static measure of relative production concentration in 1982 was nearly uniform across enterprise sizes. Finally, the change in the relative

concentration from 1978 to 1982 was positive for

the moderate enterprise size only. The scoring results are 1,2, and 3 for the very large, large, and moderate enterprises, respectively. There is evidence to

argue that no size economies exist in the Delta rice

production, given the 1979 configuration of enterprise sizes,

In Arkansas moderate enterprises have a cost advantage, when measured in cost per unit of output,

by about 4 percent. However, very large enterprises

have a cost advantage in capital ownership costs per

acre. Very large, large, and moderate enterprises can

fully use three to six, one to two, and one self-propelled, 17-foot-wide harvesters, respectively. The

static measure of relative production configuration

in 1982 was nearly uniform across enterprise sizes.

Finally, the change in the relative production concentration from 1978 to 1982 was positive for the

very large enterprise size only. The scoring results

are 2, 1, and 2 for the very large, large, and moderate enterprises, respectively. There is evidence to

argue that no size economies exist in Arkansas rice

production, given the 1979 configuration of enterprise sizes.

The absence of size economies in rice production

can be explained by examining the components of

the production cost measures (table D-12). Rice yield

in all the production areas studied is inversely related to enterprise size, except in Texas. In the case

of the Delta, large and moderate rice enterprises have

a substantial yield advantage over very large enterTable D-n .—Scoring Table Used to Determine Size Economies in Selected

Rice-Producing Areas, 1979

State, area, and Product ion Harvester Herfindahl

enterprise size cost utilization Static Dynamic Total

CA 400a

VL . . . . . . . . . . . . . .

TX 1001a

VL . . . . . . . . . . . . . .

DLT 100 and 300a

VL . . . . . . . . . . . . . .

AR 200a

VL . . . . . . . . . . . . . .

2

2

2

2

2

2

2

2

3

2

1

2

alrrigated

SOURCE: Off Ice of Technology Assessment.
328 • Technology, Public Policy, and the Changing Structure of American Agriculture

Table D-12.—Production Costs and Yield by Enterprise Size

in Selected Rice-Producing Areas, 1979

State, area, and Total cost

enterprise size $/cwt Percent

Yield Total cost

Cwt/acre Percent $/acre Percent

CA 400a

VL . . . . . . . . . . . . . . . . . 6.34

L 6.29

M“ : : : : : : : : : : : : : : : : : 6.12

TX 1001

VL . . . . . . . . . . . . . . . . . 7.70

L 7.39

M“ : : : : : : : : : : : : : : : : : 7.46

DLT 100-300a

VL . . . . . . . . . . . . . . . . . 6.78

L 6.36

M“ : : : : : : : : : : : : : : : : : 6.26

AR 200a

VL . . . . . . . . . . . . . . . . . 6.33

L 6.31

M:::::::::::::::: : 6.09

100

99

97

100

96

97

100

94

92

100

100

96

51.3 100 325 100

52.6 103 331 102

52.1 102 319 98

47.4 100 365 100

46.3 98 342 94

46.4 98 346 95

39.8 100 270 100

42.6 107 271 100

43.6 110 273 101

43.1 100 273 100

44.7 104 282 103

44.5 103 271 99

alrrigated.

SOURCE: Office of Technology Assessment

prises by about 7 to 10 percent. Total cost per acre

for very large enterprises is less than or equal to total cost per acre for large and moderate enterprises

in all the production areas again except Texas. In

general, rice production has diseconomies of size

relative to yield and no economies of size in relation to total costs per acre. Yield diseconomies are

related in large part to timeliness of fertilizer and

water application, which can be managed better at

smaller enterprise sizes than at larger ones.

Size diseconomies in rice production exist uniformly across the selected production area. These

diseconomies are primarily the result of yield diseconomies of size assistance in California, the Delta,

and Arkansas. Yield diseconomies are related to

timeliness of fertilizer, herbicide, pesticide, fungicide, and water application. In Texas size diseconomies are associated with purchased canal water

used for irrigation, which in turn allows for lower

ownership costs associated with producers’ wellpumped irrigation.

Comparative Advantage in

Rice Production

The comparative advantage of California in rice

production is the greatest of the areas studied. It is

the result of high yields, relatively inexpensive irrigation water, and reduced herbicide and fungicide

costs relative to those of other selected producing

areas. This comparative advantage is not expendable through size economies, since size economies

have been more than fully exploited in this area. California has the largest average size rice acreage per

enterprise, at 1,071 acres. This implies that production is concentrated in one or more size categories.

The combination of size economy and comparative

advantage information shows that rice enterprises

in California should not increase their size as a

means of reducing cost and thereby improving comparative advantage.

The comparative disadvantage of Texas in producing rice is the greatest of the areas studied. The data

indicate that in 1979 it was about 25 percent more

costly to produce a hundredweight of rice in Texas

than in California. This substantial lack of comparative advantage is related to relatively low yields and

high irrigation, herbicide, and fungicide costs. It is

not correctable by increasing enterprise size in attempting to be more competitive, since size economies have been more than fully exploited. If trends

in relative yield and land values continue, Texas will

decline from its already marginal competitive position.

It is about 14 percent more costly to produce a hundredweight of rice in the Delta than in California.

This lack of comparative advantage is related to additional expenditures on herbicides, fungicides, and

irrigation water. This comparative disadvantage is

not correctable by simply increasing enterprise size

in an attempt to be more competitive, since size economies have been more than fully exploited. The average enterprise size in this area is about 700 acres.

Production is very highly concentrated in one or
App. D—Analysis of Size Economies and Comparative Advantage in Crop Production in Various Areas of the United States ● 329

more of the enterprise size categories, and size economies have been more than fully exploited. The dynamic Herfindahl index shows that there was a substantial decrease in rice production concentration

between 1978 and 1982 in this area. Rice production in the absence of size economies is becoming

less concentrated and may continue to be so into

the future. The combination of size economy and

comparative advantage information implies that rice

enterprises in the Delta could not increase enterprise

size as a means of enhancing comparative advantage.

The comparative disadvantage in Arkansas in

1979 was such that it was 8 percent more costly to

produce a hundredweight of rice in Arkansas than

in California. This lack of comparative advantage

is due to additional expenditures on herbicides, fungicides, and irrigation water pumped from wells.

This comparative disadvantage is not correctable

by increasing enterprise size in an attempt to be more

competitive, since size economies do not exist. The

average enterprise size in this area is about 485 acres,

the smallest of the selected rice-producing areas. Production is distributed relatively uniformly across enterprise sizes, and there is a modest trend toward

resource dispersion, or reconcentration. The combined information on size economies and comparative advantage implies that rice enterprises in Arkansas could not increase enterprise size to enhance

comparative advantage. In fact, the current size distribution of rice enterprises is well suited for rice

production by being small on average and yet capable of fully using a single rice harvester.

For cotton the selected upland cotton-producing

areas and cultural practices include:

1. Alabama area 600—dryland

2. California area 500—irrigated

3. Mississippi area 100—-mixed

4. Texas area 200-irrigated

5. Texas area 200—dryland

Size Economies in Cotton Production

In Alabama very large cotton enterprises have the

lowest total cost per bale and lowest ownership costs

per bale by about 7 to 8 percent and lowest cost per

acre relative to large and moderate enterprises by

about 20 to 26 percent (table D-13). Very large enterprises in this area can fully use three to six selfpropelled, two-row cotton pickers. This size harvester has an annual harvesting capacity of about

400 acres. The static Herfindahl index (or measure

of relative production concentration) in 1982 was

greatest for the very large producers. Finally, the

Table D-13.—lndices Used to Determine Size Economies in Selected Cotton. Producing Areas, 1982

Harvest machinery Herfindahl indices

State, area, and Total cost per Ownership cost per full utilization Static Dynamic

enterprise size bale (percent) acre (percent) Maximum Minimum (percent) (percent)

AL 600

VL . . . . . . . . . . . . . . . . . . . . . . 100 100 6.7 2.9 100 63

L . . . . . . . . . . . . . . . . . . . . . . . 107 120 2.6 2.1 64 8

M . . . . . . . . . . . . . . . . . . . . . . . 108 126 1.8 1.3 64 8

CA 500’

VL . . . . . . . . . . . . . . . . . . . . . . 100 100 9.3 5.2 100 39

L 91 86 4.2 2.3 100 39

M:::::::::::::::::::::: : 98 98 2.0 0.9 6 9

MS 100

VL . . . . . . . . . . . . . . . . . . . . . . 100 100 11.3 5.2 100 24

L 103 101 5.1 2.3 100 24

M:::::::::::::::::::::: : 100 97 2.3 1.6 48 21

TX 200a

VL . . . . . . . . . . . . . . . . . . . . . . 100 100 4.7 2.5 100 77

L . . . . . . . . . . . . . . . . . . . . . . . 94 88 2.4 1.3 70 80

M . . . . . . . . . . . . . . . . . . . . . . . 99 94 1.1 0.6 35 80

TX 200

VL . . . . . . . . . . . . . . . . . . . . . . 100 100 29.5 5.8 100 6

L 114 143 5.0 2.7 100 6

M:::::::::::::::::::::: : 117 124 2.7 1.4 51 5

alrrlgated

SOURCE” Off Ice of Technology Assessment
330 ● Technology, Public Policy, and the Changing Structure of American Agriculture

dynamic Herfindahl index (or the change in relative concentration) from 1978 to 1982 was positive

for each enterprise size, particularly for very large

enterprises. The scoring results are 4, 1, and 1 for

very large, large, and moderate enterprises, respectively (table D-14). There is strong evidence to argue

that size economies exist for very large cotton enterprises in Alabama.

In California large cotton enterprises have the

lowest total cost per bale and ownership costs per

acre relative to very large and moderate enterprises.

Very large, large, and moderate enterprises can fully

use six to nine, three to four, and one to two selfpropelled, two-row cotton pickers, respectively. The

static Herfindahl index in 1982 was greatest for the

large and very large enterprises. Finally, the dynamic Herfindahl index for 1978 to 1982 was positive for each enterprise size, particularly for the large

and very large enterprise sizes. The scoring results

are 3, 4, and 1 for very large, large, and moderate

enterprises, respectively. There is evidence to argue that size economies do exist for very large enterprises in the production of irrigated cotton in California, given the enterprise configuration in 1982.

In Mississippi the total cost per bale and the

ownership cost per acre is nearly equal across cotton enterprise size categories. Very large, large, and

moderate enterprises can fully use 5 to 11, 3 to 5,

and 2 self-propelled, two-row cotton pickers. The

measure of relative production concentration in

1982 was greatest for very large and large enterprise

sizes. The change in relative production concentration from 1978 to 1982 was nearly constant across

all enterprise sizes. The scoring results are 4,4, and

3 for very large, large, and moderate enterprises, respectively. There is evidence to argue that size economies do not exist for large and very large cotton

enterprises relative to moderate enterprises in Mississippi, given the enterprise configuration in 1982,

In Texas (irrigated) large cotton enterprises have

the lowest total cost per bale, by about 6 percent.

Large and moderate enterprises have the lowest

ownership cost per acre. Moderate, large, and very

large enterprises can fully use one, two, and three

to four, “composite” cotton strippers with an annual harvesting capacity of about 525 acres per harvester. The measure of relative production concentration in 1982 was greatest for large and very large

enterprises. The change in relative production concentration from 1978 to 1982 was nearly constant

across enterprise size categories. The scoring results

are 3, 4, and 2 for very large, large, and moderate

enterprises, respectively. There is evidence to argue that size economies do not exist for very large

enterprises in irrigated cotton in Texas, given the

1982 configuration of enterprises.

In Texas (dryland) very large cotton enterprises

have the lowest total cost per bale, by about 14 to

Table D-14.—Scoring Table Used to Determine Size Economies in Selected

Cotton-Producing Areas, 1982

State, area, and Production Harvester Herfindahl

enterprise size cost utilization Static Dynamic Total

AL 600

VL . . . . . . . . . . . . . . 4

1

1

CA 500a

VL . . . . . . . . . . . . . . 3

4

1

4

3

3

4

2

TX 200a

VL . . . . . . . . . . . . . .

3

2

1

alrrigated,

SOURCE Office of Technology Assessment
App. D—Analysis of Size Economies and Comparative Advantage in Crop Production in Various Areas of the United States ● 331

17 percent, and lowest ownership cost per acre, by

about 24 to 43 percent. Moderate, large, and very

large enterprises can fully use 2, 3 to 5, and 6 to 30

“composite” cotton strippers, The measure of relative production concentration in 1982 was greatest

for large and very large enterprises. The change in

relative production concentration from 1978 to 1982

was positive, nearly constant, and quite small across

enterprise sizes. The scoring results are 3, 2, and

1 for very large, large, and moderate enterprises, respectively. There is evidence to argue that size economies exist for very large enterprises in dryland cotton production in Texas.

Circumstances regarding size economies in the

selected cotton-producing areas can be explained

by examining the components of the production cost

measures (table D-15). Cotton yields tend to be related to enterprise size, as in Alabama, California,

and Mississippi, by about 3 to 7 percent. In Texas,

yields tend to be inversely related to enterprise size

by about 2 to 3 percent. Total cost per acre tends

to be nearly uniform across enterprises in Alabama,

Mississippi, and Texas (irrigated). In California total cost per acre is directly related to enterprise size,

whereas in Texas (dryland) the total cost per acre

is inversely related to enterprise size.

In summary, size economies exist for very large

cotton enterprises in Alabama because these enterprises incur lower machinery and tractor-related expenses for a given field operation and still manage

to obtain a slightly higher yield. In California, size

diseconomies are primarily related to the pecuniary diseconomies of purchased irrigation water for

cotton production. In Mississippi the lack of size

economies in cotton production for large and very

large enterprises relative to moderate enterprises relates to similar preharvest and ownership costs in

conjunction with slightly higher yields, In Texas (irrigated) the lack of size economies is related to the

combination of size diseconomies in harvesting and

cultivation, along with slightly higher yields enjoyed

by large and moderate enterprises in this area. In

Texas (dryland) size economies for very large enterprises relate to the substantial preharvest and ownership cost advantages associated with lower machinery and tractor-related expenses for a given field

operation, without substantial loss in yield.

Comparative Advantage in

Cotton Production

In Alabama the average total cost per bale in producing cotton is about 23 percent higher than in Mississippi. This comparative disadvantage is due to

low yields and high fertilizer, lime, insecticide, and

harvesting costs. Size economies exist in cotton proTable D-15.—Production Costs and Yield by Enterprise Size

in Selected Cotton-Producing Areas, 1982

State, area, and Total costa

Yield Total costa

enterprise size $/bale Percent Bales/acre Percent $/acre Percent

279

298

301

100

107

108

1.52

1.47

1.47

100

97

97

424

438

443

100

103

104

298

271

291

100

91

98

2.28

2.28

2.11

100

100

93

680

619

613

100

91

90

230

237

229

100

103

100

1.79

1.79

1.74

100

100

97

412

424

399

100

103

97

319

299

315

100

94

99

0.67

0,69

0.68

100

103

102

214

206

214

100

98

100

259

298

302

100

114

117

0.44

0.45

0.45

100

103

102

114

134

136

100

118

119

aExcluding land charge.

blrrigated

SOURCE: Off Ice of Technology Assessment
332 • Technology, Public Policy, and the Changing Structure of American Agriculture

duction in this area for very large enterprises. The

average enterprise size is about 1,200 acres and is

one of the lowest of the cotton areas studied. Production concentration, on the other hand, is the highest of the areas studied, and production and resources were concentrating at a substantial rate of

36 percent between 1978 and 1982, or 9 percent per

year. Since cotton production in this area is at a substantial competitive disadvantage, producers appear

to be adopting a strategy of increasing enterprise

size as a means of exploiting size economies and increasing competitiveness. This strategy will continue to work in the future, as well.

In California the average total cost per bale of cotton is about 9 percent higher than in Mississippi.

This comparative disadvantage is due to high irrigation costs. Size economies do not exist for very

large enterprises in California, given the 1982 enterprise configuration. The average cotton enterprise is about 2,100 acres in size and is the second

most concentrated cotton-producing area in the

selected areas, The diseconomies associated with

the rates of purchased irrigation water limit the extent to which other size economies can be used to

decrease production costs.

In Mississippi the average total cost per bale of

cotton is the lowest of the areas studied. Size economies do not exist in this area for very large and large

enterprises relative to moderate enterprises. The

average cotton enterprise in this area is about 1,800

acres. Production concentration is “moderate” relative to the other areas in the study. Since cotton production is the most competitive, producers have

adopted a strategy of moderate enterprise expansion

as a strategy to increase total revenue rather than

to increase comparative advantage.

In Texas (irrigated) the average total cost per bale

of cotton is about 1 percent higher than in Mississippi. In part, this is because of a comparative advantage in soil. Size economies in Texas (irrigated) do

not exist for very large enterprise sizes under the

1982 enterprise configuration. The average enterprise size is about 1,224 acres per enterprise. Production concentration is low relative to the other

areas. Producers in this area seem to be adopting

irrigation as a means of decreasing variability as well

as increasing average yield. However, size economies do not appear to exist for very large irrigated

cotton enterprises and, therefore, do not exist as an

additional means of improving comparative advantage,

In Texas (dryland) the average total cost per bale

of cotton is about 4 percent higher than in Mississippi. Again, this is due in part to the inherent quality of the soil. Size economies remain to be exploited

by very large enterprises in this area. The average

cotton enterprise size is about 3,300 acres. The production concentration is the lowest of the cottonproducing areas studied. The change in production

concentration was about a 6-percent increase from

1978 to 1982, or about 2 percent per year. This is

very low relative to the other cotton-producing areas,

particularly when compared to irrigated cotton. In

this area yields are subject to wide variation, owing

to climate and the absence of irrigation water. Therefore, producers seem to have adopted a strategy of

nonexpansion in the face of size economies for very

large enterprises. The future success of this strategy will depend in part on relative yields and land

prices compared with those of other cotton-producing areas.
Appendix

Methodology and Detailed Results of

Microeconomic Impacts of Technology

and Public Policy for Crop Farms

Chapter 8 presented the summary results of the

macroeconomic impacts of public policies and technology on the viability of crop farms. This appendix discusses in more detail the methodology used

for the analysis and the specific results by area. For

further information the reader is advised to read

the individual commissioned papers published in

a separate volume to this report.

The first step for each production area was to describe representative farms that included moderate, large, and very large farms. The second step

involved a simulation of the representative farms

using a Monte-Carlo, whole-farm simulation model

(FLIPSIM V) under alternative farm policy, income

tax, finance, and technology scenarios.

Simulation Model

The current version of the General Firm Level

Policy Simulator—FLIPSIM V, developed by James

Richardson and Clair Nixon at Texas A&M University—was used to simulate the three representative

farms for selected policy and technology scenarios.

The model is capable of simulating the annual functions of a crop farm, i.e., production, marketing,

financial growth and decay, machinery depreciation

and replacement, family consumption, fixed and

variable costs, and participation in farm programs.

Each representative farm was simulated over the

lo-year planning horizon beginning in 1982 and extending through 1992. The planning horizon was

then repeated 50 times, using a different set of random cotton prices and yields for each iteration. At

the end of each iteration, values for key output variables were calculated.

The model began each year of the planning horizon by determining the production costs for the

current size of the farm, based on information provided for larger farms. Because the representative

farms were permitted to grow over time, crop mix

and per-acre production costs were forced to change

to correspond to those for larger representative

farms.

After determining the relevant crop mix and costs

for the farm, the model selected the random crop

prices and yields for that year. Random yields were

drawn to reflect the historical variability typical of

the study area,

FLIPSIM V simulated variable production costs

for each crop by multiplying the per-acre input

costs by planted acreages for the respective crops.

Labor costs were calculated as the sum of full-time

labor charges plus the cost of part-time labor. Parttime labor needs were based on the difference between hours of monthly labor available from fulltime employees and nonpaid family members, and

the monthly labor needs for all crops. Harvesting

costs were the product of the per-unit harvest costs,

random yield, and harvested acreage. Each farm’s

initial production and harvesting costs were expressed in 1982 dollars.

Annual crop yields were selected at random,

based on the historical yield variability observed for

the study area subject to the technology scenario

being evaluated, the year of the planning horizon,

and the size of farm. Under the base technology scenario it was assumed that the very largest farm

would adopt the new technology first. The next

smallest farm was assumed to adopt this technology in a similar pattern during subsequent years,

and the smallest farm would make the adoption

even later. The specific lag years for each commodity were based on the results of technology

workshops discussed in chapter 3.

The model calculated property taxes based on the

price of land and the property tax rate for the study

area. Other fixed costs were determined by the analyst, The model amortized all outstanding loans under the assumption that they were simple interest

mortgages. Annual interest rates for existing debt

on land, machinery, and operating loans were, respectively, 8.5, 13.4, and 14.4 percent. Annual interest rates for new debts and refinanced loans (on

long-term and intermediate-term assets) were 11.4

and 13.4 percent, respectively. Cash reserves and

off-farm investments were allowed to earn 10 percent interest annually. The market value of farm

machinery was updated under the assumption that

the real market value of used equipment decreased

1 percent per year. The market value of cropland

333
334 • Technology, Public Policy, and the Changing Structure of American Agriculture

was estimated using the historical relationship between the capital gains rate for cropland and the

rate of returns for farms. The capital gains rate was

a function of the capital gains rate for land in the

previous year and the rate of return to production

assets for the farm in the previous year,

The model next depreciated each piece of equipment on the farm for income tax purposes. Equipment purchased prior to 1981 was depreciated using

the double-declining balance method and a 5-year

to 7-year life. Equipment placed into use after 1980

was cost recovered assuming a 5-year life and the

Accelerated Cost Recovery System (ACRS) rules.

Regular-purpose and special-purpose buildings

were depreciated using ACRS rules, or the doubledeclining balance method, where applicable. Equipment that had passed its economic life was traded

for a replacement, if sufficient cash was available

to cover the required downpayment. The cost of

replacement equipment, expressed in 1982 dollars,

was held constant throughout the planning horizon.

First-year expensing and maximum investment tax

credit (ITC) were calculated for all equipment purchases.

The fraction of each crop marketed in the current

tax year was estimated internally, based on the operator’s desired taxable income ($7,400), estimated

cash receipts, and income tax deductions. If the market price was less than the effective loan rate for a

crop, it was placed in a Commodity Credit Corporation (CCC) loan when available, rather than being

sold. Stocks were released from the loan if the market price in the following year exceeded the loan rate

plus interest. Deficiency payments were paid if the

season average price was less than the target price.

The deficiency payment is a function of the payment rate, farm program yield, and harvested acreage. When an acreage set-aside or diversion program

was simulated, the model reduced planted acreage

the specified amount and accounted for increases

in production on the more productive land left in

production (slippage).

After simulating the farm policies specified by the

user, the model determined the farm operator’s yearend financial position, calculated family cash withdrawals, and calculated income taxes payable in the

following year. Cash surpluses were deposited in

an interest-bearing account at 10 percent interest.

Year-end cash flow deficits were handled in the following order: 1) grant a lien on crops in storage at

the operating loan interest rate, 2) refinance longterm equity, 3) refinance intermediate-term equity,

and/or 4) sell cropland. If the operator was unable

to cover the deficit in one of these ways, the farm

was declared insolvent and the model proceeded to

the next iteration after calculating the operator’s accrued income and self-employment taxes.

Personal income taxes and self-employment taxes

were calculated with the assumption that the operator was married, filed a joint income tax return,

and itemized personal deductions. The regular income tax liability was computed using income averaging (if qualified) and the standard tax tables. The

model selected the tax strategy that resulted in the

lower income tax liability.

The farm was permitted to grow at the end of each

year by purchasing cropland if the operator had cash

available (after meeting all expenses) to cover a 30-

percent downpayment for land and a 35-percent

downpayment for any additional machinery necessary for the proposed larger farm. The operator was

permitted to borrow against equity in land to meet

up to 50 percent of the downpayment for land. The

farm operation could also grow by leasing land if

the operator had sufficient cash available to cover

the 35-percent downpayment required for purchasing additional machinery needed to operate the

larger farm. If machinery was purchased because

of growth, the machinery was depreciated, the investment tax credit was calculated, and the operator’s income taxes were recomputed.

After checking the farm’s prospects for growth,

the model updated the farm operator’s balance sheet

and cash flow statement and prepared to simulate

the next year of the planning horizon. The steps in

the simulation process described above are repeated

for 10 years, or until the farm is declared insolvent.

After completing each iteration, the model summarized the information for numerous key output

variables and returned the farm to its initial economic situation (year one). This insured that the farm

faced the same economic, policy, and physical relationships for each of 50 iterations analyzed.

Policy and Technology Scenarios

The three representative farms for each area were

simulated for 10 years under the alternative scenarios described below. Seven farm policy scenarios

(including a continuation of the 1981 farm bill), one

income tax provision scenario, three financial bailout scenarios, and three alternative technology

scenarios were simulated for each farm. All policy

values associated with each scenario were held constant across farm sizes to allow direct comparison

of their impacts on different farm sizes. Each scenario is described in detail in this section.
App. E—Methodology and Detailed Results of Macroeconomic Impacts of Technology and Public Policy for Crop Farms ● 335

Farm Policy Scenarios

1. Base Policy .—The base policy scenario involves

continuation of the 1981 farm bill through 1992 and

continuation of the income tax provisions under

the 1982 Tax Act through 1992. Annual mean crop

yields were assumed to increase based on expected

adoption of new technology, as indicated in the previous section. For this scenario it was assumed the

following farm policies were in effect:

●

●

●

●

●

CCC- loan program is available to producers.

An acreage diversion/set-aside program in effect for 1983-85, was used, excluding payment

in kind. No acreage diversion/set-aside program

was in effect for 1986 through 1992.

A target price-deficiency payment program is

available for cotton in all years.

The $50,000-payment limitation for deficiency

and diversion payments is in effect.

Farms of all sizes are eligible to participate in

these farm program provisions.

Values for loan rates, target prices, diversion rates,

and diversion payment rates for 1983, 1984, and 1985

were set at their actual values. Loan rates and target

prices for 1986 through 1992 were held constant at

their 1985 levels.

It was assumed that the following options for

depreciating machinery and calculating income

taxes are used for the base scenario:

●

●

●

●

●

●

●

●

●

Machinery and buildings placed in use prior

to 1981 are depreciated using the doubledeclining balance method.

Machinery and buildings placed in use after

1980 are depreciated using an ACRS method.

The operator elects to claim first-year expensing for all depreciable items.

The operator elects to take maximum ITC and

reduce the basis.

The operator adjusts crop sales across tax years

to reduce current-year taxes.

The operator may use either the regular income

tax computation or income averaging to calculate Federal income tax liabilities.

There is no maximum interest deduction for calculating taxable income.

The actual self-employment tax rates and maximum income levels subject to this tax for 1983

and 1984 are used. Announced values for these

variables in 1985 through 1986 were used, and

the 1986 values were held constant through

1992.

The operator elects to trade in old machinery

on new replacements at the end of each item’s

economic life.

2. A Twenty-Percent Acreage Reduction.—The provisions of the base policy scenario were modified

by adding a 15-percent set-aside with a 5-percent

paid diversion for cotton in 1986 through 1992. Reasonable slippage (70 percent for cotton) and program

participation rates were used to estimate the resulting increase in mean prices in 1986 through 1992.

All other provisions of the base scenario were used

without change.

3. No Farm Program Payment Limitation.—All provisions of the base scenario were used except that

there was no limitation on diversion and deficiency

payments.

4. No Price Supports and No Deficiency Payments.—

The CCC loan and target price provisions under the

base scenario were assumed to have been eliminated

for all years in the planning horizon (1983-92), Annual mean prices were decreased based on the expected impact of removing the price and income support programs. Relative variability in prices about

their means was increased based on the work of Morton, Devadoss, and Heady as to the effects of no farm

program on U.S. agriculture. To isolate the impact

of price and income supports on the representative

farms, the acreage diversion and set-aside programs

in the base policy for 1983 through 1985 were assumed to remain in effect.

5. No Target Price/Deficiency Payment.—The target

price and deficiency payment provision was assumed to be eliminated for all years of the planning

horizon 1983 through 1992. All other provisions of

the base scenario were used without change to isolate the effects of removing only the deficiency

payment.

6. Target Farm Program Benefits.-All farm program

and income tax provisions of the base scenario were

used except that farms with more than $300,000 of

sales were not eligible to participate in farm program

provisions, This program restriction excluded the

very large farms from participating directly in the

program provisions (CCC loan, target price/deficiency payments, and set-aside/diversions). Mean

prices and relative variability in prices were not adjusted because sufficient “smaller” farms were assumed to be participating in the farm program for

the price support actions of the CCC loan to function normally.

7. No-Farm Program.-All farm program provisions

outlined for the base scenario were eliminated for

all 10 years of the planning horizon, Mean annual

prices and relative variance in prices for the no-price

and income supports scenario (4) were used due to

eliminating provisions of the CCC loan.
336 . Technology, Public Policy, and the Changing Structure of American Agriculture

Income Tax Scenarios

8. Reduced Income Tax Benefits and Base Farm Program.—The Federal income tax provisions in place

for the base policy scenario were made more restrictive. All farm policy provisions of the base scenario

were left unchanged. The more restrictive Federal

income tax provisions included the following:

●

●

●

●

Machinery and buildings were depreciated

using the straight-line cost recovery method.

First-year expensing provisions were eliminated for all depreciable items.

ITC provisions were continued, but the maximum ITC provision was eliminated.

The maximum annual interest expense that

could be used to reduce taxable income was

$15,600. This value represented the annual interest expense deductions a consumer might

have for a home, automobiles, and the like.

The operator must sell obsolete machinery upon

disposition rather than trading it in on new

replacements, thus forcing recapture of excess

depreciation deductions.

All other Federal income tax provisions for the

base scenario were used as outlined earlier.

9. Base Finance Scenario.—Each farm’s long-term

debt-to-asset ratio was increased to 0.55, and its

intermediate-term debt-to-asset ratio was increased

to 0,60, to represent a highly leveraged farm. Annual long-term and intermediate-term interest rates

were increased to their average values (0.1139 and

0.1343, respectively) for 1980 to 1983 to represent

a farm that had been forced to refinance its assets

during the past 4 years. The farm program provisions associated with the base policy scenario were

continued for this scenario,

10. Debt Restructure.—The length of intermediateterm loans was increased by 1 year, and a portion

of intermediate debt was converted to long-term

debt. The conversion of intermediate-term debt was

permitted as long as the long-term debt-to-asset ratio did not exceed 0.65. For some farms, this allowed

all intermediate-term debt to be converted to longterm debt, while for other farms this constraint substantially restricted debt conversion. Total debt loads

and farm program provisions were the same as those

used for the base finance scenario (9).

11. Interest Subsidy.—The annual interest rates,

debt levels, and farm program provisions in the base

finance scenario (9) were simulated, but an interest

subsidy was provided during the first 2 years. The

interest subsidy took the form of an interest rate reduction equal to 3.4 percent for long-term interest

rates and 5.4 percent for intermediate-term interest rates. These interest rate reductions were the

amounts necessary to reduce the respective interest rates (0.1 137 and 0.1343) to a 4-percent rate of

interest.

No-New-Technology Scenarios

12. No-New-Technology and Base Farm Policy .—The

Federal income tax and farm program provisions

in the base policy scenario were simulated assuming no increase in mean yields over the planning

horizon. For the no-new-technology scenarios, mean

irrigated and dryland cotton yields for all 10 years

were set equal to their respective means observed

over the period 1974-83.

13. No-New-Technology and No Deficiency Payments.—The farm program provisions in the notarget-price/deficiency payments scenario (5) were

simulated assuming the same average annual cotton yields used for the base no-new-technology scenario (12).

14. No-New-Technology and No-Farm Program.-All

farm program provisions were eliminated (scenario

7), and annual average crop yields used for the base

no new technology scenario (12) were assumed.

Evaluation Criteria

The FLIPSIM V model provides considerable detail about the viability of a representative farm at

the end of each iteration, e.g., ending leverage ratio, ending net worth, ending farm size, total assets,

total debt, net present value, and the solvency of the

farm over 10 years, By repeating each scenario for

50 iterations, the model generates the information

necessary for estimating values for key output variables. The means of these key output values are used

to compare the economic impacts of selected policy and technology scenarios on representative

farms. The following output variables for the model

were selected to compare the impacts of the

scenarios described in the previous section:

Probability of survival is defined as the probability that the representative farm will remain

solvent for 10 years. In other words, it is the

probability that the farm operator will maintain

at least the minimum financial ratios required

by bankers in the local area for all 10 years of

the planning horizon.

Probability of a positive net present value is the

probability that the representative farm will

have a positive after-tax net present value. An
App. E—Methodology and Detailed Results of Macroeconomic Impacts of Technology and Public Policy for Crop Farms ● 337

after-tax, real discount rate of 3 percent was

used to calculate the farm’s net present value.

Thus this statistic indicates the probability of

the representative farm providing at least a 3-

percent real rate of return to the operator’s initial net worth.

● After-tax net present value (NPV) is the present

value of the operator’s annual cash withdrawals

(CW) plus the present value of the change in net

worth (NW) minus the present value of annual

off-farm income (OF):

T CWt –OFt NWT NPV = ~ + — – NWO

t =l (1.03)t(1.03)T

Cash withdrawals equal family living expenses

plus State and Federal income taxes and selfemployment taxes. Initial net worth (NWo

) and

ending net worth (NWT

) explicitly consider the

value of off-farm investments and accrued

taxes. A 3-percent after-tax, real discount rate

was used to calculate net present value for all

representative farms.

• Present value of ending net worth is used to indicate the change in the farm’s real net worth

over the planning horizon. Net worth is affected

by increases (or decreases) in asset (land, machinery, and livestock) value and retained earnings. This value can be compared directly with

initial net worth to indicate the relative magnitude of real financial growth.

• Acres owned, leased, and controlled at the end

of the planning horizon for each iteration indicate the impacts of alternative scenarios on the

rate of growth for representative farms. These

three statistics provide an indication of how the

farm grew through either the purchase or lease

of land.

• Total long-term and intermediate-term debts at

the end of the planning horizon provide an insight into the financial stress of the farm over

the planning horizon. Increases in average ending debt from one scenario to another can be

due either to rapid growth through purchasing

land and machinery or to the farm operator being forced to refinance large cash flow deficits,

When surplus cash is available, the operator is

permitted to prepay intermediate-term debts

first and then prepay new long-term debts,

Therefore, large ending intermediate-term

debts indicate insufficient cash was available

to reduce intermediate-term debt through prepayment of principal.

• Ending equity ratio is the farm’s ending ratio

of total net worth to total assets. This ratio provides a “bottomline” measure for comparing

the representative farm’s ending financial position across scenarios.

Average annual net farm income is the average

net farm income received by the operator over

all years simulated, Net farm income equals total farm receipts plus total Government payments minus all cash production expenses, interest payments, labor costs, fixed cash costs,

and depreciation. This value excludes all nonfarm income and interest earned on cash reserves.

Average annual Government payment is the

average annual Government payment (deficiency and diversion payments) received over

all years simulated.

Results of Analysis

Texas Southern High Plains

Cotton Farms

The results indicate that under the most likely technology scenario and continuation of the provisions

of the 1981 farm bill, all three representative cotton

farms had a high probability of remaining solvent

through 1992 (table E-l). Additionally, all three farms

had an 88-percent or greater chance of receiving a

reasonable return to equity. All three farms were able

to grow over the 10-year planning horizon. The

greatest percentage of increase in ending farm size

was for the 1,088-acre farm, followed by the 3,383-

acre farm and the 5,570-acre farm.

Imposing an acreage reduction program (acreage

diversion and set-aside) increased net farm incomes

and average net present value for all three farms,

Acreage reduction programs increased the annual

rate of growth more for the 1,088-acre farm than for

the two larger farms.

Removing the deficiency payment program (income supports) reduced the probability of survival,

net farm incomes, and annual growth rates for all

three farms. The greatest percentage decrease in annual net farm income was experienced by the 1,088-

acre farm, followed by the 3 ,383-acre farm. Similarly,

the two smaller farms experienced greater reductions in their annual growth rates.

Removing both price supports (CCC loan) and deficiency payments reduced the probability of survival

the most for the 1,088-acre farm (36 percent),

whereas the probability of survival for the 5,570-acre

farm fell only 2 percent. All three farms had slower

rates of growth in the absence of price and income

supports. The annual rate of growth for the 1,088-
338 ● Technology, Public Policy, and the Changing Structure of American Agriculture

Table E-1 .— Comparison of Selected Farm Commodity and Income Tax Policy Scenarios on

Representative Texas Southern High Plains Cotton Farms

Alternative scenarios

Criteria 1 2 3 4 5 6 7 8

Moderate size (1,088 acres):

Probability of survival. . . . . . . . . . . . . . . . . . . . . . . . 92.0

Present value of ending net worth ($1,000) . . . . . 564.0

Ending farm size (acres) . . . . . . . . . . . . . . . . . . . . . 1,558.0

Annual net farm income ($1,000) . . . . . . . . . . . . . . 8.3

Annual Government payment ($1,000) . . . . . . . . . . 26.0

Large size (3,333 acres):

Probability of survival. . . . . . . . . . . . . . . . . . . . . . . . 90.0

Present value of ending net worth ($1,000) . . . . . 1,412.0

Ending farm size (acres) . . . . . . . . . . . . . . . . . . . . . 4,289.0

Annual net farm income ($1,000) . . . . . . . . . . . . . . 33.4

Annual Government payment ($1,000) . . . . . . . . . . 38.0

Very large size (5,570 acres):

Probability of survival 94.0

Present value of ending net worth ($1,000) . . . . . 3,027.0

Ending farm size (acres) . . . . . . . . . . . . . . . . . . . . . 6,002.0

Annual net farm income ($1,000) . . . . . . . . . . . . . . 66.6

Annual Government payment ($1,000) . . . . . . . . . . 40.2

94.0

648.0

1,635.0

13.3

22.2

94.0

1,697.0

4,455.0

53.6

35.1

96.0

3,489.0

6,047.0

100.6

39.1

94.0

601.0

1,648.0

11.9

29.5

94.0

1,853.0

4,577.0

83.3

83.3

98.0

4,047.0

6,514.0

170.6

135.8

56.0

242.0

1,216.0

–28.9

1.3

72.0

931.0

3,748.0

– 14.8

3.2

92.0

2,367.0

5,781.0

–3.2

4.8

68.0

301.0

1,274.0

–21.7

1.1

82.0

1,055.0

3,857.0

3.6

3.0

96.0

2,645.0

5,848.0

31.0

4.6

92.0

564.0

1,558.0

8.2

25.9

86.0

1,191.0

3,985.0

12.9

16.8

88.0

2,287.0

5,727.0

– 13.9

0.0

42.0

167.0

1,213.0

–40.6

0.0

62.0

801.0

3,649.0

–39.7

0.0

78.0

2,066.0

5,736.0

–40.5

0.0

88,0

516.0

1,565.0

–6.0

25.8

88.0

1,226.0

3,965.0

–7.2

37.9

94.0

2,583.0

5,746.0

– 15.6

40.4

aTh~ ~~~n~~iog are:

1. Continuation of the 1961 farm bill and 1963 Federal income tax provisions.

2. A 20-percent acreage reduction in 1966-92.

3. No farm program payment limitation in 1963-92.

4. No price supports and no deficiency payment in 1963-92.

5. No target prlceldeflclency payment in 1963-92.

6. Target farm program benefits to farms that produce less than S300,000 in program crops.

7. No farm program in 1963-92.

8. Reduced income tax benefits and the base farm program.

The impact of price supports can be derived by subtracting scenario 5 from scenario 6.

The impact of income supports can be derived by subtracting scenario 6 from scenario 1.

The impact of income supports with a $50,000 payment limitation can be found by subtracting scenario 6 from scenario 4.

SOURCE: Office of Technology Assessment.

acre farm was reduced five times more than for the

5,570-acre farm.

Removing all farm program provisions reduced

the probability of survival for all three farms. The

probability of survival declined from 92 to 42 percent for the 1,088-acre farm and from 90 to 62 percent for the 3,383-acre farm. The probability of survival for the 5,570-acre farm remained above 75

percent. Average annual net farm incomes for all

three farms were substantially less than zero, and

average net present values were considerably lower

than under the current farm program.

Imposing a more restrictive set of Federal income

tax provisions on the three representative farms reduced the average annual rate of growth more for

the two larger farms. Net farm incomes were also

reduced more for the two larger farms. Growth occurred by leasing because higher taxes reduced

available cash for land purchases (downpayments).

The results of analyzing the three farms, assuming they were highly leveraged, reveal that debt restructuring would not greatly help these farms (table E-2). Although their probabilities of survival

would not be increased, the farms would be able to

remain solvent 1 to 3 years longer. A 2-year interest

subsidy would provide greater benefits to net present

value, net farm income, and ending net worth than

would a debt restructure program.

Yield-enhancing technology anticipated over the

next 10 years for cotton did not significantly change

the average annual growth rates of the representative farms (table E-3). Changing the farm program

or Federal income tax provisions had a greater impact on farm growth.

In conclusion, the results indicate that moderate

(1,088-acre) cotton farms in the Texas Southern High

Plains depend more on farm program provisions for

their continued growth and economic viability than

do larger farms. Larger farms are better able to survive without farm program benefits because of lower

production costs (dollars/lb), higher average cotton

lint prices, and a greater asset base from which to

meet cash flow deficits. The loss of any farm program provision reduces the economic viability and
App. E—Methodology and Detailed Results of Macroeconomic Impacts of Technology and Public Policy for Crop Farms • 339

Table E-2.—Comparison of Selected Financial Bailout Scenarios for Three Representative

Texas Southern High Plains Cotton Farmsa

Alternative scenarios for Alternative scenarios for Alternative scenarios for

1,088-acre farm 3,383-acre farm 5,570-acre farm

Criteria 9 10 11 9 10 11 9 10 11

Probability of survival . . . . . . . . . . . . . . . 64.0 66.0 72.0 58.0 50.0 60.0 66.0 64.0 66.0

Present value of ending net worth

($1,000) . . . . . . . . . . . . . . . . . . . . . . . . . . 304.0 314.0 343.0 604.0 600.0 733.0 1,310.0 1,356.0 1,619.0

Ending farm size (acres) . . . . . . . . . . . . . 1,414.0 1,434.0 1,443.0 3,770.0 3,841.0 3,821.0 5,733.0 5,976.0 5,772.0

Annual net farm income ($1,000) . . . . . . –5.4 –6.4 1.3 –9.1 –21.2 6.9 –41.8 –57.3 –6.3

Annual Government payment ($1,000) . . 24.4 24.8 24.7 36.8 36.4 37.2 41.1 41.3 41.6

aThe scenarios are’

9 Continuation of the 19S1 farm bill and the 1983 Federal tax provisions for a highly leveraged farm.

10 Restructure of debt for a highly leveraged farm.

11 Interest rate subs!dy (buy-down) in the first 2 years for a highly leveraged farm

SOURCE Office of Technology Assessment

Table E.3.—Comparison of Selected Policy Scenarios Assuming No New Technology for

Three Representative Texas Southern High Plains Cotton Farmsa

Alternative scenarios for Alternative scenarios for Alternative scenarios for

1,088-acre farm 3,383-acre farm 5,570-acre farm

Criteria 12 13 14 12 13 14 12 13 14

Probability of survival . . . . . . . . . . . . . . . 92.0 68.0 42.0 88.0 78.0 60.0 94.0 90.0 76.0

Present value of ending net worth

($1,000) . . . . . . . . . . . . . . . . . . . . . . . . . . 552.0 290.0 161.0 1,325.0 966.0 738.0 2,807.0 2,322.0 1,843.0

Ending farm size (acres) . . . . . . . . . . . . . 1,590.0 1,280.0 1,206.0 4,273.0 3,818.0 3,633.0 5,960.0 5,816.0 5,724.0

Annual net farm income ($1,000) . . . . . . 7.0 –22.2 –41.0 25.4 –3.6 –45.5 47.0 0.2 –65.9

Annual Government payment ($1,000) . . 26.3 1.1 0.0 37.9 3.0 0.0 40.5 4.8 0.0

a

The scenarios are:

12 Continuation of the 1981 farm bill and the 1983 Federal tax provisions, assuming no-new-technology scenario.

13 No target price deficiency payment program, assuming no new technology scenario

14 Deficiency plus diversion payments and any other Government payments recewed for Government loans and storage costs

SOURCE” Off Ice of Technology Assessment

growth rate of the 1,088-acre farm more than the

larger farms; however, all size farms are negatively

affected.

Southern High Plains Wheat Farms

Three different size wheat farms in the Southern

High Plains, representative of a majority of the commercial agricultural production for the region, were

analyzed. The farms initially operating 1,280 acres,

1,920 acres, and 3,200 acres, reflected debt-to-asset

ratios typical of farms in the area, owned the necessary machinery complement, and farmed both owned

and leased cropland.

Analysis results indicate that under the most likely

technology scenario and a continuation of the provisions of the 1981 farm bill (base scenario), all three

representative wheat farms had a high probability

of remaining solvent through 1992 (table E-4), Additionally, all three farms had a high probability of

generating a reasonable return on equity and were

able to grow over the 10-year planning horizon. The

greatest percentage increase in average ending farm

size was for the 1,280-acre farm, followed by the

1,920-acre operation.

Imposing an acreage reduction program (acreage

set-aside and paid diversion) increased net farm incomes and average net present value for all three

farms. Acreage reduction programs increased average ending farm size slightly more for the 3,20()-acre

farm than for the 1,280-acre farm.

Removing the deficiency payment program (income supports) reduced the probability of survival

for only the 1,920-acre farm. Although each farm

suffered a reduction in average annual net farm income, the reduction was significantly greater for the

1,280-acre farm. Average annual growth rates declined more for the two smaller farms.

Removing both price (CCC loan and farmer-owned

reserve) and income supports (deficiency payments)

reduced the probability of survival for both the 1,280-

acre and 1,920-acre farms. All three farms experi-
340 ● Technology, Public Policy, and the Changing Structure of American Agriculture

Table E-4.—Comparison of Selected Farm Commodity and Income Tax Policy Scenarios on

Representative Southern Plains Wheat Farmsa

Alternative scenarios

Criteria 1 2 3 4 5 6 7 8

Moderate size (1,280 acres):

Probability of survival, . . . . . . . . . . . . . . . . . . . . . . . 100.0

Present value of ending net worth ($1,000) . . . . . 803.0

Ending farm size (acres) . . . . . . . . . . . . . . . . . . . . . 1,901.0

Annual net farm income ($1,000) . . . . . . . . . . . . . . 2.6

Annual Government payment ($1,000) . . . . . . . . . . 30.9

Large size (1,920 acres):

Probability of survival. . . . . . . . . . . . . . . . . . . . . . . . 100.0

Present value of ending net worth ($1,000) . . . . . 1,028.0

Ending farm size (acres) . . . . . . . . . . . . . . . . . . . . . 2,765.0

Annual net farm income ($1,000) . . . . . . . . . . . . . . 9.0

Annual Government payment ($1,000) . . . . . . . . . . 39.0

Very large size (3,200 acres):

Probability of survival. . . . . . . . . . . . . . . . . . . . . . . . 100.0

Present value of ending net worth ($1,000) . . . . . 1,936.0

Ending farm size (acres) . . . . . . . . . . . . . . . . . . . . . 4,218.0

Annual net farm income ($1,000) . . . . . . . . . . . . . . 48.9

Annual Government payment ($1,000) . . . . . . . . . . 44.2

100.0

1,032.0

1,955.0

18.3

31.5

100.0

1,359.0

2,890.0

28.5

39.1

100.0

2,204.0

4,365.0

59.5

45.0

100.0

811.0

1,901.0

3.1

31.6

100.0

1,117.0

2,755.0

17.3

44.7

100.0

2,231.0

4,483.0

78.4

76.9

76.0

283.0

1,565.0

–33.6

2.5

50.0

294.0

2,234.0

–52.5

4.2

100.0

1,096.0

3,552.0

–7.8

5.8

100.0

426.0

1,648.0

–21.4

2.5

90.0

475.0

2,339.0

–34.9

3.7

100.0

1,412.0

3,834.0

15.6

5.9

100.0

761.0

1,910.0

–0.9

27.7

96.0

696.0

2,618.0

– 17.6

16.2

100.0

1,087.0

3,494.0

– 13.6

0.0

48.0

189.0

1,478.0

–41 .6

0.0

32.0

179.0

2,093.0

–67.9

0.0

92.0

925.0

3,472.0

–25.1

0.0

1OO.O

710.0

1,757.0

–8.3

29.4

100.0

833.0

2,499.0

–21.8

37.3

100.0

1,657.0

3,805.0

28.1

44.1

aT1-j~ ~~-”a~i~~ a~~:

1. Continuation of the 1961 farm biil and 1983 Federal income provisions.

2. A 20-percent acreage reduction in 1966-92.

3. No farm program payment imitation in 1963-92.

4. No price supports and no deficiency payment in 1983-92.

5. No target price/deficiency payment in 1983-92.

8. Target farm program benefits to farms that produce iess than $300,000 in program crops.

7. No farm program in 1963-92.

8. Reduced income tax benefits and the base farm program.

The impact of price supports can be derived by subtracting scenario 5 from scenario 8.

The impact of income supports can be derived by subtracting scenario 6 from scenario 1.

The impact of income supports with a $50,000 payment limitation can be found by subtracting scenario 6 from scenario 4

SOURCE: Office of Technology Assessment.

enced slower rates of growth as a result of eliminating price and income supports. Average ending farm

size ranged from 19 to 16 percent less than under

the base scenario. All three farms experienced negative annual net farm incomes on the average.

Removing all farm program provisions reduced

the probability of survival for all three wheat farms.

Probability of survival (100 percent under the base

scenario) declined to 48 percent for the 1,280-acre

farm, 32 percent for the 1,920-acre farm, and 92 percent for the 3,200-acre farm. Average ending net

worth for the farms declined over the period, owing

to a decline in land values.

Imposing a more restrictive set of Federal income

tax provisions on the three representative wheat

farms slowed the average annual growth rate more

for the two larger farms. Farm growth occurred

more by leasing cropland than by purchasing land,

owing to reduced cash reserves. The 3,20()-acre farm

experienced the greatest absolute reduction in annual net farm income (about $20,000), followed by

the 1,920-acre farm.

A 2-year interest rate subsidy program would provide greater benefits to highly leveraged wheat farms

than would a debt restructure program. Probability

of survival for a highly leveraged 1,920-acre wheat

farm was increased from 40 to 80 percent by an interest rate subsidy (table E-5).

Yield-enhancing technology anticipated over the

next 10 years will likely contribute to farm growth.

The greatest benefit will accrue to those farms initially adopting the new technology (table E-6).

In conclusion, the results of this analysis indicate

that moderate wheat farms—1,280 to 1,920 acres—

in the Southern High Plains depend more on farm

program provisions than do larger farms for their

continued growth and economic viability. The loss

of any farm program provision, however, negatively

affects farms of all sizes.

Corn-Soybean Farms in the Corn Belt

All three Illinois farms had a survival probability

at or near 100 percent under the entire range of farm

program (and no program) alternatives considered

here (table E-7). But the probability of positive aftertax net present value dropped dramatically (particularly for the medium [640-acre] and large [982-acre]
App. E—Methodology and Detailed Results of Macroeconomic Impacts of Technology and Public Policy for Crop Farms ● 341

Table E-5.—Comparison of Selected Financial Bailout Scenarios for Three Representative

Southern Plains Wheat Farmsa

Alternative scenarios for Alternative scenarios for Alternative scenarios for

1,280-acre farm 1,920-acre farm 3,200-acre farm

Criteria 9 10 11 9 10 11 9 10 11

Probability of survival . . . . . . . . . . . . . . . 86.0 98.0 100.0 40.0 70.0 80.0 100.0 100.0 100.0

Present value of ending net worth

($1,000) . . . . . . . . . . . . . . . . . . . . . . . . . . 289.0 408.0 383.0 258.0 399.0 406.0 1,248.0 1,373.0 1,348.0

Ending farm size (acres) . . . . . . . . . . . . . 1,434.0 1,549.0 1,552.0 1,994.0 2,058.0 2,118.0 3,779.0 3,978.0 3,891.0

Annual net farm income ($1,000) . . . . . . –22.5 –21.2 – 14.3 –37.9 –35.1 –24.1 17.1 12.4 27.5

Annual Government payment ($1 ,000) . . 25.2 26.4 26.8 34.8 35.2 35.6 43.9 44.1 44.0

aThe scenarios are

9 Continuation of the 1981 farm bill and the 1983 Federal tax provisions for a highly leveraged farm

10 Restructure of debt for a highly leveraged farm

11 Interest rate subsidy (buy-down) in the first 2 years for a highly leveraged farm

SOURCE: Office of Technology Assessment

Table E-6.—Comparison of Selected Policy Scenarios Assuming No New Technology for

Three Representative Southern Plains Wheat Farmsa

Alternative scenarios for Alternative scenarios for Alternative scenarios for

1,280-acre farm 1,920-acre farm 3,200-acre farm

Criteria 12 13 14 12 13 14 12 13 1 4

Probability of survival . . . . . . . . . . . . . . . 100.0 90.0 32.0 100.0 44.0 10.0 100.0 82.0 28.0

Present value of ending net worth

($1,000) . . . . . . . . . . . . . . . . . . . . . . . . . . 726.0 325.0 134.0 780.0 229.0 81.0 1,131.0 562.0 220.0

Ending farm size (acres) . . . . . . . . . . . . . 1,859.0 1,632.0 1,430.0 2,605.0 2,304.0 2,048.0 3,699.0 3,542.0 3,322.0

Annual net farm income ($1,000) . . . . . . – 1.3 –28.9 –46.8 – 10.9 –52.9 –77.1 –2.1 –45.4 –85.8

Annual Government payment ($1,000) . . 30.7 2.5 0.0 38.1 3.9 0.0 43.7 5.9 0.0

aThe scenarios are:

12 Contlnuatlon of the 1981 farm bill and the 1983 Federal tax provisions, assuming no-new. technology scenario

13 No target price deficiency payment program, assuming no new technology scenario

14 Deflclency plus diversion payments and any other Government payments received for Government loans and storage costs

farms) when farm program benefits were reduced

or removed. In fact, even the loss of target price/deficiency payment programs dropped the probability

of positive after-tax net present value into the range

of 4 to 6 percent for these two representative farms.

As a general rule, the largest farm fared the best with

the loss of farm programs because it operates with

a substantial acreage of rented land and suffers relatively less from the economic drag of servicing a high

real estate debt load. Moreover, this very large unit

had much less economic incentive to grow in size

than do the two smaller farms.

All three Nebraska farms also had a survival probability at or near 100 percent under the entire range

of program and no-program alternatives (table E-8).

The loss of farm program benefits had its greatest

adverse impact on the very large (2,085-acre) farm,

probably because this unit has large machinery investments and uses much more full-time hired labor. In fact, the probability of realizing positive aftertax net present value dropped to the 8 to 12 percent

range for this very large operation when program

benefits were withdrawn or dramatically reduced.

Economic performance measures for the medium

(672-acre) and large (920-acre) farm also deteriorated

under the latter condition. Overall, the generally

stronger economic viability of the Nebraska farms

(compared with Illinois farms) was attributable to

much lower land prices and the lower debt servicing costs that result.

A modest reduction in income tax benefits did not

have major economic impacts on the moderate and

large farms in either Illinois or Nebraska. It has its

greatest impact (a reduction of $5,800 in net farm

income compared with that of the base scenario) for

the very large (2,085-acre) farm in Nebraska. Even

here, however, the impact was very small compared

with the loss of economic benefits from either the

target price/deficiency payment program or the

entire complement of existing price and income

supports.

Increasing debt loads to a level of 60 percent of

machinery value and 55 percent of land value resulted in a heavy economic dragon all three Illinois

farms, but somewhat less so for the Nebraska farms

(tables E-9, E-10). This difference results mainly from
342 ● Technology, Public Policy, and the Changing Structure of American Agriculture

Table E-7.—Comparison of Selected Farm Commodity and Income Tax Policy Scenarios on Representative

Corn-Soybean Farms in East Central Illinoisa

Alternative scenarios

Criteria 1 2 3 4 5 6 7 8

Moderate size (640 acres):

Probability of survival. . . . . . . . . . . . . . . . . . . . . . . . 100.0

Present value of ending net worth ($1,000) . . . . . 703.0

Ending farm size (acres) . . . . . . . . . . . . . . . . . . . . . 902.0

Annual net farm income ($1,000) . . . . . . . . . . . . . . 23.2

Annual Government payment ($1,000) . . . . . . . . . . 11.6

Large size (962 acres):

Probability of survival. . . . . . . . . . . . . . . . . . . . . . . . 100.0

Present value of ending net worth ($1,000) . . . . . 975.0

Ending farm size (acres) . . . . . . . . . . . . . . . . . . . . . 1,374.0

Annual net farm income ($1,000) . . . . . . . . . . . . . . 24.3

Annual Government payment ($1,000) . . . . . . . . . . 22.6

100.0

743.0

904.0

29.9

9.8

100.0

970.0

1,364.0

22.9

16.6

100.0

100.0

703.0

902.0

23.2

11.6

100.0

991.0

1,388.0

26.4

24.3

100.0

100.0

568.0

824.0

10.2

0.7

100.0

645.0

1,139.0

14.3

1.0

100,0

100.0

593.0

837.0

11.8

0.7

100.0

693.0

1,180.0

5.2

1.0

Very large size (1,630 acres):

Probability of survival. . . . . . . . . . . . . . . . . . . . . . . . 100.0 100.0 100.0

100.0

669.0

907.0

19.1

8.6

100.0

801.0

1,355.0

8.0

7.8

100.0

100.0

563.0

834.0

11.1

0.0

100.0

622.0

,134.0

0.0

100.0

719.0

893.0

19.0

11.7

100.0

852.0

1,217.0

24.9

21.9

100.0

Present value of ending net worth ($1,000) . . . . . 1,267.0 1,348.0 1,266.0 991.0 1,033.0 1,056.0 1,036.0 1,044.0

Ending farm size (acres) . . . . . . . . . . . . . . . . . . . . . 1,945.0 1,932.0 1,942.0 1,856.0 1,859.0 1,908.0 1,876.0 1,784.0

Annual net farm income ($1,000) . . . . . . . . . . . . . . 51.8 62.2 52.4 31.1 35.1 34.7 34.8 54.4

Annual Government payment ($1,000) . . . . . . . . . . 23.6 19.3 25.3 1.7 1.7 0.0 0.0 23.3

aTh~ ~~ena~i~~ a~~:

1. Continuation of the 1981 farm bill and 1963 Federal income provisions.

2, A 20-percent acreage reduction in 1966-92.

3. No farm program payment limitation in 1983-92.

4. No price supports and no deficiency payment in 1983-92.

5. No target priceldeficiency payment in 1983-92.

6. Target farm program benefits to farms that produce less than $300,000 in program crops.

7. No farm program in 1963-92

8. Reduced income tax benefits and the base farm program.

The impact of price supports can be derived by subtracting scenario 5 from scenario 6.

The impact of income supports can be derived by subtracting scenario 6 from scenario 1.

The impact of income supports with a $50,000 payment limitation can be found by subtracting scenario 6 from scenario 4.

SOURCE: Office of Technology Assessment

the much higher land prices on Illinois farms.

Whereas survival probabilities dropped to as low as

72 percent for the 640-acre Illinois farm, they

dropped only to 86 percent for the 672-acre Nebraska

unit. Similarly, the probabilities for positive aftertax net present values dropped to as low as 16 percent for the 640-acre Illinois farm, but remained at

100 percent for all three Nebraska farms.

Because of the heavy real estate debt load on Illinois farms, these farms continued to have severe

economic problems with either a debt restructuring or an interest rate subsidy type of financial

bailout. Of the two, however, the interest rate subsidy was the most beneficial alternative, particularly

for the smaller (640-acre and 982-acre) farms. Similarly, the interest rate subsidy was preferable (as

compared with debt restructuring) for the two smallest Nebraska farms. Faced with substantial incentives for additional growth in size, these farms were

not in a position to profit appreciably from debt restructuring. A financial bailout in the form of an interest rate subsidy improves net farm incomes and

provides a “margin of safety” in the event of unexpected economic adversities.

The impact of eliminating new technology fell

mainly on the very large farms (tables E-n and E12), These farms tend to be the early adopters of new

technology, which generally results in a very favorable benefit/cost ratio. One should keep in mind,

however, that the simulation analysis conducted

here did not permit feedback on the price effects

from increased output levels.

Because of highland and machinery costs, the survival probability for new entrants in Illinois was very

low (O to 4 percent). It was much higher (84 percent)

for the base scenario on the 672-acre Nebraska farm.

But this probability dropped to only 6 percent with

the loss of all farm programs. Thus the economic

survival of new entrants was particularly dependent

on price and income benefits from farm programs

(or of some other type of financial assistance).

As a practical matter, new entrants to farming can

probably survive with high land prices and high interest rates only if they are able to lease most of their

land resources or arrange for a postponement of a

portion of their “early year” debt repayment obligations.
App. E—Methodology and Detailed Results of Macroeconomic Impacts of Technology and Public Policy for Crop Farms • 343

Table E-8.—Comparison of Selected Farm Commodity and Income Tax Policy Scenarios on

Representative Irrigated Row Crop Farms in South Central Nebraska

Alternative scenarios

Criteria 1 2 3 4 5 6 7 8

Moderate size (672 acres):

Probability of survival. . . . . . . . . . . . . . . . . . . . . . . . 100.0

Present value of ending net worth ($1,000) . . . . . 670.0

Ending farm size (acres) . . . . . . . . . . . . . . . . . . . . . 921.0

Annual net farm income ($1,000) . . . . . . . . . . . . . . 26.8

Annual Government payment ($1,000) . . . . . . . . . . 17.3

Large size (920 acres):

Probability of survival . . . . . . . . . . . . . . . . . . . . . . . . 100.0

Present value of ending net worth ($1,000) . . . . . 1,349.0

Ending farm size (acres) . . . . . . . . . . . . . . . . . . . . . 1,257.0

Annual net farm income ($1,000) . . . . . . . . . . . . . . 58.4

Annual Government payment ($1,000) . . . . . . . . . . 24.1

Very large size (2,065 acres):

Probability of survival. . . . . . . . . . . . . . . . . . . . . . . . 100.0

Present value of ending net worth ($1,000) . . . . . 2,259.0

Ending farm size (acres) . . . . . . . . . . . . . . . . . . . . . 2,375.0

Annual net farm income ($1,000) . . . . . . . . . . . . . . 118.6

Annual Government payment ($1,000) . . . . . . . . . . 35.9

100.0

736.0

909.0

31.0

14.5

100.0

1,377.0

1,253.0

60.9

19.3

100.0

2,374.0

2,383.0

127.3

31.5

100.0

670.0

921.0

26.8

17.3

100.0

1,369.0

1,257.0

57.4

23.9

100.0

2,407.0

2,384.0

134.6

49.6

92.0

260.0

882.0

–9.8

1.0

100.0

739.0

1,242.0

0.1

1.3

100.0

1,013.0

2,167.0

1.3

3.0

100.0

476.0

870.0

10.6

1.0

100.0

1,084.0

1,240.0

35.7

1.3

100.0

1,863.0

2,280.0

88.0

3.0

100.0

670.0

921.0

26.8

17.3

100.0

1,180.0

1,257.0

37.4

15.3

100.0

1,270.0

2,197.0

10.8

0.0

90.0

264.0

808.0

– 11.4

0.0

100.0

750.0

1,243.0

–0.5

0.0

100.0

1,007.0

2,128.0

–0.1

0.0

100.0

628.0

917.0

26.8

17.9

100.0

1,269.0

1,234.0

58.9

24.4

100.0

2,072.0

2,330.0

112.8

35.9

aThe scenarios are.

1 Continuation of the 1981 farm bill and 1963 Federal Income tax provisions

2 A 20-percent acreage reduction In 1986-92

3 No farm program payment limitation in 1963-92

4 No price supports and no deficiency payment In 1983-92

5 No target price/deficiency payment in 1963-92

6 Target farm program benefits to farms that produce less than $300,000 in program crops

7 No farm program in 1963-92

8. Reduced income tax benefits and the base farm program

The Impact of price supports can be derived by subtracting scenario 5 from scenario 6.

The Impact of Income supports can be derived by subtracting scenario 6 from scenario 1.

The Impact of Income supports with a $50,000 payment Imitation can be found by subtracting scenario 6 from scenario 4

SOURCE Off Ice of Technology Assessment.

Table E-9.—Comparison of Selected Financial Bailout Scenarios for Three Representative

Corn-Soybean Farms in East Central Illinoisa

Alternative scenarios for Alternative scenarios for Alternative scenarios for

— 640-acre farm 982-acre farm 1,630-acre farm

Criteria 9 10 11 9 10 11 9 10 11

Probability of survival . . . . . . . . . . . . . . . 80.0 72.0 84.0 88.0 80.0 90.0 100.0 100.0 100.0

Present value of ending net worth

($1,000) . . . . . . . . . . . . . . . . . . . . . . . . . . 271.0 291.0 299.0 579.0 588.0 654.0 822.0 872.0 831.0

Ending farm size (acres) . . . . . . . . . . . . . 653.0 689.0 662.0 1,046.0 1,062.0 1,073.0 1,795.0 1,740.0 1,712,0

Annual net farm income ($1 ,000) . . . . . . –0.9 –3.3 3.8 2.0 –3.5 7.8 30.6 27.9 36.9

Annual Government payment ($1,000) . . 8.9 8.9 9.1 19.2 18.9 19.0 23.0 22.8 22.8

aThe scenartos are

9 Contlnuatlon of the 1981 farm bill and the 1963 Federal tax provisions for a highly !everaged farm

10 Restructure of debt for a highly leveraged farm.

11 Interest rate subsidy (buy-down) In the first 2 years for a highly !everaged farm

SOURCE Off Ice of Technology Assessment
344 . Technology, Public Policy, and the Changing Structure of American Agriculture

Table E-10.—Comparison of Selected Financial Bailout Scenarios for Three Representative

Irrigated Row Crop Farms in South Central Nebraskaa

Alternative scenarios for Alternative scenarios for Alternative scenarios for

672-acre farm 920-acre farm 2,083-acre farm

Criteria 9 10 11 9 10 11 9 10 11

Probability of survival . . . . . . . . . . . . . . . 96.0 86.0 98.0 100.0 100.0 100.0 100.0 100.0 100.0

Present value of ending net worth

($1,000) . . . . . . . . . . . . . . . . . . . . . . . . . . 353.0 334.0 387.0 871.0 876.0 893.0 1685.0 1820.0 1714.0

Ending farm size (acres) . . . . . . . . . . . . . 822.0 822.0 854.0 1,195.0 1,146.0 1,205.0 2,399.0 2,392.0 2,421.0

Annual net farm income ($1,000) . . . . . . 5.9 2.9 11.3 22.6 16.7 28.2 58.9 77.2 72.1

Annual Government payment ($1 ,000) . . 16.7 16.8 17.0 23.0 22.6 22.9 36,0 36.0 36.1

aThe scenarios are:

9 Contlnuatlon of the 1981 farm bill and the 1983 Federal tax provisions for a highly leveraged farm

10 Restructure of debt for a highly leveraged farm

11 Interest rate subsidy (buy-down) In the first 2 years for a highly leveraged farm

SOURCE Off Ice of Technology Assessment

Table E-11 .— Comparison of Selected Policy Scenarios Assuming No New Technology for

Three Representative Corn. Soybean Farms in East Central Illinoisa

Alternative scenarios for Alternative scenarios for Alternative scenarios for

640-acre farm 982-acre farm 1,630-acre farm

Criteria 12 13 14 12 13 14 12 13 14

Probability of survival . . . . . . . . . . . . . . . 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 98.

Present value of ending net worth

($1,000) . . . . . . . . . . . . . . . . . . . . . . . . . . 699.0 589.0 561.0 862.0 604.0 540.0 915.0 694.0 672.0

Ending farm size (acres) . . . . . . . . . . . . . 902.0 837.0 850.0 1,392.0 1,190.0 1,116.0 1,899.0 1,801.0 1,796.0

Annual net farm income ($1,000) . . . . . . 23.0 11.7 10.8 23.9 3.3 -0.8 25.3 9.8 6.1

Annual Government payment ($1,000) . . 11.6 0.7 0.0 22.9 1.0 0.0 22.9 1.7 0.0

aThe scenarios are”

12 Continuation of the 1981 farm bill and the 1983 Federal tax provisions for a highly leveraged farm

13 Restructure of debt for a highly leveraged farm

14 Interest rate subsidy (buy-down) In the first 2 years for a highly leveraged farm

SOURCE” Off Ice of Technology Assessment

Table E-12.— Comparison of Selected Policy Scenarios Assuming No New Technology

Three Representative Irrigated Row Crop Farms in South Central Nebraskaa

for

Alternative scenarios for Alternative scenarios for Alternative scenarios for

672-acre farm 920-acre farm 2,085-acre farm

Criteria 12 13 14 12 13 14 12 13 14

Probability of survival . . . . . . . . . . . . . . . 100.0 100,0 90.0 100.0 100.0 100.0 100.0 100.0 100.0

Present value of ending net worth

($1,000) . . . . . . . . . . . . . . . . . . . . . . . . . . 670.0 475,0 263.0 1,230.0 985.0 671.0 1,812.0 1,388.0 680.0

Ending farm size (acres) , . . . . . . . . . . . . 921.0 870.0 808.0 1,257.0 1,221.0 1,226.0 2,402.0 2,240.0 2,107.0

Annual net farm income ($1,000) . . . . . . 26.7 10.6 – 11.4 53.9 30.3 –2.6 77.5 51.0 – 10.9

Annual Government payment ($1 ,000) . . 17,3 0.9 0.0 23,9 1.3 0.0 35.7 3.0 0.0

aThe scenanos are

12 Contlnuatlon of the 1981 farm bill and the 1983 Federal tax provisions, assuming no.new-technology scenario

13 No target price deflc!ency payment program, assuming no.new-technology scenario.

14 Deficiency plus diversion payments and any other Government payments received for Government loans and storage costs

SOURCE Office of Technology Assessment
App. E—Methodology and Detailed Results of Macroeconomic Impacts of Technology and Public Policy for Crop Farms ● 345

General Crop Farms in the Delta Region

of Mississippi

All three representative farms had a It) O-percent

probability of survival under the entire range of policy alternatives in that equity in land and machinery did not fall below 30 and 35 percent, respectively

(table E-13). One of the principal reasons for the solvency of these farms over the 10-year planning horizon was the availability of off-farm income to meet

some of the cash flow needs. The remaining criteria

in table E-I3 are indicative of farm size, wealth, and

financial characteristics that are projected to occur

on these representative farms over the 10-year simulation under each policy alternative.

The present value of ending net worth is one measure of real wealth accumulation. In comparing the

policy scenarios for each size of farm, substantial

greater growth in real net worth occurs on the representative farms under conditions that continue

current farm commodity policy and income tax provisions with and without acreage reductions and

farm program payments limitation (scenarios 1 to

3) and with a more restrictive set of income tax provisions (scenario 8). For the 1,443-acre farm real net

worth increases by 105 to 151 percent under these

program alternatives. The largest rate of growth in

real net worth (a 151-percent increase from the initial situation) occurs for the alternative that continues the 1981 farm bill provisions, but with no farm

program payments limitations (scenario 3). A policy that continues the current farm program but with

a 20-percent acreage reduction in 1986 to 1992 results in a 135-percent growth in real net worth. Much

lower growth rates in real net worth occur for the

policy alternatives that eliminate various provisions

of the current farm program, withdraws all farm program support, or targets the benefits to farms producing less than $300,000 of program crops. Similar patterns are evident in the effects of the policy

alternatives on rates of growth in real net worth of

the 3,119-acre farm and the 6,184-acre farm,

A second noticeable pattern is the decline in the

growth rate in real wealth as the size of the representative farm increases from the 1,443-acre farm

to the 6,184-acre farm for each of the policy alternatives, Comparisons among the different farm sizes

must be made with caution because the initial total

Table E-13.—Comparison of Selected Farm Commodity and Income Tax Policy Scenarios on

Representative General Crop Farms in the Delta of Mississippi

Alternative scenarios

Criteria 1 2 3 4 5 6 7 8

Moderate size (1,443 acres):

Probability of survival. . . . . . . . . . . . . . . . . . . . . . . . 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0

Present value of ending net worth ($1,000) . . . . . 1,651.0 1,757.0 1,881.0 1,106.0 1,134.0 1,059.0 1,070.0 1,533.0

Ending farm size (acres) . . . . . . . . . . . . . . . . . . . . . 2,009.0 2,057.0 2,093.0 1,625.0 1,645.0 1,581.0 1,590.0 1,913.0

Annual net farm income ($1 ,000) . . . . . . . . . . . . . . 38.9 40.4 64.6 – 14.2 –6.9 – 16.3 – 17.6 29.9

Annual Government payment ($1,000) . . . . . . . . . . 48.2 45.2 75.4 1.9 1.9 0.0 0.0 47.9

Large size (3,119 acres):

Probability of survival. . . . . . . . . . . . . . . . . . . . . . . . 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0

Present value of ending net worth ($1,000) . . . . . 2,940.0 3,280.0 4,418.0 2,482.0 2,537.0 2,433.0 2,454.0 3,139.0

Ending farm size (acres) . . . . . . . . . . . . . . . . . . . . . 3,327.0 3,340.0 3,877.0 3,119.0 3,135.0 3,119.0 3,119.0 3,135.0

Annual net farm income ($1,000) . . . . . . . . . . . . . . 38,3 65.1 148.0 –20.6 –8.2 –28.9 –25.1 21.8

Annual Government payment ($1,000) . . . . . . . . . . 49.9 49.1 160.6 4.7 4.8 0.0 0.0 49.9

Very large size (6,184 acres):

Probability of survival. . . . . . . . . . . . . . . . . . . . . . . . 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0

Present value of ending net worth ($1,000) . . . . . 5,450.0 6,116.0 7,728.0 5,135.0 5,175.0 4,984.0 5,079.0 5,902.0

Ending farm size (acres) . . . . . . . . . . . . . . . . . . . . . 6,248.0 6,254.0 6,530.0 6,270.0 6,245.0 6,242.0 6,267.0 6,203.0

Annual net farm income ($1,000) . . . . . . . . . . . . . . 41.9 118.2 227.1 – 19.7 –0.6 –42.9 –32.4 5.9

Annual Government payment ($1,000) . . . . . . . . . . 49.9 49.8 278.0 7.9 0.0 0.0 0.0 49.9

a

The scenar(os are’

1. Continuation of the 1981 farm bill and 1983 Federal tncome provisions.

2 A 20-percent acreage reduction in 1986-92.

3. No farm program Payment limitation in 1983-92

4. No price supports and no deficiency payment in 1983-92.

5 No target price/deficiency payment in 1963-92.

6 Target farm program benef!ls to farms that produce less than $300,000 in program crops

7 No farm program in 1983-92.

8 Reduced income tax benefits and the base farm program

The impact of price supports can be derived by subtracting scenario 5 from scenario 6

The impact of Income supports can be derived by subtracting scenario 6 from scenario 1

The impact of Income supports with a $50,000 payment I!mitatlon can be found by subtracting scenario 6 from scenario 4

SOURCE: Office of Technology Assessment
346 . Technology, Public Policy, and the Changing Structure of American Agriculture

equity to asset ratios differ. However, the results indicate that the policy alternatives involving farm program payments (scenarios 1 to 3 and scenario 8) induced a greater growth rate in real wealth on the

moderate-size farm as compared with the two larger

farms.

This pattern of growth is even more evident when

examining changes in farm acreage. The 1,443-acre

farm experienced considerable growth in both owned

land acreage and/or acreage leased under scenarios

1 to 3 and scenario 8. In contrast the two larger farms

exhibited less than 7-percent growth in farm size under these scenarios, with the exception of the 3,119-

acre farm under scenario 3 wherein payments limitations are removed. The 1,443-acre farm experienced a 10- to 14-percent increase in acreage

whereas the two larger farms exhibited virtually no

growth in farm acreage for the policy alternatives

involving elimination of some or all the program payments provisions and when program payments are

targeted to farms with less than $300,000 of program

commodity sales. These results indicate that farm

program payments are an important inducement to

growth of moderate-size general crops farms in the

Delta of Mississippi Region.

The two largest representative farms reduced a

substantial portion of the long-term real estate debt

under all scenarios. The 1,443-acre farm had a much

lower rate of long-term debt payback, principally because growth in farm size occurred through purchase of additional cropland under scenarios 1 to

3, and the use of accumulated cash to purchase machinery and equipment for expansion on leased land

under scenarios 4 to 8. The 1,443-acre farm generally exhibited a larger liquidation of its intermediateterm debt than the two larger farms for each of the

policy alternatives. Each of the representative farms

tended to use income from both farm and nonfarm

sources to pay back existing debts, and the ratio of

total equity to total assets increased appreciably on

each farm for all of the policy alternatives.

The three representative general crops farms in

the Delta of Mississippi Region are very dependent

on farm program payments in maintaining net farm

income. Policy alternatives involving relatively little

or no Government payments (scenarios 4 to 7) resulted in negative average annual net farm incomes.

All three farms had a 100-percent chance of remaining solvent and having a positive after-tax net

present value over the lo-year planning horizon for

each financial bailput alternative (table E-14).

Present value of ending net worth increased substantially on each farm with the largest rate of growth

occurring under the debt restructuring alternative.

Each representative farm expanded its acreage, both

through purchasing and leasing, with the smallest

farm exhibiting the most rapid rate of growth.

The highly leveraged crops farms in this region

exhibit characteristics that indicate survival and

growth under financial bailout policies. The implementation of debt restructuring and interest rate subsidy policy alternatives would appear to stimulate

substantial growth in farm acreage in this production region.

The no-new-technology scenarios had little effect

on the probability of having a positive after-tax net

present value on each farm (table E-15). It reduced

slightly the probability under the policy of “No Farm

Program.” The probabilities of having a positive

after-tax net present value did not change from the

most likely technology situation on the 1,443-acre

farm. The impacts of these modest technology driven

yield increases on product prices were not evaluated. Consequently, in the base farm policy scenario,

the moderate-size and large-size farms show small

improvement in annual net farm income as a result

of technological advance. The very large farm shows

a substantial increase in net farm income since the

technology adoption rate was much faster on this

size of farm.

Rates of growth in cropland purchases, leasing

and total farm acreage were almost identical under

the two technology situations for a given representative farm. However, the 1,443-acre farm exhibited

substantially higher growth rates in farm acreage

than the two larger farms. Also, the rates of payback

on long-term and intermediate-term loans under the

two technology situations were nearly identical for

a given representative farm.

These results indicate that the most likely technology changes projected for the Delta of Mississippi

Region are expected to have the greatest impact on

growth in real wealth and farm acreage of the 1,443-

acre farm, The 3,119-acre farm and the 6,184-acre

farm are expected to exhibit little growth in farm

acreage over the l0-year simulation period. The economic impact expected from new technology is

rather minimal compared with the economic impact

from changing the farm commodity price and income support programs.
App. E—Methodology and Detailed Results of Macroeconomic Impacts of Technology and Public Policy for Crop Farms ● 347

Table E-14.—Comparison of Selected Financial Bailout Scenarios for Three Representative

General Crop Farms in the Delta of Mississippi

Alternative scenarios for Alternative scenarios for Alternative scenarios for

1,443-acre farm 3,1 19-acre farm 6,184-acre farm

Criteria 9 10 11 9 10 11 9 10 11

Probability of survival . . . . . . . . . . . . . . . . . . 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0

Present value of ending net worth

($1,000) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1,563.0 1,656.0 1,545.0 3,237.0 3,431.0 2,968.0 5,259.0 5,840.0 4,990.0

Ending farm size (acres) . . . . . . . . . . . . . . . . 2,109.0 2,115.0 2,025.0 3,845.0 4,719.0 3,685.0 6,606.0 7,656.0 6,453.0

Annual net farm income ($1,000) . . . . . . . . . 35.5 29.4 37.7 30.1 20.4 33.8 3.7 –14.8 5.4

Annual Government payment ($1,000). . . . . 48.4 48.4 48.3 49.9 49.9 49.9 49.9 49.9 49.9

aThe scenarios are:

9. Continuation of the 1981 farm bill and the 1983 Federal tax provisions for a highly leveraged farm

10. Restructure of debt for a highly leveraged farm.

11. Interest rate subsidy (buy-down) in the first 2 years for a highly leveraged farm.

SOURCE Office of Technology Assessment.

Table E-15.—Comparison of Selected Policy Scenarios Assuming No New Technology for

Three Representative General Crop Farms in the Delta of Mississippi

Alternative scenarios for Alternative scenarios for Alternative scenarios for

1,443-acre farm 3,1 19-acre farm 6,184-acre farm

Criteria 12 13 14 12 13 14 12 13 14

Probability of survival . . . . . . . . . . . . . . . . . . 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0

Present value of ending net worth

($1,000) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1,513.0 1,104.0 1,043.0 2,786.0 2,451.0 2,354.0 2,286.0 4,915.0 4,715.0

Ending farm size (acres) . . . . . . . . . . . . . . . . 2,006.0 1,638.0 1,587.0 3,343.0 3,148.0 3,119.0 6,322.0 6,277.0 6,261.0

Annual net farm income ($1,000) . . . . . . . . . 38.6 –7.3 – 18.3 34.0 – 11.9 –29.9 15.1 –27.5 –57.7

Annual Government payment ($1,000). . . . . 48.2 1.9 0.0 49.9 4.8 0.0 49.9 7.9 0.0

aThe scenarios are:

12. Continuation of the 1981 farm bill and the 1983 Federal tax provisions, assuming no-new-technology scenario.

13. No target price deficiency payment program, assuming no-new-technology scenario,

14 Deficiency plus diversion payments and any other Government payments received for Government ioans and storage costs

SOURCE: Office of Technology Assessment.
Appendix F

Detailed Results of Macroeconomic

Impacts of Technology and

Public Policy on Dairy Farms

Chapter 9 presented the summary results of the

macroeconomic impacts of public policies and technology on the viability of various size dairy farms.

This appendix discusses in more detail the specific

results of the analysis by area and size of farm. For

more detail than is provided in this appendix the

reader is advised to read the background paper on

which this analysis is based. The paper is published

in a separate volume to this report.

As with the crop farms analyses, the first step was

to describe representative farms that included moderate, large, and very large farms in each production area. The second step involved a simulation

of the representative farms using a Monte-Carlo,

whole farm simulation model (FLIPSIM V) under

alternative farm policy, income tax, finance, and

technology scenarios, This model is described in

appendix E.

Results for Alternative Policy

and Technology Scenarios

Minnesota Dairy-52 Cows

Given the base policy scenario, the 52-cow Minnesota dairy had a 74-percent chance for survival

after 10 years, but only a 26-percent chance of having a positive after-tax, net present value (table F1). Average present value of ending net worth decreased from the initial $417,000 to $240,000 after

10 years. Total debt increased $199,000, and the

equity-to-asset ratio declined from 0.71 to 0.44 over

the lo-year period, Average annual net farm income was a –$22,000.

Both policy alternatives involving crop programs

that increase feed prices (scenario 1) and the noTable F-1.–Comparison of Selected Policy Scenarios on a 52-Cow Minnesota Dairy

Alternative scenarios

Initial

Criteria situation Base 1 2 3 4 5 6 7 8

Probability of survival

(percent) . . . . . . . . . . . . . . . . . 58 74 62 74 58 22 74 92 50 62

Probability of positive net

present value (percent). . . . . NA 26 22 26 18 8 24 38 20 22

After-tax net present value

mean ($1,000). . . . . . . . . . . . . NA –61 –89 –61 – 100 – 198 –62 13 – 103 –88

Average present value of

ending net worth ($1,000) . . 417 240 213 240 202 114 238 310 191 214

Total debts after 10 years

($1,000) . . . . . . . . . . . . . . . . . . 168 367 392 367 403 443 370 309 400 391

Average ending equity ratio

(fraction) . . . . . . . . . . . . . . . . . 0.71 0.44 0.39 0.44 0.37 0.22 0.43 0.55 0.35 0.39

Average internal rate of return

(fraction) . . . . . . . . . . . . . . . . . NA –0.02 –0.03 –0.02 –0.04 –0.09 –0.02 0.01 –0.04 –0.03

Average annual net farm

income ($1,000) . . . . . . . . . . . NA –22 –25 –22 –27 –38 –27 –14 –26 –25

%he scenarios are:

Base—sea chapter 9.

1—A 20-percent acreage reduction crop program—9-percent higher feed costs.

2—No crop program

3—Fifty cents-per-hundredweight lower milk price.

4—No dairy price su~rt program.

5—Raduce income tax benefit program.

6—Milk supply control program.

7—No information technology,

8—No bovine growth hormone technology.

NA=Not applicable.

SOURCE: Office of Technology Assessment.

348
App. F—Methodology and Detailed Results of Macroeconomic Impacts of Technology and Public Policy for Crop Farms • 349

Table F-2.—Comparison of Selected Policy Scenarios on a 125-Cow Minnesota Dairy

Alternative scenarios

Initial

Criteria situation Base 1 2 3 4 5 6 7 8

Probability of survival

(percent) . . . . . . . . . . . . . . . . .

Probability of positive net

present value (percent). . . . .

After-tax net present value

mean ($1 ,000) . . . . . . . . . . . . .

Average present value of

ending net worth ($1 ,000) . .

Total debts after 10 years

($1,000) . . . . . . . . . . . . . . . . . .

Average ending equity ratio

(fraction) . . . . . . . . . . . . . . . . .

Average internal rate of return

(fraction) . . . . . . . . . . . . . . . . .

Average annual net farm

income ($1 ,000) . . . . . . . . . . .

NA 100 100 100 100

NA 96 88 96 86

NA 369 312 369 283

969 1,120 1,072 1,119 1,049

302 154 208 154 235

0.76 0.89 0.86 0.89 0.85

NA 0.05 0.04 0.05 0.04

NA 20 12 20 8

a

The scenarios are

Base—see chapter 9

1—A 20.percent acreage reduction crop program—9 percent higher feed costs

2—No crop program

3—Fffty cents.per-hundredweight lower m(ik prtce

4—No dairy price suport program

5—Reduce Income tax benefit program

6—Milk supply control program

7—No information technology

8—No bovtne growth hormone technology

NA = Not applicable

SOURCE Off Ice of Technology Assessment

crop program that increases feed price variability

(scenario 2) had little impact on the 52-cow Minnesota dairy because most of the feed is raised at

the dairy rather than purchased. Crop yields were

fixed, not variable, from year to year. However,

simulations where crop yields were stochastic did

not significantly change the results.

A support price 50 cents below the price in the

base scenario reduced the probability of survival

to 58 percent (scenario 3). The decline in the

present value of ending net worth was more adversely affected, and debt was $36,000 higher compared with the results from the base scenario after

10 years.

Eliminating the price-stabilizing aspects of the

dairy price support program (scenario 4) resulted

in a probability of survival of 22 percent for the 52-

cow Minnesota dairy. The equity-to-asset ratio was

0.22 after 10 years.

Eliminating income tax benefits (scenario 5) did

not change the probability of survival but did adversely affect the present value of ending net worth

and total debt when compared with the base scenario.

A supply control program (scenario 6) increased

the probability of survival to 92 percent, and the

equity-to-asset after 10 years increased from 0.44

to 0.55. Present value of ending net worth declined

98 100 100

44 94 98

59 360 461

835 1,083 1,190

518 197 93

0.67 0.87 0.93

0.01 0.05 0.05

–21 3 33

100 100

54 82

104 235

869 1,007

459 289

0.70 0.81

0.02 0.03

–15 2

to $310,000 after 10 years. Total debt increased but

less than that under the base scenario. The supply

control scenario generally was more favorable than

the base scenario for the 52-cow Minnesota dairy,

The probability of survival for the 52-cow Minnesota dairy would decline to 50 percent after 10

years if productivity gains from information and

nutrition technology do not materialize. The probability of survival without bovine growth hormone

would decline to only 62 percent because this size

dairy would adopt this technology in 1989, near the

end of the 10-year period.

Minnesota Dairy- 125 Cows

Given the base policy scenario, the 125-cow Minnesota dairy had a 100-percent chance of surviving 10 years and a 96-percent chance of having a

positive after-tax net present value (table F-2). The

present value of ending net worth increased slightly

from an initial $969,000 to $1,120,000 at the end

of the 10-year period. Both long-term and intermediate-term debt were reduced, and the equity ratio increased from an initial 0.76 to 0.89 by the end

of the 10 years.

Like the effects on the 52-cow Minnesota dairy,

policies that increase the level or variability of feed
350 • Technology, Public Policy, and the Changing Structure of American Agriculture

prices have little impact on the financial performance (scenarios 1 and 2) of the 125-cow dairy. Because this dairy produces most of its own feed requirement, it is insulated from short-run variations

in feed costs.

A support price lower by 50 cents per hundredweight reduced the present value of ending net

worth from $1,120,000 under the base scenario to

$1,049,000 (scenario 3). The total equity ratio was

0.85.

A dairy support program (scenario 4) reduced the

probability of a positive after-tax present value to

44 percent. Both long-term and intermediate-term

debt was $216,000 higher than under the base scenario,

Eliminating the income tax benefits (scenario 5)

for the 125-cow Minnesota dairy resulted in higher

debts and lower present value of ending net worth,

equity ratio, and average annual net farm income

after 10 years, compared with the results under the

base scenario.

A supply control program (scenario 6) increased

the probability of a positive after-tax net present

value to 98 percent, compared with 96 percent under

the base scenario. Under both the base and supply

control scenarios, the 125-cow dairy had a 100

percent chance of surviving the 10-year period. The

dairy showed good financial progress under the supply control program as the present value of ending

net worth was $1,190,000. The total equity ratio was

0.93 at the end of the 10-year period. Average annual net farm income for the lo-year period was

$33,000, up from $20,000 under the base scenario.

The probability of survival would remain at 100

percent without either information or bovine growth

hormone technology. However, the probability of

a positive net present value, average present value

of ending net worth, and other financial performance measures were more adversely affected than

under the base scenario.

Arizona Dairy-359 Cows

Given the base policy scenario, the 359-cow Arizona dairy had a 96 percent chance of survival and

a 96 percent change of a positive after-tax net present

value for the 10-year period (table F-3). The dairy

showed good financial improvement over the 10

years as present value of ending net worth was

$1,296,000 compared with $744,000 at the beginning

of the period, most debt was paid, and total equity

ratio increased from 0.71 to 0.93. Average annual

net farm income was $14,000.

All feed is purchased by the 359-cow Arizona

dairy, and the 9 percent increase in feed cost (scenario 1) reduced the probability of survival to 80 perTable F-3.—Comparison of Selected Policy Scenarios on a 359-COW Arizona Dairy

Alternative scenarios

Initial

Criteria situation Base 1 2 3 4 5 6 7 8

Probability of survival

(percent) . . . . . . . . . . . . . . . . . NA 98 80 98 94 42 98 96 92 94

Probability of positive net

present value (percent). . . . . NA 96 72 96 90 26 96 96 90 90

After-tax net present value

mean ($1,000) . . . . . . . . . . . . . NA 829 172 822 593 –326 812 1,134 543 592

Average present value of

ending net worth ($1,000) . . 744 1,296 768 1,288 1,120 276 1,247 1,486 1,028 1,107

Total debts after 10 years

($1,000) . . . . . . . . . . . . . . . . . . 298 41 254 43 72 603 42 26 82 78

Average ending equity ratio

(fraction) . . . . . . . . . . . . . . . . . 0.71 0.93 0.71 0.93 0.91 0.26 0.93 0.93 0.90 0.91

Average internal rate of return

(fraction) . . . . . . . . . . . . . . . . . NA 0.09 0.01 0.09 0.06 –0.15 0.09 0.11 0.06 0.07

Average annual net farm

income ($1 ,000) . . . . . . . . . . . NA 14 –64 13 –14 –121 6 54 –21 –19

%he scenarios are:

Base—see chapter 9.

1—A 20-percent acreage reduction crop program—9-percent higher feed costs.

2—No crop program

3—Fifty cents-per-hundredweight lower milk price.

4—No dairy price suport program.

5—Reduce income tax benefit program.

6—Milk supply control program.

7–No information technology.

8—No bovine growth hormone technology.

NA=Not applicable.

SOURCE: Office of Technology Assessment.
App. F—Methodology and Detailed Results of Macroeconomic Impacts of Technology and Public Policy for Crop Farms ● 351

cent, compared with 96 percent under the base scenario. However, the dairy still made good financial

progress as debts were reduced, and the present

value of ending net worth was $768,000, compared

with $744,000 at the beginning of the period. The

no-crop-program scenario (scenario 2) increased the

variability of feed prices but reduced the financial

progress of the dairy relatively little.

A support price 50 cents below the base scenario

(scenario 3) prices reduced the probability of survival and resulted in a lower present value of ending net worth. Total equity-to-asset ratio still increased to 0.91, 2 percentage points less than that

of the base scenario.

Eliminating the dairy support program (scenario

4) reduced the probability of survival to 42 percent.

The after-tax net present value was –$326. The

present value of ending net worth was $1,020,000

less than under the base and more than 60 percent

lower than at the beginning of the period. Debt was

tripled, and the total equity-to-asset ratio decreased

from 0.71 at the beginning of the period to 0.26 at

the end of the 10 years, Average annual net farm

income was –$121,000, compared with $14,000 under the base scenario.

Eliminating income tax advantages (scenario 5)

slightly reduced the present value of ending net

worth but had little effect on the probability of survival, remaining debt, and ending total equity-toasset ratio.

With supply control (scenario 6) the probability

of survival was 96 percent. Present value of ending

net worth was $190,000 higher at the end of the 10-

year period, Average annual net farm income was

$54,000.

The financial progress under either the no information technology (scenario 7) or no bovine growth

hormone (scenario 8) was somewhat less than under the base scenario that included both these technologies. The average annual net farm income became negative, and the probability of survival

declined to 92 percent for the no information scenario and to 94 percent for the no bovine growth

hormone scenario.

California Dairy-550 Cows

Given the base policy scenario, the 550-cow California dairy had a 96 percent probability of survival

and positive net present value (table F-4). The dairy

showed good financial improvement over the 10-

year period under the base scenario. Present value

of ending net worth increased from $1,261,000 at

the beginning to $2,055,000 at the end of the 10-year

Table F-4.-Comparison of Selected Policy Scenarios on a 550-COW California Dairy

Alternative scenarios

Initial

Criteria situation Base 1 2 3 4 5 6 7 8

Probability of survival

(percent) . . . . . . . . . . . . . . . . . NA 98 80 98 94 62 98 98 88 94

Probability of positive net

present value (percent). . . . . NA 96 58 98 88 32 98 96 80 96

After-tax net present value

mean ($1 ,000) . . . . . . . . . . . . . NA 1,178 157 1,169 798 –292 1,157 1,682 367 659

Average present value of

ending net worth ($1,000) . . 1,261 2,055 1,267 2,045 1,792 799 1,971 2,349 1,380 1,672

Total debts after 10 years

($1,000) . . . . . . . . . . . . . . . . . . 464 105 405 110 157 739 109 92 300 185

Average ending equity ratio

(fraction) . . . . . . . . . . . . . . . . . 0.71 0.92 0.73 0.92 0.90 0.52 0.93 0.92 0.81 0.89

Average internal rate of return

(fraction) . . . . . . . . . . . . . . . . . NA 0.08 0.00 0.08 0.06 –0.06 0,08 0.10 0.02 0.05

Average annual net farm

income ($1,000) . . . . . . . . . . . NA 10 –117 9 –35 –16 – 5 76 –88 –57

‘The scenarios are:

Base—see chapter 9.

1—A 20-percent acreage reduction crop program—9-percent higher feed costs.

2—No crop program

3—Fifly cents-per-hundredweight lower milk price.

4—No dairy price suport program.

5—Reduce income tax benefit program.

6—Milk supply control program.

7—No information technology.

8—No bovine growth hormone technology.

NA = Not applicable,

SOURCE: Office of Technology Assessment,
352 . Technology, Public Policy, and the Changing Structure of American Agriculture

period. Total debts were greatly reduced, and total

equity-to-asset ratio increased from 0.71 to 0.92 over

the lo-year period.

The crop acreage reduction policy (scenario 1),

resulting in a 9 percent increase in feed costs, reduced the probability of survival to 80 percent and

the probability of positive after-tax net present value

to 58 percent. The no-crop-program scenario (scenario 2) had relatively little impact on the 550-cow

California dairy.

A milk support price 50 cents lower (scenario 3)

than under the base scenario reduced the probability of survival to 94 percent and the present value

of ending net worth to $1,792,000. The total equityto-asset ratio after 10 years was 0.90.

The no dairy price support program (scenario 4)

reduced the probability of survival to 62 percent,

and reduced the present value of ending net worth

to $799,000. The total equity-to-asset ratio was 0.52

after 10 years.

Eliminating income tax advantages (scenario 5)

had little impact on the financial performance of the

550-cow California dairy relative to the base scenario. The total debts and the total equity-to-asset

ratio was about the same asunder the base scenario,

A mandatory supply control program (scenario 6)

resulting in a milk price $1 per hundredweight

higher than in the base scenario had a favorable impact on the 550-cow California dairy. Compared

with the base scenario the present value of ending

net worth was $2,349,000, or 14 percent higher, and

the after-tax net present value was $1,682,000, or 43

percent higher. The average annual net farm income

increased to $76,000.

The average present value of ending net worth and

the equity ratio increased from the beginning of the

period to the end even without information (scenario

7) or bovine growth hormone (scenario 8) technology. Also, total debt was reduced. However, both

the probability of survival and a positive net present

value declined, and average annual net farm income

became negative,

California Dairy-1,436 Cows

Given the base policy scenario, the 1,436-cow California dairy had a 98 percent probability of survival

and showed strong financial progress from the beginning to the end of the 10-year period (table F-5).

The present value of ending net worth was $7,332,000,

compared with $2,538,000 at the beginning of the

lo-year period. The total equity-to-asset ratio increased from 0.69 at the beginning of the period to

Table F-5.—Comparison of Selected Policy Scenarios on a 1,435-COW California Dairy

Alternative scenarios

Initial

Criteria situation Base 1 2 3 4 5 6 7 8

Probability of survival

(percent) . . . . . . . . . . . . . . . . . NA 98 98 98 98 98 98 100 96 98

Probability of positive net

present value (percent). . . . . NA 98 98 98 98 92 98 100 98 98

After-tax net present value

mean ($1,000) . . . . . . . . . . . . . NA 6,473 3,923 6,454 5,375 2,523 6,415 8,103 3,246 4,450

Average present value of

ending net worth ($1,000) . . 2,538 7,332 5,477 7,316 6,509 4,418 7,142 8,543 4,648 5,747

Total debts after 10 years

($1,000) . . . . . . . . . . . . . . . . . . 1,131 145 220 148 150 307 145 98 201 145

Average ending equity ratio

(fraction) . . . . . . . . . . . . . . . . . 0.69 0.94 0.92 0.94 0.94 0.91 0.94 0.95 0.94 0.95

Average internal rate of return

(fraction) . . . . . . . . . . . . . . . . . NA 0.15 0.08 0.16 0.15 0.07 0.16 0.19 0.10 0.12

Average annual net farm

income ($1,000) . . . . . . . . . . . NA 449 171 447 325 7 421 653 101 207

%he scenarios are:

Base-see chapter 9.

1—A 20-percent acreage reduction crop program—9-percent higher feed costs.

2—No crop program

3—Fifty cents-per-hundredweight lower milk price,

4—No dairy price suport program.

5—Reduce income tax benefit program.

6—Milk supply control program.

7—No information technology,

8—No bovine growth hormone technology.

NA = Not applicable.

SOURCE: Office of Technology Assessment.
App. F—Methodology and Detailed Results of Macroeconomic Impacts of Technology and Public Policy for Crop Farms ● 353

0.94 at the end. Total debt was cut by 87 percent from

the beginning to the end of the period.

The acreage reduction scenario, resulting in 9 percent higher feed prices, reduced the probability of

survival to 96 percent (scenario 1). Total equity-toasset ratio was 0.92.

Eliminating the crop program (scenario 2) had little impact on the financial position of the 1,436-cow

California dairy. Debt, equity-to-asset ratio, and

present value of ending net worth were all about the

same as under the base scenario.

A milk support price 50 cents lower (scenario 3)

than under the base scenario resulted in an 11 percent lower present value of ending net worth after

10 years. Total debt increased slightly, and the total

equity-to-asset ratio remained the same as the base.

Eliminating the dairy price support program (scenario 4) reduced the probability of survival to 96 percent. The present value of ending net worth was

$4,418,000, or 40 percent lower than under the base

scenario.

Eliminating the income tax advantages (scenario

5) did not affect the probability of survival, total debt,

or equity-to-asset ratio. However, the present value

of ending net worth decreased 2.6 percent from the

base scenario.

With supply control (scenario 6), the probability

of survival of the 1,436-cow California dairy increased to 100 percent. The present value of ending

net worth was $8,543,000, or about 17 percent higher

than under the base scenario. All intermediate-term

debt was paid, and only $96,000 of long-term debt

remained after 10 years under the supply control

program. Average annual net farm income increased

45 percent, to $653,000.

If the productivity gains associated with information on bovine growth hormone technologies do not

materialize, the financial performance will be adversely affected, but the probability of survival for

the 1,436-cow California dairy would remain about

the same. Average present value of ending net worth

was 37 percent lower without information and nutrition technology (scenario 7) and 22 percent lower

without bovine growth hormone technology (scenario 8).

Florida Dairies

Given the base policy scenario, the probability of

survival was 98 percent for the 350-cow and 100 percent for the 600-cow and 1,436-cow Florida dairies

(tables F-6, F-7, and F-8). Debt was reduced over the

lo-year period on all three dairies. The total equityto-asset ratio after 10 years was at least 0,84. The

present value of ending net worth increased 33 perTable F-6.—Comparison of Selected Policy Scenarios on a 350-Cow Florida Dairy

Alternative scenarios

Initial

Criteria situation Base 1 2 3 4 5 6 7 8

Probability of survival

(percent) . . . . . . . . . . . . . . . . . NA 96 58 98 86 36 94 98

Probability of positive net

present value (percent). . . . . NA 88 34 88 78 28 88 98

After-tax net present value

mean ($1 ,000) . . . . . . . . . . . . . NA 448 – 185 445 235 –305 425 663

Average present value of

ending net worth ($1 ,000) . . 757 1,004 463 1,002 825 317 936 1,164

Total debts after 10 years

($1,000) . . . . . . . . . . . . . . . . . . 304 198 577 198 300 679 226 130

Average ending equity ratio

(fraction) . . . . . . . . . . . . . . . . . 0.71 0.84 0.46 0.84 0.74 0.32 0.81 0.89

Average internal rate of return

(fraction) . . . . . . . . . . . . . . . . . NA 0.07 –0.04 0.07 0.03 –0.12 0.07 0.10

Average annual net farm

income ($1 ,000) . . . . . . . . . . . NA – 6 –83 – 6 –31 –97 – 9 25

aThe scenarios are

Base—see chapter 9

1—A 20-percent acreage reduction crop program—9-percent higher feed costs.

2–No crop program

3—Fifty cents-per-hundredweight lower milk price.

4—No dairy price suporf program

5—Reduce income tax benefit program.

6—Milk supply control program

7—No information technology

8—No bovine growth hormone technology

NA = Not applicable

SOURCE: Office of Technology Assessment,

86

66

154

706

376

0.68

0.03

–40

88

78

270

846

287

0.76

0.05

–28
354 ● Technology, Public Policy, and the Changing Structure of American Agriculture

Table F-7.–Comparison of Selected Policy Scenarios on a 600-COW” Florida Dairy

Alternative scenarios

Initial

Criteria situation Base 1 2 3 4 5 6 7 8

Probability of survival

(percent) . . . . . . . . . . . . . . . . .

Probability of positive net

present value (percent). . . . .

After-tax net present value

mean ($1,000) . . . . . . . . . . . . .

Average present value of

ending net worth ($1,000) . .

Total debts after 10 years

($1,000) . . . . . . . . . . . . . . . . . .

Average ending equity ratio

(fraction) . . . . . . . . . . . . . . . . .

Average internal rate of return

(fraction) . . . . . . . . . . . . . . . . .

Average annual net farm

income ($1,000) . . . . . . . . . . .

NA 100

NA 100

NA 1,617

1,465 2,453

468 116

0.76 0.94

NA 0.10

NA 83

96 100 100

80 100 100

602 1,612 1,281

1,778 2,455 2,255

280 116 124

0.86 0.94 0.94

0.06 0.10 0.09

–28 82 44

72 100 100 98 100

56 100 100 86 98

85 1,576 2,011 701 1,151

1,268 2,306 2,681 1,748 2,164

587 116 116 233 129

0.66 0.94 0.93 0.89 0.94

–0.04 0.11 0.12 0.06 0.08

–97 68 137 –24 23 . , .

%he scenarios are:

Base—see chapter 9.

1—A 20-percent acreage reduction crop program—9-percent higher feed costs.

2—No crop program

3—Fifty cents+ er.hundredweight lower milk price.

4—No dairy price suport program.

5—Reduce income tax benefit program.

6–Milk supply control program.

7—No information technology.

8—No bovine growth hormone technology.

NA= Not applicable.

SOURCE: Office of Technology Assessment.

Table F-8.—Comparison of Selected Policy Scenarios on a 1,436-COW Florida Dairy

Alternative scenarios

Initial

Criteria situation Base 1 2 3 4 5 6 7 8

Probability of survival

(percent) . . . . . . . . . . . . . . . . . NA 100 100 100 100 100 100 100 100 100

Probability of positive net

present value (percent). . . . . NA 100 100 100 100 100 100 100 100 100

After-tax net present value

mean ($1 ,000) . . . . . . . . . . . . . NA 8,396 6,020 8,387 7,501 4,712 8,182 9,413 5,046 6,667

Average present value of

ending net worth ($1,000) . . 3,343 9,257 7,560 9,263 8,591 6,625 8,966 10,038 6,650 7,874

Total debts after 10 years

($1,000) . . . . . . . . . . . . . . . . . . 1,053 195 195 195 195 195 195 195 195 195

Average ending equity ratio

(fraction) . . . . . . . . . . . . . . . . . 0.76 0.95 0.95 0.95 0.95 0.95 0.95 0.95 0.96 0.96

Average internal rate of return

(fraction) . . . . . . . . . . . . . . . . . NA 0.16 0.13 0.16 0.15 0.11 0.16 0,17 0.12 0.14

Average annual net farm

income ($1,000) . . . . . . . . . . . NA 635 404 634 536 242 607 769 272 433

aThe scenarios are:

Base-see chapter 9.

1—A 20-percent acreage reduction crop program—9-percent higher feed costs.

2—No crop program

3—Fifty cents-per-hundredweight lower milk price.

4—No dairy price suport program.

5—Reduce income tax benefit program.

6—Milk supply control program.

7—No information technology,

8—No bovine growth hormone technology.

NA= Not applicable.

SOURCE: Office of Technology Assessment.
App. F—Methodology and Detailed Results of Macroeconomic Impacts of Technology and Public Policy for Crop Farms ● 355

cent on the 350-cow dairy, 67 percent on the 600-

cow dairy, and 177 percent on the 1,436-cow dairy.

The crop acreage reduction policy (scenario 1) reduced the probability of surviving the 10-year period

to 58 percent for the 350-cow dairy and to 96 percent for the 600-cow dairy. The probability of survival remained at 100 percent on the large 1,436-cow

Florida dairy.

Eliminating the crop program (scenario 2) had

relatively little impact on all three Florida dairies,

The probability of survival remained unchanged at

100 percent for the 600-cow and 1,436-cow dairies

and at 96 percent on the 350-cow dairy.

A milk support price 50 cents below the base (scenario 3) did not affect the probability y of survival for

the 1,436-cow dairy but reduced the probability of

survival for the 350-cow dairy to 86 percent. The

present value of ending net worth was about 18 percent lower for the 350-cow dairy, 8 percent lower

for the 600-cow dairy, and 7 percent lower for the

1,436-cow dairy.

The no-dairy-program scenario (scenario 4) reduced the probability of survival to 36 percent for

the 350-cow dairy and to 72 percent for the 600-cow

dairy. The probability of survival remained at 100

percent for the 1,436-cow dairy, The present value

of ending net worth was at least 28 percent less under the no-dairy-program scenario than under the

base scenario for all three Florida dairies,

Eliminating income tax advantages [scenario 5)

had relatively little impact on all three Florida

dairies. The present value of ending net worth was

about 7 percent less for the 350-cow dairy but only

3 percent 1ess for the 1,436-cow dairy. Total debt remained the same for the 1,436-cow dairy but was

somewhat higher for the smaller dairies.

A supply control policy (scenario 6) improved the

financial position of each of the dairies. Compared

with the base scenario, the present value of ending

net worth increased about 16 percent for the 350-

cow dairy, 9 percent for the 600-cow dairy, and 8

percent for the 1,436-cow dairy.

Like dairies in other regions, both no information

technology (scenario 6) and no bovine growth hormone technology (scenario 8) adversely affected the

financial positions of the Florida dairies after 10

years.

Minnesota Dairies

Given the high debt (scenario 9), the 52-cow Minnesota dairy had a zero-percent probability of survival over the 10-year period (table F-9). The probability of survival remained zero even with subsidized

interest (scenario 10) and restructuring debt (scenario 11), The same result was obtained for new entrant operators (scenarios 12, 13, and 14).

The 125-cow Minnesota dairy in an initial high

debt position had a 24 percent probability of survival

(scenario 9) (table F-10). A new entrant with high

debt also had a zero probability of survival (scenario

12). Therefore, there was a zero probability of survival under both higher feed costs (scenario 13) and

no dairy programs (scenario 14),

Table F-9.—Comparison of Selected Policy Scenarios on a 52. COW Minnesota Dairy,

Assuming High Debt and New Entrant Conditions

High debt New entrants

Initial Scenarios Initial Scenarios

Criteria situation 9 10 11 situation 1 2 13 14

Probability of survival (percent). . . . . . . . . . . . . NA o 0 0 NA o 0 0

Probability of positive net present value

(percent). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . NA o 0 0 NA o 0 0

After-tax net present value mean ($1,000) . . . . NA – 103 – 108 –99 NA – 103 – 103 – 102

Average present value of ending net worth

($1,000) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 246 104 98 109 264 143 144 145

Total debts after 10 years ($1,000) . . . . . . . . . . 340 423 424 419 466 505 503 504

Average ending equity ratio (fraction) . . . . . . . 0.42 0.21 0.20 0.22 0.36 0.23 0.23 0.23

Average internal rate of return (fraction) . . . . . NA –0.17 –0.19 –0.16 NA –0.11 –0.10 –0.10

Average annual net farm income ($1,000) . . . . NA –46 –42 –44 NA –90 –90 –91

aThe scenarios are:

9–Base–continuation of present dairy policy and assuming high debt,

10—Subsidize interest rate so that effective rate on all loans is 8 percent.

11 —Restructure debt.

12—Base policy and new entrant.

13—New entrant and no price support for dairy,

14—New entrant and a 9-percent increase in feed costs.

NA = Not applicable.

SOURCE: Office of Technology Assessment
356 • Technology, Public Policy, and the Changing Structure of American Agriculture

Table F-10.—Comparison of Selected Policy Scenarios on a 125-Cow Minnesota Dairy,

Assuming High Debt and New Entrant Conditions

High debt New entrants

Initial Scenarios Initial Scenarios

Criteria situation 9 10 11 situation 1 2 13 14

Probability of survival (percent). . . . . . . . . . . . . NA

Probability of positive net present value

(percent). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . NA

After-tax net present value mean ($1,000) . . . . NA

Average present value of ending net worth

($1,000) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 554

Total debts after 10 years ($1,000) . . . . . . . . . . 718

Average ending equity ratio (fraction) . . . . . . . 0.44

Average internal rate of return (fraction) . . . . . NA

Average annual net farm income ($1,000) . . . . NA

24

24

–80

341

910

0.30

–0.06

–51

16

16

– 147

280

943

0.25

–0.09

–58

28 NA o 0 0

28 NA o 0 0

–59 NA –237 –256 –241

351 575 291 274 292

906 963 1,016 1,026 1,021

0.31 0.37 0.23 0.22 0.24

–0.06 NA –0.15 –0.16 –0.16

–49 NA –117 – 121 – 121

aThe scenarios are:

9–Base–continuation of present dairy policy and assuming high debt.

10—Subsidize interest rate so that effective rate on all loans is 8 percent.

11 —Restructure debt.

12—Base policy and new entrant.

13—New entrant and no price support for dairy.

14—New entrant and a 9-percent increase in feed costs.

NA= Not applicable.

SOURCE: Office of Technology Assessment

Table F-11.—Comparison of Selected Policy Scenarios on a 359-Cow Arizona Dairy,

Assuming High Debt and New Entrant Conditions

High debt New entrants

Initial Scenarios Initial Scenarios

Criteria situation 9 10 11 situation 1 2 13 14

Probability of survival (percent). . . . . . . . . . . . . NA 66 70 68 NA 52 6 16

Probability of positive net present value

(percent). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . NA 66 70 68 NA 52 6 16

After-tax net present value mean ($1,000) . . . . NA 417 466 440 NA 270 –320 – 173

Average present value of ending net worth

($1,000) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 471 709 746 730 528 583 64 217

Total debts after 10 years ($1,000) . . . . . . . . . . 570 326 297 325 715 459 787 700

Average ending equity ratio (fraction) . . . . . . . 0.45 0.63 0.68 0.64 0.42 0.52 0.04 0.23

Average internal rate of return (fraction) . . . . . NA 0.01 0.03 0.02 NA –0.01 –0.22 –0.08

Average annual net farm income ($1,000) . . . . NA –59 –46 –56 NA – 109 – 191 –191

a

The scenarios are:

9—Base—continuation of present dairy policy and assuming high debt.

10—Subsidize interest rate so that effective rate on all loans is 8 percent.

11 —Restructure debt.

12—Base policy and new entrant

13—New entrant and no price support for dairy,

14—New entrant and a 9-percent increase in feed costs.

NA= Not applicable.

SOURCE: Office of Technology Assessment.

Arizona Dairy-359 Cows

Given the high debt base (scenario 9), the 359-cow

Arizona dairy had a 66-percent probability of survival and improved its financial position from the

beginning to the end of the lo-year period (table F11). The present value of ending net worth was

$709,000, compared with a $471,000 beginning net

worth. Total debt was reduced $244,000 over the 10-

year period. The total equity-to-asset ratio increased

from 0.45 at the beginning of the period to 0.63 at

the end.

Restructuring debt (scenario 11) increased the

probability of survival from 66 to 70 percent and improved the present value of ending net worth and

equity-to-asset ratio compared with that of the high

debt base (scenario 9).

A new entrant with high debt had a 52-percent

probability to survive over the lo-year period (scenario 12). A new entrant under the no-dairy-pricesupport program (scenario 13) had only a 6-percent

probability of survival. Increased feed costs (scenario 14) decreased a new entrant’s probability of

survival from 52 to 16 percent.
App. F—Methodology and Detailed Results of Microeconomic Impacts of Technology and Public Policy for Crop Farms ● 357

Florida Dairy-350 Cows

Given the high debt base (scenario 9), the 350-cow

Florida dairy had only a 34-percent probability of

survival (table F-12). Present value of ending net worth

was $375,000; beginning net worth was $466,000.

The total equity-to-asset ratio decreased from 0.44

at the beginning to 0.38 at the end of the 10-year

period.

The interest subsidy (scenario 10) increased the

probability of survival from 38 percent to 42 percent and increased the present value of ending net

worth from $375,000 to $382,000, compared with

the results of high debt base (scenario 9). The debt

restructuring policy (scenario 11) improved present

value of ending net worth for the 350-cow Florida

dairy compared with the results of high debt base

scenario (scenario 9). However, the improvement

was considerably less than under the interest subsidy scenario.

A new entrant with high debt had only a 22-percent

probability of survival (scenario 12). The probability of survival declined to 2 percent, given a 9-percent higher feed cost (scenario 13). The no-dairyprice-support program reduced the probabilities of

survival of a new entrant and a high debt dairy from

22 to 2 percent (scenario 14).

Table F-12.—Comparison of Selected Policy Scenarios on a 350-COW Florida Dairy,

Assuming High Debt and New Entrant Conditions

High debt New entrants

Initial Scenarios Initial Scenarios

Criteria situation 9 10 11 situation 1 2 13 14

Probability of survival (percent). . . . . . . . . . . . . NA 34 38 36 NA 22 2 2

Probability of positive net present value

(percent). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . NA 34 38 36 NA 22 2 2

After-tax net present value mean ($1,000) . . . . NA , 6 23 NA –75 –297 –263

Average present value of ending net worth

($1,000) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 466 375 382 392 527 326 116 181

Total debts after 10 years ($1,000) . . . . . . . . . . 594 609 609 602 773 741 897 897

Average ending equity ratio (fraction) . . . . . . . 0.44 0.38 0.39 0.39 0.41 0.31 0.10 0.17

Average internal rate of return (fraction) . . . . . NA –0.05 –0.04 –0.04 NA –0.05 –0.22 –0.03

Average annual net farm income ($1,000) . . . . NA –88 –76 –85 NA – 155 – 194 –21 1

%he scenarios are:

9–Base—continuation of present dairy policy and assuming high debt

10—Subsidize interest rate so that effective rate on all loans is 8 percent.

11 —Restructure debt.

12—Base policy and new entrant

13—New entrant and no price support for dairy

14—New entrant and a 9-percent increase in feed costs.

NA = Not applicable.

SOURCE Office of Technology Assessment.
Appendix G

Workgroup, Background Papers,

and Acknowledgments

Workgroup

Seven technical workgroups comprised of preeminent physical, biological, and social scientists

from the public and private sectors provided and/or

reviewed much of the technical information for this

study. OTA greatly appreciates the time and effort

of each member of these workgroups.

Animal Technology Workgroup

Eugene C. Allen

Professor and Associate Dean

College of Agriculture

University of Minnesota

St. Paul, MN

Howard L. Bachrach

Consultant

ARS-USDA

Plum Island Animal Disease Center

Greenport, NY

John B. Campbell

Professor

North Platte Agricultural Experiment Station

North Platte, NE

Stanley E. Curtis

Professor

Department of Animal Science

University of Illinois

Urbana, IL

James R. Fischer

Associate Director

Agricultural Experiment Station

Michigan State University

East Lansing, MI

William Hansel

Liberty Hyde Bailey Professor of

Animal Physiology

Department of Physiology

New York State College of Veterinary Medicine

Cornell University

Ithaca, NY

Bennie I. Osburn

professor of Pathology

Associate Dean of Research

School of Veterinary Medicine

University of California

Davis, CA

Wilson G. Pond

Research Leader

ARS-USDA

Roman L. Hruska U.S. Meat Animal Research

Center

Clay Center, NE

Plant Technology Workgroup

Joseph A. Berry

Staff Member

Plant Biology Department

Carnegie Institution of Washington

Stanford, CA

J.A. Browning

Chairman

Department of Plant Sciences

Texas A&M University

College Station, TX

Robert T. Fraley

Manager

Plant Molecular Biology Group

Monsanto Agriculture Products Co.

St. Louis, MO

George G. Kennedy

Associate Professor

Department of Entomology

North Carolina State University

Raleigh, NC

Chester G. McWhorter

Chief, Southern Weed Science Laboratory

Agricultural Research Service

Delta State Research Center

U.S. Department of Agriculture

Stoneville, MS

358
App. G—Workgroups, Background Papers, and Acknowledgments ● 359

Louis G. Nickell

Vice President of Research and Development

Velsicol Chemical Co.

Chicago, IL

Soil and Water Technology Workgroup

Martin Alexander

Professor of Soil Microbiology

Department of Agronomy

Cornell University

Ithaca, NY

Larry L. Boersma

Professor of Soil Physics

Department of Soil Science

Oregon State University

Corvallis, OR

Charles H. Davis

Director of Chemical Division

National Fertilizer Development Center

Tennessee Valley Authority

Muscle Shoals, AL

George R. Foster

Hydraulic Engineer

National Soil Erosion Lab

ARS-USDA

Purdue University

West Lafayette, IN

Charles A. Francis

Professor

Department of Agronomy

University of Nebraska

Lincoln, NE

Richard R. Harwood

Program Officer

Winrock International

Arlington, VA

Economics and Policy Workgroup

Boyd M. Buxton

Research Leader

ERS-USDA

Department of Agriculture and

Applied Economics

University of Minnesota

St. Paul, MN

Stephen C. Cooke

Economist

Department of Agriculture and

Applied Economics

University of Minnesota

St. Paul, MN

B.R. Eddleman

Professor

Department of Agricultural Economics

Mississippi State University

Mississippi State, MS

S.R. Johnson

Professor of Economics and Administrator

Center for Agricultural Rural Development

Iowa State University

Ames, IA

Ronald D. Knutson

Professor

Department of Agricultural Economics

Texas A&M University

College Station, TX

James W. Richardson

Associate Professor

Department of Agricultural Economics

Texas A&M University

College Station, TX

Wesley B. Sundquist

Professor

Department of Agriculture and

Applied Economics

University of Minnesota

St. Paul, MN

Agricultural Finance and

Credit Policy Workgroup

Peter J. Barry

Professor

Department of Agricultural Economics

University of Illinois

Urbana, IL

John R. Brake

Professor

Department of Agricultural Economics

Cornell University

Ithaca, NY

David A. Lins

Associate Professor

Department of Agricultural Economics

University of Illinois

Urbana, IL

Ronald Meekhof

Agricultural Economist

Economic Research Service

U.S. Department of Agriculture

Washington, DC
360 ● Technology, Public Policy, and the Changing Structure of American Agriculture

John Penson

Professor

Department of Agricultural Economics

Texas A&M University

College Station, TX

Rural Community

Frederick H. Buttel

Associate Professor

Department of Rural

Cornell University

Ithaca, NY

Workshop

Sociology

Cornelia Butler Flora

Professor

Department of Sociology, Anthropology, and

Social Work

Kansas State University

Manhattan, KS

Dean MacCannell

Professor

Department of Applied Behavioral Sciences

University of California

Davis, CA

Louis E. Swanson

Assistant Professor

Department of Sociology

University of Kentucky

Lexington, KY

J.C. van Es

Professor

Department of Agricultural Economics

University of Illinois

Urbana, IL

Agricultural Research and

Extension Policy Workgroup

George Hyatt

Past Director

Cooperative Extension Service

North Carolina State University

Raleigh, NC

Ronald D. Knutson

Professor

Department of Agricultural Economics

Texas A&M University

College Station, TX

Roy L. Lovvorn

Retired

North Carolina State University and U.S.

Department of Agriculture

Raleigh, NC

Fred C. White

Professor

Department of Agricultural Economics

University of Georgia

Athens, GA

Background Papers

This report was possible because of the valuable

information and analyses contained in the background reports commissioned by OTA. These papers were reviewed and critiqued by the workgroups,

advisory panel, and outside reviewers. The papers

are available in a separate five-part volume through

the National Technical Information Service. *

Part A: The Emerging Technologies

Alexander, Martin, “Biological Nitrogen Fixation,”

paper prepared for the Office of Technology Assessment, 1985.

Allen, Eugene C., “Regulation of Growth and Development,” paper prepared for the Office of

Technology Assessment, 1985.

Bachrach, Howard L., “Genetic Engineering in Animals,” paper prepared for the Office of Technology Assessment, 1985.

Battelle Columbus Laboratories, “Communication

and Information Management, ” paper prepared

for the Office of Technology Assessment, 1985.

Battelle Columbus Laboratories, “Crop Separation,

Cleaning, and Processing,” paper prepared for the

Office of Technology Assessment, 1985.

Battelle Columbus Laboratories, “Engine and

Fuels,” paper prepared for the Office of Technology Assessment, 1985.

Battelle Columbus Laboratories, “Labor Saving

Technology,” paper prepared for the Office of

Technology Assessment, 1985.

Battelle Columbus Laboratories, “Land Managemerit, ” paper prepared for the Office of Technology Assessment, 1985.

Battelle Columbus Laboratories, “Monitoring and

Control—Animal Agriculture,” paper prepared

for the Office of Technology Assessment, 1985.

● Request for “Technology, Public Policy, and the Changing Structure

of American Agriculture, Volume II: Background Papers” should be

directed to: National Technical Information Service, U.S. Department of

Commerce, 5285 Port Royal Rd., Springfield, VA 22161 (703) 487-4650.
App. G—Workgroups, Background Papers, and Acknowledgments ● 361

Battelle Columbus Laboratories, “Monitoring and

Control—Plant Agriculture,” paper prepared for

the Office of Technology Assessment, 1985.

Battelle Columbus Laboratories, “Telecommunication, ” paper prepared for the Office of Technology Assessment, 1985.

Berry, Joseph A., “Enhancement of Photosynthetic

Efficiency,” paper prepared for the Office of Technology Assessment, 1985.

Boersma, Larry, “Water and Soil-Water-Plant Relations, ” paper prepared for the Office of Technology Assessment, 1985.

Browning, J. A., “Plant Disease and Nematode Control,” paper prepared for the Office of Technology Assessment, 1985,

Campbell, J. B., “Livestock Pest Control,” paper prepared for the Office of Technology Assessment,

1985.

Curtis, Stanley E., “Environment and Animal Behavior,” paper prepared for the Office of Technology Assessment, 1985.

Davis, Charles H., “Chemical Fertilizers,” paper prepared for the Office of Technology Assessment,

1985.

Fischer, James R., “Crop Residue and Animal Waste

Use,” paper prepared for the Office of Technology Assessment, 1985,

Foster, George R., “Soil Erosion, Productivity, and

Tillage,” paper prepared for the Office of Technology Assessment, 1985.

Fraley, Robert T., “Genetic Engineering in Plants,”

paper prepared for the Office of Technology Assessment, 1985.

Francis, Charles A., ” Multiple Cropping,” paper prepared for the Office of Technology Assessment,

1985.

Hansel, William, “Animal Reproduction,” paper

prepared for the Office of Technology Assessment, 1985.

Kennedy, George G., “Management of Insects and

Mites, ” paper prepared for the Office of Technology Assessment, 1985.

Liebhardt, William, and Harwood, Richard, “Organic Farming,” paper prepared for the Office of

Technology Assessment, 1985.

McWhorter, C. G., and Shaw, W. C., “Present Status

and Future Needs in Weed Control, ” paper prepared for the Office of Technology Assessment,

1985.

Nickell, Louis G., “Plant Growth Regulators,” paper prepared for the Office of Technology Assessment, 1985.

Osburn, Bennie I., “Animal Disease Control,” paper prepared for the Office of Technology Assessment, 1985.

Pond, Wilson G., “Animal Nutrition, ” paper prepared for the Office of Technology Assessment,

1985.

Part B: Economics and Policy Analysis

Buxton, Boyd M., “Economic, Policy, and Technology Factors Affecting Herd Size and Regional Location of U.S. Milk Production, ” paper prepared

for the Office of Technology Assessment, 1985.

Cochrane, Willard W., “Farm Commodity Policy,

Technological Advance and Structural Change,”

paper prepared for the Policy, Technology, and

Structure of Agriculture Workshop, Office of

Technology Assessment, 1983.

Cooke, Stephen C., “Size Economies and Comparative Advantage in the Production of Corn, Soybeans, Wheat, Rice, and Cotton in Various Areas

of the United States, ” paper prepared for the Office of Technology Assessment, 1985.

Eddleman, B. R., “Economic Effects of Selected Policies and Technology on the Economic Viability

of General Crops Farms in the Delta Region of Mississippi, ” paper prepared for the Office of Technology Assessment, 1985.

Richardson, James W., “Economic Impacts of Selected Policies and Technology on the Economic

Viability of Three Representative Cotton Farms

in the Texas Southern High Plains,” paper prepared for the Office of Technology Assessment,

1985.

Richardson, James W., “Economic Impacts of Selected Policies and Technology on the Economic

Viability of Three Representative Wheat Farms

in the High Plains,” paper prepared for the Office of Technology Assessment, 1985.

Sundquist, W. B., “Economic Impacts of Selected

Farm Policies, Income Tax Provisions, and Production Technology on the Economic Viability of

Corn-Soybean Farms in East Central Illinois and

Irrigated Row Crop Farms in South Central Nebraska,” paper prepared for the Office of Technology Assessment, 1985.

Part C: Agricultural Finance and Credit

Barry, Peter J., “Credit Policy and Structural Issues

in Agriculture,” paper prepared for the Policy,

Technology, and Structure of Agriculture Workshop, office of Technology Assessment, 1983.

Barry, Peter J., “Regulatory and Performance Issues

for Financial Institutions: Their Effects on Technology Adoption and Structural Change in Agriculture,” paper prepared for the Office of Technology Assessment, 1985.
362 • Technology, Public Policy, and the Changing Structure of American Agriculture

Brake, John R., “Short-Term Credit Policies for Dealing With Farm Financial Stress and Their Impacts

on Farm Structure and Adoption of New Technology, ” paper prepared for the Office of Technology Assessment, 1985.

Carman, Hoy F., “Tax Policy, Technology, and the

Structure of Agriculture,” paper prepared for the

Policy, Technology, and Structure of Agriculture

Workshop, Office of Technology Assessment,

1983.

Lins, David A., “Overview of Capital and Credit Markets Serving Agriculture: Their Impact on Technology Adoption and Structural Change,” paper

prepared for the Office of Technology Assessment, 1985.

Trechter, David, and Meekhof, Ronald, “The Role

of Federal Credit Assistance Programs in the Process of Technological Change,” paper prepared for

the Office of Technology Assessment, 1985.

Part D: Rural Communities

Buttel, Frederick H., and Lancelle, Mark, “Emerging Agricultural Technologies, Farm Structural

Change, Public Policy, and Rural Communities

in the Northeast,” paper prepared for the Office

of Technology Assessment, 1985,

Flora, Jan L., and Flora, Cornelia Butler, “Emerging Agricultural Technologies, Farm Size, Public

Policy, and Rural Communities: The Great Plains

and the West, ” paper prepared for the Office of

Technology Assessment, 1985.

MacCannell, Dean, and Dolber-Smith, Edward, “Report on the Structure of Agriculture and Impacts

of New Technologies on Rural Communities in

Arizona, California, Florida, and Texas,” paper

prepared for the Office of Technology Assessment, 1985.

McCannell, Dean, “Agribusiness and the Small

Community,” paper prepared for the Policy, Technology, and Structure of Agriculture Workshop,

Office of Technology Assessment, 1983.

Skees, Jerry R., and Swanson, Louis E., “Examining Policy and Emerging Technologies Affecting

Farm Structure in the South and the Interaction

Between Farm Structure and Well-being of Rural

Communities,” paper prepared for the Office of

Technology Assessment, 1985.

van Es, J. C., Chicoine, David L., and Flotow, Mark

A., “Agricultural Technologies, Farm Structure,

and Rural Communities in the Cornbelt: Policy

Choices and Implications for 2000,” paper prepared for the Office of Technology Assessment,

1985.

Part E: Agricultural Research and

Extension, Agricultural Structure,and

Environment and Natural Resources

Hite, James, “Environmental and Natural Resource

Impacts of Emerging Technologies in American

Agriculture,” paper prepared for the Office of

Technology Assessment, 1985, based on the Environment and Natural Resources Workshop, Nov.

15-16, 1984.

Hyatt, George, “Agricultural Extension Policy: Its

Evolution and Current Issues,” paper prepared

for the Office of Technology Assessment, 1984.

Knutson, Ronald D., “Agricultural Research and Extension Policy: Contemporary Issues,” paper prepared for the Office of Technology Assessment,

1985.

Lovvorn, Roy L., “Agricultural Research Policy: Its

Evolution and Current Issues” paper prepared for

the Office of Technology Assessment, 1984.

Sporleder, Thomas L., “Agricultural Structure Impacts of Emerging Technologies,” paper prepared

for the Office of Technology Assessment, 1985,

based on the Agricultural Structure Workshop,

Nov. 15-16, 1984.

White, Fred C., “Economic Impact of Agricultural

Research and Extension,” paper prepared for the

Office of Technology Assessment, 1984.

Acknowledgements

OTA would like to thank the members of the advisory panel who helped guide the direction of this

study and who reviewed drafts of this report. In addition, OTA acknowledges the following individuals

for reviewing background papers, reviewing individual report chapters, or providing information to

the project staff:

Walt Armbruster

Farm Foundation

Thomas Army

U.S. Department of Agriculture

Lloyd Bender

Bozeman, MT

Raymond Beneke

Iowa State University

David Bezdicek

Washington State University

Larry Bierman

J.R. Simplot Co.
App. G—Workgroups, Background Papers, and Acknowledgments • 363

J.L. Bittle

Scripps Clinic and Research Foundation

Robert Bjork

U.S. Department of Agriculture

Michael Boehlje

University of Minnesota

Elise Boulding

Dartmouth College

Dave Brown

U.S. Department of Agriculture

Orrin Burnside

University of Nebraska

Ward Butcher

Washington State University

G.S. Campbell

Washington State University

Hoy Carman

University of California—Davis

John Chapman

Valmont Industries

David Chicoine

University of Illinois

Estel Cobb

U.S. Department of Agriculture

R. James Cook

Washington State University

Nicholas Cross

Cattle Code America

Dave Dame

University of Florida

Machi Dilworth

U.S. Department of Agriculture

Paul Doak

Iowa State University

Mark Drabenstott

Kansas City Federal Reserve Bank

Roger Elmore

University of Nebraska

Robert Emerson

University of Florida

A. Farsaie

University of Maryland

Norman Faultner

Deere and Co.

Jan Flora

Kansas State University

Louise Fortman

University of California-Berkeley

Lloyd Frederick

Agency for International Development

William Fry

Cornell University

Robert Giaquinter

E.I. DuPont Experimental Station

Robert Goldberg

University of California-Los Angeles

Walter Goldschmidt

University of California-Los Angeles

J. Gorham

U.S. Department of Agriculture

Albert Grable

U.S. Department of Agricu

Mary Nell Greenwood

U.S. Department of Agricu

W.R. Guthrie

Butler Manufacturing Co.

William Harland

Bureau of the Census

Craig Harris

Michigan State University

Steve Hastings

University of Delaware

Virgil Hays

University of Kentucky

Paul Hedin

ture

ture

U.S. Department of Agriculture

William Heffernan

University of Missouri

Jim Hildreth

Farm Foundation

Fred Hines

U.S. Department of Agriculture

John Hopkin

Texas A&M University
364 • Technology, Public Policy, and the Changing Structure of American Agriculture

Dixon Hubbard

U.S. Department of Agriculture

Wallace Huffman

Iowa State University

George Irwin

Farm Credit Administration

Ray Jackson

U.S. Water Conservation Laboratory

Richard Jensen

University of Arizona

Richard Just

University of Maryland

Terry Kinney

U.S. Department of Agriculture

Waldemar Klassen

U.S. Department of Agriculture

Terry Klopfenstein

University of Nebraska

John Knepler

DICKEY-john Corp.

Mark Lancelle

Cornell University

B.A. Larkins

Purdue University

Warren Lee

Ohio State University

Jerry Mannering

purdue University

Tom McClure

Battelle Columbus Laboratories

Robert Miller

North Carolina State University

Page Morgan

Texas A&M University

A.]. Muehling

University of Illinois

Ralph Nave

U.S. Department of Agriculture

Paul Nixon

U.S. Department of Agriculture

John Nye

Purdue University

P.A. Oltenacu

Cornell University

Olga Owens

U.S. Department of Agriculture

Robert Papendick

U.S. Department of Agriculture

Kenneth Parks

Agrico Chemical Co.

Neal Peterson

U.S. Department of Agriculture

Hoyle Puckett

U.S. Department of Agriculture

Graham Purchase

U.S. Department of Agriculture

Philip Raup

University of Minnesota

Steve Rawlins

U.S. Department of Agriculture

Robert Riley

U.S. Department of Agriculture

Charles Rumburg

U.S. Department of Agriculture

Robert Schafer

U.S. Department of Agriculture

Paul Siegel

Virginia Polytechnic Institute

Jerry Skees

University of Kentucky

F.W. Slife

University of Illinois

Charles Smith

U.S. Department of Agriculture

Steve Sonka

University of Illinois

Vernon Sorenson

Michigan State University

Lauren Seth

Des Moines, IA

S.L. Spahr

University of Illinois

Thomas Sporleder

Texas A&M University
App. G—Workgroups, Background Papers, and Acknowledgments ● 365

Richard Storm

Bureau of the Census

B. Stout

Texas A&M University

Harlee Strauss

American Chemical Society

Howard Teague

U.S. Department of Agriculture

E.D. Threadgill

University of Georgia

Daniel Tompkins

U.S. Department of Agriculture

David Trechter

U.S. Department of Agriculture

F.T. Turpin

Purdue University

Frederick Weingarten

Office of Technology Assessment

Fred Westbrook

U.S. Department of Agriculture

Holly Wheeler

Applied Futures, Inc.

P.J. Wierenga

New Mexico State University

Kenneth Wilkinson

Pennsylvania State University

Ronald Wimberley

North Carolina State University

W. Fred Woods

U.S. Department of Agriculture

W.R. Woods

Purdue University

David van Doren

Ohio State University

Index

acreage allotments, 115

acreage reduction program (acreage

diversion/set-asides), 115, 175, 176, 178-179,

180, 185, 197

additives in feed, 10, 39, 43, 85

adoption of technology

by farm size, 12, 123, 130-131, 133, 289, 290

financial impacts on, 156-159

rates-of-adoption profiles, 15, 78-79, 114,

277-278

Advanced Genetic Studies, 213

Agracetus, 213

agricultural experiment stations, 19, 253, 257,

260, 275, 290

air quality, 56, 209

American Cyanamid, 273

American Express, 141

anti juvenile hormonal activity, 62

applications technology, 50, 51, 53, 60, 61, 207,

210

aquiculture, 39, 40, 207

ARS (Agriculture Research Service, see

Department of Agriculture)

artificial insemination, 5, 36, 37, 268

Asimolar conference, 213

Bacillus thurigiensis, 6, 41, 45, 51

Bank for Cooperatives, 116, 141

banking, commercial, 13, 142, 144, 147, 150-152

branching, 13, 142, 151-152

products and services, 13-14, 151

beef cattle production, 10, 77, 78, 81, 83, 97, 99-

100, 101, 103-104, 109, 115-116, 270

behavioral technology, 207

Berg, Paul, 213

Bergland, Robert, Secretary of Agriculture, 115

bGH (bovine growth hormone, see growth

hormones)

bimodal farm structure, 15, 20, 96-97, 105, 132,

286

Biotechnica International, 273

biotechnology (see also, individual technologies),

2-6, 8, 10, 12, 25, 31-32, 33-53, 81, 85, 123,

124, 125-126, 128, 129, 131, 132, 133, 134,

207

Block, John R., Secretary of Agriculture, 176

bovine growth hormone (bGH) (see growth

hormones)

capital

debt, 140, 141-145

equity, 140-141, 145-147

investment, 145, 147

profits and net income, 145-147

unrealized capital gains, 145, 146-147

requirements for production, 12, 13, 23, 114,

123, 125-127, 133, 290

requirements for technology, 139

Cargill, 110

cell culture, 6

cell fusion, 3-4, 5, 31, 32, 35, 207

Center for Biotechnology Research, 273

cleaning technology (crop), 68-69

Coca-Cola, 110

Cochrane, Willard, 163, 277

Commodity Credit Corporation (CCC), 143, 144,

145, 175, 180, 198, 202

commodity policy, 115, 131, 132, 133

comparative advantages

in crop production, 163-171

in dairy production, 189, 190

competitiveness, international, 4

computer technology (see information

technology)

concentration of land resources, 109

concentration of production, 9, 12, 14, 15, 16,

17, 18, 91, 96-97, 103-105, 109, 163-185,

189-202, 221-222

policy impacts on, 171-185, 194-202

regional differences, 222-241

confinement operations, 40, 42, 67

conservation reserves, 115

conservation tillage, 43, 54, 55, 56-57, 60, 61,

210, 212, 216

continuous cropping, 50

contracting, 12, 22, 110, 125, 133

controlled traffic farming, 55

conventional farming, 78

Cooperative Farm Credit System, 147

cooperative research agency (RAC), 290

cooperatives, 23, 285, 290, 292

corn production, 11, 14, 61, 77, 78, 80, 82-83, 97-

98, 101, 102, 103, 164-165, 169, 170, 171-

173, 176, 233, 235, 236, 270

Corps of Engineers, U.S. Army, 246

cotton production, 11, 14, 77, 80, 81, 82, 83, 101,

102, 103, 168-169, 170-171, 173-175, 176,

238, 270

credit, 137-159

policy, 116, 131, 132

regulations affecting, 150-154

role of, 147-150

creditworthiness

of borrowers, 13

of technology, 148-149

crop loss assessment, 50

369
370 • Technology, Public Policy, and the Changing Structure of American Agriculture

crop production (see also, specific crops), 3, 10,

11, 12, 14, 110, 125-132, 133-134, 163-185

crop residue technology, 42-43, 56, 126, 207

cross breeding, 41

crown gall, 49

custom-prescribed tillage, 55-56

dairy production, 10, 12, 14, 15, 17, 18, 77, 78,

80-81, 83-84, 97, 99, 101, 102, 103, 104, 132,

189-202, 232, 233, 235, 236, 244-245

costs and returns, 192-194

herd size, 190-191, 193, 194, 199

technological impacts on, 232, 244-245

debt restructuring, 15, 183-185, 201-202

Department of Agriculture (USDA), 18, 24, 25,

143, 145, 171, 196, 253, 255-259, 261, 264,

266-267, 271, 276-277, 278, 290

Agricultural Research Service (ARS), 256, 258,

259

Economic Research Service (ERS), 256, 258,

259

extension, 255, 258-259, 264, 266

research, 255-258, 264, 271

Department of Defense, 259

Department of Energy, 259

Diamond v. Chakrabarty, 273

disease control

animal, 7, 40-42, 125, 195, 207

effects of environment on, 40, 42

plant, 49-50, 77, 126, 210-211

disease resistance

animal, 31, 34, 40

plant, 45, 49-50

displaced farmers (see transition from farming)

DNA (see also, recombinant DNA technology), 6,

38, 44, 46

Dow Chemical, 273

drug residue detection, 40

Du Pont, 273

ear implants, 37, 41

economic feasibility of technologies, 13, 14

economies of scale, 14, 15, 113, 163-176, 189,

190, 202

electronic technology, 6-7, 8, 63, 65, 67-68, 69,

127

Eli Lilly, 35, 273

embryo transfers, 5-6, 10, 31, 32, 36, 40, 76, 195,

206, 268

emerging technologies, defined, 4-8, 31-71

Engenics, 273

engines, 68, 127

environmental control technologies, 42, 126, 207

environmental impacts (see technological

impacts)

environmental policy, 15, 131, 215-216

Environmental Protection Agency (EPA), 16,

213, 246

erosion (see soil erosion)

ERS (Economic Research Service, see

Department of Agriculture)

ESCOP (Agricultural Experiment Station

Committee on Policy), 274

estrous cycle detection, 7, 38, 64, 206

evaluation of products, 19, 23, 262, 265-266, 290

exports, agricultural, 4, 21-22, 285

farm bill

of 1977, 256, 257, 264, 272

of 1981, 184

of 1985, 263

Farm Credit Administration, 141

Farm Credit System, 116, 141, 152-154, 246

farmer-owned reserve, 175, 180, 198

Farmers Home Administration (FmHA), 13, 24,

116, 143, 145, 147, 154-157, 158, 159, 246,

292

Farm Foundation, 266

farm policy, 14, 15, 115-116, 131-132, 175-182,

184, 256, 257, 263, 264, 272

elimination of, 179-180, 185

farm statistics

employment, 94-95

income, 8-9, 91, 93-96

numbers of, 91, 96-97

size (see concentration of production)

FDA (see Food and Drug Administration)

Federal Land Banks, 116, 141, 144

Federal Reserve System, 138, 150

feed costs, 197, 201, 202

fertilizer technology, 53, 56, 58, 60-61, 113, 130,

209

self-fertilizing plants, 10, 31

fiber optics, 66

finance, 12, 137-159

regulation of, 150-154, 158

financial characteristics

of representative crop farms, 171-175

of representative dairy farms, 192-194, 196-197

financial situation, present, 12, 15, 23, 137-138,

140, 290

findings of report, 10-20

fiscal policy, 138-139

FmHA (see Farmers Home Administration)

Food and Drug Administration (FDA), 35, 48

Ford Foundation, 266
Index . 371

Foundation of Economic Trends, 35

freezing, of embryos, 5, 32, 36, 37

fruit production, 17, 101, 102, 103, 270

fuels, 68, 207

gene insertion, 5, 6, 10

Genentech, 35

genes, 50

gene splicing (see recombinant DNA technology)

genetic engineering

animal, 5, 31, 32, 33-43, 76, 81, 206-207, 212

controversy about, 16, 35, 43-44, 213-215

plant, 6, 31, 32, 44-53, 76, 77, 208, 210, 212

Genetic Engineering, Inc., 36

gene transfer technology, 6, 46, 47

Genex, 273

giberellins, 49

Goldschmidt, Walter, 221-222, 226, 228, 229, 239

ground-based repeater systems, 66

groundwater, depletion and pollution, 53

growth and development technology

animal, 206-207

plant, 76

growth hormones, 5, 10, 31, 34, 35, 36, 38

bovine, 10, 34, 38, 81, 85, 195, 201, 232

human, 35

growth regulator (see plant growth regulators)

herbicides, 10, 48, 59, 61, 113, 21 210, 255

human capital development, 13, 25,.270, 293

Humane Society of the United States, 35

hybridoma, 36

ice nucleation bacteria (see genetic engineering,

controversy)

identification technology (animal), 7, 39

immunization, 35, 40-41

income, farm

sources

onfarm, 145-146

off-farm, 147

supports, 15, 176, 177-178, 182, 185, 289

tax, 200-201, 202

information technology, 6-7, 8, 10, 12, 25, 31, 32-

33, 40, 52, 58, 62-67, 123, 126, 127, 128,

130, 131, 132, 133, 134, 201, 207

central computer system, 7, 32, 52, 64

local area networks (LANS), 32, 62

microcomputers, 7, 48, 62, 64, 65

software, 7, 62, 64-65

insect control technology, 7, 41-42, 45, 50-52, 62,

113, 125, 207, 210-211

slow-release technology, 41, 207

insurance companies, 142, 144

interest rate subsidy, 15, 183-184, 185

interest rates, 13

fixed, 13, 141

real, 138-139

regulation of, 150-151

variable, 13, 141

Interstate Highway System, 246

investment tax credits, 115, 116, 176, 200

1PM (integrated pest management, see pest

control technology)

irrigation technology, 7, 10-11, 53, 54, 65, 210,

212

IWMS (integrated weed management system,

see weed control technology)

J.C. Penney, 150

Joint Council on Food and Agricultural

Sciences, 258

Kellogg-Foundation, 266

Knutson, Ronald, 110

labor (see farm statistics, employment)

labor-saving technology (see also, mechanical

technology), 67-68

lactation stimulator (prolactin), 5, 36

land

availability, 10-11, 15, 84-85

use, 205, 212

land-grant universities, 24, 114, 253, 255, 256,

257, 258, 259-264, 265, 271-276, 277, 278,

290, 291, 292, 293

extension, 261-264

research, 259-261

land management technology, 54-56, 127

legislation

Agricultural Adjustment Act of 1933, 15

Dairy and Tobacco Production and

Stabilization Act of 1983, 195

Depository Institutions Deregulation and

Monetary Control Act of 1980, 150

Farm Credit Act of 1971, 152-153

amendments (1980), 153

Federal Job Training Partnership Act, Title III,

291

Food Security Act of 1985, 263

Garn-St. Germain Depository Institutions Act

of 1982, 150

Plant Variety Protection Act of 1970, 273, 276

light (see photoregulation)

limited partnership syndicates, 116

livestock production, 125-132, 133-134, 233, 238

loans

bank, 142
372 ● Technology, Public Policy, and the Changing Structure of American Agriculture

cooperative, 141

FmHA

guaranteed, 143, 156

insured, 143

Government, 143, 154-157

insurance, 142

shared appreciation mortgages (SAMs),

142-143

State, 156

supplier/merchant, 143-144

local area networks (LANS), 32, 62

low-power radio links, 32, 33

Lindow, Steve, 213

Lugar, Richard, U.S. Senator, 270

market access (see technological impacts)

marketing loan, 22

Marshall, H. Peter, 261

mathematical modeling, 36, 48, 57, 58

mechanical technology, 58-59, 67, 68, 69, 116,

124, 126-127, 128, 130, 131, 132, 133

Merrill Lynch, 150

MGI, 273

Michigan State University, 273

microbial inocula, 6, 32, 44-45, 47, 214

mite control, 7, 50-52, 125

monitoring and control technology, 8, 31, 32, 42,

63-65, 69, 127

monoclinal antibody production, 31, 35-36, 206,

207

uses for, 36, 41

Monsanto, 6, 35, 45, 273

multiple cropping, 56, 57, 58-59, 77, 209-210, 212

National Aeronautics and Space Administration

(NASA), 259

National Agricultural Research and Extension

Users Advisory Board (Users Advisory

Board), 257, 258

National Institutes of Health (NIH), 213, 259,

264

National Science Foundation (NSF), 215, 259,

264

Needs Assessment for Food and Agricultural

Sciences, 257

nematode control (see pest control technology)

Neogen, 273

new entrants to farming, 14, 184-185

nitrogen fixation technology, 32, 44, 52-53, 61-

62, 208, 212

self-reliance, 32, 53, 61

non-land-grant universities, 255, 256, 272

nonrecourse loan, 21, 22, 288, 289

nutrition

animal, 39-40, 125, 206-207

human, 39

Office of Management and Budget (OMB), 223

organic farming, 56, 61-62, 78, 127, 209

packet radio systems, 66

Panopoulos, Nicholas, 213

patent rights, 25, 265, 272-276, 293

pest control technology, 7, 50, 56, 65, 77, 195,

207, 210

integrated pest management (1PM), 7, 41, 52,

210, 266

pharmaceuticals, protein, 5, 32, 76

photoregulation, 42, 207

photosynthesis technology, 46, 47-48, 53, 77, 208

plant breeding, 44, 47, 53, 54, 58, 61-62

plant growth regulators, 48-49, 77, 208, 209, 268

plant propagation, 6, 32, 44-45

policy impacts

of

on

on

on

on

on

research and extension, 254-279

crop production, 175-185

dairy production, 194-202

environment and natural resources, 25,

215-216

rural communities, 24-25, 244-248

structure of agriculture, 20-24, 115-116.

171-185, 194-202

policy options

for balancing productivity with public interest,

293

for large, commercial farms, 21-22, 288

for moderate-size farms, 22-23, 289-291

for rural communities, 24-25, 292-293

small, part-time farms, 23-24, 291-292

pork production (see swine production)

poultry production, 10, 77, 78, 80, 81, 83, 97,

101, 102, 103, 104

poverty, rural, 242-244

price supports, 115, 176, 177, 180, 184, 185, 195,

198-199, 201, 202

processing technology (plant), 68-69

Production Credit Association, 116, 141, 144

protestants, 48, 60

quotas, dairy, 199-200

RAC (see cooperative research agency)

Ralston Purina, 110

recombinant DNA technology, 3-4, 5, 16, 31, 33-

35, 36, 38, 40-41, 47, 49, 206, 207, 213, 275

regional research, 19, 260-261, 278-279
Index ● 3 7 3

reproduction technology (animal), 7, 37-38, 40,

125, 206-207

research and extension, 15-16, 18, 21, 22, 253-

280, 287

balance between, 266-269

components of, 255-266

county level, 19, 261-262, 270, 279

Federal level, 255-261

private sector, 255, 264-266, 277

State level, 19, 24, 258, 259, 260, 261-264,

269, 277, 279

functions, 254-255

funding, 18-19, 25, 256, 266-268, 270-272, 278,

288

competitive grants, 270-272

social contract, 18, 25, 272-273, 278, 292

staff, 269-270

residue monitoring, 39

resistance (plant), 208

to disease, 10, 32

to harsh environments, 32, 48, 54, 58

to insects and pests, 10, 32, 49-51

result demonstration, 267

Rhizobium, 44, 52-53

rice production, 14, 77, 80, 81, 82, 83, 167-168,

169-170, 171, 173-174, 176

ripeners, 49

robotics, 67

Rockefeller Foundation, 265

rural agricultural counties, defined, 223

rural community impacts, 16-18, 221-249,

292-293

rural development policy, 24, 242-244, 245-248,

292

Rural Electrification Administration, 246

safeners, 48, 60

satellite technology, 33, 57, 64, 66-67

Science and Education Administration, 263

Sears Roebuck, 141, 150

Secretary of Agriculture, 115, 176, 257

set-asides, acreage, 115

sexing, of embryos, 5, 32, 36, 37

sheep production, 10

Small Business Administration (SBA), 143

Social Security System, 246

soil disease suppression, 62

soil erosion

control technology, 15, 43, 56-58, 127, 205,

209, 210, 212

estimation of, 56, 57

process of, 56

soil productivity, 52-53, 56-62, 208, 209, 210, 212

sorghum production, 176

soybean production, 10, 14, 77, 80, 81, 82, 83,

97-98, 101, 102, 103, 129, 164-165, 170-174,

209, 233, 235, 236, 270

specialist, State extension, 262, 269

specialization of production, 113, 129

Stanford University, 273

structural impacts

on environment, 211-212

on rural communities, 111-112, 221-248

on technology adoption, 123, 132-133

structural neutrality, 115

structure, agricultural

causes of change in

economic and political, 109-111, 116

institutional, 114-116

technological, 112-114

past changes, 9, 92-96

distribution of sales and income, 9, 93-94

farm size, 9, 92-96

income sources, 94-96

present, 9, 91, 97-105

by major commodity, 97-100

by region, 100-102

comparisons, 102-103

projected changes, 9, 92-96

public policy impacts on, 115-116

sugarcane production, 238

Superior Oil, 110

superovulation, 5, 31, 32, 36, 37, 38, 76

supply and demand

domestic, 84, 85

world, 3, 4, 10, 11, 84-85

surplus dairy production, 189, 195

swine production, 10, 77, 78, 80, 81, 83, 97, 100,

101, 102, 103, 104, 270

target price/deficiency payments (see price

supports)

tax-exempt bonds, 156

tax policy, 14, 115-116, 131, 132, 133, 139-140,

181, 182

technological impacts

on environment and natural resources, 15-16,

205-217

on human health, 207, 209, 210, 212

on structural elements, 123-134

barriers to entry, 12, 124, 125, 129-130,

133-134

capital and labor, 124, 125-127, 131, 133

market access, 12, 124, 125, 126, 129,

133-134

regional structure, 123, 124, 125, 130-131,

133, 134
374 • Technology, Public Policy, and the Changing Structure of American Agriculture

vertical coordination and control, 124-125,

126, 127-128, 133-134

on structure, 12, 277-278

of crop production, 181-183

of dairy production, 194-195, 196, 201, 202

on yields, 3, 10-11, 14-15, 61, 76-85, 181-183,

194-196, 201

telecommunications technology, 32-33, 65-67

high-speed, 65-66

low-power radio links, 33

Tenneco, 110

tillage technologies

conservation tillage, 43, 54-58, 59, 60, 61, 62,

210, 212, 216

no-tillage, 56-57

minimum tillage, 56, 58

reduced tillage, 56, 61

conventional tillage, 60

controlled traffic farming,

conventional tillage, 60

custom-prescribed tillage,

55

55-56

crop residue, 42-43, 56, 126, 207

multiple cropping, 56, 57, 58-59, 77, 209-210,

212

rotational tillage, 60

tobacco production, 238, 270

transition from farming, 17, 23, 24, 290-291

transponders, 7, 40, 64

tree nut production, 101, 102, 103

Tysons, 110

unemployment (see transition from farming)

Universal Soil Loss Equation, 57

University of California (Berkeley), 44

University of Pennsylvania, 35

University of Washington, 35

USDA (see Department of Agriculture)

vertical integration, 3, 9, 12, 110-111, 113, 124,

125, 127-128, 129, 133, 134

waste technology (animal), 41-42, 43, 207, 209

water

efficiency of use, 15, 48, 53-54

quality, 56, 207, 208, 209, 210, 212

Supply, 205, 207

weather prediction, 48, 66-67

weed control technology (see also, herbicides),

48, 59-60, 62, 210-211

integrated weed management system (IWMS),

59, 210

wheat production, 10, 14, 77, 80, 81, 82, 83, 97-

98, 101, 102, 103, 105, 165, 166-167, 170-

171, 173-174, 209, 233, 235, 236, 270

Wind Erosion Equation, 57

yields (crop) (see technological impacts)

o