Technology, Public Policy, and the

Changing Structure of American

Agriculture: A Special Report for the 1985

Farm Bill

March 1985

NTIS order #PB86-210374
Recommended Citation:

Technology, Public Policy, and the Changing Structure of American Agriculture: A

Special Report for the 1985 Farm Bill (Washington, DC: U.S. Congress, Office of Technology Assessment, OTA-F-272, March 1985).

Library of Congress Catalog Card Number 85-600518

For sale by the Superintendent of Documents

U.S. Government Printing Office, Washington, DC 20402
.—

Foreword

In July 1983 the Technology Assessment Board approved the requests of five

congressional committees that OTA conduct a study that would project the most

likely picture of U.S. agriculture in 2000 by analyzing the relationships between

emerging agricultural technologies, public policy, and structural change in the farm

sector. The major concern the committees expressed in requesting the study was

the lack of knowledge about the nature and impacts of emerging technologies in

combination with public policy specifically as they affect the future direction of

agriculture, The committees that requested the study are: 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).

Although the study will not be completed until late 1985, some findings from

the study, provided in this report, are relevant to specific legislation that will shortly

be debated and acted upon in Congress (the reauthorization of the Agriculture and

Food Act of 1981). Like the legislation, the report focuses on three main policy

areas: commodity, credit, and research and extension.

In the course of preparing this report, OTA drew on the experience of many

individuals. In particular, we appreciate the assistance of our advisory panel and

many workshop participants, as well as the efforts of the project’s consultants and

contractors, We would also like to acknowledge the help of the numerous reviewers

who helped ensure the accuracy of our analysis. It should be understood, however,

that OTA assumes full responsibility for this analysis and that the report does not

necessarily represent the views of the individual members of the advisory panel.

 JOHN H. GIBBON S

Director

iil
of American Agriculture Advisory Panel

Frank Baker Richard Harwood

Director, International Stockmen’s School Program Officer

Winrock International Livestock Research and International Agricultural Development

Training Center Service

James Bonnen

Professor

Department of Agricultural Economics

Michigan State University

William Brown

Chairman of the Board

Pioneer Hi-Bred International, Inc.

Frederick Buttel

Associate Professor

Department of Rural Sociology

Cornell University

Willard Cochrane

Consultant

Jack Doyle

Director

Agricultural Resources Project

Environmental Policy Institute

Marcia Dudden

Dudden Farms, Inc.

Walter Ehrhardt

Elk Ridge Mountain Farm

Charles Kidd

Dean

College of Engineering Science, Technology,

and Agriculture

Florida A&M University

Robert Lanphier III

Chairman of the Board

DICKEY-john Corp.

Edward Legates

Dean, College of Agriculture and Life Sciences

North Carolina State University

John Marvel

General Manager

Research Division

Monsanto Agriculture Products Co.

Donella Meadows

Adjunct Professor

Resources Policy Center

Dartmouth College

Don paarlberg

Consultant

Don Reeves Dean Gillette Consultant, Interreligious Taskforce on Professor U.S. Food Policy Department of Engineering

Harvey Mudd College Milo Schanzenbach

Roger Granados Schanzenbach Farms

Executive Director

La Coopertiva

iv
————.

OTA Project Staff-Technology, Public Policy,

and the Changing Structure of American Agriculture

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, * Research Assistant

Kathryn M. Van Wyk, Editor

Administrative Staff

Phyllis Balan* * and Patricia Durana, * * * Administrative Assistant

Nellie Hammond, Secretary Carolyn Swann, Secretary

Contractors/Consultants

Boyd Buxton, U.S. Department of Agriculture, St, Paul, MN

Steve Cook, University of Minnesota

B. R. Eddleman, Mississippi State University

Ronald Knutson, Texas A&M University

James Richardson, Texas A&M University

Burt Sundquist, University of Minnesota

Center for Agriculture and Rural Development, Iowa State University

*Through May 1984.

● ● Through September 1984

* ● *After September 1984.
Contents

Chapter Page

l. Overview ... .. .. .. .. .. . . ..O.... .. .. .. .. . ... O...O.. . . . . . . 3

2. The New Technologies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7

Survey of Emerging Technologies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . i’

Biotechnology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8

Information Technology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10

Impact of Emerging Technologies on Production . . . . . . . . . . . . . . . . . . . . . . . . . 12

Projections of Agricultural Yield . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13

Projections of Food Production . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14

S. The Changing Character of the U.S. Agricultural Sector... . . . . . . . . . . . . . . 1!3

The Present Structure of Agriculture . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19

Changes in Farm Size and Numbers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20

Changes in Distribution of Sales and Income . . . . . . . . . . . . . . . . . . . . . . . . . 20

Changes in Sources of Income . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21

Changes in the Structure of Debt in the Farm Sector . . . . . . . . . . . . . . . . . . 22

Defining Structural Change in Agriculture. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23

The Economic Perspective . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23

The Sociological Perspective . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25

Causes of Structural Change . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26

Technological Forces ....,. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26

Institutional Forces . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28

Economic and Political Forces . . . . . . . . . . . . . . . . . . . . . . . . . . , . . . . . . . . . . , 31

The Dynamics of Structural Change . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31

4. Economic Impacts of Emerging Technologies and

Selected Farm Policies for Various Size Crop Farms . . . . . . . . . . . . . . . . . . . . 35

The Crop Farms Analyzed . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36

Corn-Soybean Farms in the Corn Belt . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36

Wheat Farms in the Southern Plains .,, . . . . . . . . . . . . . . . . . . . . . . . . . , . . . 37

General Crop Farms in the Delta Region of Mississippi . . . . . . . . . . . . . . . . 38

Cotton Farms in the Texas Southern High Plains, . . . . . . . . . . . . . . . . . . . . . 39

Policy and Technology Scenarios . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39

Farm Policy Scenarios. . . . . . . . . . . . . . . . . . . . ..., . . . . . . . . . . . . . . . . . . . . . 40

Tax Policy Scenarios . . . . . . ., . . . . . . . . . . . . . . . . . . . ., ., . . . . . . . . , , ., , . . 45

Technology Scenarios . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ., . . . . . . . . . . . . 46

Implications for the 1985 Farm Bill . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46

Financial Stress and New Entrants Scenarios . . . . . . ., ..., . . . . . . . . ., . . . . . . 47

Financial Stress Scenarios ., ., ..., . . . . . . ., , ., ., , ., ., , ., , . . . . . . . . , . . 47

New Entrants Into Farming Scenario, . . . . . . . . . . . . . . . . . . . . . ., ., . . . . . , 48

Implications for the 1985 Farm Bill .,, , . . .,,.,..,, . . . . . . . . . . . . . . . . . . 49

!5. Economic Impacts of Emerging Technologies and

Selected Farm Policies for Various Size Dairy Farms . . . . . . . . . . . . . . . . . . . 53

Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53

Technologies and Practices ...,.....,.. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.5

Policy and Technology Scenarios. . . . . . . . . . . . . . . . . . . . . . . . . . . , . . . . . . . . . . . 56

Farm Policy Scenarios..,.. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57

Tax Policy Scenarios . . . . . . . . . . . . . . . . . . . . . . ....,,.., , .,.,, .,,.,, . . . 59

Technology Scenarios . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 60

Financial Stress Scenarios, . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . , , . . . . . . . . . . . 61

Implications for the 1985 Farm Bill, ........,.. . . . . . . . . . . . . . . . . . . . . . . . . . 61

vi
— .—

Contents-continued

Chapter Page

6. Agricultural Research and Extension Policy . . . . . . . . . . . . . . . . . . . . . . . . . . . 65

Who Profits From Technology Change . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ..,, 66

The Effect of Agricultural Research and Extension on Farm Structure . . . . . . 66

Research, Private Sector, and Extension Roles . . . . . .,..., , . . . . . . . . . . . . . . . 68

Private Sector Involvement . . . . . . , , , . . . . . . . . . . . . . . . . . . . . . . . ..., . . . . . 69

Research Involvement . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 70

Extension Roles . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 72

Implications for the 1985 Farm Bill, ,,, .,, ,,, ..,,...., . . . . . . . . . . . . . . . . . . 74

Appendix A.—Summary Analysis Tables for Crop Farms ..., , . . . . . . . . . . . . . . . 77

Appendix B.—Summary Analysis Tables for Dairy Farms . . . . . . . . . . . . . . . . . . . . 84

References , . . . . . , . . . . . , , . . . . . . . . . . . . . , . . . . . . . , . . . . . . . . . . . . . . ..., , . . . . . 89

Tables

Tabfe No, Page

2-1,

2-2.

2-3.

3-1,

3-2.

3-3

3-4<

4-1,

4-2.

4-3.

4-4.

4-5.

5-1.

5-2.

5-3.

Emerging Agricultural Production Technology Areas . . . . . . . . . . . . . . . . .,,. ‘7

Estimates of Crop Yields and Animal Production Efficiency . . . . . . . . . . . . . . 14

Projection of Major Crop Production . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15

Sales Classes of Farms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20

Distribution of Farms, Percent of Cash Receipts, Percent of Farm Income,

and Farm and Off-Farm Income per Farm by Sales Class, 1982 . . . . . . . . , . . 21

The Distribution of Farms With High Debt-to-Asset Ratios, by Sales Class

for January 1984 ...,,...,,, . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .,,,..,,. 22

Historical and Projected Percentages of Cropland Harvested by Farms

With Sales in Excess of $200,000 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24

Financial Characteristics of Three Representative Corn-Soybean Farms in

East Central Illinois . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36

Financial Characteristics of Three Representative Irrigated Corn Farms in

South Central Nebraska. . , . . . . . . . . . . . , . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37

Financial Characteristics of Three Representative Wheat Farms by Size in

the Southern Plains . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38

Financial and Resource Characteristics for Three Representative General

Crops Farms in the Delta of Mississippi, 1983 . . . . . . . . . . . . . . . . . . . . . . . . . . 38

Financial Characteristics of Three Representative Cotton Farms by Size in

the Texas Southern High Plains . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39

Total Producers and Size Distribution of Herds Selling Milkto

Plants Regulated by Federal Milk Marketing Orders, May 1983 . . . . . . . . . . . 54

Representative Dairies by Region and Herd Size . . . . . . . . . . . . . . . . . . . . . . . . 55

Financial Characteristics Assumed for Eight Dairy Operations in

Four States . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 56

Figures

Figure No. Page

3-1. Factors Influencing the Structure of Agriculture . . . . . . . , . . . . . . . . . . . . . . . . 27

5-1.How the Dairying Picture Has Changed . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54

vii
Chapter 1

Overview
Chapter 1

Overview

Continuing, rapid advances in biotechnology

and information technology promise to revolutionize agricultural production and to alter

dramatically the structure of the U.S. agricultural sector. In the next 15 years, 1.5 of the estimated 1.8 percent annual growth in production needed to balance world agricultural

supply and demand must come from increases

in agricultural yields—yields that will be possible largely through the development and

adoption of emerging technologies. While it

seems clear that these technologies must be

used if this Nation is to compete in the international marketplace, it is also clear that the

potential impacts of adopting these technologies have important policy implications for

Congress as it begins debate on the reauthorization of the 1981 farm bill,

One impact will be technology’s role, under

the current policy environment, in creating a

surplus of certain commodities in the immediate future. Overall, the agricultural community is expected to experience unpredictable

fluctuations in the balance of agricultural

supply and demand. For certain commodities,

however—notably, dairy products—a substantial potential for further U.S. surpluses exists.

The adoption of new technologies coupled with

current farm policy will exacerbate that problem. The implication for policy makers is the

need for a farm program that more easily

allows for adjustments in periods of shortages

and surpluses rather than remaining fixed.

Another impact of technology will be its continuing role in changing the structure of the

agricultural sector from a system dominated

by the moderate-size farm to one dominated by

large and very large industrialized farms. ]

Technology has provided the technical means

for structural change: mechanization has made

it possible for farmers to operate larger farms,

I Fo r purposes of this study we have defined a moderate-size

farm as having gross sales of $100,000 to $199,000; a ]arge farm,

$200,000 to $499,000; anc] a \reryr large farm, $500,000 and over.

and disease control has made it possible to use

large-scale confinement feeding. Public policy

has provided further incentives, such as price

supports and tax incentives, for farmers to expand operations.

The technologies a farmer now needs to remain competitive are costly and complex.

Farmers who lack the capital and expertise to

adopt new technology early enough to maintain a competitive edge must seek supplementary off-farm income, find some special niche

for their products, or give up farming altogether. This last alternative has become a familiar picture for the moderate-size farm,

which is fast disappearing from the agricultural

scene, As it drops from the middle of the farm

spectrum, it leaves small and part-time farms

(whose owners earn their primary income

elsewhere) clustered at one end and the large

farms (whose owners can take advantage of

economies of scale) clustered at the other,

This trend has several implications for public policy. First, if a decision is made to slow

the decline of the moderate-size farm, policymakers must provide ways for: 1) making new

technologies more available to these farms, and

2) providing training in the use of these technologies. Targeting income support to the operators of such farms would also be an effective

policy component, although even this measure

may not help dairy farmers in some regions.

Second, despite the apparent advantages of

operators of very large farms, such operators

may need a loan safety net to help them weather

price instabilities and the rigors of the world

marketplace. Unlike most of their moderatesize counterparts, such farms can survive without income supports.

Third, agricultural policy may have to include ways to help particular groups and regions make the transition to different endeavors.

For example, programs to retrain agricultural

workers for jobs in other sectors of the economy may be necessary, or farm operators in

3
4 ● A Special Report for the 1985 Farm Bill

a region may need help changing to alternative kinds of farming. The Lake States region,

for instance, shows some comparative advantages for switching from dairy production to

corn.

Finally, and perhaps most significantly, farm

programs must be considered in the context of

these strong technological, economic, and institutional forces. Farm programs can merely

speed up or slow down these forces of change

—they cannot reverse the trends.

While the forces influencing change in the

agricultural structure have been identified, they

have not primarily been studied in the overall

context of farm policy decisions. This report

attempts to do just that. It focuses on the following sections of the 1981 farm bill: Title I–

Dairy, Title III—Wheat, Title IV—Feed Grains,

Title V—Cotton, Title VI—Rice, Title VIII—

Soybeans, Title X–Grain Reserves, Title XI–

Payment Limitations, Title XIV—Research and

Extension, and Title XVI—Credit, Rural Development, and Family Farms.

Chapters 2 and 3 of this report provide background information on technology and structural change and on the procedures followed

in the conduct of this study. The remainder of

the chapters present the results of OTA’S analysis. The long-run impacts of technology, public policy, and structural changes on rural communities, the natural resource base, and the

environment will be addressed in detail in the

later full report from this study.
Chapter 2

The New Technologies
——

Chapter 2

The New Technologies

Technology has made U.S. agriculture one nologies encompass both products and processes,

of the most productive in the world. Some of and, like their predecessors, promise to reshape

that technology has taken the form of new the practice of agriculture.

products—che-rnicals to control pests, drugs to

control disease, or sensors and computers that

automatically measure moisture conditions

and irrigate the field. Other technology has

been embodied in new processes—such as the

ability to use a computer, to make better economic decisions, or to apply the best combinaThis chapter provides a brief survey of the

emerging agricultural production technologies

that could have such an impact and analyzes

the effect of various technology development

and adoption environments on agricultural

food production over the next 15 years,

tion of cultural practices. The emerging techSURVEY OF EMERGING TECHNOLOGIES

Before the turn of the century, cattle ranchers electronics will be used to increase managein Texas may be able to raise cattle as big as

elephants. California dairy farmers may be able

to control the sex of calves and to increase milk

production by more than 10 percent without

increasing feed intake. Major crops may be

genetically altered to resist pests and disease,

grow in salty soil and harsh climate, and provide their own fertilizer. And computers and

ment efficiency. These are only a few of about

150 emerging technologies in the 28 technological areas that have been identified and evaluated for this study (table 2-1). While it may

sound like science fiction, advances in biotechnology and information technology will make

these technologies a reality in the next 10 to

20 years,

Table 2.1 .—Emerging Agricultural Production Technology Areas

Animal Plant, soil, and water

Genetic engineering 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 controlling Water and soil-water-plant relations

Communication and information Soil erosion, productivity, and tillage

Telecommunication Multiple cropping

Labor-saving technologies Organic farming

Communication and information management

Monitoring and controlling

Telecommunications

Labor-saving technologies

Engine and fuels

Land management

Crop separation, cleaning, and processing

SOURCE Office of Technology Assessment

7
8 . A special Report for the 1985 Farm Bill

Biotechnology

Animal Agriculture

One of the major thrusts of genetic engineering in animals is the mass production in microorganisms of proteinaceous pharmaceuticals,

including a number of hormones, enzymes, activating factors, amino acids, and feed supplements. Previously obtained only from animal

and human organs, these biological were either unavailable in practical amounts or in

short supply and costly.

Some of these biological can be used for

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

to increase production efficiency. One of the

applications of these new pharmaceuticals is

the injection of growth hormones into animals

to increase productivity. Several firms, including Monsanto and Eli Lilly, are developing genetically engineered bovine growth hormone to stimulate lactation in cows. In trials

at Cornell University, daily doses of recombinant bovine growth hormone were administered to dairy cows. The hormone, produced

naturally by a cow’s pituitary gland, was synthesized by Genentech for Monsanto. The

results showed that each cow treated with the

hormone increased milk production by at least

12 percent without increasing feed intake.

Commercial introduction of the new hormone

now awaits approval by the Food and Drug

Administration (FDA) (Bachrach, 1984; Hansel,

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 germ lines of livestock and

poultry, opening a new world of improvement

in animal health and productivity, Unlike

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.

I Pharmaceuticals that are proteins.

‘Reproductive cells.

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,

1983). In another experiment, scientists at Ohio

University inserted rabbit genes into the embryos of mice. The genetically engineered

mice, which were 2.5 times larger than normal,

ate as much as normal mice (Mintz, 1984).

Encouraged by the success of the supermouse experiments, USDA scientists at the

Beltsville Agricultural Research Center are

now conducting a new experiment to produce

super sheep and pigs by injecting human

growth hormone gene into the germ lines of

sheep and pigs (Russell, 1984). In this experiment, USDA scientists provide Ralph Brinster

of the University of Pennsylvania with fertilized eggs from sheep and pigs at their Beltsville farms. After injecting the eggs with the

human growth hormone genes, Brinster returns the embryos to Beltsville to be inserted

into the surrogate mother animals.

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, headed by Jeremy Rifkin, and the

Humane Society of the United States. Both

charged that such experiments are a violation

of “the moral and ethical canons of civilization, ” and they sought to halt the experiment.

The researchers argued that they are continuing the experiment cautiously and countered

that the potential scientific and practical benefits far outweigh the theoretical problems

raised by the critics.
— ————

Ch. 2—The New Technologies ● 9

The success of the mice experiments indicates that analogous insertion into bovine

germ lines of additional bovine growth hormone genes, or of growth hormone genes from

larger mammals such as sperm whales or

elephants, could yield larger productivity gains

than would somatic injections of growth hormones. Moreover, the change in growth would

remain a permanently inheritable characteristic, The expression “a whale of an animal”

would no longer be just a figure of speech.

Probably, however, the growth hormone gene

from any animal may be used (not just hormones from very large animals) as long as

enough of that hormone is injected to do the

job.

Although some scientists may be too optimistic when they predict in 2 years the development of a lo, ()()()-pound cow and the

growth of a pig 12 ft long and 5 ft high (Mintz,

1984), these developments are certainly within

the realm of possibility in the next 10 to 20

years, However, some of these changes mayor

may not be desirable due to economic, environmental, anatomical, institutional, and ethical reasons.

Another technique, embryo transfer in cows,

involves artificially inseminating a superovulated donor animal4 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 other animal species (to make

chimeric animals or to permit the heterologous

species to carry the embryo to term), or frozen

in liquid nitrogen, Freezing is of great practical

importance because it allows embryos to be

stored until the estrus of the intended recipient on the farm 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 cat31njections into body cells rather than into reproductive cells.

4An animal that has been injected with a hormone to sti mulate the production of more than the normal number of eggs per

ovulation,

tle maybe those for growth hormones, prolactins (lactation stimulator), digestive enzymes,

and interferon, thereby providing both growth

and enhanced resistance to diseases.

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

involved in embryo transfers, the obvious benefits will push this percentage upward rapidly,

particularly as the costs of the procedure decrease (Brotman, 1983). One company, Genetic

Engineering Inc. (GEI), already markets frozen

cattle embryos domestically and abroad and

provides an embryo sexing service for cattle

breeders (Genetic Engineering News, 1983).

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 inoculums, plant propagation, and genetic

modification.

Microbial Inoculums.—Rhizobium seed inoculums are widely used to improve nitrogen

fixation by certain 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 inoculums. Research on other 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 inoculums,

Recently, Monsanto announced plans to

field-test genetically engineered soil bacteria

that produce naturally occurring insecticide capable of protecting plant roots against soildwelling insects (Journal of Commerce, Dec.

12, 1984). 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 regis-
10 ● A special Report for the 1985 Farm Bill

tered 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 regeneration of intact plants from single

cells or tissue explants have been developed

and are 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 seedgrown. Such technology holds promise for important forest species whose long sexual cycles

reduce the impact of traditional breeding approaches. Somatic embryoss 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.—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 deoxyribonucleic acid (DNA) from one

plant into another plant, regardless of normal

species and sexual barriers. 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

5Embryos reproduced asexually from body cells.

genes encoding photosynthetic proteins from

pea plants into petunia plants (Broglie, et al.,

1984),

Transformation technology also allows introduction of DNA coding sequences from virtually any source into plants, providing they

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; Herrera-Estrella, et

al,, 1983). By eliminating sexual barriers to

gene transfer, genetic engineering will greatly

inc:rease the genetic diversity of plants.

Information Technology

Animal Agriculture

The most significant changes in future livestock production due to information technology will come from the integration of computers and electronics into a modern livestock

production system that will make the farmer

a better manager.

Computers and electronic devices can be

used efficiently in animal feeding, reproduction, disease control, and environmental control. The first step toward efficient management

will be with electronic animal identification

(Muehling and Jones, 1983). 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

ft 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

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

stage of production. It also permits animals in

different stages of production to be penned together yet still be fed properly.
Ch. 2—The New Technologies ● 11

.— - .—

Feeding systems with sensing devices also

detect outdoor temperature so that animals can

be fed accordingly. Since the amount of feedenergy an animal needs under various weather

situations and at each stage of growth is known,

the ability to sense weather information could

fine-tune diet preparation.

A rapid analysis of the feedstuff going into

the ration will be available at the farm. In formulating a ration, it will be very helpful to get

an instant and accurate reading on the calcium,

phosphorus, and Iysine contents of the ration

ingredients, This will permit a feedback control to adjust the mill and mixer automatically

to provide an optimum feed.

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, for example,

would prove profitable in several ways:

Animals could be rebred faster after weaning and 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.

Another use of information technology is in

disease control and prevention (Osburn, 1984).

Computers and computer programs are being

used at many dairies and swine production

units and in the poultry industry. Herd recordkeeping systems for animal health are being developed and refined for various production

units. Examples of these programs now in

operation include FARMHX in Michigan and

similar systems in New York and California

(Mather, 1983). These recordkeeping systems

may be linked with animal identification systems, including radiotransmitters, as indicated

earlier. 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 medicants for treatment or prevention of disease, A review of

printouts will allow the manager or veterinarian to analyze quickly a health profile for each

animal. Bringing all of this information together will allow the veterinarian and manager

of the livestock enterprise to plan for more costeffective disease control programs and to designate the duties, such as vaccinations and

pregnancy examinations, that are to be carried

out, These programs are being applied and

refined on a few farms. By 1990 many of the

more progressive livestock producers will be

using these systems, and by 2000 these systems

will be widely applied to nearly all of the costefficient livestock production units.

Environmental control of livestock facilities

is another area where electronic devices can

be used. Microprocessors will be used to alleviate odorous gases and airborne dust in ventilation systems.

Plant Agriculture

One of the applications of information technology in plant agriculture is in the management of insects and mites (Kennedy, 1984), Improvements in the design and availability of

computer hardware and software will produce

tremendous changes in insect and mite management at all levels (research, extension, pest

management, personnel, and farmer). To be

implemented efficiently, as measured by its

contribution to crop profitability, insect and

mite management requires the processing of

voluminous quantities of information, including: 1) condition and phonological stage of the

crop, 2) status of the various insect and mite

pests and their natural enemies present in the

crop, 3) production inputs into the crop, 4) incidence of plant diseases and weed pests and

the measures used in their control, 5) weather

conditions, and 6) insect and mite management

options. Further, this information must be updated and reviewed at regular intervals, Computers can help superbly in the effective and

efficient processing of this information as well

as in the design, direction, and analysis of pest

management-related research.
12 ● A Special Report for the 1985 Farm Bill

The availability at the farm level of microcomputers equipped with appropriate software

and having access to larger centralized data

bases will greatly speed the transfer of information and facilitate pest management decisionmaking. The advantages, simply in terms

of information storage and retrieval, will be

tremendous, 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 the selection of the appropriate management unit and the design and implementation of pest management strategies for that

unit.

Centralized, computer-based, data management systems for crop, pest, and environmental

monitoring information have been developed

and are being 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 (PETE) model (Welch, et al.,

1978) is a generalized model for the prediction

of arthropod phonological events. PETE 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

(BIOSHED) developed in Michigan by Gage

and others (1982) for a large number of pests

on a wide variety of crops (Croft and Knight,

1983).

Experiences with these and other software

systems have demonstrated their great value

and identified areas where improvements are

needed. It has also pointed out that the data

base from which biological models are developed is limited, Since all biological models are

only as good as the biological information upon

which they are based, the continued development and improvement of such models for use

in integrated pest management (1PM) is contingent on continued high-quality research on

the appropriate aspects of plant and pest biology and ecology,

The advantages provided by computer software are tremendous, in terms of improved

efficiency and accuracy with which pest management decisions can be made and implemented. There is a great deal of effort currently

being devoted to the development of new software and the improvement of existing software. This, in conjunction with the rapid advances being made in computer hardware,

provides a powerful force that will lead to

dramatic changes in the implementation of

1PM and to increases in the level of sophistication of 1PM, where such increases are

desirable.

A detailed description of all technologies examined in this study will be presented in OTA’S

full report later this year,

IMPACT OF EMERGING TECHNOLOGIES ON PRODUCTION

To help analyze the impact of emerging tech- technologies. Participants in the workshops—

nologies on agricultural productivity, OTA on animal and plant agriculture—provided data

commissioned leading scientists in each of the on: 1) the timing of commercial introduction

28 technology areas studied to prepare papers of each technology area, 2) the number of years

on the state of the art. The papers were valu- needed to adopt the technology (by commodable resources for workshops conducted to ity), and 3) yield increases (by commodity)

assess the impacts of emerging production expected from the technology. Workshop par-
Ch. 2—The New Technologies ● 1 3

ticipants included physical and biological

scientists, engineers, commodity extension

specialists, economists, agribusiness representatives, and experienced farmers.

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 four alternative environments:

1.

2.

3.

4.

Baseline environment—assumes to 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 and

adoption of technology.

No-new-technology environment—assumes

that none of the technologies identified in

the study will be available for commercial

introduction by 2000.

Less-new-technology environment—assumes to 2000: a) no real rate of growth

in research and extension expenditures,

and b) all other factors less favorable than

those of the baseline scenario.

More-new-technology environment—assumes to 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 baseline scenario.

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 baseline 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.

Historical trend lines of efficiency measurements of crop and livestock production were

provided to the participants as a starting point

for their assessment of impact on productivity.

Through the Delphi process, participants collectively projected the primary impacts of the

technologies on each of the nine commodities

for 1990 and 2000 under the different environments. Based on the information obtained from

the workshops on the year of commercial introduction, the adoption profile, and the primary impacts, OTA computed crop yields and

production efficiencies for the nine commodities for 1990 and 2000 (table 2-2).

Projections of Agricultural yield

Under the baseline environment, major crop

yields are estimated to increase from now until 2000 at a rate ranging from 0.8 percent per

year, for soybeans and cotton, to 1.3 percent

per year, for wheat. Wheat yield, for example,

is projected to increase from 35.6 bushels per

acre in 1982 to 44.8 bushels per acre in 2000

at the rate of 1.3 percent per year under the

baseline environment. However, under the nonew-technology environment, wheat yield

would increase to 40,8 bushels per acre in 2000

at the rate of 0.8 percent a year. The difference

in wheat yield between the two environments,

4 bushels per acre, represents the impact of

new technologies.

Under the baseline environment, feed efficiency in animal agriculture would increase at

a rate of from 0.4 percent per year for beef to

0.8 percent for poultry, In addition, the reproduction efficiency would also increase, at an

annual rate ranging from 0.5 percent, for beef

cattle, to 0.9 percent, for swine. Milk production per cow per year would increase from

12,300 pounds (lbs) to 17,563 Ibs per cow in the

period 1982-2000. Without new technologies,

milk production per cow per year would increase to only 13,700 lbs in 2000; under the
14 . A Special Report for the 1985 Farm Bill

Table 2-2.—Estimates of Crop Yields and Animal Production Efficiency

No-new- More-newtechnology Baseline technology

environment environment environment

1982 1990 2000 1990 2000 1990 2000

Corn bu per acre 115 117 124 119 139 121 150

Cotton lb per acre 481 502 511 514 554 518 571

Rice bu per acre 105 105 109 111 124 115 134

Soybean bu per acre 30 32 35 32 37 33 37

Wheat bu per acre 36 38 41 39 45 40 46

Beef

Pounds meat per lb feed 0.070 0.071 0.066 0.072 0.072 0.072 0.073 Calves per cow 0.90 0.94 0.96 0.95 1.0 0.95 1.04

Dairy

Pounds milk per lb feed 0.94 0.94 0.95 0.95 1.03 0.96 1.11

Milk per cow per year

(thousand lb) 12.3 13.7 15.7 14.0 17.6 14.2 19.3

Poultry

Pounds meat per lb feed 0.44 0.52 0.53 0.53 0.57 0.53 0.58

Eggs per layer per year 245 255 260 258 275 257 281

Swine

Pounds meat per lb feeda

0.165 0.167 0.17 0.17 0.176 0.17 0.18

Pigs per sow per year 14.4 14.8 15.7 15.2 17.4 15.5 17.8

aTh~ value shown for swine feed efficiency for 1982 is the average of national feed efficiencies fOr the IIJ Years Prior to 1982

The national aggregate linear trend of swine feed efficiency is sllghtly negative and gwes a value of ,157 In 1982.

SOURCE. Office of Technology Assessment

more-new-technology environment, production could reach 19,300 lbs.

Projections of food Production

The data obtained from the two technology

workshops were used in an econometric model

developed by the Center for Agricultural and

Rural Development at Iowa State University to

assess the collective impact of the 28 areas of

emerging technologies on the production of various crop and livestock products.

Table 2-3 shows projections to 2000 of increased production for three major U.S. export

commodities (which comprise 60 percent of

U.S. agricultural food production exports).

Under the baseline environment, corn production is projected to increase at the rate of 1.8

percent per year from 1981 to 2000. However,

without the new technologies examined in this

study, the rate of growth would be only 1.2 percent. Under the more-new-technology environment, corn production would increase at a

much faster rate—2.2 percent per year,

About the same growth rates were obtained

for wheat production, which would increase

at 1.8 percent per year from 1981 to 2000 under

the baseline environment. Under the no-newtechnology environment, wheat production

would increase at only 1 percent per year,

A more drastic increase in soybean production is projected from now until 2000 regardless of the environment considered. The annual

production of soybeans is projected to increase

under the baseline environment at an annual

rate of 2.8 percent from 1981 to 2000. Without

new technologies, the production is still expected to increase at 2,4 percent a year. Under

the more-new-technology environment, soybean production would increase at 2.9 percent

per year.

In the world marketplace available information points to a series of periodic surpluses and

deficits in agriculture over the next two decades (Mellor, 1983; Resources for the Future,

1983). A Resources for the Future (RFF) study

indicates that global balance between cereal

production and population will remain quite

close to 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
Ch. 2—The New Technologies ● 1 5

Table 2-3.—Projection of Major Crop Production

2000

No-new- More-newtechnology Baseline technology

Crop Unit 1981 environment environment environment

Corn

Production Million bushels 8,136

Growth rate Percent

Wheat

Production Million bushels 2,704

Growth rate Percent

Soybean

Production Million bushels 1,953

Growth rate Percent

10,289.0 11,499.0 12,394,0

1.2 1.8 2,2

3,273.0 3,825.0 4,063.0

1.0 1.8 2.2

3,067.0 3,311.0 3,351.0

2.4 2.8 2.9

SOURCE Off Ice of Technology Assessment

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 to nearly double by 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 in various

forms could mean rapid increases in yields and

productivity. While such changes may improve

the competitive position of American agriculture, they have the potential for creating 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 regarding the quantity and quality

of resources available to agriculture in the

future. Land, water, and technology are likely

to 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 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 increased quantity of land used in production (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 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 for

the rest of this century.

Under the baseline environment, growth

rates in production, which include additional

land resources and new technology, will be

adequate to meet the 1.8 percent needed to balance world supply and demand in 2000. Under

the more-new-technology environment, production could increase at 2,2 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 lessnew-technology environment the production

of major crops in 2000 would drop to 1.6 per-
16 ● A Special Report for the 1985 Farm Bill

cent per year, a growth rate that would not be rent administration proposal to reduce the agriable to meet the demand. Under the no-new- cultural research budget is accepted by Contechnology environment, the annual rate of gress, the rate of production growth would be

production growth would be reduced further somewhere between 1.1 to 1.6 percent.

to 1.1 percent. It should be noted that if the cur-
Chapter 3

The Changing Character of
Chapter 3

The Changing Character of

the U.S. Agricultural Sector

Who will use a new technology is as important a consideration as which technology will

be adopted, for the distribution of technology

has a considerable impact both on agricultural

production and on the structure of the agricultural sector.

The emerging technologies examined for this

study 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. This chapter provides a perspective for analyzing technology’s

distributional impacts on agricultural structure

by surveying the characteristics of that structure and noting the past and present factors

that define it.

THE 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 dollars’ 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, while 7 percent have more

than 1,000 acres.

The number of farms reached a peak of about

6.8 million 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 in nominal terms 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

now relies 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 also 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

19
20 ● A Special Report for the 1985 Farm Bill

in terms of value of production as measured

by gross sales per year. Farms can be usefully

classified into the five categories of gross sales

shown in table 3-1.

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.

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 offfarm 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 diverse range of 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 corporations, a much higher

the other three classes. In

of contracting and vertical

higher in these classes.

percentage than in

general, the degree

integration is much

Table 3-1 .—Sales Classes of Farms

Amount of gross

Class sales per year

small . . . . . . . . . . . . . . . . . . . . . . Less than $20,()()0

Part-time . . . . . . . . . . . . . . . . . . . $20,000 to $99,999

Moderate

commercial . . . . . . . . . . . . . . .

Large

$100! 000 to $199,000

commercial . . . . . . . . . . . . . . . $200,000 to $499,999

Very large

commercial . . . . . . . . . . . . . . . $500,000 and over

SOURCE: Off Ice of Technology Assessment

Changes in Farm Size and Numbers

Major changes in the structure of U.S. agriculture can be seen in the changes in the number of farms in these classes since the 1969

Census of Agriculture. Inflation in commodity

prices has tended to move large numbers of

farms from lower sales classes into higher sales

classes. Even after the number of farms is

redistributed to counteract these nominal

changes, the real number of small farms has

declined by about 22 percent—a dramatic decline. (Recent reports that the number of small

farms has actually increased since 1978 refer

to farms that are small in acreage, not small

in sales.) The number of part-time farms has

also declined by about 18 percent. The number of moderate farms has increased substantially, by about 39 percent, and the number of

large and very large commercial farms has increased even more dramatically, by about 43

percent and 53 percent, respectively. Even

though the number of moderate farms has increased, the loss of these farms in share of sales

and net income to large and very large farms,

as shown in the next section, more accurately

indicates the changing character of American

agriculture.

Changes in Distribution of

Saks and Income

Changes in the number of farms do not alone

give the whole picture. Changes in the distribution of sales and income are more important

and clearly show the direction in which U.S.

agriculture is heading. In the sections that follow, sales and income data presented reflect

redistributions calculated to adjust for the impact of inflation.

Between 1969 and 1982, sales by small farms

declined from 9 to 6 percent. Sales from parttime farms declined from 43 to 22 percent. The

market share of moderate farms increased from

13 percent of total sales to 19 percent. In the

same period the market share of large and very
.. .

Ch. 3—The Changing Character of the U.S. Agricultural Sector ● 21

large farms increased greatly–from 36 to 57

percent.

The most telling changes of all have occurred

in the distribution of net farm income. The

large and very large farms have not only captured the majority of the market but also controlled or reduced their cost of production. In

1974 these commercial farms had a 47-percent

market share and 35 percent of net farm income after adjustment for inflation. In 1982,

just 8 years later, with their market share at 54

percent, these farms had 84 percent of net farm

income (table 3-2). Very large farms have been

responsible for the majority of this growth. This

class, which accounts for only 1.2 percent of

all farms, increased its real share of net farm

income fourfold—from 16 to 64 percent. By

comparison, small farms in 1982 had a negative net farm income, and part-time farms had

declined from 39 percent in 1974 to 5 percent

of total net farm income. Moderate farms have

seen a substantial decrease in net farm income,

from 21 percent in 1974 to 11 percent in 1982.

It is clear that if these trends continue, small

and part-time 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.

Large and very large farms will completely

dominate agriculture. The number of moderate

farms may continue to increase, but they will

have a small share of the market and a declining share of net farm income.

Moderate farms comprise most of the farms

that depend on agriculture for the majority of

their income. Traditionally, the moderate farm

has been viewed as the backbone of American

agriculture. These farms appear to be failing

in their efforts to compete for their historical

share of farm income.

Changes in Sources of Income

Employment and the sources of income of

U.S. farmers have changed greatly in the 20th

century. These changes occurred at a rapid rate

in the 1970s. The largest single source of

change was the tremendous increase in labor

productivity made 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 disposable nonfarm income.

This income differential resulted in the large

migration of the farm labor force out of agriculture and rural areas. This out-migration accelerated after the Great Depression of the

1930s because employment and per capita income opportunities increased considerably

outside of agriculture. In general, the marginal

productivity of labor was higher outside the

agricultural sector from the 1930s to the early

1970s. Therefore, migration of labor from

farming to the nonfarm sector contributed to

national economic growth.

In the 1970s, the average income differential

between farm and nonfarm households nar-
22 ● A Special Report for the 1985 Farm Bill

rowed to about 88 percent, owing 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.

In 1982 the average income of farm and nonfarm households was quite close, $27,577 and

$28,638, respectively. However, two-thirds of

the income of farm households came from offfarm sources. The majority of farm operators

today have some off-farm employment.

The average income statistics mask economic problems that exist in the middle of the

scale of sales classes of farm operations (table

3-2). Farms in the part-time class, with sales in

the range of $20,000 to $99,999, are in serious

trouble. About 580,000 farms in this class in

1982 had an average total income of about

$15,000. Their average net income from farming was only $2,033. These farms are not large

enough to generate much net farm income and

have lower-than-average off-farm incomes. In

contrast, farmers with sales of less than $20,000

have substantial off-farm incomes and low or

negative net farm income. The average off-farm

income of these individuals enables them to

maintain this way of life.

Those owning moderate farms have sufficient off-farm income to maintain a household.

However, this group may be under the most

stress. To provide an adequate total income,

moderate farm owners must earn almost as

much off-farm as on-farm income. Farmers

with sales in excess of $200,000 have moderate off-farm incomes and moderate-to-very

large net farm incomes. As a group, these

farmers are well-off.

Changes in the Structure of Debt

in The Farm Sector

At a time when agricultural production has

become more concentrated, the structure of

debt in the farm sector has also become more

concentrated. This process accelerated during

the boom years of the 1970s. The size and concentration of farm debt, combined with high

production costs and the continuing likelihood

of low commodity prices, have led to a great

deal of concern about the financial condition

of the farm sector. A substantial proportion of

the U.S. farm sector is under severe financial

stress. Financial stress is defined as the perceived inability of the firm or individual to

meet cash flow commitments in the form of

cash farm expenses, debt repayment requirements, tax payments, or family living needs.

This stress can be measured indirectly by use

of the debt-to-asset ratio. In general, the distribution of high debt-to-asset ratios is more important than the average debt-to-asset ratio of

all farms. The percentage of farms with debtto-asset ratios greater than 40 percent and

greater than 70 percent in January 1984 by

gross sales class is shown in table 3-3,

Clearly, debt use is closely related to farm

size. To the extent that debt-to-asset ratios show

potential financial problems, beginning farmers

and operators of larger farms are likely to be

in more difficulty than are other farmers,

An important aspect of outstanding debt is

the risk of default from the lender’s standpoint.

If those with the largest proportion of debts to

assets are more likely to suffer losses, then

there are important risk elements facing agricultural lenders. In January 1984, 24 percent

of the total agricultural debt was owed by

farmers with over a 70-percent debt-to-asset

ratio. Another 32 percent was owed by farmers

with debt-to-asset ratios in the range of 40 to

Table 3-3.—Distribution of Farms With High

Debt-tomAsset (d/a) Ratios, by Sales Class, January 1984

Highly Very highly

leveraged leveraged

(d/a ratios: (d/a ratios:

40 to 70”/0) over 70°\o)

“/0 of No. of 0/0 of No. Oj

Sales class class farms class farms

Less than $50,000 . . . . . 8.3 123,200 5.0 74,800

$50,000 to $99,999 . . . . . 14.7 44,000 8.7 26,400

$100,000 to $249,999 . . . 18.1 52,800 9.2 26,400

$250,000 to $499,999 . . . 19.0 17,600 12.6 11,000

$500,000 and over . . . . . 17.4 5,200 15.3 4,500

All farms . . . . . . . . . . . 11.1 242,800 6.6 143,100

SOURCE U S. Department of Agriculture, 1983 Farm Producllon Expenditure

Survey.
Ch. 3—The Changing Character of the U.S. Agricultural Sector ● 23

70 percent, Thus, over one-half of outstanding

debt was owed by operators with debts greater

than 40 percent of their assets, This is a matter of great concern for lenders, since poor

farm incomes or decreases in asset values will

more quickly erode the equity of highly leveraged operators than of high-equity operators

(Brake, 1985),

Another useful way to illustrate increasing

financial stress is through the recent increases

in debt service burdens, This increase can be

measured by the amount of interest expense

as a percentage of cash receipts after payment

of intermediate production expenses, business

taxes, wages, and rents. By this measure, the

debt burden of U.S. farms was 17 percent in

1975. By 1981 it had reached 35 percent and

has been in the range of 34 to 38 percent ever

since. This has resulted in substantial reductions in the amount of receipts remaining to

pay for the operator’s labor, for the owner’s

equity in the business, for purchases of capital durable goods, and for payments of interest and principal.

The consequences of increasing financial

stress can be seen in increasing rates of payment delinquency and foreclosure. For example, Production Credit Association loan chargeoffs were under 0.1 percent in 1978 and 1979.

By 1983 these charge-offs had risen to 1.2 percent of outstanding loans—an elevenfold increase in 4 years, Similarly, the number of

loans in process of liquidation was negligible

in the late 1970s. Data on these loans were not

even kept in the Farm Credit System. By 1982,

loans in process of liquidation approached 1

percent of outstanding loans, and as of March

1984, Production Credit Association loans in

the process of liquidation were over 2.5 percent of all outstanding loans,

DEFINING 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

Concentration refers to the proportion of production controlled by the largest firms, It is important 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 to that in many of the other economic sectors is generally low. As shown in

chapter 2, concentration has occurred to the

point where in 1982 about 28,000 very large

commercial farms—1,2 percent of all farms—

produced one-third of the total value of U.S.

farm products and accounted for over 60 percent of U.S. farm net income.
24 ● A Special Report for the 1985 Farm Bill

However, concentration in land resources is

also occurring.1 Trends in the distribution of

harvested cropland according to sales class

show that these productive acres are rapidly

becoming concentrated in the farms in the

large commercial and very large commercial

sales classes. Table 3-4 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 from

commodity to commodity. For example, beef

cattle operators with 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

I 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.

Table 3-4.—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

$500,000 + . . . . . . . . . . . . . . . 6.0 11.2 12.0 14.0

Total . . . . . . . . . . . . . . . . . . 18.0 36.5 39.0 46.0

Projection Assumptions:

I Growth ,n total hamested acres IS \lIIf?ar, r&+Ultln9 In an increase ‘f 24 ‘Illlon

z~~~e~t~~~~~&. the Ilnear trend for the two sales ClaSSeS and results In an increase of 27 milllon acres per year for the farms in the $200,000-$499,000

class and of 1 milllon acres per year for the $500,000+ class.

%he Ilnear projections are based on the acres harvested by sales classes, adjusted for inflatlon. In flatlon in commodity prices tends to move acres from

lower to upper sales classes Since !nflatlon 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

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 fed 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 (Breimyer and Barr, 1972). This would

result from increased merchandising and marketing costs, the potential unionization of agricultural workers, and 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
Ch. 3—The Changing Character of the U.S. Agricultural Sector ● 25

percent of farm sales in 1981 (Knutson, et al.,

1983). He makes the following observations on

the

1.

2.

extent of contracting:

Contracting used to be limited to perishable products; now it has expanded to virtually all commodities.

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.

Ownership 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 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

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 piece-wage 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
26 ● A Special Report for the 1985 Farm Bill

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.

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 opportunity for

input to 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—that is, are there unique

social, cultural, and traditional values in having land ownership widely dispersed, or should

agriculture join the mainstream where the

other economic sectors have long been? As

U.S. agriculture continues along the trends laid

out in this report, it will increasingly take on

characteristics of the nonfarm sector. Some

will interpret this trend as progress; others will

interpret it as a step backward.

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 (fig. 3-1).

This section briefly defines these forces.

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

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-wheeldrive 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 different scales 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,
Ch. 3—The Changing Character of the U.S. Agricultural Sector ● 27

Figure 3-1 .—Factors Influencing the Structure of Agriculture

Economic environment

 Growth of demand

● Consumer tastes and preferences

Institutional factors (agriculture specific)

● Credit institutions (PU blic 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 Off Ice of Technology Assessment

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—for example, 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 cost-declining 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

farms. Second, while the lowest average cost

of production may be attainable on a moderate 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.

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
28 ● A Special Report for the 1985 Farm Bill

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.

They 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. 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. The

result is high requirements for credit to finance

new capital investments, production, or storage.

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.

Institutional 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 Service

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 agricultural research and

extension policies is the generation of higher

levels of training and expertise embodied in

human capital, The result is more skilled

farmers, agribusinessmen, scientists, and agricultural policy makers.

Research and extension have had differential impacts on farms, farm workers, rural com-
Ch. 3— The Changing Character of the U.S. Agricultural Sector 29

munities, and even entire regions, depending

on their characteristics and the type of technology developed, Some technological innovations, particularly mechanical innovations,

have favored and hence fostered larger farms.

Other 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 the extension service 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 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 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 farms,

The Bergland initiative on research was

reemphasized 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 farmer 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—

national macroeconomic policy, for example—

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 have 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 what they would otherwise have been;

there is much less agreement about their long-
so . A Special Report for the 1985 Farm Bill

term effects on income. Price stability from

these programs has, however, enabled farmers

to adopt new and improved technologies.

Commodity programs along with technological advances influence structural change in

agriculture through the following mechanisms.

Since farmers are price takers, no one farmer

can significantly influence the aggregate supply of a commodity and hence the price that

he receives. However, the individual farmer

can do something about his operating costs. By

adopting a new technology an innovative

farmer increases productivity and lowers his

firm’s cost structure. Since price is not affected

at the early stage of technology adoption, he

reaps a profit. As his cost structure falls, the

farmer increases his output at the given price,

It is possible that innovative farmers used some

of their profits to buy up assests of less efficient

neighbors, thus starting the change in the structure of farming. As more farmers realize the

benefit of new technology and follow this innovator, the adoption of the new technology

becomes widespread. As they do this, aggregate supply increases, and the price of the

product declines. After a period of adjustment

a new equilibrium is reached at a lower price,

a situation in which the innovator no longer

receives a profit and in which the laggard adoptors of new technology suffer an economic loss.

This dynamic interaction has been referred to

as the “agricultural treadmill” (Cochrane,

1958).

Under a commodity program in which the

price of the commodity is supported, the same

treadmill concept applies. However, under such

a commodity program the price does not fall

when the aggregate supply increases, because

the product price is supported by Government

action. Instead, each early adopter continues

to reap a profit and seeks to expand output by

acquiring the land of his less innovative neighbors. Thus, farm technological advances coupled

with Government-supported product prices result in structural change in which productive

assests in farming are concentrated in the

hands of aggressive, innovative farmers. However, since the total amount of arable land is

limited, competition for this land between the

innovative farmers causes the price of land to

rise, The cost of production will thus rise until a new equilibrium is reached in which the

expanded, innovative farmers are back in a noprofit situation while the laggard adoptors end

up with a loss. In this case the equilibrium is

reached by an increase in land values rather

than a fall in product prices.

Tax Policy .—Tax laws and provisions are

widely recognized as being a determinant of

agricultural structure. There is not agreement,

however, about the relative importance of tax

policy because of tax policy’s 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

investment 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.

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).

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

large-scale feedlots in the High Plains area.
Ch. 3—The Changing Character of the U.S. Agricultural Sector ● 31

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),

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 the large four-wheeldrive 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 off-farm interests against which to offset farm losses than do moderate farms. This

could be a significant factor in accounting for

the decline of the moderate farm.

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,

Agricultural Credit Policy .—Public policy

directly influences the supply of capital to

farmers through the Farmers Home Administration (FmHA) of the USDA and the Farm

Credit System, which includes the Federal

Land Bank, Production Credit Association, and

Bank for Cooperatives. The original capital for

the Farm Credit System was supplied by the

Federal Government, but the system is now

STRUCTURAL CHANGE

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.

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.

● The institutions that lead to the development and assimilation of new technology.

“ .

The 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

more general. As this happens, supplies increase, with the tendency to force down mar-
32 ● A Special Report for the 1985 Farm Bill

ket 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. Moreover, the

higher returns gained by early adopters of technology encourage them to seek expansion of

output by acquiring more land. Given the fixed

land base, however, innovative farmers can

only grow in size by acquiring the land of their

neighbors. Thus, growth and prosperity of

large, progressive farmers can only take place

by the failure of those who are slow to adopt

technology.

These consequences often lead to suggestions of turning off the technological wheels

of progress. Such a strategy, however, would

have a devastating effect on the competitiveness of American farmers in world markets.

Instead of just some people being left behind,

the whole American farm system would be left

behind.
Chapter 4

Economic Impacts of

Emerging Technologies and

Selected Farm Policies for
Chapter 4

Economic Impacts of

Emerging Technologies and

Selected Farm Policies for

Various Size Crop Farms

The impacts of emerging technologies will

spur many adjustments at the farm level. Policymakers must thus consider several questions

as they debate the 1981 farm bill: Who will

adopt these technologies and benefit the most

from them—the moderate farms, large farms,

or very large farms? What set of farm policies

in conjunction with technology advance will

benefit each size of farm the most? What combination of emerging technologies and farm

policies encourages each size of farm to grow

or remain at its present size? How important

is technology compared to farm policy in determining farm growth? What is the likelihood of

a new entrant in agriculture remaining solvent?

To help answer these questions, this chapter

and the next will present the findings of an

analysis of selected regions in the United States

that represent significant agricultural production in the commodities considered in farm policy: dairy, corn, cotton, soybeans, rice, and

wheat, Within each production region analyzed, representative commercial farms were

identified for each of the three size categories:

moderate, large, and very Iarge.1 It was assumed that the technology development and

ISmall and part-time farms were not included because these

farm operators in general depend on off-farm employment for

their primary source of income.

adoption conditions in existence would be

those of the baseline environment outlined in

chapter Z.

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,

acreages 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.

The following sections present 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 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.

35
36 . A Special Report for the 1985 Farm Bill

THE CROP FARMS ANALYZED

Corn-Soyboan Farms in the Corn Belta

The North Central Region of the United

States produces approximately 50 percent of

the total production of corn and soybeans. Representative farms for this region are the three

farms from the corn-soybean cash grain area

of east central Illinois and the three farms from

the irrigated row crop area of south central

Nebraska.

The representative farm situations developed

and used in this analysis were constructed

from two basic data sources: 1) national costof-production surveys by the U.S. Department

of Agriculture (USDA) in 1978 and 1983, and

2) farm record data collected and analyzed by

the Universities of Illinois and Nebraska. The

size of representative farms and acreages of

owned and rented cropland were developed

from the size distributions in the USDA costof-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.

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 Agricultural Finance Survey were used to

depict the beginning financial characteristics

for the six representative farms (tables 4-1 and

4-2).

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 11-

2These representative farms were developed and analyzed in

the 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

for the Office of Technology Assessment by W. B. Sundquist.

Table 4-1 .—Financial Characteristics of Three

Representative Corn-Soybean Farms

in East Central Illinoisa

Farm size

Moderate Large Very large

Cropland acres . . . . . . . . . .

Acres owned . . . . . . . . . . . .

Acres leased . . . . . . . . . . . .

Value of owned

real estate ($1 ,OOO)b . . . .

Value of machinery ($1,000)

Long-term debt ($1,000) . .

Intermediate-term

debt ($1,000) . . . . . . . . . .

Initial net worth ($l,OOO)C 

.

Leverage ratio (fraction) . .

Long-term debt/asset

(fraction) . . . . . . . . . . . . .

Intermediate-term

debt/asset (fraction). ., .

Equity ratio (fraction) . . . .

Off-farm income ($1,000). .

Minimum family living

expenses ($1 ,000). . . . . .

Maximum family living

expenses ($1 ,000). . . . . .

Marginal propensity

to consume (fraction) . .

640

260

380

900.5

92.2

126.1

55.3

855.4

0.21

0,14

0.60

0.82

8.2

18.0

36.0

0.20

982

429

553

1,480.6

104.8

557.4

62.9

1,027.6

0.61

0.38

0.60

0.62

7.4

20.0

40.0

0.20

1,630

458

1,172

1,538.4

129.0

579.2

83.8

1,106.4

0.60

0.38

0.65

0.63

7.6

24.0

48.0

0.20

a A family size of four persons was assumed for the purposes of estimating familY

labor supply and determining appropriate income tax rates

b Includes land and buildings.

c May include assets other than land, buildings, and machinery

SOURCE: Office of Technology Assessment.

linois 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, they produce a substantial acreage of grain sorghum under a

nonirrigated (dryland) regime. Production on

this dryland acreage tends to be somewhat

riskier than for the irrigated component of their

farming operations, but irrigated farming still

has some year-to-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), and dryland sorghum (35.7 per-
Ch. 4—Economic Impacts of Emerging Technologies and Selected Farm Policies for Various Size Crop Farms ● 37

Table 4-2.—Financial Characteristics of Three

Representative Irrigated Corn Farms

in South Central Nebraskaa

Farm size

Moderate Large Very large

Cropland acres . . . . . . . . . .

Acres owned . . . . . . . . . . . .

Acres leased . . . . . . . . . . .

Value of owned

real estate ($1 ,OOO)b . . . .

Value of machinery ($1 ,000)

Long-term debt ($1 ,000) .

Intermediate-term

debt ($1,000) . . . . . . . . . .

Initial net worth ($1 ,OOO)C 

.

Leverage ratio (fraction) . .

Long-term debt/asset

(fraction) . . . . . . . . . . . . .

Intermediate-term

debt/asset (fraction).

Equity ratio (fraction) . . . .

Off-farm income ($1,000). .

Minimum family living

expenses ($1 ,000). . . . . .

Maximum family living

expenses ($1 ,000). . . . .

Marginal propensity

to consume (fraction) . .

672

302

370

477.7

102.7

123.2

40.1

448.3

0.39

0.26

0.39

0.72

8.2

18.0

36.0

0.20

920

530

390

838.4

112.1

102.0

53.7

839.0

0.20

0.12

0.48

0.84

8.2

18.0

36.0

0.20

2,085

1,042

1,043

1,648.3

183.9

291.1

98.0

1,463.1

0.27

0.18

0.53

0.79

9.7

24.0

48,0

0.20

a A family size of four persons was assumed for the purposes of e.Stimatin9 familY

labor supply and determln{ng appropriate Income tax rates

b Includes land and bulld!ngs

c May Include assets other than land, bulldlngs, and machinew

SOURCE Off Ice of Technology Assessment

cent). 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, while the price of cropland on the Nebraska farms reflects the differentials for four categories of land: 1) gravity irrigated, 2) sprinkler irrigated, 3) dryland

with irrigation potential, and 4) dryland without irrigation potential. Each of the three

Nebraska farms do, however, have the same

proportions of gravity irrigation, sprinkler irrigation, and dryland acres.

Wheat Farms in the Southern Plainsa

Approximately 65 percent of the 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 and are representative of wheat farms

in western Kansas, eastern Colorado, and the

Oklahoma and Texas Panhandle.

The three farms selected for the analysis are

the 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 tabIe 4-3. 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 who had real estate debt.

Average long- and intermediate-term debt-toasset ratios from the Agricultural Finance

Survey were used to estimate initial values for

long- and intermediate-term debts. All three

wheat farms had about the same beginning

equity levels (75 percent) (table 4-3). 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

sThese representative farms were developed and analyzed in

the paper “Economic Impacts of Selected Policies and Technology on the Economic Viability of Three Representative Wheat

Farms in the Southern Plains, ” prepared for the Office of Technology Assessment by James W. Richardson.
38 . A Special Report for the 1985 Farm Bill

Table 4-3.—Financial Characteristics of Three

Representative Wheat Farms by Size

in the Southern Plains

Farm size (acres)

Characteristics Moderate Large Verv Iarge

Cropland acres owned . . . 640 840

Cropland acres leased . . .

Acres of pastureland owned

Value of owned

cropland ($1,000) . . . . . .

Value of owned pastureland

Value of machinery ($1,000)

Value of off-farm

investments ($1 ,000) .

Beginning cash reserve

($1 ,000)

Long-term debt ($1;000)”

Intermediate-term debt

(1,000) . . . . . . . . . . . . . .

Initial net worth ($1 ,000)

Equity ratio (fraction) . .

Leverage ratio (fraction)

Long-term debt/asset

(fraction) . . . . . . . . . . .

Intermediate term

debt/asset (fraction). .

Off-farm income ($1,000)

Minimum family living

expenses ($1 ,000). . . .

Maximum family living

expenses ($1 ,000). . . .

Marginal propensity

to consume (fraction)

. .

. .

. .

. .

. .

. .

,.

.

,.

,.

.

640

120

296.0

29.4

241.9

37.3

10.0

60.2

83.2

470.3

0.77

0.31

0.19

0.34

12.4

18.0

40.0

0.25

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

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.

General Crop Farms in the

Delta Region of Mississippi

The Mississippi Delta is an excellent region

for analysis of general crop farms. Farms in

4These representative farms were developed and analyzed in

the paper “Economic Effects of Selected Policies and Technology on the Economic Viability of General Crops Farms in the

Delta Region of Mississippi, ” prepared for the Office of Technology Assessment by B, R. Eddleman.

this area can produce a variety of crops not

possible in other parts of the United States. The

representative 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 4-4 provides a summary of

the financial and resource characteristics for

the three representative farms. The long- and

intermediate-term debt-to-asset ratios for the

1,443-acre farm and the 3,119-acre farm were

obtained from USDA’s Agricultural Finance

Survey and adjusted to reflect the equity levels

as reported from a 1983 mail survey of farms

Table 4-4.—Financial and Resource Characteristics

for Three Representative General Crops Farms

in the Delta of Mississippi, 1983

Farm size

Characteristics Moderate Large Verv Iarge

. ,, . 0 . . Age or rarm operator- . . . .

Family sizea 

. . . . . . . . . . . .

Cropland acres owned . . .

Cropland acres leased . . .

Acreage of principal crops

in 1983:

Cotton . . . . . . . . . . . . . . .

Rice . . . . . . . . . . . . . . . . .

Soybeans. . . . . . . . . . . . .

Wheat (or other

small grains) . . . . . . . .

Value of owned cropland

($1,000) . . . . . . . . . . . . . . .

Value of farm machinery

($1,000) . . . . . . . . . . . . . . .

Value of off-farm

investments ($1 ,000) . . .

Beginning cash reserve

($1,000) . . . . . . . . . . . . . .

Long-term debt ($1,000) . .

Intermediate-term debt

($1,000) . . . . . . . . . . . . . . .

Net worth ($1,000) . . . . . . .

Total equity to assets

(fraction) . . . . . . . . . . . . .

Long-term debt/asset

(fraction) . . . . . . . . . . . . .

Intermediate-term

debt/asset (fraction). . . .

Off-farm income ($1,000). .

Minimum family living

expenses ($1 ,000). . . . . .

Maximum family living

expenses ($1 ,000). . . . . .

Marginal propensity

to consume (fraction) . .

44

4

533

910

395

305

640

82

799.5

378.9

129.1

31.9

331.4

243.8

748.6

0.56

0.41

0,64

18.3

18.0

27.0

0.25

44

4

1,419

1,700

1,088

574

1,190

247

2,128.5

786.7

210.3

71.1

840.8

413.0

1,921.5

0.60

0.40

0.52

18.2

24.0

36.0

0.25

44

4

3,064

3,120

2,250

871

2,539

180

4,596.0

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

a Values for the age anrj family we variables assumed for Slm14ating the effects

of alternative farm program provisions for the representative farms

SOURCE: Office of Technology Assessment.
Ch. 4—Economic Impacts of Emerging Technologies and Selected Farm Policies for Various Size Crop Farms ● 39

in the Delta. These debt ratios are the average

for part-owner 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 were adjusted by the equity levels

reported for the largest farm size group.

The mix of acreages planted in each crop

changes 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 farm 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 to each crop

was changed to reflect these relative differences in crop mix.

Cotton Farms in the Texas

Southern High Plainss

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). These size

farms account for 31 percent of the farms and

———SThese representative farms were developed and analyzed in

the paper “Economic Impacts of Selected Policies and Technology on the Economic Viability of Three Representative Cotton Farms in the Texas Southern High Plains, ” prepared for the

Office of Technology Assessment by James W, Richardson.

62 percent of the cotton lint produced in the

Texas Southern High Plains.

Table 4-5 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 USDA’s 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.

Table 4-5.—Financial Characteristics of Three

Representative Cotton Farms by Size in the

Texas Southern High Plains

Farm size

Characteristics Moderate Large Very large

Age of operator . . . . . . . . . 42 .- - .

Acres owned. . . . . . . . . . . . 381

Acres leased . . . . . . . . . . . . 707

Value of owned

cropland ($1 ,000) . . . . . 222.4

Value of machinery

($1,000). . . . . . . . . . . . . . . 144.5

Value of off-farm

investments ($1 ,000) . . . 59.0

Beginning cash reserve

($1 ,000) . . . . . . . . . . . . . . . 16.7

Long-term debt ($1,000) . . 61.1

Intermediate-term

debt ($1 ,000) . . . . . . . . . . 98.3

Initial net worth ($1,000) . 275.0

Equity ratio (fraction) . . . . 0.62

Leverage ratio (fraction) . . 0.61

Long-term debt/asset

(fraction) . . . . . . . . . . . . . 0.27

Intermediate-term

debt/asset (fraction). . . . 0.68

Off-farm income ($1,000). . 16.0

Minimum family living

expenses ($1 ,000). . . . . . 15.2

Maximum family living

expenses ($1 ,000). . . . . . 50,0

Marginal propensity

to consume (fraction) . . 0.25

SOURCE: Office of Technology Assessment

1048

2,335

611.7

420.8

110.0

52.0

120.9

203.6

854.8

0.72

0.40

0.20

0.48

0.0

29.1

50.0

0.25

3,453:;

2,117

2,015.4

713.9

213.7

85.5

488.7

475.4

2,032.3

0.67

0.49

0.24

0.67

0.0

38.0

60.0

0.25

POLICY AND TECHNOLOGY SCENARIOS

The three representative farms for each pro- farm policy scenarios (including a continuation

duction region were analyzed for the period of the 1981 farm bill), an income tax provision

1983-92 under alternative policy scenarios, Six scenario, two financial stress scenarios, a tech-

— nology option, and a new-entrant scenario

6The current version of the Firm Level Income Tax and Farm

Policy Simulator (FLIPSIM V), developed by James W. Richard- were analyzed for each farm. All assumptions

son and Clair J. Nixon, was used to simulate the three repre- and policy values associated with each scesentative farms in each region. nario were held constant across farm sizes to
40 ● A Special Report for the 1985 Farm Bill

allow direct comparison of their impacts on

different size farms. Appendix A contains summary tables of the analysis for each farm size

by region.

Farm Policy Scenarios

Current Policy

The current policy scenario involves continuation through 1992 of current income tax

provisions and of the price supports, income

support, and supply control programs of the

1981 farm bill. In addition, it is assumed that

annual mean crop yields for the three representative farms will increase as new technologies are introduced and adopted by farmers

in the baseline technology environment. For

this policy scenario it is assumed that the following farm policies are 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. T

An acreage diversion/set-aside program is

in effect for 1983-85, using the actual acreage reduction levels and diversion payment rates specified for these years.

A target price-deficiency payment program is available for corn, cotton, rice,

sorghum, and wheat in all years.

The $50,()()0-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

—

The 1977 farm bill established FOR as a 3-year extension of

the CCC loan after grain had been in the regular loan for !3

months, Stocks remain in the farm operator’s control until the

Secretary of Agriculture authorizes release.

of Agriculture Block. Loan rates and target

prices for 1985 are held constant through 1992.

No acreage reduction program was 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 an

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 reduce

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 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.

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,

have higher net farm incomes, and have stronger

financial positions.
Ch. 4—Economic Impacts of Emerging Technologies and Selected Farm Policies for Various Size Crop Farms ● 41

Results Obtained:

●

●

●

●

●

Except for Texas cotton farms, all farms

in the other four regions had a 100-percent”

probability of remaining solvent over the

l0-year period. For Texas cotton farms, the

probability 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 the average, all three farms were able

to grow by purchasing and leasing cropland. Moderate farms grew in size at a

faster rate than the very large farms. The

moderate and large grain farms grew at approximately the same rate of growth.

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 is devoted to soybeans, and this crop does not receive a

deficiency payment.)

Ratios of net farm income to total Government payments reveal that, across all regions, the moderate farms are more dependent on Government payments to maintain

their incomes than are 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

cost, 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, a

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 all regions, the larger the farms, the

greater the increase in net farm incomes.

With increased farm incomes and reduced

price risk, all three farms in all regions experienced increases in real net worth with

the price supports program.

Average ending farm sizes were not significantly different as a result 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

8The 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 anaiyzing an investment over several periods, a positive present value

would indicate an economically attractive decision; a negative

present value would not.
42 . A Special Report for the 1985 Farm Bill

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 $50,00()-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 farm. For farms of other regions, the

probability of survival was 100 percent,

with or without income support.

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 $50,00()-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 relative 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 $50,0()()-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 far-ins, 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

(Acroage 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 that participating farmers 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-
Ch. 4—Economic Impacts of Emerging Technologies and Selected Farm Policies for Various Size Crop Farms ● 43

percent set-aside with a 5-percent diversion for

corn, cotton, rice, sorghum, and wheat in 1986-

92. Normal slippageg (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.

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 a 20-

to 300-percent increase in net farm income

for almost all farms.

Average ending farm size for all three farm

sizes increased relative to the initial farm

size.

%lippage 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.

●

●

●

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 at a faster rate 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,
44 . A Special Report for the 1985 Farm Bill

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 of very large farms

decreased from 94 to 78 percent.

The probability of having a positive aftertax net present value declined significantly

for all farm sizes in each of the four regions except 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 the moderate farms.

The probability of a positive net present

value was 100 percent in the Mississippi

Delta without the farm program, Qwing

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 w a s

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 $743,000 and $1,100,800 decline 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 on average 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 dollars’ 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 set-aside

diversions). Mean prices and relative variability in prices were not adjusted because a sufficient number of “small” farms were assumed

to participate 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 with 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.
Ch. 4—Economic Impacts of Emerging Technologies and Selected Farm Policies for Various Size Crop Farms ● 45

Results Obtained:

 Moderate farms consistently producing less

than $300,000 in program crops exhibit the

same growth rates, net farm incomes, and

ending financial positions as they do under

the current policy.

 Farms that grow beyond or are initially

larger than the $300,000 threshold level of

sales experience 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 of their

level of total sales being less than $300,000.

 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 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 are

less dependent on farm programs than are

smaller size farms.

Tax Policy Scenarios

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 dis-position 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 the first 6 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
46 . A Special Report for the 1965 Farm Bill

●

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 its rate of growth in leased cropland

increased by 49 percent.

The changes in the tax provisions resulted

in reduced annual net farm incomes on 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, investment tax credit,

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

of crops only from the use of existing technologies. A comparison of these simulated results

with 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:

●

●

●

Technology advance would have the greatest impacts on wealth accumulation, net

farm income, and rate of growth in acres

controlled for very large farms that adopted

the technology first and had it in use over

a longer period of time.

The greater the increase in productivity

through technology advance the greater

should be the rate of increase in wealth,

net farm income, and rate of growth in

acres controlled.

Technology advance in the presence of

price and income support programs would

have greater impacts on growth in real

wealth, farm acres controlled, and net

farm income than it would in the absence

of these programs.

Results obtained:

●

●

●

Farm commodity policies had more effect

on the final amount of acres controlled

than did technology advance, across all

sizes of farms in all regions.

Technology advance had little impact on

the final amount of acres controlled in all

regions. Yield-enhancing benefits from

emerging technologies increased average

final farm size from o to 2 percent in the

Delta, Illinois, and Texas and from 6 to 10

percent in the Southern Plains. The greatest increase in farm size occurred on very

large farms in the Southern Plains under

the current policy scenario because these

farms are principally wheat producers,

and 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).

Farms did not exhibit any appreciably

larger rates of growth in real wealth and

farm size under price and income support

programs than under open market conditions. But in the presence of technology advance, annual net farm income increased

relatively more under the price and income support program than under open

market conditions.

Flows of new technology for all commodities in all regions were found to increase

annual net farm incomes relatively more

than real wealth and ending farm acreage

across all sizes of farms. Net farm income

was increased relatively more for the very

large farms than for the moderate and

large farms, across all farm policies

evaluated.

Implications for the 1985 Farm Bill

. Farm programs have major impacts on rates

of growth in farm size, wealth, and incomes

of commercial farmers.
Ch. 4—Economic Impacts of Emerging Technologies and Selected Farm Policies for Various Size Crop Farms 47

●

●

●

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.

Moderate farms are much more dependent

on farm programs to maintain their incomes ●

than are very large farms.

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 than to moderate farms.)

Targeting of income supports to moderate

farms is an effective policy to prolong their

survival.

Very large farms can survive without income

supports. A loan safety net may be needed

to deal with instability and world competitive environment.

FINANCIAL STRESS AND NEW ENTRANTS SCENARIOS

Financial Stress Scenarios

The financial position of many farmers is

under severe stress. As discussed in chapter

3, the situation is serious and may not improve

for some time. policy makers are considering

various solutions to this problem, Two of the

most discussed alternatives are interest subsidy

and debt restructuring. To analyze the effects

of these two financial bail-out policies, the financial position of the three representative

farms in each of the four regions was 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-toasset ratios were set equal to 60 percent, and

annual interest rates on old loans were increased to their average values for 1980-83.

Interest Subsidy

An interest subsidy is a loan at below-market

interest rates. For example, if the Government’s

cost of money is 11 percent and the Farmer’s

Home Administration makes loans at 5 percent, there is a 6-percent direct interest rate

subsidy. 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. An interest subsidy

for the first 2 years of the lo-year simulation

was provided. Interest charges on both longand intermediate-term debt were set

cent annually for the two years.

The results expected are:

● Higher probability of survival.

at 8 per-

● 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.

Deb? Restructuring

Debt restructuring refers to the rescheduling

of loan commitments. Debt may be 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

to obtain a longer repayment period. For the

highly leveraged farms, debt restructuring was

provided through increasing the length of

intermediate-term loans by 1 year and by converting a portion of the intermediate-term debt
48 . A Special Report for the 1985 Farm Bill

to long-term debt as long as the long-term debt

to asset ratio did not exceed 65 percent.

Restructuring debt has the same type of expected effects as interest rate subsidy; however,

they differ in their methods. 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 Experienced 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 survival rate

and average net farm income more than did

the restructuring of farms’ debts.

Both debt restructuring and interest subsidy

policies resulted in increased growth in real

wealth (i. e., ending net worth) on the very

large farms in all regions.

Except for Texas cotton farms, the very large

farms with high debts in each region are not

as dependent upon financial bail out strategies for survival as the moderate and large

farms.

Debt restructuring resulted in less rapid rates

of growth in rear wealth than interest rate

subsidies on moderate and large farms in

the Corn Belt and High Plains regions.

New 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 obtain 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 en-
Ch. 4—Economic Impacts of Emerging Technologies and Selected Farm Policies for Various Size Crop Farms ● 49

trants 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-aside and

paid diversion provision.

Results Obtained:

New entrants exhibited considerably lower

probabilities of survival under the base

farm policy than did current operators for

all specialized crop farms. Only the diversified crop farms in Nebraska and the

Mississippi Delta exhibited relatively high

probabilities of survival for new entrants

under current farm commodity policy.

New entrants experienced much lower

rates of real wealth accumulation than did

current operators under current policy. In

three 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.

New entrant farm operators in the High

Plains wheat and Nebraska and Illinois

crop regions were unable to increase farm

size over the l0-year period under current

farm policy. The Texas cotton farm and

Mississippi Delta crop farms experienced

considerable growth, 20 and 27 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 acreage (about

6 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 lo-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 policy resulted in new entrant

farmers in all four regions facing little

chance of surviving and becoming economically viable farming operations.

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.

Implications for the 1985 Farm Bill

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 to ease financial stress.

Very large farms with high debts are not as

dependent on these programs for survival as

moderate farms. under either of these programs, very large farms 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.
Chapter 5

Economic Impacts of

Emerging Technologies and

Selected Farm Policies for
Chapter 5

Economic Impacts of

Emerging Technologies and

Selected Farm Policies for

Various Size Dairy Farms

One of the most controversial policy areas

in the 1985 farm bill debate is expected to be

in dairy policy—in 1983 a large amount of

surplus milk production cost taxpayers approximately $2.6 billion. For that reason, there will

be many alternatives proposed to the current

dairy program. This chapter examines the current state of the dairy industry, identifies the

technologies most likely to affect the industry

from 1983 to 1992, identifies policy options

most likely to be considered in the 1985 farm

bill, and analyzes the effects of these options

on moderate, large, and very large dairy farms

in major U.S. dairy production regions.1

I The representat i~re farms were developed and analyzed in the

paper “Economic, Polic}, and Technology Factors Affecting

Ilerd Size and Regional l,ocation of U.S. Milk Product ion,”

prepared for the Office of Technology Assessment h~ Eloyd Nl,

~UXtoI1.

During the 1970s, milk production increased

41 percent in the Southwest region of the

United States and 33 percent in the Northwest,

while total milk production increased only 11

percent (fig. 1). 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, less than 5,000 well-managed dairies with

1,500 cows each could have produced all the

milk sold commercially that year.

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 miIk to plants regulated by Federal milk

marketing orders was 63 cows per farm (table

5-1). 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 5-1).

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 Lake States region. Generally, dairy herds

are much larger in the Southwest, Southeast,

and Northwest regions than in the Lake States

and Northeast regions.

From the herd size information in table 5-1,

22 dairies were selected to represent existing

herd sizes in five major dairy areas (table 5-2),

The 200-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.

53
54 ● A Special Report for the 1985 Farm Bill

Figure 5-l.— How the Dairying Picture Has Changed

(percent change in milk production in various regions from 1970-71 to 1980-81)

SOURCE: U.S. Department of Agriculture

Table 5-1 .–Total Producers and Size Distribution of Herds Selling Milk to Plants Regulated by

Federal Milk Marketing Orders, May 1983a

Average herd size (milk cows) for:

Total producers Largest Smallest

Region (State) (number) All farms 10 percent 70-89 percent 40-69 percent 40 percent

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

a The 1 zo 655 farms accounted for about 69 percent of all milk produced in May 1963, but excluded most farms in California and other states where there is no Federal

milk order,

SOURCE: Boyd M Buxton and John P, Rourke, “Size Distribution of Dairy Farms Marketing Milk Under Federal Milk Orders, ”

Service, U.S. Department of Agriculture, April 1964.

unpublished report, Economic Research
Ch, 5—Economic Impacts of Emerging Technologies and Selected Farm Policies for Various Size Dairy Farms ● 55

Table 5.2.— Representative Dairies by Region and Herd Size

.—

Herd size Cropland Housing facilities

—

Silage storage Total labor

Region/State (cows) (acres) (type) Sun shades Feed produced (type) (W/e)b

Lake States:

Minnesota . 52

Minnesota . 125

Northeast:

Pennsylvania 52

Pennsylvania . 125

Pennsylvania ., 200

New York 52

New Yor k 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

Stanchion

Free stall

No

No

Most

Most

Upright

Upright

2,03

3.30

2.2

3.8

5.54

2.21

5.54

14.36

188

449

156

375

600

156

600

1,800

Stanchion

Free stall

Free stall

Stanchion

Free stall

Free stall

No

No

No

No

No

No

Forage

Forage

Forage

Forage

Forage

Forage

Trench

Trench

Trench

Trench

Trench

Trench

Trench

Trench

NA

NA

NA

4.5

7.84

7

11

18

400

700

0

0

0

Free stall

Free stall

Open field

Open field

Open field

Yes

Yes

Yes

Yes

Yes

Forage

Forage

None

None

None

o

0

0

0

0

0

Corral

Corral

Corral

Corral

Corral

Corral

Yes

Yes

Yes

Yes

Yes

Yes

None

None

None

None

None

None

NA

NA

NA

NA

NA

NA

13

7

12

16

9

16

Free stall

Corral

Corral

No

No

No

Silage

Most

None —.

Trench

Trench

NA

2.96

5.0

10.5

51

400

0

a Housing types are

● Stanch/on A conventional barn with locking stanchions In which cows are Im I I ked and fed

● Free sta// A covered barn w(th Indlvldual stalls In wh{ch cows freely enter and exit

● Open fie/d A field where cows are kept that IS large enough to mal ntal n p!ant cover

● Corra/ A drvlot open pen where cows are kept and fed at a fencel I ne feeder

b Labor In worker equivalents of 2,500 hours annuall Y

NA—not applicable

SOURCE Off Ice of Technology Assessment

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 exists, but rather

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 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.

approximate modern sizes and types of oper- Open fields with sun shades are assumed in ations. Florida. one-half acre per cow is provided,

The 52-cow dairies in Minnesota, Pennsyl- allowing fields to remain grass-covered to minvania, and New York use the conventional imize mud problems. Cows are milked twice

stanchion barns for housing and milking cows a day in a milking parlor, After leaving the

(table 5-2). For larger herds in the Lakes States, milking parlor, they are fed concentrates in a
56 Ž A Special Report for the 1965 Farm Bill

feed barn before being released back to the dairy, Dairy operations in Pennsylvania, New

field. Roughage is fed loose in the open fields, York, and Georgia purchase most of the conThe source of feed follows the common prac- centrates but produce most of the forage used

by their dairy herds. All feed is assumed to be tice existing in the various States. For New

Mexico, Arizona, California, and Florida, most produced on-farm for the Minnesota and the

feed is purchased from off the dairy operation. ZOO-cow Idaho dairies.

The same is assumed for the 550-cow Idaho

POLICY AND TECHNOLOGY SCENARIOS

Eight representative dairy operations of the

22 presented in table 5-2 were selected to simulate selected policy and technology scenarios.

The likelihood of a particular dairy remaining

solvent under alternative policies is directly affected by its financial characteristics. 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 5-3. The averages

were approximated from the U.S. Department

of Agriculture (USDA) farm financial survey.

2The 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 representative

farms in each region.

The eight dairy operations in three regions

were simulated for 10 years under the alternative scenarios described below. Seven policy

scenarios (including the 1983 base described

in a previous section) and two technology

scenarios were simulated for each dairy. The

assumptions and policy values associated with

each scenario were held constant across all

dairies to allow direct comparison of their impacts on different size dairies in different

regions.

Two financial stress scenarios (interest subsidy and debt restructuring) were evaluated for

the Minnesota 52-cow and 125-cow, Arizona

359-cow, and Florida 350-cow dairies, assuming an initial high debt position and assuming

a new entrant with high debt position. Each

scenario is described below, along with the exTable 5-3.—Financial Characteristics Assumed for Eight Dairy Operations in Four States

Herd size in:

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

Buildings ($1,000) . . . . . . . . . . . . . . . . . . 92.7

Farm machinery ($1,000). . ...........104.1

All livestock ($1,000) . . . . . . . . . . . . . . . . 77.9

Off-farm investments ($1,000) . . . . . . . . 5.5

Beginning cash reserves ($1,000) . . . . . . . 12.0

Debt:

Long-term ($1,000) . . . . . . . . . . . . . . . . . . 111.2

Intermediate-term ($1,000) . . . . . . . . . . . 57.1

Initial net worth ($1 ,000) . ..............417.1

Equity ratio (fraction) . . . . . . . . . . . . . . . . . 0.71

Family living:

Minimum ($1,000). , . . . . . . . . . . . . . . . . . 20.0

Maximum ($1,000) . . . . . . . . . . . . . . . . . . 32.0

Marginal propensity (fraction) . . . . . . . . 0.3

Off-farm income ($1 ,000) . . . . . . . . . . . . . . 0

SOURCE Office of Technology Assessment.

679.1

176.7

159.0

181.4

13.1

62.5

213.9

88.5

969.4

0.76

25.0

35.0

0.4

0

39.4

192.8

120.3

599.6

0

89.8

67.3

230.4

744.2

0.71

25

30

0.3

0

160.0

284.4

183.1

960.7

0

137.5

155.5

308.8

1,261.3

0.73

27

38

0.4

0

312.0

512.6

303.0

2,505.0

0

35.9

288.6

842.4

2,537.5

0.69

30

40

0.4

0

262,5

87.9

114.6

525.5

0

70.0

143.7

160.0

756.9

0.71

25

30

0.35

0

450.0

108.9

180.0

981.4

0

212.0

218.0

243.9

1,464.7

0.76

27

38

0.4

0

1,074.0

211.7

260.7

2,344,3

0

505.5

475.7

468.9

3,343.0

0.76

30

40

0.4

0
Ch. 5—Economic Impacts of Emerging Technologies and Selected Farm Policies for Various Size Dairy Farms ● 57

pected results and the observed results from

the analysis. Appendix B contains summary

tables of the analysis for each farm size by

region.

Farm Policy Scenarios

Current Policy

The current policy assumes the continuation

of the Dairy and Tobacco production Stabilization Act of 1983 through September 30, 1985.

The Government stocks of dairy products 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 1983 act.

All features of the 1983 act are scheduled to

expire on September 30, 1985. It is assumed

that the support price will remain at the 1985

level through 1986, then rise to $13.11 for manufacturing milk through the end of the lo-year

simulation period.

Results Expected.—Under current policy, it

is 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 is also expected

that well-managed dairies in all regions should

be able to survive under a continuation of the

current program. Farms that are 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 l0-year planning horizon. The 52-cow dairy experienced a 54-

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 (0o percent) due to having

the highest unit cost of production. It lost

an average of $27,000 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

current policy.

Results Expected.—Feed cost represents

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 most

likely 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.

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 to that

of the current policy, the average annual

net farm income of the 1,436-cow California dairy declined 64 percent from $375,000

to $136,000.

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

on 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,
58 ● A Special Report for the 1985 Farm Bill

subjecting the dairyman who purchases feed

to greater risk. For this scenario the Commodity Credit Corporation (CCC) loan, farmerowned 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 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 Lower Price

All the assumptions of the current policy

were retained except that the mean milk prices

were reduced 50 cents per hundredweight

(cwt) and the variability of milk price is increased. This scenario was included in the

analysis because of the current high level of

Government stocks and program costs.

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 are already in financial difficulties under the base policy.

Results Obtained:

●

●

●

No

All farms were more negatively affected

by this policy than by current policy. All

farms experienced more losses under this

policy in net farm income, net present value, and net worth.

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 70 to 38

percent and the probability of a positive

net present value declined from 24 to 14

percent, Other dairies that were adversely

affected included the smaller Florida and

California farms. Therefore, reduced price

supports would force many small dairies

out of business.

Dairy Program

With no dairy program, the price of milk

would drop about 8 percent across the regions

(about $1/cwt) to the variable cost of production in Minnesota and California as excess

stocks and production are eliminated. It was

assumed that this would take 4 years. After

that, prices were expected to increase 6.6 percent ($0.80 /cwt), equal to the average total cost

of production for large-scale dairies in Minnesota and Californians Historical price relationships were maintained,

~The 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 LongTerm Impacts, Economic Report ER83-9, Department of Agricultural and Applied Economics, University of Minnesota, June

1983.
Ch. 5—Economic Impacts of Emerging Technologies and Selected Farm Policies for Various Size Dairy Farms ● 59

Results Expected.—Without a dairy price

support program there would be no guaranteed

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 and large farms

analyzed. The lowest probability of survival was 34 percent for the 52-cow Minnesota dairy.

Net present value declined significantly

for all farms. For example, the very 1arge

California dairy experienced a 43-percent

decline in net present value and a 27-

percent decrease in net worth.

However, the very large farms were still

able to survive in all regions.

SuppIy Control

All assumptions of the base current policy

were retained, except that mandatory quotas

were 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 program is implemented, However, this

economic advantage could be capitalized into

the quota value, thereby eroding the advantage

for new entrants or producers who would have

to purchase quotas to expand milk production.

Results Obtained:

Probability of survival was increased for

all farms of all regions. The 52-cow Minnesota dairy experienced the largest increase in the probability of survival from

70 percent under the base scenario to 92

percent,

Average net present value increased for all

dairy farms. The 52-cow Minnesota dairy

increased from negative $77,000 to $22,000,

Ending net worth was increased for all

dairies due to retained earnings and repayment of debt.

Net farm income for Minnesota dairies

was increased by $15,000. These dairies

previously had the lowest income.

Tax Policy Scenarios

All assumptions of the current policy were

retained except for more restrictive Federal income tax provisions, including 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 could be used to reduce taxable income was $15,600.

The operator must sell obsolete machinery upon 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 increase

the tax liability, reducing the net income of the

operation and leaving less for debt retirement

and increases in net worth.
60 . A Special Report for the 1985 Farm Bill

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

Computer-Controlled

A technology now available but not widely

Scenarios

Feeding

adopted is individual cow feeding by using

computer-controlled feed stalls. with this technology concentrates fed to individual cows can

be controlled in total and over time. One experiment suggests that average daily milk production per cow can be increased 2 pounds

with a 0.1 percent higher butterfat content

without increasing total feed fed to the herd

(Wildhaber, et al., 1984). The estimated added

investment costs for computer feeding for the

three largest dairies were:

Minnesota 125-cow herd . . . . . . . , . . . . . $18,750

Florida 1,436-cow herd . . . . . . ... , ... , $157,960

California 1,436-cow herd . . . . . . . . . . . . $157,900

Investment included a neck responder for

each cow, a feeder stall with storage and auger

feeder, and a computer, It was assumed that

this technology would be adopted only by the

largest dairies in each region; thus, only three

dairies were analyzed.

All other assumptions of the current policy

were retained except that allowances were

made for added investment and operating costs

and for higher average milk production per

cow. The gain in milk production was expected

to exceed the added cost, giving dairy producers a more favorable financial position,

Growth Hormone

A technology not yet in commercial use but

demonstrated in experimental work is bovine

growth hormone. Injecting milk cows with this

hormone every other day would result in increased milk production. preliminary results

are that with this technology, milk production

per cow during the last two-thirds of the lactation period is increased 30 to 40 percent without additional feed (about 23 percent annually).

The cost for the hormone can be expected to

decline since it can probably be produced

cheaply.

Injections given every other day and costing

$1 each are assumed in this analysis. Combining this cost with increased hauling and other

costs of added milk results in about a $185-

increase in cost per cow per year. Once again,

it was assumed that only the largest farms

would adopt, and allowances were made for

added cost and yields,

Results Expected

The expected impact of adopting these technologies is to improve greatly the financial performance of the larger adopting dairies. The

probability of survival and all measures of financial performance would be improved for

the adopting dairies. The disparity in costs and

returns for moderate and very large dairies

could be significantly increased,

Results Obtained:

Large increases in net farm income, net present value, and net worth were experienced

by the adopting dairies, These increases

were significantly larger for the bovine

growth hormones.

Any lag in the adoption of new cost-reducing technologies seriously adversely affected

the ability of dairies to compete.
Ch. 5—Economic Impacts of Emerging Technologies and Selected Farm Policies for Various Size Dairy Farms . 61 —

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 intermediateterm loans. In the second case, interest rates,

total debt loads, and other assumptions of the

high-debt dairies remained the same as under

current policy.

●

●

●

●

●

IMPLiCATiONS FOR

Policies and technologies that are favorable

for the dairy industry 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 Southeast and

Southwest.

Traditional dairy regions will continue to experience increased competitive pressure

The impact of higher feed costs and 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 and new entrants with high

debt had significantly lower probabilities

of surviving than dairies with initial financial situations assumed in current policy.

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 highdebt producers in this region cannot survive under even the current dairy policy.

THE 1985 FARM BILL

from larger scale, more efficient producers

in other parts of the United States, Substantial restructuring of dairies in the Lake States

and Northeast will be required for them to

compete.

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 6

Agricultural Research
—.....—. —.. —

Chapter

Agricultural Research

and Extension Policy

Much of the success of American agriculture

is attributable to the creation of its agricultural

research and extension system (Ruttan, 1982;

Cochrane, 1958). For well over a century, 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 longstanding part of

U.S. agricultural policy—the first Federal appropriations to agricultural research in 1856,

the establishment of the land grant university

system in 1862, and the creation of the FederalState-local extension partnership in 1914 (Knutson, et al., 1983).

The agricultural research and extension system continues to be an important contributor

to a plentiful and low-cost food and fiber

supply, as well as to the positive U.S. balance

of agricultural trade. For the period 1945-79,

technological innovations brought about by the

system increased agricultural output 85 percent, with no change in the level of agricultural

inputs (USDA, 1980).

Agriculture’s entrance into the era of biotechnology and information technology raises sevIAgricultural research and extension policy issues were identified and analyzed in papers prepared by the OTA research and

extension policy workgroup. Authors of the papers were Ronald

Knutson, Roy Lovvorn, George Hyatt, and Fred White. This

chapter is based on an integration prepared by Ronald Knutson, of the workgroup’s findings.

eral 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:

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 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?

These are the major issues that will be addressed in this chapter, The answers are based

on previous OTA studies, on an extensive body

of literature regarding the impact of technology on agriculture, and on papers commissioned by OTA regarding the status of the agricultural research and extension system as it

relates to developments in biotechnology and

information technology.

65
66 . A Special Report for the 1985 Farm Bill

The point that technology is one of the driving forces behind structural change in agriculture has perhaps been most clearly argued by

Willard Cochrane (1983), who notes that the

first adopters of new technology are also the

immediate beneficiaries in that their costs per

unit of production are lowered and their profits are thus increased. The profits of those

firms supplying the products of new technology also increase. In addition, higher profits

for the farmers encourage the adopting farmers

to expand output—even to the extent of increasing the scale of their farm operation. However,

as output expands, prices decline; later technology adopters thus realize less profit. Those

farmers who are the last to adopt new technologies may actually be forced either to adopt or

to get out of agriculture.

Three important lessons arise from this

description of the process of technological

change:

● 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, structure is affected to the extent that

research discoveries or extension programs favor farm operations of a certain

scale. The significance of technology’s role

in fostering structural change makes it an

important factor to consider when designing research and extension programs.

● Research and extension are vital to maintaining the competitiveness and compara-

●

tive advantage of U.S. agriculture in international trade. Competition in export

markets is becoming increasingly keen as

countries strive to expand output and export to earn foreign exchange. Throughout the 1970s, exports were the driving

force behind farm prices and incomes. A

return to agricultural prosperity awaits a

resurgence of exports. Growth in export

markets cannot be maintained without the

benefits of continuous adoption of costreducing technologies.

The ultimate beneficiary of agricultural research and extension is the consumer—

domestic and foreign. Larger supplies,

lower food prices, and better quality have

almost invariably been the main results of

agricultural research. This does not mean

that research operates contrary to the interest of all farmers; rather, research

directly benefits the more progressive

farmers. Research is also critical for expanding markets for farm products and for

overcoming the constant threat of disease

and other vagaries of nature.

The result of these gains and losses has been

a handsome rate of return from public investment in agriculture. Rates of return on public

investment in agricultural research typically

fall in the 30 to 60 percent range (Ruttan, 1982).

Rates of return for extension have been estimated to run even higher—particularly in the

case of specific extension activities (White,

1984). The high rate of return indicates that

agricultural research and extension services

have been highly productive.

The impacts of research and extension on innovations, favor and hence foster larger

farms, farm workers, agribusiness, and rural farms, Other innovations could be applied on

communities depend on the type of technology farms of any size, but are often first adopted

developed and the rate of adoption. Some tech- by larger farms (Paarlberg, 1981; Perrin and

nological innovations, particularly mechanical Winkelman, 1976; White, 1984).
Ch. 6—Agricultural Research and Extension Policy . 67

The extent to which agricultural research

and extension affect farm structure has become

an item of increasing debate and concern, Jim

Hightower (1973) focused and fueled the controversy by concluding that “Agriculture’s preoccupation with scientific and business efficiency has produced a radical restructuring of

rural America and consequently urban America . , . . America’s land grant college complex

has wedded itself to an agribusiness vision of

automated, vertically integrated and corporatized agriculture, ” Hightower’s perspective appears to be that agricultural research and extension should be structurally neutral (i.e., not

favor one farm size over another), but if it

favors anything, it should favor moderate and

smaller farms.

The impact of agricultural research and extension on farm structure can best be understood by considering the separate impacts of

research, extension, and technological adoption on farm structure.

A research program that is structurally neutral would develop technologies that can be

used by any size farm. There is limited evidence about whether the type of agricultural

research being conducted by public institutions

is structurally neutral (White, 1984). Biologicalchemical technologies, the focus of most land

grant research, are more likely to be structurally neutral than is mechanical research, which

is primarily done in the private sector. Mechanical innovations such as the cotton picker, combine, and mechanical tomato harvester have favored large farms by reducing labor requirements and lowering costs on large farms (Schmitz

and Seckler, 1970). The biological-chemical

technologies over the past 50 years have accounted for about a doubling of output in most

farm commodities–i.e., wheat, corn, rice, and

cotton. However, mechanization and economies of size have accounted for a tenfold to

twentyfold increase in output, and this has not

been structurally neutral. In general, there has

been no widespread public recognition of the

consequences of such technological developments before their release and widespread

adoption (White, 1984).

Dissemination that is structurally neutral entails dissemination of research results by research and extension staff to all farmers. Although the extension service disseminates

research results through a wide range of publications, public meetings, and result demonstrations, these means are more readily accessed by the more knowledgeable and better

educated farmers, who more often are the operators of larger, more progressive farms. Since

the topics covered in publications and public

meetings are heavily influenced by current research results, any bias toward larger farms in

these results would be carried over into those

publications and meetings. On the other hand,

one of the criticisms of extension has also been

that operators of the larger, more progressive

farms are more knowledgeable about the state

of the art than are extension staff. This claim

is more likely true of county-level staff than of

the State specialist staff.

Adoption that is structurally neutral involves

the equal willingness and ability of operators

of all farm sizes to adopt new technology.

Adoption neutrality would be hampered if research and/or dissemination were not structurally neutral. But even when research and extension activities are structurally neutral,

adoption may not be neutral because adoption

of new technology is dependent on many factors, including the potential profitability of

technology, the capital investment required,

the natural resources controlled by farmers, the

economic environment within which farmers

operate, and the technical skills of the farmer.

The structural trend in agriculture is quite

clearly toward a bimodal distribution—small

and large farms surviving, with moderate

farms struggling to exist, Small farms are surviving and even increasing in number because

they have off-farm income against which to offset farm losses. Large farms are increasing in

number because their operators are more efficient and can purchase inputs at lower prices,

sell their products at higher prices, obtain more

farm program benefits, and therefore have

higher incomes (Smith, et al., 1984].
68 ● A Special Report for the 1985 Farm Bill

Considering the number and complexity of

these factors, it would be difficult to achieve

a farm structure that maintains the moderate

farm simply by focusing more research and extension resources on producing and disseminating technologies specifically oriented toward

the moderate farm segment. Instead, research

and extension activities would have to be integrated into other targeted policy tools to

achieve the desired structural goals.

Since dissemination and adoption would appear to be more important than research to

structural change, the emphasis in a program

to achieve greater neutrality would logically fall

on highly applied research and extension functions targeted toward the competitiveness and

survival of moderate farms. Such a program

would have to:

Increase public research efforts aimed at

developing farming and management systems that allow moderate farms to achieve

the same technical or production efficiencies as their larger scale counterparts.

Provide higher levels of support for farmer

cooperative research and educational

activities aimed at serving family farm

agriculture, With proper orientation,

farmer cooperatives should be able to

allow moderate farms the same input

economies as larger farms.

● Increase emphasis on the use of modern

marketing and management tools by operators of moderate farms. An understanding of contracting, futures markets, options markets, and committed cooperatives

will be critical to the future survival of the

moderate farm system. In addition, moderate farms will have to use state-of-the-art

computer information and financial systems. Public research and extension will

play the major role in seeing that this

knowledge base is developed and reaches

farmers.

Reorienting the research and extension system in this manner carries some risk, The competitive position of American agriculture in an

open world economy could be jeopardized if,

while concentrating on improving the competitive position of moderate farms, technological

advances for larger farms stagnated. Therefore,

while directing more efforts toward moderate

farms, research and extension must continue

to foster improvements in production, marketing, and management systems for all farm

sizes. Accomplishing such changes would require additional staff, retraining of existing

staff, more resources, and a reorientation of existing resources.

RESEARCH, PRIVATE SECTOR, AND EXTENSION ROLES

One of the most important contemporary

issues that the agricultural research and extension system has had to deal with is that of establishing both the broad priorities for research

and extension and the roles of the components

of the research and extension system. Since the

passage of the 1977 farm bill, considerable progress has been made in establishing roles and

priorities in the various components of the agricultural research system. The Joint Council and

the Users Advisory Board, given sufficient time

and encouragement to perform, have the potential for dealing effectively with the priorities

issue. Positive progress is indicated by the recently reIeased Joint Council Needs Assessment for Food and Agricultural Sciences.

The primary question regarding the roles

issue involves the line of demarcation between

the U.S. Department of Agriculture (USDA)

anci the land grant programs. This issue has

been treated quite differently by research and

extension. OTA’S agricultural research system

study concluded that USDA research should

concentrate on those agricultural problems that

are important to the Nation and for which no

one State or private group has the resources,

facilities, or incentive to solve (OTA, 1981).
— — . — -.. ...—.—.—

Ch. 6—Agricultural Research and Extension Policy ● 6 9

Such a role can logically be assigned to the

USDA Agricultural Research Service and the

USDA Economic Research Service. Concentrating only on national and regional problems

would represent a marked shift by the Agricultural Research Service from its past decentralization policies involving increasing emphasis

on research having a State or local focus.

Private Sector involvement

The land grant university system was established largely because it was concluded that in

a decentralized competitive structure, the private 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 grant system, this premise went largely unchallenged

until the 1970s.

As a result, private sector grants for agricultural research have historically come primarily from foundations such as Ford or Rockefeller and from a small number of grants for

university developmental research associated

with the introduction of new products. With

the advent of biotechnology, the interest of private firms in agricultural research increased

sharply. While much of this interest appears

to be a spinoff of 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 in more 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.

These rights, in turn, gave rise to increased private sector interest in supporting university research that could result in profitable, patented

discoveries.

The current magnitude of private sector commitment to agricultural research is largely unknown. Studies suggest that it may approach

$3 billion (National Agricultural Research and

Extension Users Advisory Board, 1983). Approximately half of the amount 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 shortrun 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 of such research. As

noted previously, private firms have tended to

cut back on research first in times of adversity.

The private sector also plays a role in education. 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 relates more to alerting farmers

to the availability of new products than to

evaluating objectively 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); extension involvement is more

efficient. However, the biotechnology era holds

potential for increased antagonism between

private sector firms and extension because the

extension service evaluates the comparative

performance of new biotechnological products,

a role not always appreciated by firms producing products that have relatively lower levels

of performance,

With a few important exceptions, such as integrated pest management (1PM) checkoff programs, the private sector’s direct financial support for agricultural extension programs has

been limited, but appears to be growing. It

might be argued that limited private sector

funding is essential for keeping extension edu-
70 ● A Special Report for the 1985 Farm Bill

cation programs objective. Greater dangers

may lie more in increased private sector funding of extension than of research. In the funding of both, it is critical to maintain the objectivity and availability of information flows.

Research Involvement

Land grant universities were created to serve

the public. The agricultural component of the

land grant universities has unique responsibilities to conduct and extend the results of

research for the public benefit. Traditionally,

those research results have been readily and

freely available to the public, since they have

no private property or exclusivity rights attached to them. Research results that were to

be held in confidence or had proprietary rights

attached to them were frowned upon. Policy

changes that have occurred over the past 15

years hold the potential for substantially changing this traditional concept of ready and free

access to land grant university research. Some

changes have already occurred; others may occur very rapidly. In other words, changes in

property rights and exclusivity rules may have

also changed the very concept of the land grant

system.

Questions of how the land grant universities

might adjust to the new concept of research

property rights and the related opportunities

for increased private sector funding have been

the subject of extensive study. However, the

impact of these factors on the unique nature

or “social contract” of the land grant system

has received little attention.

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 prevent

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 innovation 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. Most of these firms have developed

their own in-house research capability, employing molecular biologists, biochemists, geneticists, plant breeders, and veterinarians.

Relationships are also developing between

universities and many of these firms. 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

and 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 land

grant college) created the entity Neogen to seek

venture capital for limited partnerships to develop and market innovations arising out of research. The formation of Neogen points up a

significant problem being encountered by

universities in the biotechnology era. Neogen

was formed, in part, for the purpose of retaining faculty members who are getting offers

from biotechnology companies, In Neogen,

faculty members are allowed to develop their

entrepreneurial talent and gain financial rewards while remaining at the university,
Ch. 6—Agricu/tural Research and Extension Policy . 71

The formation of Neogen reflects the reality

that biotechnology development is resulting in

or might result in a substantial drain on university basic and applied research talent. If leading

faculty members are not overtly hired away

from universities, they may form their own

companies or become consultants. The establishment of biotechnology property rights has

substantially heightened scientists’ interest in

private sector employment opportunities. In

the process, questions have arisen over who

should maintain the property right—the university, the private firm, or the scientist.

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 Engenics and its five corporate

sponsors receive the royalty-bearing licenses.

Neogen will buy patent rights from Michigan

State University, while the inventor will get a

15-percent royalty or a stock option in Neogen.

It does not take much imagination to recognize the potentially profound implications of

such developments on the land grant university system. While public sector-private sector

arrangements were kept previously at arms

length, private sector arrangements now integrate business into the university fabric.

Questions develop over who controls the

university research agenda, the allegiance of

scientists to their university employer, the willingness of scientists to discuss research discoveries related to potentially patentable products,

and potential favoritism shown particular companies by the university because of its research

ties,

The advent of patent rights, exclusive licensing, and private sector investment in public

sector research may change the distribution of

benefits from land grant research discoveries.

These changes warrant direct public discussion and consideration by policy makers. They

occur for at least five reasons:

● By exclusive licensing or transferring of

patent rights to private firms, the right to

use discoveries is no longer freely avail-

●

●

●

●

able—even if information on the discovery

itself is freely available.

Certain individuals and 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.

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.

Society thus pays twice: once for the cost

of the research and again for its benefits.

Without the conferral of property rights,

rents are minimized by competition.

Private sector-public sector inequities are

virtually assured in any granting of research property rights to an individual

firm. This occurs because a relatively

small private sector investment brings access to a much broader range of current

and prior research.

The existence of patent rights, trade secrets,

and confidential information has many potentially 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 previously would have been

readily available.

The argument does not, however, flow exclusively against the conferral of private sector property rights by the land grants. There

are

●

three main counterbalancing arguments:

With the conferral of private property

rights and the associated private sector investment, the quantity of research discoveries may increase. Robert Evenson (1983),

for example, found a sharp acceleration in

private plant breeding programs after the

1970 Plant Variety Protection Act was

enacted into law. Over 1,088 patent-like

certificates were granted by February 1,

1983.
72 ● A Special Report for the 1985 Farm Bill

●

●

Without land grant university involvement

in private sector-funded research, the

universities may not be able to retain the

top-quality scientists needed to conduct

agricultural research on the frontiers of

knowledge. In the process, the agricultural

research, extension, and teaching programs would all suffer.

Patent monopoly rights may be necessary

to attract the capital investment needed to

translate the scientific advances of land

grant universities 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.

If policy makers want land grant universities

to refrain from conferring property rights, it

will be necessary for policy makers to provide

the level of funding whereby land grant universities can compete with non-land grant universities that confer such rights. This basic decision may be the most important related public

policy decision since the land grant system was

created. Once the land grant system starts actively competing for private sector grants and

conferring licensing rights, there will be no

turning back.

Extendon Roles

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 widely

studied and critically evaluated within and

without the system in a series of projects extending back to the mid-1960s. In light of these

analyses, the agricultural research system has

adjusted the distribution of its resources in recognition of potential advances evolving from

biotechnology and information technology.

Similar progress is not apparent in extension.

Extension administrators suggest that this is

the case because most of the extension planning occurs at the local level through advisory

committees. Yet such a system does not obviate

the need for setting national plans and priorities. 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,

1983). There is also 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 also dramatically reemphasized its direct education role in the past

20 years (Hyatt, 1984). Although Federal extension specialists were generally viewed as having a vast subject matter base in their own right

and were frequently called upon to engage in

staff training and to conduct educational programs, these specialists are viewed today more

as program leaders, coordinators, and facilitators. The education function is thus left to

State specialists and agents. These changes

were at least partially forced by reductions in

personnel ceilings and limited appropriations.

Regardless of the cause, this change in strategy has not been beneficial to the overall national extension education program, which is

left to cope with a lack of progress in national

planning and needs assessment and a deterioration in the quality of educational service to

the States.

As in research, there are issues of national

significance that the USDA Extension Service

is better able to deal with educationally than

are the States. While ultimately the States must

still take the lead in extending educational programs to farmers, the USDA Extension Service can play an important role in making the

information and related educational materials

available on a timely basis. (For another perspective see Hyatt, 1984.) Currently, this role

is being played on, at best, a spotty basis. A key

mission of the Federal Extension Service

should be to facilitate technology transfer between USDA research agencies and the State

extension services as well as between States.

If this function is not adequately performed,

research agencies become motivated to develop

their own outreach programs. The need then

is for increased integration of the research and

extension function—not greater fragmentation.
Ch. 6—Agricultural Research and Extension Policy ● 7 3

To add Federal extension national program

leaders who are knowledgeable about the state

of the art of technology would be substantially

more expensive. Such staff would have to be

recognized as national extension coordinators

and be provided compensation consistent with

that role. Finally, they would have to have access to resources whereby State specialists and

researchers coordinated to develop state-of-theart educational materials

all States.

The biotechnology era

important challenges to

munity—challenges that

that could be used in

presents some very

the extension comcould determine extension’s future usefulness as an educational

aid to farmers. With renewed emphasis on

basic agricultural research, substantial concern

arises about whether a gap in applied research

will develop, This could occur as applied scientists are attracted into basic research that offers higher rewards, leaving open the jobs in

applied research. The potential for such a gap

is reduced by increased private sector interest

and involvement in biotechnology research

and development (R&D). However, as the private sector performs a larger share of the applied research, extension may become even

more involved in the evaluation of technologies

and products flowing out of the private sector.

Without such evaluation individual farmers

and ranchers will incur the costs of experimenting to determine which combinations are optimum for use in production. These costs will

be converted into a decline in the number of

farms (for those who used the wrong input

combinations), higher food costs, and reduced

competitiveness in international commodity

markets.

Substantial challenge is involved in extension’s adjusting to this new role. While in some

States technology and extension are already

deeply involved in the evaluation of new products, in other States product evaluation has

been primarily a function of experiment stations. In the future, experiment stations will

likely be doing less of this work, and extension’s responsibilities will correspondingly increase, Meeting 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 conflict-oriented product evaluation

programs. To the extent that this occurs, the

usefulness of extension to the farmer will

decline.

Many of the technologies 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.

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 during

the biotechnology era. Without such training,

extension will play an increasingly less important role in production agriculture. Technology transfer will occur less efficiently with

more structural impacts—larger farms will benefit at the expense of smaller farms.

At current funding levels, the most difficult

issue facing extension is whether to limit its

role and coverage to those functions for which

it has the greatest expertise. Without criteria

for limiting the role of extension, extension

activities might become so dispersed and out

of focus that their effectiveness would be impaired. Regardless of whether the problem is

related to agriculture or not, extension may be

called upon to solve it. It is not possible for extension to be everything to everybody, particularly in times of limited resources.

The Joint Council has not given sufficient attention to the role of extension. As a starting

point for defining that 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.
74 “ A Special Report for the 1985 Farm Bill

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 station. Viewing extension

in a broader context than this runs the 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 basic

research and extension, i.e., applied research.

Another dimension of this role problem involves the tendency for the experiment station

to become more involved in extension-type

educational programs as a way of gaining pub-

●

●

●

IMPLICATIONS FOR

Granting of property rights and exclusive

licensing of technological discoveries have

brought the unique nature or “social contract” of land grant universities into question. These new rules may change the distribution of benefits from land grant research

discoveries. These changes warrant direct

public discussion and consideration by policymakers.

Progress of the agricultural research community in establishing priorities is more advanced than that of extension.

The agricultural research system has adjusted the allocation of resources in recognition of potential advances evolving from

biotechnology and information technology.

Similar progress is not apparent in extension.

lic recognition and support. Considerable care

must be taken not to foster such duplication

of efforts.

The 1890 land grant universities have evolved

into institutions that have a comparative advantage in studying problems that are unique to

small farmers—particularly those that depend

on agriculture for a majority of their income.

Satisfactory performance of this function requires a recognition of this role and a closer

working relationship with the 1862 land grant

university in both research and extension programs (Lovvorn, 1984).

THE 1985 FARM BILL

● There is a need to address the following extension issues:

—clientele and mission of extension,

—organizational structure of the extension

system,

—role of Federal extension service, and

—need for extension to conduct applied research.

● Research and extension policy is a critical

component of agricultural structure policy.

For moderate farms to be able to compete,

for example, ways must be developed for

making new technologies more available to

moderate farms and for providing training

in the use of these technologies.
Appendixes
—

Appendix A. -Summary Analysis Tables

for Crop Farms

Table A-1 .—Comparison of Selected Farm Commodity and Income Tax Policy Scenarios on Representative

Corn-Soybean Farms jn East Central Illinois -

Alternative Scenarios= -

Criteria I II VII!

Moderate size-(640 ;cres):

Probability of survival . . . ... ... , ... . 100,0 100.0

Present value of ending net worth ($1 ,000) ., 703,0 743.0

Ending farm size (acres) . . . . . . . . . . ... 902.0 904.0

Annual net farm income ($1,000) . . . . ... 23.2 29.9

Annual government payment ($1,000) . . . . . . 11,6 9.8

Ill Iv v VI Vll —

100.0

703.0

902.0

23.2

11.6

100.0

568.0

824.0

10.2

0.7

100.0

593.0

837.0

11.8

0,7

100.0

669.0

907.0

19.1

8.6

100.0

563.0

834.0

11,1

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

1,044,0

1,784.0

54.4

23.3

Large size (982 acres):

Probability of survival ... . . . . ... ... 100.0 100.0

Present value of ending net worth ($1 ,000) 975.0 970.0

Ending farm size (acres) . . . . . . . . . . . . . 1,374.0 1,364.0

Annual net farm income ($1 ,000) . . . . . . . . . . 24.3 22.9

Annual government payment ($1,000) . . . . ., . 22.6 16.6

100.0

991.0

1,388.0

26.4

24.3

100.0

645.0

1,139,0

14,3

1,0

100.0

693.0

1,180.0

5.2

1,0

100,0

801,0

1,355,0

8.0

7.8

100,0

622.0

1,134.0

1,1

0.0

Very large size (1,630 acres):

Probability of survival ., . . . . ... . . . . 100.0 100,0

Present value of ending net worth ($1 ,000) . 1,267.0 1,348,0

Ending farm size (acres) . . . . . ... . . . . 1,945.0 1,932.0

Annual net farm income ($1,000) . . . . . . . . . 51.8 62.2

Annual government payment ($1,000) . . . . . . . 23.6 19.3

100.0

1,266,0

1,942.0

52.4

25.3

100.0

991.0

1,856.0

31.1

1.7

100,0

1,033,0

1,859.0

35.1

1.7

100,0

1,056,0

1,908.0

34.7

0,0

100,0

1,036,0

1,876,0

34.8

0.0

Table A.2.—Comparison of Selected Farm Commodity and Income Tax Policy Scenarios on

Representative Irrigated Row Crop Farms in South Central Nebraska

Alternative Scenarios a

Criterl a I - II 111 --- lv -

V VI ‘- Vll

Moderate size (672 acres):

—————

Probability of survival . . ... ... ... . . . . . . 100,0 100.0 100.0 92.0 100.0 100.0 90.0

Present value of ending net worth ($1 ,000) . 670.0 736,0 670.0 260.0 476.0 670.0 264.0

Ending farm size (acres) . ... . 921.0 909.0 921.0 882.0 870.0 921.0 808.0

Annual net farm income ($1 ,000) ., . . . . . . . . . 26.8 31.0 26.8 –9.8 10.6 26.8 – 11.4

Annual government payment ($1 ,000) 17.3 14,5 17.3 1.0 1.0 17.3 0.0

Large size (920 acres):

Probability of survival . . . . . . . . . . . . . . . . . . 100.0 100.0 100.0 100.0 100.0 100.0 100.0

Present value of ending net worth ($1 ,000) . 1,349.0 1,377.0 1,369.@ 739.0 1,084.0 1,180.0 750.0

Ending farm size (acres) . . . . . . . . . . . . . . . . 1,257.0 1,253.0 1,257.0 1,242,0 1,240.0 1,257.0 1,243.0

Annual net farm income ($1 ,000) . . . . . . . . . . . . 58.4 60.9 57.4 0.1 35.7 37.4 –0.5

Annual government payment ($1,000) . . . . . . . 24.1 19.3 23.9 1,3 1.3 15.3 0.0

Very large size (2,085 acres):

Probability of survival ., . . . . . . . . . . . . . . . . . . . . 100.0 100.0 100.0 100.0 100.0 100.0 100,0

Present value of ending net worth ($1,000) . . . . 2,259.0 2,374.0 2,407.0 1,013.0 1,863.0 1,270.0 1,007.0

Ending farm size (acres) . . . . . . . . . . . . . . . . . 2,375.0 2,383.0 2,384.0 2,167.0 2,280.0 2,197.0 2,128.0

Annual net farm Income ($1,000) . . . . . . . . . . 118.6 127.3 134.6 1.3 88.0 10,8 –0.1

Annual government payment ($1,000) ... . . . . 35.9 31.5 49.6 3.0 3.0 0.0 0.0

a

The Scenar)os are

1—Contlnuatlon of the 1981 Farm BIII and 1983 Federal Income tax provisions

II –A 20° c Acreage Reduc[lon (n 19861992

Ill – No Farm Program Payment Llmltatlon In 1983-1992

IV– No Price Support and No Deflclency Payment In 19831992

V–No Target Price/Deftclency Payment In 1983.1992

VI —Target Farm Program Benefits to farms that produce less t~an $300,000 In program crops

VII—NO Farm Program (n 1983.1992

Vlll —Reduced Income Tax Benef(ts 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 Llmltat!on can be found by subtracting Scenario 6 from Scenario 4

Vlll

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

77
78 . A Special Report for the 1985 Farm Bill

Table A-3.—Comparison of Selected Farm Commodity and Income Tax Policy Scenarios on

Representative Southern Plains Wheat Farms

Alternative Scenarios a

Criteria I II Ill IV v VI Vll Vlll

Moderate size (1,280 acres):

Probability of survival . . . . . . . . . . . . . . . . . . . . . . . . 100.0 100.0 100.0 76.0 100.0 100.0 48.0 100.0

Present value of ending net worth ($1 ,000) . . . . . 803.0 1,032.0 811<0 283.0 426.0 761.0 189.0 710.0

Ending farm size (acres) . . . . . . . . . . . . . . . . . . . . . 1,901.0 1,955.0 1,901.0 1,565.0 1,648.0 1,910.0 1,478.0 1,757.0

Annual net farm income ($1,000) . . . . . . . . . . . . . . 2.6 18.3 3.1 –33.6 –21.4 –0.9 –41 .6 –8.3

Annual government payment ($1,000) . . . . . . . . . . 30.9 31.5 31.6 2.5 2.5 27.7 0.0 29.4

Large size (1,920 acres):

Probability of survival . . . . . . . . . . . . . . . . . . . . . . . . 100.0 100.0 100.0 50.0 90.0 96.0 32.0 100.0

Present value of ending net worth ($1,000) . . . . . 1,028.0 1,359.0 1,117.0 294.0 475,0 696.0 179.0 833.0

Ending farm size (acres) . . . . . . . . . . . . . . . . . . . . . 2,765.0 2,890.0 2,755.0 2,234.0 2,339.0 2,618.0 2,093.0 2,499.0

Annual net farm income ($1,000) . . . . . . . . . . . . . . 9.0 28.5 17.3 –52.5 –34.9 – 17.6 –67.9 –21.8

Annual government payment ($1,000) . . . . . . . . . . 39.0 39.1 44.7 4.2 3.7 16.2 0.0 37.3

Very large size (3,200 acres):

Probability of survival . . . . . . . . . . . . . . . . . . . . . . . . 100.0 100.0 100.0 100.0 100.0 100.0 92.0 100.0

Present value of ending net worth ($1,000) . . . . . 1,936.0 2,204.0 2,231.0 1,096.0 1,412.0 1,087.0 925.0 1,657.0

Ending farm size (acres) . . . . . . . . . . . . . . . . . . . . . 4,218.0 4,365.0 4,483.0 3,552.0 3,834.0 3,494.0 3,472.0 3,805.0

Annual net farm income ($1,000) . . . . . . . . . . . . . . 48.9 59.5 78.4 –7.8 15.6 – 13.6 – 25.1 28.1

Annual government payment ($1,000) . . . . . . . . . . 44.2 45.0 76.9 5.8 5.9 0.0 0.0 44.1

Table A-4.—Comparison of Selected Farm Commodity and Income Tax Policy Scenarios on

Representative General Crop Farms in the Delta of Mississippi

Alternative Scenarios a

Criteria I II Ill Iv v VI Vll Vlll

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 92.6 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 65.1 147.9 – 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 ofending net worth ($1,000) . . . . . 5,450.0 6,117.0 7,728.0 5,135.0 5,175.0 4,964.0 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) . . . . . . . . . . . . . . 41.8 118.2 277.1 – 19.7 –0.6 – 42.9 – 32.4 5.9

Annual government payment ($1,000) . . . . . . . . . . 49.9 49.8 277.9 7.9 7.9 0.0 0.0 49.9

aThe Scenarios are:

l—Cont!nuatlon of the 1981 Farm Bill and 1983 Federal income tax provisions.

II—A 20°/0 Acreage Reduction in 1986-1992,

III—No Farm Program Payment Limitation in 1983.1992,

IV–NO Price Support and No Deficiency Payment in 1983.1992

V—No Target Price/Deficiency Payment in 1983-1992.

Vi—Target Farm Program Benefits to farms that produce less than $300,000 In program crops.

VII–NO Farm Program In 1983-1992

Vlll —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
App. A —Summary Analysis Tables for Crop Farms ● 79

Table A-5.—Comparison of Selected Farm Commodity and Income Tax Policy Scenarios on

Representative Texas Southern High Plains Cotton Farms

Alternative Scenarios a

Criteria I II Ill Iv v VI Vll Vlll

Moderate size (1,088 acres):

Probability of survival . . . . . . . . . . . . . . . . . . . . . . . . 92.0 94.0 94.0 56.0 68.0

Present value of ending net worth ($1 ,000) ., . . . 564.0 648.0 601.0 242.0 301.0

Ending farm size (acres) . . . . . . . . . . . . . . . . . . . . . 1,558.0 1,635.0 1,648.0 1,216,0 1,274.0

Annual net farm income ($1 ,000) . . . . . . . . . . . . 8.3 13.3 11.9 –28.9 –21.7

Annual government payment ($1,000) ... . . . . . 26.0 22.2 29.5 1.3 1.1

Large size (3,383 acres):

Probability of survival ., . . . . . . . . . . . . . . . . 90.0 94.0 94.0 72.0 82.0

Present value of ending net worth ($1 ,000) . . . . . 1,412.0 1,697.0 1,853.0 931.0 1,055.0

Ending farm size (acres) . . . . . . . . . . . . . . . . . . . . 4,289.0 4,455.0 4,577.0 3,748.0 3,857.0

Annual net farm income ($1 ,000) . . . . . . . . . . . . 33.4 53.6 83.3 – 14.8 3.6

Annual government payment ($1,000) . . . . 38.0 35.1 83.3 3.2 3.0

Very large size (5,570 acres):

Probability of survival 94.0 96.0 98.0 92.0 96.0

Present value of ending net worth ($1,000) . . . . 3,027.0 3,489,0 4,047.0 2,367.0 2,645.0

Ending farm size (acres) . . . . . . . . . . . . . . . . . . . . 6,002.0 6,047.0 6,514.0 5,781.0 5,848.0

Annual net farm income ($1,000) . . . . . . . . . . . . . 66.6 100,6 170.6 –3.2 31.0

Annual government payment ($1,000) . . . . . . . . . . 40.2 39.1 135.8 4.8 4.6

a

The Scenar!os are

l—Continuation of the 1981 Farm BIII and 1983 Federal income tax provisions

II—A 20°/0 Acreage Reduction in 1986.1992

III–No Farm Program Payment Limitation In 1983.1992

IV—NO Price Support and No Deflclency Payment in 1983.1992

V–No Target Price/Deficiency Payment In 1983-1992

VI —Target Farm Program Benefits to farms that produce less than $300,000 in program crops

VII–NO Farm Program In 1983.1992

Vlll —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 dertved by subtracting Scenario 6 from Scenario 1

The Impact of Income Supports w!th a $50,000 Payment Llm!tatlon can be found by subtracting Scenario 6 from Scenario 4

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
80 ● A Special Report for the 1985 Farm Bill

Table A-6.—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 lx x xl lx x x l lx x x l

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

Table A-7.—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 lx x xl - lx x xl lx x xl

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

Table A-8.—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 lx x xl - lx x xl lx x xl

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.() 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

Table A-9.—Comparison of Selected Financial Bailout Scenarios for Three Representative

General Crop Farms in the Delta of Mississippia

Alternative Scenarios for Alternative Scenarios for Alternative Scenarios for

1,443-acre Farm 3,1 19-acre Farm 6,184-acre Farm

Criteria lx x xl - lx x xl lx x xl

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) . . . . . . . . . . . . . 2109.0 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

a

The Scenarios are”

IX—Continuation of the 1981 Farm BIII and the 1983 Federal tax provisions for a highly leveraged farm

X—Restructure of debt for a highly leveraged farm.

X1—lrrterest rate subs!dy (buy-down) in the first two years for a highly leveraged farm
App. A —Summary Analysis Tables for Crop Farms ● 81

Table A-IO—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 lx x xl lx x xl lx x xl

Probability of survival . . . . . . . . . . . 64.0 66.0 72.0 56.0 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

a

The scenar!os are

IX —Contlnuatlon of the 1981 Farm BIII and the 1983 Federal tax provisions for a highly leveraged farm

X—Restructure of debt for a highly leveraged farm

Xl—Interest rate subsidy (buy-down) In the first two years for a highly leveraged farm

Table A-n .—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 X11 X111 Xlv X11 X111 Xlv X11 X111 Xlv

Probability of suwival . . . . . . . . . . 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

Table A-12.—Comparison of Selected Policy Scenarios Assuming No New Technology 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 2,085-acre Farm

Criteria X11 X111 Xlv X11 X111 Xlv X11 X111 Xlv

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.o 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

Table A-l 3.—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 X11 X111 Xlv - Xll X111 Xlv X11 X111 Xlv

Probability of~urvival ., . . . . . . . . . . . . 100.0 90.0 32.0 100.0 Z4.O 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

a

The Scenarios are

X1l —Conttnuatlon of the 1981 Farm BIII and the 1983 Federal tax provisions, assuming no new technology scenar!o

Xl If—No Target Pr!ce/Deflclency Payment Program, assuming no new technology scenario

XIV— Deflclency plus diversion payments and any other government payments received for government loans and storage costs
8.2 ● A Special Report for the 1985 Farm Bill

Table A-14.—Comparison of Selected Policy Scenarios Assuming No New Technology for

Three Representative General Crop Farms in the Delta of Mississippia

Alternative Scenarios for Alternative Scenarios for Alternative Scenarios for

1,443-acre Farm 3,1 19-acre Farm 6,184-acre Farm

Criteria X11 X111 Xlv X11 X111 Xlv X11 X111 Xlv

Probability of suwival . . . . . . . . . . . . . . . 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) ~ . . . . . . . . . . . . . . . . . . . . . . . . . 1,613.0 1,104.0 1,043.0 2,786.0 2,451.0 2,354.@ 5,286.0 4,915.0 4,714.0

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

Table A-15.—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 X11 X111 Xlv X11 X111 Xlv X11 X111 Xlv

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

($l,ooo) . . . . . . . . . . . . . . . . . . . . . . . . . . 552.o 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:

X1l—Continuation of the 1981 Farm Bill and the 1983 Federal tax prowslons, assuming no new technology scenario

XIII—NO Target Price/Deficiency Payment Program, assuming no new technology scenario,

XIV—Deficiency plus dlverslon payments and any other government payments received for government loans and storage costs
—

App. A—Summary Analysis Tables for Crop Farms ● 8 3

Table A-16.—Comparison of Selected Policy

Scenarios for a New Entrant on a Representative

640 Acre Corn”Soybean Farm in

East Central Illinoisa

Alternative Scenarios for

640-acre Farm

Criteria x v Xvl Xvll

Probability of survival . . . . . . . 2.0 0.0 4.0

Present value of ending net

worth ($1 ,000) ... . . 221.0 202.0 197.0

Ending farm size (acres) . 640.0 640.0 640.0

Annual net farm income

($1,000) . . . . . . . . . . . . . . . . . . –56.9 –61 .1 –62,8

Annual government payment

($1,000) . . . . . . . . . . . . . ... 9.6 6.1 0.0

Table A-17.—Comparison of Selected Policy

Scenarios for a New Entrant on a Representative

672 Acre Irrigated Row Crop Farm in

South Central Nebraskaa

—

Alternative Scenarios for

672-acre Farm

Criteria x v Xvl Xvll

Probability of survival . . . . . . . 84.0 42.0 6.0

Present value of ending net

worth ($1 ,000) . . . . . . . . . . . . 187.0 106.0 356.0

Ending farm size (acres) ., . . 674.0 666.0 672.0

Annual net farm income

($1,000) . . . . . . . . . . . . . . –19.2 – 35.8 – 56.6

Annual government payment

($1,000) . . . . . . . . . . . . . . 14.5 1.2 0.0

Table A-18.—Comparison of Selected Policy

Scenarios for a New Entrant on a Representative

Southern Plains Wheat Farma

Alternative Scenarios for

1,280-acre Farm

Criteria x v Xvl Xvll

Probability of survival . . . . . . . 2.0 0.0 0.0

Present value of ending net

worth ($1 ,000) . . . . . . . . . . . . . 39.0 26,0 45.0

Ending farm size (acres) . . . . . 1,280.0 1,280.0 1,280.0

Annual net farm income

($1,000) . . . . . . . . . . . . . . . . . . -94.2 -103.2 – 121,9

Annual government payment

($1,000) . . . . . . . . . . . . . . . . . . 18.1 7.4 0,0

Table A-19.—Comparison of Selected Policy

Scenarios for a New Entrant on a Representative

1443 Acre General Crop Farm in

the Delta of Mississippia

Alternative Scenarios for

1,443-acre Farm

Criteria x v Xvl Xvll

Probability of survival . . . . . . . 100.0 76,0 62.0

Present value of ending net

worth ($1 ,000) . . . . . . . . . . . . . 985,0 395.0 319.0

Ending farm size (acres) . . . . . 1,830.0 1,459.0 1,443.0

Annual net farm income

($1,000) . . . . . . . . . . . . . . . . . . -18.8 -76.8 –91 .3

Annual government payment

($1,000) . . . . . . . . . . . . . . . . . 47.3 2.3 0.0

Table A-20.—Comparison of Selected Policy

Scenarios for a New Entrant on a Representative

Texas Southern High Plains Cotton Farma

Alternative Scenarios for

1,088-acre Farm

Criteria x v X v l Xvl l

Probability of survival . . . . . . . 50.0 16.0 10.0

Present value of ending net

worth ($1 ,000). . . . . . . . . . 235.0 53.0 41.0

Ending farm size (acres) . . . . . 1,306.0 1,155.0 1,126.0

Annual net farm income

($1,000) . . . . . . . . . . . . . . . . . . -35.7 –66. 5 – 84.9

Annual government payment

($1,000) . . . . . . . . . . . . . . . . . 21.7 2.0 0.0

a

The Scenanos are

XV —Contlnuatlon of the 1981 Farm BIII and the 1983 Federal tax pro.

vlslons

XVI—NO Target Prtce/Deflclency Payment Program In 19811992

XVII—NO Farm Program In 1983.1992
Appendix B. -Summary Analysis Tables

for Dairy Farms

Table B-1 .—Comparison of Selected Commodity Policy, Tax and Technology Scenarios on

Representative Dairy Farms in Minnesotae

Alternative Scenanos

Initial

Criteria situation Base I II Ill Iv v VI Vll Vlll

52 cow Minnesota dairy:

Probability of survival NA 70.0 68.0 68.0 38 34.0 70.0 92,0 –

Present value of ending net worth

—

($1,000). ,, 417.0 224.0 222.0 223,0 157,0 149.0 221.0 314,0 –

Ending equity ratio (fraction)

—

0.71 0.41 0.41 0,41 0.30 0.29 0.41 0,57 –

Annual net farm income ($1 ,000).

—

NA –27. 0 – 2 7 –27, 0 –35. 0 –36. 0 – 27.0 –15.0 – —

125 cow Minnesota dairy:

Probability of surviva l NA 100,0 100.0 100.0 100,0 98,0 100,0 100,0 100.0 100.0

Present value of ending net worth

($1!000). ...,,.,,, , 969.0 1,012.0 1,019.0 1,012,0 888.0 872.0 965.0 1,111,0 116.0 1,140.0

Ending equity ratio (fraction) 0.76 0.83 0.83 0.82 0.72 0.71 0.79 0.89 0.92 0.91

Annual net farm income ($1 ,000). NA 0.0 0.0 0.0 – 18,0 – 19,0 –9. 0 15,0 21.0 44,0

Table B-2.—Comparison of Selected Commodity Policy, Tax and Technology Scenarios on

Representative Dairy Farms in the Southweste

Alternative Scenarios

Initial

Criteria situation Base I II Ill Iv v VI Vll Vlll

359 cow Arizona daity:

Probability of survival NA 96.0 86.0 96.0

Present value of ending net worth

($1,000) ,. 744,0 1,404.0 1,018.0 1,399. 0

Ending equity ratio (fraction ) 0.71 0,94 0.84 0.94

Annual net farm income ($1 ,000). NA 28,0 –32. 0 27.0

550 cow California dairy:

Probability of survival NA 96.0 76,0 96.0

Present value of ending net worth

($1,000). 1,261.0 1,824.0 1,107.0 1,813.0

Ending equity ratio (fraction ) 0,71 0.90 0.68 0.90

Annual net farm income ($1 ,000) NA –29. 0 – 142.0 –30. 0

1,436 cow California dairy:

Probability of survival NA 96.0 92.0 96.0

Present value of ending net worth

($1 ,000) 2,538.0 6,507,0 4,968.0 6,496. 0

Ending equity ratio” (fraction)” : : 0.69 0.92 0.89 0.92

Annual net farm income ($1 ,000) NA 375.0 136.0 373.0 —

aThe Scenarios are

Base–Contlnuatlon of present policy

I–A 20 percent acreage reduction In 1986.1992–9 percent higher feed costs

II–No crop program

111–Fifty cent per C W1 lower milk price

IV–NO dairy price support program

V–Reduce Income tax benefit program

V1–Milk supply control program

V1l– Computer feeding technology

Vlll– Bovine growth hormone technology

96.0 92.0 96.0 100,0 — —

1,334.0

0.94

13,0

1,098,0

0.88

–22, 0

1,325,0

0.94

31.0

1,7190

0.95

93,0

—

—

—

—

—

—

84.0 76,0 96.0 98,0 — —

1,368,0

0.79

– 101.0

1,074,0

0.67

– 147,0

1,716.0

0.90

–30. 0

2,312,0

0,94

65.0

—

—

—

—

—

—

9 4 0 92.0 96.0 100,0 100.0 100,0

5,448.0

0.89

211.0

4,774.0

0.88

99,0

6,278.0

0.92

371.0

9,553.0

0,95

7980

8,750.0

.96

614.0

11,084.0

0,94

1,154,0

84
— —

App. —Summary Analysis Tables for Dairy Farms w 85

Table B-3.—Comparison of Selected Commodity Policy, Tax and Technology Scenarios on

Representative Dairy Farms in Floridaa

Alternative Scenarios

.

In[t[al

Crltena sltuatlon Base I II Ill Iv v VI Vll Vlll

350 cow Florida dairy:

Probability of survival NA 9 8 0 7 0 0 9 6 0 82 6 8 0 9 2 0 1000

Present value of ending net worth

($1 .000) 757.0 1,009 0 5780 1,005 0 933.0 648.0 911 0 1,3270 –

Ending equity ratio (fraction) 0.71 0 8 5 0 5 7 0 8 5 0 7 4 0 5 9 0 8 0 0 9 3

Annual net farm Income ($1 ,000) NA –8 O –71 o –8 O –17 o –66 o –3 o 2 8 0

600 cow Florida dairy:

Probablllty of survival NA 100.0 88.0 1000 9 8 0 8 4 0 1000 1000 – —

Present value of ending net worth

($1 ,000) 1,4650 2,1700 1,5470 2,169 0 2,1300 1,6300 1,9960 2,326. 0

Ending equity ratio (fraction) O 76 0 9 4 081 0 9 4 091 0.78 0 9 2 0 9 4

Annual net farm income ($1 ,000) NA 3 3 0 –62 O 3 2 0 3 0 0 –46 O 3 3 0 6 3 0 —

1,436 cow Florida dairy:

Probability of survival NA 1000 100.0 1000 1000 1000 1000 100,0 1000 1000

Present value of ending net worth

($1 !000) 3,3430 8,6910 7,338.0 8,670.0 7,9420 7,7660 8,396.0 9,9540 10,576 0 12,3580

Ending equity ratio (fraction) O 76 0 9 5 0.95 0.95 0 9 5 0 9 5 0 9 5 0 9 5 0 9 5 0 9 5

Annual net farm Income ($1 ,000) NA 5860 411,0 585.0 5040 4630 5820 7490 7820 1.2860

a

The Scenarios are

Base– Conflnuatlon of pre;ent policy

I–A 20 percent acreage ‘eductlon m 1986-”99? 9 oercent higher feed costs

II–No crop program

1ll– F(fty cent per cwt lower milk price

IV–NO dairy price support program

V–Reduce income tax benefit program

VI –Milk supply control program

V1l– Computer Ieedlng technology

Vlll–Bovine growth hormone technology
86 ● A Special Report for the 1985 Farm Bill

Table B-4.—Comparison of Selected Policy Scenarios on Representative Minnesota Dairy Farms That Have

High Debt or Are New Entrants With High Debt

Financial Stress Scenariosa New Entrant Scenariosb

Initial Initial

Criteria situation lx x xl situation xii X111 Xlv

52 cow Minnesota dairy:

Probability of survival . . . . . . . . . . . . . . . . . . . . . NA 0.0 0.0 0.0 NA 0.0 0.0 0.0

Present value of ending net worth ($1 ,000) . . . 246.0 97.0 93.0 101.0 264.0 138.0 122.0 138.0

Ending equity ratio (fraction) . . . . . . . . . . . . . . . 0.42 0.19 0.19 0.20 0.36 0.21 0.19 0.22

Annual net farm income ($1 ,000). . . . . . . . . . . NA –57.0 –46 –57.0 NA –98.0 – 105.0 – 98.0

125 cow Minnesota dairy:

Probability of survival . . . . . . . . . . . . . . . . . . . . . NA 0.0 0.0 0.0 NA 0.0 0.0 0.0

Present value of ending net worth ($1 ,000) . . . 554.0 238.0 235.0 235.0 575.0 304,0 298.0 303.0

Ending equity ratio (fraction) . . . . . . . . . . . . . . . 0.44 0.23 0.22 0.22 0.37 0.23 0.22 0.23

Annual net farm income ($1 ,000). . . . . . . . . . . . NA –92.0 – 73.0 –94.0 NA – 147,0 –163.0 –147.0

Table B-5.—Comparison of Selected Policy Scenarios on Representative Florida and Arizona Dairy Farms

That Have High Debt or Are New Entrants With High Debt

Financial Stress Scenariosa New Entrant Scenariosb

Initial Initial

Criteria situation lx x xl situation X11 X111 Xlv

350 cow Florida dairy:

Probability of survival . . . . . . . . . . . . . . . . . . . . .

Present value of ending net worth ($1,000) . . .

Ending equity ratio (fraction) . . . . . . . . . . . . . . .

Annual net farm income ($1 ,000). . . . . . . . . . . .

359 cow Arizona dairy:

Probability of survival$

Present value of ending net worth ($1 ,000) . . .

Ending equity ratio (fraction) . . . . . . . . . . . . . . .

Annual net farm income ($1,000). . . . . . . . . . . .

NA

466.0

0.44

NA

NA

471.0

0.45

NA

26.0 42.0

334.0 391.0

0.35 0.40

– 101.0 --78.00

66.0 70.0

760.0 840.0

0.64 0.68

–59.0 –35.0

26.0

337.0

0.35

– 101.0

66.0

763.0

0.63

–60.0

NA

527.0

0.41

NA

NA

528.0

0.42

NA

22.0

345.0

0.32

– 174.0

60.0

717.0

0.58

– 102.0

12.0 2.0

165.0 180.0

0.15 0.17

–245.0 –230.0

38.0 24.0

372.0 262.0

0.33 0.27

– 182.0 – 194.0

a~~e financial Scenarios are:

IX—Continuation of present dairy policy and assuming high debt.

X–Subsidize interest rate so effective rate on all loans is 8 percent

Xl–Restructure debt.

bThe new entrant scenarios are.

Xl I—Base Policy and new entrant.

Xlll – New entrant and no price support for dairy products.

XlV—New entrant and a 9-percent increase in feed costs.

 . .

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