a

GAO

United States Government Accountability Office

Report to the Ranking Democratic 

Member, Committee on Agriculture, 

Nutrition, and Forestry, U.S. Senate

September 2006 AGRICULTURAL 

CONSERVATION

USDA Should Improve 

Its Process for 

Allocating Funds to 

States for the 

Environmental Quality 

Incentives Program

GAO-06-969
What GAO Found

United States Government Accountability Office

Why GAO Did This Study

Highlights

Accountability Integrity Reliability

September 2006

AGRICULTURAL CONSERVATION

USDA Should Improve Its Process for 

Allocating Funds to States for the 

Environmental Quality Incentives 

Program 

Highlights of GAO-06-969, a report to the 

Ranking Democratic Member, Committee 

on Agriculture, Nutrition, and Forestry, 

U.S. Senate 

The Environmental Quality

Incentives Program (EQIP) assists

agricultural producers who install 

conservation practices, such as

planting vegetation along streams 

and installing waste storage 

facilities, to address impairments 

to water, air, and soil caused by

agriculture or to conserve water. EQIP is a voluntary program 

managed by the U.S. Department of 

Agriculture’s (USDA) Natural 

Resources Conservation Service 

(NRCS). NRCS allocates about $1

billion in financial and technical

assistance funds to states annually. 

About $650 million of the funds are 

allocated through a general 

financial assistance formula. 

As requested, GAO reviewed

whether USDA’s process for 

allocating EQIP funds to states is 

consistent with the program’s 

purposes and whether USDA has

developed outcome-based 

measures to monitor program 

performance. To address these 

issues, GAO, in part, examined the

factors and weights in the general

financial assistance formula. 

What GAO Recommends

GAO recommends, among other 

things, that NRCS document its

rationale for the factors and 

weights in its general financial 

assistance formula and use current 

and accurate data. USDA agreed

with GAO that the formula needed

review. USDA did not agree with 

GAO’s view that NRCS’s funding

process does not clearly link to

EQIP’s purpose of optimizing 

environmental benefits. It believes

that the funding process clearly 

links to EQIP’s purpose, but it has

not documented the link.

NRCS’s process for providing EQIP funds to states is not clearly linked to 

the program’s purpose of optimizing environmental benefits; as such, NRCS 

may not be directing funds to states with the most significant environmental 

concerns arising from agricultural production. To allocate most EQIP funds, 

NRCS uses a general financial assistance formula that consists of 31 factors, 

including such measures as acres of cropland, miles of impaired rivers and 

streams, and acres of specialty cropland. However, this formula has several

weaknesses. In particular, while the 31 factors in the financial assistance

formula and the weights associated with each factor give the formula an 

appearance of precision, NRCS does not have a specific, documented 

rationale for (1) why it included each factor in the formula, (2) how it 

assigns and adjusts the weight for each factor, and (3) how each factor 

contributes to accomplishing the program’s purpose of optimizing 

environmental benefits. Factors and weights are important because a small

adjustment can shift the amount of funding allocated to each state on the 

basis of that factor and, ultimately, the amount of money each state receives. 

For example, in 2006, a 1 percent increase in the weight of any factor would 

have resulted in $6.5 million more allocated on the basis of that factor and a 

reduction of 1 percent in money allocated for other factors. In addition to 

weaknesses in documenting the design of the formula, some data NRCS uses 

in the formula to make financial decisions are questionable or outdated. For 

example, the formula does not use the most recent data available for 6 of the 

31 factors, including commercial fertilizers applied to cropland. As a result,

any recent changes in a state’s agricultural or environmental status are not 

reflected in the funding for these factors. During the course of GAO’s 

review, NRCS announced plans to reassess its EQIP financial assistance 

formula. 

NRCS recently developed a set of long-term, outcome-based performance 

measures to assess changes to the environment resulting from EQIP 

practices. The agency is also in the process of developing computer models 

and other data collection methods that will allow it to assess these 

measures. Thus, over time, NRCS should ultimately have more complete 

information on which to gauge program performance and better direct EQIP 

funds to areas of the country that need the most improvement.

www.gao.gov/cgi-bin/getrpt?GAO-06-969.

To view the full product, including the scope 

and methodology, click on the link above. 

For more information, contact Daniel Bertoni 

at (202) 512-3841 or bertonid@gao.gov. 
Page i GAO-06-969 Agricultural Conservation

Contents

Letter 1

Results in Brief 3

Background 5

NRCS’s Process for Allocating EQIP Funds to the States Does Not 

Clearly Address the Program’s Purpose of Optimizing 

Environmental Benefits 12

NRCS Has Begun to Develop More Outcome-Oriented Performance 

Measures 20

Conclusions 22

Recommendations for Executive Action 23

Agency Comments and Our Evaluation 23

Appendixes

Appendix I: Objectives, Scope, and Methodology 25

Appendix II: EQIP 2006 Funding Allocation Formulas 28

Appendix III: Statistical Techniques to Determine Influential Factors in 

the 2006 EQIP Financial Allocation Formula 33

Principal Components Regression 33

Factor Analysis of EQIP Environmental Variables 39

Appendix IV: Initial EQIP Funding Provided to the States, Fiscal Year 

2006 44

Appendix V: Historical EQIP Funding Levels, Fiscal Years 2001-2006 46

Appendix VI: Fiscal Year 2005 EQIP Obligations by Conservation 

Practice 48

Appendix VII: Comments from the U. S. Department of Agriculture 55

Appendix VIII: GAO Contact and Staff Acknowledgments 57

Tables Table 1: Fiscal Year 2006 Categories of EQIP Funding 7

Table 2: EQIP General Financial Assistance Formula Factors and 

Weights, Fiscal Year 2006 9

Table 3: EQIP Annual Performance Measures, Fiscal Year 2006 20

Table 4: EQIP Long-term Measures 21

Table 5: Factors, Data Sources, and Weights in the EQIP General 

Financial Assistance Formula for Allocating Funding to the 

States in Fiscal Year 2006 28

Table 6: Fiscal Year 2006 Formula for Allocating Ground and 

Surface Water Conservation Financial Assistance 31
Contents

Page ii GAO-06-969 Agricultural Conservation

Table 7: Factors Used in the Fiscal Year 2006 Formula for 

Allocating EQIP Performance Bonuses 31

Table 8: Fiscal Year 2006 Formula for Allocating Klamath Basin 

Program Financial Assistance 32

Table 9: Standardized Principal Components Estimators of the 

Original Variables and Statistical Significance 38

Table 10: Rotated Factor Pattern Matrix 41

Figure Figure 1: Initial EQIP Funding to States, Fiscal Year 2006 11

Abbreviations

CAFO Concentrated Animal Feeding Operations

EQIP Environmental Quality Incentives Program

NRCS Natural Resources Conservation Service

NRI National Resources Inventory

USDA U.S. Department of Agriculture

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Page 1 GAO-06-969 Agricultural Conservation

United States Government Accountability Office

Washington, D.C. 20548

A

September 22, 2006 Letr

The Honorable Tom Harkin

Ranking Democratic Member

Committee on Agriculture, Nutrition, and Forestry

United States Senate

Dear Senator Harkin:

Approximately two-thirds of the continental U.S.’s land area is used as 

range, forest, crop, or pasture land. The production of food and fiber on 

these lands contributes to the health of the U.S. population and the strength 

of the nation’s economy. If not properly managed, however, agricultural 

production on these lands can damage the environment and the nation’s 

natural resources, as when routine agricultural activities produce 

sediment, fertilizer runoff, and animal waste that can impair the nation’s 

waterways. Improper management of natural resources can also reduce the 

productive capacity of agricultural land; for example, excessive soil 

erosion may lead to soil lacking in nutrients. Agriculture is also a major 

user of both groundwater and surface water, contributing, in part, to water 

scarcity in the western United States. Responsible production management 

practices can mitigate many of these problems. 

The Environmental Quality Incentives Program (EQIP) provides financial 

and technical assistance to agricultural producers who enter into contracts 

with the U.S. Department of Agriculture’s (USDA) Natural Resources 

Conservation Service (NRCS) to install conservation practices on their 

land. A primary purpose of EQIP is to optimize the environmental benefits 

achieved using program funds. Managed by NRCS, EQIP is a voluntary 

program established in 1996 that currently provides about $1 billion 

annually in cost-share and incentive payments to farmers and ranchers in 

all 50 states, as well as U.S. territories, whose production practices may put 

soil, water, air, and related natural resources at risk for environmental 

damage.1

 The program provides funds to help implement conservation 

practices, such as planting vegetation along rivers and streams—known as 

riparian buffers—to prevent sediment and other materials from polluting 

the waters, and constructing waste storage facilities to reduce the level of 

1

The Commonwealth of Puerto Rico, the U.S. Virgin Islands, and Pacific Basin territories 

also receive EQIP assistance. For the purposes of this report, these are referred to as states, 

with Puerto Rico and the U.S. Virgin Islands considered a single entity under EQIP. 
Page 2 GAO-06-969 Agricultural Conservation

nutrients from livestock production that enter neighboring bodies of water. 

The Farm Security and Rural Investment Act of 2002 (the act) reauthorized 

EQIP and increased annual authorized program funding from about $200 

million in 1997 to current levels of over $1 billion.2

NRCS allocates the majority of EQIP funds through a general financial 

assistance formula with 31 factors related to the availability of natural 

resources and the presence of environmental concerns or problems. NRCS 

assigns each of the formula’s factors a weight that determines the funds to 

be allocated to states based on that factor. The agency also periodically 

modifies factor weights. Additional funds are distributed using a second 

technical assistance formula that considers ongoing and expected future 

conservation work, as well as through a performance bonus formula 

designed to reward states for optimizing environmental benefits and 

efficient program management.3

 States disburse EQIP funds to producers 

to install conservation practices on their land.

As requested, we assessed the extent to which (1) USDA’s process for 

providing funds to the states is consistent with the program’s purpose of 

optimizing environmental benefits and (2) USDA has developed measures 

to monitor program performance.

To address these issues, we reviewed relevant statutory provisions and 

NRCS’s regulations and guidelines for implementing EQIP and spoke with 

officials in NRCS’s national headquarters. To review NRCS’s efforts to 

allocate EQIP funding to the states, we analyzed documents accounting for 

NRCS’s disbursements of EQIP funds. We examined the factors and 

weights in the formula for general financial assistance and discussed the 

role of the data source for each factor in the formula with NRCS’s EQIP 

officials. We gathered comments from stakeholders about the strengths 

and weaknesses of NRCS’s EQIP funding approach, selecting stakeholders 

from environmental and farm organizations to obtain a broad set of views 

on the effectiveness of the formula in allocating funds. To evaluate the 

2

Pub. L. No. 107-171, § 2701, 116 Stat. 134, 278 (2002).

3

EQIP money is also provided to states for practices that promote groundwater and surface 

water conservation, water conservation activities in the Klamath Basin in California and 

Oregon, salinity control measures in the Colorado River Basin, and through grants to 

encourage innovative conservation practices. Regional equity funding is provided to ensure 

all states receive at least $12 million from a total of five USDA conservation programs, 

including EQIP. In fiscal year 2006, the threshold was lowered administratively to $11 

million.
Page 3 GAO-06-969 Agricultural Conservation

extent to which NRCS has developed sufficient outcome-based measures 

to monitor program performance, we spoke with representatives from the 

NRCS teams responsible for strategic planning and oversight activities and 

representatives from the EQIP program team. We examined documentation 

of EQIP performance measures and reviewed NRCS’s Performance Results 

System. 

A more detailed description of our objectives, scope and methodology is 

presented in appendix I. We performed our work between December 2005 

and August 2006 in accordance with generally accepted government 

auditing standards. 

Results in Brief NRCS’s funding process is not clearly linked to EQIP’s purpose of 

optimizing environmental benefits; as such, NRCS may not be directing 

EQIP funds to states with the most significant environmental concerns 

arising from agricultural production. NRCS’s general financial assistance 

formula has several weaknesses that raise questions about the formula’s 

usefulness for effectively directing funds to states. Specifically, while the 31 

factors in the financial assistance formula, and the weights associated with 

each factor, give the formula an appearance of precision, NRCS does not 

have a specific, documented rationale for why it included each factor in the 

formula or for how it assigns and periodically adjusts factor weights. 

Factors and weights are important for ensuring that funds are distributed 

to states to address the nation’s most significant environmental problems 

arising from agriculture. Small adjustments in the weights of the factors 

can shift the amount of funding directed at a particular resource concern 

and, ultimately, the amount of money each state receives. For example, in 

2006, a 1 percent increase in the weight of any of the 31 factors would have 

resulted in $6.5 million more allocated on the basis of that factor at the 

expense of other factors. In addition, some data in the EQIP financial 

assistance formula is questionable or outdated. First, 5 of the data 

sources—such as acres of nonirrigated cropland and federal grazing land—

were used in the formula more than once. Using the same data for multiple 

factors may result in factors being indirectly weighted more than intended 

and may make the formula less reliable for allocating state funding. 

Second, NRCS could not identify the source of the data used in 10 of the 31 

factors in the formula, such as livestock animal units and animal waste 

generation and, therefore, we could not verify the accuracy of the data or 

the basis on which the agency was allocating funding. Finally, the formula 

does not use the most current data available for at least 6 of the 31 factors. 

For example, the formula uses 1995 data to measure commercial fertilizer 
Page 4 GAO-06-969 Agricultural Conservation

use on cropland, but we identified 2005 data that would have made this 

factor more current. Because it was not clear how NRCS originally 

calculated this data, we could not quantify the effect of using more recent 

data. However, using less recent data raises questions about whether the 

formula allocates funds to areas of the country that currently have the 

greatest environmental needs. When we brought our concerns to NRCS’s 

attention, officials agreed that the formula, including weights and data 

sources, needed to be reviewed. NRCS subsequently announced plans to 

issue a request for proposal soliciting comments and suggested revisions to 

NRCS’s formulas for allocating conservation funds, including the EQIP 

financial assistance formula. 

As part of its 2005 strategic planning effort, NRCS developed long-term, 

outcome-based measures to assess changes to the environment resulting 

from EQIP practices. NRCS has developed baselines for these measures 

and plans to assess and report on them once computer models and other 

data collection methods that estimate environmental change are 

completed. In the meantime, NRCS will continue to use the results of its 

existing annual measures to assess performance. As NRCS collects 

additional data about its accomplishment of long-term performance 

measures, it may ultimately have more complete information on which to 

gauge program performance. Such information could help the agency 

refine its process for allocating funds to the states via its financial 

assistance formula by directing funds toward areas of the country that need 

the most improvement. 

We are making recommendations to the Secretary of Agriculture to better 

align NRCS’s process for allocating EQIP funds with the program’s stated 

purpose of optimizing environmental benefits. In particular, we are 

recommending that NRCS ensures that its rationale for the factors and 

weights is documented and linked to program priorities, its data sources 

are accurate and current, and it uses information about long-term program 

performance to ensure funds are directed to areas of the highest priority. 

We provided USDA with a draft of this report for review and comment. 

USDA agreed that the EQIP allocation formula needs review. USDA did not 

agree with our assessment that NRCS’s funding process lacks a clear link to 

the program’s purpose of optimizing environmental benefits. The agency 

stated that its use of factors related to the natural resource base and 

condition of those resources shows the general financial assistance 

formula is tied to the program’s purpose of optimizing environmental 

benefits. USDA also stated that, while some formula data sources and 

weights will be updated, the types of factors used would be needed in any 
Page 5 GAO-06-969 Agricultural Conservation

process that attempts to inventory and optimize environmental benefits. 

While this may in fact be the case, USDA needs to document this 

connection—that is, why factors were chosen and weights assigned. USDA 

could make the connection between the formula and the program’s 

purpose of optimizing environmental benefits more evident if it provided 

additional information describing its reasons for including or excluding 

factors in the formula and its rationale for assigning and modifying weights.

Background The U.S. agricultural sector benefits our economy and the health of our 

nation. However, if not properly managed, agricultural activities can impair 

the nation’s water, air, and soil; disrupt habitat for endangered species; and 

constrain groundwater resources. For example, sediment produced during 

routine agricultural activities may run off the land and reach surface 

waters, including rivers and lakes. Sediment can destroy or degrade aquatic 

habitat and can further impair water quality by transporting into area 

waters both the pesticides applied to cropland and the nutrients found in 

fertilizers and animal waste.4

 These and other water quality issues are of 

concern in a number of U.S. agriculture-producing regions, including the 

Midwest and along the Mississippi River. Agriculture is also a major user of 

groundwater and surface water, which has led to water resource concerns 

across the country, particularly in the West. In 2000, irrigation accounted 

for 65 percent of the nation’s consumption of fresh water. Agricultural 

production can also impair air quality, when wind carries eroded soil, 

odors, and smoke, and may lead to the loss of wetlands, which provide 

wildlife habitat, filter pollutants, retain sediment, and moderate hydrologic 

extremes.

EQIP is one of a number of USDA conservation programs designed to 

mitigate agriculture’s potentially negative environmental effects. EQIP 

provides cost-share funds and incentive payments for land used for 

agricultural production and supports around 190 conservation practices, 

including constructing facilities to temporarily store animal waste; planting 

rows of trees or shrubs to reduce wind erosion and provide food for 

wildlife; and planning the amount, form, placement, and timing of the 

application of plant nutrients. EQIP is designed to fund conservation 

4

Nutrients such as nitrogen and phosphorous often exist in large quantities on farms with 

animal feeding operations—facilities where animals are fed and raised in confined or 

semiconfined conditions.
Page 6 GAO-06-969 Agricultural Conservation

practices in a manner that helps the program achieve the following national 

priorities identified by NRCS:

• reducing nonpoint source pollution (nutrients, sediment, pesticides, or 

excess salinity), groundwater contamination, and pollution from point 

sources (such as concentrated animal feeding operations); 

• conserving groundwater and surface water resources; 

• reducing emissions that contribute to air quality impairment; 

• reducing soil erosion from unacceptable levels on agricultural land; and 

• promoting at-risk species habitat conservation.

The Federal Agriculture Improvement and Reform Act of 1996 created 

EQIP by combining four existing conservation programs into a single 

program.5

 The Farm Security and Rural Investment Act of 2002, the farm 

bill, reauthorized EQIP and increased its authorized funding from about 

$200 million in 1997 to current levels of over $1 billion.6

 The 2002 act 

required that at least 60 percent of EQIP funds be made available for 

conservation practices relating to livestock production.7

 In addition, it 

authorized EQIP funds for specific conservation purposes—(1) funds for 

producers to install water conservation practices to improve groundwater 

and surface water conservation (the Ground and Surface Water 

Conservation component of EQIP) and (2) funds for water conservation

5

Pub. L. No. 104-127, § 334, 110 Stat. 888, 997 (1996). The act combined the Agricultural 

Conservation Program, Great Plains Conservation Program, Water Quality Incentives 

Program, and Colorado River Basin Salinity Control Program into the EQIP program.

6

EQIP is funded through USDA’s Commodity Credit Corporation. The 2002 Farm Bill 

authorized $1.2 billion using the funds, facilities, and authorities of the Commodity Credit 

Corporation to carry out the EQIP program for fiscal year 2006. 16 U.S.C. § 3841(a)(6). 

However, in the annual appropriations act for fiscal year 2006, Congress capped program 

funding for EQIP by limiting the amount of funding available to pay salaries and expenses of 

personnel in carrying out EQIP to $1.017 billion. Pub. L. No. 109-97, § 735, 119 Stat. 2155 

(2005). Similarly, for fiscal years 2003 through 2005, Congress has capped funding levels for 

EQIP at levels below those authorized in the 2002 Farm Bill.

7

The 1996 act required that 50 percent of EQIP funding be targeted at practices relating to 

livestock production. 
Page 7 GAO-06-969 Agricultural Conservation

practices in the Klamath Basin located on the California/Oregon border 

(the Klamath Basin component of EQIP).8

Annually, NRCS headquarters officials determine the amount of funding 

each state receives, while state and local NRCS officials decide what 

conservation practices to fund in their state and local communities. The 

total amount of EQIP funding a state receives can be derived by adding 

together that state’s funding for all categories. Table 1 describes the 

different categories of funding that states received for fiscal year 2006 and 

NRCS’s process for allocating that funding. 

Table 1: Fiscal Year 2006 Categories of EQIP Funding

8

The act authorized $360 million from 2002 to 2007 for these components of EQIP.

EQIP funding category Funding purpose Process for allocating funding

Percentage of 

total funding

General financial assistance Cost-share and incentive payments for 

installing conservation practices.

Funds are divided among states using a 

31-factor formula that considers the 

presence of available natural resources and 

environmental concerns in each state. 

Each factor is assigned a weight, which 

determines the amount of money to be 

given to states based on that factor.

65%

General technical assistance Funds for technical specialists’ time. 

Among other activities, specialists process 

EQIP administrative paperwork, advise 

farmers about the installation of practices, 

and inspect installed practices.

Technical assistance dollars are divided 

among states based on the number of 

ongoing EQIP contracts and expected 

future technical specialist needs.

19

Ground and Surface Water 

Conservationa

Funds for conservation practices that 

improve groundwater and surface water 

conservation. Practices must result in a net 

savings of groundwater or surface water 

resources.

Groundwater and surface water funds are 

allocated to eight High Plains Aquifer 

states, nine western drought states, and 

other states with agricultural water needs 

using a formula based on groundwater, 

irrigation, and other agricultural water 

usage factors.

7

Performance incentive 

bonusesa

Bonuses designed to reward states that 

achieve a high level of program efficiency 

and optimize environmental benefits. States 

can use bonuses as they do other EQIP 

financial and technical assistance.

Performance bonuses are divided among 

states using a formula with seven factors.

4
Page 8 GAO-06-969 Agricultural Conservation

Source: GAO analysis of NRCS documentation.

Note: EQIP funds are also provided to producers through Conservation Innovation Grants, funds 

competitively awarded for the development and adoption of innovative conservation approaches and 

technologies. In fiscal year 2006, around $20 million in grants was approved by NRCS. Conservation 

Innovation Grants are awarded through national and state competitions to producers demonstrating 

innovative approaches to conservation. Because the grant money for national competitions is not 

provided to states along with their initial EQIP allocations, it is not reflected in this table. 

a

NRCS provides these funds to the states through both financial and technical assistance; the majority 

of the assistance is in the form of financial assistance.

b

In fiscal year 2006, the threshold was lowered administratively to $11 million.

As the table shows, each category of EQIP funding is allocated to the states 

using a different process. For the general financial assistance formula, the 

availability of natural resources accounts for approximately half of the 

funds allocated, and the presence of environmental concerns or problems 

accounts for the remainder. 9

 Table 2 shows the factors and weights used in 

the financial assistance formula for fiscal year 2006.

EQIP Colorado Salinitya Funds for salinity control measures in the 

Colorado River Basin.

Colorado Salinity dollars are divided 

between Colorado, Utah, and Wyoming 

based on the amount of land in each state 

needing salinity control treatment.

2

EQIP regional equitya Funds provided to states that receive less 

than $12 million from NRCS conservation 

programs (including EQIP) in a given fiscal 

year.b

 States can use funds as they do 

other EQIP financial and technical 

assistance.

Regional equity funds are provided to 

states that receive less than $12 million 

from NRCS conservation programs 

(including EQIP) in a given fiscal year.b

 

Headquarters officials determine the 

amount of funds to be provided to each 

state and from which program the funds will 

come.

2

Klamath Basina Funds to carry out water conservation 

activities in the Klamath Basin in California 

and Oregon.

Klamath Basin funding is split evenly 

between California and Oregon.

1%

(Continued From Previous Page)

EQIP funding category Funding purpose Process for allocating funding

Percentage of 

total funding

9

Other USDA conservation programs, such as the Conservation Technical Assistance 

Program, Farm and Ranch Lands Protection Program, and Wildlife Habitat Incentives 

Program, also use formulas to allocate funding.
Page 9 GAO-06-969 Agricultural Conservation

Table 2: EQIP General Financial Assistance Formula Factors and Weights, Fiscal 

Year 2006

Source: NRCS.

a

The factor names in this chart are NRCS terminology. In certain cases, they may not represent what is 

actually being measured. For example, the factor for acres of cropland and pastureland soils affected 

Factora Weight

Acres of nonirrigated cropland 3.2

Acres of irrigated cropland 4.3

Acres of federal grazing lands 0.5

Acres of nonfederal grazing lands 4.3

Acres of forestlands 1.1

Acres of specialty cropland 3.2

Acres of wetlands and at-risk species habitat 4.6

Acres of bodies of water 3.2

Livestock animal unitsb 5.8

Animal waste generation 5.8

Waste management capital cost 3.5

Acres of American Indian tribal lands 3.3

Number of limited resource producers 5.0

Acres of grazing land lost to conversion 0.8

Air quality nonattainment areas 1.4

Acres of pastureland needing treatment 5.5

Acres of cropland eroding above Tc 6.2

Acres of fair and poor rangeland 6.2

Acres of forestlands eroding above Tc 1.4

Acres of cropland and pastureland soils affected by saline and/or sodic 

conditionsd

2.6

Miles of impaired rivers and streams 3.6

Potential for pesticide and nitrogen leaching 1.3

Potential for pesticide and nitrogen runoff 1.7

Ratio of livestock animal units to cropland 1.7

Number of concentrated animal feeding operations/animal feeding operationse 2.8

Ratio of commercial fertilizers to cropland 0.9

Wind erosion above Tc 4.2

Phosphorous runoff potential 3.9

Riparian areas 0.8

Carbon sequestration 3.6

Coastal zone land 3.6
Page 10 GAO-06-969 Agricultural Conservation

by saline and/or sodic conditions only measures the presence of salts on cropland and pastureland 

and does not include data on the presence of sodium on these lands.

b

Animal units are a standard way of quantifying livestock of different types and sizes (e.g., cattle, dairy, 

poultry, etc.) One animal unit is equivalent to 1,000 pounds of live animal weight.

c

T is a term that refers to a tolerable rate of erosion. T is the maximum rate of annual soil loss that will 

permit crop productivity to be sustained economically and indefinitely on a given soil. 

d

Saline and sodic soils are soils that contain salts and sodium. Excess amounts of salt and sodium in 

soils may adversely affect soil quality and crop productivity.

e

Animal feeding operations are facilities where animals are raised in confined or semiconfined 

situations usually with feed brought to the animals. When large enough or when in environmentally 

sensitive locations, these facilities are designated as concentrated animal feeding operations and 

become subject to regulatory requirements to prevent point source pollution.

In fiscal year 2006, approximately $652 million was divided among the 

states through the general financial assistance formula.10 For example, 

according to the formula, EQIP funding for nonirrigated cropland 

(accounting for 3.2 percent of financial assistance) totaled $20.9 million. 

The state with the most acres of nonirrigated cropland received $1.7 

million of the funds associated with this factor, and the state with the 

fewest acres of nonirrigated cropland received approximately $1,100. A 

state’s total allocation is composed of the funds it receives for each of the 

31 factors.

Although about 65 percent of EQIP funds are provided through the general 

financial assistance formula, other categories of funding can have a 

significant effect on the total amount of funds an individual state receives. 

For example, 35 percent of Utah’s fiscal year 2006 allocation was from 

general financial assistance. The largest category of EQIP funds Utah 

received—38 percent—was Colorado Salinity funds. Appendix II provides 

additional information on the 2006 funding allocation formulas for general 

financial assistance, Ground and Surface Water Conservation, performance 

incentive bonuses and Klamath Basin funding categories. 

Figure 1 shows the initial distribution of NRCS’s fiscal year 2006 EQIP 

allocations to the states in November 2005. States had to return any unused 

funds by June 2006 for redistribution to states with a need for additional 

10More specifically, approximately $662.6 million was allocated for general financial 

assistance in fiscal year 2006. Of that, $652 million was allocated among the 48 continental 

states. The remainder—$10.6 million—was provided to Alaska, Hawaii, the Pacific Basin, 

and Puerto Rico and the U.S Virgin Islands—states and territories for which NRCS does not 

have consistent data reflecting the availability of natural resources and the extent of 

environmental problems. NRCS allocates money for these states separately from its 

allocations for the other 48 states.
Page 11 GAO-06-969 Agricultural Conservation

funds. Appendix IV describes the amount of funding each state initially 

received in fiscal year 2006.

Figure 1: Initial EQIP Funding to States, Fiscal Year 2006

Source: Art Explosion (map); GAO analysis of NRCS documentation.

Pacific Basin

American Samoa

Guam

Northern Marianas

HI

AK

WA

OR

CA

ID

NV

AZ

NM

UT

MT

WY

CO

TX

OK

ND

NE

MO

WI

IA

AR

KS

LA

MN

MI

IL IN OH

KY

TN

WV

PA

NY

NJ

VA

NC

SC

M AL GA S

FL

ME

MA

VT

NH

RI

CT

DE

MD

PR

SD

$1,000,000-$10,000,000

$10,000,001-$25,000,000

$25,000,001-$40,000,000

$40,000,001-$100,000,000

FY 2006 EQIP state funding levels
Page 12 GAO-06-969 Agricultural Conservation

NRCS’s Process for 

Allocating EQIP Funds 

to the States Does Not 

Clearly Address the 

Program’s Purpose of 

Optimizing 

Environmental 

Benefits 

NRCS’s process for providing EQIP funds to the states is not clearly linked 

to the program’s purpose of optimizing environmental benefits. In 

particular, NRCS’s general financial assistance formula, which accounts for 

approximately two-thirds of funding provided to the states, does not have a 

specific, documented rationale for each of the formula’s factors and 

weights. In addition, the financial assistance formula relies on some 

questionable and outdated data. As a result, NRCS may not be directing 

EQIP funds to states with the most significant environmental concerns 

arising from agricultural production. 

NRCS Does Not Have A 

Specific, Documented 

Rationale for Formula 

Factors and Weights

Although the 31 factors and weights used in the general financial assistance 

formula give it an appearance of precision, NRCS does not have a clearly 

documented rationale for including each factor in the formula and 

assigning or modifying each weight. The original EQIP formula was created 

in 1997 by an interagency task force that modified the formula created for a 

different conservation program—the Conservation Technical Assistance 

Program.11 The task force added and deleted factors and adjusted factor 

weights so that the EQIP formula better corresponded to the Federal 

Agriculture Improvement and Reform Act of 1996’s requirement that 50 

percent of funds be targeted at funding livestock-related practices. 

Since the creation of the financial assistance formula, NRCS has 

periodically modified factors and weights to emphasize different program 

elements and national priorities, most recently in fiscal year 2004 following 

the passage of the 2002 Farm Security and Rural Investment Act. 

Furthermore, NRCS officials stated that they meet annually to review the 

allocation of funds to states. However, throughout this process, NRCS has 

not documented the basis for its decisions to modify factors and weights or 

documented how changes to its formula achieve the program’s purpose of 

optimizing environmental benefits. Thus, it is not always clear whether the 

formula factors and weights guide funds to the states as effectively as 

possible. For example, it is unclear why NRCS includes a factor in the 

formula that addresses the waste management costs of small animal 

feeding operations but not a factor that addresses such costs for large 

11The Conservation Technical Assistance Program provides technical assistance to help 

people conserve, maintain, and improve their natural resources.
Page 13 GAO-06-969 Agricultural Conservation

operations—large operations can also damage the environment and are 

eligible for EQIP funding.12 By not including the costs of the larger 

operations in its financial assistance formula, some states may not be 

receiving funds to address their specific environmental concerns. In 

addition, NRCS has not demonstrated that it has the most appropriate 

water quality factors in its formula. For example, the formula includes a 

factor addressing river and stream impairment but no factor for impaired 

lakes and other bodies of water. Moreover, it is not certain whether the 

impaired rivers and streams factor results in funds being awarded on the 

basis of general water quality concerns or water pollution specifically 

caused by agricultural production. As a result, it was not certain whether 

the formula allocates funds as effectively as possible to states with water 

quality concerns arising from agricultural production.

While the factors in the EQIP general financial assistance formula 

determine what resource and environmental characteristics are considered 

when allocating funds, the weights associated with these factors directly 

affect how much total funding is provided for each factor and, thus, the 

amount of money each state receives. Factors and weights are key to 

ensuring states with the greatest environmental problems receive funding 

to address these problems. Small differences in the weights of the factors 

can shift the amount of financial assistance directed at a particular 

resource concern and, ultimately, the amount of money provided to a state. 

In 2006, if the weight of any of the 31 factors had increased by 1 percent, 

$6.5 million would have been allocated on the basis of that factor at the 

expense of one or more other factors. Such a shift could impact the amount 

of financial assistance received by each state. For example, a 1 percent 

increase in the weight of the specialty cropland factor with a corresponding 

decrease of 1 percent in the American Indian tribal land factor could result 

in large changes to the distribution of EQIP general financial assistance. 

According to our analysis, the state benefiting the most from such a change 

would receive $2.6 million more (a 7.2 percent increase in that state’s level 

of general financial assistance) and the state benefiting least from such a 

change would lose $2.7 million (a 13.5 percent decrease in that state’s level 

of general financial assistance). The potential for the weights to 

significantly affect the amount of funding a state receives underscores the 

12The waste management capital cost factor considers the costs associated with animal 

feeding operations with fewer than 1,000 animal units. It does not include the costs of 

operations with more than 1,000 animal units. Since 2002, operations with more than 1,000 

animal units have been eligible to receive EQIP funding for waste storage facilities.
Page 14 GAO-06-969 Agricultural Conservation

importance of having a well-founded rationale for assigning them. To date, 

NRCS has not documented its rationale for choosing the weights.

Some stakeholders we spoke with questioned NRCS’s assignment of 

weights to certain factors in the financial assistance formula because they 

did not believe NRCS’s formula adequately reflected the states’ 

environmental priorities. For example, NRCS’s general financial assistance 

formula allocates 6.3 percent of EQIP funds to the states based on factors 

specifically associated with animal feeding operations.13 However, states 

spent more of their EQIP financial assistance on related practices, which 

suggests that the weights in the financial assistance formula may not reflect 

states’ priorities. In fiscal year 2005, states spent a total of 11 percent of 

EQIP financial assistance, or $91.1 million, on one such practice—the 

construction of waste storage facilities for animal feeding operations. (App. 

VI outlines the practices funded in fiscal year 2005, including other 

practices to control pollution from animal feeding operations.) More 

generally, other stakeholders said that, as the program develops, NRCS 

should give additional weight to factors related to the presence of 

environmental concerns in a state and place less emphasis on factors 

related to natural resources in a state. They believed this reassignment of 

weights would better ensure that states contending with the most 

significant environmental problems receive the most funding. Currently, 

factors related to the presence of environmental concerns account for 

approximately half of the total funding, while factors relating to the 

availability of natural resources account for the remainder. Factors related 

to the availability of natural resources provide states that have significant 

amounts of a particular type of land—such as grazing land or cropland—

with more funds, regardless of whether that land is impaired. 

Although NRCS has stated that it meets annually to review its allocation of 

funds to states, officials told us they had not conducted any statistical 

analysis to examine the influence of factors on funding outcomes. 

Statistical analyses can provide information on how the factors in the 

allocation formula have affected the distribution of funds, thereby

13The financial assistance formula allocates 3.5 percent of funds using the waste 

management capital cost factor and 2.8 percent using the number of concentrated animal 

feeding operations/animal feeding operations factor. According to NRCS officials, states 

with animal feeding operations may receive additional funds based on the livestock animal 

units, animal waste generation, and ratio of animal units to cropland formula factors.
Page 15 GAO-06-969 Agricultural Conservation

providing information to improve program implementation.14 To better 

understand the effect of the factors on the allocations to states, we used 

two types of statistical analysis to assess the effects of the EQIP financial 

assistance formula on state funding: (1) regression analysis to show which 

factors are the most influential in determining funding levels and (2) factor 

analysis to understand how factors can be grouped and identified with 

program priorities. 

Our regression analysis for the fiscal year 2006 funding allocation shows 

that the factors that were the most important in explaining the distribution 

of general financial assistance to states were acres of fair and poor 

rangeland, acres of nonfederal grazing lands, livestock animal units, acres 

of irrigated cropland, acres of American Indian tribal lands, and wind 

erosion above T. This analysis suggests that regions of the country with 

these types of characteristics are more likely to benefit from the current 

formula. On the other hand, a few factors, such as acres of forestlands, 

potential for pesticide and nitrogen leaching, and air quality nonattainment 

areas were not significantly related to the allocation, indicating that they 

had little or no impact on the formula. 

Our factor analysis, which groups the data into a smaller number of 

categories that actually drive the formula, found that the largest grouping 

with the greatest amount of correlation, included acres of nonfederal 

grazing land, acres of fair and poor rangeland, livestock animal units, and 

wind erosion above T—all indicative of dryland agriculture and livestock 

feeding and ranching. These results correspond with those of our 

regression analysis and help to show how the current national allocation 

formula prioritizes money to states. A complete explanation of both 

analyses is included in appendix III.

Financial Assistance 

Formula Relies on Some 

Questionable and Outdated 

Data

Weaknesses in the financial assistance formula are compounded by NRCS’s 

use of questionable and outdated data. Accurate data are key to ensuring 

that funds are distributed to states as intended. However, we identified 

several methodological weaknesses in the data sources: (1) data that were 

14We discuss the importance of retrospective economic analysis and its usefulness in 

managing programs, in GAO, Economic Performance: Highlights of a Workshop on 

Economic Performance Measures, GAO Workshop, GAO-05-796SP, (Washington, D.C.: July 

18, 2005).
Page 16 GAO-06-969 Agricultural Conservation

used more than once in the formula, (2) data sources whose accuracy 

could not be verified, and (3) data that was not as recent as possible.

First, 5 of the 29 data sources behind the factors in the financial assistance 

formula were used more than once, potentially causing NRCS to 

overemphasize some environmental concerns at the expense of others. 

Specifically: 

• NRCS uses the same data to estimate pesticide and nitrogen runoff and 

phosphorous runoff in its formula. According to NRCS, because data 

measuring the potential for phosphorous runoff were unavailable, it 

substituted data measuring the potential for pesticide and nitrogen 

runoff. The agency did so believing that similar characteristics cause 

both types of runoff. However, an NRCS official responsible for deriving 

the runoff and leaching indicators commented that the substitution of 

one type of runoff data for another was problematic because the 

mechanisms through which pesticides and nitrogen are transported offsite to cause environmental problems are different from those of 

phosphorous. A 2006 NRCS cropland report estimates that the intensity 

of nitrogen and phosphorous losses may differ geographically.15 For 

example, nitrogen dissolved in surface water runoff in the upper 

Midwest accounts for 28 percent of the national total, while 

phosphorous dissolved in surface water runoff in the same region 

accounts for 45 percent of the national total. This difference in the effect 

of these two pollutants in the same region raises questions about the 

appropriateness of substituting one type of data for the other. Until 

adequate data are available for a given factor, it may not be appropriate 

to include that factor in the general financial assistance formula.

• NRCS’s formula uses nonirrigated cropland, federal grazing land, 

nonfederal grazing land, and forestland once for estimating acreage and 

then again for estimating carbon sequestration.16 According to NRCS, 

15USDA’s report, Model Simulation of Soil Loss, Nutrient Loss, and Change in Soil 

Organic Carbon Associated with Crop Production, USDA, Conservation Effects 

Assessment Project, June 2006, estimates nitrogen and phosphorous losses across the 

country on cropland.

16Carbon sequestration is the retention of carbon through physical or biological processes 

that prevent or delay its emission into the atmosphere. For example, conservation tillage 

leaves more crop residue on land and retains more carbon than many traditional cultivation 

practices. Sequestering carbon may help mitigate climate change by reducing the amount of 

carbon dioxide in the atmosphere.
Page 17 GAO-06-969 Agricultural Conservation

the agency did not have good source data to measure potential areas 

where management practices could improve levels of carbon 

sequestration so it substituted these other data sources. While we could 

not fully assess the soundness of NRCS’s estimate of carbon 

sequestration, some academic stakeholders we spoke with questioned 

whether NRCS had estimated carbon sequestration as effectively as 

possible and noted that alternate data sources were available. In 

discussing these alternate sources with NRCS, the EQIP Manager said 

the agency had not previously considered using these sources for the 

EQIP formula, but that they could prove relevant.

Using the same data for multiple factors may result in factors being 

indirectly weighted higher than intended. For example, the effective weight 

of the pesticide nitrogen runoff factor is 5.6 percent—the sum of the 

original pesticide nitrogen runoff weight (1.7 percent) and the phosphorous 

runoff weight (3.9 percent). Using data created for one factor for a second 

factor also makes the formula less transparent and potentially less reliable 

for allocating state funding. 

Second, NRCS could not confirm the source of data used in 10 factors in 

the formula; as such, we could not determine the accuracy of the data, 

verify how NRCS generated the data, or fully understand the basis on 

which the agency allocates funding. Specifically, we could not confirm the 

source of data for acres of federal grazing land, livestock animal units, 

animal waste generation, acres of cropland eroding above T, acres of 

forestlands eroding above T, ratio of animal units to cropland, miles of 

impaired rivers and streams, ratio of commercial fertilizers to cropland, 

riparian areas, and coastal zone land.17 For example, we could not verify 

how data for the livestock animal units and animal waste factors were 

generated, and NRCS said it had not retained documentation of how the 

data for these factors were calculated. As a result, it was uncertain whether 

NRCS had chosen the most appropriate data as its basis for allocating 

funds to states with pollution problems from livestock and animal waste or 

whether the data were accurately calculated. EQIP officials told us that, in 

most cases, the data sources had been chosen and incorporated into the 

17For nine of these factors, NRCS provided documentation that allowed us to verify data for 

all but two states. However, this documentation did not provide sufficient evidence to allow 

us to verify the source of the data for these factors or understand how the data for these 

factors were estimated. For one factor, the documentation NRCS provided to corroborate 

the data used in its financial assistance formula did not match what was used in the formula.
Page 18 GAO-06-969 Agricultural Conservation

formula before they were involved with EQIP and that documentation had 

not been kept to identify how data sources were used.

In addition, for one factor— the number of limited resource producers in a 

state—we found that the data did not measure what its factor name 

indicated. NRCS defines a limited resource producer as one who had, for 

the last 2 years, (1) farm sales not more than $100,000 and (2) a household 

income at or below the poverty level, or less than 50 percent of the county 

median household income.18 However, the data NRCS uses in the general 

financial assistance formula only captures farms with low sales, which 

does not necessarily indicate whether producers on those farms have 

limited means. As a result, NRCS may not be directing funds to states 

having farmers with the most limited resources. A description of each 

factor in the fiscal year 2006 general financial assistance formula can be 

found in appendix II.

Third, NRCS does not use the most current data for six factors in the 

formula—livestock animal units, animal waste generation, number of 

limited resource producers, miles of impaired rivers and streams, ratio of 

livestock animal units to cropland, and ratio of commercial fertilizers to 

cropland.19 According to NRCS, the source of data on the ratio of 

commercial fertilizers to cropland was a 1995 report by the Association of 

American Plant Food Control Officials; we found a 2005 version of the 

same report with more current data. In other cases, we identified more 

current, alternate sources of data. For example, the formula currently uses 

1996 EPA data for its waste management capital cost factor but could use 

2003 NRCS data that estimates waste management costs. 20 Not using 

18Specifically, EQIP regulations (7 C.F.R. § 1466.3) define a limited resource producer as 

a person with (1) direct or indirect gross farm sales not more than $100,000 in each of the 

previous 2 years (to be adjusted for inflation) and (2) a total household income at or below 

the national poverty level for a family of four, or less than 50 percent of county median 

household income in each of the previous 2 years.

19In the case of the livestock animal units, animal waste generation, ratio of animal units to 

cropland, miles of impaired rivers and streams, and ratio of commercial fertilizers to 

cropland factors, where we were not able to verify the source of the data, we relied on EQIP 

program officials’ statements about when data sources were created to determine if a more 

current source existed.

20NRCS’s 2003 report, Costs Associated with Development and Implementation of 

Comprehensive Nutrient Management Plans, estimates the record-keeping, nutrient 

management, off-farm transport and land treatment costs associated with livestock farms. 

This data is available on a state-by-state basis.
Page 19 GAO-06-969 Agricultural Conservation

recent data raises questions about whether the formula allocates funds to 

areas of the country that currently have the greatest environmental needs, 

because recent changes in a state’s agricultural or environmental status 

may not be reflected. According to our analysis, by using more current data 

for the number of limited resource producers factor, one state would have 

received approximately $151,000 more in fiscal year 2006 (a 0.2 percent 

increase in that state’s general financial assistance), and another state 

would have received approximately $138,000 less (a 1.3 percent decrease in 

that state’s general financial assistance).21 Because we were unable to 

determine how NRCS used the data for developing the remaining five 

factors, we could not determine what impact using more current data for 

those factors would have on financial assistance provided to states. 

According to NRCS, the alternate sources we identified appeared to be 

acceptable for use in the formula, and the agency is in the process of 

updating the formula’s livestock data.

In addition to these six factors, data used to measure acres of riparian 

areas, fair and poor rangeland, and forestland eroding above T are about 20 

years old and will likely become more inappropriate over time. 

When we brought our concerns to NRCS’s attention, officials agreed that 

the formula, including weights and data sources, needed to be reexamined. 

NRCS subsequently announced plans to issue a request for proposal 

soliciting comments and suggested revisions to NRCS’s formulas for 

allocating conservation funds, including the EQIP financial assistance 

formula. In addition, according to NRCS’s EQIP Manager, the agency is in 

the process of consolidating the data used in the financial assistance 

formulas for its conservation programs into a single database. As a part of 

this process, the agency plans to review its data sources for the formula 

factors and update them with more relevant and current data when 

possible.

21As noted above, the data NRCS uses for the limited resource producers factor has 

shortcomings. For purposes of demonstrating how changes over time could affect the 

distribution of general financial assistance, we used the same data NRCS used in its 

formula—farms with low sales.
Page 20 GAO-06-969 Agricultural Conservation

NRCS Has Begun to 

Develop More 

Outcome-Oriented 

Performance Measures

NRCS has recently begun to develop program-specific, long-term measures 

to monitor EQIP’s outcomes. In 2000, we reported that performance 

measures tied to outcomes would better communicate the results NRCS 

intended its conservation programs to achieve.22 As part of its 2005 

strategic planning effort, NRCS developed outcome-based, long-term 

measures to assess changes to the environment resulting from the 

installation of EQIP conservation practices.23 These measures include such 

things as reduced sediment delivery from farms, improved soil condition 

on working cropland, and increased water conservation. Previously, in 

2002, NRCS established annual measures that primarily assess program 

outputs—the number and type of conservation practices installed. Table 3 

outlines NRCS’s seven annual performance measures for fiscal year 2006, 

and table 4 describes its seven long-term EQIP performance measures 

approved in 2005. 

Table 3: EQIP Annual Performance Measures, Fiscal Year 2006

22GAO, Natural Resources Conservation Service: Additional Actions Needed to Strengthen 

Program and Financial Accountability, GAO/RCED-00-83 (Washington, D.C.: Apr. 7, 2000).

23According to the Director of NRCS’s Strategic Planning and Performance Division, NRCS 

has had program-neutral, outcome-based measures in place since 1997 to which EQIP was 

expected to contribute.

Performance 

measure Measure unit

Progress as of 

September 1, 2006

Fiscal year

target as of 

September 1, 2006a

Comprehensive 

nutrient management 

plans applied

Number of plans 2,189 2,488

Comprehensive 

nutrient management 

plans written

Number of plans 2,231 2,435

Grazing land with 

conservation practices 

to protect the resource 

base

Acres 11,640,329 10,454,337

Improved irrigation 

efficiency

Acre-feet 641,158b 543,204c
Page 21 GAO-06-969 Agricultural Conservation

Source: NRCS.

a

According to NRCS, performance targets may change as additional funds are provided to the states 

and as states return unused funds to headquarters.

b

This figure represents combined progress for EQIP, Ground and Surface Water Conservation, and 

Klamath Basin.

c

This figure represents a combined target for EQIP, Ground and Surface Water Conservation, and 

Klamath Basin.

Table 4: EQIP Long-term Measures

Source: NRCS.

a

The National Resources Inventory (NRI) includes data on soil type, soil characteristics, and soil 

interpretations, in addition to historical information on land use, management practices, and erosion. 

These data, along with historical climate data, are being used to assess soil quality by deriving a Soil 

Conditioning Index value for each NRI sample site. This index quantifies the effects of cropping 

sequences, tillage, and other management inputs on soil organic matter content, which serves as an 

indicator of soil quality.

Nonfederal land 

managed to protect 

species with declining 

populations

Acres 1,163,850 381,124

Reduction of cropland 

soils damaged by 

erosion

Acres 1,345,101 1,360,622

Soil erosion reduced Tons 16,230,336 9,912,788

(Continued From Previous Page)

Performance 

measure Measure unit

Progress as of 

September 1, 2006

Fiscal year 

target as of 

September 1, 2006a

Performance measure Measure unit Baseline year Proposed target

Improve soil condition on working 

cropland

Millions of acres moved to a 

soil conditioning index level > 

than 0.a

.5 in 2005 2.7 by 2010

Reduce potential sediment 

delivery from agricultural 

operations

Million tons per year 2.4 in 2004 18.5 by 2010

Reduce potential nitrogen 

delivery from agriculture

Tons 18,200 in 2005 100,000 by 2010

Reduce potential phosphorus 

delivery from agriculture

Tons 2,700 in 2005 14,000 by 2010

Increase water conservation Acre-feet 600,000 in 2005 4,200,000 by 2010

Improve grassland condition, 

health, and productivity

Million acres 10.3 in 2005 52 by 2010

Improve the quality of habitat for 

at-risk species

Million acres .45 million in 2005 2.4 by 2010
Page 22 GAO-06-969 Agricultural Conservation

According to NRCS, it has developed baselines for its long-term, outcomebased performance measures and plans to assess and report on them once 

computer models and other data collection methods that estimate 

environmental change are completed. The Director of the NRCS Strategic 

Planning and Performance Division said NRCS expects to assess and report 

on the status of all measures by 2010 but will be able to assess the results of 

some measures, such as improved soil condition on working land, sooner. 

In the meantime, the agency will continue to utilize its existing annual 

measures to assess performance. The Director of NRCS’s Strategic 

Planning and Performance Division acknowledged that the long-term 

measures were not as comprehensive as needed but represented measures 

NRCS could reasonably assess using modeling and data collection methods 

that would soon become available. NRCS plans to continue to improve its 

performance measures going forward.

Although we did not assess the comprehensiveness of the EQIP 

performance measures, the additional information they provide about the 

results of EQIP outcomes should allow NRCS to better gauge program 

performance. Such information could also help the agency refine its 

process for allocating funds to the states via its financial assistance formula 

by directing funds toward practices that address unrealized performance 

measures and areas of the country that need the most improvement. The 

Chief of NRCS’s Environmental Improvement Programs Branch agreed that 

information about program performance might eventually be linked back 

to the EQIP funding allocation process. However, the agency does not yet 

have plans to do so. 

Conclusions As a key NRCS conservation program with over $1 billion in annual 

funding, EQIP was designed to help producers mitigate the potentially 

negative environmental impacts of agricultural production. However, the 

program may not be fully optimizing the environmental benefits resulting 

from practices installed using EQIP dollars because of weaknesses in 

NRCS’s process for allocating funds to the states. Moreover, outdated and 

duplicate formula data sources may further compromise EQIP’s 

effectiveness in allocating funds. Currently, it is not clear that factors, 

weights, and data sources in the general financial assistance formula help 

the agency direct funding to the areas of the nation with the greatest 

environmental threats arising from agricultural production. NRCS has an 

opportunity to address this issue as it moves forward on its plans to 

reexamine its conservation funding formulas. Furthermore, the agency may 

be able to use information gathered from the results of its outcome-based 
Page 23 GAO-06-969 Agricultural Conservation

performance measures to refine the financial assistance formula, making it 

easier for NRCS to direct EQIP funds at the most pressing environmental 

problems related to agriculture production.

Recommendations for 

Executive Action

To achieve EQIP’s purpose of optimizing environmental benefits, we 

recommend that the Secretary of Agriculture direct the Chief of the Natural 

Resources Conservation Service to take the following two actions:

• ensure that the rationale for the factors and weights used in the general 

financial assistance formula are documented and linked to program 

priorities, and data sources used in the formula are accurate and 

current; and

• continue to analyze current and newly developed long-term 

performance measures for the EQIP program and use this information 

to make any further revisions to the financial assistance formula to 

ensure funds are directed to areas of highest priority. 

Agency Comments and 

Our Evaluation

We provided USDA with a draft of this report for review and comment. 

USDA agreed that the EQIP allocation formula needs review. USDA did not 

agree with our assessment that NRCS’s funding process lacks a clear link to 

the program’s purpose of optimizing environmental benefits. The agency 

stated that its use of factors related to the natural resource base and 

condition of those resources shows the general financial assistance 

formula is tied to the program’s purpose of optimizing environmental 

benefits. USDA stated that, while some formula data sources and weights 

will be updated, the types of factors used would be needed in any process 

that attempts to inventory and optimize environmental benefits. While this 

may in fact be the case, USDA needs to document this connection—that is, 

why factors were chosen and weights assigned. USDA could make the 

connection between the formula and the program’s purpose of optimizing 

environmental benefits more evident if it provided additional information 

describing its reasons for including or excluding factors in the formula and 

its rationale for assigning and modifying weights.

Appendix VII presents USDA’s comments.
Page 24 GAO-06-969 Agricultural Conservation

We are sending copies of this report to interested congressional 

committees, the Secretary of Agriculture, the Director of the Office of 

Management and Budget, and other interested parties. We also will make 

copies available to others upon request. In addition, the report will be 

available at no charge on the GAO Web site at http://www.gao.gov .

If you or your staff have any questions about this report, please contact me 

at (202) 512-3841 or bertonid@gao.gov. Contact points for our Offices of 

Congressional Relations and of Public Affairs may be found on the last 

page of this report. GAO staff who made major contributions to this report 

are listed in appendix VIII.

Sincerely yours,

Daniel Bertoni

Acting Director, Natural Resources 

and Environment
Page 25 GAO-06-969 Agricultural Conservation

Appendix I

Objectives, Scope, and Methodology AAppenednxidexiIs

At the request of the Ranking Democratic Member, Senate Committee on 

Agriculture, Nutrition, and Forestry, we reviewed the extent to which (1) 

the U.S. Department of Agriculture’s (USDA) process for allocating 

Environmental Quality Incentives Program (EQIP) funds to states is 

consistent with the program’s purpose of optimizing environmental 

benefits and (2) USDA has developed measures to monitor program 

performance.

To review the Natural Resources Conservation Service’s (NRCS) process 

for allocating EQIP funding to the states, we examined EQIP funding 

documents and spoke with NRCS officials from the Financial Assistance 

Program Division, Budget Planning and Analysis Division, and Financial 

Management Division. Our analysis considered each of the different 

categories of EQIP funding, including EQIP general financial assistance, 

EQIP technical assistance, regional equity funds, performance bonuses, 

Conservation Innovation Grants, Colorado Salinity funds, Ground and 

Surface Water Conservation funds, and Klamath Basin funds. We gathered 

comments from stakeholders about the strengths and weaknesses of 

NRCS’s EQIP funding approach. We selected stakeholders from 

environmental and farm organizations to get a broad set of views on the 

effectiveness of the formula in allocating funds. Specifically, we spoke with 

representatives from environmental organizations, including 

Environmental Defense, the National Association of Conservation 

Districts, the Soil and Water Conservation Society, and the Sustainable 

Agriculture Coalition, as well as farm organizations, including the 

American Farm Bureau and the National Pork Producers Council. We also 

discussed the EQIP funding allocation process with selected participants 

on state technical committees—the Iowa Department of Natural 

Resources, Iowa Farm Bureau, and Nebraska Department of 

Environmental Quality; academic stakeholders; and former NRCS 

employees who participated in the development of the original formula.1

 

We examined the factors and weights in the financial assistance formula 

and discussed their purpose with EQIP program officials. We performed 

statistical analysis of the financial assistance formula to determine what 

impact the different factors had on overall funding. A discussion of the 

analysis we performed can be found in appendix III. We searched for 

information about the source of data for each factor in the formula in order 

to formulate an understanding of what each factor measured and verify the 

1

State Technical Committees offer advice to NRCS state officials on establishing EQIP 

activities at the state level.
Appendix I

Objectives, Scope, and Methodology

Page 26 GAO-06-969 Agricultural Conservation

accuracy of the data being used by NRCS. NRCS did not retain 

documentation of the source data for 10 factors and, as a result, we were 

unable to verify all data used in the financial assistance formula. To 

estimate the number of factors using outdated data, we searched for more 

updated versions of the same data sources NRCS said it used in its formula. 

We did not include more updated, but different, sources of data in our 

count.

To understand Congress’s and NRCS’s goals for EQIP, we reviewed the 

Federal Agriculture Improvement and Reform Act of 1996, Farm Security 

and Rural Investment Act of 2002, associated regulations, and related 

appropriations laws. We reviewed program documentation describing the 

purpose and priorities of EQIP and discussed the documentation with 

EQIP officials. To understand agency conservation priorities, we analyzed a 

2005 database of conservation practices funded using EQIP, Ground and 

Surface Water Conservation, and Klamath Basin funds.

To determine how the factors and weights in the formula aligned with 

resource concerns across the nation, we conducted research on the impact 

agricultural production has on the environment. We spoke with NRCS 

officials from selected states—Iowa, Maryland, Mississippi, Missouri, 

Montana, Nebraska, New Mexico, Rhode Island, and Texas—to better 

understand resource concerns important to their state and how they used 

funds received from headquarters to address those concerns. We also 

spoke with officials from three county offices within these states. This 

geographically diverse group included states that received varying amounts 

of EQIP funding and engaged in a range of types of agricultural production.

To review what measures are in place to monitor EQIP program 

performance, we spoke with representatives from the NRCS teams 

responsible for strategic planning and oversight activities—the Operations 

Management and Oversight Division, Oversight and Evaluation staff, and 

Strategic and Performance Planning Division—and representatives from 

the Financial Assistance Program Division. We examined agency strategic 

planning and performance documents. We reviewed documentation of 

agency and EQIP goals and performance measures and reviewed the Webbased NRCS Performance Results System.2

 We also spoke with 

representatives from NRCS and nongovernmental organizations working 

2

NRCS’s Performance Results System can be found at 

http://ias.sc.egov.usda.gov/prshome/default.html.
Appendix I

Objectives, Scope, and Methodology

Page 27 GAO-06-969 Agricultural Conservation

on the Conservation Effects Assessment Project and reviewed related 

documentation to determine how that initiative might influence the 

development of future EQIP goals. Our analysis did not include an 

independent verification of NRCS’s compliance with internal controls.3

We performed our work between December 2005 and August 2006 in 

accordance with generally accepted government auditing standards. 

3

The following OIG and GAO reports have recently addressed the issue of internal control at 

NRCS: Audit Report. Natural Resources Conservation Service Application Controls–

Program Contracts System (ProTracts). Report No. 10501-5-FM. (Washington, D.C.: July 

2006); GAO, Conservation Security Program: Despite Cost Controls, Improved USDA 

Management Is Needed to Ensure Proper Payments and Reduce Duplication with Other 

Programs, GAO-06-312 (Washington, D.C.: Apr. 28, 2006); Audit Report. Natural Resources 

Conservation Service. Environmental Quality Incentives Program. Report No. 10099-18-

KC. (Washington, D.C.: February 2005); and Audit Report. Natural Resources Conservation 

Service. Environmental Quality Incentives Program. Cost Share Practice Approvals and 

Specifications in Nebraska. Report No. 10005-1-KC. (Mission, Kans.: November 2002).
Page 28 GAO-06-969 Agricultural Conservation

Appendix II

EQIP 2006 Funding Allocation Formulas ApendxiI

Tables 5, 6, 7, and 8, respectively, describe the formulas for allocating 

general financial assistance, Ground and Surface Water Conservation 

funds, performance bonuses, and Klamath Basin funds. In the case of the 

general financial assistance formula, we have identified the source of data 

for each factor and described what each factor measures. 

Table 5: Factors, Data Sources, and Weights in the EQIP General Financial Assistance Formula for Allocating Funding to the 

States in Fiscal Year 2006

Factor Source Description Weight

Acres of nonirrigated cropland 1997 National Resources Inventory 

(Revised December 2000)

Nonirrigated cultivated and 

noncultivated cropland acres

3.2%

Acres of irrigated cropland 1997 National Resources Inventory 

(Revised December 2000)

Irrigated cultivated and noncultivated 

cropland acres

4.3

Acres of federal grazing lands a b 0.5

Acres of nonfederal grazing lands 1997 National Resources Inventory 

(Revised December 2000)

Nonfederal, rural acres of pastureland, 

rangeland, and grazed forestland

4.3

Acres of forestlands 1997 National Resources Inventory 

(Revised December 2000)

Nonfederal, rural acres of forestland 1.1

Acres of specialty cropland 1997 National Resources Inventory 

(Revised December 2000)

Acres of land used as vineyards or to 

grow fruits, nuts, berries, bush fruit, or 

other specialty crops

3.2

Acres of wetlands and at-risk species 

habitat

1997 National Resources Inventory 

(Revised December 2000)

Acres of wetlands and deepwater 

habitats on water areas and nonfederal 

land 

4.6

Acres of bodies of water 1997 National Resources Inventory 

(Revised December 2000)

Surface area (in acres) of water areas 3.2

Livestock animal units 1997 NRCS calculation based on data 

gathered prior to 1997 (exact year 

unknown)c

b 5.8

Animal waste generation NRCS calculation based on 1987 Census 

of Agriculture and other datac

b 5.8

Waste management capital cost 1996 Environmental Protection Agency 

Clean Water Needs Survey Report to 

Congress

Modeled estimates of state needs for 

controlling nonpoint source pollution 

from confined animal facilities with fewer 

than 1,000 animal units

3.5

Acres of American Indian tribal lands 1997 Bureau of Indian Affairs data Acres of American Indian reservations 

and Tribal Trust Land

3.3

Number of limited resource producers 1997 Census of Agriculture Number of farms with sales under 

$100,000

5.0

Acres of grazing land lost to conversion 1997 National Resources Inventory 

(Revised December 2000)

Acres of grazing and pastureland 

converted to another form of land or 

development between 1982 and 1997

0.8
Appendix II

EQIP 2006 Funding Allocation Formulas

Page 29 GAO-06-969 Agricultural Conservation

Air quality nonattainment areas NRCS analysis of 2005 Environmental 

Protection Agency air quality data

Measure of air quality nonattainment 

based on the percent of a state affected 

by certain air quality pollutants and the 

number of air quality standards not met 

by that state

1.4

Acres of pastureland needing treatment 1992 National Resources Inventory Acres of pastureland needing 

conservation treatment

5.5

Acres of cropland eroding above T 1992 National Resources Inventoryc d 6.2

Acres of fair and poor rangeland 1987 National Resources Inventory Acres of rangeland in fair and poor 

condition

6.2

Acres of forestlands eroding above T 1987 National Resources Inventorye f 1.4

Acres of cropland and pastureland soils 

affected by saline and/or sodic 

conditions

1997 National Resources Inventory 

(Revised December 2000)

Acres of cultivated and noncultivated 

cropland and pastureland with the 

presence of salts

2.6

Miles of impaired rivers and streams Environmental Protection Agency 1994 

National Water Quality Inventoryc

b 3.6

Potential for pesticide and nitrogen 

leaching

1997 NRCS analysisg NRCS formula based on data about 

land vulnerability to manure nitrogen, 

commercial nitrogen, and pesticide 

leaching

1.3

Potential for pesticide and nitrogen 

runoff

1997 NRCS analysisg NRCS formula based on data about 

land vulnerability to manure nitrogen, 

commercial nitrogen, and pesticide 

runoff

1.7

Ratio of livestock animal units to 

cropland

a b 1.7

Number of concentrated animal feeding 

operations/animal feeding operations

2003 NRCS report based on 1997 Census 

of Agriculture datah

Number of farms needing a 

comprehensive nutrient management 

plan

2.8

Ratio of commercial fertilizers to 

cropland

NRCS calculation based on 1995 data 

from the Association of American Plant 

Food Control Officials and 1997 NRI 

cropland datac

b 0.9

Wind erosion above T 1997 National Resources Inventory 

(Revised December 2000)

Cultivated and noncultivated cropland 

with a 4-year average rate of estimated 

soil loss due to wind erosion greater 

than T—a tolerable rate of erosion 

above which soil productivity is believed 

to decrease

4.2

Phosphorous runoff potential 1997 NRCS analysisg Same data used for factor measuring 

potential for pesticide and nitrogen 

runoff

3.9

Riparian areas 1982 National Resources Inventoryc i 0.8

(Continued From Previous Page)

Factor Source Description Weight
Appendix II

EQIP 2006 Funding Allocation Formulas

Page 30 GAO-06-969 Agricultural Conservation

Sources: GAO analysis of NRCS and USDA data.

Note: We used NRCS’s own terminology for the factor names in this chart. In some instances, names 

do not precisely capture what is being measured.

a

We were unable to verify the source of data for this factor.

b

Because we could not verify certain data sources, we were unable to provide an accurate description 

of what each factor measured. Blank cells indicate that we were unable to accurately describe what the 

factor measured.

c

Data source as reported by NRCS. We were unable to verify the source of data for this factor.

d

According to an NRI official, cropland eroding above T could have been estimated in one of two 

ways—(1) acres of cropland where the total wind, sheet and rill erosion rates exceeded T or (2) acres 

of cropland where either wind erosion, sheet and rill erosion, or both, exceeded T. We were not able to 

confirm how the data was estimated.

e

NRCS could not confirm the source or date of this data. The National Resources Inventory believed 

this data was from work NRI performed in 1987.

f

According to an NRI official, this factor measures acres of nonfederal, rural forestland with estimated 

average annual sheet and rill erosion above T. We were not able to obtain documentation to confirm 

this definition.

g

“Potential Priority Watersheds for Protection of Water Quality from Nonpoint Sources Related to 

Agriculture.” Poster Presentation at the 52nd Annual SWCS Conference Toronto, Ontario, Canada, 

July 22-25, 1997 (Revised October 7, 1997).

h

Costs Associated with Development and Implementation of Comprehensive Nutrient Management 

Plans Part I—Nutrient Management, Land Treatment, Manure and Wastewater Handling and Storage, 

and Recordkeeping (NRCS, June 2003).

i

According to NRCS, the definition for riparian areas in the 1982 National Resources Inventory was 

acres of riparian areas—the banks, shorelines, or edges of the rising ground bordering a natural or 

manmade watercourse or water area (riparian areas are not limited to natural areas).

j

According to NRCS, this factor considers data on square miles of coastlines.

Carbon sequestration 1997 National Resources Inventory 

(Revised December 2000) and unknown 

data source

Sum of data from other factors in the 

financial assistance formula—

nonirrigated cropland, federal grazing 

lands, nonfederal grazing lands and 

forestlands 

3.6

Coastal zone land NRCS calculation based on 1992 National 

Oceanic and Atmospheric Administration 

and unknown datac

j 3.6%

(Continued From Previous Page)

Factor Source Description Weight
Appendix II

EQIP 2006 Funding Allocation Formulas

Page 31 GAO-06-969 Agricultural Conservation

Table 6: Fiscal Year 2006 Formula for Allocating Ground and Surface Water Conservation Financial Assistance

Source: NRCS.

Table 7: Factors Used in the Fiscal Year 2006 Formula for Allocating EQIP Performance Bonuses

Source: NRCS.

a

Comprehensive nutrient management plans are conservation plans unique to livestock operations. 

These plans document practices and strategies adopted by livestock operations to address natural 

resource concerns related to manure and organic by-products and their potential impacts on water 

quality.

Targeted area Allocation methodology Weight 

High Plains Aquifer states—Colorado, Kansas, 

Nebraska, New Mexico, Oklahoma, South 

Dakota, Texas, and Wyoming

Percentage of state’s acreage in the High Plains 

Aquifer

40.6%

Western drought states—Arizona, California, 

Idaho, Montana, Nevada, North Dakota, Oregon, 

Utah, and Washington

Amount of irrigated acreage in each state 41.5%

Additional states with agricultural water 

needs—Alabama, Arkansas, Delaware, Florida, 

Georgia, Hawaii, Iowa, Louisiana, Maine, 

Minnesota, Mississippi, Missouri, North Carolina, 

Puerto Rico, Wisconsin

Proportional comparison of agriculture to 

nonagricultural use of water

17.9%

Factor Description Weight

Ratio of technical assistance obligations to total 

obligations

Ratio of obligated EQIP funds used for technical 

assistance in fiscal year 2005 to total obligated 

funds

25%

Livestock-related contracts Ratio between the number of EQIP contracts issued 

for Comprehensive Nutrient Management Plans to 

the number of farms needing such plansa

15

Cost-share obligations versus payments Ratio of cost-share dollars obligated to cost-share 

dollars paid in fiscal years 2004 and 2005

15

Technical service provider obligations and 

disbursements

Ratio of disbursements to obligations in fiscal years 

2004 and 2005 to technical service providers—

contractors that help producers install practices

15

Weighted cost-share percentage Average cost-share rate by state, excluding limited 

resource farmer cost-share and incentive payments

10

Limited resource farmer Percentage of total EQIP contracts entered into with 

limited resource farmers

10

Program national priorities Ratio between acres treated with conservation 

practices that address the national priorities to the 

total agricultural base

10%
Appendix II

EQIP 2006 Funding Allocation Formulas

Page 32 GAO-06-969 Agricultural Conservation

Table 8: Fiscal Year 2006 Formula for Allocating Klamath Basin Program Financial 

Assistance

Source: NRCS.

State Weight

California 50%

Oregon 50%
Page 33 GAO-06-969 Agricultural Conservation

Appendix III

Statistical Techniques to Determine Influential 

Factors in the 2006 EQIP Financial Allocation 

Formula ApendxiI

Using statistical techniques—that is, principal components regression and 

factor analysis—we analyzed the Environmental Quality Incentives 

Program (EQIP) formula used to allocate fiscal year 2006 financial 

assistance to the states to identify the environmental factors that most 

influenced the allocations. Sixty-five percent of the total EQIP funds for 

2006 were based on the allocation formula for financial assistance. 

Principal Components 

Regression

In order to determine the relationships between the allocation and the 

environmental factors (variables), we typically would apply regression 

techniques to a model, expressed as

(1) (i = 1,..., 48) 

In equation (1), the dependent variable is the funding allocation for state i, 

the x’s are the j factors in the allocation formula, β 0, β 1,...,βj are the 

regression coefficients, and εi

 is the model error for the ith state.

When we used this model, however, standard regression techniques were 

not possible because many of the environmental factors used in the 

allocation formula were highly collinear.1

 Collinearity occurs when 

variables are so highly correlated that it is difficult to distinguish their 

independent influences on the dependent variable—in this case, state 

allocation funding. In a regression analysis, highly correlated independent 

variables cause the following effects: (1) regression coefficients change, 

depending on which variables are included or excluded in the model, (2) 

standard errors are large, (3) regression coefficients are large with random 

signs, and (4) achieving statistical significance of the collinear parameters 

is difficult. Moreover, multicollinearity poses a problem if the purpose of 

the model is to estimate, or explain, rather than predict, the individual 

contributions of variables. Following Fekedulegn et al., (2002), Norton

1

We used both the variance inflation factor (VIF), as well as inspection of the eigenvalues to 

determine the extent of multicollinearity in the model. Many of the eigenvalues were close 

to zero, indicating a serious problem with multicollinearity. 

i i i j ji i A = β + β x + β x + ... + β x + ε 0 1 1 2 2
Appendix III

Statistical Techniques to Determine 

Influential Factors in the 2006 EQIP 

Financial Allocation Formula

Page 34 GAO-06-969 Agricultural Conservation

(1984), and others, we used principal components regression analysis since 

this technique is recommended when there is multicollinearity in the data.2

Before running the regression analysis, we performed the principal 

components analysis.3

 This procedure generates a set of latent variables, 

called principal components—uncorrelated linear transformations of the 

original variables.4

 At this stage, even though the new variables are not 

collinear, the same magnitude of variance is retained. Therefore, the 

elimination of small principal components reduces the total variance and 

substantially improves the diagnostic capability of the model. In order to 

eliminate these small principal components, various selection procedures 

are used. Following Fekedulegn (2002), we chose the cumulative 

eigenvalue product rule, which keeps the first principal components whose 

combined product is greater than 1.00 (Guiot et al., 1982).5

 The principal 

components themselves are expressed as

(2) Z = X*V. 

2

Fekedulegn, B. Desta, J.J. Colbert, R.R. Hicks, Jr., and Michael E. Schuckers, “Coping with 

Multicollinearity: An Example on Application of Principal Components Regression in 

Dendroecology,” Forest Service, Northeastern Research Station, Research Paper NE-721, 

USDA, 2002; Norton, D.A., “Tree Growth—Climate Relationships in Subalpine Nothofagus 

Forests, South Island, New Zealand,” New Zealand Journal of Botany, 1984, vol. 22: 471-481.

3

In order to perform the principal components analysis, we used the SAS PRINCOMP 

procedure. 

4

The vectors are said to be uncorrelated or orthogonal (perpendicular) to each other when 

they yield a zero valued scalar product.

5

Guiot, J., Berger, A.L., Munaut, A.V., 1982. “Response Functions,” In: Hughes, M.K., Kelly, 

P.M., Pilcher, J.R., LaMarche, V.C., eds. Climate from Tree Rings. Cambridge, UK: 

Cambridge University Press: 38-45.
Appendix III

Statistical Techniques to Determine 

Influential Factors in the 2006 EQIP 

Financial Allocation Formula

Page 35 GAO-06-969 Agricultural Conservation

In equation (2), Z is an (i x j) matrix of principal components, X is an (i x j) 

matrix of standardized environmental factors, and V is a (j x j) matrix of 

eigenvectors.6,7

After the principal components analysis and the elimination of smaller 

principal components as described above, we used the data in a crosssectional multivariate regression expressed as

(3) .

In equation (3), A is an (i x 1) vector for the allocation of funding for the 

states (the dependent variable in the regression), β01 is an (i x 1) vector of 

the intercept terms, Z is an (i x j) matrix of principal components, and α is 

a (j x 1) vector of new coefficients of the principal components. However, 

this procedure will usually leave some principal components that are not 

statistically significant. Therefore, to further eliminate the nonsignificant 

principal components, we used the SAS stepwise regression procedure.8

 

Specifically, we eliminated “r” principal components in the analysis, which 

consisted of the (1) number eliminated using the eigenvalue product rule 

and (2) number eliminated from the stepwise regression. We were then left 

with (j – r) principal components estimators or coefficients and the 

reduced form in equation 3 becomes

(4) .

In equation (4), α is the vector of coefficients associated with the reduced 

set of (j–r) principal components and Z is an (i x (j–r)) matrix of principal 

components. With the r components eliminated, the principal components 

estimators—in terms of the standardized environmental factors of the 

6

V is a (j x j) matrix of “orthonormal” eigenvectors, meaning that it has the property that its 

transpose and its inverse matrix are equal. Equivalently, any pair from the set of column 

vectors, or row vectors, of the matrix are perpendicular or orthogonal (have a scalar 

product of zero), while any individual vector from the set has a norm of one (scalar product 

with itself of one).

7

An eigenvector of a transformation is a vector whose direction is unchanged by that 

transformation. The factor by which the magnitude is scaled is called the eigenvalues of that 

vector.

8

The stepwise regression procedure examines the impact of each variable to the model on a 

step-by-step basis. A variable that cannot contribute much to the variance explained is 

eliminated. 

A = β 1 + Zα + ε 0

= + α + ε A b j−r j−r 1 ˆ 0 Z
Appendix III

Statistical Techniques to Determine 

Influential Factors in the 2006 EQIP 

Financial Allocation Formula

Page 36 GAO-06-969 Agricultural Conservation

allocation model—are obtained by multiplying the new vector of 

coefficients by the associated vectors in the matrix of eigenvectors:

(5) bs

pc = Vj x (j-r) α(j-r) x 1 .

In equation (5), bs

pc (subscript pc stands for principal components) is the 

vector of j standardized principal component estimators of the regression 

coefficients of the environmental factors, Vis the (j x ( j - r)) matrix of 

eigenvectors, and α is the reduced vector of ((j – r) x 1) estimated 

coefficients as in equation 4. Once we have the standardized coefficients of 

the principal components estimators of the factors, we can transform them 

back into the coefficients of the original environmental factors. For the 

standardized estimators, the method for this transformation is expressed 

as 

(6) 

In equation (6), Sxj is the standard deviation of the original jth environmental 

factor, xj

, bs

j,pc is the jth standardized estimator, and bj,pc is the coefficient of 

the original environmental factor. 

While we can obtain the regression coefficients of the original 

environmental factors (the bj,pc’s) that have been corrected for 

multicollinearity, we cannot directly compare them because most have 

different units. For instance, some environmental and resource factors 

used in the formula are measured in acres, while others may be measured 

in terms of animal units. In other words, the largest coefficient may not be 

the most influential in the regression. Therefore, when comparing the 

relative importance of the factors (variables) in the regression, we mainly

X j

s

j pc

j pc S

b

b ,

, =
Appendix III

Statistical Techniques to Determine 

Influential Factors in the 2006 EQIP 

Financial Allocation Formula

Page 37 GAO-06-969 Agricultural Conservation

discuss the standardized estimators of the environmental factors used in 

the allocation formula.9

Data Used For the 48 contiguous states, we used a cross-section of data for the 

dependent variable—the allocation variable—and the independent 

variables—the environmental variables (factors). We could not incorporate 

Alaska or Hawaii because we lacked complete data. We excluded two 

factors—independent variables—from the regression analysis because 

they were linear combinations of factors already included in the data. For 

instance, we could not include the carbon sequestration factor because it is 

the sum of four factors already included in the formula allocation model: 

acres of nonirrigated cropland, forestland, federal grazing land, and 

nonfederal grazing land. We also excluded the factor for pesticide and 

nitrogen runoff because it contains the same data as the phosphorous 

runoff potential factor. Although the U.S. Department of Agriculture 

(USDA) weights these factors differently, they are still linear combinations 

and, for regression analysis, must be excluded. In all, we ran the regression 

using the 2006 state allocations for the 48 states as our dependent variable 

and the 29 environmental and resource factors in the formula for our 

independent variables.

Results After reducing the components from the eigenvalues product rule and the 

stepwise regression, we were left with 13 principal components from the 

original 29. We then transformed the parameter estimates of the stepwise 

regression, , back into the coefficients of the standardized principal 

components of the environmental factors, the bs

pc. The results for these 

standardized coefficients—bs

pc, the t-values, and the probability values of 

t—sorted by the size of the standardized coefficient are shown in table 9. 

9

To test for the statistical significance of the principal components estimators, it has been 

shown that the proper test statistic to use is a t-test with (n – k – 1) degrees of freedom. 

Therefore, for a two-sided test: H0 : βj

s

 = 0 vs. Ha : βj

s

≠0, with significance level α, the null 

hypothesis, H0 , should be rejected if the test statistic is greater than or equal to the critical 

value, (t α/2, n-k-1 ). The actual test statistic, where bs

j,pc is the standardized principal 

component estimator of βj

s

 is t = bs

j,pc / s.e.(bsj,pc ) where s.e.(bj,pc ) is the standard error of the 

coefficients of original environmental factors. It is estimated by dividing the standard error 

of the standardized principal component estimator, bs

j,pc, by the standard deviation of the 

corresponding environmental factor. Mansfield et al. (1977) and Gunst and Mason (1980).

αˆ
Appendix III

Statistical Techniques to Determine 

Influential Factors in the 2006 EQIP 

Financial Allocation Formula

Page 38 GAO-06-969 Agricultural Conservation

Specifically, a standardized coefficient of a factor measures the expected 

change in the dependent variable for a one unit change in the standardized 

independent variable, in this case the ith factor, all other things being equal. 

Those variables that had the largest standardized coefficient as well as 

being highly statistically significant were acres of fair and poor rangeland, 

acres of nonfederal grazing land, acres of irrigated cropland, acres of 

American Indian tribal lands, wind erosion above T, and livestock animal 

units. As table 9 shows, as one would expect with a formula, most of the 

factors in the regression were highly significant and positively related to 

the allocation, except the four factors, acres of forestlands, potential for 

pesticide and nitrogen leaching, air quality nonattainment areas, and acres 

of federal grazing lands.

Table 9: Standardized Principal Components Estimators of the Original Variables and Statistical Significance

Factor

Standardized 

coefficient t-value p-value

Acres of fair and poor rangeland 1399095 35.322 <0.0001

Acres of nonfederal grazing lands 1389052 35.7784 <0.0001

Acres of irrigated cropland 1372591 13.448 <0.0001

Acres of American Indian tribal lands 1313695 13.2325 <0.0001

Wind erosion above T 1210688 18.4507 <0.0001

Livestock animal units 1197842 37.3831 <0.0001

Riparian areas 935709 12.5955 <0.0001

Number of limited resource producers 776918 17.9933 <0.0001

Acres of cropland eroding above T 748625 11.1347 <0.0001

Acres of bodies of water 699697 8.91583 <0.0001

Acres of cropland and pastureland soils affected by saline and/or sodic 

conditions 654109 6.14821 <0.0001

Acres of specialty cropland 648891 10.1944 <0.0001

Acres of pastureland needing treatment 625264 11.5436 <0.0001

Animal waste generation 537929 6.19323 <0.0001

Acres of wetlands and at-risk species habitat 528679 10.9819 <0.0001

Waste management capital cost 504716 7.58475 <0.0001

Coastal zone land 501769 9.1963 <0.0001

Acres of grazing land lost to conversion 449376 6.32035 <0.0001

Miles of impaired rivers and streams 446096 3.99622 0.0008

Ratio of commercial fertilizers to cropland 409840 4.10749 0.0007

Acres of nonirrigated cropland 403724 7.66134 <0.0001
Appendix III

Statistical Techniques to Determine 

Influential Factors in the 2006 EQIP 

Financial Allocation Formula

Page 39 GAO-06-969 Agricultural Conservation

Source: GAO analysis of USDA data.

Note: All variables above the bolded line are positive and statistically significant at the 0.05 percent 

level. Our analysis does not include an adjustment made to the variable “acres of American Indian 

tribal lands” affecting two states. We do not expect that this adjustment would have a material affect on 

the results.

Factor Analysis of 

EQIP Environmental 

Variables

We used the factor analysis technique to reduce the original set of variables 

(environmental factors) in the EQIP formula to a smaller set of underlying 

factors that actually drive the variables and the relationships among these 

variables.10 Factor analysis has been used previously by researchers to 

identify, group, and interpret various environmental concerns, such as soil 

quality, that cannot be measured directly, but must be inferred by 

measuring other attributes that serve as indicators.11 For this formula, the 

underlying factors should mimic, in some sense, the underlying 

environmental concerns, such as water quality and quantity, soil 

productivity, and wildlife habitat preservation. 

Acres of forestlands eroding above T 393498 5.97645 <0.0001

Phosphorous runoff potential 306870 5.30147 <0.0001

Number of concentrated animal feeding operations/animal feeding 

operations 251359 4.95367 0.0001

Ratio of livestock animal units to cropland 213299 2.08889 0.0512

Acres of forestlands 89181 1.19149 0.2489

Potential for pesticide and nitrogen leaching 45721 0.77 0.4513

Air quality nonattainment areas -33022 -0.64754 0.5255

Acres of federal grazing lands -280851 -2.53757 0.0206

(Continued From Previous Page)

Factor

Standardized 

coefficient t-value p-value

10As in the regression analysis, we do not include the carbon sequestration variable or the 

pesticide and nitrogen runoff variable as these are linear combinations of other data in the 

allocation formula. Also, this analysis does not include an adjustment made in the data for 

the factor “acres of American Indian Tribal Lands” between two states. We do not expect 

that this adjustment would have a material affect on the results.

11Specifically, factor analysis was used in a study to identify and interpret soil quality factors 

at a regional level. Brejda, John J. and Thomas B. Moorman, “Identification and 

Interpretation of Regional Soil Quality Factors for the Central High Plains of the Midwestern 

USA,” In: D.E. Stott, R.H. Mohtar, and G.C. Steinhardt (eds.), Sustaining the Global Farm, 

selected papers from the 10th International Soil Conservation Organization Meeting held 

May 24–29, 1999, Purdue University and USDA-ARS National Soil Erosion Research 

Laboratory.
Appendix III

Statistical Techniques to Determine 

Influential Factors in the 2006 EQIP 

Financial Allocation Formula

Page 40 GAO-06-969 Agricultural Conservation

Explanation of the 

Technique

Factor analysis is a technique used to explain the correlations between 

variables and to derive a new set of underlying variables, called “factors,” 

that give a better understanding of the data being analyzed. Using this 

technique allows us to determine what smaller number of factors accounts 

for the correlation in the larger set of variables in the formula. 

In factor analysis, each observed variable, x, can be expressed as a 

weighted composite of a set of underlying, latent variables (f’s) such that

(7) . 

In equation (7), the correlation between the observed variables, the x’s, can 

be explained in terms of the underlying (latent) factors. These latent 

factors explain the common variance between the variables. For example, 

given a set of observed variables, factor analysis forms a set of factors that 

are as independent from each other as possible, while the observed 

variables within each factor are as highly correlated as possible.

To perform the factor analysis, we used the SAS PROC FACTOR procedure, 

choosing the principal factors method to extract the factors. One part of 

the analysis was to determine the number of factors to extract. 

Hypothetically, there can be one factor for every variable, but the goal is to 

reduce this number to a subset of factors that drive, or control, the values 

of the variables being measured. We postulated that the underlying factors 

should mimic, in some sense, the underlying environmental concerns, such 

as water quality and quantity, soil productivity, and wildlife habitat. 

However, since the data contain certain variables such as acres of 

nonirrigated cropland, acres of nonfederal grazing land, or acres of 

American Indian tribal lands, the latent factors may be different in 

character. To determine the number of factors, there are several 

computational methods and more subjective methods such as ease of 

interpretability of factors. We used both the ease of interpretability of the 

factors, as well as the “scree test.”12 As is typically done to achieve a more 

meaningful and interpretable solution, we applied a rotation technique to 

the initial factor pattern matrix.13

12A “scree test” is a graphic method for determining the number of factors. The eigenvalues 

are plotted in the sequence of the principal factors. The number of factors is chosen where 

the plot levels off to a linear decreasing pattern.

13In this case, we applied an orthogonal rotation to the initial factor pattern matrix. 

i i i ik k i x = a f + a f +L+ a f +e 1 1 2 2
Appendix III

Statistical Techniques to Determine 

Influential Factors in the 2006 EQIP 

Financial Allocation Formula

Page 41 GAO-06-969 Agricultural Conservation

Results We used the rotated factor pattern matrix to interpret the meaning of the 

latent factors, which we identified through their correlations with the 

environmental factors (variables), as shown in table 10. The factor loadings 

that have an absolute value equal to or greater than 0.4 are shaded, and 

several variables are significantly correlated with more than one factor—

called a “split loading.”14 

Table 10: Rotated Factor Pattern Matrix 

14In factor analysis terminology, a “loading” is the correlation between a variable and a 

factor. A correlation above 0.40 is considered a statistically significant correlation.

Latent factor

Factor or variable from formula 1 2 3 4

Acres of nonfederal grazing land 0.95432 -0.14193 0.00329 0.10117

Acres of fair and poor rangeland 0.89044 -0.18858 0.04876 -0.03961

Livestock animal units 0.77845 0.51746 0.07331 0.19868

Wind erosion above T 0.76534 0.12833 0.11974 -0.29702

Acres of cropland eroding above T 0.69604 0.613 -0.01102 -0.21171

Riparian areas 0.68226 0.16742 0.37957 0.23914

Acres of grazing land lost to conversion 0.64431 0.26539 -0.0365 0.11935

Acres of irrigated cropland 0.61829 0.0418 0.14401 0.4548

Acres of pastureland needing treatment 0.60112 0.28467 0.24348 0.11009

Number of limited resource producers 0.59529 0.56896 0.23444 0.20013

Acres of federal grazing land 0.46349 -0.4339 -0.32474 0.01709

Acres of cropland and pastureland soils affected by saline and/or 

sodic conditions 0.39952 -0.17105 -0.18815 0.39389

Number of concentrated animal feeding operations/animal feeding 

operations 0.05818 0.87513 -0.03339 -0.0465

Phosphorous runoff potential 0.12202 0.86976 0.28758 0.13109

Waste management capital cost 0.1018 0.76694 0.04769 0.07004

Acres of nonirrigated cropland 0.46277 0.71639 -0.15479 -0.23632

Potential for pesticide and nitrogen leaching 0.01166 0.69366 0.46219 0.29581

Animal waste generation 0.2898 0.57515 0.26865 0.19698

Acres of American Indian tribal lands 0.24861 -0.37768 -0.26536 0.01991

Ratio of livestock animal units to cropland 0.08217 -0.47811 -0.05783 0.29948

Acres of wetlands and at-risk species habitat 0.14074 0.18228 0.86993 -0.1914
Appendix III

Statistical Techniques to Determine 

Influential Factors in the 2006 EQIP 

Financial Allocation Formula

Page 42 GAO-06-969 Agricultural Conservation

Source: GAO analysis of USDA data.

Note: Variable loadings with a significant correlation are shaded.

The factor analysis technique also calculates the amount of common 

variance explained by each latent factor. For these data, the variances are: 

factor 1— 6.44, factor 2—5.49, factor 3—3.56, and factor 4—3.00, 

accounting for about 71 percent of the common variance in the data. 

Overall, the four factors (1) all relate to environmental concerns, as well as 

agricultural resources, and (2) each latent factor contributes a decreasing 

amount of common variance to the total variation among all of the 

variables. We interpreted the EQIP data that went into the factor analysis to 

represent (1) dryland agriculture and cattle feeding, (2) water quality 

concerns relating to concentrated livestock feeding operations and 

nonirrigated cropland, (3) wildlife habitat preservation, and (4) specialty 

crops/intensive agriculture and water quality/quantity concerns. Specifics 

of the factor analysis follow:

Factor 1: This factor contributes the most variation to the factor analysis 

and seems to be associated with dryland agriculture and cattle grazing and 

feeding. The variables—acres of nonfederal grazing lands, acres of fair and 

poor rangeland, wind erosion above T, acres of cropland eroding above T, 

and acres of irrigated cropland—are all descriptors of this type of 

agriculture. In addition, factor 1 is also strongly correlated with the 

livestock animal units variable, although it has a split loading with factor 2. 

While the number of limited resource producers variable has a split loading 

between this factor and factor 2, it is most heavily loaded with this factor.

Acres of bodies of water 0.35908 0.10564 0.81371 -0.01889

Coastal zone land 0.1042 -0.05594 0.79583 0.29895

Acres of forestlands 0.13012 0.24312 0.69724 0.20541

Acres of specialty cropland 0.09679 -0.10928 0.30919 0.78195

Acres of forestlands eroding above T 0.15413 -0.00078 -0.04538 0.77575

Miles of impaired rivers and streams 0.20763 0.12325 0.1149 0.60384

Ratio of commercial fertilizer to cropland -0.15704 0.21558 0.05047 0.52823

Air quality nonattainment areas -0.19264 -0.2472 -0.00932 0.32038

(Continued From Previous Page)

Latent factor

Factor or variable from formula 1 2 3 4
Appendix III

Statistical Techniques to Determine 

Influential Factors in the 2006 EQIP 

Financial Allocation Formula

Page 43 GAO-06-969 Agricultural Conservation

Factor 2: This factor, like factor 1, has to do with livestock operations, as 

well as with other important livestock-related variables that affect water 

quality. Here, the highest loading is with the variable, number of 

concentrated animal feeding operations/animal feeding operations, 

(CAFOs) (0.88), although it has the split loading with livestock animal units 

(0.52). In addition, factor 2 showed high loadings for phosphorous runoff 

potential and potential for pesticide and nitrogen leaching, which may be 

related to sediment losses from both animal and cropland agriculture. 

Moreover, as cropland and CAFOs are usually in the same location, one 

would expect the variable for acres of nonirrigated cropland to also have a 

high loading, which it does (0.72). 

Factor 3: This factor seems to be related to environmental concerns about 

wildlife habitat, with the highest loading going to acres of wetland and atrisk species habitat (0.87), as well as to acres of bodies of water, (0.81) 

coastal zone land (0.80) and acres of forestlands (0.70). Potential for 

pesticide and nitrogen leaching (0.46) showed a split loading with factor 2.

Factor 4: This factor seems to represent variables relating to specialty 

crop and intensive agriculture, with high loadings for acres of specialty 

crops, ratio of commercial fertilizer to cropland, and acres of irrigated 

cropland, (which had a split loading with factor 1). Also, acres of cropland 

and pastureland affected by saline and/or sodic conditions, a soil condition 

that often accompanies irrigated soils, is almost significantly correlated to 

Factor 4 (0.39). This factor also highly loads with miles of impaired rivers 

and streams, which may be an indication of water quality and quantity 

concerns associated with soils that require irrigation. Factor 4 is also highly 

associated with acres of forestlands eroding above T, many of which are 

found in the same areas that contain acres of irrigated cropland.

The two variables—air quality nonattainment areas and acres of American 

Indian tribal lands—did not load onto any of the latent factors. When this 

happens, the variable has a unique variance that is not explained by the 

common factors. 
Page 44 GAO-06-969 Agricultural Conservation

Appendix IV

Initial EQIP Funding Provided to the States, 

Fiscal Year 2006 ApendxiIV

State

General 

financial 

assistance

General 

technical 

assistance

Colorado 

Salinitya

Performance 

bonusesa

Regional 

equitya

Ground and 

Surface 

Watera

Klamath 

Basina

Total EQIP 

fundingb

Alabama $11,692,291 $3,409,837 $0 $1,499,189 $0 $169,802 $0 $16,771,119 

Alaska 2,065,044 855,803 0 1,729,833 2,189,241 0 0 6,839,921 

Arizona 19,838,811 4,909,614 0 1,383,866 0 2,195,991 0 28,328,282 

Arkansas 15,285,890 4,275,715 0 1,845,155 0 3,197,575 0 24,604,335 

California 35,601,305 9,917,421 0 0 0 12,139,356 5,244,128 62,902,210 

Colorado 19,166,118 6,402,301 9,746,600 1,153,222 0 4,731,333 0 41,199,573 

Connecticut 2,276,617 1,298,554 0 0 2,409,129 0 0 5,984,300 

Delaware 4,262,315 1,120,129 0 1,499,189 559,577 177,342 0 7,618,551 

Florida 18,598,404 5,661,746 0 0 0 904,160 0 25,164,310 

Georgia 13,596,275 3,956,923 0 922,578 0 574,887 0 19,050,663 

Hawaii 2,830,085 1,315,702 0 0 1,826,989 1,537,667 0 7,510,443 

Idaho 10,402,360 3,120,403 0 2,075,800 0 4,764,140 0 20,362,703 

Illinois 13,490,217 3,506,538 0 0 0 0 0 16,996,755 

Indiana 10,199,898 2,756,379 0 0 0 0 0 12,956,277 

Iowa 19,857,205 5,539,542 0 0 0 212,556 0 25,609,303 

Kansas 20,568,274 5,916,955 0 0 0 4,277,167 0 30,762,396 

Kentucky 10,470,827 3,014,900 0 0 0 0 0 13,485,727 

Louisiana 12,681,547 3,641,657 0 2,075,800 0 493,370 0 18,892,373 

Maine 4,969,838 1,612,816 0 1,499,189 492,709 387,111 0 8,961,663 

Maryland 6,057,344 1,919,649 0 0 0 0 0 7,976,993 

Massachusetts 2,252,718 1,061,011 0 0 2,057,775 9,740 0 5,381,243 

Michigan 15,171,136 4,586,470 0 0 0 0 0 19,757,606 

Minnesota 25,108,644 6,645,257 0 0 0 246,344 0 32,000,245 

Mississippi 12,880,865 3,440,744 0 1,499,189 0 2,953,823 0 20,774,620c

Missouri 18,150,708 4,835,351 0 0 0 403,185 0 23,389,244 

Montana 22,189,687 5,834,973 0 1,153,222 0 2,491,718 0 31,669,601 

Nebraska 20,543,213 5,607,316 0 0 0 5,634,218 0 31,784,747 

Nevada 4,568,635 1,578,562 0 0 1,266,836 842,096 0 8,256,129 

New 

Hampshire 1,665,326 771,967 0 0 3,022,681 0 0 5,459,975 

New Jersey 2,831,241 1,055,596 0 0 1,627,774 0 0 5,514,610 

New Mexico 16,436,791 5,168,241 0 2,306,444 0 1,332,763 0 25,244,238 

New York 11,000,760 3,416,030 0 0 0 0 0 14,416,790 

North Carolina 14,156,869 3,741,418 0 0 0 300,995 0 18,199,282 
Appendix IV

Initial EQIP Funding Provided to the States, 

Fiscal Year 2006

Page 45 GAO-06-969 Agricultural Conservation

Source: GAO analysis of NRCS data.

Note: Dollars allocated at the national level to producers through Conservation Innovation Grants are 

not included.

a

NRCS provides these funds to the states through both financial and technical assistance, the majority 

of which are financial assistance.

b

The source for data on total EQIP funding, except for Mississippi, is NRCS at 

http://www.nrcs.usda.gov/PROGRAMS/2006_allocations/2006Allocationstostatesbyprog/FY2006progr

am_allocations_by_states.html. Due to rounding, totals may not equal the sum of funding from all 

categories.

c

Mississippi’s funding total is approximately $189,000 more than what was reported by NRCS. The 

$189,000 represents a payment transfer made from the Mississippi state office to headquarters for 

training. In order to consistently represent the initial amount of funding each NRCS state office 

received from headquarters, we included this $189,000 in Mississippi’s funding total.

d

Totals for Puerto Rico also include funding provided to the U.S. Virgin Islands.

e

Total funding may not equal the sum of state funding due to rounding.

North Dakota 16,419,674 4,644,465 0 1,960,477 0 117,791 0 23,142,407 

Ohio 11,752,789 3,834,502 0 1,268,544 0 0 0 16,855,835 

Oklahoma 20,967,673 5,195,174 0 2,075,800 0 966,706 0 29,205,352 

Oregon 11,345,753 3,096,493 0 1,614,511 0 2,251,318 5,593,064 23,901,139 

Pennsylvania 10,470,862 3,364,889 0 0 0 0 0 13,835,751 

Rhode Island 940,474 337,025 0 0 3,600,911 0 0 4,878,410 

South Carolina 5,845,873 2,103,593 0 1,845,155 0 0 0 9,794,621 

South Dakota 15,806,924 4,488,602 0 1,153,222 0 555,182 0 22,003,929 

Tennessee 10,020,289 2,821,655 0 0 0 0 0 12,841,944 

Texas 65,270,552 17,442,105 0 1,383,866 0 7,193,968 0 91,290,491 

Utah 8,918,556 3,728,733 9,746,600 2,075,800 0 1,289,849 0 25,759,537 

Vermont 2,323,794 1,157,690 0 0 2,370,729 0 0 5,852,213 

Virginia 10,803,694 2,909,515 0 1,268,544 0 0 0 14,981,753 

Washington 13,651,196 3,463,804 0 1,037,900 0 2,087,065 0 20,239,965 

West Virginia 4,328,232 1,594,233 0 0 1,525,651 0 0 7,448,115 

Wisconsin 16,218,507 4,262,300 0 0 0 174,865 0 20,655,672 

Wyoming 10,914,358 2,962,722 44,000 2,191,122 0 2,242,966 0 18,355,168 

Pacific Basin 1,370,851 239,153 0 0 0 0 0 1,610,004 

Puerto Ricod 4,364,658 1,391,991 0 0 1,050,000 180,892 0 6,987,541

Totale $662,601,964 $190,934,165 $19,537,200 $38,517,615 $24,000,000 $67,037,941 $10,837,192 $1,013,466,074 

(Continued From Previous Page)

State

General 

financial 

assistance

General 

technical 

assistance

Colorado 

Salinitya

Performance 

bonusesa

Regional 

equitya

Ground and 

Surface 

Watera

Klamath 

Basina

Total EQIP 

fundingb
Page 46 GAO-06-969 Agricultural Conservation

Appendix V

Historical EQIP Funding Levels, Fiscal Years 

2001-2006 ApendxiV

State 2001 2002 2003 2004 2005 2006a

Alabama $3,682,300 $7,113,500 $10,682,200 $13,637,700 $16,285,108 $16,771,119

Alaska 498,700 996,200 1,330,900 9,087,600 7,345,521 6,839,921

Arizona 7,107,300 12,314,800 13,579,100 20,017,300 22,584,523 28,328,282

Arkansas 4,557,300 8,353,300 12,880,500 20,835,800 23,652,812 24,604,335

California 9,184,200 19,137,900 48,581,600 56,981,700 62,114,391 62,902,210

Colorado 7,074,000 14,432,500 25,560,300 36,931,700 39,185,835 41,199,573

Connecticut 790,300 1,541,700 2,379,700 8,021,300 6,171,688 5,984,300

Delaware 968,500 1,939,200 3,032,600 5,366,500 6,532,427 7,618,551

Florida 5,365,800 10,178,300 15,554,300 22,392,900 24,123,030 25,164,310

Georgia 4,107,200 8,010,200 12,167,200 16,188,600 18,674,184 19,050,663

Hawaii 648,900 1,384,200 1,969,800 8,060,300 8,192,003 7,510,443

Idaho 3,328,800 6,416,800 17,727,900 18,994,300 19,174,741 20,362,703

Illinois 4,156,300 7,798,000 12,108,000 16,729,200 17,969,667 16,996,755

Indiana 2,757,600 5,140,200 8,111,900 11,599,400 12,574,260 12,956,277

Iowa 7,036,400 8,994,800 14,231,400 23,399,700 25,856,704 25,609,303

Kansas 5,014,700 10,448,800 19,763,400 28,144,400 30,447,213 30,762,396

Kentucky 3,111,400 5,913,300 8,958,100 12,039,300 13,288,086 13,485,727

Louisiana 3,947,800 7,089,900 10,913,400 15,156,500 18,048,303 18,892,373

Maine 1,982,000 3,070,500 4,380,300 9,155,900 9,806,574 8,961,663

Maryland 2,067,600 3,396,800 5,125,400 6,701,100 7,732,193 7,976,993

Massachusetts 933,600 1,715,200 2,632,600 6,453,000 4,952,573 5,381,243

Michigan 4,334,400 8,225,400 12,713,300 17,463,300 18,629,584 19,757,606

Minnesota 5,788,200 11,483,700 19,012,100 29,423,700 32,924,161 32,000,245

Mississippi 5,218,700 8,298,100 11,860,200 19,492,400 21,420,866 20,774,620b

Missouri 5,042,500 9,944,300 15,271,600 22,394,800 23,379,201 23,389,244

Montana 6,463,100 13,295,500 19,354,600 28,432,400 31,810,709 31,669,601

Nebraska 4,805,400 10,673,400 20,441,800 29,600,300 32,123,093 31,784,747

Nevada 1,432,700 2,773,700 4,467,000 9,452,900 8,914,534 8,256,129

New Hampshire 614,800 1,265,400 1,779,700 2,297,000 5,726,909 5,459,975

New Jersey 965,100 1,891,900 2,919,200 5,784,000 4,386,375 5,514,610

New Mexico 5,796,800 12,460,100 16,143,600 27,889,800 29,802,972 25,244,238

New York 3,822,800 6,774,000 10,355,500 12,484,700 13,128,566 14,416,790

North Carolina 4,572,300 8,590,100 13,169,500 16,473,100 17,985,395 18,199,282

North Dakota 4,263,300 8,710,200 14,394,600 19,181,100 22,014,952 23,142,407

Ohio 3,250,900 6,505,300 10,150,400 13,412,400 15,823,019 16,855,835
Appendix V

Historical EQIP Funding Levels, Fiscal Years 

2001-2006

Page 47 GAO-06-969 Agricultural Conservation

Source: GAO analysis of NRCS data.

a

The data source for fiscal year 2006 total EQIP funding, except for Mississippi, was NRCS's Web site: 

http://www.nrcs.usda.gov/PROGRAMS/2006_allocations/2006Allocationstostatesbyprog/FY2006progr

am_allocations_by_states.html . 

b

Mississippi’s funding total for 2006 is approximately $189,000 more than what was reported by NRCS. 

The $189,000 represents a payment transfer made from the Mississippi state office to headquarters for 

training. 

c

Totals for Puerto Rico also include funding provided to the U.S. Virgin Islands.

Oklahoma 5,018,900 9,290,600 13,913,400 25,378,800 29,017,864 29,205,352

Oregon 4,383,600 7,404,400 17,950,700 21,615,200 21,839,220 23,901,139

Pennsylvania 3,364,400 5,960,600 9,056,800 11,853,900 12,828,822 13,835,751

Rhode Island 422,900 841,400 1,309,100 1,026,800 5,461,693 4,878,410

South Carolina 2,281,700 4,453,700 6,812,400 9,025,800 9,663,381 9,794,621

South Dakota 4,695,000 10,424,500 13,595,200 19,076,300 20,547,674 22,003,929

Tennessee 3,114,500 5,913,100 8,935,400 11,513,300 12,759,284 12,841,944

Texas 15,187,200 28,700,500 57,717,300 78,565,800 90,007,418 91,290,491

Utah 4,655,000 10,139,900 14,565,800 20,976,900 23,107,745 25,759,537

Vermont 1,281,800 1,907,700 2,687,100 7,341,400 5,739,903 5,852,213

Virginia 3,172,500 6,216,300 9,494,700 12,366,100 13,336,380 14,981,753

Washington 4,194,000 7,420,300 12,937,800 18,549,900 20,694,391 20,239,965

West Virginia 1,807,400 3,507,700 5,313,600 8,690,200 7,404,453 7,448,115

Wisconsin 4,554,800 8,730,200 13,486,800 18,960,500 20,962,647 20,655,672

Wyoming 3,684,700 7,217,800 11,335,200 16,135,900 17,803,201 18,355,168

Pacific Basin 346,300 582,100 958,200 866,800 1,771,577 1,610,004

Puerto Ricoc 964,700 1,808,800 2,928,100 6,660,200 6,150,928 6,987,541

Total $197,821,100 $376,796,800 $626,701,300 $908,279,900 $991,878,752 $1,013,466,074

(Continued From Previous Page)

State 2001 2002 2003 2004 2005 2006a
Page 48 GAO-06-969 Agricultural Conservation

Appendix VI

Fiscal Year 2005 EQIP Obligations by 

Conservation Practice ApendxiVI

Practice name/payment type Numbera

EQIP dollars 

obligated

Ground and 

Surface 

Water 

Conservation 

dollars 

obligated 

Klamath 

dollars 

obligated 

Total dollars 

obligated 

Percentage 

of total

Waste storage facility 2,983 $91,086,442 $13,500 $0 $91,099,942 11.43%

Fence 19,303 59,497,144 80,456 72,012 59,649,612 7.48

Irrigation system, sprinkler 3,633 40,316,414 14,649,127 3,184,141 58,149,682 7.29

Brush management 9,664 38,418,198 8,160 0 38,426,358 4.82

Nutrient management 28,203 31,364,894 202,457 0 31,567,351 3.96

Pipeline 10,337 30,219,008 13,115 1,350 30,233,473 3.79

Irrigation water conveyance, pipeline, high 

pressure, underground, plastic 2,911 21,842,555 3,850,154 1,453,781 27,146,491 3.41

Irrigation system, trickle 1,321 16,623,856 9,606,358 0 26,230,214 3.29

Pasture and hayland planting 11,582 23,856,494 118,962 0 23,975,456 3.01

Pest management 19,190 22,803,131 227,884 0 23,031,015 2.89

Heavy use area protection 4,689 22,119,580 0 0 22,119,580 2.77

Residue management, no till and strip till 8,355 21,516,049 281,657 0 21,797,706 2.73

Trough or tank 13,770 20,854,462 15,177 1,875 20,871,514 2.62

Prescribed grazing 12,736 17,266,545 285,858 537,144 18,089,547 2.27

Grade stabilization structure 3,630 15,101,275 54,118 9,000 15,164,393 1.90

Residue management, mulch till 3,397 14,289,883 185,416 0 14,475,299 1.82

Well 3,275 12,745,858 56,865 0 12,802,723 1.61

Irrigation water conveyance, pipeline, low 

pressure underground, plastic 1,447 8,916,088 2,329,292 1,428,530 12,673,910 1.59

Manure transfer 1,223 11,800,218 57,660 0 11,857,878 1.49

Pond 4,006 11,375,676 31,678 0 11,407,354 1.43

Irrigation land leveling 1,190 9,387,978 1,966,210 9,600 11,363,788 1.43

Terrace 2,961 11,340,023 11,870 0 11,351,893 1.42

Structure for water control 3,456 8,323,788 2,217,993 325,373 10,867,154 1.36

Underground outlet 2,811 8,702,037 124,150 0 8,826,187 1.11

Pumping plant for water control 2,203 7,287,132 878,852 591,736 8,757,720 1.10

Forest stand improvement 2,772 8,612,338 0 47,520 8,659,858 1.09

Conservation crop rotation 2,682 2,579,277 6,072,364 0 8,651,641 1.09

Composting facility 513 8,061,771 0 0 8,061,771 1.01

Grassed waterway 2,635 7,071,664 5,096 0 7,076,760 0.89

Irrigation water management 5,319 3,694,001 2,844,830 250,556 6,789,387 0.85
Appendix VI

Fiscal Year 2005 EQIP Obligations by 

Conservation Practice

Page 49 GAO-06-969 Agricultural Conservation

Water and sediment control basin 1,732 6,531,431 20,100 0 6,551,531 0.82

Waste utilization 2,245 5,751,350 0 0 5,751,350 0.72

Tree/shrub establishment 2,618 5,355,933 5,739 23,835 5,385,507 0.68

Cover crop 2,997 5,150,505 224,470 1,440 5,376,415 0.67

Range planting 1,810 5,221,400 151,840 315 5,373,555 0.67

Atmospheric resource quality management 2,150 5,308,280 0 0 5,308,280 0.67

Access road 1,067 4,673,395 0 3,750 4,677,145 0.59

Irrigation water conveyance, ditch and canal, 

nonreinforced concrete 324 3,718,626 805,511 0 4,524,137 0.57

Streambank and shoreline protection 502 4,467,492 15,486 15,000 4,497,978 0.56

Agrochemical mixing facilityb 379 4,436,661 0 0 4,436,661 0.56

Wastewater and feedlot runoff controlb 98 3,750,526 0 0 3,750,526 0.47

Forest site preparation 1,609 3,728,573 0 0 3,728,573 0.47

Critical area planting 5,204 3,016,223 38,433 1,125 3,055,781 0.38

Incentive payment for comprehensive 

nutrient management plan 1,423 3,049,132 0 0 3,049,132 0.38

Subsurface drain 722 2,967,580 0 0 2,967,580 0.37

Sediment basin 322 2,744,387 1,645 0 2,746,032 0.34

Waste treatment lagoon 333 2,072,400 630,734 0 2,703,134 0.34

Irrigation water conveyance, pipeline, rigid 

gated pipeline 737 2,396,798 156,156 63,362 2,616,316 0.33

Closure of waste impoundment 137 2,428,288 0 0 2,428,288 0.33

Residue management, direct seedb 638 2,404,419 5,000 0 2,409,419 0.30

Diversion 1,068 2,264,307 1,380 0 2,265,687 0.28

Prescribed burning 1,763 2,243,540 0 0 2,243,540 0.28

Irrigation system, tailwater recovery 172 1,675,379 429,927 13,500 2,118,806 0.27

Use exclusion 1,264 2,116,869 1,770 0 2,118,639 0.27

Animal trails and walkways 545 2,007,572 0 0 2,007,572 0.25

Spring development 1,278 1,984,575 0 0 1,984,575 0.25

Windbreak/shelterbreak establishment 1,277 1,796,732 21,978 0 1,818,710 0.23

Long term no tillb 463 1,717,823 0 0 1,717,823 0.22

Stream crossingb 668 1,617,152 0 0 1,617,152 0.20

Irrigation storage reservoir 69 739,821 815,416 0 1,555,237 0.20

Reimbursement for technical service 

provider, design 2,862 1,516,129 36,669 0 1,552,797 0.19

(Continued From Previous Page)

Practice name/payment type Numbera

EQIP dollars 

obligated

Ground and 

Surface 

Water 

Conservation 

dollars 

obligated 

Klamath 

dollars 

obligated 

Total dollars 

obligated 

Percentage 

of total
Appendix VI

Fiscal Year 2005 EQIP Obligations by 

Conservation Practice

Page 50 GAO-06-969 Agricultural Conservation

Pond sealing or lining, flexible membrane 56 1,548,195 0 0 1,548,195 0.19

Roof runoff management 729 1,542,321 1,500 0 1,543,821 0.19

Alum treatment of poultry litterb 603 1,207,459 0 0 1,207,459 0.15

Irrigation regulating reservoir 165 944,204 250,180 0 1,194,384 0.15

Wildlife upland habitat management 1,285 1,167,347 22,897 0 1,190,244 0.15

Animal mortality facility 242 1,144,067 0 0 1,144,067 0.14

Open channel 32 983,142 0 0 983,142 0.12

Lined waterway or outlet 166 955,446 0 0 955,446 0.12

Firebreak 601 919,207 8,280 0 927,487 0.12

Irrigation system, surface and subsurface 132 667,096 236,679 0 903,775 0.11

Forest harvest trails and landings 249 866,311 0 0 866,311 0.11

Residue management, seasonal 298 801,742 10,122 0 811,864 0.10

Irrigation water conveyance, pipeline, 

unspecified type 125 681,791 72,246 0 754,037 0.09

Reimbursement for technical service 

provider, construction 1,864 709,354 7,626 0 716,981 0.09

Obstruction removal 207 697,581 5,550 0 703,131 0.09

Mulching 568 677,376 12,475 0 689,851 0.09

Pond sealing or lining, bentonite 54 676,910 0 0 676,910 0.08

Wildlife habitat restoration and management 238 613,289 1,065 0 614,354 0.08

Wastewater, milkhouse treatment systemb 30 586,991 0 0 586,991 0.07

Water harvesting catchment 49 436,851 148,500 0 585,351 0.07

Storm water wet detention/chemical 

treatment systemb 3 581,251 0 0 581,251 0.07

Field border 724 524,775 4,860 0 529,635 0.07

Filter strips 801 527,986 1,550 0 529,536 0.07

Toxic salt reduction 223 528,113 0 0 528,113 0.07

Precision land forming 57 527,826 0 0 527,826 0.07

Riparian forest buffer 453 469,753 0 2,250 472,003 0.06

Land smoothing 223 434,267 3,688 7,628 445,583 0.06

Well decommissioning 484 429,531 15,286 0 444,817 0.06

Windbreak/shelterbreak renovation 228 427,053 0 0 427,053 0.05

Reimbursement for technical service 

provider, certification 2,127 410,614 11,466 0 422,079 0.05

Dike 95 418,129 0 0 418,129 0.05

(Continued From Previous Page)

Practice name/payment type Numbera

EQIP dollars 

obligated

Ground and 

Surface 

Water 

Conservation 

dollars 

obligated 

Klamath 

dollars 

obligated 

Total dollars 

obligated 

Percentage 

of total
Appendix VI

Fiscal Year 2005 EQIP Obligations by 

Conservation Practice

Page 51 GAO-06-969 Agricultural Conservation

Incineratorb 72 409,113 0 0 409,113 0.05

Reimbursement for technical service 

provider, planning 282 389,035 0 0 389,035 0.05

Animal use area protectionb 107 388,377 0 0 388,377 0.05

Hedgerow planting 111 376,818 0 0 376,818 0.05

Tree/shrub pruning 253 358,758 0 0 358,758 0.04

Barnyard runoff managementb 19 344,198 0 0 344,198 0.04

Feed management 287 327,729 0 0 327,729 0.04

Conservation cover 489 302,024 10,041 0 312,065 0.04

Chiseling and subsoiling 228 305,754 4,800 0 310,554 0.04

Wildlife habitat, early successional 362 310,026 0 0 310,026 0.04

Anaerobic digester, ambient temperature 2 300,001 0 0 300,001 0.04

Residue management 184 284,296 0 0 284,296 0.04

Fish passage 22 262,056 7,789 9,911 279,756 0.04

Surface drainage, main or lateral 15 263,977 3,411 0 267,388 0.03

Wastewater treatment strip 98 261,499 0 0 261,499 0.03

Irrigation water conveyance, pipeline, steel 97 226,747 23,293 1,200 251,240 0.03

Shellfish aquaculture managementb 63 247,909 0 0 247,909 0.03

Irrigation water conveyance, pipeline, 

nonreinforced concrete 14 240,950 0 0 240,950 0.03

Closure of waste impoundmentb 91 224,816 0 0 224,816 0.03

Fish stream improvement 34 202,469 1,006 0 203,475 0.03

Aquaculture ponds 200 6,000 196,524 0 202,524 0.03

Anaerobic digester, controlled temperature 5 195,000 0 0 195,000 0.02

Agricultural fuel containment facilityb 63 194,607 0 0 194,607 0.02

Stream channel stabilization 26 185,418 200 0 185,618 0.02

Pond sealing or lining, soil dispersant 30 171,387 5,370 0 176,757 0.02

Irrigation field ditch 55 175,048 0 0 175,048 0.02

Residue management, ridge till 97 146,536 150 0 146,686 0.02

Transition to organic productionb 99 132,781 0 0 132,781 0.02

Forage harvest management 154 62,461 66,996 0 129,457 0.02

Shallow water for wildlife 162 101,607 14,627 0 116,234 0.01

Wetland restoration 40 115,663 0 0 115,663 0.01

Dam, floodwater retarding 13 115,385 0 0 115,385 0.01

(Continued From Previous Page)

Practice name/payment type Numbera

EQIP dollars 

obligated

Ground and 

Surface 

Water 

Conservation 

dollars 

obligated 

Klamath 

dollars 

obligated 

Total dollars 

obligated 

Percentage 

of total
Appendix VI

Fiscal Year 2005 EQIP Obligations by 

Conservation Practice

Page 52 GAO-06-969 Agricultural Conservation

Clearing and snagging 12 106,250 0 0 106,250 0.01

Digester, complete mixb 13 105,600 0 0 105,600 0.01

Dam, diversion 25 96,877 6,330 0 103,207 0.01

Wildlife watering facility 141 88,417 0 0 88,417 0.01

Wetland enhancement 31 79,020 0 0 79,020 0.01

Grazing land mechanical treatment 68 74,389 0 0 74,389 0.01

Solid/liquid waste separation facility 1 72,000 0 0 72,000 0.01

Contour farming 129 69,690 0 0 69,690 0.01

Livestock shade structureb 23 67,146 0 0 67,146 0.01

Stripcropping, contour 63 65,270 0 0 65,270 0.01

Silage leachate collection and transferb 2 65,190 0 0 65,190 0.01

Hillside ditch 34 57,056 0 0 57,056 0.01

Irrigation water conveyance, pipeline, 

aluminum tubing 9 15,995 2,250 35,412 53,657 0.01

Land clearing 59 50,970 0 0 50,970 0.01

Digester, plug flowb 13 49,500 0 0 49,500 0.01

Cross wind ridges 33 45,956 0 0 45,956 0.01

Farm evaluationb 42 45,500 0 0 45,500 0.01

Pond sealing or lining, unspecified type 6 42,495 0 0 42,495 0.01

Riparian herbaceous buffer 70 41,440 0 0 41,440 0.01

Fuel break 17 38,556 0 0 38,556 0.00

Pathogen managementb 69 34,500 0 0 34,500 0.00

Waste field storage areab 11 31,874 0 0 31,874 0.00

Waterspreading 6 31,264 0 0 31,264 0.00

Fish raceway or tank 1 27,125 0 0 27,125 0.00

Irrigation canal or lateral 4 25,157 1,406 0 26,563 0.00

Temporary steel workb 6 25,416 0 0 25,416 0.00

Contour orchard and other fruit area 130 24,796 0 0 24,796 0.00

Wetland, constructed 5 23,621 0 0 23,621 0.00

Vegetative barrier 13 22,494 0 0 22,494 0.00

Alley cropping 86 21,963 0 0 21,963 0.00

Regulating water in drainage systems 13 19,466 0 0 19,466 0.00

Surface roughening 11 18,207 0 0 18,207 0.00

(Continued From Previous Page)

Practice name/payment type Numbera

EQIP dollars 

obligated

Ground and 

Surface 

Water 

Conservation 

dollars 

obligated 

Klamath 

dollars 

obligated 

Total dollars 

obligated 

Percentage 

of total
Appendix VI

Fiscal Year 2005 EQIP Obligations by 

Conservation Practice

Page 53 GAO-06-969 Agricultural Conservation

Irrigation water conveyance, ditch and canal, 

flexible membrane 8 913 16,359 0 17,272 0.00

Channel vegetation 15 14,730 2,400 0 17,130 0.00

Sinkhole treatmentb 10 13,988 0 0 13,988 0.00

Irrigation water conveyance, ditch and canal, 

unspecified type 2 13,260 0 0 13,260 0.00

Vertical drain 12 13,251 0 0 13,251 0.00

Recreation trail and walkway 1 13,000 0 0 13,000 0.00

Wildlife wetland habitat management 71 11,860 0 0 11,860 0.00

Waste facility cover 1 10,080 0 0 10,080 0.00

Contour buffer strips 26 10,052 0 0 10,052 0.00

Soil salinity management, nonirrigated 24 9,390 0 0 9,390 0.00

Spoil spreading 5 9,021 0 0 9,021 0.00

Surface drainage, field ditch 16 7,010 870 0 7,880 0.00

Well testingb 36 7,560 0 0 7,560 0.00

Herbaceous wind barriers 13 7,139 0 0 7,139 0.00

Wetland creation 5 6,990 0 0 6,990 0.00

Pasture and hayland management 3 5,898 0 0 5,898 0.00

Monitoring well 1 4,875 0 0 4,875 0.00

Invasive plant species controlb 4 4,292 0 0 4,292 0.00

Row arrangement 3 3,126 0 0 3,126 0.00

Infiltration ditchesb 8 2,768 0 0 2,768 0.00

Air managementb 4 2,570 0 0 2,570 0.00

Anionic polyacrylamide erosion control 5 2,359 0 0 2,359 0.00

Soil salinity controlb 4 2,232 0 0 2,232 0.00

Cross wind trap strips 2 2,030 0 0 2,030 0.00

Silvopasture establishment 3 1,890 0 0 1,890 0.00

Cross wind stripcropping 3 1,440 0 0 1,440 0.00

Irrigation water conveyance, pipeline, 

reinforced plastic mortar 2 1,260 0 0 1,260 0.00

Cisternb 1 750 0 0 750 0.00

Irrigation water conveyance, pipeline, 

corrugated metal pipeline 2 0 12 527 539 0.00

Snow harvestingb 2 500 0 0 500 0.00

Dry hydrant 1 373 0 0 373 0.00

(Continued From Previous Page)

Practice name/payment type Numbera

EQIP dollars 

obligated

Ground and 

Surface 

Water 

Conservation 

dollars 

obligated 

Klamath 

dollars 

obligated 

Total dollars 

obligated 

Percentage 

of total
Appendix VI

Fiscal Year 2005 EQIP Obligations by 

Conservation Practice

Page 54 GAO-06-969 Agricultural Conservation

Source: GAO analysis of NRCS data.

Note: This table only provides data on financial assistance obligations. It does not contain data on 

technical assistance obligations. The data used were generated in March 2006 and represent 

obligations as of that date. NRCS said the database from which these data were generated is 

continually modified as contracts are altered or cancelled.

a

In fiscal year 2005, NRCS entered into 49,406 contracts for the EQIP program. Each contract included 

one or more practices. This column represents the total number of practices for which EQIP, Ground 

and Surface Water Conservation, and Klamath Basin funds were obligated. 

b

This represents an interim state practice, rather than a national approved practice. Interim state 

practices are tested by NRCS for 2 years, after which they are approved for national use, extended for 

further testing, added to an existing state standard, or cancelled.

c

Totals may not add due to rounding.

Water well testing 1 50 0 0 50 0.00

Totalc 248,998 $738,429,353 $50,729,316 $8,091,873 $797,250,542 100.00%

(Continued From Previous Page)

Practice name/payment type Numbera

EQIP dollars 

obligated

Ground and 

Surface 

Water 

Conservation 

dollars 

obligated 

Klamath 

dollars 

obligated 

Total dollars 

obligated 

Percentage 

of total
Page 55 GAO-06-969 Agricultural Conservation

Appendix VII

Comments from the U. S. Department of 

Agriculture ApendxiVI
Appendix VII

Comments from the U. S. Department of 

Agriculture

Page 56 GAO-06-969 Agricultural Conservation
Page 57 GAO-06-969 Agricultural Conservation

Appendix VIII

GAO Contact and Staff Acknowledgments ApendxiVI

GAO Contact Daniel Bertoni (202) 512-3841

Staff 

Acknowledgments 

In addition to the individual named above, Ronald E. Maxon, Jr., Assistant 

Director; William Bates; Thomas Cook; Barbara El Osta; Paige Gilbreath; 

Lynn Musser; Omari Norman; and Carol Herrnstadt Shulman made key 

contributions to this report.

(360644)
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