Agricultural Conservation: USDA Should Improve Its Process for Allocating Funds to States for the Environmental Quality Incentives Program
Published: 2006-09-22T00:00:00

Fast Facts:

Highlights:
section_title: Why GAO Did This Study
paragraphs: 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
section_title: What GAO Found
paragraphs: 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.

section_title: Letter
paragraphs: 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. 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  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.
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. 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  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.
subsections: section_title: Background
paragraphs: 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. 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  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. 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. The 2002 act  required that at least 60 percent of EQIP funds be made available for  conservation practices relating to livestock production. 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 practices in the Klamath Basin located on the California/Oregon border  (the Klamath Basin component of EQIP).
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.
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. Table 2 shows the factors and weights used in  the financial assistance formula for fiscal year 2006.
In fiscal year 2006, approximately $652 million was divided among the  states through the general financial assistance formula. 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  funds. Appendix IV describes the amount of funding each state initially  received in fiscal year 2006.
section_title: NRCS’s Process for Allocating EQIP Funds to the States Does Not Clearly Address the Program’s Purpose of Optimizing Environmental Benefits
paragraphs: 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.
subsections: section_title: NRCS Does Not Have A Specific, Documented Rationale for Formula Factors and Weights
paragraphs: 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. 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  operations—large operations can also damage the environment and are  eligible for EQIP funding. 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  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. 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 providing information to improve program implementation. 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.
section_title: Financial Assistance Formula Relies on Some Questionable and Outdated Data
paragraphs: 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  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 off- site 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. 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. According to NRCS,  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. 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  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. 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. 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. Not using  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). 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.
section_title: NRCS Has Begun to Develop More Outcome-Oriented Performance Measures
paragraphs: 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. 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. 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.
According to NRCS, it has developed baselines for its long-term, outcome- based 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.
section_title: Conclusions
paragraphs: 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  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.
section_title: Recommendations for Executive Action
paragraphs: 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.
section_title: Agency Comments and Our Evaluation
paragraphs: 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.
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.
section_title: Objectives, Scope, and Methodology
paragraphs: 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.   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  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 Web- based NRCS Performance Results System. We also spoke with  representatives from NRCS and nongovernmental organizations working  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.
We performed our work between December 2005 and August 2006 in  accordance with generally accepted government auditing standards.
section_title: EQIP 2006 Funding Allocation Formulas
paragraphs: 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.
section_title: Statistical Techniques to Determine Influential Factors in the 2006 EQIP Financial Allocation Formula
paragraphs: 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.
subsections: section_title: Principal Components Regression
paragraphs: 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, β,…,β are the  regression coefficients, and ε 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.
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. (1984), and others, we used principal components regression analysis since  this technique is recommended when there is multicollinearity in the data.
Before running the regression analysis, we performed the principal  components analysis. This procedure generates a set of latent variables,  called principal components—uncorrelated linear transformations of the  original variables. 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). The principal  components themselves are expressed as (2) Z = X*V.
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.
After the principal components analysis and the elimination of smaller  principal components as described above, we used the data in a cross- sectional multivariate regression expressed as (3)  1 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.   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) . pc = Vj x (j-r) α(j-r) x 1 .
In equation (5), bvector of j standardized principal component estimators of the regression  coefficients of the environmental factors, V is 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  pc (subscript pc stands for principal components) is the  (6)
In equation (6), S is the standard deviation of the original jth environmental  factor, x, bj,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 discuss the standardized estimators of the environmental factors used in  the allocation formula.
subsections: section_title: Data Used
paragraphs: 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.
section_title: Results
paragraphs: 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.
section_title: Factor Analysis of EQIP Environmental Variables
paragraphs: 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. 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. 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.
subsections: section_title: Explanation of the Technique
paragraphs: 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.” As is typically done to achieve a more  meaningful and interpretable solution, we applied a rotation technique to  the initial factor pattern matrix.
section_title: Results
paragraphs: 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.”
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.
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 at- risk 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.
section_title: Initial EQIP Funding Provided to the States, Fiscal Year 2006
paragraphs: Totals for Puerto Rico also include funding provided to the U.S. Virgin Islands.
Total funding may not equal the sum of state funding due to rounding.
section_title: Historical EQIP Funding Levels, Fiscal Years 2001-2006
paragraphs: Totals for Puerto Rico also include funding provided to the U.S. Virgin Islands.
section_title: Fiscal Year 2005 EQIP Obligations by Conservation Practice
paragraphs: Ground and  Surface  Water  Conservation  dollars  obligated  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.
Totals may not add due to rounding.
section_title: Comments from the U. S. Department of Agriculture
section_title: GAO Contact and Staff Acknowledgments
subsections: section_title: GAO Contact
section_title: Staff Acknowledgments
paragraphs: 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.