Agricultural Conservation: USDA Should Improve Its Process for Allocating Funds to States for the Environmental Quality Incentives Program
Released: 2006-09-27T00:00:00

Letter

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. 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.

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. 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.

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 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.

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. 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.

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 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.

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.

Objectives, Scope, and Methodology

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.

EQIP 2006 Funding Allocation Formulas

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.

Statistical Techniques to Determine Influential Factors in the 2006 EQIP Financial Allocation Formula

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, 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.

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

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.

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. 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.

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.” As is typically done to achieve a more meaningful and interpretable solution, we applied a rotation technique to the initial factor pattern matrix.

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.”

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.

Initial EQIP Funding Provided to the States, Fiscal Year 2006

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.

Historical EQIP Funding Levels, Fiscal Years 2001-2006

Totals for Puerto Rico also include funding provided to the U.S. Virgin Islands.

Fiscal Year 2005 EQIP Obligations by Conservation Practice

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.

Comments from the U. S. Department of Agriculture

GAO Contact and Staff Acknowledgments

GAO Contact

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.