An Econometric Evaluation of the North Central

IPM Center Funded NRCS & IPM Working Group

on the EQIP 595 Practice Adoption

May 30, 2012

Steven Miller, Ph.D.

Andrea Leschewski

Summary

This study evaluates the performance of the North Central IPM Center funded NRCS Working Group on

EQIP 595 practice adoption using econometric techniques. As the North Central Work Group was an

innovator in planning and operationalizing EQIP 595 programs, this analysis also shows the returns to

investing in program delivery. EQIP 595 establishes a set of environmental practices under the heading

of Integrated Pest Management (IPM), for farm producers to adopt. However adopting new practices

encompasses added risks and expenses for farmers. Hence, the NRCS has established incentive

programs administered at the state level to encourage farm adoption of such environmental practices.

The North Central Workgroup has been instrumental in encouraging state NRCS programs to expand

EQIP 595 incentives, particularly for specialty crop growers, where incentives were largely found to be

inadequate for encouraging adoption.

The analysis used annual, county-level EQIP 595 data from the NRCS ProTracts database from 2008 to

2011 to gauge the relative outcomes of state adoption rates across the four USDA regions. Statistical

tests were conducted to determine whether the North Central states were more successful in

generating EQIP activity as measured by contract generation, acres under contract and dollars

committed to environmental practices. The findings conclude that states that make up the North

Central Region tended to outperform other states in terms of planned acres under contract and dollars

obligated. The findings are consistent across both program crops and specialty crops. The North Central

Region excelled at generating planned contracts for program crops, but not for specialty crops. In total,

for each activity the North Central Region tended to do as well or better than the national average,

suggesting that early efforts to develop incentive programs were effective at encouraging adoption of

targeted practices.

1

Introduction The Environmental Quality Incentives Program (EQIP), administered by the U.S. Department of

Agriculture Natural Resources Conservation Service (NRCS), is a voluntary program created to help

agricultural producers meet local and federal environmental regulations and to support the

implementation of agricultural practices that conserve natural resources. This program encourages

adoption and implementation of conservation practices and structures through contracts for cost-

sharing implementation of new practices and structures. Within EQIP is a multitude of practices

covering soil, water, plant, animal and air quality, energy conservation, and related resources on

agricultural land and non-industrial private forestland. One set of practices that producers encouraged

by NRCS is Integrated Pest Management or EQIP 595 practices. EQIP 595 payments are used to

encourage farmer into adopting IPM practices around prevention, monitoring, and suppression

strategies of weeds, insects, diseases, and animals (NRCS 2008). EQIP 595 payments are available to all

farmers in livestock, crop, or forest production and are administered at the state level.

Despite the availability of EQIP 595 payments to aid farmers, less than one percent of EQIP funds were

used to support the adoption of IPM practices between 1997 and 2002 (Hoard and Brewer, 2006) and

the current disposition is that it remains underutilized. There exists some debate as to the reason for

low rates of IPM adoption. While it appears that funding mostly existed at the state-level, farmers have

not been eager to pursue EQIP 595 contracts. The North Central IPM Center, Working Group instigated

an early effort for remedying the low participation rates through research and promoting EQIP 595

incentives to state NRCS programs. Several factors contributing to low participation has been identified,

including lack of knowledge, financial constraints, productivity concerns, and farm-specific

characteristics.

Many farmers cite a lack of knowledge as a major barrier to implementing IPM. Farmers explain that

they lack necessary knowledge at all stages of the IPM implementation process, including information

on who benefits from IPM, what programs are available to provide assistance for implementation, how

to actually implement IPM, and the technologies used for IPM (Brewer , et al., 2009; Alston and Reding,

1998; Hammond, et al., 2006; Rodriguez, et al., 2009). Several financial concerns also deter the

adoption of IPM by farmers. These concerns include the possibility that the cost of IPM practices will

exceed the cost of conventional practices and that lenders are less likely to fund IPM practices (Brewer

2009; Alston 1998; Hammond 2006; Rodriguez 2009). Additionally, many farmers feel that the EQIP 595

payments are not substantial enough to effectively aid in the implementation of IPM. EQIP 595

payments are set on a per-acre basis and are based on the estimated costs of implementing IPM for row

crops. Most specialty crops, however, are more capital intensive, making the EQIP 595 payments an

ineffective incentive for specialty crop farmers to adopt IPM (Brewer, et al., 2009; Hirsch and Miller,

2008). In 2006, the state of Wisconsin addressed this issue by increasing the EQIP 595 payment from $2

per acre to $39 per acre for orchards (Hirsch and Miller, 2008) with substantial results. While 595

contracts tend to favor larger operations as 595 payments and plan development payments are paid on

a per-acre basis, implementation costs are largely independent of number of acres. Hence, large

2

producers may face lower implementation costs than smaller producers, making the incentive more

attractive to the larger the operation (Hirsch and Miller, 2008; Fernandez-Cornejo 1998).

Producers have also expressed concerns of the effectiveness of IPM practices on production – fearing

that abandoning conventional pest management practices in favor of IPM will lead to lower crop

performance. Producers cite that IPM practices offer shorter intervals of protection, produce lower

yield quantities, and produce lower yield quality (Alston and Reding, 1998; Rodriguez, et al., 2009).

Further, farmers are concerned that the IPM practices will be incompatible with their current practices,

forcing them to retrain their current staff and purchase new equipment (Brewer, et al., 2009; Rodriguez,

et al., 2009). From the farmer’s perspective, these potential costs raise the risk of adopting IPM over

current conventional practices.

Some researchers found that the characteristics of farmers themselves also play a crucial role in

whether or not IPM strategies are adopted. These studies contend that older farmers have greater

resistance to change and feel the “old way” of farming is better than using IPM strategies (Alston and

Reding, 1998; Rodriguez 2009). It has also been shown that farmers with a primary job outside farming

are less likely to adopt IPM strategies, due to lower levels of knowledge and less time to dedicate to

farming (Alston 1998; Fernandez-Cornejo 1998).

Besides environmental impacts, IPM has the potential to generate real economic outcomes. Such

outcomes may be positive-positive in that both producers and society gain, positive-negative where one

party benefits at the expense of another, or negative-negative, where IPM practices costs more than it

benefits for both producers and society. There is a great deal of interest in understanding the economic

consequences of IPM as well as the policy implications of EQIP 595 in particular. However, this research

is currently in its infancy. Some early work in this regard views IPM cost savings to the producer as

reduced pesticide expenditures and applications and potentially health-related impacts as short-run

returns, and reduced cost of pest adaptation and increased soil services as long-term benefits. Costs of

IPM adaptations include barriers to adoption discussed above such as learning costs, capital

expenditures, increased labor and scouting costs, and especially risks to crop output in the form of yield

and quality. Understanding the non-incentive return to IPM adoption is important for policy

consideration, and findings will have wide implications for growers (J. Fernandez-Cornejo, 1996, Nutan

Kaushik et al., 2012).

The existing research on barriers to adopting IPM practices and to entering EQIP 595 contracts suggest

real potential for policy intervention. State administered EQIP 595 funds have experienced varying

degrees of participation, and participation is likely to reflect the mix of commodities grown in the state.

For example, states with a high presence of tree nut and fruit crops and have 595 payment rates for

such crops comparable to rates for grain crops, will likely not experience a significant amount of interest

from tree nut and fruit crop growers. That is because tree nut and fruit growers face steeper

opportunity costs to abandoning conventional practices on a per acre basis. This includes high fixed

capital costs and relatively higher revenues per acre compared to grain crop producers. Hence, it is

evident that states have significant leeway in determining their EQIP 595 outcomes.

3

Introduction For the NRCS, states fall within one of four USDA regions. Each region has an IPM Center –

Northeastern, Southern, western and North Central. These IPM Centers are vital for sources of

information and guidance for state EQIP 595 programs and research on issues around IPM and pest

management in general. The North Central IPM Center approached the Center for Economic Analysis at

Michigan State University to evaluate the standing of its member states in terms of generating EQIP 595

activities. This report establishes a broad-level approach to evaluating state-level outcomes across the

four IPM Centers to gauge the relative performance of counties in the North Central region.

EQIP 595 incentives have plaid a minor role in the EQIP program. To date, only about two percent of all

national EQIP funding goes to IPM practices. However, states have experienced varying degrees of

participation in EQIP 595, with some states lagging others in program innovations. Table 1 shows the

aggregate counts of EQIP 595 contracts by state over the years of 2008 through 2011. The first panel

shows such counts for all crops and non-crop contracts, the second panel, shows counts for what we

classify as cash or grain crops and the third panel shows that for a sub-class of specialty crops.1 Evident

in Table 1 is that there exists a great deal of variation in the number of contracts applied and planned

across both states and time. This is not so evident, but there also tends to be a shift over time from

applied contracts to planned contracts. This may be a temporal effect of the selected years, or it can

reflect a trend. Regardless, consideration of the source of this trend is outside the scope of this study.

The current study applies econometric approaches to measure the comparative growth in EQIP 595

activity and to test hypotheses that early investment in the North Central region has accelerated

adoption of EQIP 595 activities. If the hypothesis is correct, the early efforts of the North Central NRCS

working group will result in greater EQIP 595 activity relative to peer regions. Such activity includes

contracts, dollar commitments and acres under contract. Gauging the relative performance of states or

regions requires developing a model design and data that facilitates testing the equality of

performances.

Data Used The data used in this analysis is provided by the Resource Economics, Analysis and Policy Division of the

NRCS (REAP) from the NRCS ProTracts database, collected October 1, 2011. The data provides monthly

totals for every county reporting at least one EQIP 595 planned or applied contract, including planned

and applied contracts, acres and dollars.2 Table 2 describes the data variables.

1 Cash crops include Barley, Corn, Forage/Hay, Oats, Rice, Sorghum, Soybeans and Wheat. Tree Nuts, Grapes,

Berries, Vegetables & Fruits include, Berries, Fruits, Grapes, Nuts and Vegetables. 2 Dollars represent NRCS committed co-pays.

4

Table 1: Applied and Planned Contracts by State – 2008-2011

A P A P A P A P A P A P A P A P A P A P A P A P

AK 2 1 3 3 11 11 2 13 AK 1 0 3 3 2 3 2 4 AK 2 1 3 3 11 11 2 13

AL 49 27 15 6 13 7 9 13 AL 30 15 11 2 3 1 4 7 AL 30 16 11 2 3 1 4 7

AR 0 0 4 3 0 0 0 5 AR 0 0 3 2 0 0 0 1 AR 0 0 4 3 0 0 0 5

AZ 5 0 7 5 3 5 1 2 AZ 5 0 5 1 2 2 0 1 AZ 5 0 5 3 3 3 0 2

CA 17 10 31 28 25 26 7 37 CA 4 1 4 4 7 7 2 7 CA 17 8 28 26 21 24 6 32

CO 27 15 27 29 19 28 4 18 CO 24 11 16 18 17 24 2 14 CO 24 11 16 19 17 24 2 14

CT 4 3 6 6 4 7 0 7 CT 0 0 0 0 0 0 0 2 CT 4 2 5 5 4 7 0 7

DE 3 1 3 3 3 3 0 3 DE 2 1 3 3 3 3 0 2 DE 3 1 3 3 3 3 0 3

FL 37 26 18 26 26 36 2 34 FL 27 17 7 10 19 26 1 21 FL 28 19 14 18 23 33 1 29

GA 21 7 75 53 61 53 27 59 GA 1 1 47 33 39 36 19 39 GA 6 1 63 44 49 45 20 44

HI 3 2 3 3 3 4 1 3 HI 0 0 1 0 1 1 0 0 HI 3 1 3 3 2 2 1 3

IA 12 3 31 36 9 9 3 10 IA 12 3 27 31 5 6 2 8 IA 12 3 30 35 9 9 2 9

ID 16 7 5 7 5 14 2 19 ID 7 5 4 5 5 11 0 10 ID 7 5 4 5 5 12 2 16

IL 0 0 0 4 0 0 0 1 IL 0 0 0 4 0 0 0 0 IL 0 0 0 4 0 0 0 1

IN 44 19 45 44 42 44 22 42 IN 44 19 35 36 29 30 21 40 IN 44 19 35 36 30 31 21 42

KS 35 13 25 23 16 24 11 28 KS 20 7 14 12 12 15 5 16 KS 20 7 14 12 12 15 5 16

KY 0 0 3 3 1 1 0 0 KY 0 0 1 1 1 1 0 0 KY 0 0 3 3 1 1 0 0

LA 0 0 5 2 2 4 1 2 LA 0 0 2 0 2 3 0 2 LA 0 0 3 0 2 4 0 2

MA 10 7 5 6 9 9 2 5 MA 3 2 1 0 2 6 0 0 MA 10 6 5 6 7 9 2 5

MD 8 1 2 4 2 3 0 2 MD 8 1 2 4 2 3 0 1 MD 8 1 2 4 2 3 0 2

ME 6 3 6 5 6 8 2 7 ME 1 1 1 0 2 4 0 0 ME 4 3 6 5 6 8 2 7

MI 33 32 19 30 22 36 6 37 MI 25 27 12 19 12 22 3 18 MI 31 31 17 27 21 35 6 36

MN 64 29 56 53 58 61 15 41 MN 63 25 52 47 54 55 15 36 MN 63 25 52 47 54 56 15 39

MO 58 21 38 25 24 31 5 24 MO 54 19 32 23 22 30 4 21 MO 55 19 33 23 23 31 4 21

MS 61 6 40 7 37 27 29 45 MS 43 4 34 6 32 15 25 33 MS 43 4 35 7 34 21 26 38

MT 37 25 7 8 4 8 0 4 MT 30 19 7 8 3 6 0 3 MT 32 20 7 8 4 8 0 4

NC 26 3 17 2 18 4 4 17 NC 19 3 12 1 10 4 3 13 NC 21 3 14 1 12 4 3 13

ND 30 10 31 27 28 29 7 17 ND 29 9 30 25 28 28 7 16 ND 29 9 30 26 28 28 7 16

NE 8 6 22 21 11 12 0 1 NE 6 4 20 19 11 12 0 0 NE 6 4 21 20 11 12 0 0

NH 6 6 4 4 3 4 0 0 NH 4 3 1 2 1 2 0 0 NH 5 4 2 3 2 3 0 0

NJ 7 2 7 7 1 5 0 4 NJ 5 0 2 2 0 2 0 1 NJ 7 1 7 7 0 5 0 4

NM 5 2 7 4 2 1 0 4 NM 5 1 2 1 1 1 0 2 NM 5 2 6 4 1 1 0 4

NV 4 3 3 3 2 6 0 0 NV 4 3 3 3 2 5 0 0 NV 4 3 3 3 2 5 0 0

NY 0 0 5 8 4 8 0 4 NY 0 0 3 5 1 2 0 0 NY 0 0 5 8 4 8 0 4

OH 46 4 16 15 6 8 0 5 OH 15 1 7 7 0 0 0 1 OH 15 1 15 14 6 8 0 5

OK 35 15 24 26 28 27 24 29 OK 31 12 20 19 24 22 16 21 OK 31 12 21 20 24 23 16 23

OR 12 3 23 16 12 18 3 15 OR 6 0 12 9 11 12 1 7 OR 10 2 20 14 12 17 2 13

PA 18 4 7 7 5 9 2 7 PA 8 0 4 4 2 3 1 2 PA 17 3 7 7 5 9 2 7

RI 4 4 0 2 4 4 0 1 RI 3 3 0 1 1 2 0 0 RI 3 3 0 1 4 4 0 1

SC 9 4 20 9 18 12 3 15 SC 7 3 6 6 9 9 2 8 SC 7 3 18 9 12 9 2 14

SD 28 21 27 22 18 25 4 30 SD 19 15 23 21 13 19 4 26 SD 19 15 23 21 13 19 4 26

TN 36 5 1 1 0 0 0 7 TN 29 5 0 0 0 0 0 6 TN 29 5 1 1 0 0 0 6

TX 104 42 13 15 3 6 2 17 TX 94 38 7 8 1 5 1 10 TX 94 38 12 12 3 6 2 15

UT 19 16 22 23 15 21 0 20 UT 18 16 22 22 13 20 0 18 UT 18 16 22 22 13 20 0 19

VA 37 12 30 22 14 14 0 3 VA 30 11 23 16 12 12 0 0 VA 33 11 27 20 13 13 0 3

VT 7 4 5 3 7 8 0 0 VT 3 3 2 2 2 3 0 0 VT 5 4 2 2 3 4 0 0

WA 24 19 19 22 22 25 11 24 WA 16 11 14 13 15 18 8 17 WA 20 14 18 22 19 24 11 22

WI 20 4 23 24 14 18 2 13 WI 16 3 17 18 12 15 1 8 WI 19 4 21 23 14 18 2 13

WV 5 0 2 2 0 0 0 2 WV 1 0 0 0 0 0 0 2 WV 5 0 2 2 0 0 0 2

WY 8 3 3 3 3 5 0 3 WY 8 3 2 2 3 4 0 3 WY 8 3 2 2 3 4 0 3

U.S. 1,050 450 810 710 643 728 213 699 U.S. 780 325 556 478 448 510 149 447 U.S. 861 363 700 615 550 640 172 610

A=Applied

P=Planned

Source: Resource Economics, Analysis and Policy Division

20112008 2009 2010 2011 2008 2009 2010 2011 2008 2009 2010

FY FY FY

Contract Counts : Al l Crops Contract Counts : Cash Crops

Contract Counts : Tree Nuts , Grapes , Berries ,

Vegetables & Frui ts

5

Table 2: Measures of EQIP 595 Activities Variable Description

Applied Count number of contracts which have at least one applied IPM action included

Applied Acres total number of acres the IPM action(s) was/were applied to

Dollars Paid funding paid for conservation practices completed

Planned Count number of IPM activities that are planned under contracts that have not yet been implemented

Planned Acres number of acres that are planned that have not yet been implemented

Dollars obligated funding that would be paid out if the entire contract was completed on schedule

REAP provided EQIP activity measures by commodity for each county and year. REAP provided data on

27 different commodities and added one category of IPM practices not specified to crop production. For

the purpose of this study, we aggregate commodities into three broad groups as shown in Table 3.

Those groups include “Cash” crops, a combination of specialty food crops that make up Tree nuts,

grapes, berries, vegetables and fruits, and a final catch-all group “other.”

Table 3 also reports total number of contracts, which have at least one applied IPM action for the latest

reported fiscal year of 2011. In total 581 contracts with IPM components were applied in 2011, where

about 65 percent (380) of them were attributed to crops that are categorized as cash crops. Only 93 of

the total contracts were for crops categorized as tree nuts, grapes, berries, vegetables and fruits. The

remaining 108 contracts with IPM components are lumped into the category “other.” The delineation of

applied counts by commodities for years 2008 to 2010 largely reflect that in Table 3, but as discussed

below and shown in Table 1, there appears to be a great deal of overall variability year-over-year.

Figures 1 and 2 show the applied and planned contract counts of the four USDA IPM Regional Centers.

Applied counts include all contracts executed in the reporting fiscal year. However, where the contract

cannot be fully executed within the originating year, the contract will be carried over to the next year as

a planned contract. Figure 1 shows executed contracts by fiscal year while Figure 2 shows planned

contracts by originating year. As evident, applied counts have been in sharp decline since 2008 while

planned counts have been buoyed.

Based on contract counts, it appears that EQIP 595 activity growth is in decline. In this, it is important to

consider that each new contract is a new IPM practice, which under ideal situations, persists well into

the future. As such a decline in contract counts reflects a slowing in growth, not a decline in IPM

practices. Regardless, the growth in planned contract counts is not sufficient to offset the decline in

applied contracts, resulting in a gradual decline of new contracts.

6

Table 3: REAP Commodity Categories and 2011 Contract Counts

Commodity 2011 Applied Count

Cash 380 Barley - Corn 92 Forage/Hay 204 Oats - Rice - Soybeans 57 Sorghum - Wheat 27

Tree Nuts, Grapes, Berries, Vegetables & Fruits 93

Berries 7 Fruits 10 Grapes 32 Nuts 1 Vegetables 43

Others 108 Cotton 21 Oil Seed - Peanuts 2 Coffee - Ginseng - Grass Seed - Ornamental Plants 1 Other Crop 8 Sod - Sugar Maple - Tobacco - Trees 12 Potatoes 1 Sugar Beets - No Crops 63

Total 581

REAP provides total counts of acres by which contracts are made. As shown in Figures 3 and 4, the

numbers of new acres under IPM reflect the findings in Figures 1 and 2. New applied acres on contract

have declined since 2008, while new planned acres have largely increased. However, the increase in

planned acres is not enough to offset the decline in applied acres, suggesting that growth is slowing.

7

Figure 1: Cumulative Applied EQIP IPM Contract Counts (2008-2011)

Figure 2: Cumulative Planned EQIP IPM Contract Counts (2008-2011)

0

1,000

2,000

3,000

4,000

5,000

6,000

7,000

8,000

2008 2009 2010 2011

NC

NE

S

W

0

1,000

2,000

3,000

4,000

5,000

6,000

7,000

2008 2009 2010 2011

NC

NE

S

W

8

Figure 3: Cumulative Applied EQIP IPM Contract Acres (2008-2011)

Figure 4: Cumulative Planned EQIP IPM Contract Acres (2008-2011)

A final measure of interest is the total value of IPM commitments under EQIP contracts. Figures 5 and 6

show the annual EQIP funds paid and obligated, respectively. Here, dollars paid are those dollars of the

NCRS commitment to producers entering and executing a negotiated IPM contract, and dollars obligated

are those committed for a contract that has yet to be executed.

Once again, there is a distinct decline in the dollars paid through new EQIP contracts, but dollars

obligated have increased. However, unlike the prior graphs, the increase in dollars obligated is sufficient

to have a notable negating effect when combined with dollars paid. That is, while total dollars paid

declined by $28.9 million between 2008 and 2011, total dollars obligated increased by $23.2 million.

0

500,000

1,000,000

1,500,000

2,000,000

2,500,000

2008 2009 2010 2011

NC

NE

S

W

0

200,000

400,000

600,000

800,000

1,000,000

1,200,000

1,400,000

1,600,000

1,800,000

2008 2009 2010 2011

NC

NE

S

W

9

Figure 5: Cumulative New EQIP IPM Dollars Paid (2008-2011)

The preceding discussion highlights that the level of IPM activity through EQIP has largely declined since

2008, however the dollars attributed to IPM activity has experienced moderated declines. The findings

are consistent with EQIP programs increasing incentive rates as the marginal cost of inducing further

IPM practice adoption increases. That is, the first movers require fewer incentives to participate than

the hold-outs.

Figure 6: New EQIP IPM Dollars Obligated (2008-2011)

The Analysis The evaluation takes the form of a statistical equation for testing the hypothesis that the NC Region’s

level of EQIP 595 activity has grown at a higher pace than its peer regions. As the REAP data covers all

$0

$2,000,000

$4,000,000

$6,000,000

$8,000,000

$10,000,000

$12,000,000

$14,000,000

2008 2009 2010 2011

NC

NE

S

W

$0

$2,000,000

$4,000,000

$6,000,000

$8,000,000

$10,000,000

$12,000,000

$14,000,000

$16,000,000

$18,000,000

2008 2009 2010 2011

NC

NE

S

W

10

counties with at least one EQIP 595 contract over the years 2008 to 2011, the data constitutes a

longitudinal set of observations. The longitudinal data affords estimation by county, rather than

estimation over all counties. For this date, This dataset is made strongly balanced by adding zeros for

county-crop-year combinations that had no reported activity. That is, omitted entries imply no contract

activity. The regression equation in longitudinal form is given in equation 1.

( ) (Equation 1)

In Equation 1, Ir is a vector of indicator variables for each county3 and measures the inert

differences in region r’s EQIP 595 activity from the overall county average level a. The coefficients b0

and bNC and associated variables retain the interpretation from Equation 1. The term controls for

non-linearity in overall trends from 2008 to 2011. The estimated error term is unobserved errors in

the model fit for region r in time t.

The test of whether the growth in EQIP 595 activity of the North Central region exceeds that of the

nation as a whole does not change and follows the discussion above. That test is carried out by testing

the alternative that the North Central region grew by the same or less than the nation. More

specifically, for each EQIP activity, we will test the following hypothesis.4

Rejecting the hypothesis that bNC is negative indicates that bNC is greater than zero with some stated

degree of confidence.

Regressions were run for nine measures of EQIP activity, including applied and planned contract counts

and acres, dollars paid and dollars obligated per fiscal year, and aggregate counts, acres and dollars that

combine applied and planned into single measures of activity. Further delineation is made between

cash, or grain, crops and specialty food crops of tree nuts, grapes, berries, vegetables & fruits.

Regression results are shown in the Appendix. A total of 27 regressions are generated to test the

assumption that the North Central region’s growth trajectory is greater than the rest of the nation.

Table 4 shows the results of statistical tests on the bNC coefficient where the hypothesis tested is that bNC

is equal to or less than zero.

A summary of findings is shown in Table 4, where entries containing “Fail to Reject” indicate activities

where the NC region’s level of activity is not significantly different from the nation as a whole. The other

cells contain a percentage representing the percent chance that the annual change in the respective

EQIP activity is equal to or less than the annual change for the nation. That is, an entry of “5%” indicates

there is only a five percent chance that the NC region’s level of activity is equal to or lower than that of

the nation.

3 Takes a value of one for the county the variable represents and zero for all other counties. This specification is

consistent with a fixed-effects model where individual counties are treated as fixed effects. 4 is assumed to be normally distributed with mean 0 and some constant variance of .

11

Table 4: Tests of the Hypothesis bNC ≤ 0

As evident in Table 4, the North Central region is generating about as many new contracts a year as the

national average. However, it appears to lead the nation in planned counts for cash crops. In terms of

acres under contract, the North Central region appears to be more successful in the number of acres

planned across the two commodity groups. Additionally, there is significant evidence that the North

Central region brought in more applied acres for all crops.5 Finally, it appears that the North Central

region out-performs with total value of EQIP dollars obligated, though not necessarily dollars paid.

Aggregating dollars paid and obligated into a single measure reveals that we cannot conclude that the

North Central region has significantly impacted financial commitment over other regions.

The next step we undertake is to compare the predicted outcomes of the North Central region to the

nation. Using the regression equations in the Appendix A, predictions are carried out across a six-year

horizon to gauge how the dynamic outcomes. Table 5 shows the differences in EQIP 595 outcomes of

the North Central Region relative to the nation using a six-year simulation.6 The outcomes of Table 5

reflect the findings in Table 4, but provides estimates of the magnitudes of the outcomes.

Table 5: Simulated NC Region outcomes relative to U.S. 6-Year Trajectories

All Crops Cash Crops

Veg., Tree

nuts and Fruit

Applied Contract Counts 1.0% 6.4% 3.6%

Planned Contract Counts 1.3% 12.8% 2.7%

Applied Acres 12.0% -33.8% -31.0%

Planned Acres 36.5% 43.9% 52.7%

Dollars Paid 0.2% 3.3% 2.4%

Dollars Obligated 13.3% 34.8% 12.5%

Table 5 shows that planned acres and dollars obligated are magnitudes larger for the North Central

region for both cash crops and specialty crops. It also shows that across all EQIP 595 activities, the

North Central Region is at least as successful or more successful in terms of engendering participation

except for applied acres. It is interesting that the North Central Region is expected to have lower

numbers of acres under EQIP 595 for both cash crops and specialty crops, but that once combining cash

5 includes crops not included in other regressions

6 Year 1 of the simulation would coincide with 2008

All Crops Cash Crops

Veg., Tree Nuts and

Fruit

Applied Counts Fail to Reject Fail to Reject Fail to Reject

Planned Counts Fail to Reject 1% Fail to Reject

Applied and Planned Counts Fail to Reject 1% 5%

Applied Acres 5% Fail to Reject Fail to Reject

Planned Acres 1% 1% 1%

Applied and Planned Acres 1% 1% 1%

Dollars Paid Fail to Reject Fail to Reject Fail to Reject

Dollars Obligated 1% 1% 1%

Dollars Paid and Obligated Fail to Reject Fail to Reject Fail to Reject

12

and specialty crops, the North Central Region tends to best the nation. The conflicting results suggests

instability in the estimates, but may be an artifact of the category of EQIP activity under “other”7

Summary As state NRCS offices have a clear potential to impact the rate of IPM adoption in agricultural producers,

it is important for the regional IPM centers to undertake the necessary steps to educate policy makers

and producers about the potential benefits and challenges of the EQIP 595 program. This study set out

to develop a model to evaluate relative performance of the North Central Regional IPM Center.

Performance is measured in relative terms to the U.S. as a whole. Doing this creates a conservative

measure of relative performance, as the region’s performance is measured against the average

performance of other U.S. centers and itself. EQIP 595 activity is defined as the change in the number of

contracts applied or planned, the number of acres these contracts encompass and the total dollars

committed toward adopting IPM practices.

Regression results show that between 2008 and 2011 the EQIP 595 activity of counties within the North

Central Center was equal to or greater than that of the U.S. This is especially realized when viewing

performance in terms of contracts on all crop types. When comparing outcomes of cash, or grain, crops

only, the results are a bit less pronounced, but nonetheless favorable in term of the NC region

performance. The NC region’s relative performance tended to be lowest for specialty crops, but rarely

worse than that of the nation. The regressions also show that for all counties, there is a tendency for

planned activities to supplant applied activities. This may be a simple temporal issue with the data and

the economic environment during the research frame of 2008 to 2011. Nonetheless, there is one

instance, in which the NC region appeared to produce outcomes below the U.S. average. That is, total

number of new acres applied in cash crops and specialty crops tends to be lower for the north central

region than for the U.S. However, when comparing all crops, this is not the case. For acres applied, it

appears that the North Central region had strength in generating contracts for other crops that include

an eclectic range of specialty crops.

7 This would suggest that most of the “tree” and “No Crop” activities take place in the North Central region.

However, a review of the data suggest that the NC region does not dominate tree and non-crop activities.

13

References

Alston, D. G., and M. E. Reding. "Factors influencing adoption and educational outreach of integrated

pest management." Journal of Extension 36, no. 3(1998).

Brewer, M. J., E. G. Rajotte, J. R. Kaplan, P. B. Goodell, D. J. Biddinger, J. N. Landis, R. M. Nowierski,

B. F. Smallwood, and M. E. Whalon. "Opportunities, Experiences, and Strategies to Connect

Integrated Pest Management to US Department of Agriculture Conservation Programs."

American Entomologist 55, no. 3(2009): 140-146.

Fernandez-Cornejo, J. "The Microeconomic Impact of Ipm Adoption: Theory and Application."

Agricultural and Resource Economics Review, no. 25(1996): 149-60.

Fernandez-Cornejo, J. "Environmental and economic consequences of technology adoption: IPM in

viticulture." Agricultural Economics 18, no. 2(1998): 145-155.

Hammond, C. M., E. C. Luschei, C. M. Boerboom, and P. J. Nowak. "Adoption of Integrated Pest

Management Tactics by Wisconsin Farmers1." Weed Technology 20, no. 3(2006): 756-767.

Hirsch, R. M., and M. M. Miller. "Progressive planning to address multiple resource concerns: Integrated

pest management in Wisconsin orchards." Journal of Soil and Water Conservation 63, no.

2(2008): 40A-43A.

Hoard, R. J., and M. J. Brewer. "Adoption of Pest, Nutrient, and Conservation Vegetation Management

Using Financial Incentives Provided by a U.S. Department of Agriculture Conservation

Program." HortTechnology 16, no. 2(2006): 306-311.

Kaushik, Nutan; Sharma, Vivek and Joshi, Vister. "Evaluation, Validation and Economic Analysis of

Biointensive Ipm in Okra (Abelmoschus Esculentus L. Moench) in India," Anonymous, Seventh

International Integrated Pest Management Symposium. Memphis Tennessee, (2012).

NRCS. “Natural Resource Conservation Service Conservation Practice Standard: Pest Management Code

595.” NRCS. (2008).

Rodriguez, J. M., J. J. Molnar, R. A. Fazio, E. Sydnor, and M. J. Lowe. "Barriers to adoption of

sustainable agriculture practices: Change agent perspectives." Renewable Agriculture and Food

Systems 24, no. 01(2009): 60-71.

Appendix A: Regression Estimates

14

Appli

ed C

ou

nts

Appli

ed C

ou

nts

Appli

ed C

ou

nts

acC

oef

.Std

. E

rr.

zP

>|z

|ac

Coef

.Std

. E

rr.

zP

>|z

|ac

Coef

.Std

. E

rr.

zP

>|z

|

c_nc_

tren

d0.0

29

0.0

93

0.3

10

0.7

56

c_nc_

tren

d0.2

25

0.1

87

1.2

00

0.2

29

c_nc_

tren

d0.1

44

0.1

69

0.8

50

0.3

94

tren

d-6

.050

0.5

11

-11.8

40

0.0

00

tren

d-7

.088

1.1

77

-6.0

20

0.0

00

tren

d-7

.916

1.0

69

-7.4

00

0.0

00

tren

d2

0.7

94

0.1

00

7.9

10

0.0

00

tren

d2

0.8

22

0.2

38

3.4

50

0.0

01

tren

d2

0.9

68

0.2

14

4.5

30

0.0

00

_co

ns

11.9

62

0.5

65

21.1

80

0.0

00

_co

ns

15.8

30

1.2

20

12.9

70

0.0

00

_co

ns

16.9

99

1.1

35

14.9

80

0.0

00

sigm

a_u

3.3

94

sigm

a_u

0.0

00

sigm

a_u

0.0

00

sigm

a_e

8.3

14

sigm

a_e

12.0

51

sigm

a_e

12.0

23

rho

0.1

43

rho

0.0

00

rho

0.0

00

Pla

nn

ed C

ou

nts

Pla

nn

ed C

ou

nts

Pla

nn

ed C

ou

nts

pc

Coef

.Std

. E

rr.

zP

>|z

|p

cC

oef

.Std

. E

rr.

zP

>|z

|p

cC

oef

.Std

. E

rr.

zP

>|z

|

c_nc_

tren

d0.0

33

0.0

84

0.4

00

0.6

91

c_nc_

tren

d0.4

94

0.1

10

4.5

00

0.0

00

c_nc_

tren

d0.1

58

0.1

43

1.1

00

0.2

71

tren

d1.5

44

0.3

87

3.9

90

0.0

00

tren

d3.3

34

0.6

91

4.8

30

0.0

00

tren

d2.8

72

0.9

07

3.1

70

0.0

02

tren

d2

-0.1

74

0.0

76

-2.2

80

0.0

22

tren

d2

-0.4

11

0.1

40

-2.9

40

0.0

03

tren

d2

-0.2

06

0.1

81

-1.1

30

0.2

57

_co

ns

-0.1

90

0.4

35

-0.4

40

0.6

61

_co

ns

-1.5

89

0.7

16

-2.2

20

0.0

26

_co

ns

-1.0

36

0.9

63

-1.0

80

0.2

82

sigm

a_u

4.1

54

sigm

a_u

0.0

00

sigm

a_u

0.0

00

sigm

a_e

6.2

95

sigm

a_e

6.7

07

sigm

a_e

9.9

15

rho

0.3

03

rho

0.0

00

rho

0.0

00

Appli

ed a

nd P

lan

ned C

ou

nts

Appli

ed a

nd P

lan

ned C

ou

nts

Appli

ed a

nd P

lan

ned C

ou

nts

cC

oef

.Std

. E

rr.

z P

>z

aC

oef

.Std

. E

rr.

z P

>z

pC

oef

.Std

. E

rr.

z P

>z

c_nc_

tren

d0.0

23

0.1

49

0.1

60

0.8

75

c_nc_

tren

d183.3

46

69.6

90

2.6

30

0.0

09

c_nc_

tren

d1062.6

71

471.8

96

2.2

50

0.0

24

tren

d-4

.491

0.6

79

-6.6

10

0.0

00

tren

d-1

065.1

00

408.6

40

-2.6

10

0.0

09

tren

d-3

373.6

14

2133.6

16

-1.5

80

0.1

14

tren

d2

0.6

20

0.1

33

4.6

50

0.0

00

tren

d2

131.2

01

80.2

97

1.6

30

0.1

02

tren

d2

283.8

75

418.6

99

0.6

80

0.4

98

_co

ns

11.7

71

0.7

64

15.4

10

0.0

00

_co

ns

2612.2

05

449.8

88

5.8

10

0.0

00

_co

ns

17092.4

20

2401.6

35

7.1

20

0.0

00

sigm

a_u

7.5

22

sigm

a_u

2081.7

15

sigm

a_u

23897.1

80

sigm

a_e

11.0

43

sigm

a_e

6652.2

50

sigm

a_e

34663.8

05

rho

0.3

17

rho

0.0

89

rho

0.3

22

All

Cro

ps

Cash

Cro

ps

Vegeta

ble

s, T

ree N

uts

an

d F

ruit

Appendix A: Regression Estimates

15

Appli

ed A

cres

Appli

ed A

cres

Appli

ed A

cres

aaC

oef

.Std

. E

rr.

zP

>|z

|aa

Coef

.Std

. E

rr.

zP

>|z

|aa

Coef

.Std

. E

rr.

zP

>|z

|

c_nc_

tren

d63.9

84

38.5

47

1.6

60

0.0

97

c_nc_

tren

d-3

66.5

90

116.9

70

-3.1

30

0.0

02

c_nc_

tren

d-3

48.6

14

98.0

14

-3.5

60

0.0

00

tren

d-1

296.0

18

232.1

66

-5.5

80

0.0

00

tren

d-1

859.6

95

397.5

12

-4.6

80

0.0

00

tren

d-1

915.7

25

331.6

22

-5.7

80

0.0

00

tren

d2

168.5

67

45.6

24

3.6

90

0.0

00

tren

d2

213.7

63

78.7

63

2.7

10

0.0

07

tren

d2

244.7

17

65.1

82

3.7

50

0.0

00

_co

ns

2514.0

56

255.2

52

9.8

50

0.0

00

_co

ns

4307.9

19

450.1

86

9.5

70

0.0

00

_co

ns

4116.2

79

384.7

30

10.7

00

0.0

00

sigm

a_u

1034.2

11

sigm

a_u

7004.2

07

sigm

a_u

6423.6

59

sigm

a_e

3776.9

81

sigm

a_e

3290.7

66

sigm

a_e

3081.5

69

rho

0.0

70

rho

0.8

19

rho

0.8

13

Pla

nn

ed A

cres

Pla

nn

ed A

cres

Pla

nn

ed A

cres

pa

Coef

.Std

. E

rr.

zP

>|z

|p

aC

oef

.Std

. E

rr.

zP

>|z

|p

aC

oef

.Std

. E

rr.

zP

>|z

|

c_nc_

tren

d122.4

36

36.4

22

3.3

60

0.0

01

c_nc_

tren

d302.7

08

118.5

60

2.5

50

0.0

11

c_nc_

tren

d308.7

62

97.9

26

3.1

50

0.0

02

tren

d229.8

02

216.3

34

1.0

60

0.2

88

tren

d1149.4

37

467.1

35

2.4

60

0.0

14

tren

d997.3

94

375.8

22

2.6

50

0.0

08

tren

d2

-37.3

66

42.5

11

-0.8

80

0.3

79

tren

d2

-169.5

56

93.1

68

-1.8

20

0.0

69

tren

d2

-153.7

37

74.2

17

-2.0

70

0.0

38

_co

ns

98.1

49

238.0

11

0.4

10

0.6

80

_co

ns

-762.2

28

502.7

58

-1.5

20

0.1

29

_co

ns

-688.2

96

417.0

72

-1.6

50

0.0

99

sigm

a_u

1036.8

11

sigm

a_u

5556.1

13

sigm

a_u

5230.3

87

sigm

a_e

3521.8

15

sigm

a_e

4079.8

80

sigm

a_e

3641.7

96

rho

0.0

80

rho

0.6

50

rho

0.6

73

Appli

ed a

nd P

lan

ned A

cres

Appli

ed a

nd P

lan

ned A

cres

Appli

ed a

nd P

lan

ned A

cres

cC

oef

.Std

. E

rr.

z P

>z

aC

oef

.Std

. E

rr.

z P

>z

pC

oef

.Std

. E

rr.

z P

>z

c_nc_

tren

d0.7

19

0.2

43

2.9

60

0.0

03

c_nc_

tren

d-4

6.1

16

201.2

94

-0.2

30

0.8

19

c_nc_

tren

d2930.4

82

917.7

53

3.1

90

0.0

01

tren

d-3

.753

1.5

30

-2.4

50

0.0

14

tren

d-4

79.9

70

661.1

42

-0.7

30

0.4

68

tren

d2371.3

56

4160.6

44

0.5

70

0.5

69

tren

d2

0.4

11

0.3

10

1.3

30

0.1

85

tren

d2

5.3

51

130.7

88

0.0

40

0.9

67

tren

d2

-787.9

05

834.4

35

-0.9

40

0.3

45

_co

ns

14.2

41

1.5

86

8.9

80

0.0

00

_co

ns

3248.5

79

765.3

47

4.2

40

0.0

00

_co

ns

13930.7

70

4384.0

41

3.1

80

0.0

01

sigm

a_u

0.0

00

sigm

a_u

13051.1

74

sigm

a_u

34382.9

51

sigm

a_e

14.5

53

sigm

a_e

5431.3

41

sigm

a_e

37685.6

61

rho

0.0

00

rho

0.8

52

rho

0.4

54

All

Cro

ps

Cash

Cro

ps

Vegeta

ble

s, T

ree N

uts

an

d F

ruit

Appendix A: Regression Estimates

16

Do

llars

Pa

idD

oll

ars

Pa

idD

oll

ars

Pa

id

apC

oef

.Std

. E

rr.

zP

>|z

|ap

Coef

.Std

. E

rr.

zP

>|z

|ap

Coef

.Std

. E

rr.

zP

>|z

|

c_n

c_tr

end

9.1

71

265.2

62

0.0

30

0.9

72

c_n

c_tr

end

323.1

59

644.0

59

0.5

00

0.6

16

c_nc_

tren

d200.1

91

585.8

53

0.3

40

0.7

33

tren

d-7

941.2

75

1457.5

13

-5.4

50

0.0

00

tren

d-6

948.3

81

2624.1

28

-2.6

50

0.0

08

tren

d-1

0988.1

30

2715.6

81

-4.0

50

0.0

00

tren

d2

842.7

54

286.3

21

2.9

40

0.0

03

tren

d2

320.8

74

524.0

98

0.6

10

0.5

40

tren

d2

968.7

89

539.6

01

1.8

00

0.0

73

_co

ns

19516.5

50

1610.7

87

12.1

20

0.0

00

_co

ns

22243.2

40

2805.9

57

7.9

30

0.0

00

_co

ns

28641.6

10

2928.6

37

9.7

80

0.0

00

sigm

a_u

9549.0

13

sigm

a_u

28740.3

46

sigm

a_u

23635.8

43

sigm

a_e

23691.1

79

sigm

a_e

23191.0

30

sigm

a_e

27847.1

35

rho

0.1

40

rho

0.6

06

rho

0.4

19

Do

lla

rs O

bli

ga

ted

Do

llars

Obli

ga

ted

Do

llars

Obli

ga

ted

pp

Coef

.Std

. E

rr.

zP

>|z

|p

pC

oef

.Std

. E

rr.

zP

>|z

|p

pC

oef

.Std

. E

rr.

zP

>|z

|

c_n

c_tr

end

732.9

56

297.8

25

2.4

60

0.0

14

c_n

c_tr

end

2681.3

77

462.2

31

5.8

00

0.0

00

c_nc_

tren

d1639.7

59

525.0

02

3.1

20

0.0

02

tren

d4684.0

35

1365.6

22

3.4

30

0.0

01

tren

d9287.9

01

2909.0

32

3.1

90

0.0

01

tren

d8466.1

89

3328.6

82

2.5

40

0.0

11

tren

d2

-558.8

80

268.0

13

-2.0

90

0.0

37

tren

d2

-1150.2

52

588.5

39

-1.9

50

0.0

51

tren

d2

-714.3

31

665.9

20

-1.0

70

0.2

83

_co

ns

-2424.1

26

1534.3

65

-1.5

80

0.1

14

_co

ns

-7358.8

01

3015.9

11

-2.4

40

0.0

15

_co

ns

-5687.4

43

3532.5

88

-1.6

10

0.1

07

sigm

a_u

14792.9

59

sigm

a_u

0.0

00

sigm

a_u

0.0

00

sigm

a_e

22204.7

80

sigm

a_e

28826.0

22

sigm

a_e

35722.1

02

rho

0.3

07

rho

0.0

00

rho

0.0

00

Do

lla

rs P

aid

an

d O

bli

ga

ted

Do

llars

Pa

id a

nd O

bli

ga

ted

Do

llars

Pa

id a

nd O

bli

gate

d

cC

oef

.Std

. E

rr.

z

P

>z

aC

oef

.Std

. E

rr.

z

P>

zp

Coef

.Std

. E

rr.

z P

>z

c_n

c_tr

end

0.3

01

0.2

49

1.2

10

0.2

26

c_n

c_tr

end

-50.5

73

165.8

21

-0.3

00

0.7

60

c_nc_

tren

d1677.1

20

882.0

55

1.9

00

0.0

57

tren

d-5

.044

1.5

78

-3.2

00

0.0

01

tren

d-7

05.7

60

535.4

30

-1.3

20

0.1

87

tren

d-2

482.8

83

4560.5

27

-0.5

40

0.5

86

tren

d2

0.7

63

0.3

16

2.4

20

0.0

16

tren

d2

56.8

14

105.0

47

0.5

40

0.5

89

tren

d2

294.9

55

908.8

37

0.3

20

0.7

46

_co

ns

15.9

63

1.6

74

9.5

30

0.0

00

_co

ns

3161.1

81

640.1

73

4.9

40

0.0

00

_co

ns

22037.4

20

4877.5

70

4.5

20

0.0

00

sigm

a_u

0.0

00

sigm

a_u

12078.7

28

sigm

a_u

27630.5

72

sigm

a_e

16.2

63

sigm

a_e

4922.9

55

sigm

a_e

46536.1

42

rho

0.0

00

rho

0.8

58

rho

0.2

61

All

Cro

ps

Cash

Cro

ps

Vegeta

ble

s, T

ree N

uts

an

d F

ruit