"Estimating the Determinants and Effects of Participation in the USDA's Conservation Reserve

Program."

Prepared for: Camp Resources XVAugust 7-8, 2008

Jacob N. BrimlowPh.D. Candidate

Agricultural and Resource EconomicsNCSU

USDA Conservation Reserve Program

(CRP)

• Goals:

• reduce soil erosion, enhance air and water quality, expand and improve wildlife habitat and wetlands

• Retires cropland using 10-15 year contracts

• Compensates landowners using annual rental payments, cost share assistance, and incentive payments

National Enrollment : 36.8 million acres ($1.8b/yr)

Minnesota Enrollment: 1.8 million acres

**

Enrolling in the CRP ( “general” sign-ups since 1990)

Landowner “Bids”:

The Environmental Benefits Index (EBI) Score

• Landowner chooses land to enroll, conservation practice to adopt, and rental rate/cost share assistance to request

• Seven EBI factors are tallied to compute overall score

- cost factor penalizes higher rental rate bids

• Bids are ranked by EBI score, and EBI cutoff determined by FSA after all bids are received

Question

Does enrollment in the CRP affect the value of enrolled farmland?

Who cares?

Hypothesis

Does enrollment in the CRP affect the value of enrolled farmland?

Hypothesis

Yes

Does enrollment in the CRP affect the value of enrolled farmland?

Hypothesis

Note: CRP is voluntaryAssume: Landowners are profit

maximizers

landowners will not enroll in CRP unless profits increase

parcels restricted under CRP will be worth at least as much as those that are not

Hypothesis: Ceteris paribus, CRP enrollment will have a non-negative effect on the value of enrolled farmland

Estimation

• y - vector of explanatory variables (productivity, location, etc)

• A* - CRP enrollment

and I think

• z - vector of explanatory variables (productivity, location, etc)

Enrollment

Does CRP enrollment depend, empirically, on variables likely to influence land value?

Estimation: Enrollment

• County Data• Parks and Kramer (1995)• Esseks and Kraft (1988)• Plantinga, et. al. (1990)• Goodwin and Smith (2003)• Isik and Yang (2004)

• County/Farm Data• Roberts and Lubowski (2007) • Jake

• z - vector of explanatory variables (productivity, location, etc)

• A* - CRP enrollment (continuous or binary)

• Key Variables: Land Productivity, Government payments, Erosion, CRP bids/payments

• Results: Mixed

Literature

Estimation

• y - vector of explanatory variables (productivity, location, etc)

• A* - CRP enrollment

and I’m pretty sure

• z - vector of explanatory variables (productivity, location, etc)

selection bias/correlated errors...

• Positive Effect• Shoemaker (1989)*

• No Effect (Insignificant)• Vitaliano and Hill (1994)• Nickerson and Lynch (2001)

• Negative Effect• Taff (2004)*• Shultz and Taff (2004)• Anderson and Weinhold (2005)• Taff and Weisberg (2007)*• Goodwin, et. al. (working)

*Estimate the effect of enrollment in the CRP

• Key Variables: Land Productivity, Location

• Estimation Issues: Selection bias, data quality, sample size

Estimation: Farmland Value and

Conservation Programs

Farmland Value

Township Data• (Log of) Farmland value per acre 2007• Proportion of township enrolled in CRP• Productivity (CPI): Scale 1-100

• weighted average productivity of township by soil• proportion of land in productivity “grade”

• Population• growth 1990-2000, 2000-2007• level 1990, 2000-2007

• Location (county, NASS region)

Let’s talk about...

- data resolution (county, township, parcel, mixed)

- data type (spatial?)

- look at eligible cropland only

- estimation strategy (two-stage/IV, diff in diff, spatial?)

- option values (option to enroll, option to develop)

Thank You!

0

100

A

$/yr

150

0 .5 A

50

A

$/yr

Average Productivity: 100

Average Productivity: 100

$CRP

AA

Parcel 2Parcel 1

Enrollment: 0 Enrollment: .5A

17

Quick View: Enrollment Data

– Farm-level Data:

• CRP enrollment (tillable acres)

• Average productivity (CER...more to follow...)

• Tillable acreage

• Location (county, NASS region)

–

Quick View: Enrollment Regression

Productivity Data

Parcel/Farm AnalysisCrop Equivalency Rating (CER)- county index 0-100- captures erosion, climate, soil permeability- generated in some counties as early as 1972- only available in select counties

Township AnalysisCrop Productivity Index (CPI)- county index 0-100- captures ability of soil grow corn- NO erosion or climate adjustments- generated 2007- available state-wide

Censored Regression

Acres = A

$/year

A1*, A2* = 0 A3* A5* = A

c

NP1

A4*

NP2

NP3

NP4

NP5

Observation summary: 4240 left-censored 246 uncensored 42 right-censored

Model Implication

Where A* is the acreage enrolled, c is the per acre payment for retiring land from production, and z is a vector of variables that affect the net productivity of land.

Test using:

Crop Equivalency Rating (CER)

• Reflects the net economic return per acre of soil when property is managed for the highest net return.

– adjusted for weather

– CER’s are relative to other properties in each county

• But, – limits sample size

– out of date

•

Enrollment Summary Stats

A

$/year

0 A

Model

NP1= a0 + a1 z1 + a2 A

NP2= a0 + a1 z2 + a2 A

A

$/year

0 A1*

s1

s2

b

A

c

Model

NP1= a0 + a1 z1 + a1 A

NP2= a0 + a1 z2 + a1 A

The Conservation Reserve Program

(CRP)

Enrollment: Remaining Issues

1. Productivity Data (Crop Equivalency Rating - CER)- covers only some counties, and is out of date

2. Productivity-Acreage Link- confidentiality issues have made finer resolution difficult

3. Government Payments Data- land characteristics or dollars ?