1. "Estimating the Determinants and Effects of Participation in the USDA's Conservation Reserve Program." Prepared for: Camp Resources XV August 7-8, 2008 Jacob N. Brimlow Ph.D. Candidate Agricultural and Resource Economics NCSU

2. 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 **

3. 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

4. Question Does enrollment in the CRP affect the value of enrolled farmland? Who cares?

5. Hypothesis Does enrollment in the CRP affect the value of enrolled farmland?

6. Hypothesis Yes Does enrollment in the CRP affect the value of enrolled farmland?

7. Hypothesis Note: CRP is voluntary Assume: 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

8. Estimation y - vector of explanatory variables (productivity, location, etc) A* - CRP enrollment and I think z - vector of explanatory variables (productivity, location, etc)

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

10. 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

11. 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...

12. 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

13. 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) –

16. 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)

17. Thank You!

18. 0 100 A $/yr 150 0.5 A 50 A $/yr Average Productivity: 100 $CRP AA Parcel 2Parcel 1 Enrollment: 0 Enrollment:.5A

19. 17 Quick View: Enrollment Data –Farm-level Data: CRP enrollment (tillable acres) Average productivity (CER...more to follow...) Tillable acreage Location (county, NASS region) –

20. Quick View: Enrollment Regression

21. Productivity Data Parcel/Farm Analysis Crop 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 Analysis Crop Productivity Index (CPI) - county index 0-100 - captures ability of soil grow corn - NO erosion or climate adjustments - generated 2007 - available state-wide

22. Censored Regression Acres = A $/year A 1 *, A 2 * = 0A3*A3*A 5 * = A c NP 1 A4*A4* NP 2 NP 3 NP 4 NP 5 Observation summary: 4240left-censored 246 uncensored 42 right-censored

23. 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:

24. 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

25. Enrollment Summary Stats

26. A $/year 0 A Model NP 1 = a 0 + a 1 z 1 + a 2 A NP 2 = a 0 + a 1 z 2 + a 2 A

27. A $/year 0 A1*A1* s1s1 s2s2 b A c Model NP 1 = a 0 + a 1 z 1 + a 1 A NP 2 = a 0 + a 1 z 2 + a 1 A

28. The Conservation Reserve Program (CRP)

29. 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 ?