the supply of land for conservation uses: evidence from the conservation reserve program

Resources, Conservation and Recycling

31 (2001) 199–215

The supply of land for conservation uses:evidence from the conservation reserve program

Andrew J. Plantinga a,*, Ralph Alig b, Hsiang-tai Cheng a

a Department of Resource Economics and Policy, Uni6ersity of Maine, Orono, ME 04469-5782, USAb USDA Forest Ser6ice, Pacific Northwest Research Station, 3200 Jefferson Way, Cor6allis,

OR 97331, USA

Received 28 July 1999; accepted 20 July 2000

Abstract

From 1987 to 1990, the Conservation Reserve Program (CRP) operated similarly to acompetitive market for conservation lands. Using CRP data on counties from this period, weestimate supply functions for conservation lands for nine US regions. The results allowregions to be grouped according to low (Mountain, North Plains), moderate (Cornbelt, LakeStates, South Plains), and high (Appalachian, Delta States, Northeast, Southeast) costs basedon acreage enrolled. In addition, they identify farmers’ perceived opportunity costs ofenrolling cropland in a conservation program. The results provide potentially useful informa-tion to CRP administrators following the recent reauthorization of the program and alsoyield insights into the costs of other land conservation efforts. © 2001 Elsevier Science B.V.All rights reserved.

Keywords: Land; Conservation; Economic analyses

www.elsevier.com/locate/resconrec

1. Introduction

An important role of government is to intervene in markets that do not achievean efficient allocation of resources. This includes the regulation of markets withsignificant externalities (e.g. pollution) and the provision of public goods such asparks and national defense. Among other objectives, the Conservation Reserve

* Corresponding author. Tel.: +1-207-5813156; fax: +1-207-5814278.E-mail address: [email protected] (A.J. Plantinga).

0921-3449/01/$ - see front matter © 2001 Elsevier Science B.V. All rights reserved.

PII: S0921 -3449 (00 )00085 -9

A.J. Plantinga et al. / Resources, Conser6ation and Recycling 31 (2001) 199–215200

Program (CRP), administered by the US Department of Agriculture, seeks toredress externalities and public goods problems in markets for agricultural land. Aprimary goal of the program is the reduction of soil erosion through the retirementof marginal agricultural lands and conversion to permanent cover (Osborn et al.,1995). Beginning in 1991, the program encouraged the enrolment of land providingenvironmental amenities with public good characteristics such as wildlife habitatand converted wetlands.

The CRP was established in Title XII of the Food Security Act of 1985 as avoluntary land retirement program with a goal of enrolling 40–45 million acres ofcropland by the end of the 1990 crop year (Osborn et al., 1995). To put theenrolment target in perspective, the area of cropland in the US in 1987 wasapproximately 423 million acres. Participating farmers or farm operators agree toconvert cropland to a qualifying conservation use such as grass or trees for a periodof 10 years, during which time no harvesting or pasturing is permitted. In return,participants are given annual payments to compensate for foregone agriculturalreturns and a share of the cost of establishing vegetative cover. Eligibility require-ments changed during the course of the program but, generally speaking, land hadto be highly erodible to qualify during the signups through 1990. With thereauthorization of the CRP in 1990, the eligibility requirements and bid acceptanceprocedures were modified to target environmental benefits.

The voluntary nature of the CRP necessitated a change early in the programfrom a bid to an offer system (US Government Accounting Office, 1995). Inresponse to low participation during the first signup periods (roughly, those in1986), program administrators specified the payments in each region (referred to asmaximum allowable rental rates) and allowed farmers to enroll as many eligibleacres as desired at the designated rate. Thus, after 1986 and prior to 1991 when theprogram began targeting environmental benefits, the CRP operated similarly to acompetitive market for conservation lands. Competitive markets are characterizedby many buyers and sellers, all of whom take the price for goods as given. Sellersin a competitive market maximize profits by supplying additional units of theirgood until the cost of producing the last unit (the marginal cost) equals the price.With the CRP, the rental rate set by program administrators is analogous to a pricefor land conservation. Taking this price as given, landowners have an incentive toenroll additional parcels of land as long as the price exceeds the marginal cost ofenrolment. The cost of enrolment is the foregone returns from the land in crops ornon-agricultural uses such as development.

In this study, we use county-level CRP data to estimate grassland supplyfunctions for US agricultural regions and the years, 1987–1990. The large majorityof CRP lands was enrolled during this period and, as discussed in the next section,the enrolment procedure used during this time enables us to estimate readily thesupply functions. After 1990, changes in enrolment criteria preclude the use of themethods used in this study. The supply functions measure the total acreage ofconservation lands enrolled at different prices. These results are of particularinterest following the recent reauthorization of the CRP. The duration of CRPcontracts is 10 years and so contracts from the first signups have begun to expire.

A.J. Plantinga et al. / Resources, Conser6ation and Recycling 31 (2001) 199–215 201

Decisions are now being made with regard to enrolment criteria, the renewal ofcontracts, and the enrolment of new land (Babcock, 1996). Our results are of valueto program administrators since they quantify acreage response by farmers andidentify differences by regions, states, and counties in the willingness of farmers toparticipate in the program. As well, they provide vital information for otherlarge-scale land conservation efforts, such as afforestation programs designed tosequester carbon in biomass and, thereby, reduce the threat of global warming.

2. The supply of land to the CRP

The demand curve measures the most that consumers are willing to pay for thelast unit of a good and the supply curve measures the least suppliers are willing toaccept for the last unit (Fig. 1).1 In a competitive market, the equation of demandand supply defines an equilibrium and establishes the market price and quantity ofgoods. Estimation of the relationship between the price and the quantity suppliedis complicated by the well-known ‘identification problem’. A time-series of pricesand quantities for goods often appear as a smattering of points because the positionof the demand and supply curves can shift from period to period (Fig. 1). In thiscase, regressing prices on quantities yields meaningless results. In general, the

Fig. 1. Demand and supply curves.

1 To clarify the terminology used here, the supply curve measures the relationship between price andtotal acres enrolled, assuming all other variables (e.g. the net returns to cropping) are held constant. Thesupply function measures the acres enrolled as a function of price and other variables. These othervariables are referred to as supply shifters since, when varied, they shift the supply curve.


Fig. 2. Demand and supply curves for CRP lands.

solution to the identification problem is to account for the factors that shift demandand supply and model the demand and supply relations as a system of simultaneousequations (see, for example, Judge et al., 1988).

Particular features of the CRP program during the period 1987–1990 make thetask of recovering the supply relationship much easier. As noted, farmers wereallowed to enroll as much land as desired at the specified rental rate. This impliesthat the government’s demand curve for conservation land is horizontal (Fig. 2)2. Ina given year, we observe price–quantity pairs for counties with CRP enrolment, asillustrated in Fig. 2. The position of the supply curves differs among countiesbecause of differences in the opportunity costs of enrolment. At a given price, moreland will be enrolled in counties with low returns to cropping (County 3) than incounties with high returns (County 1), all else equal. It is possible, however, tocontrol for the factors that determine the position of the supply curves forindividual counties. In this case, variation in the government’s demand acrosscounties will trace out the underlying supply relationship. Accordingly, we canrecover the supply function by regressing enrolled acres on price and supplyshifters.

The validity of this approach depends on the assumption that demand isunrelated to the factors affecting supply. When this assumption does not hold, theendogeneity of related factors makes standard estimators such as least squaresbiased and inconsistent (see Judge et al., 1988). According to Osborn et al. (1995),rental rates are specified by the Secretary of Agriculture and tend to be similar in

2 The government’s demand curve becomes vertical at the amount of eligible acreage. We ignore thisportion of the demand curve because counties rarely reached the eligibility limits.


counties with similar soil erosion rates and crop production levels. In most regions,CRP rental rates are considerably higher than market rental rates for farmland(Moulton and Richards, 1990; Canning, 1991). These studies indicate that thegovernment’s demand for CRP land depends in part on policy objectives and maynot always be linked closely to prevailing economic conditions in a region.

Before discussing the methods used in this analysis, it is worth mentioning thatseveral earlier studies used CRP data to estimate supply relationships (Moulton andRichards, 1990; Parks and Kramer, 1995; Parks and Schorr, 1997). Parks andKramer (1995) model the shares of eligible land in counties enrolled in the CRPwetlands category. Their empirical results indicate that enrolment increases, all elseequal, with higher CRP payments and lower returns to crop production. Parks andSchorr (1997) estimate similar models for metropolitan and nonmetropolitancounties in the Northeast. Their results for metropolitan counties suggest there is anactive margin between agriculture and developed uses but not between agricultureand the CRP. The latter result is explained by the higher returns to developed usescompared with payments received in the CRP.

Moulton and Richards (1990) use CRP data to derive supply curves for carbonsequestration related to tree planting on marginal agricultural lands. Their ap-proach is to first identify the average rental rates3 and the amount of land in eachregion eligible for enrolment. Assuming all eligible acres are enrolled at the specifiedrates, a supply curve is constructed by plotting the rental rates in ascending orderagainst the cumulative acres enrolled. Our study differs from earlier work in that weconsider enrolment of land in the grassland category, accounting for roughly 90%of total CRP enrolment, and include all counties where enrolment occurred. Aswell, based on our estimation results, we construct supply curves for differentregions and enrolment periods. This allows us to detect regional differences as wellas shifts in the supply relationships over time.

3. Methods

Models of optimal land use imply that profit-maximizing land managers willallocate a parcel of land to the use providing the highest level of net returns (e.g.Lichtenberg, 1989) or discounted returns in a dynamic setting under appropriateconditions (see Plantinga, 1996). In the case of the CRP, we assume that landown-ers will enroll eligible land in the CRP if the present value of CRP paymentsexceeds the present value of net returns from agricultural and developed uses.Accordingly, land is enrolled if

P [(1+r)10−1]r(1+r)10 +FV\

ANR [(1+r)10−1]r(1+r)10 +FV (1)

3 In Moulton and Richards (1990), the CRP payments are inflated by up to 20% to attract previousnon-participants and include cost share payments for tree establishment.


where P is the annual CRP payment (rental rate plus annualized cost sharepayment), r is the interest rate, 10 years is the duration of the CRP contract, FV isthe future net returns to the land (in present value terms) following CRP enrolment,and ANR is the annual net returns to the land in the most profitable alternative use,assumed to be cropland or development. The annual CRP payment is fixed for thelength of the contract. Thus, if landowners expect annual net returns to remainconstant for the length of the contract, the enrolment decision depends on therelative magnitude of P and ANR.

Empirical researchers typically estimate land use models using aggregate data(e.g. county data) since field-level observations are rarely available (e.g. Lichten-berg, 1989; Stavins and Jaffe, 1990; Parks and Kramer, 1995; Parks and Schorr,1997; Plantinga, 1996). Based on profit-maximization rules such as Eq. (1), theaggregate supply of land to a given use is specified as a function of the net returnsto the given use, the returns to alternative uses, and variables measuring thephysical characteristics of land. A consistent finding of empirical studies is thataggregate land use patterns depend on economic rents and land quality.

We employ methods similar to those used in aggregate land use studies toestimate grassland supply functions for nine US farm production regions (Fig. 3)and the years 1987–1990 (signup periods 4–9).4 Almost 90% of enrolled CRPacreage was planted in permanent grasses or had established grass cover. About 7%of the acres were planted in trees, primarily in the Southeast region (see below). The

Fig. 3. US farm production regions.

4 We omit the Pacific region since CRP enrolment is concentrated in relatively few counties. Over 50%of the CRP acreage is found in seven counties; 80% is found in 15 counties.


primary data source is the CRP Summary File (US Department of Agriculture andEconomic Research Service, 1995). The Summary File provides county-level obser-vations of the acres enrolled by treatment (e.g. grass, trees), the total acres enrolledin each of the eight years from 1986 to 1993, and total rental and cost-sharepayments. These data are used to construct measures of the grassland acres enrolled(Ait) and the per acre payment (Pit) for each county i and year t of the program.Summary statistics for Ait and Pit are reported in Table 1.

Since enrolment in the CRP is limited to cropland satisfying certain eligibilityrequirements, it is necessary to control for the acreage of eligible land in eachcounty. As noted above, the thrust of the eligibility requirements during the periodof analysis pertain to the erodibility of the land (Osborn et al., 1995). Erodibility isdefined using the Land Capability Class (LCC) system.5 LCC ratings indicate thepotential for agricultural production on a parcel of land. The rating is based on 12measures of agricultural productivity, including soil depth, slope, and drainage. Foreach characteristic, a rating from I to VIII is assigned, where I indicates the greatestpotential for agricultural production. The characteristic receiving the lowest score isassumed to be the limiting factor for agriculture and this determines the overallLCC rating for the parcel.

During all CRP signups, land in Land Capability Class (LCC) VI through VIIIwas eligible; land in LCC II through V was eligible provided it met certainerodibility criteria, which changed during the course of the program. Roughmeasures of eligible land are developed from the 1987 National Resources Inven-tory, which reports the area of land in each county by Land Capability Class andSubclass, and the 1987 Census of Agriculture, which gives the area of cropland ineach county. Eligible land (Eit) is measured as the lesser of cropland acreage inLCCs IIe, IIIe, IVe, Ve, VI, VII, VIII and 25% of total cropland acreage.6 Subclassratings indicate the dominant agricultural limitation of the land and subclass ‘e’lands are susceptible to erosion. Total eligible acreage at the start of program andfor each region is listed in Table 1.7 Eligible acreage in succeeding years isdetermined according to Eit+1=Eit−Ait−Oit where Oit equals the non-grasslandacres (e.g. trees) enrolled in county i in time t.

The Census of Agriculture provides information on cropland returns for everycounty in the US. In their studies of the CRP, Parks and Kramer (1995), Parks andSchorr (1997) use Census data on the value of crops sold and production costs tomeasure the returns to keeping land in agriculture. The value of crops sold does not

5 For more details on the LCC system, see US Department of Agriculture (1973).6 CRP enrolment was limited normally to 25% of cropland acreage in a county.7 By summing our county estimates, we estimate the total eligible land in the US at the start of the

program at 97 million acres. This figure is somewhat larger than the 70 million acre estimate in Osbornet al. (1995). The discrepancy arises from our inclusion of all subclass ‘e’ land in LCC II–V; only aportion of subclass ‘e’ land meets the CRP’s definition of highly erodible (i.e. land with an annualerosion rate greater than 3 T). Refining our measure of eligible land is impossible at the country leveldue to data limitations.


Table 1Summary statistics for conservation reserve program variables

DeltaVariable LakeAppalachian MountainCornbelt

480 181 225341 179Number ofcounties

Grassland acres (A)

Annual totals38 6871986 157 61996 338 544 615225 556

156 2501987 1 226 067437 791 2 535 3502 342 39986 107 533 295814 878 1 659 0241988 181 66661 178 348 809 802 8821989 105 875 695 99740 419 221 444444 501 556 55746 0271990

125 42018 132 16 948 62 159 69 7321991333 77229 579 22 486 131 550 94 7721992

20 296 144 968 79 264369 6881993 34 923

Payments (P)115 70 8094 74Regional average

($ per acre)39 12 40Minimum 40 49

109 149519 159255Maximum

Eligible acres (E)2 482 956 9 488 521 10 407 435Pre-program total 5 395 143 22 258 028

Pacific SoutheastVariable Southern plainsNortheast Northern plains

316 68 272Number of 258167counties

Grassland Acres (A)Annual totals1986 140 59613 917 39 773 206 408225 1181987 2 172 112 895 161 144 903 2 201 63060 247

403 347 76 1522 578 139 1 380 57953 9781988110 550 49 6681989 687 56237 462 1 672 02493 437 15 3731 507 484 397 7551990 17 91310 6211991 89475766 41 21533 22044 324 918595 239 100 79882341992

71 7137095 32 019 14 280 106 3491993

Payments (P)85 82Regional average 89112 83

($ per acre)49Minimum 2747 3445

125 131174 159Maximum 354

Eligible acres (E)Pre-program total 4 348 5483 347 094 3 098 111 10 935 88125 438 298


account for crops that are grown and fed to livestock, a common practice in manyparts of the country. The cost measure includes expenditures on labor, materials,and supplies that support farm operations other than crop production. We con-structed similar variables from Census data and included them in a subset of theregional models. In all cases, associated coefficients were not significantly differentfrom zero, which we attribute to the imprecision of the variables in measuringcropland returns.

Instead, we measure cropland returns (CRit) as the average LCC of eligible land.LCC ratings indicate the physical limitations of the land for agricultural produc-tion. For instance, prime agricultural land with a class I rating has no limitationswhereas class VII land has significant limitations (e.g. steep slopes, shallow soils)for agricultural production. Consequently, returns to crop production tend to behigher on higher quality land. Moreover, within farm production regions — thelevel of aggregation for our supply analysis — crop choice and farming practicesare fairly similar and, during a given year, farmers in a region will face similar cropprices and input costs. Accordingly, cropland returns will be similar on lands of thesame quality and the average LCC of eligible land serves as an appropriate proxyfor average returns in a county.

Economic and demographic factors are often used to explain land use patterns(e.g. Alig, 1986; Plantinga et al., 1990). Variables measuring population density(POPit) and median household income (INCit) are developed from the 1990 USPopulation Census. POPit is computed as the number of people in county i in 1990divided by the land area of the county in square miles. INCit is the reported medianhousehold income in county ‘i’ in 1990. County population and income tend toexhibit a high degree of stability over time and, thus, we can use 1990 measures —which, of the available data, are closest in time to our period of analysis — toexplain CRP enrolment between 1987 and 1990.

Population density and household income are general indicators of opportunitiesfor conversion to non-agricultural uses of the land. It is hypothesized that farmersare less likely to commit their land to agricultural uses for 10 years (the duration ofCRP contracts) in counties where opportunities to convert cropland to non-agricul-tural uses are greater. However, in some instances counties with relatively highpopulation densities and incomes may have relatively large cropland acreages andCRP enrolments. A populous and wealthy county (e.g. Marin County, California)may zone land for agriculture to prevent development and provide open space.

The scope of the study, nine farm production regions, 43 states, and almost 2400counties, limits the set of explanatory variables, for which consistent measures canbe found. To control for possible omitted relevant variables, we include fixed effectsparameters aj for each state j in a region. We define Dij as a dummy variable thattakes a value 1 if county i is in state j and 0 otherwise and J as the number of stateswithin a region. For the Northeast region, a single fixed effects parameter isestimated for states other than Maryland, New York, and Pennsylvania due tolimited observations.

Over time, higher CRP payments were required to attract farmers with higherreservation prices. To account for potentially nonstationary supply relationships,


we estimate separate equations for each of the years 1987–1990. In addition, weexpect there to be regional differences in farmers’ responses to the CRP related todifferences in farming practices. Accordingly, we estimate separate sets of equationsfor each of the nine farm production regions. A simple specification of the supplyfunction is used for all nine regions and years,

ln Ait=b1 ln Pit+b2 ln Eit+b3 ln CRit+b4 ln POPit+b5 ln INCit+%j ajDij

+oit (2)where oit is a random error term with a zero mean and variance s8.

The bs in Eq. (2) measure the percentage change in enrolled acreage for a 1%increase in the corresponding independent variable, all else equal. Accordingly, weexpect the coefficients b1 and b2 to have positive signs. All else equal, enrolmentshould be higher in counties with higher rental payments and more eligible acreage.The coefficient on eligible acreage may be less or greater than unity. In the lattercase, since the total enrolled acreage in a county is typically a small share of totaleligible acreage, a 1% increase in eligible acreage may result, all else equal, in agreater than 1% increase in enrolled acreage. The sign of b3 is expected to bepositive since a higher average LCC rating indicates lower quality lands and cropreturns and, thus, a greater likelihood of CRP enrolment. The signs of b4 and b5

are expected to be negative; however, in regions where countervailing effects arepresent (see above), the coefficients may not be significantly different from zero.

Table 2Estimation results for the Cornbelt regiona

1987 19901988Variable 1989

2.87 (0.50)* 3.59 (0.57)*ln P 1.40 (0.53)*1.26 (0.52)*0.14 (0.12)0.13 (0.13)0.20 (0.16)ln E 0.80 (0.17)*

0.77 (0.14)* 0.75 (0.15)*ln CR 0.89 (0.15)*0.75 (0.16)*−0.29 (0.11) −0.20 (0.11)*ln POP −0.37 (0.11)* −0.45 (0.13)*

0.32 (0.63)−0.88 (0.55)−1.07 (0.58)*ln INC −1.56 (0.55)*0.17 (5.79) −1.76 (6.06)IL −7.51 (5.80) −23.28 (6.64)*

−7.52 (5.82) −23.70 (6.65)*IN −1.89 (6.08)0.26 (5.81)1.25 (5.82) −1.27 (6.09)IA −6.97 (5.84) −22.73 (6.67)*

−1.56 (6.00)0.05 (5.74) −23.93 (6.57)*MO −7.95 (5.75)−23.45 (6.61)*OH 0.11 (5.77) −7.62 (5.78)−2.08 (6.04)

0.290.300.27Adjusted R2 0.44c Observations 478478478478

a Dependent variable, ln A ; *, P=0.05; S.E. are in parentheses.

8 The dependent variable is censored since in some counties and years of the program no acreage isenrolled. This causes obvious problems since the logarithm of zero is undefined. There is no generallyaccepted procedure for dealing with this problem although many researchers use the ad hoc approachof replacing zero values with small positive values. We replace zeros with ones. Since most of ournon-zero observations are relatively large numbers (greater than 50), all transformed variables exceedzero.


Table 3Estimation results for the Lake States regiona

1987Variable 1988 1989 1990

2.37 (0.41)* 1.74 (0.39)* 2.40 (0.47)* 2.24 (0.51)*ln P1.52 (0.15)* 1.15 (0.15)*ln E 1.42 (0.18)* 0.93 (0.16)*

ln CR −0.57 (0.15)* −0.48 (0.15)* −0.60 (0.20)* −0.20 (0.22)ln POP −0.29 (0.18) −0.36 (0.16)* −0.35 (0.20)* −0.50 (0.23)*

−1.21 (0.90) 0.29 (0.85)ln INC −0.60 (1.02) 0.74 (1.15)MI 0.55 (8.52) −3.27 (8.09) −4.65 (9.79) −18.35 (11.06)*

1.41 (8.61) −2.46 (8.17)MN −4.26 (9.89) −16.98 (11.18)1.05 (8.60) −2.26 (8.15)WI −3.94 (9.87) −16.50 (11.16)0.62 0.50Adjusted R2 0.47 0.43

223 223 223 223c Observations


4. Results

Regression results for the Cornbelt, Lake States, Mountain, North Plains, andSouth Plains regions are reported in Tables 2–6. Results are similar for theNortheast, Appalachian, Southeast, and Delta States regions. To conserve space inreporting the results, we pooled the data for these regions and estimated a single setof parameters for an aggregate East Coast region (Table 7).9 Considering the scopeof the analysis and the use of aggregate (in our case, county) data, the model in Eq.(2) appears to capture the hypothesized relationships well. For most regions andyears, the estimates of b1, b2, and b3 have the expected signs and are significantly

Table 4Estimation results for the Mountain regiona

Variable 1987 1988 1989 1990

0.66 (1.36) 3.86 (1.52)*2.51 (1.27)*ln P 2.84 (1.22)*0.47 (0.08)*0.50 (0.30)*ln E 0.59 (0.09)* 0.60 (0.07)*

1.85 (0.22)* 2.17 (0.25)*ln CR 1.81 (0.20)*1.55 (0.32)*−0.27 (0.15)* −0.12 (0.14) −0.03 (0.16) −0.03 (0.18)ln POP

−0.13 (1.25)0.36 (1.03) 1.54 (1.00)* 0.89 (1.11)ln INC−36.24 (12.48)* −43.77 (14.05)*−31.01 (11.58)*CO −50.21 (11.27)*

−41.77 (13.96)*−30.61 (11.50)* −48.71 (11.19)* −33.86 (12.39)*IDMT −42.07 (13.80)*−34.98 (12.25)*−48.73 (11.06)*−31.37 (11.37)*

−36.19 (12.34)*−51.22 (11.16)*−33.03 (11.47)* −45.18 (13.90)*NM−34.64 (12.38)* −41.96 (13.94)*−29.78 (11.49)*UT −47.69 (11.18)*

−33.07 (11.56) −50.79 (11.24)*WY −37.95 (12.44)* −44.67 (14.02)*0.41Adjusted R2 0.550.56 0.49

179179179c Observations 179


9 For the pooled model, we estimate regional fixed effects parameters.


Table 5Estimation results for the North Plains regiona

19881987 1989 1990Variable

0.84 (0.86)0.29 (0.80) 0.36 (0.82)−0.32 (0.80)ln Pln E 0.45 (0.17)* 0.56 (0.10)* 0.57 (0.08)*1.00 (0.18)*

1.18 (0.18)* 1.06 (0.17)*1.26 (0.18)*0.69 (0.19)*ln CR−0.19 (0.10)* −0.25 (0.09)*ln POP −0.17 (0.09)* −0.17 (0.09)*

0.39 (0.68) 0.95 (0.65)0.82 (0.65)ln INC −0.28 (0.66)−20.89 (8.70)*KS −22.76 (8.29)*−9.78 (8.18) −19.03 (8.17)*−22.24 (8.63)* −24.37 (8.22)*−20.92 (8.10)*−10.38 (8.11)NE

−19.63 (8.04)*−10.64 (8.05) −20.28 (8.57)* −22.11 (8.16)*ND−20.41 (7.98)*−10.97 (7.99) −20.76 (8.50)* −22.30 (8.10)*SD

0.44 0.500.46Adjusted R2 0.30315315 315 315c Observations


different from zero at the 5% level. Exceptions are the price coefficients in theNorth Plains model and the crop returns coefficients in the Lake States model. Theprice coefficients in the North Plains model are not significantly different from zero,which might be explained by lack of variation in the price variable across counties.The crop returns coefficients for the Lake States are negative and significantlydifferent from zero, which is contrary to expectations. We expect CRP enrolment toincrease, all else equal, as the average quality of eligible land declines.

For almost all regions and years, the coefficient on eligible acreage is positive andsignificantly different from zero. It is important to recognize that the share ofeligible land enrolled is less than 50% in all regions except one so the eligibilityrequirements do not impose a binding constraint on enrolment. However, theremay be a limited number of farmers willing to commit their land to a conservationuse for 10 years and, thus, the ‘effective’ eligible acreage may be considerablysmaller. Most of the population coefficients are negative (the East Coast region is

Table 6Estimation results for the South Plains regiona

19891988 19901987Variable

4.24 (0.98)*ln P 3.99 (0.94)*5.23 (0.89)*4.52 (0.92)*0.83 (0.16)*1.84 (0.27)*ln E 0.48 (0.07)*0.69 (0.08)*

0.08 (0.40) 1.17 (0.34)*1.24 (0.32)*0.88 (0.32)*ln CR−0.86 (0.14)* −0.63 (0.14)*ln POP −0.56 (0.15)* −0.38 (0.16)*

−0.66 (0.87)0.12 (0.78)ln INC −0.44 (0.84)−0.86 (0.76)−28.22 (9.61)*−35.42 (8.83)*OK −28.50 (9.56)*−22.24 (8.85)*−25.88 (9.66)*−28.27 (9.60)*−35.53 (8.89)*TX −22.82 (8.93)*

0.31Adjusted R2 0.42 0.31 0.33c Observations 258258258 258



Table 7Estimation results for the East Coast regiona

19901989Variable 1987 1988

ln P 1.01 (0.30)* 1.46 (0.29)* 1.76 (0.30)*1.62 (0.29)*0.11 (0.07)ln E 1.01 (0.12)* 0.29 (0.10)*0.35 (0.11)*0.95 (0.12)*ln CR 0.96 (0.14)* 1.34 (0.14)* 1.12 (0.13)*0.04 (0.11)ln POP 0.09 (0.11) 0.12 (0.11) 0.04 (0.10)

ln INC −1.37 (0.43)*−2.36 (0.44)* −1.46 (0.42)*−1.81 (0.43)*−4.53 (4.09)Appalachian −3.52 (4.23)3.09 (4.24) −3.28 (4.16)

−1.66 (4.12)Delta states 3.59 (4.15) −2.97 (4.00)−1.99 (4.07)−5.32 (4.16)Northeast −4.00 (4.30)2.07 (4.32) −4.27 (4.24)

Southeast −3.30 (4.20)2.98 (4.22) −4.25 (4.07)−3.05 (4.14)Adjusted R2 0.29 0.22 0.19 0.14c Observations 855855 855855


an exception) and many are significantly different from zero. Fewer of the incomecoefficients are negative and significant. As anticipated, these variables may captureopposing effects on CRP enrolment.

The fitted model is used to construct supply curves for each year and region.10

The curves depict the relationship between the CRP offer price and the totalacreage enrolled, holding constant other factors (eligible acreage, crop returns,socioeconomic characteristics). The results for the Lake States (Fig. 4) illustrate the

Fig. 4. Supply curves for the Lake States region, 1987–1990.

10 For each county, the acreage enrolled for given prices is computed using the fitted Eq. (2) andcorresponding county-level values of the other independent variables. The acreages for counties aresummed to find the associated point on the aggregate supply curve.


Fig. 5. Supply curves by region, 1987–1990 average.

pattern found in most regions. The supply curve is relatively flat in 1987 as a largenumber of acres are enrolled. Following 1987, the supply curve shifts back andbecomes increasingly steeper. The steeper curves correspond to a decline in the‘effective’ eligible acreage and the corresponding need for higher prices, all elseequal, to attract farmers with higher opportunity costs.

To illustrate the average supply responses in each region during the period ofanalysis, a set of regional supply curves is constructed by averaging curves for theyears 1987–1990 (Fig. 5). As might be expected, farmers in the East require highpayments to enroll even small amounts of cropland. This result is explained by thesmall area of eligible land and opportunities for conversion to non-agriculturaluses. In the Northeast, many farmers have the option of converting their land todeveloped uses (Parks and Schorr, 1997). In the Southeast, forestry is a viablealternative to agriculture; as noted, farmers in this region planted a large share ofenrolled CRP land in trees. Farmers are most responsive in the Mountain andNorth Plains region, the two regions with the greatest enrolment of grassland acres.The remaining regions, Cornbelt, Lake States, and South Plains, exhibit similarsupply relationships, though South Plains farmers appear to be more responsive athigher prices than farmers in the Cornbelt and Lake States.

5. Discussion

The results reveal that US regions fall into one of three groups with similar landconservation costs. The high cost regions are the Appalachian, Delta States,Northeast, and Southeast. Relative to other regions, high payments are required toenroll even small amounts of cropland. The low cost category includes the


Mountain and North Plains regions. The relatively elastic supply curves for theseregions suggest the most cost-effective opportunities for enrolling land. The mod-erate cost regions are the Cornbelt, Lake States, and South Plains. Among these,the Cornbelt appears to be the lowest cost region at fairly low prices and theSouth Plains the low cost region at high prices.

This study exploits the fact that during the period 1987–1990, the CRP oper-ated in much the same way as a competitive market for conservation lands.Accordingly, the estimated supply relationships reflect the actual response byfarmers to prevailing prices for their land. In many studies of land conservationprograms (e.g. Moulton and Richards, 1990; Parks and Hardie, 1995), analyststypically assume that farmers will accept average agricultural returns and plant-ing cost share payments as compensation for converting land. For instance,Moulton and Richards (1990) assume that farmers will enroll large amounts ofadditional land in a CRP-type program if prices for conservation land areslightly higher than CRP rental rates.

There are, however, a number of reasons why landowners may not respondin the manner specified by researchers. Esseks and Kraft (1988) found thatsome Illinois farmers, eligible for the CRP, did not believe they qualified. Fur-ther, in some cases, contractual arrangements between owners and mortgageand lease holders precluded enrolment. Even if foregone agricultural returnsare an appropriate measure of opportunity costs, the government cannotidentify the minimum amount of compensation required by individual farmers.This asymmetric information between farmers and the government enablesthe farmers to claim higher rates of compensation (Smith, 1995). As discussedby Stavins (1999), there are a number of additional factors that may influenceenrolment decisions in practice, including (1) the partial irreversibility of land-use changes coupled with uncertainty about future economic returns, whichmay give rise to option values (Dixit and Pindyck, 1994) i.e. a premium tokeep land in its current use; (2) nonpecuniary returns to landowners, suchas recreational uses of conservation lands; and (3) liquidity constraints or ‘deci-sion-making inertia’ that result in delayed response to changes in economicincentives.

The supply estimates presented in this paper are derived from data on actualenrolment decisions and, thus, measure farmers’ perceived opportunity costs ofenrolling cropland in a conservation program. Accordingly, the estimates reflectthe influence of the factors discussed above that may affect enrolment decisionsin practice but are not included in other analyses of conservation programs.Plantinga et al. (1999), Stavins (1999) use a similar approach to the one em-ployed in this study to estimate supply curves for forest carbon sequestration.These studies estimate higher costs for carbon sequestration than those reportedin earlier studies, such as Moulton and Richards (1990). One explanation is thatthe earlier studies fail to account for many of the factors that influence enrol-ment decisions in practice.


6. Conclusions

In this study, we have quantified the response of farmers to economic incentivesfor land conservation. To some extent, our supply estimates reflect specific provi-sions of the CRP such as eligibility requirements and the 10 year contract duration.Moreover, the steepness of the supply curves for the East Coast region are relatedin part to the large acreages of tree plantings in the Southeast region. Thesequalifications withstanding, the regional supply relationships reflect the relativewillingness of farmers to retire agricultural land. With the reauthorization of theprogram in 1990, the US Congress capped total CRP enrolment at 40–45 millionacres, a figure later revised to 38 million acres. Since this cap had almost been metby 1990 (Table 1), relatively few acres were enrolled after this time. Accordingly,the estimates presented here, particularly those for 1990, should approximatecurrent conditions.11

The US Congress reauthorized the CRP in 1996 but did not specify enrolmentand re-enrolment criteria. Current debates concern the nature of environmentalbenefits to be targeted (e.g. wildlife habitat, wetlands, soil erosion), the scale of theprogram, and the appropriate level of CRP payments. The Secretary of Agriculturehas announced that average CRP payments for newly enrolled land will besignificantly lower than payments for land currently in the program. The results ofthis analysis suggest that at low payments (e.g. less than $40 per acre per year) verylittle land will be enrolled except in the North Plains region. Closer to $60 per acreper year, significant acreage response can also be expected in the Mountain region.Substantial acreage only at annual payments above $80 per acre will be enrolled inthe Lake States, Cornbelt, and South Plains regions. Even at high payments, littleland is likely to be enrolled in the Appalachian, Delta States, Northeast, andSoutheast regions.

The results of this analysis yield insights into the costs of other land conservationprograms. Currently, there is a great deal of interest in the prospect of plantingtrees on agricultural land to offset emissions of carbon dioxide. Indeed, afforesta-tion receives explicit recognition in the Kyoto Protocol and the US Climate ChangeAction Plan. In this study, we provide estimates of the payments that would likelybe required to enroll large amounts of agricultural land in a carbon sequestrationprogram. It is notable that the greatest acreage response is found in US regions(e.g. the North and South Plains) where tree establishment is difficult due toclimatic conditions. In these regions, additional incentives may be needed to inducelandowners to convert land to forest. An alternative approach would be toencourage farmers to adopt soil conservation practices that increase the amount ofcarbon stored in agricultural soils.

11 The 1995 Farm Bill instituted changes in farm programs that reduce or eliminate commodity pricesupports and payments. Since our crop return variable (CR) does not measure net returns explicitly, thelevel of crop subsidies is reflected in the estimates of b3. To the extent that changes in the farm programreduce agricultural returns and, thus, the opportunity costs of enrolling land in conservation programs,our model may underestimate the supply response by farmers.


Acknowledgements

The authors acknowledge helpful comments from Kevin Boyle on an earlier draftof this article. Maine Agricultural and Forest Experiment Station Publication No.2436.

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