Role of Ethanol Plants in Dakotas Land Use ChangeGaurav Arora*, David A. Hennessy**, Hongli Feng***

IntroductionRecent research has identified changing land-use patterns in the Prairie Pothole Region of the US Great Plains. The Dakotas, with 271,000 ha net conversions from grasslands to corn/soybean production from 2006 to 2011 (Wright and Wimberley, 2013), are central to this phenomenon. The implications range from reduced biodiversity and loss of habitat for waterfowl species, to costly crop practices due to low agricultural productivity on these marginal lands (Johnston, 2014). Although much has been done in characterizing these transitions, efforts lack in explaining the market forces underlying these changes. We utilize quasi-experimental techniques and high-resolution satellite data to consider how the presence of an ethanol plant has affected cropping patterns for the Dakotas.

MethodsWe use the Difference-in-Difference (DID) approach in conjunction with Propensity Score Matching (PSM). Possible endogeneity of an ethanol plant’s location in anticipation of land-use change is a major concern. The DID approach circumvents this problem by eliminating trend- and individual- fixed effects and PSM controls for the presence of unobservables that may otherwise result in biased estimates (List et al. 2003).

DataFor empirical analysis, we focus on two ethanol plants –Red Trail Energy (RTE) and Blue Flint (BF) - in North Dakota. Both RTE and BF commenced operations in 2007. They produce 50 and 65 million gallons of ethanol every year, respectively.

Dakotas’ Land-Use data are acquired from United States Department of Agriculture’s (USDA) Cropland Data Layer (CDL), available online. CDL provides data from 1997-2013 for North Dakota and 2006-2013 for South Dakota. Soils data are accessed from USDA National Resource Conservation Service’s Web Soil Survey using the STATSGO2 application.

Regression FrameworkUsing the matched sample of T and C groups, we estimate:

Defining Treatment (T) and Control (C) GroupsWe assume transportation costs, monotonic in Euclidian distance from the plant, to be paid for by the corn producer. All else equal, a representative supplier nearer to the plant has higher incentive to produce corn than the one farther away. Consequently, T samples in our case are closer to the ethanol plant than C samples.

We also utilize a 1:1 Nearest Neighbor PSM Algorithm in conjunction with DID to compare groups with similar propensity for being treated conditional on soil characteristics – Slope and Land Capability Class. We estimate an Average Treatment Effect (ATT and denoted by ) for individual ethanol plants.

Preliminary Estimation ResultsWe use the following controls for regression results in the following tables.

Treatment Effect: In most cases, our treatment coefficients’ estimates are insignificant. Although, we find some significant impacts, negative for corn/soybean and positive for wheat, these estimates are sensitive to choice of regressor and hence unreliable. Moreover, their significance does not hold for their counterparts in other time periods.

Other Control Variables: Our results also reveal that higher grass or pasture acreage, prior to the treatment, has significantly positive impact on production of corn/soy and wheat. However, more wheat, prior to treatment, has a stronger positive impact on corn/soybean acreage than grass or pasture. This reflects a lower cost of conversion from wheat to corn/soy than from grass/pasture to corn/soy. Further, we do not find our coefficient estimates for these controls to vary between treatment and control groups.

Caveat and Future Work Our ad-hoc treatment and control groups are not exhaustive, since we deal with a non-exogenously defined change/policy. Replicating our analysis with falsified treatments (i.e., placebo tests) without an ethanol plant, spatially and temporally, will serve as robustness checks.

Our preliminary results indicate that land use impacts of ethanol plants depend on local conditions. We will conduct similar analysis with respect to several other ethanol plants in the region to further investigate the role of local factors.

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ReferencesJohnston, C.A. 2014. “Agricultural expansion: land

use shell game in the U.S. Northern Plains,” Landscape Ecology 29(1):81-95.

Wright, C.K., Wimberly, M.C. 2013. “Recent land use change in the Western Corn Belt threatens grasslands and wetlands,” Proceedings of the National Academy of Sciences USA, 110:4134-4139.

List, J.A., Millimet, D.L., Fredriksson, P.G., McHone, W.W. 2003. “Effects of Environmental Regulations on Manufacturing Plant Births: Evidence from A Propensity Score Matching Estimator,” Review of Economics & Statistics 85(4): 944-952.

AcknowledgementThis research is part of a collaborative project supported by the USDA-National Institute of Food and Agriculture, award number 2014-67003-21772.

Figure 1. Land-use composition for North DakotaPre- and Post Treatment Years

Figure 2. Treatment and Control GroupsSource: North Dakota 337504.21 m East, 5249274.59 m North

Google Earth. April 9, 2014, October 20,2014.

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*Graduate Student, Department of Economics, Iowa State University**Professor of Economics, Iowa State University***Adjunct Associate Professor, Department of Economics, Iowa State University

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