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Demand for corn from ethanol plants and feedlots related to corn cropping intensifica4on in Kansas J. Christopher Brown, Vijay Barve, Eric Hanley, Dana Peterson, Nathaniel Brunsell, University of Kansas
Introduc@on Farmers’ cropping decisions are a product of a complex socio-‐economic, poli;cal, cultural, and natural environment in which factors opera;ng at a number of different spa;al scales impact what farmers ul;mately decide to do with their land in any given year or over a set of years. Some environmentalist interests are concerned that increased demand for corn for ethanol produc;on is leading to conversion of non-‐cropland to corn produc;on (extensifica;on). Ethanol industry interests counter that more than enough corn supply comes from already exis;ng cropland (intensifica;on). In this study, we determine which response to corn demand, intensifica;on or extensifica;on, is supported most by an analysis of land-‐use/land-‐cover (LULC) data across the state of Kansas and measures of corn demand (ethanol plant presence and caJle feedlot opera;ons).
Data and Methods Our study period is 2007-‐2009. We created a unique value-‐added dataset of LULC across the state of Kansas for all years represen;ng field-‐level crop coverage. USDA Na;onal Agricultural Sta;s;cal Service Cropland Data Layer (CDL) classifica;ons for 2007-‐2009 were generalized at the field level using the Common Land Unit (CLU) Field Boundary data layer for Kansas that was made available in 2006 by the Farm Service Agency of the USDA and purchased from the firm Farm Market ID. Generalizing the CDL data to the CLU layer smoothed the data to allow for more accurate change detec;on between years. From these data, we created two dependent variables represen;ng sequences of LULC that are consistent with the two responses: extensifica;on: Non-‐croplandà Corn à Corn, dividing that area by the total rural area. We repeated this procedure for all areas where the LULC sequence was consistent with intensifica;on: Non-‐corn croplandà Corn à Corn. Our independent variables are measures of corn demand for each county. We calculated for each county the distance from the county centroid to the nearest ethanol plant (PDist) and the total head of caJle for each county (from NASS, Na;onal Agricultural Sta;s;cs Service, 2007). All ethanol plants were in opera;on by 2007, except for two (Rice and Republic coun;es), which came online in May 2008. We then performed two Ordinary Least Squares (OLS) regressions with these variables and the fo l lowing contro l var iab les (precipita;on index, yield). Model We specified the following model using OLS: Yi= β0 + (β1Xi) + εi where i is a county index, Yi represents intensifica;on and extensifica;on of corn produc;on by county, Xi is a vector of covariates in original units, and εi an error term.
Results and Discussion Results strongly favor the intensifica;on over the extensifica;on response to increased corn demand from feedlots and ethanol plants. For every addi;onal 10,000 heads of caJle, rural acreage in intensive corn produc;on increases by .275 percent. The further a county is away from an ethanol plant, however, the lower the percentage of rural acreage devoted to intensive corn produc;on; for every ten miles of addi;onal distance from an ethanol plant, the percentage of rural acreage in intensive corn produc;on drops by .907 percent. The general rela;onships hold and are sta;s;cally significant in the models that account for spa;al autocorrela;on in the respec;ve error and spa;al lag models. Future work on this issue will address the current study’s limita;ons. First, we do not model the precise ;me an ethanol plant came online, nor do we model differences in capacity or the fact that many plants also use other crops in addi;on to corn, such as sorghum. We also consider only a limited number of years. Eventually, our project will examine these rela;onships from 2000-‐2014 during which different scenarios will play out over ;me. We also cannot explain the precise mechanisms behind the rela;onship among our variables. We intend to explore the data from farmer surveys and interviews in the coming years to complement our sta;s;cal analyses.
Acknowledgements The authors recognize funding from the following sources for comple;on of this research: NSF EPSCoR Grant No. EPS-‐0903806.
Map below illustrates the minimum distance from each county centroid (blue lines) to the nearest ethanol plant loca;on (red dots).
Yellow areas in these county maps below are where intensifica;on of corn cropping occurred between 2007-‐2009.
Ethanol plant in GarneJ, KS
CaJle Feedlot in Finney County, KS
Spa;al diagnos;cs of the OLS using a distance-‐based con;guity matrix indicated the need to account for spa;al auto-‐correla;on in the error term and in the dependent variable, depending on the variable in ques;on.
