Indirect Land Use Changes Resulting from Increased Ethanol Feedstock Production in Kansas


Many factors, both domestic and international, have created a "perfect storm" for the corn ethanol boom of the past decade. This has created enormous changes in row-crop agriculture because of increased production of ethanol feedstocks (corn and sorghum) and concerns have arisen over using farmland to produce fuel rather than food. A direct land use change is when a farmer cultivates a crop as a biofuel feedstock; an indirect land use change occurs when a farmer cultivates a crop that was displaced by feedstock production. This paper is an attempt to verify that indirect land use change is occurring in Kansas due to increased ethanol feedstock production using county-level crop production data and regression analysis.

We created spatial variables to determine the effect that increasing corn/sorghum acreage near ethanol plants have on three crops (wheat, soybean and grasslands) further from the plants. Overall, we find that acreage for all three crops decrease. Increasing sorghum acreage most significantly impacts wheat acreage, with little change from increasing corn acreage. For soybean acreage, the opposite is true: increasing corn acreage has a small effect while increasing sorghum acreage has none. Grassland acreage is the most significantly impacted, with similar changes coming from both increasing corn and sorghum acreage. Although we found the effects that increased feedstock production has on wheat, soybean and grassland acreage, we cannot definitively conclude that indirect land use change is occurring.


Spikes in oil prices and increasing conflict in the Middle East caused global concerns over oil supplies in the early 2000's. The Federal Government responded with the Energy Policy Act of 2005 and later with the Energy Independence and Security Act of 2007 to reduce dependency on foreign oil imports and to support renewable fuels ("Renewable Fuel Standard"). These factors created a "corn-ethanol boom" that started between 2003 and 2006 (Figure 1).

Figure 1

U.S. Ethanol Production

The Energy Independence and Security Act of 2007 along with skyrocketing oil prices in 2008 boosted corn ethanol production even higher (Figure 1 and Figure 2). Total ethanol production followed an exponential increase from the early 2000's until it reached the 2010 level, where it has remained relatively constant ever since.

Figure 2

U.S. Oil Price

Kansas, one of the top ethanol producing states in the U.S., currently has 12 ethanol plants capable of producing more than 500 million gallons per year (Figure 3; "Alternative Energy"). All 12 plants use crop starches as a feedstock and 11 of these use only corn and sorghum. These two feedstocks can be used interchangeably in the ethanol production process ("Kansas Ethanol Production").

Figure 3

KS Ethanol Production

Although renewable biofuels are a promising substitute for fossil fuels, many issues still prevent widespread acceptance. Major concerns exist about how growing crops for fuel impacts farmers' decisions, especially what crops they choose to grow, because shifts in crop production directly affect crop prices and can indirectly affect food prices (Bai et al., 2012). In recent years, farmers have expanded corn acreage, replacing hay, cotton, sorghum, wheat and CRP acreage. (Malcolm et al., 2009; Wallander et al., 2011). Recent years have also seen dramatic changes in crop prices both in the U.S. as a whole and Kansas individually (Figure 4 and Figure 5). It is important that we understand how ethanol production affects land use and crop production, both directly and indirectly.

Figure 4

U.S. Crop Prices

Figure 5

KS Crop Prices

A direct land use change occurs when a farmer cultivates a crop as an ethanol feedstock (corn, sorghum); an indirect land use change occurs when a farmer cultivates a crop that was displaced by ethanol feedstock production (U.S. House, 2010). Vorotnikova and Seale confirmed that since the Energy Policy Act of 2005, the acreage of most crops (excluding corn and less notably soybeans) has decreased. The USDA's Economic Research Service found that most of the increase in corn acreage came from farmers previously growing soybeans (a direct change), but farmers previously growing other crops increased their soybean acreage to offset the change (an indirect change; Wallander et al., 2011). Additionally, they found that approximately one-third of the total increase in crop acreage came from converting hay, grazing, CRP and idle land into row crop production (Wallander et al., 2011). Another group of researchers found that the most significant contributors to land use changes are crop prices, changes in input costs and technological changes, all of which affect the profitability of each crop (U.S House, 2010).

It is already well established that opening a new ethanol plant directly impacts the price of the feedstock being used. One group of researchers found that the price of corn (when the main feedstock in the area) increases by about 6-24% after a plant is opened and that the magnitude dependends on the level of cooperation between farmers and the plant (Bai et al., 2012). Another research team found similar results, with a 1.5-cent to 12-cent increase per bushel, with an average increase of 5.9-cents per bushel (McNew and Griffith, 2005). The higher corn prices lead to a 44%-86% increase in corn acreage for ethanol production in the immediate vicinity. However, there is very little research on the extant of influence that plants have on other crops grown in the broader area surrounding the plant. It is likely that increased corn acreage leads to a decreased supply of wheat, soybeans and other crops, which in turn leads to greater demand and a higher local price for these commodities. In turn, farmers who are not close enough to the plant to benefit from higher corn prices will instead grow these other crops and reap the benefits of higher prices.

Purpose and Objectives

The purpose of this research is to statistically verify and explain indirect land use changes resulting from ethanol feedstock production in Kansas. Specifically, we attempt to determine what impact ethanol plants have on wheat, soybean and grassland acreage allocations, the relative magnitude of these impacts, what factors contribute to indirect land use change and what implications these changes have in the big picture of ethanol production. The objective of the research discussed in this paper is to discover the indirect effects that ethanol plants have on crop production in Kansas at the county level. This paper attempts to show a correlation between ethanol plants and shifts in crop production based on distance from those ethanol plants. We expect to see decreases in wheat, soybean and grassland acreage in counties near the plants. However, we expect to see increases in wheat and soybean acreages, and decreases in grassland acreage in counties further from a plant.


The dataset used was collected by the USDA's Farm Service Agency and National Agricultural Statistics Service, sorted and arranged using Microsoft Excel and broken down into individual county-level data and by year, from 1999-2010. Furthermore, counties were grouped into three tiers (Figure 4): tier 1 (counties with an ethanol plant), tier 2 (counties bordering tier 1 counties) and tier 3 (counties not in tier 1 or 2).

Figure 6

KS County Map


We used linear regression to determine the effects of the dependent variables on the independent variables using a panel dataset of 105 counties over 12 years. The linear regression equation is as follows:

Linear Regression Equation

Y is the value of the dependent variable, alpha is the constant, X is the value of the independent variable and beta is the coefficient of X.

The dependent variables used in the regressions include: wheat acreage, soybean acreage and grassland acreage (alfalfa, hay and forages). The independent variables include: prices (corn, wheat, sorghum, soybeans, and livestock), government payments (corn, wheat, sorghum, soybeans and CRP rent), average slope, cattle population, average precipitation, average temperature, inputs (wage rate and price of diesel), biofuel proxy (calculated as capacity of each ethanol plant per mile), spatial variables (the inverted distances between counties in tiers 1 and 2, tiers 2 and 3, and tiers 1 and 3), and a dummy variable (0 if prior to 2008 and 1 if 2008 and later).


We ran separate regressions for each independent variable (wheat, soybean and grassland acreages) with all of the dependent variables. For the results, we only analyzed the effect of the spatial variables described above, as these variables reveal any indirect land use changes. The results of the regressions were taken and arranged in the tables below. In total there are 12 dependent variables: they are divided by crop (corn or sorghum acreage), time (lagged or not lagged) and the tiers involved (tier 1-2, tier 1-3, or tier 2-3). For example, tier 1-2 indicates that a change in corn/sorghum acreage in tier 1 results in a change in wheat/soybean/grassland acreage in tier 2. The second column, beta, describes the marginal change in acreage (the change in wheat/soybean/grassland acreage that occurs for every 1 acre change in corn/sorghum). Furthermore, the "Significance" column indicates the likelihood that beta is statistically significant (* indicates >90%, ** indicates >95% and *** indicates >99%); therefore, only the values that are statistically significant are discussed here.

Wheat Acreage

For corn acreage, there is only one statistically significant variable: a shift from tier 1-2 with a magnitude of -1.15 (not lagged). For sorghum acreage there are three significant variables, all of which cause wheat acreage to decrease. Two are shifts from tier 1-2 (the first is lagged and the second is not lagged) while the third is a shift from tier 2-3 (lagged). The magnitudes are -4.02, -1.39 and -2.90, respectively. This demonstrates that sorghum acreage has a much greater effect on wheat acreage than corn acreage does. The majority of the change occurs in tier 2 due to shifts in corn/sorghum acreage in tier 1.

Wheat Regression Table

Soybean Acreage

For soybean acreage, all three significant variables are caused by increases in corn acreage: two are from tier 1-2 (one lagged and the other not lagged) and the third is from tier 2-3 (lagged). Their respective magnitudes are -0.55, -0.50 and -0.93. This demonstrates that soybean acreage changes very little compared to wheat and grassland acreages and most of it is due to increases in corn acreage.

Soybean Regression Table

Grassland Acreage

Grassland acreage is impacted more than wheat and soybean acreages combined. For corn acreage, there are three significant variables: two are shifts from tier 1-2 (the first is lagged and the second is not lagged) while the third is a shift from tier 2-3 (not lagged). The magnitudes are -1.21, -1.13 and -5.36, respectively. For sorghum acreage, there are two significant variables: one is a shift from tier 1-2 (lagged) while the other is a shift from tier 2-3 (lagged). Their magnitudes are -2.28 and -2.60, respectively.

Grassland Regression Table


As stated earlier, the purpose of this research is to statistically verify and explain the indirect land use changes resulting from ethanol feedstock production in Kansas. We found that grassland acreage decreases substantially with increases in corn/sorghum acreage and that wheat and soybean acreage decreases with increases in corn/sorghum acreage but at a much lower rate than grassland acreage. There are three possible explanations for these findings:

1. Corn/sorghum acreage replaces all lost wheat, soybean and grassland acreage (a direct change) and this acreage is not relocated.

2. Corn/sorghum acreage replaces all lost wheat and soybean acreage, and some lost grassland acreage (a direct change), and wheat/soybean acreage replaces some of the lost grassland acreage (an indirect change).

3. Corn/sorghum acreage replaces all lost wheat and soybean acreage (a direct change), but wheat/soybean acreage replaces all lost grassland acreage (an indirect change).

Because we are unable to definitively conclude which of these possibilities is occurring, more research must be done before we can verify whether or not indirect land use changes are resulting from ethanol feedstock production in Kansas.


  • Alternative Energy. (n.d.). Kansas Department of Commerce. Retrieved July 18, 2014, from
  • Bai, Y., Ouyang, Y., & Pang, J. (2012). Biofuel supply chain design under competitive agricultural land use and feedstock market equilibrium. Energy Economics, 34(5), 1623-1633.
  • Kansas Ethanol Production. (n.d.). Kansas Ethanol Production | Kansas Corn. Retrieved July 26, 2014, from
  • Malcolm, S.A., Aillery, M., and Weinberg, M. (2009). Ethanol and a Changing Agricultural Landscape. Economic Research Report 86. U.S. Dept. of Agriculture Economic Research Service.
  • McNew, K., & Griffith, D. (2005). Measuring the impact of ethanol plants on local grain prices. Applied Economic Perspectives and Policy, 27(2), 164-180.
  • Renewable Fuel Standard (RFS). (2013, December 10). EPA. Retrieved July 18, 2014, from
  • U.S. House. 2010 Agriculture, Rural Development, Food and Drug Administration, and Related Agencies Appropriations Bill. Measuring the Indirect Land-Use Change Associated With Increased Biofuel Feedstock Production. H. Rpt. 111-181.
  • Vorotnikova, E., & Seale Jr, J. L. (2014). Effect of Relative Price Changes of Top Principle Crops on US Farm Land Allocation before and after 2005 Energy Policy Act (EPA).
  • Wallander, S., Claassen, R., and Nickerson, C. (2011). The Ethanol Decade: An Expansion of U.S. Corn Production, 2000-09, EIB-79, U.S. Department of Agriculture, Economic Research Service.
  • About Me

    • My name is Eliott Pruett and I am currently a junior at the University of Arkansas.
    • I am pursuing a major in Crop Science with minors in Crop Biotechnology and Agribusiness.
    • I grew up on a poultry farm outside of Fayetteville, Arkansas.
    • As long as I'm doing something outdoors, I'm happy.
    • After I graduate I hope to pursue a Masters Degree in either Plant Breeding or Sustainable Agriculture.


    • Dr. Jason Bergtold: Associate Professor, Department of Agricultural Economics, Kansas State University
    • Brian Lauer: IGERT Fellow and Graduate Student, Department of Agricultural Economics, Kansas State University
    • This material is based upon work supported by National Science Foundation Grant: REU Site: Summer Academy in Sustainable Bioenergy; NSF Award No.: SMA-1359082, awarded to Kansas State University.

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