Crop Production/Industries, Environmental Economics and Policy, Land Economics/Use,

Hendricks, Nathan P.

Sumner, Daniel A.

English

Research Papers in Economics

 
 
Spatially Explicit Estimates of Crop Rotation 
Responses 
 
 
 
 
 
 
 
Nathan P. Hendricks and Daniel A. Sumner 
 
Department of Agricultural & Resource Economics 
University of California, Davis 
 
 
 
 
 
 
 
 
 
 
Poster prepared for presentation at the Agricultural & Applied Economics  
Association’s 2011 AAEA & NAREA Joint Annual Meeting, Pittsburgh, Pennsylvania, July 
24-26, 2011 
 
 
 
 
 
 
 
 
 
 
 
 
Copyright 2011 by Nathan P. Hendricks and Daniel A. Sumner. All rights reserved. 
Readers may make verbatim copies of this document for non-commercial purposes by any 
means, provided that this copyright notice appears on all such copies. Spatially Explicit Estimates of Crop Rotation Responses
Nathan P. Hendricks and Daniel A. Sumner
Agricultural & Resource Economics, University of California, Davis
Overview
Crop transition models are estimated using high resolution 
data from the Cropland Data Layer.  The response to price is 
allowed to vary across different soils types.  Initial 
estimates indicate that there is substantial heterogeneity in 
acreage response to price and that the response tends to 
be larger in areas with lower runoff potential.
Data Visualization
Data
Results
Methods
Although commodity prices as a 
whole peaked in 2008, the 
relative price of corn increased 
first and peaked in 2007.  
Although not shown, this led to a 
sharp increase in the acres 
planted to corn in 2007 in the 
Corn Belt, largely offset by a 
decrease in soybean acres.  Acres 
that were corn after corn 
increased sharply in 2007 and 
then increased again in 2008, 
even though total corn acres 
decreased in 2008.
The four maps show the spatial 
distribution of crop transitions. It 
is clear from the maps that 
monoculture is spatially 
concentrated in certain locations 
and it appears that the price 
response has a spatial component 
as well.
Corn after Corn - 2006 Corn after Corn - 2008
The Markov transition probabilities are used to calculate the 
probability of planting corn in each CLU, unconditional of the 
previous crop (bottom left map).  The change in the probability of 
planting corn given the previous crop was corn or soybeans is also 
calculated.  Combining these estimates gives the average short-run 
change in the probability of planting corn for a change in the price of 
corn (bottom right map).  As expected, we estimate a positive price 
response for nearly every CLU.  The steady-state change in the 
probability of planting corn is smaller than the short-run effect, but is 
not reported here.  Generally areas with a higher probability of 
planting corn also have a larger acreage response to price, although 
there appear to be deviations from this pattern.
Crops
Cropland Data Layer – NASS
Illinois: 1999-2010
Iowa & Indiana: 2000-2010 
Prices
Jan-March average price of new harvest 
futures contract – adjusted for loan 
deficiency payments
Unit of Analysis 
(“field” boundaries)
2007 Common Land Unit GIS data layer 
released by Farm Service Agency
Soils SSURGO database - NRCS
April-May Precip PRISM Climate Group
Legend
Corn-Corn
Other Transitions
Missing
The Cropland Data Layer provides high resolution (less than 1 acre) crop 
data derived from satellite imagery.  However, each pixel does not 
represent a separate decision since there are many pixels within each 
field.  Pixels at field boundaries are also more likely to be misclassified.  
The Common Land Unit (CLU) boundaries are used to avoid such 
problems.  We calculate the centroid of each CLU then a point is chosen 
that is slightly offset diagonally from the centroid.  These points 
predominantly fall in the middle of “fields” and provide the unit of analysis 
for the empirical model.  Soil and precipitation variables are also spatially 
merged at each of the points.  There are about 850,000 points each year 
that are classified as either corn or soybeans.
We estimate two Markov transition linear probability models: the 
probability of planting corn given corn was planted last year and given that 
soybeans were planted last year.  The coefficients on corn and soybean 
prices are allowed to vary across different soil types because we 
hypothesize that differences in relative yields on different soils will lead to 
different price responses.  The soil taxonomy from NRCS is used to identify 
soils that have similar characteristics.  In addition, interaction terms with 
percent clay, percent silt, and slope are included in the model. Estimates 
from the transition models are used to calculate the probability of 
planting corn (unconditional of the previous crop) and the change in the 
probability of planting corn for a $1/bu increase in the price of corn, 
holding the price of soybeans constant for each point in our dataset, 
which is then mapped using the Common Land Unit GIS data layer.
Soybeans after Soybeans - 2006
Note: Soybeans includes double-
cropped soybeans and wheat.
Soybeans after Soybeans - 2008
Short-run Change in Probability of Planting Corn for 
$1 Increase in Price of Corn
Probability of Planting Corn Unconditional of 
Previous Crop at Average Prices
The first graph indicates that corn production is more likely to 
occur on land that has a lower runoff potential.  Furthermore, 
when the price of corn increases, the acreage response is 
larger in areas with a lower runoff potential.  Future work will 
compare the supply parameters with other environmental 
indicators, but these initial results indicate that the effect of 
corn production on water quality will be biased if researchers 
assume corn supply is homogenous across space in the Corn 
Belt.
Correlation of Corn Supply with Water 
Runoff Potential
Legend
High : 0.136213
Low : -0.0381087
Legend
High : 0.702459
Low : 0.325954
Relative Prices Aggregate Crop Transitions
Legend
Soy-Soy
Other Transitions
Missing
Legend
Corn-Corn
Other Transitions
Missing
Legend
Soy-Soy
Other Transitions
Missing

Spatially Explicit Estimates of Crop Rotation Responses

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