Skip to main content
ARS Home » Midwest Area » Ames, Iowa » Corn Insects and Crop Genetics Research » Research » Publications at this Location » Publication #203651

Title: Use of aggregated environment data to predict spatial variation of crop yields across a landscape

item Williams, Carol
item Liebman, Matt
item Edwards, Jode
item James, David
item Herzmann, Daryl
item Singer, Jeremy
item Arritt, Ray

Submitted to: American Society of Agronomy Abstracts
Publication Type: Abstract Only
Publication Acceptance Date: 11/16/2006
Publication Date: 11/16/2006
Citation: Williams, C., Liebman, M., Edwards, J.W., James, D.E., Herzmann, D., Singer, J.W., Arritt, R. 2006. Use of aggregated environment data to predict spatial variation of crop yields across a landscape. American Society of Agronomy Abstracts. P314-3.

Interpretive Summary:

Technical Abstract: Crop yield variability is effected by environmental heterogeneity at various scales. “Scaling-up” of locally-derived process-based models has not been universally successful in accurately modeling patterns of yield association with environmental characteristics at broader spatial scales. Use of remotely-sensed spatially-referenced environmental data and geographic information systems (GIS), combined with autoregression statistical techniques offers an opportunity to meet the challenge of predicting broader-scale patterns of yield variability in relation to crop-relevant environmental parameters. Such information could aid in improved strategic agro-ecological and economic decision-making beyond the farm enterprise. The aim of this study was to model spatial distribution of crop yields and their interannual variability among counties in Iowa, U.S.A, based on the hypothesis that mean county-level environmental characteristics were predictors of mean county-level, long-term crop yields. We used a raster GIS to derive values for a limited set of environmental predictors which were then used to predict yield using autoregression. Predictors included county-level means and standard deviations of climatic, edaphic and topographic environmental attributes. Both predictor types were significant in predicting yields of corn (Zea mays), soybean (Glycine max), alfalfa (Medicago sativa), and oat (Avena sativa).