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United States Department of Agriculture

Agricultural Research Service

Title: Relationship of Corn and Soybean Yield to Soil and Terrain Properties

Authors
item Kaspar, Thomas
item Pulido, D - IOWA STATE UNIV.
item Fenton, T - IOWA STATE UNIV.
item Colvin, Thomas
item Karlen, Douglas
item Jaynes, Dan
item Meek, David

Submitted to: Agronomy Journal
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: January 5, 2004
Publication Date: May 10, 2004
Citation: Kaspar, T.C., Pulido, D.J., Fenton, T.E., Colvin, T.S., Karlen, D.L., Jaynes, D.B., Meek, D.W. 2004. Relationship of corn and soybean yield to soil and terrain properties. Agronomy Journal. 96(3):700-709.

Interpretive Summary: Farmers will be better able to implement site-specific management practices when they understand the causes of spatial and temporal variability of corn and soybean yield in their fields. Because crop yield is strongly related to landscape position and soil properties, this information should be useful for interpreting yield maps. Our objectives were to determine if a data set containing 20 soil and terrain variables would explain spatial yield variability better than a subset of seven more easily-measured variables and to determine whether the relative importance of factors in explaining yield variability differed between corn and soybean or between wet and dry years. The 20-variable data set explained more of the spatial variation in yield than the subset of seven variables and showed that landscape position, pH, slope curvature, and closed depressions affected yield. Soybean yield was affected more by pH and less by curvature than corn yield. Both corn and soybean yield was negatively affected by closed depressions and lower landscape positions in wet years, whereas these factors had less effect in dry years. Yield variability in this field could be reduced by improving drainage, by reducing erosion and increasing organic matter on shoulders and hilltops, and by utilizing iron-chlorosis-tolerant soybean cultivars. Although the information and results of this study can only be applied to this field, the approach and data analysis techniques could be used on any field. Improving the ability to interpret yield maps will someday allow farmers to adjust inputs of fertilizers and agricultural chemicals to match expected yields in their fields and therefore increase profitability and reduce environmental impacts.

Technical Abstract: Farmers will be better able to implement site-specific management practices when they understand the causes of spatial and temporal variability of corn and soybean yield in their fields. Our objectives were to determine if a data set containing 20 soil and terrain variables would explain spatial yield variability better than a subset of seven more easily-measured variables and to determine whether the relative importance of factors in explaining yield variability differed between corn and soybean or between wet and dry years. Corn and soybean yield data were collected over eleven years along eight transects in a 16-ha field in central Iowa. Soil and terrain variables measured included: A horizon depth, carbonate depth, pH, coarse sand, sand, silt, clay, organic C, N, Fe, K, P, and Zn; and seven easily-measured variables; electrical conductivity, soil color, elevation, slope, profile curvature, plan curvature, and depression depth. Factor analysis of the variables followed by regression of yield on the resulting factors showed that the 20-variable set explained more of the spatial variation in yield than the subset of seven-variables and identified landscape, curvature, pH, and closed depression factors. Further, the analysis of the 20-variable data set showed that soybean yield was affected more by pH and less by curvature than corn yield. Similarly, yield was negatively affected by closed depressions and lower landscape positions in wet years, whereas these factors had either no effect or a positive effect in dry years. Even with 20 measured variables and multiple years of yield data, more than 26% of the yield spatial variability remains unexplained.

Last Modified: 10/25/2014
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