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United States Department of Agriculture

Agricultural Research Service

Title: CALIBRATION OF A CROP GROWTH MODEL TO PREDICT SPATIAL YIELD VARIABILITY)

Author
item Paz, Joel
item Batchelor, William
item Colvin, Thomas
item Logsdon, Sally
item Kaspar, Thomas - Tom
item Karlen, Douglas - Doug

Submitted to: American Society of Agricultural Engineers Meetings Papers
Publication Type: Abstract Only
Publication Acceptance Date: 8/14/1997
Publication Date: N/A
Citation:

Interpretive Summary:

Technical Abstract: Soybean yields have been shown to be highly variable across fields. Past efforts to correlate yield in small sections of fields to soil type, elevation, fertility, and other factors in an attempt to characterize yield variability have had limited success. In this paper, we demonstrate how a process oriented crop growth model (CROPGRO-Soybean) can be used to characterize spatial yield variability of soybean, and to test hypotheses related to causes of yield variability. In this case, the model was used to test the hypothesis that variability in water stress corresponds well with final soybean yield variability within a field. Soil parameters in the model related to rooting depth and hydraulic conductivity were calibrated in each of 224 grids in a 16 ha field in Iowa using 3 years of yield data. In the best case, water stress explained 69% of the variability in yield for all grids over 3 years. The root mean square error was 286 kg ha**-1 representing approximately 12% of the 3 year mean measured yield. Results could further be improved by including factors that were not measured, such as plant population, disease, and accurate computation surface water runon into a grid. Results of this research show the importance of including measurements of soil moisture holding capacity and drainage characteristics, as well as root depth as data layers that should be considered in any data collection effort.

Last Modified: 8/24/2016
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