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Estimation of Soybean Yields at County and State Levels Using GLYCIM: A Case Study for Iowa

Acock, USDA-ARS-NRI Systems Res. Lab, Lab., BARCW, Bldg. 007, Room 008, Beltsville, MD 20705
Dep. of Botany, Duke Univ., Univ., Durham, NC 27708.
Acock, USDA-ARS-NRI Systems Res. Lab, Lab., BARCW, Bldg. 007, Room 008, Beltsville, MD 20705

^* Corresponding author.

Global climate change could have a significant impact on important food crops such as soybean [Glycine max (L.) Merr.]. To assess the impact of these environmental changes, estimates of crop yields are needed, with special reference to regional responses in key production areas. The objective of this study was to examine the performance of the GLYCIM soybean crop model in modeling regional soybean yields in Iowa at the state and county level, over a 20-yr period. The GLYCIM model has been extended to run for multiple years and combined with aggregations of soil and weather data to provide simulation over large areas. Soils data were aggregated to the soil association level in 18 sample counties so as to provide two-county data sets in each Crop Reporting District (CRD). Weather files for each CRD spanning the period 1972 to 1991 were used to provide current climate information. Simulation runs were made for each year and county. Statewide yields were estimated as an average of county yields. Statistical measures of simulation success indicated that, using aggregated characteristics, the model was best able to simulate the 20-yr mean yield at the state and county level, as well as long-term yield variability. Poorer results of modeling yields in particular years were obtained. This validation establishes the model's ability to simulate long-term yield averages under current conditions, and lays the groundwork for future simulations using altered climate scenarios.

Received for publication September 27, 1994.

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