Crop growth models are computer programs that integrate information on daily weather, genetics, management, soil characteristics, and pest stress to determine daily plant growth and subsequent yield. Researchers at Iowa State University (ISU) have used a soybean growth model as a tool to evaluate causes of yield variability in several fields in Iowa. The idea is to calibrate the model to mimic historic yields within small grids in a field. Once calibrated, the model can be used to evaluate performance of prescriptions over many different weather conditions.
A case study was conducted on the McGarvey field on Ron Heck's farm near Perry, IA. The model was calibrated to account for water stress, soybean cyst nematode (SCN), and weed densities on 100 grids within a 50-acre field. By incorporating these factors, the model explained approximately 84 percent of soybean yield variability in 1997. Estimates were then made to determine the yield increase that could be realized by eliminating water stress (Figure 1), SCN (Figure 2), and weeds (Figure 3). According to our estimates, water stress reduced the overall yield in the field by 786.2 bushels. Eliminating SCN effects by planting an SCN-resistant variety could return 232 bushels over the 50-acre field. Eliminating weed competition by improving weed control could return up to 76 bushels for the field. Weed effects were estimated using the WeedSOFT program, developed at the University of Nebraska.
These results confirm that water stress accounts for the majority of soybean yield variation. Both weeds and SCN can be controlled by following current ISU Extension guidelines. However, a scouting plan should be implemented to determine if SCN is a factor in your field. Currently, irrigation is the only management practice that can eliminate water stress. However, research is being conducted to identify drought-tolerant varieties that may perform better in dry parts of a field. This research may offer a new management solution to reduce the effects of variable water stress and improve yields within a field and capture lost yield potential.
Figure 1. Predicted soybean yield loss due to water stress on the McGarvey field (1997).
Figure 2. Predicted soybean yield loss due to Soybean Cyst Nematode on the McGarvey field (1997).
Figure 3. Predicted yield loss due to weeds on the McGarvey field (1997). Weed losses were estimated using the WeedSOFT program.
This work was funded by the Iowa Soybean Promotion Board.
This article originally appeared on page 2 of the IC-482 (PrecisAg) -- May 5, 1999 issue.