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Symposium Papers

Crop Simulation Models Can be Used as Dryland Cropping Systems Research Tools

Dep. of Agron., Kansas State Univ., Manhattan, KS 66506

^* Corresponding author (sstaggen{at}ksu.edu)

Received for publication February 5, 2004. Dryland cropping systems research in the semiarid Great Plains region requires a substantial investment in land, labor, and other resources. The objective of this analysis was to illustrate that crop simulation models can assist scientists in making more efficient use of these resources by providing insight on potential plant responses to alterations in cropping systems before conducting field research. Models included in DSSAT 3.5 were used to simulate two cropping systems studies that evaluated the inclusion of grain sorghum [Sorghum bicolor (L.) Moench] into a traditional wheat (Triticum aestivum L.)–fallow system in western Kansas and soybean [Glycine max (L.) Merr.] into continuous grain sorghum in north-central Kansas. CERES-Wheat overestimated wheat yields by 16% although no consistent reason was identified for these errors. The model also simulated complete plant stand losses from winter injury in 5 yr when no stand losses were observed. CERES-Sorghum underestimated grain sorghum yields by approximately 27% across both studies. Overestimating the impact of water stress on plant growth appeared to be common at the western site, and a lack of response to N when grown in rotation with soybean appeared to be the primary sources of error at the northern site. Using uniform genetic coefficients to span a 19-yr study also contributed to errors in simulating sorghum yields. CROPGRO simulated soybean within 20% and closely mimicked annual responses of soybean yields to weather patterns. If researchers used these results to evaluate the objectives of both studies before conducting fieldwork, despite the errors, the overall trends would have been similar to those measured in the field. These results would have also enabled researchers to focus their research efforts, thus more efficiently using their resources.

Abbreviations: LAI, leaf area index • RMSE, root mean square error • WF, wheat–fallow • WSF, wheat–sorghum–fallow