Reducing corn yield variability and enhancing yield through the use of corn-specific growth models.

摘要

Crop simulation models (CSMs) can evaluate the effects of management and environmental scenarios on crop growth and yields. Two corn (Zea mays L.) crop growth simulation models, Hybrid-Maize, and CERES-Maize, were calibrated and validated under mid-Atlantic United States conditions to provide better understanding of corn response to variable environmental conditions and developing management that decreases temporal yield variation. Calibration data were from small-plot population by maturity studies conducted across five site years. Model validation was performed on data from large, replicated trials from across Virginia. Both CSMs under-predicted corn grain yield. CERES-Maize grain yield prediction error was consistent across the range of plant density, whereas accuracy of Hybrid-Maize varied with density. Validation results of the calibrated CSMs showed reasonable accuracy in simulating planting date and environment on a range of corn hybrids. Because each model has unique strengths and assessment modules, the CSM can be matched to the individual use.