Spatial validation of crop models for precision agriculture

Spatial measurements of yield using technological advances like on-the-go yield monitoring systems have clearly shown large within-field variability in crop yields suggesting that field yields could be increased or cost decreased by varying management over space. This study evaluated the utility of the CROPGRO-Soybean simulation model and remote sensing in the interpretation of a soybean yield map. CROPGRO was executed on areas within the field defined as reasonably uniform by a Normalized Difference Vegetative Index (NDVI) analysis. The model was able to closely predict the crop yield variability measured within the field when the measured soil type and plant population were used as model inputs. Remote sensing was useful in finding spatial patterns across the field to target sampling and to provide spatial inputs for the model. Results of this study showed that a combination of crop model and remote sensing can identify management zones and causes for yield variability, which are prerequisites for zone-specific management prescriptions.