Simulation of sprinkler irrigation water uniformity impact on corn yield

Abstract

In a previous work, the spatial and temporal wind effects on corn yield were analysed using Ador-Crop (based onthe FAO crop model CropWat) and a solid set sprinkler irrigation model. The combined model could explain only 25% of the variability of measured yield. The objective of this work was to evaluate the predictive capacity of two more advanced crop models (EPICphase and DSSAT) when coupled to the solid set sprinkler irrigation model. EPICphase explained 44% of total dry mater (TDM) and grain yield (GY) variability when measured irrigation was used. The combination of EPICphase and the solid set sprinkler irrigation model explained better the variability of TDM than that of GY (42% and 35%, respectively), although the error in the estimation of GY with the coupled model was higher than when measured irrigation doses were considered (1.55 t ha the FAO crop model CropWat) and a solid set sprinkler irrigation model. The combined model could explain only 25% of the variability of measured yield. The objective of this work was to evaluate the predictive capacity of two more advanced crop models (EPICphase and DSSAT) when coupled to the solid set sprinkler irrigation model. EPICphase explained 44% of total dry mater (TDM) and grain yield (GY) variability when measured irrigation was used. The combination of EPICphase and the solid set sprinkler irrigation model explained better the variability of TDM than that of GY (42% and 35%, respectively), although the error in the estimation of GY with the coupled model was higher than when measured irrigation doses were considered (1.55 t ha the FAO crop model CropWat) and a solid set sprinkler irrigation model. The combined model could explain only 25% of the variability of measured yield. The objective of this work was to evaluate the predictive capacity of two more advanced crop models (EPICphase and DSSAT) when coupled to the solid set sprinkler irrigation model. EPICphase explained 44% of total dry mater (TDM) and grain yield (GY) variability when measured irrigation was used. The combination of EPICphase and the solid set sprinkler irrigation model explained better the variability of TDM than that of GY (42% and 35%, respectively), although the error in the estimation of GY with the coupled model was higher than when measured irrigation doses were considered (1.55 t ha–1 vs. 1.22 t ha–1). The DSSAT model explained 39% and 38% of the variability in TDM and GY, respectively, when measured irrigation data was used. When DSSAT was considered in the coupled model, better results were obtained for TDM (R2 = 41%) than GY (R2 = 31%). The EPICphase model simulated grain yield more accurately than the DSSAT model because it produced a better prediction of the maximum LAI. The combination of the sprinkler irrigation model with the EPICphase or DSSAT models simulated crop growth and yield more accurately than when combined with the Ador-Crop model.