[en] 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&ndash;1 vs. 1.22 t ha&ndash;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.

[es] En trabajos anteriores se ha estudiado el efecto espacial y temporal del viento sobre el rendimiento del cultivo de ma&iacute;z utilizando el modelo Ador-Crop (basado en el modelo de FAO CropWat) y un modelo de simulaci&oacute;n de la distribuci&oacute;n del agua de riego por aspersi&oacute;n en cobertura total. El modelo combinado pudo explicar s&oacute;lo el 25% de la variabilidad de la reducci&oacute;n de rendimiento medida. El objetivo de este trabajo fue estimar la capacidad predictiva de dos modelos de cultivo m&aacute;s avanzados (EPICphase y DSSAT) que el modelo Ador-Crop en el mismo caso de estudio. El modelo EPICphase explic&oacute; el 44% de la variabilidad de la biomasa y del rendimiento en grano. La combinaci&oacute;n de EPICphase y el modelo de riego por aspersi&oacute;n explic&oacute; un poco mejor la variabilidad de la biomasa que la del rendimiento en grano (42% y 35%, respectivamente), aunque el error en la estimaci&oacute;n del rendimiento en grano con el modelo acoplado fue mayor que con las dosis de riego medidas (1,55 t ha&ndash;1 vs. 1,22 t ha&ndash;1). El modelo DSSAT explic&oacute; el 39% de la variabilidad de la biomasa y el 38% de la variabilidad del rendimiento en grano. Cuando se combin&oacute; el modelo de riego por aspersi&oacute;n con el modelo DSSAT, se obtuvo un mejor resultado en la simulaci&oacute;n de la biomasa (R2 = 41%) que en la del rendimiento en grano (R2 = 31%). El modelo EPICphase simul&oacute; de forma m&aacute;s precisa el rendimiento en grano que el modelo DSSAT porque predijo mejor el &iacute;ndice de &aacute;rea foliar m&aacute;ximo. La combinaci&oacute;n del modelo de riego por aspersi&oacute;n con EPICphase y DSSAT simul&oacute; mejor el crecimiento y rendimiento del cultivo que cuando se combin&oacute; con el modelo Ador-Crop.