SIMULATION OF CORN (Zea mays L.) YIELD IN NORTHERN SINALOA USING THE AQUACROP MODEL

The intensification of climate variability has caused uncertainty in available water volumes in several irrigation areas of Mexico; this has led to instability in the productivity of corn crop (Zea mays L.). Biological simulation models, once calibrated, are a feasible computational tool to study the behavior of crops under climatic conditions and different scenarios of agronomic and water management. In this study the AquaCrop model was calibrated and validated to simulate the development of corn crop in northern Sinaloa, Mexico, with three conditions of water availability: full irrigation (RT) and deficit irrigation (80 % and 60 % with respect to RT). To calibrate and validate the model, observed and simulated data of green canopy cover, biomass production at harvest and grain yield were compared, using experimental data from commercial corn plots established in the fall-winter agricultural seasons (O-I) 2003-2004, 2007-2008 and 2008-2009, and the spring-summer seasons (O-I), 2008 and 2009. The grain yield predictions of the model were good with a value of 0.79 t ha(-1) for the root mean square error (RMSE) and Willmott's index of agreement (d) was of 0.85. The AquaCrop model previously calibrated and validated is an alternative for the answer of corn crop with deficit irrigation and contrasting climatic conditions.