CMS-CERES-Maize model (DSSAT v 4.0): Calibration, Validation and Simulation of maize growth and yield

In recent years, the use of crop model has been increasing exponentially for agro-climatic and site specific resource management to increase the grain yield. Among them, the CMS-CERES-Maize model is one, but, very little academic literature exists on how to best utilize the resource using simulation model to optimize the yield in reference to developing countries like Nepal. Best fit (calibration and validation)of model using the available datasets is prerequisites for its application. Site specific management of resources like nitrogen (dose, time and method), water (amount and time), etc. have increased the yield of maize minimizing their losses in field.Likewise, climate of the year, sowing dates, initial moisture content of the field,etc. also significantly affect on the yield performance. From the findings of this study, we concluded that the change in magnitude of climatic parameters creates stress on the limited resource management to increase the grain yield. Further, The analysis should help shed some light on this new and exciting field and should be especially useful to professionals and anyone else involve in agriculture.

Mr. Tej Narayan Bhusal is an assistant professor at IAAS/TU, Nepal (http:/iaas.edu.np). He has been studied MSc Agriculture (Agronomy) and BSc Agriculture in 2006 and 2008, respectively at IAAS/TU, Nepal. His research interest is in crop modelling, breeding of field crops and sustainable agriculture.