CSM CERES Maize model (DSSAT v4.0)

In recent years, the use of crop models has grown exponentially for agro-climatic and site-specific resource management aimed at increasing grain yield. Among these models, CMS-CERES-Maize is one of the most prominent. However, limited academic literature exists on how to best utilize such simulation models to optimize yield in developing countries like Nepal. Calibration and validation using available datasets are prerequisites for effective application. Site-specific management of resources-such as nitrogen (dose, timing, and method) and water (amount and timing)-has been shown to increase maize yield while minimizing losses. Similarly, factors such as annual climate variation, sowing dates, and initial soil moisture content significantly affect yield performance. Findings from this study suggest that changes in the magnitude of climatic parameters can place stress on resource management systems, thereby affecting grain yield. This analysis contributes to a better understanding of this emerging field and may be particularly useful to professionals and others involved in agriculture.