Overview

The global food supply relies heavily on cereals, and maize is one of the most important due to its adaptability across diverse agro-ecological regions and its high economic value. Its strong yield potential has increased the demand for improved regional management practices. Accurate regional-level yield estimation is therefore essential but remains challenging. Traditional monitoring methods are limited, leading to growing interest in maize growth simulation models. However, regional yield simulations are still weak, highlighting the need for region-specific models. Weather plays a major role in crop growth, and using historical averages cannot capture the daily interactions among weather, soil, water, and nutrients. Reliable seasonal weather forecasts at the start of the cropping season can improve yield estimates, creating the need for daily weather records that closely match actual statistical characteristics. This research aimed to develop a maize growth simulation model with an integrated weather sub-model. It was carried out in two phases: developing and evaluating a weather generator (CMWSim), followed by creating and testing an integrated maize model (MAIZESim).

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