A DSSAT Model Approach for Yield Optimization: A Case Study of Mae Na Rua, Phayao, Thailand

Rice production in northern Thailand, particularly in the Mae Na Ruea Sub-district, has been significantly affected by climate change, with increasing temperatures and decreasing water availability leading to reduced yields. This study aims to determine the optimal rice planting periods to maximize yields under changing climatic conditions by using the Decision Support System for Agrotechnology Transfer (DSSAT) model. The research utilizes the CERES-Rice module within DSSAT to simulate rice growth and yields across 15 planting periods from mid-April to late July 2023. Weather, soil, and crop management data were input into the model to predict yields under various environmental conditions. The results indicate that mid-May (W5) is the optimal planting time, with a predicted yield of 4294 kg/ha, 41.95% higher than the average. In contrast, early May (W3) showed the lowest yield at 1889 kg/ha. The simulated yield for weeks (W9) and (W10) were 3,018 kg/ha and 2,987 kg/ha, respectively, which were only 0.23% and 1.26% lower than the average. This demonstrates the model's a high degree of precision. The study highlights the significant impact of planting time on yield outcomes, emphasizing the need for adaptive strategies to cope with climate variability. However, the model's limitations, including underestimation of early-season drought stress and lack of consideration for factors such as irrigation and pest pressures, suggest the need for further research to enhance prediction accuracy and address long-term environmental variability.