Field‐Based Evaluation of a SIMPLE Crop Model for Sesame ( <i>Sesamum indicum</i> L.): Growth Simulation and Yield Prediction

The study assesses how well the SIMPLE crop model predicts sesame growth and yield under diverse environmental conditions in Cho Moi District, An Giang Province, Vietnam, year 2023–2024. Validation of the model was conducted using field data and sensitivity analysis was carried out for key parameters. Results showed the model performs reliably, with prediction errors (relative root mean square error [RRMSE]) below 6.5% across both seasons. Sensitivity tests using Morris and Fourier amplitude sensitivity test methods revealed that accumulated temperature ( T sum ), sensitivity of radiation use efficiency to drought stress ( S Water ), and how efficiently plants convert sunlight into biomass (radiation use efficiency [RUE]) strongly influence yield. Particularly, T sum drove yield changes in the autumn–winter (AW) season, while S Water became critical in the drier winter–spring (WS) season, underlining how seasonal weather shifts reshape crop responses. Simulations further projected that a 5°C temperature rise would shorten the growing period by 10–12 days, slashing biomass by over 15%, and reducing yields by 15.5%. Higher CO 2 levels (600 ppm) boosted biomass by 16.8%, but this gain was erased when combined with heat stress. The research stresses the importance of sensitivity analysis in crop modeling, not only for improving accuracy but also for guiding targeted investments in climate‐resilient farming practices. For regions like Vietnam’s Mekong Delta, where water and heat extremes threaten staple crops, such tools are vital for tailoring adaptive strategies to local conditions.