Soybean Glicine max. (L.) Merr. is one of the most major food crops. In some areas, its responses to different climates have not been well studied, particularly in tropical countries where other crops are more dominant. Accordingly, we adopted the SIMPLE crop model to investigate the responses of soybeans to the climate. We conducted two experiments on crop growth in the Summer–Autumn season of 2020, and Winter–Spring 2021 in the Hoa Binh Commune, in the Mekong Delta, Vietnam, which is an area that is vulnerable to climate change impacts, to obtain data for our model input and assessment. The assessment was concerned with the effects of climate variables (temperature and CO₂) on soybean biomass and yield. The results indicated that the SIMPLE model performed well in simulating soybean yields, with an RRMSE of 9–10% overall. The drought stress results showed a negative impact on the growth and development of soybeans, although drought stress due to less rainfall seemed more serious in Spring–Winter 2021 than in Summer–Autumn 2020. This study figured out the trend that higher temperatures can shorten biomass development and lead to yield reduction. In addition, soybeans grown under high CO₂ concentrations of 600 ppm gave a higher biomass and a greater yield than in the case with 350 ppm. In conclusion, climate variance can affect the soybean yield, which can be well investigated using the SIMPLE model.

Rice is the essential food crop of An Giang Province. Vietnam and the whole world are facing several problems hindering climate change, such as increased temperature and CO2 concentration that many manufacturers’ companies and managers need to estimate output to make production plans or adjust policies. In this study, the model known as SIMPLE was applied to simulate the biomass and yield of rice in 2 crop seasons Autumn - Winter 2020 (AW) and Winter - Spring 2020 - 2021 (WS), in Cho Moi district, An Giang province, Vietnam (10° 23' 47"N, 105° 27' 41"E) and analyzed the effects of climate variabilities and scenarios on simulation results. Heat stress showed a relatively negative impact on the growth and development of rice in AW more seriously than WS due to climate variabilities. Climate change scenario RCP8.5 (RCP - Representative Concentration Pathway) has predicted that atmosphere temperature may increase above 4 °C and CO2 concentration to reach 900 ppm by the end of the 21st century. As a result, from the model, for every 100 ppm CO2 concentration increase, the cumulative rice biomass increased by 8 and 10 % in AW and WS, respectively. Moreover, conditions assumed from the model that increased 5 °C caused a decrease in cumulative biomass up to 7.2 % in AW season compared to 3.1 % in WS season. However, with responses of 5 °C increasing in the model, rice yield decreased relatively rapidly from 8.5 % in AW and 7 % WS. HIGHLIGHTS The model known as SIMPLE has been used in this study RMSE of our model differs from the observed yield from 4.2 % (Winter-Spring crop-WS) to 5.5 % (Autumn-Winter crop-AW) For every 100-ppm CO2 concentration increased, the cumulative rice biomass increased by 8 and 10 % in AW and WS, respectively Increasing 5 °C, rice yield decreased 8.5 % in AW and 7 % WS Sensitivity analysis showed that RUE (Radiation Use Efficiency) has the most influencing factor on rice yield GRAPHICAL ABSTRACT

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.