Enhancing crop yield predictions under drought: Integrating Accumulated Drought Degree Days into the WOFOST model

Water limitations significantly constrain crop growth and yield, yet current crop models like WOFOST often inadequately represent the cumulative impacts of drought, resulting in unreliable yield predictions. This study employs two years of field data to evaluate yield simulations for soybean and oilseed rape under varying water-deficit conditions using the WOFOST model. Our analysis revealed that the original model fails to account for drought stress effects, leading to systematic overestimations on yield prediction. To resolve this limitation, we introduced an Accumulated Drought Degree Days (ADDD) metric using a three-threshold method to quantify drought stress, and then the original WOFOST model was modified by integrating an innovative ADDD-based decay factor. The modified WOFOST model effectively captured yield variations across different moisture regimes, achieving high explanatory power (R² [1:1] = 0.93 for soybean and 0.78 for oilseed rape, respectively). Comparative analyses showed the yield simulation errors were significantly reduced, with NRMSE by ∼26 % from 34.92 % to 8.72 % for soybean and ∼12 % from 26.41 % to 13.95 % for oilseed rape. These findings demonstrate substantial improvements in accuracy of yield simulation and highlight the necessity of considering an ADDD-based factor in the WOFOST model. Furthermore, the proposed approach minimizes reliance on extensive computational resources and hard-to-obtain parameters. It thus provides a practical and efficient tool for yield assessment under future climate change scenarios.