Integrating dynamic planting dates into Noah-MP-Crop for sorghum simulation in semi-arid regions

Process-based crop models embedded within land surface schemes provide a physically consistent framework for assessing crop–climate interactions. However, their application in semi-arid regions is often constrained by limited field data and simplified management assumptions. In particular, fixed planting dates remain a major source of uncertainty for rainfed systems where sowing decisions are strongly controlled by rainfall timing and intra-seasonal variability.

We develop and evaluate a framework for simulating rainfed sorghum growth using the Noah-MP-Crop model, with dynamic planting dates derived from satellite observations. Sowing timing is inferred from temporal trajectories of the GLASS Leaf Area Index (LAI) product, enabling spatially and interannually varying planting information to be incorporated. The approach is applied over the semi-arid eastern Nile basin, where sorghum production is highly sensitive to seasonal rainfall variability.

The model is implemented within the WRF-Hydro modeling system and driven by ERA5-Land atmospheric forcing and IMERG satellite-based precipitation. A stepwise calibration strategy is adopted, targeting crop phenology, leaf area development, and carbon allocation processes. Model performance is evaluated against satellite-derived LAI, independent energy flux estimates, and observed yield data, with comparisons between simulations using fixed and dynamic planting assumptions.

Results show that dynamic planting dates substantially improve the timing and magnitude of simulated LAI, particularly during early growth stages. In contrast, energy fluxes exhibit weaker sensitivity to planting date representation, reflecting the dominant control of atmospheric demand and radiation on surface energy partitioning in semi-arid conditions. Furthermore, simulations using dynamic planting dates show improved agreement with observed yields, indicating that a realistic representation of sowing variability translates into better seasonal productivity estimates. The findings highlight the importance of representing realistic sowing variability for crop growth simulation, while also illustrating the potential of combining open satellite products with process-based models in data-limited regions.

This work demonstrates a practical methodology for integrating dynamic planting information into land surface crop models, providing a transferable approach for improved crop–climate assessments and future seasonal yield prediction applications.