Advancing crop growth modeling: Challenges, model integration, and pathways for improved decision support

Crop growth models (CGMs) are essential tools for simulating plant growth, optimizing management, and supporting climate-resilient agriculture, yet selecting appropriate models and ensuring accurate predictions remain challenging due to data limitations, model structure constraints, and diverse agroecological conditions. This narrative review synthesizes CGM development, applications, and limitations, emphasizing systematic model selection, calibration, and validation to enhance prediction reliability, alongside best practices in sensitivity analysis, uncertainty assessment, and data integration. A major focus is on model integration and coupling, linking CGMs with hydrological, climate, economic, and spatial models to capture complex biophysical and socio-economic interactions, while multi-model approaches; including ensembles, modular frameworks, and cross-model comparisons, address single-model limitations, improve predictive reliability, and enable scenario-based analyses. Emerging technologies such as remote sensing, real-time sensors, AI, and digital twins further enhance adaptive modeling, automated calibration, and actionable decision support, supporting sustainable management, informing climate adaptation, and enabling evidence-based policy development. By combining systematic model selection, model coupling, and technological innovations, this review provides a roadmap for advancing CGMs toward more comprehensive, accurate, and actionable simulations, bridging the gap between modeling science and practical agricultural decision-making.