Abstract
Increasing global temperatures and climate variability are significantly altering crop patterns and yields. Monitoring these changes across large areas presents practical challenges due to the limitations of ground observations. Crop Simulation Models (CSMs) provide a strong alternative for assessing crop growth, resource utilization, and yield under different environmental conditions. This review offers a critical evaluation of key CSMs, including general-purpose models (DSSAT, APSIM, EPIC, WOFOST) and crop-specific tools (Sirius, WheatGrow, RiceGrow), underscoring their differences in complexity, adaptability, and scalability. Integrating CSMs with remote sensing, sensor networks, and real-time data greatly enhances prediction accuracy. Technological innovations such as artificial intelligence, machine learning, and high-resolution climate data have further improved model precision and operational efficiency. DSSAT and APSIM, widely used around the world, are highly flexible across various agro-ecological zones, but they necessitate significant calibration and computational resources. In comparison, crop-specific models achieve precise targeting but have limited adaptability. EPIC continues to be important for environmental assessments, despite its high data demands. However, challenges remain, including limited interoperability between models, difficulties in data integration, and inadequate consideration of socio-economic factors. To address these issues, the review highlights future enhancements such as improving interoperability across modeling platforms, adopting spatially explicit gridded simulations, refining global datasets (NASAPOWER, CHIRPS, ISRIC, IFPRI-SPAM), and incorporating socio-economic factors. These advancements are crucial for fostering sustainable agricultural practices and making informed decisions in the face of climate change uncertainties.