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Global food security is facing formidable challenges due to rising temperatures, frequent extreme weather events, growing water scarcity, cropland reduction, fluctuations in international food trade, and rising food demand. Crop production systems are complex, multi-factor dynamic systems influenced collectively by crop cultivars, climatic conditions, soil properties, and management practices, which exhibit strong spatiotemporal variability. Crop growth models have emerged as essential tools in smart agriculture, integrating knowledge from crop physiology, ecology, meteorology, soil science, and agronomy to simulate crop growth processes dynamically. This systematic review focuses on six key aspects of crop growth modeling: (1) introduction of major crop models; (2) assessing climate change impacts on crop yields; (3) predicting yield potential and yield gaps; (4) identifying yield-limiting factors; (5) formulating adaptation strategies; and (6) challenges and future research directions. Future research should focus on the deep integration of crop growth models with remote sensing, the Internet of Things (IoT), big data, cloud computing, and artificial intelligence technologies to establish intelligent "space-air-ground" decision-making systems that support precision, unmanned, and climate-resilient agriculture.
Ye et al. (Mon,) studied this question.