Agricultural production is strongly influenced by weather and climate variables, including temperature, rainfall, humidity, solar radiation, wind speed, and soil moisture. Agricultural meteorology employs computer models to translate these observations into actionable information for crop management and decision-making. Process-based models such as DSSAT, APSIM, WOFOST, and CERES simulate crop growth and yield. Recent advances in artificial intelligence, machine learning, remote sensing, and big data have enhanced predictive capabilities. Integrating crop-growth processes with data-driven approaches can strengthen climate resilience, optimize resource use, and improve agricultural sustainability.
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