• Remote sensing and spectral imagery provide a core, scalable data stream for crop monitoring, biomass estimation, and early yield proxies across landscapes [1], [4], [15], [17].

• Process-based crop growth models and simulation frameworks unify weather, soil, and management to predict yields and explore scenario-based decision options [8], [9], [13], [14], [16].

• Water, evapotranspiration, and resource-use optimization form core constraints and management targets, tying ET estimation, LAI, and water-limited growth to yield outcomes [4], [6], [13], [19].

• Yield components and physiological determinants such as kernel number, harvest index, and grain filling respond to irradiance, temperature, and stress, guiding breeding and agronomic strategies [5], [7], [10], [18].

• Global forecasting, policy framing, and economics-integrated models support planning under climate variability and agricultural change, connecting policy, production, and technology [2], [16], [20].