AI for Sustainable Development Goals: Leveraging Machine Learning for Climate-Resilient Wheat Production in Wasit, Iraq
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Wheat production in semi-arid regions faces considerable challenges due to increased temperature variability, irregular rainfall, and reduced freshwater availability. These climate-induced stresses have led to unstable yields and threatened national food security in Iraq. This research presents a machine-learning-based decision-support system designed to enhance climate resilience in wheat cultivation in Wasit Governorate. The system integrates multi-temporal satellite vegetation indices, surface soil moisture estimations, and meteorological reanalysis data to generate early-season yield forecasts, irrigation scheduling recommendations, and drought early warnings. By linking predictive modeling with practical field-level decision-making, the system supports more efficient resource use, mitigates climate risks, and aligns agricultural practices with sustainability objectives.
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