Precision Agronomy and Data-Intensive Farming Systems for Yield Optimization Under Climate Stress

Abstract

Anthropogenic climate change poses an unprecedented threat to global agricultural systems through shifting temperature regimes, altered precipitation patterns, and increased frequency of extreme weather events. This review evaluates the integration of precision agronomy and data-intensive farming systems as a cohesive framework for yield optimization under climate stress. We synthesize current research on advanced spatial soil diagnostics using electromagnetic induction and geostatistical modeling, demonstrating that apparent electrical conductivity (ECₐ) mapping enables rapid, non-invasive characterization of root-zone salinity with prediction accuracies (R² = 0.86–0.89) comparable to traditional laboratory methods. The review examines canopy-atmosphere supply-demand dynamics through the Crop Water Stress Index (CWSI), validated by strong correlations with stomatal conductance (R² = 0.63) and transpiration rates (R² = 0.73), and evaluates variable rate irrigation systems achieving water savings of 9–50% while maintaining crop yields. Variable-rate nitrogen strategies reduce fertilizer inputs by 18.4–49.6% and lower carbon footprints by 6.4–22.0% without compromising grain yield. Predictive machine learning models, particularly Random Forest ensembles optimized with genetic algorithms, achieve high spatial yield prediction accuracy (R² = 0.91–0.92) under drought stress. Socio-economic analysis reveals that precision agriculture technologies positively influence natural capital (score: 2.31) and financial capital (score: 2.28), though adoption barriers persist including high initial investment costs and technical complexity. We conclude that precision agronomy represents a transformative pathway for climate-smart agriculture, yet successful implementation requires integrated policy support, targeted subsidies, and context-specific adaptation strategies for diverse farming communities.