Crop Yield Prediction Under Climate Stress: Integrating Degradation Effects and Adaptive Capacity

Authors: David Johnson*, Emma Wong, Frank Chen

Abstract

Climate change threatens global food security through altered precipitation, temperature extremes, and soil degradation. Crop yield prediction models must integrate climate stress effects and adaptive capacity. This study develops a machine learning framework combining climate variables, soil properties, and degradation metrics to predict crop yields under future climate scenarios. We integrate remotely-sensed vegetation indices (NDVI, EVI), soil moisture from satellite data, and in-situ climate observations from 500+ agricultural districts across diverse climates (humid tropical, semi-arid, temperate). Ground-truth yield data from 2010-2024 provides training labels. Our approach uses gradient boosting (XGBoost) with feature engineering: (1) climate stress indices (thermal stress days, water deficit), (2) soil degradation proxies (organic matter decline rate), (3) adaptive capacity indicators (irrigation access, crop diversity). The model predicts yields with R² = 0.74 across diverse regions and crops (maize, wheat, rice, sorghum). Climate stress accounts for 35-45% of yield variance; soil degradation explains 15-25%; management practices (irrigation, fertilization) explain 20-30%. Under RCP 8.5 scenarios (2050), yields decline 15-30% in water-stressed regions (sub-Saharan Africa) without adaptation; high-adaptation pathways (improved varieties, irrigation expansion, conservation agriculture) reduce losses to 5-10%. Temporal analysis reveals increasing climate volatility: coefficient of variation in yields increases 40% from 2010-2024 compared to 1990-2010 baseline. Yield forecasts 2-3 months before harvest using seasonal climate forecasts achieve correlation 0.65 with actual yields, enabling early warning and policy interventions. Our framework explicitly models interaction between climate stress and adaptive capacity, showing that adaptation effectiveness varies by region (higher in temperate areas, lower where resource constraints limit adoption). This work supports climate-informed agricultural planning and early warning systems for food security.