Integrated Environmental Metrics for Predicting Farm Household Income in Degradation‐Prone Regions

ABSTRACT This study develops and validates an integrated soil–land–climate (SLC) framework to predict farm household income in South Sindh and South Punjab, Pakistan. Using a Multinomial Endogenous Switching Regression (MESR) model, we assess how soil fertility, water scarcity, and climate stress impact agricultural productivity and income. Results show that a one‐unit increase in soil fertility index raises farm income by 1.39 units ( p < 0.01), while water availability increases income by 2.43 units ( p < 0.05). Climate change perception demonstrates the strongest effect, boosting income by 7.39 units ( p < 0.01). CSA adoption reduces income risk by 49% ( p < 0.01) and revenue skewness by 38% ( p < 0.05). The SLC framework revealed feedback loops in which water scarcity accelerated soil salinization ( r = 0.62, p < 0.01). The joint adoption of CSA practices results in a 45.9% increase in income and a 49.0% reduction in downside risk. The validity of the MESR model was confirmed with robust statistical results. Soil fertility (coefficient = 0.23) and farm size (coefficient = 0.18) are key factors influencing farm income, while rainfall variability (coefficient = −0.21) shows a climate variability impact. Policy simulations indicate that improving soil health raises annual income, while drip irrigation subsidies targeting farms > 8 km from markets yield 3:1 benefit–cost ratios. This study provides evidence for climate adaptation policies in Pakistan by recommending targeted subsidies for drip irrigation, soil amendments, and strengthening of FBOs.