Work

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

Year: 2026

Type: article

Abstract: 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.

Source: Land Degradation and Development

DOI: 10.1002/ldr.70462

Authors: Li Feng, Muhammad Irfan, Qunxia Li, Aqsa Mehreen

Institutions: Beijing Wuzi University, Bahauddin Zakariya University, University of Science and Technology Beijing

Language: English

Topic: Climate change impacts on agriculture

Subfield: Ecology, Evolution, Behavior and Systematics

Field: Agricultural and Biological Sciences

Domain: Life Sciences

SDGs: Zero hunger, Climate action, No poverty, Life on land, Clean water and sanitation

Open Access status: bronze

Funders: National Office for Philosophy and Social Sciences, Beijing Municipal Natural Science Foundation