MACHINE LEARNING–BASED CLIMATE-SMART SOIL HEALTH PREDICTION AND CROP YIELD OPTIMIZATION IN ARID AGRO-ECOSYSTEMS OF PAKISTAN

Abstract

Machine learning–based climate-smart agriculture has emerged as a transformative approach for addressing soil degradation, declining crop productivity, and climate variability in arid agro-ecosystems. In Pakistan, agricultural sustainability is increasingly threatened by soil fertility loss, water scarcity, salinity, and erratic climatic conditions, which significantly reduce crop yield potential. This study examined the role of machine learning–based soil health prediction in optimizing crop yield and enhancing sustainable agricultural productivity in arid regions. A quantitative, explanatory, and cross-sectional research design was adopted. Data were collected from 306 respondents, including agricultural researchers, agronomists, data analysts, extension officers, and farmers. Statistical techniques including descriptive analysis, correlation, and multiple regression were applied using SPSS. The results revealed that machine learning–based soil health prediction has a strong and significant positive impact on crop yield optimization. Climate-smart agriculture also demonstrated a significant contribution to sustainable productivity, while institutional and technological support was identified as a key enabling factor for successful implementation. The regression model explained 65.9% of the variance in crop yield optimization, indicating strong predictive validity. The study concludes that machine learning technologies offer a powerful and innovative solution for improving soil management, enhancing agricultural efficiency, and strengthening climate resilience in arid farming systems.