Sustainable agricultural productivity in semi-arid regions is often limited by soil degradation, inefficient water use, and climate variability. This study presents an integrated hydro-soil optimization framework that combines laboratory-based soil analysis, quantitative modeling, and statistical validation to improve resource management and crop outcomes. The approach incorporates key components such as soil carbon estimation, water balance modeling, and irrigation efficiency assessment to evaluate how effectively natural resources are utilized. The findings reveal that the soils under study are moderately alkaline, with a pH of 8.70, which can restrict nutrient availability and affect crop growth. Additionally, the soils exhibit low organic carbon content (0.23%), indicating poor soil fertility and reduced capacity for moisture retention. A high Free Swell Index of approximately 84% suggests significant shrink–swell behavior, which can lead to structural instability and negatively impact root development and water infiltration.To validate the effectiveness of watershed-based interventions, statistical techniques were applied. Regression analysis produced a strong coefficient of determination (R² = 0.82), indicating a high level of correlation between implemented strategies and improved outcomes. Furthermore, ANOVA results (p < 0.05) confirm that these interventions have a statistically significant impact on soil moisture retention and agricultural productivity.The proposed framework demonstrates that integrating hydro-soil parameters with data-driven decision-making can enhance agricultural productivity by 30–45%. It also contributes to improved groundwater recharge, addressing long-term water sustainability challenges. Overall, this study offers a scalable and scientifically robust model that can be adapted to similar semi-arid regions, supporting sustainable rural development through optimized soil and water resource management.
Jethva et al. (2026) studied this question.