The Impact of Climate-Smart Technology Adoption on Agricultural Productivity and Environmental Sustainability

Climate-Smart Agriculture (CSA) Agricultural Productivity Environmental Sustainability Fergana Valley Fertilizer Micro-Dosing Farm Income Irrigated Agroecosystems Resource Use Efficiency (RUE) Soil Conservation

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This study provides an empirical assessment of emerging opportunities and offers a conceptual framework for understanding the potential impacts of climate-smart agriculture (CSA) adoption on agricultural productivity and environmental sustainability. Focusing on Uzbekistan, the research employs quantitative analysis of farm-level data, adoption gradient modeling, and return-on-investment (ROI) estimation to examine how CSA technologies influence key farm-level outcomes, including yields, income, resource-use efficiency (RUE), soil erosion, and water quality under world constraints. In cotton-wheat systems, the usage of six or more CSA is associated with a 71% increase in farm income, a 43% rise in crop yields, and a 48% improvement in resource-use efficiency (RUE), compared to farms with low levels of CSA usage. Fertilizer micro-dosing is associated with an average increase in cotton yields of 245.8 kg ha⁻¹ yr⁻¹ and delivers a ROI of 456%. Multivariate regression models account for 57.3% of the variation in yield and 61.8% in farm income, underscoring the explanatory power of CSA adoption patterns. Comparative analyses demonstrate that organic matter-based practices consistently outperform capital-intensive alternatives in both economic and environmental terms. The methodological approach integrates monitoring, reporting, and verification (MRV) indicators, payback period estimations, and threshold analyses tailored to risk-sensitive smallholder contexts. The findings provide robust empirical support for evidence-informed CSA policy formulation, including the design of targeted subsidies, extension services, and investment strategies in Uzbekistan. By reconciling global CSA implementation paradigms with localized constraints, the study generates scalable and empirically validated approaches, offering methodological relevance for analogous agroecological and institutional contexts.