Conservation agriculture (CA) has emerged as a promising approach for enhancing agricultural sustainability by improving soil health, resource use efficiency, climate resilience, and environmental quality. However, the performance of CA in India remains highly heterogeneous due to wide variations in agro ecological conditions, cropping systems, residue availability, mechanization, and socio economic constraints. This review critically synthesizes current evidence on the impacts of CA in Indian agro ecosystems using a structured literature review methodology inspired by the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) framework. The review evaluates the effects of minimum soil disturbance, permanent soil cover, crop diversification, and controlled traffic/permanent raised beds (PRBs) on soil health, soil–water dynamics, crop productivity, weed and pest ecology, greenhouse gas (GHG) emissions, and climate resilience. The synthesis distinguishes well established evidence from context dependent responses, demonstrating that CA generally improves aggregate stability, soil organic carbon, infiltration, water-use efficiency, and long term yield stability, while responses related to carbon sequestration, nutrient stratification, GHG emissions, and crop productivity vary with soil type, climate, residue management, cropping system, and duration of adoption. The review also highlights key adoption barriers, including residue livestock competition, limited access to appropriate machinery, knowledge and extension gaps, and regional disparities. Finally, it identifies critical research priorities related to long term system performance, integrated nutrient and water management, climate smart mechanization, and policy support. CA improves soil structure, SOC, infiltration and moisture retention. Enhances yield stability and water use in irrigated cereal systems. Trade-offs include weeds, residue use conflicts and CH4 in rice. Mechanization and institutions drive outcomes beyond biophysics. Scaling needs region-specific, system-based adaptive design.
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Sawant et al. (2026) studied this question.
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