Machine Learning-driven Prioritization of Adoption Drivers and Constraints in Agro-climatic Zone-specific Integrated Farming Systems

The experiment was conducted during the month of April to November in 2022 to study integrated farming systems (IFSs) in West Bengal. IFSs offered holistic solutions to food security, resource efficiency, and rural livelihoods, yet adoption remained limited in India due to socio-economic, ecological, and institutional barriers. A significant gap existed in empirical evidence regarding the relative importance of the enabling and constraining factors influencing the adoption and scalability of IFS in the Indian context. This study aimed to bridge that gap by identifying and ranking key factors influencing the adoption of IFSs in Eastern India. A multi-stage sampling approach was used to select 60 farmers practicing IFS for data collection across six agro-climatic zones (ACZs) in West Bengal, India from April to November in 2022. This study identified and ranked key factors, based on relative closeness values (ranging from 0 to 1), influencing IFS adoption in West Bengal through a SWOC (Strengths, Weaknesses, Opportunities, and Challenges)– TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution) analysis, a Multi-Criteria Decision-Making (MCDM) method grounded in machine learning principles. Zone-specific insights revealed strengths and/ or opportunities like women’s participation, income enhancement, farm production improvement, sustainable livelihood security, and scope of organic farming, alongside major weaknesses and challenges like intensive water requirement, higher labour engagement, greater capital start-up cost, natural calamities, and market volatility. The integrated methodology presented a replicable model for contextual planning and informed decision-making in West Bengal and similar agro-climatic regions across India and beyond.