Comparative analysis of resource use efficiency and productivity among AI-adopting and non-adopting sugarcane farmers in Western Maharashtra

The present study examined resource-use efficiency and productivity differentials between AI-adopting and non-adopting sugarcane farmers in Western Maharashtra during the agricultural year 2025-26. Primary data were collected from 90 farmers (45 AI and 45 non-AI) selected from nine tehsils across Pune, Satara and Solapur districts. Cobb-Douglas production functions were estimated separately for each system. The AI model explained 87 per cent of output variation, while the non-AI model explained 84 per cent. In AI cultivation, machine power, phosphorus, potassium, plant-protection expenditure and micronutrient expenditure exerted positive and statistically significant effects on output, whereas irrigation expenditure showed a negative significant relationship. Under non-AI cultivation, manure, phosphorus and potassium had positive significant effects, while irrigation expenditure was negatively significant. Resource-use analysis for AI sugarcane indicated under-utilisation of manure, machine power, nitrogen, phosphorus and potassium, with particularly high MVP/MC ratios for phosphorus (51.78), potassium (42.00) and machine power (26.55). Plant protection and micronutrient expenditure were over-utilised, and irrigation recorded a negative MVP/MC ratio. In non-AI cultivation, manure, machine power, phosphorus and potassium were under-utilised, while human labour, nitrogen, irrigation, plant protection and micronutrients showed ratios below unity. AI farmers obtained substantially higher physical productivity (248.23 t/ha versus 177.89 t/ha) and a higher B:C ratio at Cost C (2.02 versus 1.47). The results underscore the productivity advantage of AI-based practices and the scope for reallocation of under-utilised inputs in both systems.