Intercropping System Optimization Using Python-Based Goal and Fuzzy Goal Programming

This study employs fuzzy goal programming (FGP) and goal programming (GP) approach to plan the production of sorghum–Bengal gram–sunflower intercropping in the Northern Dry Zone of Karnataka. In this research, seven goals were established, increasing the annual revenue from sorghum–Bengal gram–sunflower intercropping, reducing agricultural expenses, decreasing labor input, optimizing the application of urea fertilizer, optimizing the application of fertilizer, enhancing the use of manure, and reducing the utilized land area. The research employed a survey technique. The data gathering method involved purposive sampling. Participants were farmers practicing rice–corn–soybean intercropping. The number of participants were thirty individuals, and data was collected through random sampling. This model solved by Lingo and Python software. Both provided identical results. The results demonstrated that optimal outcomes can be achieved with an increase in the yearly revenue by intercropping sorghum–Bengal gram–sunflower on an area of 1.95, 0.04 and 0.05 ha.