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

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Abstract

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.

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The corresponding author can provide the datasets upon request.

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Acknowledgements

The research work was made possible with the support of UVCE, Bangalore, Karnataka, India and Bangalore University, Bangalore, Karnataka, India which provided the necessary facilities.

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The success of this research work is a testament to the invaluable collaboration and collective efforts of all contributing authors. Each author brought their unique expertise and unwavering dedication, enriching the work with diverse perspectives and insightful contributions.

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Correspondence to K. Veena.

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Veena, K., Pushpa, C.N., Thriveni, J. et al. Intercropping System Optimization Using Python-Based Goal and Fuzzy Goal Programming. SN COMPUT. SCI. 5, 1120 (2024). https://doi.org/10.1007/s42979-024-03368-1

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