Optimal Cropping and Irrigation Scheduling for Sugarcane Farms Using Fuzzy Multi-Objective Linear Programming Under Uncertain Water Availability

Lokesh Kumar

Abstract: Water scarcity poses a major challenge for sustainable sugarcane production, particularly in semi-arid regions where water availability is uncertain. This paper develops a Fuzzy Multi-Objective Linear Programming (FMOLP) model to optimize both cropping pattern and irrigation scheduling for sugarcane farms under uncertain hydrological conditions. The model simultaneously maximizes net economic returns and water-use efficiency, while minimizing deficit irrigation risks. A triangular fuzzy membership approach is applied to represent uncertainty in rainfall, irrigation water, and crop yield parameters. The proposed model is validated using data from a sugarcane-growing region. Results show that the FMOLP framework achieves a 17.8% increase in net benefit and 12.5% reduction in irrigation demand compared to conventional deterministic models. Sensitivity analysis reveals that the optimal solution is robust under varying water supply levels and membership function spreads. The study provides an adaptable decision-support framework for sustainable irrigation management in sugarcane systems facing climate and water variability.

Keywords: Water scarcity, Sugarcane farming, Fuzzy optimization, Irrigation planning, Sustainable agriculture

How to Cite?: Lokesh Kumar, "Optimal Cropping and Irrigation Scheduling for Sugarcane Farms Using Fuzzy Multi-Objective Linear Programming Under Uncertain Water Availability", Volume 15 Issue 2, February 2026, International Journal of Science and Research (IJSR), Pages: 162-166, https://www.ijsr.net/getabstract.php?paperid=SR26202180532, DOI: https://dx.doi.org/10.21275/SR26202180532