“Weighted FGP For Groundwater-Conserving Crop Planning: A Baghpat Case Study”
DOI:
https://doi.org/10.29284/40kmmx72Keywords:
Weighted Fuzzy goal programming, crop allocation, groundwater uncertainty, Pareto optimality, Multi-objective optimization, Sensitivity analysis.Abstract
Groundwater extraction in the Baghpat district in western Uttar Pradesh, India, is 50% more than recharge, making it an overexploited area. In order to adapt the cropping pattern and maximize net profit and total output while minimizing groundwater usage, this research uses weighted fuzzy goal programming, or weighted FGP. Based on stakeholder choices, profit, production, and water objectives were given weights of 0.50, 0.20, and 0.30, respectively. The model was solved in less than 0.3 seconds using Python-PuLP. When all individual objectives are fully achieved, the results indicate an overall satisfaction level of λ = 0.892 (μ₁ = μ₂ = μ₃ = 1.0). The ideal plan lowers sugarcane area by 32% (from 76,047 ha to 51,789 ha), increases mustard and pulses by 80% and 27%, decreases groundwater consumption by 31.7% (1,127.5 MCM compared 1,650 MCM), and boosts net profit by 12.9% (₹562.4 Cr versus ₹498 Cr) in comparison to the actual 2023–24 pattern. Robustness is confirmed by sensitivity analysis (±20% tolerance variation), which shows water savings of up to −9.8% and profit improvements of up to +14.2%. On the profit–water Pareto frontier, the weighted FGP solution outperforms both the current pattern and clear goal programming. The results provide policymakers a workable plan for long-term groundwater sustainability and agricultural diversification in overfished northwest India.
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