A Fuzzy Credibility-Constrained Fuzzy Multi-Objective Programming Model for Optimizing Irrigation Strategies to Balance Citrus Yield and Quality Under Uncertainty

Optimizing irrigation strategies to simultaneously enhance crop yield and fruit quality under water scarcity is a critical challenge for sustainable agriculture. This study addresses this challenge by developing a novel fuzzy credibility-constrained fuzzy multi-objective programming (FCC-FMOP) model for irrigation planning under uncertainty. The model incorporates stochastic hydrologic factors, decision-maker preferences, and complex interrelationships among fruit quality attributes and market dynamics. Applied to a citrus-producing region in Southwest China prone to seasonal drought, the approach demonstrates its capability to reconcile multiple objectives under fuzzy constraints. The key findings include the following: (1) The IVIF-TOPSIS analysis quantitatively revealed that yield was the paramount objective for decision-makers in the study region, followed by single fruit weight, highlighting the economic drivers that must be balanced with qualitative metrics. (2) The FCC-FMOP model effectively balances yield and quality objectives while adapting to real-world fuzzy constraints, proving to be both robust and practical. (3) Compared with conventional practices, the proposed irrigation strategy—calibrated under varying credibility levels (β = 0.55, 0.75, and 0.95)—significantly improves yield, fruit weight, hue angle, water content, and soluble sugar content. Performance evaluation using synthetic degree (SD), the sustainability index (SI), and approximation degree (AD) confirmed the model’s superiority over single-objective models and conventional practices. The FCC-FMOP model provides a scalable and decision-maker-oriented tool for sustainable irrigation management in water-limited environments.