Optimizing agricultural water-land resource allocation in water-economic-environment cycles considering uncertainties of spatiotemporal water footprints

Abstract

Water and croplands are spatiotemporally heterogeneous in agricultural production and their sustainable use is important for supporting water–economy–environment cycles. This study proposes a fuzzy multi-objective programming model based on water footprint (WF) theory to achieve a universally optimal land-water allocation strategy under uncertainties. The model balances the trade-offs among water-saving, economic benefits, and environmental benefits while ensuring food security and addressing uncertainties from climate, agricultural production, and market fluctuations. Subsequently, a tri-intuitionistic fuzzy decomposition simplex aggregation algorithm is proposed to handle these uncertainties and generate the optimal spatial cropping patterns via integrating fuzzification, defuzzification, decomposition, aggregation, and simplicity mechanisms. The applicability and effectiveness of the methodology were validated in Jiangsu Province, China. Results indicated cotton had the highest total WF (4454 ± 480 m³/ton) among six crops based on estimations obtained from daily climate observations conducted from 2002 to 2022 in 21 water function zones of Jiangsu, followed by rape (1907 ± 152 m³/ton), wheat (1223 ± 89 m³/ton), rice (899 ± 70 m³/ton), peanut (1080 ± 124 m³/ton) and maize (780 ± 78 m³/ton). Optimal results from the Pareto front demonstrated improvements of 6.3 %, 2.1 %, and 3.2 % in water-saving, economic benefit, and environmental benefits objectives, respectively, compared to the actual scenario. The optimal cropping pattern suggested increasing the proportion of crops with high profit and low fertilizer (i.e., rape) during the dry season, and crops with low irrigation dependency (i.e., maize) during the rainy season. This study provides a scientific methodology and guidelines for decision-makers to balance the trade-offs in water–economy–environment cycles in agricultural sustainability.