Water and energy are critical yet interconnected resources in agriculture, linking to land use, crop production, and greenhouse gas (GHG) emissions within the Water-Energy-Food (WEF) and Carbon nexus. This study develops a single-objective constrained nonlinear programming model to maximize total cropping profit by optimizing irrigation water and land allocation, while internalizing relevant environmental costs. The profit function integrates crop revenues minus variable costs, including water, energy, and a carbon price applied to GHG emissions. Crop yield is simulated using Stewart’s water production functions; energy use is linked to irrigation water via diesel pumping; and emissions are quantified for both irrigation and all other farm operations. Constraints include total water availability, maximum irrigation rates, and irrigated area limits. Applied to a case study of cotton-wheat rotation in Toowoomba region, Australia, using government data and expert interviews, the model demonstrates optimal allocations for different resources. Under current costs and a 38.92 GL water limit, the optimum allocation is 72% (7,768 ha) of irrigated area to wheat (2.02 ML/ha) and 28% (3,003 ha) to cotton (7.74 ML/ha). Gross margins are AUD 4,132/ha for cotton and AUD 1,584/ha for wheat. Total GHG emissions are 20.91 ktCO 2 e (wheat) and 9.77 ktCO 2 e (cotton), with intensities of 2.69 tCO 2 e/ha (wheat) and 3.25 tCO 2 e/ha (cotton). The model provides a transferable tool for quantifying trade-offs between profitability, resource efficiency, and environmental performance in irrigated cropping systems.
Gao et al. (Fri,) studied this question.