Green and efficient fine control of regional irrigation water use coupled with crop growth-carbon emission processes

Abstract

The effective, environmentally friendly, and optimally deployed management of regional agricultural water resources is crucial to ensuring food security and the sustainable use of water resources in the face of challenging agricultural issues, such as increasing greenhouse gas (GHG) emissions, water scarcity, and rapid population and economic growth. In this study, a multiobjective model for the optimal distribution of regional agricultural water resources was built, using the GIS-DSSAT and GIS-DNDC models, to simulate the regional spatial raster crop growth process and carbon emission process. The model allowed for the creation of synergies in both crop production and emission reduction. The model was solved using a fuzzy planning algorithm, and applied to the main Sanjiang Plain grain production area in Heilongjiang Province, yielding the best water allocation scheme and a set of planting structure adjustment schemes for the main grain crops of rice, maize, and soybeans in 1074 Sanjiang Plain response units. In contrast to the current method, which relies heavily on soil and water resources, the model developed in this paper reduced greenhouse gas (GHG) emissions by 10 %, energy consumption by 14.4 %, and regional irrigation water use by 34.48 %. It achieved the dual goal of reducing GHG emissions and conserving water, while increasing the synergy between increased regional food production and decreased emissions by 21 % compared to the status quo. Climate change will pose series of challenges to agricultural production, particularly the arable land and water resource usage. By optimizing cropping structures and irrigation systems, climate-adaptive management can not only reduce the area of arable land and water consumption, but also decrease carbon emissions; for the SSP2–4.5 and SSP3–7.0 scenarios, this trend resulted in increases in economic benefits of 2.4 % and 3.3 %, respectively, and decreases in carbon emission scenarios of 14.6 % and 20.7 %, respectively. The yields and GHG emissions of the response units were sensitive to these changes. This study offers decision-making support for the intense, effective, and low-carbon management of water resources.