College of Water Resources and Civil Engineering, China Agricultural University, Beijing, 100083, China.
Key Laboratory for City Cluster Environmental Safety and Green Development of the Ministry of Education, School of Ecology, Environment and Resources, Guangdong University of Technology, Guangzhou, 510006, China.
J Environ Manage. 2024 Jan 1;349:119322. doi: 10.1016/j.jenvman.2023.119322. Epub 2023 Oct 31.
Optimization of crop structure is an efficient way to reduce greenhouse gas (GHGs) from agriculture production. However, carbon footprint have rarely been incorporated into previous planting structure optimization models due to the challenges of assessing the spatial and temporal distribution of agricultural carbon footprint for multiple crops in irrigated districts. In addition, previous planting structure models suffered from strong subjectivity in objective function determination, and the obtained non-dominated solution set offered difficulties to decision-makers in selecting specific implementation options. To fill such gaps, an integrated accounting-assessment-optimization-decision making (AAODM) approach was proposed, which remedies the shortcomings of previous crop planting structure optimization models in carbon footprint mitigation, and overcomes the subjectivity of objective function determination and the difficulty in selecting specific implementation options. Firstly, life cycle assessment (LCA) method was used to account for the multi-year agricultural carbon footprints of multiple crops in the irrigation district. The optimization objective functions of planting structure optimization models can then be determined based on the assessment method of carbon footprint influencing factors. Next, the Non-dominated Sorting Genetic Algorithm-II (NSGA-II) was used to generate a non-dominated solution set of the optimization model. The optimal planting structure can be finally obtained based on decision making methods by determining the maximum harmonic mean (HM) and knee points (KPs) of the non-dominated solution set. The developed AAODM approach was then applied to a case study of agricultural crop management in Bayan Nur City, China. The results showed that the level of economic development was a key factor influencing the increase in carbon footprint in Bayan Nur City over the past 20 years. The regulation of the level of economic development would significantly influence the agricultural carbon footprint in Bayan Nur City. Moreover, two optimal crop cultivation patterns were provided for decision-makers by selecting solutions from the Pareto front with decision making methods. The comparison results with other methods showed that the solutions obtained by NSGA-II were superior to MOPSO in terms of carbon reduction. The developed AAODM approach for agricultural GHG mitigation could help agricultural production systems in achieving low carbon emissions and high efficiency.
优化作物结构是减少农业生产温室气体（GHG）排放的有效途径。然而，由于评估灌溉区多种作物农业碳足迹的时空分布具有挑战性，以前的种植结构优化模型很少将碳足迹纳入其中。此外，以前的种植结构模型在目标函数确定方面存在很强的主观性，并且获得的非支配解集在为决策者选择具体实施选项方面带来了困难。为了填补这些空白，提出了一种综合核算-评估-优化-决策（AAODM）方法，该方法弥补了以前作物种植结构优化模型在减少碳足迹方面的不足，克服了目标函数确定的主观性和具体实施选项选择的困难。首先，使用生命周期评估（LCA）方法核算灌溉区多年多种作物的农业碳足迹。然后，可以根据碳足迹影响因素的评估方法确定种植结构优化模型的优化目标函数。接下来，使用非支配排序遗传算法-II（NSGA-II）生成优化模型的非支配解集。最后，可以根据确定非支配解集的最大调和平均值（HM）和拐点（KPs）的决策方法来获得最优种植结构。然后，将所开发的 AAODM 方法应用于中国巴彦淖尔市农业作物管理的案例研究。结果表明，过去 20 年来，经济发展水平是影响巴彦淖尔市碳足迹增加的关键因素。经济发展水平的调节将显著影响巴彦淖尔市的农业碳足迹。此外，通过使用决策方法从帕累托前沿选择解决方案，为决策者提供了两种最优的作物种植模式。与其他方法的比较结果表明，在减少碳排放方面，NSGA-II 获得的解决方案优于 MOPSO。用于农业 GHG 减排的 AAODM 方法可以帮助农业生产系统实现低碳排放和高效率。
