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A bi-level multiobjective stochastic approach for supporting environment-friendly agricultural planting strategy formulation

Author:
Zhang, Fan, Yue, Qiong, Engel, Bernard A., Guo, Shanshan, Guo, Ping, Li, Xiaolin
Source:
The Science of the total environment 2019 pp. 133593
ISSN:
0048-9697
Subject:
carbon dioxide, decision making, ecosystem services, entropy, environmental impact, greenhouse gas emissions, irrigation water, least squares, models, planting, pollution, pollution control, runoff, semiarid zones, sustainable development, water use efficiency, watersheds, China
Abstract:
In this study, integration of analytic hierarchy process method and entropy method (AHP-EW) for quantifying the knowledge and experience accumulated by regional managers as well as the socioeconomic situation, the partial least squares regression (PLS) for reflecting the relationship between irrigation water use efficiency and agronomic inputs, and the ecosystem service value for measuring environmental impacts of changing crop planting area were considered in one framework simultaneously. With help of these efforts, a bi-level multiobjective stochastic approach to improve irrigation water use efficiency and decrease the pollution production of agronomic measures in the process of agricultural production. The proposed framework integrate bi-level multiobjective programming and stochastic expectation programming to not only make tradeoffs among multiple concerns from two-level decision makers, but also deal with the randomness of runoff. Then, the proposed approach was applied to a real-world case in the middle reaches of the Heihe River basin, northwest China. Results show that the developed approach can improve irrigation water use efficiency, reduce CO2 emission, expand ecosystem service values, and provide more profitable and environment-friendly agricultural planting strategies to decision makers, which can further contribute to the sustainable development of agriculture. Furthermore, by comparing the bi-level multiobjective stochastic programming (BMSP) model with the other six models originated from developed model, it can be found that 1) the single objective model can obtain the best value of that objective, but cannot readily consider other important aspects; 2) the multiobjective models can make tradeoffs among multiple objectives; 3) the BMSP model can reflect the leader-follower relationship in the optimization process. The approach is applicable for arid and semiarid regions that face similar problems to determine agricultural planting strategies.
Agid:
6552657