Development of an assessment-based planting structure optimization model for mitigating agricultural greenhouse gas emissions
Copyright © 2023 Elsevier Ltd. All rights reserved..
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.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2024 2023 |
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| Erschienen: |
2024 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:349 |
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| Enthalten in: |
Journal of environmental management - 349(2023) vom: 01. Jan., Seite 119322 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Han, Yuhan [VerfasserIn] |
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| Links: |
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| Themen: |
7440-44-0 |
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| Anmerkungen: |
Date Completed 30.11.2023 Date Revised 30.11.2023 published: Print-Electronic Citation Status MEDLINE |
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| doi: |
10.1016/j.jenvman.2023.119322 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| 520 | |a Copyright © 2023 Elsevier Ltd. All rights reserved. | ||
| 520 | |a 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 | ||
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a Carbon footprint | |
| 650 | 4 | |a Crop planting structure | |
| 650 | 4 | |a Irrigation district | |
| 650 | 4 | |a Life cycle assessment | |
| 650 | 4 | |a Multi-objective optimization | |
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| 650 | 7 | |a Carbon |2 NLM | |
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| 700 | 1 | |a Tan, Qian |e verfasserin |4 aut | |
| 700 | 1 | |a Zhang, Tong |e verfasserin |4 aut | |
| 700 | 1 | |a Wang, Shuping |e verfasserin |4 aut | |
| 700 | 1 | |a Zhang, Tianyuan |e verfasserin |4 aut | |
| 700 | 1 | |a Zhang, Shan |e verfasserin |4 aut | |
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