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Multi-Agent Modeling and Simulation of Farmland Use Change in a Farming–Pastoral Zone: A Case Study of Qianjingou Town in Inner Mongolia, China

Author

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  • Xuehong Bai

    (Key Laboratory for Resources Use and Environmental Remediation, Institute of Geographical Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
    University of Chinese Academy of Sciences, Beijing 100049, China
    These authors contributed equally to this work.)

  • Huimin Yan

    (Key Laboratory for Resources Use and Environmental Remediation, Institute of Geographical Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China)

  • Lihu Pan

    (School of Computer, Taiyuan University of Science and Technology, Taiyuan 030024, China
    These authors contributed equally to this work.)

  • He Qing Huang

    (Key Laboratory for Resources Use and Environmental Remediation, Institute of Geographical Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
    These authors contributed equally to this work.)

Abstract

Farmland is the most basic material condition for guaranteeing rural livelihoods and national food security, and exploring management strategies that take both stable rural livelihoods and sustainable farmland use into account has vital significance in theory and practice. Farmland is a complex and self-adaptive system that couples human and natural systems, and natural and social factors that are related to its changing process need to be considered when modeling farmland changing processes. This paper uses Qianjingou Town in the Inner Mongolian farming–pastoral zone as a study area. From the perspective of the relationship between household livelihood and farmland use, this study establishes the process mechanism of farmland use change based on questionnaire data, and constructs a multi-agent simulation model of farmland use change using the Eclipse and Repast toolbox. Through simulating the relationship between natural factors (including geographical location) and household behavior, this paper systematically simulates household farmland abandonment and rent behaviors, and accurately describes the dynamic interactions between household livelihoods and the factors related to farmland use change. These factors include natural factors (net primary productivity, road accessibility, slope and relief amplitude) and social factors (household family structures, economic development and government policies). Ultimately, this study scientifically predicts the future farmland use change trend in the next 30 years. The simulation results show that the number of abandoned and sublet farmland plots has a gradually increasing trend, and the number of non-farming households and pure-outworking households has a remarkable increasing trend, whereas the number of part-farming households and pure-farming households has a decreasing trend. Household livelihood sustainability in the study area is confronted with increasing pressure, and household non-farm employment has an increasing trend, while regional appropriate-scale agricultural management is maintained. The research results establish the theoretical foundation and a basic method for developing sustainable farmland use management that can meet the willingness of households and guarantee grain and ecological security.

Suggested Citation

  • Xuehong Bai & Huimin Yan & Lihu Pan & He Qing Huang, 2015. "Multi-Agent Modeling and Simulation of Farmland Use Change in a Farming–Pastoral Zone: A Case Study of Qianjingou Town in Inner Mongolia, China," Sustainability, MDPI, vol. 7(11), pages 1-32, November.
    • Handle: RePEc:gam:jsusta:v:7:y:2015:i:11:p:14802-14833:d:58397
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    References listed on IDEAS

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    1. Davide Natalini & Giangiacomo Bravo, 2013. "Encouraging Sustainable Transport Choices in American Households: Results from an Empirically Grounded Agent-Based Model," Sustainability, MDPI, vol. 6(1), pages 1-20, December.
    2. An, Li, 2012. "Modeling human decisions in coupled human and natural systems: Review of agent-based models," Ecological Modelling, Elsevier, vol. 229(C), pages 25-36.
    3. Holden, Stein & Shiferaw, Bekele & Pender, John, 2004. "Non-farm income, household welfare, and sustainable land management in a less-favoured area in the Ethiopian highlands," Food Policy, Elsevier, vol. 29(4), pages 369-392, August.
    4. Berger, Thomas, 2001. "Agent-based spatial models applied to agriculture: a simulation tool for technology diffusion, resource use changes and policy analysis," Agricultural Economics, Blackwell, vol. 25(2-3), pages 245-260, September.
    5. Pender, John, 2004. "Development pathways for hillsides and highlands: some lessons from Central America and East Africa," Food Policy, Elsevier, vol. 29(4), pages 339-367, August.
    6. Hare, M & Deadman, P, 2004. "Further towards a taxonomy of agent-based simulation models in environmental management," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 64(1), pages 25-40.
    7. Koczberski, Gina & Curry, George N., 2005. "Making a living: Land pressures and changing livelihood strategies among oil palm settlers in Papua New Guinea," Agricultural Systems, Elsevier, vol. 85(3), pages 324-339, September.
    8. Dingde Xu & Jifei Zhang & Golam Rasul & Shaoquan Liu & Fangting Xie & Mengtian Cao & Enlai Liu, 2015. "Household Livelihood Strategies and Dependence on Agriculture in the Mountainous Settlements in the Three Gorges Reservoir Area, China," Sustainability, MDPI, vol. 7(5), pages 1-20, April.
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    1. Huimin Yan & Lihu Pan & Zhichao Xue & Lin Zhen & Xuehong Bai & Yunfeng Hu & He-Qing Huang, 2019. "Agent-Based Modeling of Sustainable Ecological Consumption for Grasslands: A Case Study of Inner Mongolia, China," Sustainability, MDPI, vol. 11(8), pages 1-24, April.
    2. Camelia Delcea & Liviu-Adrian Cotfas & Ramona Paun, 2018. "Agent-Based Evaluation of the Airplane Boarding Strategies’ Efficiency and Sustainability," Sustainability, MDPI, vol. 10(6), pages 1-26, June.

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