IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v7y2015i11p14802-14833d58397.html
   My bibliography  Save this article

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

Listed:
  • 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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/7/11/14802/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2071-1050/7/11/14802/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. 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.
    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. 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.
    4. 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.
    5. 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.
    6. 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.
    7. 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.
    8. 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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. James D. A. Millington & Hang Xiong & Steve Peterson & Jeremy Woods, 2017. "Integrating Modelling Approaches for Understanding Telecoupling: Global Food Trade and Local Land Use," Land, MDPI, vol. 6(3), pages 1-18, August.
    2. Coronese, Matteo & Occelli, Martina & Lamperti, Francesco & Roventini, Andrea, 2023. "AgriLOVE: Agriculture, land-use and technical change in an evolutionary, agent-based model," Ecological Economics, Elsevier, vol. 208(C).
    3. Müller-Hansen, Finn & Heitzig, Jobst & Donges, Jonathan & Cardoso, Manoel F. & Dalla-Nora, Eloi L. & Andrade, Pedro R. & Kurths, Jürgen & Thonicke, Kirsten, 2019. "Can intensification of cattle ranching reduce deforestation in the Amazon? Insights from an agent-based social-ecological model," SocArXiv x5q9j, Center for Open Science.
    4. Noeldeke, Beatrice & Winter, Etti & Ntawuhiganayo, Elisée Bahati, 2022. "Representing human decision-making in agent-based simulation models: Agroforestry adoption in rural Rwanda," Ecological Economics, Elsevier, vol. 200(C).
    5. Nicholas R. Magliocca, 2020. "Agent-Based Modeling for Integrating Human Behavior into the Food–Energy–Water Nexus," Land, MDPI, vol. 9(12), pages 1-25, December.
    6. Bert, Federico E. & Rovere, Santiago L. & Macal, Charles M. & North, Michael J. & Podestá, Guillermo P., 2014. "Lessons from a comprehensive validation of an agent based-model: The experience of the Pampas Model of Argentinean agricultural systems," Ecological Modelling, Elsevier, vol. 273(C), pages 284-298.
    7. Robert Huber & Hang Xiong & Kevin Keller & Robert Finger, 2022. "Bridging behavioural factors and standard bio‐economic modelling in an agent‐based modelling framework," Journal of Agricultural Economics, Wiley Blackwell, vol. 73(1), pages 35-63, February.
    8. Sumanta Prakash Shee & Ramkrishna Maiti, 2019. "Land acquisition, livelihood and income: the case of JSW Bengal Steel Plant at Salboni Block, Paschim Medinipur, West Bengal, India," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 21(6), pages 2997-3014, December.
    9. Fraser J. Morgan & Philip Brown & Adam J. Daigneault, 2015. "Simulation vs. Definition: Differing Approaches to Setting Probabilities for Agent Behaviour," Land, MDPI, vol. 4(4), pages 1-24, September.
    10. Bazzana, Davide & Foltz, Jeremy & Zhang, Ying, 2022. "Impact of climate smart agriculture on food security: An agent-based analysis," Food Policy, Elsevier, vol. 111(C).
    11. Müller-Hansen, Finn & Heitzig, Jobst & Donges, Jonathan F. & Cardoso, Manoel F. & Dalla-Nora, Eloi L. & Andrade, Pedro & Kurths, Jürgen & Thonicke, Kirsten, 2019. "Can Intensification of Cattle Ranching Reduce Deforestation in the Amazon? Insights From an Agent-based Social-Ecological Model," Ecological Economics, Elsevier, vol. 159(C), pages 198-211.
    12. Berger, Thomas & Schreinemachers, Pepijn, 2006. "From Bioeconomic Farm Models to Multi-Agent Systems: Challenges for Parameterization and Validation," 2006 Annual Meeting, August 12-18, 2006, Queensland, Australia 25577, International Association of Agricultural Economists.
    13. Berger, Thomas & Schreinemachers, Pepijn & Woelcke, Johannes, 2006. "Multi-agent simulation for the targeting of development policies in less-favored areas," Agricultural Systems, Elsevier, vol. 88(1), pages 28-43, April.
    14. Barbier, Edward B., 2012. "Natural capital, ecological scarcity and rural poverty," Policy Research Working Paper Series 6232, The World Bank.
    15. Diego Ferraro & Daniela Blanco & Sebasti'an Pessah & Rodrigo Castro, 2021. "Land use change in agricultural systems: an integrated ecological-social simulation model of farmer decisions and cropping system performance based on a cellular automata approach," Papers 2109.01031, arXiv.org, revised Sep 2021.
    16. Malawska, Anna & Topping, Christopher John, 2016. "Evaluating the role of behavioral factors and practical constraints in the performance of an agent-based model of farmer decision making," Agricultural Systems, Elsevier, vol. 143(C), pages 136-146.
    17. Stefano Balbi & Carlo Giupponi, 2009. "Reviewing agent-based modelling of socio-ecosystems: a methodology for the analysis of climate change adaptation and sustainability," Working Papers 2009_15, Department of Economics, University of Venice "Ca' Foscari".
    18. Chen, Serena H. & Jakeman, Anthony J. & Norton, John P., 2008. "Artificial Intelligence techniques: An introduction to their use for modelling environmental systems," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 78(2), pages 379-400.
    19. Claudia Parra Paitan & Peter H. Verburg, 2019. "Methods to Assess the Impacts and Indirect Land Use Change Caused by Telecoupled Agricultural Supply Chains: A Review," Sustainability, MDPI, vol. 11(4), pages 1-24, February.
    20. Yang Chen & Martha M. Bakker & Arend Ligtenberg & Arnold K. Bregt, 2016. "How Are Feedbacks Represented in Land Models?," Land, MDPI, vol. 5(3), pages 1-20, September.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:7:y:2015:i:11:p:14802-14833:d:58397. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.