IDEAS home Printed from https://ideas.repec.org/a/eee/agiwat/v298y2024ics037837742400180x.html
   My bibliography  Save this article

Coupling behavioral economics and water management policies for agricultural land-use planning in basin irrigation districts: Agent-based socio-hydrological modeling and application

Author

Listed:
  • Wang, Shunke
  • Chang, Jingjing
  • Xue, Jie
  • Sun, Huaiwei
  • Zeng, Fanjiang
  • Liu, Lei
  • Liu, Xin
  • Li, Xinxin

Abstract

Optimizing the cropping structure is of great significance for ensuring efficient water use and ecological sustainability in irrigation districts experiencing water shortages. However, how farmers choose the crops to plant under the intervention of factors such as behavioral economics and policies has been less considered in agricultural land-use planning. This study proposes an agent-based socio-hydrological model embedding a geographic information system (ABSHM-GIS). In ABSHM-GIS, a bottom-up research methodology coupled with farmers’ behavioral economics and government policies is used to examine the factors influencing farmers’ cropping structure and the impact of cropping decisions on farmers’ income and the spatial and temporal distributions of water resources. In this study, ABSHM-GIS was applied in the context of planting structure and crop subsidy policy in Qira oasis, Xinjiang, China as a case study area. Results showed that government intervention in cropping structure was effective in reducing water use for agricultural irrigation (18–30 million m3/year) relative to farmer intervention, but it also reduced farmer returns (17,850–25,860 yuan/ha). During the period of agricultural irrigation, regulating the increase in the lower residual volume of irrigation gates 2–4 in the range of 666–869 m3/day and that of irrigation gates 5–9 in the range of 211–342 m3/day could effectively ensure the even spatial distribution of surface water and groundwater. In the planting structure, an increase in government subsidy by 300 yuan/ha could save an average of 2 million m3/year of water resources for ecological water use to ensure ecological safety. The behavioral economics showed that the learning factor was the main behavioral factor that changed the decision-making behavior of farmers. The ABSHM-GIS model proposed in this paper could help researchers reveal the causes and provide insights into the emergence of macro-patterns, such as changes in cropping structure and irrigated agriculture, as a result of microfarmers’ economic behavior in a bottom-up research approach.

Suggested Citation

  • Wang, Shunke & Chang, Jingjing & Xue, Jie & Sun, Huaiwei & Zeng, Fanjiang & Liu, Lei & Liu, Xin & Li, Xinxin, 2024. "Coupling behavioral economics and water management policies for agricultural land-use planning in basin irrigation districts: Agent-based socio-hydrological modeling and application," Agricultural Water Management, Elsevier, vol. 298(C).
  • Handle: RePEc:eee:agiwat:v:298:y:2024:i:c:s037837742400180x
    DOI: 10.1016/j.agwat.2024.108845
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S037837742400180X
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.agwat.2024.108845?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Xiaoling Su & Jianfang Li & Vijay Singh, 2014. "Optimal Allocation of Agricultural Water Resources Based on Virtual Water Subdivision in Shiyang River Basin," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 28(8), pages 2243-2257, June.
    2. Jonas Jägermeyr & Amandine Pastor & Hester Biemans & Dieter Gerten, 2017. "Reconciling irrigated food production with environmental flows for Sustainable Development Goals implementation," Nature Communications, Nature, vol. 8(1), pages 1-9, August.
    3. Fan Fan & Bei Li & Weifeng Zhang & John R. Porter & Fusuo Zhang, 2021. "Evaluation of Sustainability of Irrigated Crops in Arid Regions, China," Sustainability, MDPI, vol. 13(1), pages 1-15, January.
    4. Man Li & Wenchao Xu & Tingju Zhu, 2019. "Agricultural Water Allocation under Uncertainty: Redistribution of Water Shortage Risk," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 101(1), pages 134-153.
    5. Huang, Qiuqiong & Wang, Jinxia & Li, Yumin, 2017. "Do water saving technologies save water? Empirical evidence from North China," Journal of Environmental Economics and Management, Elsevier, vol. 82(C), pages 1-16.
    6. Roobavannan, M. & Kandasamy, J. & Pande, S. & Vigneswaran, S. & Sivapalan, M., 2020. "Sustainability of agricultural basin development under uncertain future climate and economic conditions: A socio-hydrological analysis," Ecological Economics, Elsevier, vol. 174(C).
    7. Ghazali, Mahboubeh & Honar, Tooraj & Nikoo, Mohammad Reza, 2018. "A hybrid TOPSIS-agent-based framework for reducing the water demand requested by stakeholders with considering the agents’ characteristics and optimization of cropping pattern," Agricultural Water Management, Elsevier, vol. 199(C), pages 71-85.
    8. 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.
    9. Dai, Z.Y. & Li, Y.P., 2013. "A multistage irrigation water allocation model for agricultural land-use planning under uncertainty," Agricultural Water Management, Elsevier, vol. 129(C), pages 69-79.
    10. Santosh Kaini & Santosh Nepal & Saurav Pradhananga & Ted Gardner & Ashok K. Sharma, 2021. "Impacts of climate change on the flow of the transboundary Koshi River, with implications for local irrigation," International Journal of Water Resources Development, Taylor & Francis Journals, vol. 37(6), pages 929-954, November.
    11. Yuan, Shiwei & Li, Xin & Du, Erhu, 2021. "Effects of farmers’ behavioral characteristics on crop choices and responses to water management policies," Agricultural Water Management, Elsevier, vol. 247(C).
    12. Alireza Nouri & Bahram Saghafian & Majid Delavar & Mohammad Reza Bazargan-Lari, 2019. "Agent-Based Modeling for Evaluation of Crop Pattern and Water Management Policies," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 33(11), pages 3707-3720, September.
    13. Mansour, Shawky & Al-Belushi, Mohammed & Al-Awadhi, Talal, 2020. "Monitoring land use and land cover changes in the mountainous cities of Oman using GIS and CA-Markov modelling techniques," Land Use Policy, Elsevier, vol. 91(C).
    14. Lisa Huber & Nico Bahro & Georg Leitinger & Ulrike Tappeiner & Ulrich Strasser, 2019. "Agent-Based Modelling of a Coupled Water Demand and Supply System at the Catchment Scale," Sustainability, MDPI, vol. 11(21), pages 1-15, November.
    15. Abbas Mirzaei & Mansour Zibaei, 2021. "Water Conflict Management between Agriculture and Wetland under Climate Change: Application of Economic-Hydrological-Behavioral Modelling," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 35(1), pages 1-21, January.
    16. Barton, D.N. & Saloranta, T. & Moe, S.J. & Eggestad, H.O. & Kuikka, S., 2008. "Bayesian belief networks as a meta-modelling tool in integrated river basin management -- Pros and cons in evaluating nutrient abatement decisions under uncertainty in a Norwegian river basin," Ecological Economics, Elsevier, vol. 66(1), pages 91-104, May.
    17. Leonardo D. Garcia & Camilo Lozoya & Antonio Favela-Contreras & Emanuele Giorgi, 2023. "A Comparative Analysis between Heuristic and Data-Driven Water Management Control for Precision Agriculture Irrigation," Sustainability, MDPI, vol. 15(14), pages 1-14, July.
    18. Jule Thober & Birgit Müller & Jürgen Groeneveld & Volker Grimm, 2017. "Agent-Based Modelling of Social-Ecological Systems: Achievements, Challenges, and a Way Forward," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 20(2), pages 1-8.
    19. Alvaro Calzadilla & Katrin Rehdanz & Richard S.J. Tol, 2011. "Water scarcity and the impact of improved irrigation management: a computable general equilibrium analysis," Agricultural Economics, International Association of Agricultural Economists, vol. 42(3), pages 305-323, May.
    Full references (including those not matched with items on IDEAS)

    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. Huber, Robert & Bakker, Martha & Balmann, Alfons & Berger, Thomas & Bithell, Mike & Brown, Calum & Grêt-Regamey, Adrienne & Xiong, Hang & Le, Quang Bao & Mack, Gabriele & Meyfroidt, Patrick & Millingt, 2018. "Representation of decision-making in European agricultural agent-based models," Agricultural Systems, Elsevier, vol. 167(C), pages 143-160.
    2. 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).
    3. 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.
    4. 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.
    5. Yuan, Shiwei & Li, Xin & Du, Erhu, 2021. "Effects of farmers’ behavioral characteristics on crop choices and responses to water management policies," Agricultural Water Management, Elsevier, vol. 247(C).
    6. Hua Xing & Shuhong Mo & Xiaoyan Liang & Ying Li, 2021. "Water Resources Allocation Based on Complex Adaptive System Theory in the Inland River Irrigation District," Sustainability, MDPI, vol. 13(15), pages 1-19, July.
    7. F. LeRon Shults & Wesley J. Wildman, 2020. "Human Simulation and Sustainability: Ontological, Epistemological, and Ethical Reflections," Sustainability, MDPI, vol. 12(23), pages 1-16, December.
    8. An, Li & Grimm, Volker & Sullivan, Abigail & Turner II, B.L. & Malleson, Nicolas & Heppenstall, Alison & Vincenot, Christian & Robinson, Derek & Ye, Xinyue & Liu, Jianguo & Lindkvist, Emilie & Tang, W, 2021. "Challenges, tasks, and opportunities in modeling agent-based complex systems," Ecological Modelling, Elsevier, vol. 457(C).
    9. Anbari, Mohammad Javad & Zarghami, Mahdi & Nadiri, Ata-Allah, 2021. "An uncertain agent-based model for socio-ecological simulation of groundwater use in irrigation: A case study of Lake Urmia Basin, Iran," Agricultural Water Management, Elsevier, vol. 249(C).
    10. Bourceret, Amélie & Amblard, Laurence & Mathias, Jean-Denis, 2022. "Adapting the governance of social–ecological systems to behavioural dynamics: An agent-based model for water quality management using the theory of planned behaviour," Ecological Economics, Elsevier, vol. 194(C).
    11. Meike Will & Jürgen Groeneveld & Karin Frank & Birgit Müller, 2021. "Informal risk-sharing between smallholders may be threatened by formal insurance: Lessons from a stylized agent-based model," PLOS ONE, Public Library of Science, vol. 16(3), pages 1-18, March.
    12. Will, Meike & Groeneveld, Jürgen & Lenel, Friederike & Frank, Karin & Müller, Birgit, 2023. "Determinants of Household Vulnerability in Networks with Formal Insurance and Informal Risk-Sharing," Ecological Economics, Elsevier, vol. 212(C).
    13. Lisa Huber & Nico Bahro & Georg Leitinger & Ulrike Tappeiner & Ulrich Strasser, 2019. "Agent-Based Modelling of a Coupled Water Demand and Supply System at the Catchment Scale," Sustainability, MDPI, vol. 11(21), pages 1-15, November.
    14. Guangyao Deng & Liujuan Wang & Yanan Song, 2015. "Effect of Variation of Water-Use Efficiency on Structure of Virtual Water Trade - Analysis Based on Input–Output Model," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 29(8), pages 2947-2965, June.
    15. Ficko, Andrej & Boncina, Andrej, 2013. "Probabilistic typology of management decision making in private forest properties," Forest Policy and Economics, Elsevier, vol. 27(C), pages 34-43.
    16. Disha Gupta, 2023. "Free power, irrigation, and groundwater depletion: Impact of farm electricity policy of Punjab, India," Agricultural Economics, International Association of Agricultural Economists, vol. 54(4), pages 515-541, July.
    17. Dinar, Ariel, 2012. "Economy-wide implications of direct and indirect policy interventions in the water sector: lessons from recent work and future research needs," Policy Research Working Paper Series 6068, The World Bank.
    18. Baccar, Mariem & Raynal, Hélène & Sekhar, Muddu & Bergez, Jacques-Eric & Willaume, Magali & Casel, Pierre & Giriraj, P. & Murthy, Sanjeeva & Ruiz, Laurent, 2023. "Dynamics of crop category choices reveal strategies and tactics used by smallholder farmers in India to cope with unreliable water availability," Agricultural Systems, Elsevier, vol. 211(C).
    19. Bingkui Qiu & Shasha Lu & Min Zhou & Lu Zhang & Yu Deng & Ci Song & Zuo Zhang, 2015. "A Hybrid Inexact Optimization Method for Land-Use Allocation in Association with Environmental/Ecological Requirements at a Watershed Level," Sustainability, MDPI, vol. 7(4), pages 1-25, April.
    20. Gianluca Fabiani & Nikolaos Evangelou & Tianqi Cui & Juan M. Bello-Rivas & Cristina P. Martin-Linares & Constantinos Siettos & Ioannis G. Kevrekidis, 2024. "Task-oriented machine learning surrogates for tipping points of agent-based models," Nature Communications, Nature, vol. 15(1), pages 1-13, December.

    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:eee:agiwat:v:298:y:2024:i:c:s037837742400180x. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/agwat .

    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.