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Effects of farmers’ behavioral characteristics on crop choices and responses to water management policies

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  • Yuan, Shiwei
  • Li, Xin
  • Du, Erhu

Abstract

Understanding farmers’ decision-making on crop choices and water use is essential for agricultural water resource management. However, previous studies have limited understanding of how farmers’ behavioral characteristics affect their crop choices and water use in agricultural systems. To fill this research gap, in this study we develop an agent-based model (ABM) that incorporates two types of behavioral characteristics (i.e., perception to the uncertainty in future crop prices, planting cost and precipitation, and tolerance to the variation of crop profits) to investigate their effects on crop choices and water use under the influence of water management policies. The ABM is applied to the Heihe River Basin (HRB), an arid endorheic river basin in northwestern China as a demonstration. The modeling results show that farmers with adventurous perceptions and high tolerance level (Type I) tend to choose high-profit crops. They are more likely to have a single-crop pattern with high crop profits and high water consumption. In comparison, farmers with cautious perceptions and low tolerance level (Type II) prefer steady profit crops. They typically pay more attention to the variation of crop profits, resulting in a mixed crop pattern with low crop profits and low water consumption. In addition, the two types of farmers exhibit varied responses to water management policies. The Type I farmers are more sensitive to the changes in the volume of water permits and irrigation efficiency, and as a result, are more sensitive to the changes of water management policies than the Type II farmers do. We also find that the effects of farmers’ behavioral characteristics vary at the irrigation district level, county level and entire study area level. These findings emphasize the importance of incorporating farmers’ behavioral characteristics into crop choice and water use models. The modeling result could provide policy implications for designing location-based water management policies that account for the heterogeneity in farmers’ behavioral characteristics and responses to water policies.

Suggested Citation

  • 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).
  • Handle: RePEc:eee:agiwat:v:247:y:2021:i:c:s037837742032237x
    DOI: 10.1016/j.agwat.2020.106693
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    References listed on IDEAS

    as
    1. Tian, Qing & Holland, John H. & Brown, Daniel G., 2016. "Social and economic impacts of subsidy policies on rural development in the Poyang Lake Region, China: Insights from an agent-based model," Agricultural Systems, Elsevier, vol. 148(C), pages 12-27.
    2. Lu, Zhixiang & Wei, Yongping & Xiao, Honglang & Zou, Songbing & Ren, Juan & Lyle, Clive, 2015. "Trade-offs between midstream agricultural production and downstream ecological sustainability in the Heihe River basin in the past half century," Agricultural Water Management, Elsevier, vol. 152(C), pages 233-242.
    3. Timothy G. Conley & Christopher R. Udry, 2010. "Learning about a New Technology: Pineapple in Ghana," American Economic Review, American Economic Association, vol. 100(1), pages 35-69, March.
    4. Juan He & Roderick Rejesus & Xiaoyong Zheng & Jose Yorobe, 2018. "Advantageous Selection in Crop Insurance: Theory and Evidence," Journal of Agricultural Economics, Wiley Blackwell, vol. 69(3), pages 646-668, September.
    5. Masih Akhbari & Neil Grigg, 2013. "A Framework for an Agent-Based Model to Manage Water Resources Conflicts," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 27(11), pages 4039-4052, September.
    6. Deng, Xiaohong & Xu, Zhongmin & Song, Xiaoyu & Zhou, Jian, 2017. "Transaction costs associated with agricultural water trading in the Heihe River Basin, Northwest China," Agricultural Water Management, Elsevier, vol. 186(C), pages 29-39.
    7. Guifang Li & Dingyang Zhou & Minjun Shi, 2019. "How Do Farmers Respond to Water Resources Management Policy in the Heihe River Basin of China?," Sustainability, MDPI, vol. 11(7), pages 1-19, April.
    8. 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.
    9. Seo, S. Niggol & Mendelsohn, Robert, 2008. "An analysis of crop choice: Adapting to climate change in South American farms," Ecological Economics, Elsevier, vol. 67(1), pages 109-116, August.
    10. Nilgun B. Harmancioglu, 2017. "Overview of Water Policy Developments: Pre- and Post-2015 Development Agenda," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 31(10), pages 3001-3021, August.
    11. Carr, Gemma & Potter, Robert B. & Nortcliff, Stephen, 2011. "Water reuse for irrigation in Jordan: Perceptions of water quality among farmers," Agricultural Water Management, Elsevier, vol. 98(5), pages 847-854, March.
    12. Aliasghar Montazar & H. Riazi & S. Behbahani, 2010. "Conjunctive Water Use Planning in an Irrigation Command Area," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 24(3), pages 577-596, February.
    13. Andriyas, Sanyogita & McKee, Mac, 2014. "Exploring irrigation behavior at Delta, Utah using hidden Markov models," Agricultural Water Management, Elsevier, vol. 143(C), pages 48-58.
    14. Li An & Alex Zvoleff & Jianguo Liu & William Axinn, 2014. "Agent-Based Modeling in Coupled Human and Natural Systems (CHANS): Lessons from a Comparative Analysis," Annals of the American Association of Geographers, Taylor & Francis Journals, vol. 104(4), pages 723-745, July.
    15. Gars, Jared & Ward, Patrick S., 2019. "Can differences in individual learning explain patterns of technology adoption? Evidence on heterogeneous learning patterns and hybrid rice adoption in Bihar, India," World Development, Elsevier, vol. 115(C), pages 178-189.
    16. Andrée, Bo Pieter Johannes & Diogo, Vasco & Koomen, Eric, 2017. "Efficiency of second-generation biofuel crop subsidy schemes: Spatial heterogeneity and policy design," Renewable and Sustainable Energy Reviews, Elsevier, vol. 67(C), pages 848-862.
    17. 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.
    18. Sulewski, Piotr & Kłoczko-Gajewska, Anna, 2014. "Farmers’ risk perception, risk aversion and strategies to cope with production risk: an empirical study from Poland," Studies in Agricultural Economics, Research Institute for Agricultural Economics, vol. 116(3), pages 1-8, December.
    19. Li, Jiang & Mao, Xiaomin & Li, Mo, 2017. "Modeling hydrological processes in oasis of Heihe River Basin by landscape unit-based conceptual models integrated with FEFLOW and GIS," Agricultural Water Management, Elsevier, vol. 179(C), pages 338-351.
    20. Rianne van Duinen & Tatiana Filatova & Peter Geurts & Anne van der Veen, 2015. "Empirical Analysis of Farmers' Drought Risk Perception: Objective Factors, Personal Circumstances, and Social Influence," Risk Analysis, John Wiley & Sons, vol. 35(4), pages 741-755, April.
    21. Masih Akhbari & Neil Grigg, 2015. "Managing Water Resources Conflicts: Modelling Behavior in a Decision Tool," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 29(14), pages 5201-5216, November.
    22. Pyle, David H & Turnovsky, Stephen J, 1970. "Safety-First and Expected Utility Maximization in Mean-Standard Deviation Portfolio Analysis," The Review of Economics and Statistics, MIT Press, vol. 52(1), pages 75-81, February.
    23. Venu Kandiah & Andrew R. Binder & Emily Z. Berglund, 2017. "An Empirical Agent‐Based Model to Simulate the Adoption of Water Reuse Using the Social Amplification of Risk Framework," Risk Analysis, John Wiley & Sons, vol. 37(10), pages 2005-2022, October.
    24. Rianne Duinen & Tatiana Filatova & Wander Jager & Anne Veen, 2016. "Going beyond perfect rationality: drought risk, economic choices and the influence of social networks," The Annals of Regional Science, Springer;Western Regional Science Association, vol. 57(2), pages 335-369, November.
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    2. Alam, Mohammad Faiz & McClain, M. & Sikka, Alok & Pande, S., 2022. "Understanding human-water feedbacks of interventions in agricultural systems with agent based models: a review," Papers published in Journals (Open Access), International Water Management Institute, pages 1-17(10):10.
    3. Ran Sun & James Nolan & Suren Kulshreshtha, 2022. "Agent-based modeling of policy induced agri-environmental technology adoption," SN Business & Economics, Springer, vol. 2(8), pages 1-26, August.
    4. Gaofeng Ren & Xiao Cui, 2024. "The Government–Farmer Cooperation Mechanism and Its Implementation Path to Realize the Goals of Optimizing Grain Planting Structure," Land, MDPI, vol. 13(3), pages 1-25, March.
    5. Yunxian Yan & Lingqing Wang & Jun Yang, 2022. "The Willingness and Technology Preferences of Farmers and Their Influencing Factors for Soil Remediation," Land, MDPI, vol. 11(10), pages 1-15, October.

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