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A multistage irrigation water allocation model for agricultural land-use planning under uncertainty

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  • Dai, Z.Y.
  • Li, Y.P.

Abstract

In practical agricultural water management problems, fluctuating water availabilities and demands, varying crop yields and economic profits, as well as changing irrigation patterns in both temporal and spatial scales are challenged decision makers. These challenges are being further compounded by rapid socioeconomic development associated with increased food requirement and decreased resources accessibility. A multistage irrigation water allocation (MIWA) model is developed for agricultural water management and cropland use planning in response to such complexities. The MIWA model is derived from incorporating interval parameters within a multistage stochastic programming (MSP) framework, such that uncertainties expressed as interval parameters and probability distributions can be tackled, and the real-time dynamic irrigation water management can be conducted. It can also support the analysis of various policy scenarios that are associated with different levels of economic consequences when the pre-regulated crop targets are violated over a multistage context. The MIWA model is then applied to a real case of planning agricultural water management and cropland use pattern in Zhangweinan River Basin, which is one of the driest regions in China and faces serious water scarcity. Solutions of irrigation targets for multiple crops as well as actual water-allocation patterns in different growth stages can help determine optimized water and land use in agricultural system, which could hedge appropriately against future available water levels in more profitable and sustainable ways.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:agiwat:v:129:y:2013:i:c:p:69-79
    DOI: 10.1016/j.agwat.2013.07.013
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    5. Chen, Shu & Shao, Dongguo & Gu, Wenquan & Xu, Baoli & Li, Haoxin & Fang, Longzhang, 2017. "An interval multistage water allocation model for crop different growth stages under inputs uncertainty," Agricultural Water Management, Elsevier, vol. 186(C), pages 86-97.
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    13. Shukui Tan & Lu Zhang & Min Zhou & Yanan Li & Siliang Wang & Bing Kuang & Xiang Luo, 2017. "A hybrid mathematical model for urban land-use planning in association with environmental–ecological consideration under uncertainty," Environment and Planning B, , vol. 44(1), pages 54-79, January.
    14. Elmira Valipour & Hamed Ketabchi & Reza Safari shali & Saeed Morid, 2023. "Equity, Social Welfare, and Economic Benefit Efficiency in the Optimal Allocation of Coastal Groundwater Resources," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 37(8), pages 2969-2990, June.
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    16. Min Zhou & Shukui Tan & Lizao Tao & Xiangbo Zhu & Ghulam Akhmat, 2015. "An interval fuzzy land-use allocation model (IFLAM) for Beijing in association with environmental and ecological consideration under uncertainty," Quality & Quantity: International Journal of Methodology, Springer, vol. 49(6), pages 2269-2290, November.
    17. Li, Mo & Guo, Ping & Singh, Vijay P., 2016. "An efficient irrigation water allocation model under uncertainty," Agricultural Systems, Elsevier, vol. 144(C), pages 46-57.
    18. Lina Zhang & Xiaoling Zhang & Fengping Wu & Qinghua Pang, 2020. "Basin Initial Water Rights Allocation under Multiple Uncertainties: a Trade-off Analysis," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 34(3), pages 955-988, February.
    19. Hamze, Mohamad & Cheviron, Bruno & Baghdadi, Nicolas & Lo, Madiop & Courault, Dominique & Zribi, Mehrez, 2023. "Detection of irrigation dates and amounts on maize plots from the integration of Sentinel-2 derived Leaf Area Index values in the Optirrig crop model," Agricultural Water Management, Elsevier, vol. 283(C).

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