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Agricultural Water Management Model Based on Grey Water Footprints under Uncertainty and its Application

Author

Listed:
  • Ge Song

    (College of Water Resources & Civil Engineering, China Agricultural University, Beijing 100083, China
    State Key Laboratory of Hydraulics and Mountain River Engineering, Sichuan University, Chengdu 610065, China)

  • Chao Dai

    (School of Civil and Environmental Engineering, Nanyang Technological University, Singapore 639798, Singapore)

  • Qian Tan

    (College of Water Resources & Civil Engineering, China Agricultural University, Beijing 100083, China
    Institute of Environmental and Ecological Engineering, Guangdong University of Technology, Guangzhou 510006, China)

  • Shan Zhang

    (College of Water Resources & Civil Engineering, China Agricultural University, Beijing 100083, China)

Abstract

The grey water footprint theory was introduced into a fractional programming model to alleviate non-point source pollution and increase water-use efficiency through the adjustment of crop planting structure. The interval programming method was also incorporated within the developed framework to handle parametric uncertainties. The objective function of the model was the ratio of economic benefits to grey water footprints from crop production, and the constraints contained water availability constraints, food security constraints, planting area constraints, grey water footprint constraints and non-negative constraints. The model was applied to the Hetao Irrigation District of China. It was found that, based on the data in the year of 2016, the optimal planting plans generated from the developed model would reduce 34,400 m 3 of grey water footprints for every 100 million Yuan gained from crops. Under the optimal planting structure, the total grey water footprints would be reduced by 21.9 million m 3 , the total economic benefits from crops would be increased by 1.138 billion Yuan, and the irrigation water would be saved by 44 million m 3 . The optimal results could provide decision-makers with agricultural water use plans with reduced negative impacts on the environment and enhanced economic benefits from crops.

Suggested Citation

  • Ge Song & Chao Dai & Qian Tan & Shan Zhang, 2019. "Agricultural Water Management Model Based on Grey Water Footprints under Uncertainty and its Application," Sustainability, MDPI, vol. 11(20), pages 1-18, October.
    • Handle: RePEc:gam:jsusta:v:11:y:2019:i:20:p:5567-:d:274864
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    References listed on IDEAS

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    1. Irina Marinov & Anca Marinov, 2014. "A Coupled Mathematical Model to Predict the Influence of Nitrogen Fertilization on Crop, Soil and Groundwater Quality," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 28(15), pages 5231-5246, December.
    2. Marina Segura & Concepción Maroto & Concepción Ginestar & Baldomero Segura, 2018. "Optimization Models to Improve Estimations and Reduce Nitrogen Excretion from Livestock Production," Sustainability, MDPI, vol. 10(7), pages 1-14, July.
    3. Zhu, H. & Huang, W.W. & Huang, G.H., 2014. "Planning of regional energy systems: An inexact mixed-integer fractional programming model," Applied Energy, Elsevier, vol. 113(C), pages 500-514.
    4. Ozdemir, Mujgan S. & Saaty, Thomas L., 2006. "The unknown in decision making: What to do about it," European Journal of Operational Research, Elsevier, vol. 174(1), pages 349-359, October.
    5. Eros Borsato & Alejandro Galindo & Paolo Tarolli & Luigi Sartori & Francesco Marinello, 2018. "Evaluation of the Grey Water Footprint Comparing the Indirect Effects of Different Agricultural Practices," Sustainability, MDPI, vol. 10(11), pages 1-15, November.
    6. Qiuchi Xue & Xin Yang & Jianjun Wu & Huijun Sun & Haodong Yin & Yunchao Qu, 2019. "Urban Rail Timetable Optimization to Improve Operational Efficiency with Flexible Routing Plans: A Nonlinear Integer Programming Model," Sustainability, MDPI, vol. 11(13), pages 1-26, July.
    7. C. Dai & Y. P. Cai & W. T. Lu & H. Liu & H. C. Guo, 2016. "Conjunctive Water Use Optimization for Watershed-Lake Water Distribution System under Uncertainty: a Case Study," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 30(12), pages 4429-4449, September.
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