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Simulation-optimization based real-time irrigation scheduling: A human-machine interactive method enhanced by data assimilation

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  • Li, Xuemin
  • Zhang, Jingwen
  • Cai, Ximing
  • Huo, Zailin
  • Zhang, Chenglong

Abstract

Efficient irrigation scheduling is crucial for both improving crop production and saving irrigation water use in arid/semi-arid agricultural regions threatened by water shortage and soil salinity. However, irrigation scheduling optimization is hindered by the uncertainties of data and optimization model, and adopting the optimal irrigation scheduling is subject to farmers' acceptance. To effectively tackle these challenges, this paper presents a novel human-machine interactive framework for real-time irrigation scheduling (RIS). The developed modeling framework couples a simulation-optimization model, irrigation farmers, and a data assimilation procedure within the human-machine interactive framework for RIS. The proposed approach is capable of: 1) searching optimal irrigation scheduling through the simulation-optimization model; 2) making actual irrigation decisions based on farmers' experiences, knowledge, behaviors, or optimal solutions; and 3) updating soil water content based on the model simulations and real-time observations at each time period. The RIS is applied to a real-world case in a typical arid agricultural region of China. Based on the comparisons with historical irrigation records and a tradition simulation-optimization model, the proposed RIS can not only achieve higher economic benefit with less irrigation water allocation quotas, but also improve irrigation efficiency. This study contributes to the methodology by integrating computer model, real-time observations and farmers' experiences to optimization modeling framework for supporting sustainable irrigation water management.

Suggested Citation

  • Li, Xuemin & Zhang, Jingwen & Cai, Ximing & Huo, Zailin & Zhang, Chenglong, 2023. "Simulation-optimization based real-time irrigation scheduling: A human-machine interactive method enhanced by data assimilation," Agricultural Water Management, Elsevier, vol. 276(C).
    • Handle: RePEc:eee:agiwat:v:276:y:2023:i:c:s0378377422006060
    DOI: 10.1016/j.agwat.2022.108059
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    References listed on IDEAS

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    1. Li, Xuemin & Zhang, Chenglong & Huo, Zailin & Adeloye, Adebayo J., 2020. "A sustainable irrigation water management framework coupling water-salt processes simulation and uncertain optimization in an arid area," Agricultural Water Management, Elsevier, vol. 231(C).
    2. Karami, Ezatollah, 2006. "Appropriateness of farmers' adoption of irrigation methods: The application of the AHP model," Agricultural Systems, Elsevier, vol. 87(1), pages 101-119, January.
    3. Li, Dazhi & Hendricks Franssen, Harrie-Jan & Han, Xujun & Jiménez-Bello, Miguel Angel & Martínez Alzamora, Fernando & Vereecken, Harry, 2018. "Evaluation of an operational real-time irrigation scheduling scheme for drip irrigated citrus fields in Picassent, Spain," Agricultural Water Management, Elsevier, vol. 208(C), pages 465-477.
    4. Bontemps, Christophe & Couture, Stéphane, 2002. "Irrigation water demand for the decision maker," Environment and Development Economics, Cambridge University Press, vol. 7(4), pages 643-657, October.
    5. Playan, Enrique & Mateos, Luciano, 2006. "Modernization and optimization of irrigation systems to increase water productivity," Agricultural Water Management, Elsevier, vol. 80(1-3), pages 100-116, February.
    6. Xu, Xu & Huang, Guanhua & Qu, Zhongyi & Pereira, Luis S., 2010. "Assessing the groundwater dynamics and impacts of water saving in the Hetao Irrigation District, Yellow River basin," Agricultural Water Management, Elsevier, vol. 98(2), pages 301-313, December.
    7. Li, Jiang & Song, Jian & Li, Mo & Shang, Songhao & Mao, Xiaomin & Yang, Jian & Adeloye, Adebayo J., 2018. "Optimization of irrigation scheduling for spring wheat based on simulation-optimization model under uncertainty," Agricultural Water Management, Elsevier, vol. 208(C), pages 245-260.
    8. Rose, David C. & Parker, Caroline & Fodery, Joe & Park, Caroline & Sutherland, William J. & Dicks, Lynn V., 2018. "Involving stakeholders in agricultural decision support systems: Improving user-centred design," International Journal of Agricultural Management, Institute of Agricultural Management, vol. 6(3-4), January.
    9. Fazlullah Akhtar & Bernhard Tischbein & Usman Awan, 2013. "Optimizing Deficit Irrigation Scheduling Under Shallow Groundwater Conditions in Lower Reaches of Amu Darya River Basin," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 27(8), pages 3165-3178, June.
    10. Rose, David C. & Sutherland, William J. & Parker, Caroline & Lobley, Matt & Winter, Michael & Morris, Carol & Twining, Susan & Ffoulkes, Charles & Amano, Tatsuya & Dicks, Lynn V., 2016. "Decision support tools for agriculture: Towards effective design and delivery," Agricultural Systems, Elsevier, vol. 149(C), pages 165-174.
    11. Garg, N.K. & Dadhich, Sushmita M., 2014. "Integrated non-linear model for optimal cropping pattern and irrigation scheduling under deficit irrigation," Agricultural Water Management, Elsevier, vol. 140(C), pages 1-13.
    12. Zwart, Sander J. & Bastiaanssen, Wim G. M., 2004. "Review of measured crop water productivity values for irrigated wheat, rice, cotton and maize," Agricultural Water Management, Elsevier, vol. 69(2), pages 115-133, September.
    13. Ren, Dongyang & Xu, Xu & Engel, Bernard & Huang, Quanzhong & Xiong, Yunwu & Huo, Zailin & Huang, Guanhua, 2019. "Hydrological complexities in irrigated agro-ecosystems with fragmented land cover types and shallow groundwater: Insights from a distributed hydrological modeling method," Agricultural Water Management, Elsevier, vol. 213(C), pages 868-881.
    14. Ines, Amor V.M. & Honda, Kiyoshi & Das Gupta, Ashim & Droogers, Peter & Clemente, Roberto S., 2006. "Combining remote sensing-simulation modeling and genetic algorithm optimization to explore water management options in irrigated agriculture," Agricultural Water Management, Elsevier, vol. 83(3), pages 221-232, June.
    15. Epperson, James E. & Hook, James E. & Mustafa, Yasmin R., 1993. "Dynamic programming for improving irrigation scheduling strategies of maize," Agricultural Systems, Elsevier, vol. 42(1-2), pages 85-101.
    16. Wen, Yeqiang & Shang, Songhao & Yang, Jian, 2017. "Optimization of irrigation scheduling for spring wheat with mulching and limited irrigation water in an arid climate," Agricultural Water Management, Elsevier, vol. 192(C), pages 33-44.
    17. Linker, Raphael & Ioslovich, Ilya & Sylaios, Georgios & Plauborg, Finn & Battilani, Adriano, 2016. "Optimal model-based deficit irrigation scheduling using AquaCrop: A simulation study with cotton, potato and tomato," Agricultural Water Management, Elsevier, vol. 163(C), pages 236-243.
    18. Shu Chen & Dongguo Shao & Xudong Li & Caixiu Lei, 2016. "Simulation-Optimization Modeling of Conjunctive Operation of Reservoirs and Ponds for Irrigation of Multiple Crops Using an Improved Artificial Bee Colony Algorithm," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 30(9), pages 2887-2905, July.
    19. Sun, Hong-Yong & Liu, Chang-Ming & Zhang, Xi-Ying & Shen, Yan-Jun & Zhang, Yong-Qiang, 2006. "Effects of irrigation on water balance, yield and WUE of winter wheat in the North China Plain," Agricultural Water Management, Elsevier, vol. 85(1-2), pages 211-218, September.
    20. Zhang, Fan & Guo, Shanshan & Liu, Xiao & Wang, Youzhi & Engel, Bernard A. & Guo, Ping, 2020. "Towards sustainable water management in an arid agricultural region: A multi-level multi-objective stochastic approach," Agricultural Systems, Elsevier, vol. 182(C).
    21. Lu, Yang & Chibarabada, Tendai P. & Ziliani, Matteo G. & Onema, Jean-Marie Kileshye & McCabe, Matthew F. & Sheffield, Justin, 2021. "Assimilation of soil moisture and canopy cover data improves maize simulation using an under-calibrated crop model," Agricultural Water Management, Elsevier, vol. 252(C).
    22. R. González Perea & E. Camacho Poyato & P. Montesinos & J. A. Rodríguez Díaz, 2016. "Optimization of Irrigation Scheduling Using Soil Water Balance and Genetic Algorithms," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 30(8), pages 2815-2830, June.
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