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Multi-objective optimization of rice irrigation modes using ACOP-Rice model and historical meteorological data

Author

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  • Chen, Mengting
  • Linker, Raphael
  • Wu, Conglin
  • Xie, Hua
  • Cui, Yuanlai
  • Luo, Yufeng
  • Lv, Xinwei
  • Zheng, Shizong

Abstract

Current rice production in China is associated with low rainfall use efficiency. In order to increase rainfall use efficiency and develop simple water-saving irrigation modes that could be readily implemented by farmers, a new multi-objective optimization framework for irrigation modes of paddy rice was developed, based on a modified version of the AquaCrop model called ACOP-Rice model. The optimization focused on the water level at which irrigation was triggered for five growth periods and the irrigation frequency, rainfall use efficiency and yield were optimized. The procedure was tested on nine rice production cases in China for which over 60 years of historical meteorological data and irrigation guidelines were available. Analysis of the weather data showed that rainfall distribution varied greatly between the different locations and growth periods. The results obtained by following the current guidelines were compared to three optimal solutions that corresponded to minimum number of irrigation events, maximum rainfall use efficiency and “balanced” performance in which equal attention was given to rainfall use efficiency and irrigation frequency, respectively. Overall, the optimization led to lowering the water depth at which irrigation was triggered. The optimal water level after irrigation varied between the different cases, depending on the combined effects of rainfall distribution, operation constraints and length of growth period. Compared to the current guidelines, the optimized irrigation modes reduced the proportion of drainage caused by rainfall after irrigation. For optimal solutions with minimum number of irrigation events, maximum rainfall use efficiency and “balanced” performance, the number of irrigation events was reduced by 57 %, 18 % and 44 % on average (9.4, 3.0 and 7.4 fewer irrigation events per year) while on average rainfall use efficiency improved by 5 %, 19 % and 17 % without significant yield loss.

Suggested Citation

  • Chen, Mengting & Linker, Raphael & Wu, Conglin & Xie, Hua & Cui, Yuanlai & Luo, Yufeng & Lv, Xinwei & Zheng, Shizong, 2022. "Multi-objective optimization of rice irrigation modes using ACOP-Rice model and historical meteorological data," Agricultural Water Management, Elsevier, vol. 272(C).
    • Handle: RePEc:eee:agiwat:v:272:y:2022:i:c:s0378377422003705
    DOI: 10.1016/j.agwat.2022.107823
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    1. Chao Bao & Chuang-lin Fang, 2012. "Water Resources Flows Related to Urbanization in China: Challenges and Perspectives for Water Management and Urban Development," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 26(2), pages 531-552, January.
    2. Linker, Raphael, 2020. "Unified framework for model-based optimal allocation of crop areas and water," Agricultural Water Management, Elsevier, vol. 228(C).
    3. Shirazi, Sana Zeeshan & Mei, Xurong & Liu, Buchun & Liu, Yuan, 2021. "Assessment of the AquaCrop Model under different irrigation scenarios in the North China Plain," Agricultural Water Management, Elsevier, vol. 257(C).
    4. Luo, Wanqi & Chen, Mengting & Kang, Yinhong & Li, Wenping & Li, Dan & Cui, Yuanlai & Khan, Shahbaz & Luo, Yufeng, 2022. "Analysis of crop water requirements and irrigation demands for rice: Implications for increasing effective rainfall," Agricultural Water Management, Elsevier, vol. 260(C).
    5. Kelly, T.D. & Foster, T., 2021. "AquaCrop-OSPy: Bridging the gap between research and practice in crop-water modeling," Agricultural Water Management, Elsevier, vol. 254(C).
    6. Guo, Daxin & Olesen, Jørgen Eivind & Manevski, Kiril & Ma, Xiaoyi, 2021. "Optimizing irrigation schedule in a large agricultural region under different hydrologic scenarios," Agricultural Water Management, Elsevier, vol. 245(C).
    7. Xu, Junzeng & Bai, Wenhuan & Li, Yawei & Wang, Haiyu & Yang, Shihong & Wei, Zheng, 2019. "Modeling rice development and field water balance using AquaCrop model under drying-wetting cycle condition in eastern China," Agricultural Water Management, Elsevier, vol. 213(C), pages 289-297.
    8. 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.
    9. Alvar-Beltrán, J. & Heureux, A. & Soldan, R. & Manzanas, R. & Khan, B. & Dalla Marta, A., 2021. "Assessing the impact of climate change on wheat and sugarcane with the AquaCrop model along the Indus River Basin, Pakistan," Agricultural Water Management, Elsevier, vol. 253(C).
    10. 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.
    11. Liu, Xiao & Yang, Dawen, 2021. "Irrigation schedule analysis and optimization under the different combination of P and ET0 using a spatially distributed crop model," Agricultural Water Management, Elsevier, vol. 256(C).
    12. Seyed Ahmadi & Elnaz Mosallaeepour & Ali Kamgar-Haghighi & Ali Sepaskhah, 2015. "Modeling Maize Yield and Soil Water Content with AquaCrop Under Full and Deficit Irrigation Managements," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 29(8), pages 2837-2853, June.
    13. Cao, Jingjing & Tan, Junwei & Cui, Yuanlai & Luo, Yufeng, 2019. "Irrigation scheduling of paddy rice using short-term weather forecast data," Agricultural Water Management, Elsevier, vol. 213(C), pages 714-723.
    14. Akpoti, Komlavi & Dossou-Yovo, Elliott R. & Zwart, Sander J. & Kiepe, Paul, 2021. "The potential for expansion of irrigated rice under alternate wetting and drying in Burkina Faso," Agricultural Water Management, Elsevier, vol. 247(C).
    15. Chen, Mengting & Cui, Yuanlai & Wang, Xiaonan & Xie, Hengwang & Liu, Fangping & Luo, Tongyuan & Zheng, Shizong & Luo, Yufeng, 2021. "A reinforcement learning approach to irrigation decision-making for rice using weather forecasts," Agricultural Water Management, Elsevier, vol. 250(C).
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    Cited by:

    1. Hua, Keji & He, Jun & Liao, Bin & He, Tianzhong & Yang, Peng & Zhang, Lei, 2023. "Multi-objective decision-making for efficient utilization of water and fertilizer in paddy fields: A case study in Southern China," Agricultural Water Management, Elsevier, vol. 289(C).
    2. Zhao, Xueyin & Chen, Mengting & Xie, Hua & Luo, Wanqi & Wei, Guangfei & Zheng, Shizong & Wu, Conglin & Khan, Shahbaz & Cui, Yuanlai & Luo, Yufeng, 2023. "Analysis of irrigation demands of rice: Irrigation decision-making needs to consider future rainfall," Agricultural Water Management, Elsevier, vol. 280(C).

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