IDEAS home Printed from https://ideas.repec.org/a/eee/agiwat/v272y2022ics0378377422003705.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0378377422003705
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.agwat.2022.107823?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    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. 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).
    4. 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).
    5. 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.
    6. 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).
    7. 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).
    8. 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).
    9. 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.
    10. 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).
    11. 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).
    12. 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).
    13. 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.
    14. 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.
    15. 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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. 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).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. 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).
    2. 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).
    3. Kelly, T.D. & Foster, T. & Schultz, David M., 2023. "Assessing the value of adapting irrigation strategies within the season," Agricultural Water Management, Elsevier, vol. 275(C).
    4. Zhang, Ting & Zuo, Qiang & Ma, Ning & Shi, Jianchu & Fan, Yuchuan & Wu, Xun & Wang, Lichun & Xue, Xuzhang & Ben-Gal, Alon, 2023. "Optimizing relative root-zone water depletion thresholds to maximize yield and water productivity of winter wheat using AquaCrop," Agricultural Water Management, Elsevier, vol. 286(C).
    5. Wu, Hui & Yue, Qiong & Guo, Ping & Xu, Xiaoyu & Huang, Xi, 2022. "Improving the AquaCrop model to achieve direct simulation of evapotranspiration under nitrogen stress and joint simulation-optimization of irrigation and fertilizer schedules," Agricultural Water Management, Elsevier, vol. 266(C).
    6. Serra, J. & Paredes, P. & Cordovil, CMdS & Cruz, S. & Hutchings, NJ & Cameira, MR, 2023. "Is irrigation water an overlooked source of nitrogen in agriculture?," Agricultural Water Management, Elsevier, vol. 278(C).
    7. Tsakmakis, I.D. & Kokkos, N.P. & Gikas, G.D. & Pisinaras, V. & Hatzigiannakis, E. & Arampatzis, G. & Sylaios, G.K., 2019. "Evaluation of AquaCrop model simulations of cotton growth under deficit irrigation with an emphasis on root growth and water extraction patterns," Agricultural Water Management, Elsevier, vol. 213(C), pages 419-432.
    8. Mandal, Uday & Dhar, Anirban & Panda, Sudhindra N., 2021. "Enhancement of sustainable agricultural production system by integrated natural resources management framework under climatic and operational uncertainty," Agricultural Water Management, Elsevier, vol. 252(C).
    9. Linker, Raphael & Kisekka, Isaya, 2022. "Concurrent data assimilation and model-based optimization of irrigation scheduling," Agricultural Water Management, Elsevier, vol. 274(C).
    10. Richwell Mubita Mwiya & Zhanyu Zhang & Chengxin Zheng & Ce Wang, 2020. "Comparison of Approaches for Irrigation Scheduling Using AquaCrop and NSGA-III Models under Climate Uncertainty," Sustainability, MDPI, vol. 12(18), pages 1-20, September.
    11. 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).
    12. Xu, Xianghui & Chen, Yingshan & Zhou, Yan & Liu, Wuyuan & Zhang, Xinrui & Li, Mo, 2023. "Sustainable management of agricultural water rights trading under uncertainty: An optimization-evaluation framework," Agricultural Water Management, Elsevier, vol. 280(C).
    13. Qian Li & Yan Chen & Shikun Sun & Muyuan Zhu & Jing Xue & Zihan Gao & Jinfeng Zhao & Yihe Tang, 2022. "Research on Crop Irrigation Schedules Under Deficit Irrigation—A Meta-analysis," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(12), pages 4799-4817, September.
    14. Wang, Haidong & Cheng, Minghui & Liao, Zhenqi & Guo, Jinjin & Zhang, Fucang & Fan, Junliang & Feng, Hao & Yang, Qiliang & Wu, Lifeng & Wang, Xiukang, 2023. "Performance evaluation of AquaCrop and DSSAT-SUBSTOR-Potato models in simulating potato growth, yield and water productivity under various drip fertigation regimes," Agricultural Water Management, Elsevier, vol. 276(C).
    15. Chen, Shu & Shao, Dongguo & Tan, Xuezhi & Gu, Wenquan & Lei, Caixiu, 2017. "An interval multistage classified model for regional inter- and intra-seasonal water management under uncertain and nonstationary condition," Agricultural Water Management, Elsevier, vol. 191(C), pages 98-112.
    16. Ran, Hui & Kang, Shaozhong & Li, Fusheng & Du, Taisheng & Tong, Ling & Li, Sien & Ding, Risheng & Zhang, Xiaotao, 2018. "Parameterization of the AquaCrop model for full and deficit irrigated maize for seed production in arid Northwest China," Agricultural Water Management, Elsevier, vol. 203(C), pages 438-450.
    17. Lu, Junsheng & Geng, Chenming & Cui, Xiaolu & Li, Mengyue & Chen, Shuaihong & Hu, Tiantian, 2021. "Response of drip fertigated wheat-maize rotation system on grain yield, water productivity and economic benefits using different water and nitrogen amounts," Agricultural Water Management, Elsevier, vol. 258(C).
    18. Tsakmakis, I.D. & Gikas, G.D. & Sylaios, G.K., 2021. "Integration of Sentinel-derived NDVI to reduce uncertainties in the operational field monitoring of maize," Agricultural Water Management, Elsevier, vol. 255(C).
    19. Tsai, Wen-Ping & Cheng, Chung-Lien & Uen, Tinn-Shuan & Zhou, Yanlai & Chang, Fi-John, 2019. "Drought mitigation under urbanization through an intelligent water allocation system," Agricultural Water Management, Elsevier, vol. 213(C), pages 87-96.
    20. Yuan, Chengfu & Feng, Shaoyuan & Huo, Zailin & Ji, Quanyi, 2019. "Effects of deficit irrigation with saline water on soil water-salt distribution and water use efficiency of maize for seed production in arid Northwest China," Agricultural Water Management, Elsevier, vol. 212(C), pages 424-432.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:agiwat:v:272:y:2022:i:c:s0378377422003705. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/agwat .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.