IDEAS home Printed from https://ideas.repec.org/a/eee/agiwat/v256y2021ics0378377421003498.html
   My bibliography  Save this article

Irrigation schedule analysis and optimization under the different combination of P and ET0 using a spatially distributed crop model

Author

Listed:
  • Liu, Xiao
  • Yang, Dawen

Abstract

In recent years, as drought intensifies and agricultural water consumption increases, it is of great significance to optimize the irrigation schedule to ensure regional food security. This paper constructs the distributed AquaCrop model and multi-objective genetic algorithm (NSGA - Ⅱ) simulation - optimization (MGSO) model to facilitate the development of a rational irrigation schedule. The distributed AquaCrop model considers the spatial variability of soil, climate, crops, and management practices, which can be batch calibrated using the XGBoost method. The MGSO model is for irrigation schedules under the combination of different Precipitation and ET0. In this paper, crop yield, ET, and water use efficiency (WUE) were simulated and analyzed in 13 irrigation zones in Northeast China, where the existing irrigation schedules were analyzed and optimized. The results showed that the distributed AquaCrop model could simulate regional crops well. Crop yields in the study area ranged from 3 to 10ton/hm2. The western part of Heilongjiang province and the northern part of Jilin province has a higher yield. The simulation results of Heilongjiang Province are more accurate, and the relative error is minor. The joint distribution model constructed by the Frank Copula function can describe the joint probability distribution characteristics of precipitation and ET0. According to the simulation results, each typical station has a different performance under the existing irrigation schedule under different situations. The crop yield and WUE of some stations changed significantly. The maximum and minimum yield difference was 22% for Harbin, 32% for Heihe, and 21% for Dunhua. It is mainly due to the irrigation amount in some scenarios that do not meet crop water requirements. Under the optimized irrigation schedule, the crop yield and WUE in different scenarios have been improved by the MGSO model in the Harbin station

Suggested Citation

  • 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).
    • Handle: RePEc:eee:agiwat:v:256:y:2021:i:c:s0378377421003498
    DOI: 10.1016/j.agwat.2021.107084
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0378377421003498
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.agwat.2021.107084?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. Han, Congying & Zhang, Baozhong & Chen, He & Liu, Yu & Wei, Zheng, 2020. "Novel approach of upscaling the FAO AquaCrop model into regional scale by using distributed crop parameters derived from remote sensing data," Agricultural Water Management, Elsevier, vol. 240(C).
    2. Wang, Youzhi & Guo, Shanshan & Yue, Qing & Mao, Xiaomin & Guo, Ping, 2021. "Distributed AquaCrop simulation-nonlinear multi-objective dependent-chance programming for irrigation water resources management under uncertainty," Agricultural Water Management, Elsevier, vol. 247(C).
    3. Alonso Campos, J.C. & Jiménez-Bello, M.A. & Martínez Alzamora, F., 2020. "Real-time energy optimization of irrigation scheduling by parallel multi-objective genetic algorithms," Agricultural Water Management, Elsevier, vol. 227(C).
    4. 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.
    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. Qiao, Jianmin & Cao, Qian & Liu, Yupeng & Wu, Quanyuan, 2018. "Scale dependence and parameter sensitivity of the EPIC model in the agro-pastoral transitional zone of north China," Ecological Modelling, Elsevier, vol. 390(C), pages 51-61.
    7. Er-Raki, S. & Bouras, E. & Rodriguez, J.C. & Watts, C.J. & Lizarraga-Celaya, C. & Chehbouni, A., 2021. "Parameterization of the AquaCrop model for simulating table grapes growth and water productivity in an arid region of Mexico," Agricultural Water Management, Elsevier, vol. 245(C).
    8. Li, Jinpeng & Wang, Yunqi & Zhang, Meng & Liu, Yang & Xu, Xuexin & Lin, Gang & Wang, Zhimin & Yang, Youming & Zhang, Yinghua, 2019. "Optimized micro-sprinkling irrigation scheduling improves grain yield by increasing the uptake and utilization of water and nitrogen during grain filling in winter wheat," Agricultural Water Management, Elsevier, vol. 211(C), pages 59-69.
    9. Li, Jiang & Shang, Songhao & Jiang, Hongzhe & Song, Jian & Rahman, Khalil Ur & Adeloye, Adebayo J., 2021. "Simulation-based optimization for spatiotemporal allocation of irrigation water in arid region," Agricultural Water Management, Elsevier, vol. 254(C).
    10. Mohamed Sallah, Abdoul-Hamid & Tychon, Bernard & Piccard, Isabelle & Gobin, Anne & Van Hoolst, Roel & Djaby, Bakary & Wellens, Joost, 2019. "Batch-processing of AquaCrop plug-in for rainfed maize using satellite derived Fractional Vegetation Cover data," Agricultural Water Management, Elsevier, vol. 217(C), pages 346-355.
    11. de Wit, Allard & Boogaard, Hendrik & Fumagalli, Davide & Janssen, Sander & Knapen, Rob & van Kraalingen, Daniel & Supit, Iwan & van der Wijngaart, Raymond & van Diepen, Kees, 2019. "25 years of the WOFOST cropping systems model," Agricultural Systems, Elsevier, vol. 168(C), pages 154-167.
    12. Jin, Xiuliang & Li, Zhenhai & Feng, Haikuan & Ren, Zhibin & Li, Shaokun, 2020. "Estimation of maize yield by assimilating biomass and canopy cover derived from hyperspectral data into the AquaCrop model," Agricultural Water Management, Elsevier, vol. 227(C).
    13. Zhu, Xiufang & Xu, Kun & Liu, Ying & Guo, Rui & Chen, Lingyi, 2021. "Assessing the vulnerability and risk of maize to drought in China based on the AquaCrop model," Agricultural Systems, Elsevier, vol. 189(C).
    14. Li, Jiamin & Inanaga, Shinobu & Li, Zhaohu & Eneji, A. Egrinya, 2005. "Optimizing irrigation scheduling for winter wheat in the North China Plain," Agricultural Water Management, Elsevier, vol. 76(1), pages 8-23, July.
    15. 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).
    16. 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.
    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. 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).
    2. 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).
    3. Wang, Yue & Jiang, Kongtao & Shen, Hongzheng & Wang, Nan & Liu, Ruizhe & Wu, Jiujiang & Ma, Xiaoyi, 2023. "Decision-making method for maize irrigation in supplementary irrigation areas based on the DSSAT model and a genetic algorithm," Agricultural Water Management, Elsevier, vol. 280(C).
    4. Zhang, Fan & Cui, Ningbo & Guo, Shanshan & Yue, Qiong & Jiang, Shouzheng & Zhu, Bin & Yu, Xiuyun, 2023. "Irrigation strategy optimization in irrigation districts with seasonal agricultural drought in southwest China: A copula-based stochastic multiobjective approach," Agricultural Water Management, Elsevier, vol. 282(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. 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).
    2. 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).
    3. Zhang, Chao & Xie, Ziang & Wang, Qiaojuan & Tang, Min & Feng, Shaoyuan & Cai, Huanjie, 2022. "AquaCrop modeling to explore optimal irrigation of winter wheat for improving grain yield and water productivity," Agricultural Water Management, Elsevier, vol. 266(C).
    4. 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).
    5. Avargani, Habib Karimi & Hashemy Shahdany, S. Mehdy & Kamrani, Kazem & Maestre, Jose, M. & Hashemi Garmdareh, S. Ebrahim & Liaghat, Abdolmajid, 2022. "Prioritization of surface water distribution in irrigation districts to mitigate crop yield reduction during water scarcity," Agricultural Water Management, Elsevier, vol. 269(C).
    6. Aster Tesfaye Hordofa & Olkeba Tolessa Leta & Tena Alamirew & Abebe Demissie Chukalla, 2022. "Response of Winter Wheat Production to Climate Change in Ziway Lake Basin," Sustainability, MDPI, vol. 14(20), pages 1-17, October.
    7. Wang, Youzhi & Guo, Xinwei & Zhang, Fan & Yin, Huijuan & Guo, Ping & Zhang, Wenge & Li, Qiangkun, 2022. "The spatially-distributed ANN-optimization approach for water-agriculture-ecology nexus management under uncertainties and risks," Agricultural Water Management, Elsevier, vol. 271(C).
    8. Cheng, Minghui & Wang, Haidong & Fan, Junliang & Xiang, Youzhen & Liu, Xiaoqiang & Liao, Zhenqi & Abdelghany, Ahmed Elsayed & Zhang, Fucang & Li, Zhijun, 2022. "Evaluation of AquaCrop model for greenhouse cherry tomato with plastic film mulch under various water and nitrogen supplies," Agricultural Water Management, Elsevier, vol. 274(C).
    9. Dhouib, M. & Zitouna-Chebbi, R. & Prévot, L. & Molénat, J. & Mekki, I. & Jacob, F., 2022. "Multicriteria evaluation of the AquaCrop crop model in a hilly rainfed Mediterranean agrosystem," Agricultural Water Management, Elsevier, vol. 273(C).
    10. Wang, Chong & Zhao, Jiongchao & Feng, Yupeng & Shang, Mengfei & Bo, Xiaozhi & Gao, Zhenzhen & Chen, Fu & Chu, Qingquan, 2021. "Optimizing tillage method and irrigation schedule for greenhouse gas mitigation, yield improvement, and water conservation in wheat–maize cropping systems," Agricultural Water Management, Elsevier, vol. 248(C).
    11. Alvar-Beltrán, Jorge & Saturnin, Coulibaly & Grégoire, Baki & Camacho, Jose Luís & Dao, Abdalla & Migraine, Jean Baptiste & Marta, Anna Dalla, 2023. "Using AquaCrop as a decision-support tool for improved irrigation management in the Sahel region," Agricultural Water Management, Elsevier, vol. 287(C).
    12. Cao, Zhaodan & Zhu, Tingju & Cai, Ximing, 2023. "Hydro-agro-economic optimization for irrigated farming in an arid region: The Hetao Irrigation District, Inner Mongolia," Agricultural Water Management, Elsevier, vol. 277(C).
    13. Mahboobe Ghobadi & Mahdi Gheysari & Mohammad Shayannejad & Hamze Dokoohaki, 2023. "Analyzing the Effects of Planting Date on the Uncertainty of CERES-Maize and Its Potential to Reduce Yield Gap in Arid and Mediterranean Climates," Agriculture, MDPI, vol. 13(8), pages 1-17, July.
    14. Li, Pei & Huang, Qiang & Huang, Shengzhi & Leng, Guoyong & Peng, Jian & Wang, Hao & Zheng, Xudong & Li, Yifei & Fang, Wei, 2022. "Various maize yield losses and their dynamics triggered by drought thresholds based on Copula-Bayesian conditional probabilities," Agricultural Water Management, Elsevier, vol. 261(C).
    15. 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).
    16. 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).
    17. Zhao, Nana & Liu, Yu & Cai, Jiabing & Paredes, Paula & Rosa, Ricardo D. & Pereira, Luis S., 2013. "Dual crop coefficient modelling applied to the winter wheat–summer maize crop sequence in North China Plain: Basal crop coefficients and soil evaporation component," Agricultural Water Management, Elsevier, vol. 117(C), pages 93-105.
    18. Bohan, David & Schmucki, Reto & Abay, Abrha & Termansen, Mette & Bane, Miranda & Charalabiis, Alice & Cong, Rong-Gang & Derocles, Stephane & Dorner, Zita & Forster, Matthieu & Gibert, Caroline & Harro, 2020. "Designing farmer-acceptable rotations that assure ecosystem service provision inthe face of climate change," MPRA Paper 112313, University Library of Munich, Germany.
    19. 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).
    20. Walaa El-Nashar & Ahmed Elyamany, 2023. "Adapting Irrigation Strategies to Mitigate Climate Change Impacts: A Value Engineering Approach," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 37(6), pages 2369-2386, May.

    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:256:y:2021:i:c:s0378377421003498. 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.