IDEAS home Printed from https://ideas.repec.org/a/eee/agiwat/v280y2023ics0378377423000963.html
   My bibliography  Save this article

Decision-making method for maize irrigation in supplementary irrigation areas based on the DSSAT model and a genetic algorithm

Author

Listed:
  • Wang, Yue
  • Jiang, Kongtao
  • Shen, Hongzheng
  • Wang, Nan
  • Liu, Ruizhe
  • Wu, Jiujiang
  • Ma, Xiaoyi

Abstract

Supplementary irrigation area in north China is an important agricultural production area, but water shortages have limited the agricultural production considerably. Irrigation is required to ensure crop yield during growth seasons with variable rainfall. Most irrigation schedules are based on typical meteorological years. However, it is very difficult to predict the meteorological information of irrigation year in advance, which limits the planning of irrigation schedules. Therefore, taking the central part of Shanxi Province (supplementary irrigation area) as an example, this study focuses on the optimal irrigation decision-making method of supplementary irrigation area. The irrigation schedule of summer maize from 1970 to 2020 was firstly optimized by using DSSAT model and genetic algorithm. On this basis, a decision-making method based on the ratio of cumulative rainfall to cumulative ETc between plantation date to the current date to determine the irrigation time is developed. The results indicated that using P/ETc (the ratio of cumulative rainfall to cumulative crop water requirement) as the criterion leads to superior decision-making. Two supplementary irrigation events can meet the growth demand of summer maize. Irrigation events decisions were made on days 44 and 58 after summer maize sowing, the accuracy of this method for two irrigation events decisions is 82.35 % and 86.27 %, respectively, and the average accuracy of the two irrigation event decisions was 84.31 %. At the same time, a support vector machine algorithm is used to make decisions about irrigation, comparing the decision accuracy with the results of the decision method proposed in this paper. The accuracy of the support vector machine decisions for the first and second irrigation events was 82.35 % and 80.40 %, respectively, and the average accuracy of the two irrigation event decisions was 81.40 %. The decision model was also superior to that involving support vector machine algorithms for the irrigation decision of summer maize can be used for irrigation decision-making in similar supplementary irrigation areas with variable rainfall.

Suggested Citation

  • 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).
    • Handle: RePEc:eee:agiwat:v:280:y:2023:i:c:s0378377423000963
    DOI: 10.1016/j.agwat.2023.108231
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0378377423000963
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.agwat.2023.108231?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. Attia, Ahmed & Rajan, Nithya & Xue, Qingwu & Nair, Shyam & Ibrahim, Amir & Hays, Dirk, 2016. "Application of DSSAT-CERES-Wheat model to simulate winter wheat response to irrigation management in the Texas High Plains," Agricultural Water Management, Elsevier, vol. 165(C), pages 50-60.
    2. Jiang, Yiwen & Zhang, Lanhui & Zhang, Baoqing & He, Chansheng & Jin, Xin & Bai, Xiao, 2016. "Modeling irrigation management for water conservation by DSSAT-maize model in arid northwestern China," Agricultural Water Management, Elsevier, vol. 177(C), pages 37-45.
    3. Jones, M.R. & Singels, A. & Ruane, A.C., 2015. "Simulated impacts of climate change on water use and yield of irrigated sugarcane in South Africa," Agricultural Systems, Elsevier, vol. 139(C), pages 260-270.
    4. 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).
    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. Dahri, Shahzad Hussain & Shaikh, Irfan Ahmed & Talpur, Mashooque Ali & Mangrio, Munir Ahmed & Dahri, Zakir Hussain & Hoogenboom, Gerrit & Knox, Jerry W., 2024. "Modelling the impacts of climate change on the sustainability of rainfed and irrigated maize in Pakistan," Agricultural Water Management, Elsevier, vol. 296(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. 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).
    2. Zhong, Honglin & Sun, Laixiang & Fischer, Günther & Tian, Zhan & van Velthuizen, Harrij & Liang, Zhuoran, 2017. "Mission Impossible? Maintaining regional grain production level and recovering local groundwater table by cropping system adaptation across the North China Plain," Agricultural Water Management, Elsevier, vol. 193(C), pages 1-12.
    3. Santillán-Fernández, A. & Santoyo-Cortés, V.H. & García-Chávez, L.R. & Covarrubias-Gutiérrez, I. & Merino, A., 2016. "Influence of drought and irrigation on sugarcane yields in different agroecoregions in Mexico," Agricultural Systems, Elsevier, vol. 143(C), pages 126-135.
    4. 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).
    5. Shirazi, Sana Zeeshan & Mei, Xurong & Liu, Buchun & Liu, Yuan, 2022. "Estimating potential yield and change in water budget for wheat and maize across Huang-Huai-Hai Plain in the future," Agricultural Water Management, Elsevier, vol. 260(C).
    6. Shi, Xinrui & Batchelor, William D. & Liang, Hao & Li, Sien & Li, Baoguo & Hu, Kelin, 2020. "Determining optimal water and nitrogen management under different initial soil mineral nitrogen levels in northwest China based on a model approach," Agricultural Water Management, Elsevier, vol. 234(C).
    7. 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).
    8. Attia, Ahmed & El-Hendawy, Salah & Al-Suhaibani, Nasser & Alotaibi, Majed & Tahir, Muhammad Usman & Kamal, Khaled Y., 2021. "Evaluating deficit irrigation scheduling strategies to improve yield and water productivity of maize in arid environment using simulation," Agricultural Water Management, Elsevier, vol. 249(C).
    9. 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).
    10. Zhu, Hongyan & Zheng, Bingyan & Nie, Weibo & Fei, Liangjun & Shan, Yuyang & Li, Ge & Liang, Fei, 2024. "Optimization of maize irrigation strategy in Xinjiang, China by AquaCrop based on a four-year study," Agricultural Water Management, Elsevier, vol. 297(C).
    11. 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).
    12. Espoir M. Bagula & Jackson Gilbert M. Majaliwa & Gustave N. Mushagalusa & Twaha A. Basamba & John-Baptist Tumuhairwe & Jean-Gomez M. Mondo & Patrick Musinguzi & Cephas B. Mwimangire & Géant B. Chuma &, 2022. "Climate Change Effect on Water Use Efficiency under Selected Soil and Water Conservation Practices in the Ruzizi Catchment, Eastern D.R. Congo," Land, MDPI, vol. 11(9), pages 1-22, August.
    13. Kothari, Kritika & Ale, Srinivasulu & Bordovsky, James P. & Thorp, Kelly R. & Porter, Dana O. & Munster, Clyde L., 2019. "Simulation of efficient irrigation management strategies for grain sorghum production over different climate variability classes," Agricultural Systems, Elsevier, vol. 170(C), pages 49-62.
    14. Kaur, Rajbir & Arora, VK, 2018. "Assessing spring maize responses to irrigation and nitrogen regimes in north-west India using CERES-Maize model," Agricultural Water Management, Elsevier, vol. 209(C), pages 171-177.
    15. Bai, Yu & Gao, Jinhua, 2021. "Optimization of the nitrogen fertilizer schedule of maize under drip irrigation in Jilin, China, based on DSSAT and GA," Agricultural Water Management, Elsevier, vol. 244(C).
    16. A. Mukherjee & A. K. S. Huda, 2018. "Assessment of climate variability and trend on wheat productivity in West Bengal, India: crop growth simulation approach," Climatic Change, Springer, vol. 147(1), pages 235-252, March.
    17. Mansour, Elsayed & Abdul-Hamid, Mohamed I & Yasin, Mohamed T & Qabil, Naglaa & Attia, Ahmed, 2017. "Identifying drought-tolerant genotypes of barley and their responses to various irrigation levels in a Mediterranean environment," Agricultural Water Management, Elsevier, vol. 194(C), pages 58-67.
    18. 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.
    19. Zhao, Jie & Han, Tong & Wang, Chong & Jia, Hao & Worqlul, Abeyou W. & Norelli, Nicole & Zeng, Zhaohai & Chu, Qingquan, 2020. "Optimizing irrigation strategies to synchronously improve the yield and water productivity of winter wheat under interannual precipitation variability in the North China Plain," Agricultural Water Management, Elsevier, vol. 240(C).
    20. Zhong, Honglin & Sun, Laixiang & Fischer, Günther & Tian, Zhan & Liang, Zhuoran, 2019. "Optimizing regional cropping systems with a dynamic adaptation strategy for water sustainable agriculture in the Hebei Plain," Agricultural Systems, Elsevier, vol. 173(C), pages 94-106.

    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:280:y:2023:i:c:s0378377423000963. 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.