IDEAS home Printed from https://ideas.repec.org/a/eee/agiwat/v295y2024ics0378377424000933.html
   My bibliography  Save this article

Towards efficient irrigation management at field scale using new technologies: A systematic literature review

Author

Listed:
  • Bounajra, Afaf
  • Guemmat, Kamal El
  • Mansouri, Khalifa
  • Akef, Fatiha

Abstract

Life on earth is linked to water resources. Recently, alarm bells have been ringing in global organizations to raise awareness of the importance of rational use of water resources, which are becoming an increasingly scarce commodity. The majority of the world's freshwater is used for agricultural irrigation, hence there is a need to adopt an intelligent irrigation strategy that will lead to sustainable agricultural management. To reap the full benefits, irrigation strategy must be accompanied by a good understanding of field characteristics. Several studies have benefited from the improvement of new technologies for irrigation scheduling, but taking only soil water properties as a basis for research, and to our knowledge there is no systematic literature review study to date that aims at irrigation scheduling taking into consideration the characteristics of the crop field for intelligent and efficient agricultural management. This literature review article aims to explore the new Internet of Things and Artificial Intelligence technologies used on the one hand for monitoring and predicting the coefficients that control the crop evapotranspiration process responsible for crop water losses, namely the reference crop evapotranspiration coefficient ETo and the crop coefficient Kc, and on the other hand for a good and intelligent understanding of the: physical, chemical, biological and hydrological characteristics of a specific field, and which affect the crop evapotranspiration process and therefore yield. Following a systematic literature review methodology led us to a refined selection of 55 journal articles for further analysis. We have identified that the profitability of a crop field is closely linked to the right strategies adopted in a specific crop plot, and these strategies can only be defined after a good understanding of the field's characteristics. We were able to discuss these field characteristics through the primary studies which enabled us to develop an intelligent model that brings together the different approaches adopted for irrigation scheduling and farm management and to identify gaps and limitations in the use of new technologies for farm management at field scale, and thus pave the way for further research.

Suggested Citation

  • Bounajra, Afaf & Guemmat, Kamal El & Mansouri, Khalifa & Akef, Fatiha, 2024. "Towards efficient irrigation management at field scale using new technologies: A systematic literature review," Agricultural Water Management, Elsevier, vol. 295(C).
  • Handle: RePEc:eee:agiwat:v:295:y:2024:i:c:s0378377424000933
    DOI: 10.1016/j.agwat.2024.108758
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0378377424000933
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.agwat.2024.108758?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. Shao, Guomin & Han, Wenting & Zhang, Huihui & Zhang, Liyuan & Wang, Yi & Zhang, Yu, 2023. "Prediction of maize crop coefficient from UAV multisensor remote sensing using machine learning methods," Agricultural Water Management, Elsevier, vol. 276(C).
    2. Lu, Yingjie & Li, Tao & Hu, Hui & Zeng, Xuemei, 2023. "Short-term prediction of reference crop evapotranspiration based on machine learning with different decomposition methods in arid areas of China," Agricultural Water Management, Elsevier, vol. 279(C).
    3. Di Nunno, Fabio & Granata, Francesco, 2023. "Future trends of reference evapotranspiration in Sicily based on CORDEX data and Machine Learning algorithms," Agricultural Water Management, Elsevier, vol. 280(C).
    4. Bwambale, Erion & Abagale, Felix K. & Anornu, Geophrey K., 2022. "Smart irrigation monitoring and control strategies for improving water use efficiency in precision agriculture: A review," Agricultural Water Management, Elsevier, vol. 260(C).
    5. Zhang, Yu & Han, Wenting & Zhang, Huihui & Niu, Xiaotao & Shao, Guomin, 2023. "Evaluating maize evapotranspiration using high-resolution UAV-based imagery and FAO-56 dual crop coefficient approach," Agricultural Water Management, Elsevier, vol. 275(C).
    6. Feng, Xuyu & Liu, Haijun & Feng, Dongxue & Tang, Xiaopei & Li, Lun & Chang, Jie & Tanny, Josef & Liu, Ronghao, 2023. "Quantifying winter wheat evapotranspiration and crop coefficients under sprinkler irrigation using eddy covariance technology in the North China Plain," Agricultural Water Management, Elsevier, vol. 277(C).
    7. Zhangzhong, Lili & Gao, Hairong & Zheng, Wengang & Wu, Jianwei & Li, Jingjing & Wang, Dequn, 2023. "Development of an evapotranspiration estimation method for lettuce via mobile phones using machine vision: Proof of concept," Agricultural Water Management, Elsevier, vol. 275(C).
    8. Becker, Rike & Schüth, Christoph & Merz, Ralf & Khaliq, Tasneem & Usman, Muhammad & Beek, Tim aus der & Kumar, Rohini & Schulz, Stephan, 2023. "Increased heat stress reduces future yields of three major crops in Pakistan’s Punjab region despite intensification of irrigation," Agricultural Water Management, Elsevier, vol. 281(C).
    9. Alibabaei, Khadijeh & Gaspar, Pedro D. & Assunção, Eduardo & Alirezazadeh, Saeid & Lima, Tânia M., 2022. "Irrigation optimization with a deep reinforcement learning model: Case study on a site in Portugal," Agricultural Water Management, Elsevier, vol. 263(C).
    10. Shao, Guomin & Han, Wenting & Zhang, Huihui & Liu, Shouyang & Wang, Yi & Zhang, Liyuan & Cui, Xin, 2021. "Mapping maize crop coefficient Kc using random forest algorithm based on leaf area index and UAV-based multispectral vegetation indices," Agricultural Water Management, Elsevier, vol. 252(C).
    11. Sawadogo, Alidou & Dossou-Yovo, Elliott R. & Kouadio, Louis & Zwart, Sander J. & Traoré, Farid & Gündoğdu, Kemal S., 2023. "Assessing the biophysical factors affecting irrigation performance in rice cultivation using remote sensing derived information," Agricultural Water Management, Elsevier, vol. 278(C).
    12. Han, Ming & Zhao, Chengyi & Šimůnek, Jirka & Feng, Gary, 2015. "Evaluating the impact of groundwater on cotton growth and root zone water balance using Hydrus-1D coupled with a crop growth model," Agricultural Water Management, Elsevier, vol. 160(C), pages 64-75.
    13. DeJonge, Kendall C. & Kaleita, Amy L. & Thorp, Kelly R., 2007. "Simulating the effects of spatially variable irrigation on corn yields, costs, and revenue in Iowa," Agricultural Water Management, Elsevier, vol. 92(1-2), pages 99-109, August.
    14. Chen, Shuai & Mao, Xiaomin & Shang, Songhao, 2022. "Response and contribution of shallow groundwater to soil water/salt budget and crop growth in layered soils," Agricultural Water Management, Elsevier, vol. 266(C).
    Full references (including those not matched with items on IDEAS)

    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. Imran Ali Lakhiar & Haofang Yan & Chuan Zhang & Guoqing Wang & Bin He & Beibei Hao & Yujing Han & Biyu Wang & Rongxuan Bao & Tabinda Naz Syed & Junaid Nawaz Chauhdary & Md. Rakibuzzaman, 2024. "A Review of Precision Irrigation Water-Saving Technology under Changing Climate for Enhancing Water Use Efficiency, Crop Yield, and Environmental Footprints," Agriculture, MDPI, vol. 14(7), pages 1-40, July.
    2. Guilherme Jesus & Martim L. Aguiar & Pedro D. Gaspar, 2022. "Computational Tool to Support the Decision in the Selection of Alternative and/or Sustainable Refrigerants," Energies, MDPI, vol. 15(22), pages 1-20, November.
    3. Dong, Juan & Xing, Liwen & Cui, Ningbo & Guo, Li & Liang, Chuan & Zhao, Lu & Wang, Zhihui & Gong, Daozhi, 2024. "Estimating reference crop evapotranspiration using optimized empirical methods with a novel improved Grey Wolf Algorithm in four climatic regions of China," Agricultural Water Management, Elsevier, vol. 291(C).
    4. Li, Yunfeng & Yu, Qihua & Ning, Huifeng & Gao, Yang & Sun, Jingsheng, 2023. "Simulation of soil water, heat, and salt adsorptive transport under film mulched drip irrigation in an arid saline-alkali area using HYDRUS-2D," Agricultural Water Management, Elsevier, vol. 290(C).
    5. Phogat, V. & Skewes, M.A. & McCarthy, M.G. & Cox, J.W. & Šimůnek, J. & Petrie, P.R., 2017. "Evaluation of crop coefficients, water productivity, and water balance components for wine grapes irrigated at different deficit levels by a sub-surface drip," Agricultural Water Management, Elsevier, vol. 180(PA), pages 22-34.
    6. Rozenstein, Offer & Fine, Lior & Malachy, Nitzan & Richard, Antoine & Pradalier, Cedric & Tanny, Josef, 2023. "Data-driven estimation of actual evapotranspiration to support irrigation management: Testing two novel methods based on an unoccupied aerial vehicle and an artificial neural network," Agricultural Water Management, Elsevier, vol. 283(C).
    7. Zhang, Xianbo & Yang, Hui & Shukla, Manoj K. & Du, Taisheng, 2023. "Proposing a crop-water-salt production function based on plant response to stem water potential," Agricultural Water Management, Elsevier, vol. 278(C).
    8. Wang, Wendi & Straffelini, Eugenio & Tarolli, Paolo, 2023. "Steep-slope viticulture: The effectiveness of micro-water storage in improving the resilience to weather extremes," Agricultural Water Management, Elsevier, vol. 286(C).
    9. Ruiqi Zhang & Chunguang Hu & Yucheng Sun, 2024. "Decoding the Characteristics of Ecosystem Services and the Scale Effect in the Middle Reaches of the Yangtze River Urban Agglomeration: Insights for Planning and Management," Sustainability, MDPI, vol. 16(18), pages 1-26, September.
    10. França, Ana Carolina Ferreira & Coelho, Rubens Duarte & da Silva Gundim, Alice & de Oliveira Costa, Jéfferson & Quiloango-Chimarro, Carlos Alberto, 2024. "Effects of different irrigation scheduling methods on physiology, yield, and irrigation water productivity of soybean varieties," Agricultural Water Management, Elsevier, vol. 293(C).
    11. Fabio Di Nunno & Marco De Matteo & Giovanni Izzo & Francesco Granata, 2023. "A Combined Clustering and Trends Analysis Approach for Characterizing Reference Evapotranspiration in Veneto," Sustainability, MDPI, vol. 15(14), pages 1-23, July.
    12. Feng, Genxiang & Zhu, Chengli & Wu, Qingfeng & Wang, Ce & Zhang, Zhanyu & Mwiya, Richwell Mubita & Zhang, Li, 2021. "Evaluating the impacts of saline water irrigation on soil water-salt and summer maize yield in subsurface drainage condition using coupled HYDRUS and EPIC model," Agricultural Water Management, Elsevier, vol. 258(C).
    13. Boyer, Christopher N. & Larson, James A. & Roberts, Roland K. & McClure, Angela T. & Tyler, Donald D. & Smith, S. Aaron, 2014. "Probability of Irrigated Corn Being Profitable in a Humid Region," 2014 Annual Meeting, February 1-4, 2014, Dallas, Texas 162470, Southern Agricultural Economics Association.
    14. Zihao Wu & Yiyun Chen & Yuanli Zhu & Xiangyang Feng & Jianxiong Ou & Guie Li & Zhaomin Tong & Qingwu Yan, 2023. "Mapping Soil Organic Carbon in Floodplain Farmland: Implications of Effective Range of Environmental Variables," Land, MDPI, vol. 12(6), pages 1-15, June.
    15. Bali, Khaled M. & Mohamed, Abdelmoneim Zakaria & Begna, Sultan & Wang, Dong & Putnam, Daniel & Dahlke, Helen E. & Eltarabily, Mohamed Galal, 2023. "The use of HYDRUS-2D to simulate intermittent Agricultural Managed Aquifer Recharge (Ag-MAR) in Alfalfa in the San Joaquin Valley," Agricultural Water Management, Elsevier, vol. 282(C).
    16. Li, Pei & Ren, Li, 2023. "Evaluating the differences in irrigation methods for winter wheat under limited irrigation quotas in the water-food-economy nexus in the North China Plain," Agricultural Water Management, Elsevier, vol. 289(C).
    17. Kun Liu & Zhen Zhang & Yu Shi & Xizhi Wang & Zhenwen Yu, 2024. "Optimizing Ridge–Furrow Ratio to Improve Water Resource Utilization for Wheat in the North China Plain," Agriculture, MDPI, vol. 14(9), pages 1-17, September.
    18. Zhaoshuang He & Yanhua Chen & Yale Zang, 2024. "Wind Speed Forecasting Based on Phase Space Reconstruction and a Novel Optimization Algorithm," Sustainability, MDPI, vol. 16(16), pages 1-29, August.
    19. Stephen Luo Sheng Yong & Jing Lin Ng & Yuk Feng Huang & Chun Kit Ang & Norashikin Ahmad Kamal & Majid Mirzaei & Ali Najah Ahmed, 2024. "Enhanced Daily Reference Evapotranspiration Estimation Using Optimized Hybrid Support Vector Regression Models," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 38(11), pages 4213-4241, September.
    20. Qian Cheng & Honggang Xu & Shuaipeng Fei & Zongpeng Li & Zhen Chen, 2022. "Estimation of Maize LAI Using Ensemble Learning and UAV Multispectral Imagery under Different Water and Fertilizer Treatments," Agriculture, MDPI, vol. 12(8), pages 1-21, August.

    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:295:y:2024:i:c:s0378377424000933. 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.