IDEAS home Printed from https://ideas.repec.org/a/gam/jagris/v15y2025i5p527-d1602348.html
   My bibliography  Save this article

Model Predictive Control of Adaptive Irrigation Decisions Incorporating Rainfall Intensity and Soil Properties

Author

Listed:
  • Ao Liu

    (College of Surveying and Geo-Informatics, North China University of Water Resources and Electric Power, Zhengzhou 450046, China
    PowerChina Jiangxi Electric Power Engineering Co., Ltd., Nanchang 330096, China)

  • Dongbao Zhao

    (College of Surveying and Geo-Informatics, North China University of Water Resources and Electric Power, Zhengzhou 450046, China)

  • Yichang Wei

    (College of Surveying and Geo-Informatics, North China University of Water Resources and Electric Power, Zhengzhou 450046, China)

Abstract

To mitigate the loss of surface runoff and deep percolation in the water-scarce area and enhance the utilization of rainfall resources, this study adaptively determines the soil water content threshold triggering such losses by incorporating rainfall intensity (RI) and soil properties (SP) based on the model predictive control (MPC) framework. These thresholds then serve as the target soil water content before rainfall, and a model predictive control incorporating RI and SP (RISPMPC) irrigation decision-making is proposed. We conducted irrigation simulation experiments in Ya’an City, Sichuan Province, across four RI levels and six soil texture types. The results were compared with those obtained from conventional model predictive control (CMPC), rule-based closed-loop irrigation decision (RBC), and a newly developed zone-based model predictive control (ZMPC). Results demonstrate that RISPMPC enhances the utilization of rainfall resources across different scenarios. In soils with strong infiltration capabilities, such as loamy sand, loam, and clay loam, RISPMPC reduces irrigation water use by 26%, 5%, and 3% compared to RBC, CMPC, and ZMPC, respectively. In contrast, for soils with poor infiltration capabilities, including silty soil, clay A, and clay B, RISPMPC’s water-saving efficiency strongly correlates with rainfall intensity levels, achieving maximum savings of 61%, 36%, and 34% compared to the same three methods. Furthermore, in all cases, RISPMPC demonstrates the highest maximum effective rainfall utilization rate (MERU). As soil infiltration capability decreases and rainfall intensity increases, the MERU gap between RISPMPC and the other three methods widens significantly, underscoring RISPMPC’s robustness in environments where rainwater utilization is challenging. Therefore, RISPMPC can improve the utilization efficiency of rainwater resources and effectively alleviate agricultural water scarcity issues.

Suggested Citation

  • Ao Liu & Dongbao Zhao & Yichang Wei, 2025. "Model Predictive Control of Adaptive Irrigation Decisions Incorporating Rainfall Intensity and Soil Properties," Agriculture, MDPI, vol. 15(5), pages 1-19, February.
    • Handle: RePEc:gam:jagris:v:15:y:2025:i:5:p:527-:d:1602348
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2077-0472/15/5/527/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2077-0472/15/5/527/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Olutobi Adeyemi & Ivan Grove & Sven Peets & Tomas Norton, 2017. "Advanced Monitoring and Management Systems for Improving Sustainability in Precision Irrigation," Sustainability, MDPI, vol. 9(3), pages 1-29, February.
    2. 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).
    3. Liang, Xi & Liakos, Vasilis & Wendroth, Ole & Vellidis, George, 2016. "Scheduling irrigation using an approach based on the van Genuchten model," Agricultural Water Management, Elsevier, vol. 176(C), pages 170-179.
    4. CHEN,Ru & HUANG,Yongye & JI,Xinglu & XU,Yuehua & XUE,Xiaomin & WANG,Jinzheng, 2018. "Effects of Different Irrigation Methods on Growth, Fruit Quality and Yield of Apple Trees," Asian Agricultural Research, USA-China Science and Culture Media Corporation, vol. 10(07), July.
    5. Olayide, Olawale Emmanuel & Tetteh, Isaac Kow & Popoola, Labode, 2016. "Differential impacts of rainfall and irrigation on agricultural production in Nigeria: Any lessons for climate-smart agriculture?," Agricultural Water Management, Elsevier, vol. 178(C), pages 30-36.
    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. Mompremier, R. & Her, Y. & Hoogenboom, G. & Migliaccio, K. & Muñoz-Carpena, R. & Brym, Z. & Colbert, R.W. & Jeune, W., 2021. "Modeling the response of dry bean yield to irrigation water availability controlled by watershed hydrology," Agricultural Water Management, Elsevier, vol. 243(C).
    2. Emami, Somayeh & Dehghanisanij, Hossein & Hajimirzajan, Amir, 2024. "Agent-based simulation model to evaluate government policies for farmers’ adoption and synergy in improving irrigation systems: A case study of Lake Urmia basin," Agricultural Water Management, Elsevier, vol. 294(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. 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.
    5. 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.
    6. Oludare Sunday Durodola & Khaldoon A. Mourad, 2020. "Modelling the Impacts of Climate Change on Soybeans Water Use and Yields in Ogun-Ona River Basin, Nigeria," Agriculture, MDPI, vol. 10(12), pages 1-23, December.
    7. Imran, Muhammad Ali & Ali, Asghar & Ashfaq, Muhammad & Hassan, Sarfraz & Culas, Richard & Ma, Chunbo, 2019. "Impact of climate smart agriculture (CSA) through sustainable irrigation management on Resource use efficiency: A sustainable production alternative for cotton," Land Use Policy, Elsevier, vol. 88(C).
    8. Collins C. Okolie & Gideon Danso-Abbeam & Okechukwu Groupson-Paul & Abiodun A. Ogundeji, 2022. "Climate-Smart Agriculture Amidst Climate Change to Enhance Agricultural Production: A Bibliometric Analysis," Land, MDPI, vol. 12(1), pages 1-23, December.
    9. Zhen Shi & Huinan Huang & Yingju Wu & Yung-Ho Chiu & Shijiong Qin, 2020. "Climate Change Impacts on Agricultural Production and Crop Disaster Area in China," IJERPH, MDPI, vol. 17(13), pages 1-23, July.
    10. Edmond Totin & Alcade C. Segnon & Marc Schut & Hippolyte Affognon & Robert B. Zougmoré & Todd Rosenstock & Philip K. Thornton, 2018. "Institutional Perspectives of Climate-Smart Agriculture: A Systematic Literature Review," Sustainability, MDPI, vol. 10(6), pages 1-20, June.
    11. Angelin Blessy & Avneesh Kumar & Prabagaran A & Abdul Quadir Md & Abdullah I. Alharbi & Ahlam Almusharraf & Surbhi B. Khan, 2023. "Sustainable Irrigation Requirement Prediction Using Internet of Things and Transfer Learning," Sustainability, MDPI, vol. 15(10), pages 1-20, May.
    12. Kedir Y. Ahmed & Allen G. Ross & Seada M. Hussien & Kingsley E. Agho & Bolajoko O. Olusanya & Felix Akpojene Ogbo, 2023. "Mapping Local Variations and the Determinants of Childhood Stunting in Nigeria," IJERPH, MDPI, vol. 20(4), pages 1-16, February.
    13. Siva K. Balasundram & Redmond R. Shamshiri & Shankarappa Sridhara & Nastaran Rizan, 2023. "The Role of Digital Agriculture in Mitigating Climate Change and Ensuring Food Security: An Overview," Sustainability, MDPI, vol. 15(6), pages 1-23, March.
    14. Yeboah, Samuel, 2023. "Unlocking the Potential of Technological Innovations for Sustainable Agriculture in Developing Countries: Enhancing Resource Efficiency and Environmental Sustainability," MPRA Paper 118215, University Library of Munich, Germany, revised 26 Jul 2023.
    15. Gezahegn, Tafesse & Van Passel, Steven & Berhanu, Tekeste & D'Haese, Marijke & Maertens, Miet, 2020. "Structural and Institutional Heterogeneity among Agricultural Cooperatives in Ethiopia: Does it Matter for Farmers’ Welfare?," Journal of Agricultural and Resource Economics, Western Agricultural Economics Association, vol. 46(2), August.
    16. Li Bin & Muhammad Shahzad & Hira Khan & Muhammad Mehran Bashir & Arif Ullah & Muhammad Siddique, 2023. "Sustainable Smart Agriculture Farming for Cotton Crop: A Fuzzy Logic Rule Based Methodology," Sustainability, MDPI, vol. 15(18), pages 1-18, September.
    17. 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).
    18. Matchaya, Greenwell C. & Tadesse, Getaw & Kuteya, Auckland N., 2022. "Rainfall shocks and crop productivity in Zambia: Implication for agricultural water risk management," Agricultural Water Management, Elsevier, vol. 269(C).
    19. Jakhongirmirzo Mirzaqobulov & Kedar Mehta & Sana Ilyas & Abdulkhakim Salokhiddinov, 2024. "The Role of Collector-Drainage Water in Sustainable Irrigation for Agriculture in the Developing World: An Experimental Study," World, MDPI, vol. 6(1), pages 1-19, December.
    20. Stavros Sakellariou & Marios Spiliotopoulos & Nikolaos Alpanakis & Ioannis Faraslis & Pantelis Sidiropoulos & Georgios A. Tziatzios & George Karoutsos & Nicolas R. Dalezios & Nicholas Dercas, 2024. "Spatiotemporal Drought Assessment Based on Gridded Standardized Precipitation Index (SPI) in Vulnerable Agroecosystems," Sustainability, MDPI, vol. 16(3), pages 1-16, February.

    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:gam:jagris:v:15:y:2025:i:5:p:527-:d:1602348. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    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.