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Advanced Monitoring and Management Systems for Improving Sustainability in Precision Irrigation

Author

Listed:
  • Olutobi Adeyemi

    (Engineering Department, Harper Adams University, Newport, Shropshire TF10 7BP, UK)

  • Ivan Grove

    (Engineering Department, Harper Adams University, Newport, Shropshire TF10 7BP, UK)

  • Sven Peets

    (Engineering Department, Harper Adams University, Newport, Shropshire TF10 7BP, UK)

  • Tomas Norton

    (Engineering Department, Harper Adams University, Newport, Shropshire TF10 7BP, UK
    M3-BIORES research group, Division of Animal and Human Health Engineering, Department of Biosystems, Katholieke Universiteit L, Kasteelpark Arenberg 30, 3001 Leuven, Belgium)

Abstract

Globally, the irrigation of crops is the largest consumptive user of fresh water. Water scarcity is increasing worldwide, resulting in tighter regulation of its use for agriculture. This necessitates the development of irrigation practices that are more efficient in the use of water but do not compromise crop quality and yield. Precision irrigation already achieves this goal, in part. The goal of precision irrigation is to accurately supply the crop water need in a timely manner and as spatially uniformly as possible. However, to maximize the benefits of precision irrigation, additional technologies need to be enabled and incorporated into agriculture. This paper discusses how incorporating adaptive decision support systems into precision irrigation management will enable significant advances in increasing the efficiency of current irrigation approaches. From the literature review, it is found that precision irrigation can be applied in achieving the environmental goals related to sustainability. The demonstrated economic benefits of precision irrigation in field-scale crop production is however minimal. It is argued that a proper combination of soil, plant and weather sensors providing real-time data to an adaptive decision support system provides an innovative platform for improving sustainability in irrigated agriculture. The review also shows that adaptive decision support systems based on model predictive control are able to adequately account for the time-varying nature of the soil–plant–atmosphere system while considering operational limitations and agronomic objectives in arriving at optimal irrigation decisions. It is concluded that significant improvements in crop yield and water savings can be achieved by incorporating model predictive control into precision irrigation decision support tools. Further improvements in water savings can also be realized by including deficit irrigation as part of the overall irrigation management strategy. Nevertheless, future research is needed for identifying crop response to regulated water deficits, developing improved soil moisture and plant sensors, and developing self-learning crop simulation frameworks that can be applied to evaluate adaptive decision support strategies related to irrigation.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jsusta:v:9:y:2017:i:3:p:353-:d:91671
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    References listed on IDEAS

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    9. Juan F. Velasco-Muñoz & José A. Aznar-Sánchez & Luis J. Belmonte-Ureña & Isabel M. Román-Sánchez, 2018. "Sustainable Water Use in Agriculture: A Review of Worldwide Research," Sustainability, MDPI, vol. 10(4), pages 1-18, April.
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    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. 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).
    13. Nandan, Rohit & Woo, Dong K. & Kumar, Praveen & Adinarayana, J., 2021. "Impact of irrigation scheduling methods on corn yield under climate change," Agricultural Water Management, Elsevier, vol. 255(C).
    14. Lucio Di Matteo & Alessandro Spigarelli & Sofia Ortenzi, 2020. "Processes in the Unsaturated Zone by Reliable Soil Water Content Estimation: Indications for Soil Water Management from a Sandy Soil Experimental Field in Central Italy," Sustainability, MDPI, vol. 13(1), pages 1-15, December.
    15. George P. Petropoulos & Prashant K. Srivastava & Maria Piles & Simon Pearson, 2018. "Earth Observation-Based Operational Estimation of Soil Moisture and Evapotranspiration for Agricultural Crops in Support of Sustainable Water Management," Sustainability, MDPI, vol. 10(1), pages 1-20, January.
    16. Abimbola, Olufemi P. & Franz, Trenton E. & Rudnick, Daran & Heeren, Derek & Yang, Haishun & Wolf, Adam & Katimbo, Abia & Nakabuye, Hope N. & Amori, Anthony, 2022. "Improving crop modeling to better simulate maize yield variability under different irrigation managements," Agricultural Water Management, Elsevier, vol. 262(C).
    17. Dileep Kumar Gupta & Prashant K. Srivastava & Ankita Singh & George P. Petropoulos & Nikolaos Stathopoulos & Rajendra Prasad, 2021. "SMAP Soil Moisture Product Assessment over Wales, U.K., Using Observations from the WSMN Ground Monitoring Network," Sustainability, MDPI, vol. 13(11), pages 1-18, May.
    18. Nouri, Milad & Homaee, Mehdi & Pereira, Luis S. & Bybordi, Mohammad, 2023. "Water management dilemma in the agricultural sector of Iran: A review focusing on water governance," Agricultural Water Management, Elsevier, vol. 288(C).

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