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Root zone soil moisture prediction models based on system identification: Formulation of the theory and validation using field and AQUACROP data

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  • Delgoda, Dilini
  • Saleem, Syed K.
  • Malano, Hector
  • Halgamuge, Malka N.

Abstract

In model-based irrigation control, the root zone soil moisture deficit (RZSMD) is maintained based on the water balance. To predict RZSMD in real-time, effective rainfall, irrigation and crop evapotranspiration need to be calculated online. Estimating the first two variables is more important yet tedious due to practical limitations of knowing the amount of water actually infiltrated into the soil. In order to solve this problem, we propose to apply system identification on water balance data to obtain a linear time series model. We further investigate how to carry out the modelling (i) under saturated conditions, (ii) when there is a rule-based irrigation control, and (iii) under measurement noise in the soil moisture readings. Using synthetic data we obtained a model fit above 80% in all cases. Additionally, we show the model optimality and applicability with an independent dataset, using residual tests. For two sets of field data, we observed model fits of 84% and 63%, and satisfaction in all residual tests. Simplicity in the model reduces calibration efforts whereas its linearity and adequacy recommend it for real-time irrigation control applications. In summary, the results indicate that a first order linear time series model based on system identification can successfully predict RZSMD in a real-time irrigation control system.

Suggested Citation

  • Delgoda, Dilini & Saleem, Syed K. & Malano, Hector & Halgamuge, Malka N., 2016. "Root zone soil moisture prediction models based on system identification: Formulation of the theory and validation using field and AQUACROP data," Agricultural Water Management, Elsevier, vol. 163(C), pages 344-353.
    • Handle: RePEc:eee:agiwat:v:163:y:2016:i:c:p:344-353
    DOI: 10.1016/j.agwat.2015.08.011
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    References listed on IDEAS

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    1. Rao, N. H. & Sarma, P. B. S. & Chander, Subhash, 1992. "Real-time adaptive irrigation scheduling under a limited water supply," Agricultural Water Management, Elsevier, vol. 20(4), pages 267-279, February.
    2. Gowing, J. W. & Ejieji, C. J., 2001. "Real-time scheduling of supplemental irrigation for potatoes using a decision model and short-term weather forecasts," Agricultural Water Management, Elsevier, vol. 47(2), pages 137-153, March.
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    Cited by:

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    2. Fawen, Li & Manjing, Zhang & Yong, Zhao & Rengui, Jiang, 2023. "Influence of irrigation and groundwater on the propagation of meteorological drought to agricultural drought," Agricultural Water Management, Elsevier, vol. 277(C).
    3. 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).
    4. Gong, Xuewen & Li, Xiaoming & Li, Yu & Bo, Guokui & Qiu, Rangjian & Huang, Zongdong & Gao, Shikai & Wang, Shunsheng, 2023. "An improved model to simulate soil water and heat: A case study for drip-irrigated tomato grown in a greenhouse," Agricultural Water Management, Elsevier, vol. 277(C).
    5. Ahmed A. Abdelmoneim & Roula Khadra & Angela Elkamouh & Bilal Derardja & Giovanna Dragonetti, 2023. "Towards Affordable Precision Irrigation: An Experimental Comparison of Weather-Based and Soil Water Potential-Based Irrigation Using Low-Cost IoT-Tensiometers on Drip Irrigated Lettuce," Sustainability, MDPI, vol. 16(1), pages 1-15, December.

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