Irrigation Science

, 26:505 | Cite as

A step towards new irrigation scheduling strategies using plant-based measurements and mathematical modelling

  • Kathy Steppe
  • Dirk J. W. De Pauw
  • Raoul Lemeur
Original Paper


Because of the increasing worldwide shortage of freshwater and costs of irrigation, a new plant-based irrigation scheduling method is proposed. In this method, two real-time plant-based measurements (sap flow and stem diameter variations) are used in combination with a mathematical water flow and storage model in order to predict the stem water potential. The amount of required irrigation water is derived from a time integration of the sap flow profile, while the timing of the irrigation is controlled based on a reference value for the predicted stem water potential. This reference value is derived from the relationship between midday values of maximum photosynthesis rates and stem water potential. Since modelling is an important part of the proposed methodology, a thorough mathematical analysis (identifiability analysis) of the model was performed. This analysis showed that an initial (offline) model calibration was needed based on measurements of sap flow, stem diameter variation and stem water potential. Regarding irrigation scheduling, however, only sap flow and stem diameter variation measurements are needed for online simulation and daily model calibration. Model calibration is performed using a moving window of 4 days of past data of stem diameter variations. The research tool STACI (Software Tool for Automatic Control of Irrigation) was used to optimally combine the continuous measurements, the mathematical modelling and the real-time irrigation scheduling. The new methodology was successfully tested in a pilot-scale setup with young potted apple trees (Malus domestica Borkh) and its performance was critically evaluated.


Apple Tree Irrigation Schedule Irrigation Event Plant Water Status Stem Water Potential 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



We wish to thank the Research Foundation—Flanders (FWO) for the Postdoctoral Fellow funding granted to the first author and the Special Research Fund (BOF) of Ghent University for the Postdoctoral Fellow funding granted to the second author. This project was supported by a grant from the Institute for the Promotion of Innovation by Science and Technology in Flanders (IWT-Vlaanderen). We also wish to thank Philip Deman, technician of the Laboratory of Plant Ecology, for his enthusiastic support and valuable contribution to the experimental set-up, Tom De Swaef for his help with the measurements, and the anonymous reviewers for their valuable comments on this paper.


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Copyright information

© Springer-Verlag 2008

Authors and Affiliations

  • Kathy Steppe
    • 1
  • Dirk J. W. De Pauw
    • 2
  • Raoul Lemeur
    • 1
  1. 1.Faculty of Bioscience Engineering, Department of Applied Ecology and Environmental Biology, Laboratory of Plant EcologyGhent UniversityGhentBelgium
  2. 2.Faculty of Bioscience Engineering, Department of Applied Mathematics, Biometrics and Process Control, KERMIT: Knowledge Based SystemsGhent UniversityGhentBelgium

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