Irrigation Science

, 28:143 | Cite as

Spatial extrapolation of the vine (Vitis vinifera L.) water status: a first step towards a spatial prediction model

  • C. Acevedo-OpazoEmail author
  • B. Tisseyre
  • H. Ojeda
  • S. Guillaume
Original Paper


The goal of this study is to propose a model that allows for spatial extrapolation of the vine water status over a whole field from a single reference site. The precision of the model was tested using data of spatial plant water status from a commercial vineyard block located in the Languedoc-Roussillon region, France. Observations of plant water status were made on 49 sites (three vines per site) on a regular grid at various times in the growing seasons over two non-irrigated fields planted with Shiraz and Mourvèdre cultivars. Plant water status was determined by measuring predawn leaf water potential (PLWP). Results showed a significant within-field variability of PLWP over space and time, and the existence of significant linear relationship amongst PLWP values measured at different dates. Based on these results, a linear model of spatial extrapolation of PLWP values was proposed. This model was able to predict spatial variability of PLWP with a spatial and temporal mean error less than 0.1 MPa on Shiraz as well as on Mourvèdre. This model provides maps of spatial variability in PLWP at key phenological stages on the basis of one measurement performed on a reference site. The model calibration is, in its current state, based on a significant database of PLWP measurements. This makes unrealistic its application to commercial vineyards. However, the approach constitutes a significant step towards the spatial extrapolation of vine water status. Finally, the study mentions alternative ways to build up such models using auxiliary information such as airborne imagery, apparent soil conductivity and easily measured vine/canopy development parameters.


Reference Site Water Restriction Plant Water Status Differential Global Position System Predawn Leaf 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.


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Copyright information

© Springer-Verlag 2009

Authors and Affiliations

  • C. Acevedo-Opazo
    • 1
    Email author
  • B. Tisseyre
    • 2
  • H. Ojeda
    • 3
  • S. Guillaume
    • 4
  1. 1.Universidad de Talca, Facultad de Ciencias Agrarias, CITRATalcaChile
  2. 2.Montpellier SupAgroUMR ITAPMontpellier Cedex 1France
  3. 3.INRA, Experimental Station of Pech RougeGruissanFrance
  4. 4.Cemagref, UMR ITAPMontpellierFrance

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