A model for the spatial prediction of water status in vines (Vitis vinifera L.) using high resolution ancillary information

A model for the spatial prediction of water status in vines (Vitis vinifera L.) using high... This paper establishes and tests a model to extrapolate vine water status spatially across a vineyard block. The proposed spatial model extrapolates predawn leaf water potential (PLWP), measured at a reference location, to other unsampled locations using a linear combination of spatial ancillary information sources (AIS) and the reference measurement. In the model, the reference value accounts for temporal variability and the AIS accounts for spatial variation of vine water status, which enables extrapolation over the whole domain (vine fields in this case) at any time when a reference measurement is made. The spatial model was validated for two fields planted with Syrah and Mourvèdre during the seasons 2003–2004 and 2005–2006, respectively, in the south of France. The proposed spatial model significantly improved the prediction of vine water status, especially under conditions of high water restriction (PLWP < −0.4 MPa), compared with a non-spatial model. The model was robust to the choice of reference site. The results also highlighted that AIS pertaining to canopy growth are the most relevant variables for predicting PLWP under these experimental conditions. Preliminary results showed the potential to calibrate the model from a limited number of field measurements, making it a realistic option for adoption in commercial vineyards. The success of the spatial model in improving the quality of prediction of PLWP means it could be incorporated into a decision-support tool to improve irrigation management within a vineyard. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Precision Agriculture Springer Journals

A model for the spatial prediction of water status in vines (Vitis vinifera L.) using high resolution ancillary information

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Springer US
Copyright © 2010 by Springer Science+Business Media, LLC
Life Sciences; Agriculture; Soil Science & Conservation; Remote Sensing/Photogrammetry; Statistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences; Atmospheric Sciences
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