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Technical Brief

Predicting the Leaf Area of Vitis vinifera L. cvs. Cabernet Sauvignon and Shiraz

Yann Guisard, Colin J. Birch, Dejan Tesic
Am J Enol Vitic. June 2010 61: 272-277; published ahead of print June 01, 2010 ; DOI: 10.5344/ajev.2010.61.2.272
Yann Guisard
1Lecturer, Charles Sturt University/National Wine and Grape Industry Centre, Wagga Wagga, NSW, 2678, Australia; 2Associate Professor, University of Tasmania, Burnie Campus, Burnie, 7320, Tasmania, formerly Senior Lecturer, The University of Queensland, Gatton, QLD, 4343, Australia; 3formerly Senior Lecturer, Charles Sturt University/National Wine and Grape Industry Centre, Wagga Wagga, NSW, 2678, Australia.
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  • For correspondence: yguisard@csu.edu.au
Colin J. Birch
1Lecturer, Charles Sturt University/National Wine and Grape Industry Centre, Wagga Wagga, NSW, 2678, Australia; 2Associate Professor, University of Tasmania, Burnie Campus, Burnie, 7320, Tasmania, formerly Senior Lecturer, The University of Queensland, Gatton, QLD, 4343, Australia; 3formerly Senior Lecturer, Charles Sturt University/National Wine and Grape Industry Centre, Wagga Wagga, NSW, 2678, Australia.
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Dejan Tesic
1Lecturer, Charles Sturt University/National Wine and Grape Industry Centre, Wagga Wagga, NSW, 2678, Australia; 2Associate Professor, University of Tasmania, Burnie Campus, Burnie, 7320, Tasmania, formerly Senior Lecturer, The University of Queensland, Gatton, QLD, 4343, Australia; 3formerly Senior Lecturer, Charles Sturt University/National Wine and Grape Industry Centre, Wagga Wagga, NSW, 2678, Australia.
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Abstract

The planimetric area of grapevine leaf blades (LA) is required as input data in many grapevine growth models and quantitative studies of the soil/plant/atmosphere continuum. A subset of 300 scanned grapevine leaves was used to identify and compare allometric statistical models predicting the leaf area of grapevines (cultivars Cabernet Sauvignon and Shiraz). The mean absolute error (MAE), root mean square error (RMSE), and Δ (RMSE – MAE) were used as discriminatory criteria. Six families of models drawn from the literature were compared with stepwise regression using up to six possible predictor variables. Each family was fitted to each cultivar for three vineyard sites. Generic models were computed by aggregating the data across sites and cultivars. The Queensland (stepwise regressions) family performed best, closely followed by Elsner2 and Montero. The MAE of some generic models was at times less than that of their components because of the influence of sites and/or cultivars. Site- and cultivar-specific stepwise regressions are generally the most accurate methodology for estimating leaf surface area. Simple models were generally less accurate than models integrating several predictor variables.

  • leaf surface area
  • grapevines
  • statistical models
  • allometric relationships
  • Received May 2009.
  • Revision received November 2009.
  • Accepted December 2009.
  • Copyright © 2010 by the American Society for Enology and Viticulture
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Predicting the Leaf Area of Vitis vinifera L. cvs. Cabernet Sauvignon and Shiraz
Yann Guisard, Colin J. Birch, Dejan Tesic
Am J Enol Vitic.  June 2010  61: 272-277;  published ahead of print June 01, 2010 ; DOI: 10.5344/ajev.2010.61.2.272

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Predicting the Leaf Area of Vitis vinifera L. cvs. Cabernet Sauvignon and Shiraz
Yann Guisard, Colin J. Birch, Dejan Tesic
Am J Enol Vitic.  June 2010  61: 272-277;  published ahead of print June 01, 2010 ; DOI: 10.5344/ajev.2010.61.2.272
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