RT Journal Article SR Electronic T1 Predicting the Leaf Area of Vitis vinifera L. cvs. Cabernet Sauvignon and Shiraz JF American Journal of Enology and Viticulture JO Am J Enol Vitic. FD American Society for Enology and Viticulture SP 272 OP 277 DO 10.5344/ajev.2010.61.2.272 VO 61 IS 2 A1 Yann Guisard A1 Colin J. Birch A1 Dejan Tesic YR 2010 UL http://www.ajevonline.org/content/61/2/272.abstract AB 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.