The planimetric leaf area of grapevines 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 for the prediction of the leaf area of grapevines (cultivars Cabernet Sauvignon and Shiraz). The Mean Absolute Error (MAE), the Root Mean Square Error (RMSE) and ' (RMSE ' MAE) were used as discriminatory critera. Six families of models drawn from the literature were computed as well as a stepwise regression using up to six possible predictor variables. Each family was fitted to each cultivar for three vineyard sites. In addition, generic models were computed by aggregating the data across sites and cultivars. The "Queensland' (stepwise regressions) family performed best, closely followed by the "Elsner2" and "Montero". The MAE of some generic models was sometimes smaller than that of their components, due to the influence of sites and/or cultivars. Site and cultivar specific stepwise regressions are proposed as being generally the most accurate methodology for the estimation of leaf surface area. Simple models were generally less accurate than models integrating several predictor variables.
|Number of pages||6|
|Journal||American Journal of Enology and Viticulture|
|Publication status||Published - Jun 2010|