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Accurate and nondestructive methods to determine individual leaf areas of plants are a useful tool in physiological and agronomic research. Determining the individual leaf area (LA) of rose (Rosa hybrida L.) involves measurements of leaf parameters such as length (L) and width (W), or some combinations of these parameters. Two-year investigation was carried out during 2007 (on thirteen cultivars) and 2008 (on one cultivar) under greenhouse conditions, respectively, to test whether a model could be developed to estimate LA of rose across cultivars. Regression analysis of LA vs. L and W revealed several models that could be used for estimating the area of individual rose leaves. A linear model having L×W as the independent variable provided the most accurate estimate (highest r2, smallest MSE, and the smallest PRESS) of LA in rose. Validation of the model having L×W of leaves measured in the 2008 experiment coming from other cultivars of rose showed that the correlation between calculated and measured rose LA was very high. Therefore, this model can estimate accurately and in large quantities the LA of rose plants in many experimental comparisons without the use of any expensive instruments

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Accurate and nondestructive methods to determine individual leaf areas of plants are a useful tool in physiological and agronomic research. Determining the individual leaf area (LA) of small fruit like raspberry (Rubus idaeus L.), redcurrant (Ribes rubrum L.), blackberry (Rubus fruticosus L.), gooseberry (Ribes grossularia L.), and highbush blueberry (Vaccinium corymbosum L.) involves measurements of leaf parameters such as length (L) and width (W) or some combinations of these parameters. A 2-year investigation was carried out during 2006 (on seven raspberry, seven redcurrant, six blackberry, five gooseberry, and two highbush blueberry cultivars) and 2007 (on one cultivar per species) under open field conditions to test whether a model could be developed to estimate LA of small fruits across cultivars. Regression analysis of LA versus L and W revealed several models that could be used for estimating the area of individual small fruit leaves. A linear model having LW as the independent variable provided the most accurate estimate (highest R 2, smallest mean square error, and the smallest predicted residual error sum of squares) of LA in all small fruit berries. Validation of the model having LW of leaves measured in the 2007 experiment coming from other cultivars of small fruit berries showed that the correlation between calculated and measured small fruit berries LAs was very high. Therefore, these models can estimate accurately and in large quantities the LA of small fruit plants in many experimental comparisons without the use of any expensive instruments.

The aim of the present experiment was to evaluate the currently used allometric models for Vitis vinifera L., as well as to develop a simple and accurate model using linear measurements [leaf length (L) and leaf width (W)], for estimating the individual leaf area (LA) of nine grapevine genotypes. For model construction, a total of 1,630 leaves coming from eight genotypes in 2010 was sampled during different leaf developmental stages and encompassed the full spectrum of leaf sizes. The model with single measurement of L could be considered an interesting option because it requires measurement of only one variable, but at the expense of accuracy. To find a model to estimate individual LA accurately for grapevine plants of all genotypes, both measurements of L and W should be involved. The proposed linear model [LA = -0.465 + 0.914 (L × W)] was adopted for its accuracy: the highest coefficient of determination (> 0.98), the smallest mean square error, the smallest prediction sum of squares, and the reasonably close prediction sum of squares value to error sum of squares. To validate the LW model, an independent data set of 200 leaves coming from another genotype in 2011 was used. Correlation coefficients showed that there was a highly reliable relationships between predicted leaf area and the observed leaf area, giving an overestimation of 0.8% in the prediction.