Knowledge of the Leaf Area Index (LAI) of sweet cherry orchards is crucial for diagnosis about their production potential. The objective of this research was to develop simple, precise and non-destructive methods for estimating LA/tree (and based on that the LAI, dividing LA/tree by the area allocated to each tree) in orchards trained as tatura-trellis. Therefore, four models were evaluated: two simple linear regression models using as explanatory variables the Trunk Cross-Sectional Area (TCSA; dm2) (30 cm above the floor) or the Wood Volume of the Central Leader (WVCL; dm3) (considering the trunk as a cone: WVCL = π?r2?h/3) and two multiple regression models combining Mean Leaf Area (MLA; dm2) with the two previously mentioned variables. In all cases, LA/tree was the dependent variable. The TCSA and WVCL of 27 ‘Sweetheart’/’SL64’ trees conducted as tatura-trellis were registered, covering a wide range of tree sizes. At full canopy, all leaves of each tree were counted and 2% of them were randomly taken as a sample, from which the MLA was estimated. Leaf Area (LA) per tree was calculated multiplying the number of leaves by MLA. The best models to explain LA/tree variability were multiple linear regression equations combining WVCL and MLA (LA/tree = -9.18 + 37.31 ? MLA + 0.83 ? WVCL), or TCSA and MLA (LA/tree = -11.23 + 37.2 ? MLA + 15.91 ? TCSA), with R2 of 0.83 and 0.84, respectively. The R2 of simple linear regression models utilizing WVCL or TCSA were also high (0.69 and 0.70, respectively). However, the non-inclusion of MLA in these models, would limit their applicability in cultivars with different leaf morphology. The multiple regression equation including WVCL and MLA as explanatory variables seems to be the most robust model to be applied in other situations, because it considers two dimensions of the trunk and the leaf morphology.
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