Variables influencing cork thickness in spanish cork oak forests A modelling approach
In this study, we evaluate the influence of different variables on cork thickness in cork oak forests. For this purpose, first we fitted a multilevel linear mixed model for predicting average cork thickness, and then identified the explanatory covariates by studying their possible correlation with random effects. The model for predicting average cork thickness is described as a stochastic process, where a fixed, deterministic model, explains the mean value, while unexplained residual variability is described and modelled by including random parameters acting at plot, tree, plot x cork harvest and residual within-tree levels, considering the spatial covariance structure between trees within the same plot. Calibration is carried out by using the best linear unbiased predictor (BLUP) theory. Different alternatives were tested to determine the optimum subsample size which was found to be appropriate at four trees. Finally, the model was applied and its performance in the estimation of cork production was tested and compared with the cork weight model traditionally used in Spain. © INRA, EDP Sciences, 2007.
| Main Authors: | , , , |
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| Format: | artículo biblioteca |
| Language: | English |
| Published: |
BioMed Central
2007
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| Subjects: | Cork thickness, Mixed model, Calibration, Quercus suber L., |
| Online Access: | http://hdl.handle.net/20.500.12792/4095 http://hdl.handle.net/10261/292096 |
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| Summary: | In this study, we evaluate the influence of different variables on cork thickness in cork oak forests. For this purpose, first we fitted a multilevel linear mixed model for predicting average cork thickness, and then identified the explanatory covariates by studying their possible correlation with random effects. The model for predicting average cork thickness is described as a stochastic process, where a fixed, deterministic model, explains the mean value, while unexplained residual variability is described and modelled by including random parameters acting at plot, tree, plot x cork harvest and residual within-tree levels, considering the spatial covariance structure between trees within the same plot. Calibration is carried out by using the best linear unbiased predictor (BLUP) theory. Different alternatives were tested to determine the optimum subsample size which was found to be appropriate at four trees. Finally, the model was applied and its performance in the estimation of cork production was tested and compared with the cork weight model traditionally used in Spain. © INRA, EDP Sciences, 2007. |
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