Journal
FOREST SYSTEMS
Volume 29, Issue 3, Pages -Publisher
INST NACIONAL INVESTIGACION & TECNOLOGIA AGRARIA & ALIMENTARIA-INIA-CSIC
DOI: 10.5424/fs/2020293-15500
Keywords
Tree growth; forest growth modelling; forest inventory; hierarchical data structure; Italy
Categories
Funding
- Carabinieri Command for Forest, Environmental and Agri-food protection
Ask authors/readers for more resources
Aim of study: Assessment of growth is essential to support sustainability of forest management and forest policies. The objective of the study was to develop a species-specific model to predict the annual increment of tree basal area through variables recorded by forest surveys, to assess forest growth directly or in the context of more complex forest growth and yield simulation models. Area of study: Italy. Material and methods: Data on 34638 trees of 31 different forest species collected in 5162 plots of the Italian National Forest Inventory were used; the data were recorded between 2004 and 2006. To account for the hierarchical structure of the data due to trees nested within plots, a two-level mixed-effects modelling approach was used. Main results: The final result is an individual-tree linear mixed-effects model with species as dummy variables. Tree size is the main predictor, but the model also integrates geographical and topographic predictors and includes competition. The model fitting is good (McFadden's Pseudo-R-2 0.536), and the variance of the random effect at the plot level is significant (intra-class correlation coefficient 0.512). Compared to the ordinary least squares regression, the mixed-effects model allowed reducing the mean absolute error of estimates in the plots by 64.5% in average. Research highlights: A single tree-level model for predicting the basal area increment of different species was developed using forest inventory data. The data used for the modelling cover 31 species and a great variety of growing conditions, and the model seems suitable to be applied in the wider context of Southern Europe.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available