Abstract
This study tested the effects of inserting climatic variables inEucalyptus grandisas covariables of a dominant height model, which for site index classification is usually related to age alone. Dominant height values ranging from 1 to 12 years of age located in the Southeast region of Brazil were used, as well as data from 19 automatic meteorological stations from the area. The Chapman-Richards model was chosen to represent dominant height as a function of age. To include the environmental variables a modifier was included in the asymptote of the model. The asymptote was chosen since this parameter is responsible for the maximum value which the dominant height can reach. Of the four environmental variables most responsible for database variation, the two with the highest correlation to the mean annual increment in dominant height (mean monthly precipitation and temperature) were selected to compose the asymptote modifier. Model validation showed a gain in precision of 33% (reduction of the standard error of estimate) when climatic variables were inserted in the model. Possible applications of the method include the estimation of site capacity in regions lacking any planting history, as well as updating forest inventory data based on past climate regimes.
Highlights
In its wider scope, forest management involves the choices of strategies that ensure the sustainability of the enterprise as a whole
MAI dominant height −0.35 −0.25 −0.17 0.27 sdm); mean minimum temperature; mean precipitation. These variables were responsible in explaining 84.24% of the variability of the database, this does not guarantee that they possess correlation with dominant height
A correlation matrix was elaborated using the variables selected in the Principal Components Analysis (PCA) and mean annual dominant height increment, MAI (Table 3)
Summary
Forest management involves the choices of strategies that ensure the sustainability of the enterprise as a whole. According to Louw and Scholes [1], the classification of forest site productivity requires the knowledge of the geology, topography, climate, soils, and biotic factors that occur in the local. These authors sustain that the classification of forest sites should have ecological bases and not aligned with productivity (even if a covariance occurs). The use of site factors that influence forest growth (e.g., soil characteristics, climatic conditions) for the classification of site quality is reliable to differentiate broad regions of growth, in forestry the use of more direct stand characteristics (e.g., volume, height) is more common due to practical reasons
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