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Allometric equations for predicting mineralomass in high-forest chestnut stands in Portugal

Author:
Patrício, M. S., Tomé, M.
Source:
Acta horticulturae 2018 no.1220 pp. 125-132
ISSN:
0567-7572
Subject:
Castanea sativa, adults, allometry, bark, biomass, branches, calcium, ecosystems, equations, felling, flowers, harvesting, least squares, leaves, magnesium, models, nitrogen, nutrients, phosphorus, potassium, prediction, trees, woodlands, Portugal
Abstract:
The assessment of nutrients in biomass tree-components is a time-consuming and expensive process, often involving tree felling, not always possible or desirable. Thus, mineralomass prediction equations are an important tool for the quantification of the nutrients exported in management and harvesting activities towards its replacement and sustainable management, as well as to evaluate the effect of other disturbances in the balance of ecosystems. Thus, given the importance of the relationship of biomass and nutrients (mineralomass) for dynamic and sustainable management of chestnut woodlands, above-ground mineralomass was studied in sweet chestnut (Castanea sativa Mill.) high-forest stands located in northern Portugal. Nutrient-specific prediction equations that allow estimating the mineralomass (N, P, K, Ca, Mg, S, B and C) stocked in the trees above the ground, considering the tree as a whole (stem + bark + branches + leaves + flowers) and seperately for each tree component: stem-wood, stem-bark, branches, leaves and flowers, based on tree dendrometric variables, DBH (diameter breast height) and total height, were developed.. Linear and non-linear regression estimation methods were used. Data analysis was based on information collected in destructive analysis of 34 felled trees, distributed by the existing diameter classes (10-65 cm) in three adult chestnut stands. Several linear and nonlinear equations were fitted by the least squares method to select models. A simultaneous fit by SUR method using iterative seemingly unrelated regression (ITSUR) was used for the final selected models. The best-fitting models are presented.
Agid:
6256547