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The state of mineral nutrition as well as accumulation and translocation of selected heavy metals As, Cr, Hg, and Pb were evaluated in poplar clones Robusta (Populus x euramericana) and I-214 (Populus x euramericana). These clones are characterized by fast growth and biomass production of relatively high quality. Soil water quantity and its dynamics is one of the most important factors for growth of poplar clones, in relation to their high transpiration and nutrient transport. The research was conducted in two locations in eastern and three in western Slovakia. The poplars were grown on alluvial plains continuously influenced by ground waters from surrounding rivers. Differences in As, Cr, and Pb concentrations were recorded among the locations only, whereas between these single-clones no differences were confirmed. Different accumulation patterns were found for some elements (Ca, K, Si) in poplars compared to those of softwood (spruce) and hardwood (beech) species. The mineral elements (Ca, K, Mg, Na, Si) were accumulated in above-ground parts as follows: stem > “small-wood”/bark > wood. Cr was the most represented risk element in biomass, while Hg reached the highest concentrations and in contrast to essential elements was accumulated predominantly in wood. A potential source of Hg was soil/water environment.
Height-diameter models define the general relationship between the tree height and diameter at each growth stage of the forest stand. This paper presents generalized height-diameter models for mixed-species forest stands consisting of Norway spruce (Picea abies Karst.), Silver fir (Abies alba L.), and European beech (Fagus sylvatica L.) from Slovakia. The models were derived using two growth functions from the exponential family: the two-parameter Michailoff and three-parameter Korf functions. Generalized height-diameter functions must normally be constrained to pass through the mean stand diameter and height, and then the final growth model has only one or two parameters to be estimated. These “free” parameters are then expressed over the quadratic mean diameter, height and stand age and the final mathematical form of the model is obtained. The study material included 50 long-term experimental plots located in the Western Carpathians. The plots were established 40–50 years ago and have been repeatedly measured at 5 to 10-year intervals. The dataset includes 7,950 height measurements of spruce, 21,661 of fir and 5,794 of beech. As many as 9 regression models were derived for each species. Although the “goodness of fit” of all models showed that they were generally well suited for the data, the best results were obtained for silver fir. The coefficient of determination ranged from 0.946 to 0.948, RMSE (m) was in the interval 1.94–1.97 and the bias (m) was –0.031 to 0.063. Although slightly imprecise parameter estimation was established for spruce, the estimations of the regression parameters obtained for beech were quite less precise. The coefficient of determination for beech was 0.854–0.860, RMSE (m) 2.67–2.72, and the bias (m) ranged from –0.144 to –0.056. The majority of models using Korf’s formula produced slightly better estimations than Michailoff’s, and it proved immaterial which estimated parameter was fixed and which parameters were free.
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