& Key message Local site-level calibration of allometric models was scrutinized. Two Bayesian calibration methods were compared to local random effects estimations. The Bayesian calibration methods proved more effective than local estimation of random effects in reducing prediction bias. The simplest literature-based calibration can be recommended. The local calibration had minor effects on stem volume estimations. & Context The spatial variability of trees allometry has long prompted the necessity for local, site-or stand-level calibrations. Mixed-effect models have enabled a quantitative progress through a local calibration where data are available. More recently, Bayesian statistics brought new alternatives owing to their formal definition of the random effects from prior information. & Aims To compare three local calibration methods: (i) a calibration based on the estimation of the local random effects, (ii) a Bayesian calibration where prevailing measurements are used to produce prior estimations, and (iii) a Bayesian calibration reproducing a calibration based on literature data only. & Methods The three calibrations were compared using a stem taper model developed for Norway spruce in Romania. The taper model was fitted to a large dataset, then applied locally to two high-elevation sites with contrasting growing conditions. & Results The local calibration of mixed-effect models resulted in small gains and high biases. The Bayesian calibrations yielded better results, mostly because the Monte Carlo Markov Chain implementation permitted to tune of all the model's parameters simultaneously. The differences in stem volume estimations were however always very small ranging from − 5.2 to 3.3% of the non-calibrated volume. & Conclusion The Bayesian literature-like calibration performed as well as the calibration using the large dataset (4-97% bias reduction according to the tree) and can be preferred for its ease of use.
In this paper, site-specific allometric biomass models were developed for European beech (Fagus sylvatica L.) and silver fir (Abies alba Mill.) to estimate the aboveground biomass in Șinca virgin forest, Romania. Several approaches to minimize the demand for site-specific observations in allometric biomass model development were also investigated. Developing site-specific allometric biomass models requires new measurements of biomass for a sample of trees from that specific site. Yet, measuring biomass is laborious, time consuming, and requires extensive logistics, especially for very large trees. The allometric biomass models were developed for a wide range of diameters at breast height, D (6–86 cm for European beech and 6–93 cm for silver fir) using a logarithmic transformation approach. Two alternative approaches were applied, i.e., random intercept model (RIM) and a Bayesian model with strong informative priors, to enhance the information of the site-specific sample (of biomass observations) by supplementing with a generic biomass sample. The appropriateness of each model was evaluated based on the aboveground biomass prediction of a 1 ha sample plot in Șinca forest. The results showed that models based on both D and tree height (H) to predict tree aboveground biomass (AGB) were more accurate predictors of AGB and produced plot-level estimates with better precision, than models based on D only. Furthermore, both RIM and Bayesian approach performed similarly well when a small local sample (of seven smallest trees) was used to calibrate the allometric model. Therefore, the generic biomass observations may effectively be combined with a small local sample (of just a few small trees) to calibrate an allometric model to a certain site and to minimize the demand for site-specific biomass measurements. However, special attention should be given to the H-D ratio, since it can affect the allometry and the performance of the reduced local sample approach.
This study analyses the influence of a planting scheme on physical properties of Norway spruce wood. The research material consisted of 326 Norway spruce trees (Picea abies (L.) H. Karst) selected from an experimental plot with four planting variants (2500, 3330, 5000, and 7510 trees·ha−1). The research aspects were: (1) wood density (measured by volumetric method and using microdrilling resistance as proxy), (2) microdrilling resistance, and (3) sound speed. There was a decrease in wood density values (from 0.3376 to 0.3367 g·cm−3) and in microdrilling resistance values (from 15.136% to 14.292%) as the number of trees·ha−1 used for plantation increased from 2500 to 5000. The planting variant with 7510 trees·ha−1 had the largest value (0.3445 g·cm−3 for wood density and 15.531% for microdrilling resistance). Sound speed decreased from 1032.8 to 989.8 m·s−1 as the number of trees·ha−1 increased from 2500 to 7510. These results show a relationship between DBH values and studied physical properties. This relationship is more evident for variants with low planting density (e.g., 2500, 3330 trees· ha−1) than that of dense planting variants (e.g., 7510 trees· ha−1). The explanation may be that the growth of trees in dense plantings is slower; in less dense planting variants, the increase in wood is greater, and as a result, wood volumetric density dependence on the DBH value is greater.
This study analyses the possibility of assessing standing-tree wood density by microdrilling during tending forestry work carried out on Norway spruce stands. The research material comes from 4 experimental plots and consists of 270 trees (78 trees = control variant, 85 trees = moderate variant, and 107 trees = strong variant). The research objectives were to: (1) highlight wood density particularities, (2) identify wood resistance to microdrilling particularities, and (3) assess standing-tree wood density by microdrilling. For the control variant, average density recorded values of 0.357 ± 0.021 and 0.386 ± 0.027 g·cm−3; in the moderate variant, values were between 0.359 ± 0.029 and 0.393 ± 0.027 g·cm−3; and the strong variant was characterized by the limits of 0.364 ± 0.020 and 0.397 ± 0.027 g·cm−3. Average microdrilling resistance values were between 16.6 ± 2.6 and 22.5 ± 3.0% for the control variant; the moderate variant was characterized by the limits of 18.3 ± 3.1 and 23.4 ± 3.3%; and the strong variant recorded value of 19.7 ± 2.6 and 20.5 ± 2.6 (1.5)%. The linear regression results showed that microdrilling resistance increased as wood density increased. Additionally, generalized linear models showed that, when using covariates of microdrill resistance and tree diameter at breast height, there was a significant influence on the dependent variable, wood density, for all considered work variants. These results suggest that it is possible to consistently estimate both quality and resistance in Norway spruce standing trees using microdrilling. Our findings suggest that wood density and microdrilling resistance are dependent on biometric and qualitative characteristics, as well as the amount of tending forestry work conducted on Norway spruce stands.
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