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Concerns about the sustainability of tropical forestry motivated this study on post-logging timber and carbon dynamics over a 20-year period in Paragominas, Pará, Brazil. Previously unlogged forest was subjected to conventional logging (CL), reduced-impact logging (RIL), or was set aside as an unlogged control. All trees ⩾25 cm DBH and all trees of commercial species ⩾10 cm DBH were monitored in a 24.5 ha plot in each treatment, with a 5.25 ha subplot in each for monitoring all trees ⩾10 cm DBH. Above-ground biomass and bole volumes of merchantable species were tracked based on 10 inventories made between 1993 and 2014. Pre-logging biomass and bole volumes of commercial species were estimated as 237, 231, and 211 Mg ha −1 , and 78, 80, and 70 m 3 ha −1 , in the RIL, CL, and unlogged plots, respectively. One year after logging, biomass was reduced 14% by RIL and 24% by CL with corresponding merchantable species volume reductions of 21% and 31%. By 2014, biomass and bole volumes of commercial species had recovered 95% and 98% of their pre-logging stocks in the RIL plot but only 76% and 72% in the CL plot, respectively; timber volumes from large trees (⩾50 cm DBH) were only recovered to 81% in the RIL plot and to 53% in the CL plot. Over the first twenty years after logging, average volume increments from commercial species were substantially higher in the RIL plot (0.72 m 3 ha −1 year −1 ) than in the CL plot (0.08 m 3 ha −1 year −1 ). Recovery of both biomass and timber volumes were temporarily reversed between 2009 and 2014 due to a 4-fold increase in annual mortality rates in the RIL plot and a 5.5-fold increase in the CL plot (as well as a 3-fold increase in the control plot), all presumably related to the extreme drought of 2010. Our findings support the claim that use of RIL techniques accelerates rates of biomass and timber stock recovery after selective logging. [ABSTRACT FROM AUTHOR]

Abstract: In the wet tropics, near the Atlantic Coast of Brazil, drought may reduce plantation yields by as much one-third over a six-to-seven-year rotation. For land owners, annual variation in production cannot be estimated with empirical models. In this paper, we examine whether the process-based growth model, 3-PG is sufficiently sensitive to climatic variation to provide a virtual record of changes in growing stock across 180,000ha eucalypt plantation estate. We first mapped variation in climate and soil properties, and then ran simulations for the current planted forest with ages varying from one to seven years. Model predictions of stand volume and mean tree diameter agreed closely with measurements acquired on 60 reference plots monitored over the test period; the prediction of mean annual increment (MAI) was less reliable. Available soil water (ASW) and leaf area index (LAI) were also measured and compared with the model estimations. Vapour pressure deficit (VPD) and ASW accounted for most of the variation in yields. We conclude that this spatial modelling approach offers a reasonable alternative to extensive ground surveys, particularly when climatic variation extends beyond the historical average for a region. [Copyright &y& Elsevier]

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• Caatinga management promotes low GHG emission and sustainable wood fuel supply. • Growth depends on rainfall and hardly on soil or original standing stock. • Forest management practices/regulations should be based on local rainfall patterns. • Available rainfall data are an accurate tool to predict growth of managed stands. All over the world, there is increasing demand for wood and other goods from seasonally dry tropical forests; the "Caatinga" forest in northeast Brazil is a case in point. In order to set up sustainable forest management protocols, a comprehensive understanding of the main drivers of forest growth is needed, but few studies have focused on this subject in the Caatinga biome. Traditionally, periodic annual increment (PAI) has been calculated by dividing standing stock by the legally imposed minimum cutting cycle of 15 years, but it is doubtful that this guarantees sustainability. We use data from 20 coupes spread over 10 managed areas and apply both multiple regression and tree regression techniques to correlate PAI with 27 environmental variables including mean annual rainfall and many soil properties. We find that neither the time since harvesting nor the stock before harvest are significantly related to PAI. Instead, using a simple linear regression model, we show that rainfall can explain most (72%) of the variation, while a tree regression model, which captures non-linear relations between rainfall and PAI, explains 96% of the variation. On the other hand, no soil factors contribute significantly to the overall explanation of growth after harvest. We conclude that planning of sustainable management could be greatly improved by use of our regression models and rainfall data which are widely available at local level across the Caatinga. Moreover, this would obviate the need for costly forest inventories. [ABSTRACT FROM AUTHOR]

• Integration of PCA and ANN provide an excellent approach to modeling MAI7. • Soil physical attributes are more associated with MAI7 by Spearman's correlation. • Three latent variables explain 84% of the total variance of variables biophysical. • ANN precisely models the MAI7 from three latent variables. Modeling the productive capacity of forest sites from biophysical factors is important when site-dominant height data is not available. For this reason, we aim with this study to model the mean annual volume increment at age 7 (MAI7) of Eucalyptus clonal plantations, to evaluate the accuracy of the modeling, and to fit an empirical equation. For this, we used data from twenty-two variables collected in 51 plots distributed randomly in three classes of MAI7 predicted. Initially, Spearman's rank correlation was used for primary mining of these variables, then principal components analysis (PCA) was used to create orthogonal latent variables that were used as input in the artificial neural network (ANN) to MAI7 predict. Spearman's and PCA analysis proved to be excellent for data mining because when used together were enabled to reduce the number of variables and create a variable that represented the maximum variance of the variables which were significantly associated with the MAI7. All ANN trained exhibited high learning capability when using as input to the latent variables created in conjunction with the classes of MAI7, but the ANN trained with architecture 6-9-1 made prediction with greater precision for the test set and too showed high accuracy to the train set. Therefore, combining PCA with ANN was an excellent approach to developing an empirical equation to accurately predict MAI7 of clonal plantings of hybrid of Eucalyptus located in the southeast of Pará State, Brazil from biophysical variables. [ABSTRACT FROM AUTHOR]