RESUMO -Os objetivos deste trabalho foram quantificar a biomassa e a estocagem de carbono em uma Floresta Estacional Semidecidual,com área de 44,11 ha, localizado no Parque Tecnológico de Viçosa, MG e avaliar as diferenças entre as metodologias de quantificação de biomassa propostas pelo Painel Intergovernamental sobre Mudanças do Clima (IPCC) e utilizando equações regionais. Para a quantificação da biomassa e estocagem de carbono da área utilizou-se duas metodologias distintas, uma com equações regionais e outra sugerida pelo IPCC. Os estoques totais de biomassa e de carbono foram de 116,98 t ha -1 e 56,31 t ha -1 ,respectivamente, pela metodologia de equações regionais, considerando os valores acima e abaixo do solo, sub-bosque e serapilheira. E pela metodologia sugerida pelo IPCC, os estoques totais de biomassa e de carbono foram de 107,59 t ha -1 e 48,70 t ha -1 , respectivamente. De acordo com os resultados a metodologia do IPCC subestimou a biomassa e o carbono em relação às equações regionais.Palavras-chave: Mata Atlântica; Sequestro de carbono; Metodologias de quantificação de carbono.
QUANTIFYING BIOMASS AND CARBON STOCK IN A SEASONAL SEMIDECIDUOUS FOREST IN VIÇOSA, MG, BRAZIL
Agrosilvopastoral and silvopastoral systems can increase carbon sequestration, offset greenhouse gas (GHG) emissions and reduce the carbon footprint generated by animal production. The objective of this study was to estimate GHG emissions, the tree and grass aboveground biomass production and carbon storage in different agrosilvopastoral and silvopastoral systems in southeastern Brazil. The number of trees required to offset these emissions were also estimated. The GHG emissions were calculated based on pre-farm (e.g. agrochemical production, storage, and transportation), and on-farm activities (e.g. fertilization and machinery operation). Aboveground tree grass biomass and carbon storage in all systems was estimated with allometric equations. GHG emissions from the agroforestry systems ranged from 2.81 to 7.98 t CO2e ha−1. Carbon storage in the aboveground trees and grass biomass were 54.6, 11.4, 25.7 and 5.9 t C ha−1, and 3.3, 3.6, 3.8 and 3.3 t C ha−1 for systems 1, 2, 3 and 4, respectively. The number of trees necessary to offset the emissions ranged from 17 to 44 trees ha−1, which was lower than the total planted in the systems. Agroforestry systems sequester CO2 from the atmosphere and can help the GHG emission-reduction policy of the Brazilian government.
Forests in the southwestern Amazon are rich, diverse, and dense. The region is of high ecological importance, is crucial for conservation and management of natural resources, and contains substantial carbon and biodiversity stocks. Nevertheless, few studies have developed allometric equations for this part of the Amazon, which differs ecologically from the parts of Amazonia where most allometric studies have been done. To fill this gap, we developed allometric equations to estimate the volume, biomass, and carbon in commercial trees with diameter at breast height (DBH) ≥ 50 cm in an area under forest management in the southeastern portion of Brazil’s state of Acre. We applied the Smalian formula to data collected from 223 felled trees in 20 species, and compared multiple linear and nonlinear models. The models used diameter (DBH) measured at 1.30 m height (d), length of the commercial stem (l), basic wood density (p), and carbon content (t), as independent variables. For each dependent variable (volume, biomass, or carbon) we compared models using multiple measures of goodness-of-fit, as well as graphically analyzing residuals. The best fit for estimating aboveground volume of individual stems using diameter (d) and length (l) as variables was obtained with the Spurr model (1952; logarithmic) (root mean square error (RMSE) = 1.637, R² = 0.833, mean absolute deviation (MAD) = 1.059). The best-fit equation for biomass, considering d, l, and p as the explanatory variables, was the Loetsch et al. (1973; logarithmic) model (RMSE = 1.047, R² = 0.855, MAD = 0.609). The best fit equation for carbon was the Loetsch et al. (1973; modified) model, using the explanatory variables d, l, p, and t (RMSE = 0.530, R² = 0.85, MAD = 0.304). Existing allometric equations applied to our study trees performed poorly. We showed that the use of linear and nonlinear allometric equations for volume, biomass, and carbon can reduce the errors and improve the estimation of these metrics for the harvested stems of commercial species in the southwestern Amazon.
Models to predict tree survival and mortality can help to understand vegetation dynamics and to predict effects of climate change on native forests. The objective of the present study was to use Artificial Neural Networks, based on the competition index and climatic and categorical variables, to predict tree survival and mortality in Semideciduous Seasonal Forests in the Atlantic Forest biome. Numerical and categorical trees variables, in permanent plots, were used. The Agricultural Reference Index for Drought (ARID) and the distance-dependent competition index were the variables used. The overall efficiency of classification by ANNs was higher than 92% and 93% in the training and test, respectively. The accuracy for classification and number of surviving trees was above 99% in the test and in training for all ANNs. The classification accuracy of the number of dead trees was low. The mortality accuracy rate (10.96% for training and 13.76% for the test) was higher with the ANN 4, which considers the climatic variable and the competition index. The individual tree-level model integrates dendrometric and meteorological variables, representing a new step for modeling tree survival in the Atlantic Forest biome.
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