Global review and state-of-the-art of biomass and carbon stock in the Amazon.

Understanding how studies have been carried out in the region helps to understand the Amazon rainforest potential in mitigating climate change. In addition, evaluating scientific production is essential to verify the main methods and places researched. Thus, this study objective was to build an overview and identify the main gaps regarding research related to biomass and carbon stocks with the coverage limit of the Amazon rainforest. Therefore, an analysis of the publications indexed in the Scopus database was developed until 2020, performing a bibliometric analysis and a systematic and state-of-the-art review. Initially 2042 publications were obtained, of which 786 met the inclusion criteria. The first work indexed in the Scopus database related to the theme was published in 1982. Over time, it was possible to observe an increasing behavior in relation to the interest in the theme. Natural environments were the most researched and change in the land use and occupation of the Amazon Rainforest is still poorly evaluated. Brazil is the country with the highest number of studies, followed by Peru and Colombia. Guyana and Suriname appear as possible scientific gaps and potential environments to be studied. Studies preferentially explore the biomass carbon, with the soil being little evaluated when compared to the total amount of papers found. When observing only the biomass studies, the aboveground biomass is the most evaluated, while the roots and necromass are little studied. The main variables used in biomass equations were diameter at 1.3 m above ground and total tree height. The biomass to carbon conversion factor has been widely used, but it can generate unreliable results. It is recommended to carry out local assessments of the carbon content, especially using the dry combustion method, which generates less waste, with more precise results and shorter execution time of the analyses. Such assessments present values for the location that can avoid false or misinterpretations of the biomass and carbon stocks in the Amazon Rainforest.

Selection criteria for linear regression models to estimate individual tree biomasses in the Atlantic Rain Forest, Brazil.

BACKGROUND: Biomass models are useful for several purposes, especially for quantifying carbon stocks and dynamics in forests. Selecting appropriate equations from a fitted model is a process which can involves several criteria, some widely used and others used to a lesser extent. This study analyzes six selection criteria for models fitted to six sets of individual biomass collected from woody indigenous species of the Tropical Atlantic Rain Forest in Brazil. Six models were examined and the respective fitted equations evaluated by the residual sum of squares, adjusted coefficient of determination, absolute and relative estimates of the standard error of estimate, and Akaike and Schwartz (Bayesian) information criteria. The aim of this study was to analyze the numeric behavior of these model selection criteria and discuss the ease of interpretation of them. The importance of residual analysis in model selection is stressed. RESULTS: The adjusted coefficient of determination ([Formula: see text]) and the standard error of estimate in percentage (Syx%) are relative model selection criteria and are not affected by sample size and scale of the response variable. The sum of squared residuals (SSR), the absolute standard error of estimate (Syx), the Akaike information criterion and the Schwartz information criterion, in turn, depend on these quantities. The best fit model was always the same within a given data set regardless the model selection criteria considered (except for SSR in two cases), indicating they tend to converge to a common result. However, such criteria are not always closely related across different data sets. General model selection criteria are indicative of the average goodness of fit, but do not capture bias and outlier effects. Graphical residual analysis is a useful tool to this detection and must always be used in model selection. CONCLUSIONS: It is concluded that the criteria for model selection tend to lead to a common result, regardless their mathematical formulation and statistical significance. Relative measures of goodness of fitting are easier to interpret than the absolute ones. Careful graphical residual analysis must always be used to confirm the performance of the models.

Dynamics of carbon and CO2 removals by Brazilian forest plantations during 1990-2016.

BACKGROUND: We analyzed the dynamics of carbon (C) stocks and CO2 removals by Brazilian forest plantations over the period 1990-2016. Data on the extent of forests compiled from various sources were used in the calculations. Productivities were simulated using species-specific growth and yield simulators for the main trees species planted in the country. Biomass expansion factors, root-to-shoot ratios, wood densities, and carbon fractions compiled from literature were applied. C stocks in necromass (deadwood and litter) and harvested wood products (HWP) were also included in the calculations. RESULTS: Plantation forests stocked 231 Mt C in 1990 increasing to 612 Mt C in 2016 due to an increase in plantation area and higher productivity of the stands during the 26-year period. Eucalyptus contributed 58% of the C stock in 1990 and 71% in 2016 due to a remarkable increase in plantation area and productivity. Pinus reduced its proportion of the carbon storage due to its low growth in area, while the other species shared less than 6% of the C stocks during the period of study. Aboveground biomass, belowground biomass and necromass shared 71, 12, and 5% of the total C stocked in plantations in 2016, respectively. HWP stocked 76 Mt C in the period, which represents 12% of the total C stocked. Carbon dioxide removals by Brazilian forest plantations during the 26-year period totaled 1669 Gt CO2-e. CONCLUSIONS: The carbon dioxide removed by Brazilian forest plantations over the 26 years represent almost the totality of the country´s emissions from the waste sector within the same period, or from the agriculture, forestry and other land use sector in 2016. We concluded that forest plantations play an important role in mitigating GHG (greenhouse gases) emissions in Brazil. This study is helpful to improve national reporting on plantation forests and their GHG sequestration potential, and to achieve Brazil's Nationally Determined Contribution and the Paris Agreement.

A model based on environmental factors for diameter distribution in black wattle in Brazil.

This article discusses the dynamics of a diameter distribution in stands of black wattle throughout its growth cycle using the Weibull probability density function. Moreover, the parameters of this distribution were related to environmental variables from meteorological data and surface soil horizon with the aim of finding a model for diameter distribution which their coefficients were related to the environmental variables. We found that the diameter distribution of the stand changes only slightly over time and that the estimators of the Weibull function are correlated with various environmental variables, with accumulated rainfall foremost among them. Thus, a model was obtained in which the estimators of the Weibull function are dependent on rainfall. Such a function can have important applications, such as in simulating growth potential in regions where historical growth data is lacking, as well as the behavior of the stand under different environmental conditions. The model can also be used to project growth in diameter, based on the rainfall affecting the forest over a certain time period.