The inverted forest: Aboveground and notably large belowground carbon stocks and their drivers in Brazilian savannas.

Savannas contribute to ca. 30 % of the total terrestrial net primary productivity and are responsible for significant carbon storage. Savannas in South America are mostly found within the Cerrado Domain, which is very threatened and presents remarkable carbon pools. Herein, we used a unique dataset of 21 Cerrado sites spanning 144 permanent field plots in Southeastern Brazil to assess the general patterns of above and belowground carbon stocks. We identified the main environmental and tree diversity drivers of aboveground wood carbon and productivity, belowground carbon stocks (roots and soil), carbon ratios (root:shoot and above:below) and total carbon stocks in the Cerrado through a combination of climatic estimates, fire frequency data, field measurements of vegetation, roots, soil carbon, nutrients and texture, and assessment of different components of diversity (species, functional and phylogenetic). Our findings reveal average aboveground, root, and soil carbon stocks of 20.4, 14.24, and 123.13 Mg.ha-1, respectively. Average Root:Shoot and Above:Below confirm the "inverted forest" concept with values of 1.58 and 0.21, respectively. Total carbon was 145.62 Mg.ha-1, reinforcing the great amount of carbon storage in the Cerrado and its role in the carbon cycle and dynamics. Tree diversity variables (mainly species diversity and functional composition variables) had more significant effects over aboveground variables, whereas environmental variables had more significant effects over belowground variables. Ratios and total carbon mixed up these effects. The impressive values of carbon storage, especially belowground, point out the need to better manage and protect the Cerrado. Moreover, our findings might be particularly relevant for discussions on restoration programs focused on the trees-for­carbon idea that do not consider species diversity and belowground carbon stocks.

Assessment of iron-rich tailings via portable X-ray fluorescence spectrometry: the Mariana dam disaster, southeast Brazil.

On November 5, 2015, the Fundão dam collapsed and released > 60 million m3 of iron-rich mining sediments into the Doce river basin, covering >1000 ha of floodplain soils across ~80 km from the rupture. The characterization of alluvial mud covering and/or mixed with native soil is a priority for successful environmental rehabilitation. Portable X-ray fluorescence (pXRF) spectrometry was used to (1) assess the elemental composition of native soils and alluvial mud across impacted riparian areas; and 2) predict fertility properties of the mud and soils that are crucial for environmental rehabilitation and vegetation establishment (e.g., pH, available macro and micronutrients, cation exchange capacity, organic matter). Native soils and alluvial mud were sampled across impacted areas and analyzed via pXRF and conventional laboratory methods. Random forest (RF) regression was used to predict fertility properties using pXRF data for pooled soil and alluvial mud samples. Mud and native surrounding soils were clearly differentiated based on chemical properties determined via pXRF (mainly SiO2, Al2O3, Fe2O3, TiO2, and MnO). The pXRF data and RF models successfully predicted pH for pooled samples (R2 = 0.80). Moderate predictions were obtained for soil organic matter (R2 = 0.53) and cation exchange capacity (R2 = 0.54). Considering the extent of impacted area and efforts required for successful environmental rehabilitation, the pXRF spectrometer showed great potential for screening impacted areas. It can assess total elemental composition, differentiate alluvial mud from native soils, and reasonably predict related fertility properties in pooled heterogeneous substrates (native soil + mud + river sediments).

Temporal decomposition sampling and chemical characterization of eucalyptus harvest residues using NIR spectroscopy and chemometric methods.

Near-infrared (NIR) spectroscopy and chemometric methods were used to predict the chemical properties of decomposing eucalyptus harvest residues to better understand the decomposition process of these materials. Leaves, twigs, branches, and bark from a decomposition experimental set up in commercial plantations were sampled for one year. The contents of carbon (C), nitrogen (N), extractives (EX), acid-soluble lignin (SL), Klason insoluble lignin (KL) and holocellulose (HC) were determined by the reference method in the collected samples. Principal component analysis (PCA) was employed to distinguish the types of harvest residues throughout the decomposition period. Multi-residue regression models were built from the NIR spectra using partial least squares regression (PLS). Two feature selection methods, i.e., ordered predictors selection (OPS) and genetic algorithm (GA), were applied and compared. The OPS and GA did not differ statistically; however, compared with the GA, OPS was more computationally efficient and selected fewer variables. Using the PLS-OPS models, the root mean square errors of prediction (RMSEP) for C, N, EX, SL, KL and HC were 19.70, 0.08, 0.74, 0.39, 28.13 and 33.99, respectively, and the prediction correlations (Rp) for these properties were 0.94, 0.99, 0.99, 0.99, 0.96 and 0.98, respectively. PLS-discriminant analysis (PLS-DA) was used to classify the samples over the decomposition time and provided a good separation. Some mismatches obtained in the modeled classes were explained by the differences in the decomposition rate and changes in the chemical composition of the different harvest residue components that were evaluated. The results showed the feasibility of NIR spectroscopy and chemometric methods to evaluate the chemistry of decomposing eucalyptus harvest residues, indicating that these methods can be used as rapid and inexpensive alternatives to conventional methods to help understand the decomposition process.