Search

The objective of this study was to estimate, by the indirect method, the biomass and the carbon accumulated in riparian forest restoration plantings. Twenty-one sites were selected, including degraded areas, disturbed areas and native forest around the reservoirs of the Camargos and Volta Grande hydroelectric power plants, in the Grande river, for estimating the biomass and carbon stock. The results showed that the plantings done in degraded areas were not capable to sequester the atmospheric carbon in the biomass similarly to the native forest fragment, but the plantings in better sites performed this function more efficiently than the native forest. The biomass and the carbon accumulated in the arboreal stratum were higher in the older stands in the better sites. The increments of biomass and carbon were smaller in the stands that suffer frequente disturbances. The older stands did not stock more carbon in the soil than the younger ones, indicating that the riparian forest plantings did not have time yet to perform this function by the litter fall and decomposition of the organic matter. Many of the restoration plantings under better soil conditions, up to the age of 17, played the role of carbon sequestration in a similar or more efficient way than the native forest fragment, showing the potential of ciliary reforestation in sequestration and immobilization of carbon. [ABSTRACT FROM AUTHOR]

• The spatial distribution of soil C in the Cerrado of MG, Brazil, was determined. • The upper 30 cm of soil holds 45% of the stock found in the first meter. • Average soil C stock in MG Cerrado is greater than one reported for Brazilian Cerrado. • MG Cerrado woodland remnants store 1.03 Pg of C in the first meter of soil. • Regional-scale studies are needed to refine national estimates. In Brazil, studies addressing soil carbon (C) stocks under native vegetation are scarce and often limited to land use change comparisons at the plot level, while studies across larger areas are based on legacy data that address multiple land uses, use wet-oxidations methods to determine C concentrations (C c) and lack proper soil bulk density (ρ b) determinations. Together, these factors may result in either underestimations or biased soil C stocks, thereby hampering the development of adequate land use policy. In this study, we aimed to (i) determine the soil C stocks at different depths (0–10, 10–20, 20–40, 40–60 and 60–100 cm) in Cerrado woodland remnants in Minas Gerais (MG), southeastern Brazil, (ii) study the spatial distribution of soil C stocks using regression-kriging (RK), and (iii) provide a description of the soil C stock distribution and its relationship with variables that drive the soil C stock in the MG Cerrado. We designed a large soil inventory, in which soil samples were collected for soil C c and ρ b determination, to compute soil C stocks in the Cerrado biome of MG. Afterwards, we used RK to model the soil C stocks across the MG Cerrado to a depth of 1 m. Our results show that using RK to map C stocks across different soil layers in the MG Cerrado resulted in accurate estimates. The soil C stocks in the MG Cerrado tend to increase in the west-east direction, are positively correlated with altitude and silt + clay content, and are negatively correlated with the mean annual temperature and total annual precipitation. On average, the C stored at the 0–40 cm layer accounted for 56% of the total C stocked in the top 1 m of soil. Approximately 1.03 Pg of C is stored in the MG Cerrado soils at a depth of 1 m. The average soil C stock in the MG Cerrado is approximately 53% larger than the average indicated for the Brazilian Cerrado. Our results highlight the need for more detailed regional scale soil C inventories in the country to refine national C accounting. We believe our study can aid the development of adequate land use policy and could be used as a baseline for land use change studies in the MG Cerrado. [ABSTRACT FROM AUTHOR]

Soil moisture temporal variability is an important concept in understanding forest management. The objective of this study was to analyse the effect of planting density on soil profile moisture in a clonal Eucalyptus plantation. Soil moisture was observed every 15 days at 25 points distributed throughout the eucalyptus stand between November 2013 and October 2015 to characterize the moisture according to the following three types of spacings: 3 x 2, 3 x 3, and 3 x 5 m. At each point, the 0 - 10, 10 - 20, 20 - 30, 30 - 40, and 60 - 100 cm layers were evaluated using a Profile Probe PR2 model. Soil moisture showed high spatial variability in the 0 - 10 cm layer, whereas from 30 to 100 cm deep, the moisture tended to become more homogeneous throughout the area. The dry periods presented greater temporal soil moisture stability when compared to the rainy seasons. The 3 x 2 m spacing has greater temporal soil moisture stability when compared to the others, while the 3 x 3 m spacing presented greater temporal variability. [ABSTRACT FROM AUTHOR]

The total soil organic carbon (C) is regulated by climatic factors, altitude, texture and soil depth. Due to higher storage in surface, it is common exponential model fit to the C content in the soil profile. This study was carried out in order to study the influence of climatic factors, altitude and soil depth on the vertical distribution (0-100 cm) of C in five cerrado fragments located in the municipalities of Januária and Bonito de Minas, MG. Soil samples were collected in the following layers: 0-10, 10-20, 20-40, 40-60 and 60-100 cm. The maximum and minimum levels of C varies depending on the sampling site, fragment and soil layer sampled; larger variations in the levels of C are noted for the 0-10 cm layer, whose C content did not correlate with root and litter biomasses, and with other soil C conditioning factors tested. Specific logarithmic equations are provided for each of the five fragments in order to estimate the C contents (in the 0-100 cm soil layers) as a function of the average temperature, precipitation, altitude and fragment soil layer sampled. [ABSTRACT FROM AUTHOR]

• Soil texture of 1565 samples was determined and analyzed with pXRF in Brazil. • pXRF data was successfully predicted soil texture through SVM and RF algorithms. • Predictions were accurate independently of soil class, parent material, and horizon. • Fe and Al positively correlated to clay content due to Fe- and Al-oxides in soils. • Si positively correlated to sand, due to dominance of quartz in this fraction. Soil texture is an important feature in soil characterization, although its laboratory determination is costly and time-consuming. As an alternative, this study aimed at predicting soil texture from portable X-ray fluorescence (pXRF) spectrometry data in Brazilian soils. 1565 soil samples (503 from superficial and 1062 from subsuperficial horizons) were analyzed in the laboratory for soil texture and scanned with the pXRF. Elemental contents determined by pXRF were correlated with soil texture and used to calibrate regression models through the generalized linear model (GLM), support vector machine (SVM), and random forest (RF) algorithm. Models were created with 70% of the data using three datasets: i) only superficial horizon data; ii) only subsuperficial horizon data; and iii) data from both horizons. Validation was performed with 30% of the data. Clay content was positively correlated with Fe (0.79) and Al 2 O 3 (0.41) reflecting the great residual concentration of Fe- and Al-oxides in this fraction. This same fraction correlated negatively with SiO 2 (-0.75), while the sand fraction correlated positively with SiO 2 corresponding to quartz dominance in the sand fraction of Brazilian soils. For the separated superficial and subsuperficial horizon datasets, SVM promoted the best predictions of clay (R2 0.83; RMSE = 7.04%) and sand contents (R2 0.87; RMSE = 9.11%), while RF provided the best results for silt (R2 0.60; RMSE = 6.33%). When combining both datasets, RF was better for sand prediction (R2 0.73; RMSE = 5.79%), while SVM promoted better predictions for silt (R2 0.72; RMSE = 5.77%) and clay (R2 0.84; RMSE = 7.08%). Elemental contents obtained by pXRF are capable of accurately predicting soil texture for a great variety of Brazilian soils. [ABSTRACT FROM AUTHOR]