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Eucalyptus species play an important role in the global carbon cycle, especially in reducing the greenhouse effect as well as storing atmospheric CO₂. Thus, assessing the amount of CO₂ released by the soil in forest areas can generate important information for environmental monitoring. This study aims to verify the relation between soil carbon dioxide (CO₂) flux (FCO₂), spectral bands, and vegetation indices (VIs) derived from a UAV-based multispectral camera over an area of eucalyptus species. Multispectral imageries (green, red-edge, and near-infrared) from the Parrot Sequoia sensor, derived vegetation indices, and the FCO₂ data from a LI-COR 8100 analyzer, combined with soil moisture and temperature data, were collected and related. The vegetation indices ATSAVI (Adjusted Transformed Soil-Adjusted VI), GSAVI (Green Soil Adjusted Vegetation Index), and SAVI (Soil-Adjusted Vegetation Index), which use soil correction factors, exhibited a strong negative correlation with FCO₂ for the species E. camaldulensis, E. saligna, and E. urophylla species. A Multivariate Analysis of Variance showed significance (p < 0.01) for the species factor, which indicates that there are differences when considering all variables simultaneously. The results achieved in this study show a specific correlation between the data of soil CO₂ emission and the eucalypt species, providing a distinction of values between the species in the statistical data. [ABSTRACT FROM AUTHOR]

The purpose of this study was to estimate the temporal variability of CO2 emission (FCO2) from O2 influx into the soil (FO2) in a reforested area with native vegetation in the Brazilian Cerrado, as well as to understand the dynamics of soil respiration in this ecosystem. The database is composed of soil respiration data, agroclimatic variables, improved vegetation index (EVI), and soil attributes used to train machine learning algorithms: artificial neural network (ANN) and an adaptive neuro-fuzzy inference system (ANFIS). The predictive performance was evaluated based on the mean absolute error (MEA), root mean square error (RMSE), mean absolute percentage error (MAPE), agreement index (d), confidence coefficient (c), and coefficient of determination (R2). The best estimation results for validation were FCO2 with multilayer perceptron neural network (MLP) (R2 = 0.53, RMSE = 0.967 µmol m−2 s−1) and radial basis function neural network (RBF) (R2 = 0.54, RMSE = 0.884 µmol m−2 s−1) and FO2 with MLP (R2 = 0.45, RMSE = 0.093 mg m−2 s−1) and RBF (R2 = 0.74, 0.079 mg m−2 s−1). Soil temperature and macroporosity are important predictors of FCO2 and FO2. The best combination of variables for training the ANFIS was selected based on trial and error. The results were as follows: FCO2 (R2 = 16) and FO2 (R2 = 29). In all models, FCO2 outperformed FO2. A primary factor analysis was performed, and FCO2 and FO2 correlated best with the weather and soil attributes, respectively. [ABSTRACT FROM AUTHOR]

Soil CO2 emission (FCO2) is a critical component of the global carbon cycle, but it is a source of great uncertainty due to the great spatial and temporal variability. Modeling of soil respiration can strongly contribute to reducing the uncertainties associated with the sources and sinks of carbon in the soil. In this study, we compared five machine learning (ML) models to predict the spatiotemporal variability of FCO2 in three reforested areas: eucalyptus (RE), pine (RP) and native species (RNS). The study also included a generalized scenario (GS) where all the data from RE, RP and RNS were included in one dataset. The ML models include generalized regression neural network (GRNN), radial basis function neural network (RBFNN), multilayer perceptron neural network (MLPNN), adaptive neuro-fuzzy inference system (ANFIS) and random forest (RF). Initially, we had 32 attributes and after pre-processing, including Pearson's correlation, canonical correlation analysis (CCA), and biophysical justification, only 21 variables remained. We used as input variables 19 soil properties and climate variables in reforested areas of eucalyptus, pine and native species. RF was the best model to predict soil respiration to RE [adjusted coefficient of determination (R2 adj): 0.70 and root mean square error (RMSE): 1.02 µmol m−2 s−1], RP (R2 adj: 0.48 and RMSE: 1.07 µmol m−2 s−1) and GS (R2 adj: 0.70 and RMSE: 1.05 µmol m−2 s−1). Our findings support that RF and GRNN are promising for predicting soil respiration of reforested areas which could help to identify and monitor potential sources and sinks of the main additional greenhouse gas over ecosystems. [ABSTRACT FROM AUTHOR]

With the advancement of remote sensing, it is now possible to identify and characterize greenhouse gas emissions under deferment land uses. Given the above, this study aimed to characterize the spatial–temporal variability and the main factors controlling the average atmospheric CO2 column (Xco2) in the macroregion of Ribeirão Preto (MRP), São Paulo, a significant sugarcane producer in Brazil. We obtained remote sensing data from January 2015 to December 2018. The variables used were Xco2 and sun-induced fluorescence of chlorophyll (SIF) by NASA's Orbiting Carbon Observatory-2 satellite (OCO-2), relative humidity (RH), global radiation (Qg), and the average temperature at 2 m (T2m) by the NASA-POWER platform, and leaf area index (LAI) and evapotranspiration by Penman–Monteith (ET) by MODIS sensor. We evaluated the data in trimester's averages, where descriptive statistics, Pearson correlation and linear regression have been applied. The spatial distribution was made by the inverse distance weighted (IDW). The minimum (390.40 ± 0.41 ppm) and maximum (394.75 ± 0.34 ppm) mean of Xco2 was observed in the first quarter of 2015 and third quarter of 2017. The Xco2 obtained negative correlations with the SIF (−0.81), LAI (−0.81), RH (−0.74), ET (−0.84), and Qg (−0.51). Hotspots and coldspots of Xco2 tend to vary over the years. We conclude that the temporal variation of Xco2 above sugarcane areas in southern Brazil is well represented by a periodic function. Our results indicate photosynthesis and soil exposure after harvest are factors that could act as source and sink of CO2. [ABSTRACT FROM AUTHOR]

There is a growing need of sustainable solutions for balancing agricultural production with the reduction of its environmental impacts. The rapid increase in sugarcane cultivation and the progressive conversion of pre-harvest burning (BH) to green harvest (GH) have brought into debate the contribution of agricultural sector to the greenhouse gas (GHG) mitigation. This study focused on the estimated GHG emission from sugarcane cultivation during years in which sugarcane areas in southern Brazil expanded and passed throughout an important transition, from 2006 to 2012, when harvest adopted was changed from burned to not-burned based. Sugarcane management and harvest were mapped through visual interpretation of Landsat-type satellite images, and the areas under sugarcane cultivation were distinguished according to each agricultural phase and harvest regime (i.e., manual harvest with burning vs. green mechanized harvest). Based on a broad data review and applying the IPCC (2006) methodologies, the results were expressed in terms of kilograms of carbon dioxide equivalent (kg CO2eq ha−1). Avoiding burn prior to harvest, even during expansion of sugarcane areas, promoted a mean reduction of GHG emission from 901 to 686 kg CO2eq ha−1 relative to harvest phase (24% lower) and an increase from 1418.3 to 1507.9 kg CO2eq ha−1 related to the ratoon maintenance phase (6% higher). Analyzing the total GHG emission per unit of cultivated sugarcane area (hectare), it was observed a decrease from 2275 to 2034 kg CO2eq ha−1 (11% reduction). The gradual transition of pre-harvest burning on that period has contributed to the reduction of GHG emission associated with sugarcane production being an important step towards GHG mitigation while still providing more sustainable sugar and ethanol production in southern Brazil. [ABSTRACT FROM AUTHOR]

Soils feature significant variations in soil carbon stocks through land-use changes, management practices, and intrinsic characteristics. The aim of this study was to estimate the changes in soil carbon stock under different scenarios of land use and agricultural management in the Maranhão State, Brazil, considering the conversion from the conventional agriculture into conservationist management systems. Changes in soil carbon stocks were estimated from the scenario t0 to the current scenario (2010), followed by the adoption of a conservationist scenario by the year 2030. Soils under pasturelands presented the highest average of carbon stocks (62.19 Mg ha−1), followed by forestry lands (61.60 Mg ha−1) and agricultural lands (38.28 Mg ha−1). The conversion of native vegetation into an intensive agricultural use contributed to soil carbon losses of 1.57 Mt C, with pasturelands accounting for 1.36 Mt C and agricultural lands for 0.21 Mt C by 2010. The replacement of intensive agricultural systems into conservationist systems in the current areas has a technical potential for soil carbon sequestration of 0.6 Mt by 2030, with livestock and agricultural lands accounting for 0.54 and 0.03 Mt C, respectively. [ABSTRACT FROM AUTHOR]

The spatial structure of soil CO2 emission (FCO2) and soil attributes are affected by different factors in a highly complex way. In this context, this study aimed to characterize the spatial variability patterns of FCO2 and soil physical, chemical, and microbiological attributes in a sugarcane field area after reform activities. The study was conducted in an Oxisol with the measurement of FCO2, soil temperature (Ts), and soil moisture (Ms) in a regular 90 × 90-m grid with 100 sampling points. Soil samples were collected at each sampling point at a depth of 0–0.20 m to determine soil physical (density, macroporosity, and microporosity), particle size (sand, silt, and clay), and chemical attributes (soil organic matter, pH, P, K, Ca, Mg, Al, H + Al, cation exchange capacity, and base saturation). Geostatistical analyses were performed to assess the spatial variability and map soil attributes. Two regions (R1 and R2) with contrasting emission values were identified after mapping FCO2. The abundance of bacterial 16S rRNA, pmoA, and nifH genes, determined by real-time quantitative PCR (qPCR), enzymatic activity (dehydrogenase, urease, cellulase, and amylase), and microbial biomass carbon were determined in R1 and R2. The mean values of FCO2 (2.91 µmol m−2 s−1), Ts (22.6 °C), and Ms (16.9%) over the 28-day period were similar to those observed in studies also conducted under Oxisols in sugarcane areas and conventional soil tillage. The spatial pattern of FCO2 was similar to that of macropores, air-filled pore space, silt content, soil organic matter, and soil carbon decay constant. No significant difference was observed between R1 and R2 for the copy number of bacterial 16S rRNA and nifH genes, but the results of qPCR for the pmoA gene presented differences (p < 0.01) between regions. The region R1, with the highest FCO2 (2.9 to 4.2 µmol m−2 s−1), showed higher enzymatic activity of dehydrogenase (33.02 µg TPF g−1 dry soil 24 h−1), urease (41.15 µg NH4–N g−1 dry soil 3 h−1), amylase (73.84 µg glucose g−1 dry soil 24 h−1), and microbial biomass carbon (41.35 µg C g−1 soil) than R2, which had the lowest emission (1.9 to 2.7 µmol m−2 s−1). In addition, the soil C/N ratio was higher in R2 (15.43) than in R1 (12.18). The spatial pattern of FCO2 in R1 and R2 may not be directly related to the total amount of the microbial community (bacterial 16S rRNA) in the soil but to the specific function that these microorganisms play regarding soil carbon degradation (pmoA). [ABSTRACT FROM AUTHOR]

Agriculture is a great emission source of CO2 into the atmosphere, contributing significantly to the greenhouse effect. Considering the hypothesis that there are differences in soil carbon dynamics due to the distinct physiological and morphological characteristics of sugarcane cultivars, the aim of this study was to characterize the short-term soil CO2 emission associated with soil attributes in agricultural areas under cultivation of five sugarcane cultivars. The experiment was conducted in an area of high-clay Oxisol (Hapludox, USDA Soil Taxonomy) located at the Cerrado biome, Midwestern region of Brazil. Over the course of 20 days, ten measurements of soil CO2 emission (FCO2), soil temperature (Ts), and soil moisture (Ms) were carried out. Subsequently, soil samples were collected at a depth of 0-0.20 m to determine soil physical and chemical attributes. In timescale, FCO2, Ts, and Ms varied depending on the amount of straw produced by each cultivar. The cultivars RB935608, RB935744, and SP832847 induced a higher soil CO2 emission since they are associated with controlling factors of the primary CO2 production process (higher organic matter content and lower C/N ratio in the soil). Thus, strategies to reduce greenhouse gas emissions in agriculture, such as the choice of sugarcane cultivars that provide lower soil CO2 emissions, are essential to mitigate important environmental issues such as the global warming. [ABSTRACT FROM AUTHOR]

Background: Since sugarcane areas have increased rapidly in Brazil, the contribution of the sugarcane production, and, especially, of the sugarcane harvest system to the greenhouse gas emissions of the country is an issue of national concern. Here we analyze some data characterizing various activities of two sugarcane mills during the harvest period of 2006-2007 and quantify the carbon footprint of sugar production. Results: According to our calculations, 241 kg of carbon dioxide equivalent were released to the atmosphere per a ton of sugar produced (2406 kg of carbon dioxide equivalent per a hectare of the cropped area, and 26.5 kg of carbon dioxide equivalent per a ton of sugarcane processed). The major part of the total emission (44%) resulted from residues burning; about 20% resulted from the use of synthetic fertilizers, and about 18% from fossil fuel combustion. Conclusions: The results of this study suggest that the most important reduction in greenhouse gas emissions from sugarcane areas could be achieved by switching to a green harvest system, that is, to harvesting without burning. [ABSTRACT FROM AUTHOR]

Methane and carbon dioxide seasonal cycles during years 1998 and 1999 at two Brazilian urban and inland sites are presented. The mixing ratio averages over the studied period of time were 1.80 ppm CH4 and 384.7 ppm CO2. A comparison is made between continental averages and the averages of the three nearest global network background sites of NOAA-CMDL comprising Ascension Island, Namibia and Easter Island. Inland sites had 0.08 ppm or 4.9% more CH4 and 19.0 ppm or 4.9% more CO2 than background over the same time span. The CH4 summer minimum observed in remote sites was also detected inland. During the month of October 98 and 99 inland mixing ratios were frequently similar to background. [ABSTRACT FROM AUTHOR]