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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 Amazon region comprises the largest tropical forest on the planet and is responsible for absorbing huge amounts of CO2 from the atmosphere. However, changes in land use and cover have contributed to an increase in greenhouse gas emissions, especially CO2, and in endangered indigenous lands and protected areas in the region. The objective of this study was to detect changes in CO2 emissions and removals associated with land use and land cover changes in the Brazilian Legal Amazon (BLA) through the analysis of multispectral satellite images from 2009 to 2019. The Gross Primary Production (GPP) and CO2Flux variables were estimated by the MODIS sensor onboard Terra and Aqua satellite, representing carbon absorption by vegetation during the photosynthesis process. Atmospheric CO2 concentration was estimated from the GOSAT satellite. The variables GPP and CO2Flux showed the effective flux of carbon in the BLA to atmosphere, which were weakly correlated with precipitation (r = 0.191 and 0.133). The forest absorbed 211.05 TgC annually but, due to its partial conversion to other land uses, the loss of 135,922.34 km2 of forest area resulted in 5.82 TgC less carbon being absorbed. Pasture and agriculture, which comprise the main land conversions, increased by 100,340.39 km2 and absorbed 1.32 and 3.19 TgC less, and emitted close to twice more, than forest in these areas. Atmospheric CO2 concentrations increased from 2.2 to 2.8 ppm annually in BLA, with hotspots observed in the southeast Amazonia, and CO2 capture by GPP showed an increase over the years, mainly after 2013, in the north and west of the BLA. This study brings to light the carbon dynamics, by GPP and CO2Flux models, as related to the land use and land cover in one of the biggest world carbon reservoirs, the Amazon, which is also important to fulfillment of international agreements signed by Brazil to reduce greenhouse gas emissions and for biodiversity conservation and other ecosystem services in the region. [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]

Food production in sustainable agricultural systems is one of the main challenges of modern agriculture. Vegetable intercropping may be a strategy to mitigate greenhouse gas (GHG) emissions, replacing monoculture systems. The objective is to identify the main emissions sources and to estimate GHG emissions of intercropping and monoculture production of collard greens, New Zealand spinach and chicory. Four scenarios were evaluated: ICS -- intercropping collard greens and spinach; MCS -- monoculture collard greens and spinach; ICC -- intercropping collard greens and chicory; MCC - monoculture collard greens and chicory. The boundaries' reach from "cradleto- gate" and the calculation of GHG emissions were performed using IPCC methodology and specific factors (Tier 2). The total GHG emitted was standardized as CO2 equivalent (CO2eq). The GHG emissions in ICS and ICC scenarios were approximately 31% lower than in MCS and MCC scenarios. Carbon footprint in ICS (0.030 kg CO2eq kg-1 vegetables year-1) and ICC (0.033 kg CO2eq kg-1 vegetables year-1) scenarios were also lower than in MCS (0.082 kg CO2eq kg-1 vegetables year-1) and MCC (0.071 kg CO2eq kg-1 vegetables year-1) scenarios. Fertilizers, fuel (diesel) and irrigation were the main contributing sources for total GHG emitted and carbon footprint in all evaluated scenarios. The results suggest that intercropping systems may reduce GHG emissions associated with the production of vegetables evaluated as compared with monoculture. [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]

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]

The production of the main soil greenhouse gases (GHG: carbon dioxide [CO2], methane [CH4,] and nitrous oxide [N2O]) is influenced by agricultural practices that cause changes in soil physical, chemical and biological attributes, directly affecting their emission to the atmosphere. The aim of this study was to investigate the infield soil carbon dioxide emissions (FCO2) and soil CO2, methane and nitrous oxide production potentials (PCO2, PCH4 and PN2O, respectively) under laboratory conditions and their relationship to soil attributes in a mechanically harvested sugarcane area. Soil carbon dioxide emissions presented an infield average emission value of 1.19 μmol CO2 m-2 s-1, while GHG production in the laboratory was 2.34 μg C-CO2 g-1 soil d-1 and 0.20 ng N-N2O g-1 soil d-1 for PCO2 and PN2O, respectively. No significant production or oxidation was observed for CH4. Factor analysis showed the formation of two independent processes that explained almost 72% of the total variance observed in the data. The first process was related to FCO2 transport and its relation to soil physical attributes such as microporosity, macroporosity, the C/N ratio, soil moisture and soil bulk density, showing the dependence between FCO2 and soil porosity. The second process was related to soil CO2 and N2O production potentials under laboratory conditions and their relation to soil chemical attributes such as the sum of bases, pH and available phosphorus, which affect microbial activity and contribute to GHG production. Although presented as independent, these processes are coupled and occur simultaneously in the soil, in addition to providing information about their variability and showing if the infield emissions are due to gas transport processes or soil carbon levels and their quality. [ABSTRACT FROM AUTHOR]

The objective was to determine the influence of partial cultivation of only the crop row on carbon dioxide (CO2) emissions in an Oxisol and Ultisol. The experimental design was a large paired-plot design with treatments consisting of targeted chiseling to only the planting row to chiseling the whole field. Soil CO2 emissions, soil temperature, and soil moisture were measured daily for 12 d after tillage. In the Oxisol, CO2 emissions were higher on the second day after tillage and then decreased until Day 6. When analyzing the effect of tillage, chiseling in the planting row (CPR) showed, in general, lower CO2 emissions (2.54 μmol m-2 s-1) when compared to chiseling in total area (CTA) (3.32 μmol m-2 s-1), regardless of the day aft er tillage. However, the linear regression analysis between soil CO2 emissions and soil moisture in the Ultisol was significant for CPR (R2 = 0.79, p < 0.01) and CTA (R² = 0.58, p < 0.01). Total soil CO2 emissions were higher in the Oxisol for both tillage systems (CPR = 1020.9 kg CO2 ha-1 and CTA = 1336.3 kg CO2 ha-1) than the Ultisol (CPR = 587.0 kg CO2 ha-1 and CTA = 649.3 kg CO2 ha-1). Th ese results indicate that soil tillage by using CPR favors a lower soil CO2 emission to the atmosphere and contributes to a reduction in soil degradation when cultivated with sugarcane (Saccharum officinarum L.). [ABSTRACT FROM AUTHOR]

Soil carbon dioxide emission (ECO2) is a process determined by biotic and abiotic factors influenced by land use and management practices. In grassland ecosystems, grazing intensity may affect C input from plants into soil, and thus may also change soil respiration rate. Indeed, limited information is available regarding the effects of grazing management on ECO2. This study was conducted to evaluate ECO2 seasonal variation, and its relationship to soil temperature (Tsoil) and precipitation, in an area with different pasture heights of Marandu palisade grass [Brachiaria brizantha (A. Rich.) Stapf.]. The pasture heights (15, 25, and 35 cm) were distributed in a completely randomized design with 12 repetitions in each area. The evaluations of ECO2 and Tsoil occurred from January 2011 to February 2013, totaling 73 d of observation that were distributed according to climatic seasons. The results showed that seasonal variation of ECO2 was directly related to variations in precipitation and Tsoil. Soil CO2 emission was higher in summer and lower in winter. The data variability of ECO2 was higher in rainy, hot summers than in dry, cold winters. A positive linear association between ECO2 and Tsoil was observed in summer and autumn. In most seasons measured, ECO2 reduced with increasing pasture height. Accordingly, 2-yr data indicated that accumulated ECO2 decreased with increasing pasture height. Thus to better understand the effect of pasture height on ECO2, a shorter period of measurements is not suitable since a different result was observed in the first year of evaluation. [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]

Soil quality (SQ) assessments are fundamental to design more sustainable land uses and management practices. However, SQ is a complex concept and there is not a universal approach to evaluate SQ across different conditions of climate, soil, and cropping system. Large-scale sugarcane production in Brazil is predominantly based on conventional tillage and high mechanization intensity, leading to SQ degradation. Thus through this study, we aim to assess the impact of sustainable management practices, including cover crops and less intensive tillage systems, in relation to the conventional system, using a soil quality index composed of abiotic indicators. Additionally, we developed a decision tree model to predict SQ using a minimum set of variables. The study was conducted in the municipality of Ibitinga, São Paulo, Brazil. The experimental design used was in strips, with four cover crops and three tillage systems. We evaluated three sugarcane cultivation cycles (2015/16, 2016/17, and 2017/18 crops). To calculate the SQ index, we selected five abiotic indicators: macroporosity, potassium content, calcium content, bulk density, and mean weight-diameter of soil aggregates. Based on our SQ index, our findings indicated that the soil quality was driven by the production cycle of sugarcane. Although a reduction of soil quality occurs between the plant cane and first ratoon cane cycles, from the second ratoon cane there is a trend of the gradual restoration of soil quality due to the recovery of both the soil's physical and chemical attributes. Our study also demonstrated that the cultivation of sunn hemp and millet as cover crops, during the implementation of sugarcane plantation, enhanced soil quality. Due to the advantages provided by the use of these two cover crops, we encourage more detailed and long-term studies, aiming to test the efficiency of intercropping involving sunn hemp and millet during the re-planting of sugarcane. [ABSTRACT FROM AUTHOR]

The detection of Solar-Induced chlorophyll Fluorescence (SIF) by remote sensing has opened new perspectives on ecosystem studies and other related aspects such as photosynthesis. In general, fluorescence high-resolution studies were limited to proximal sensors, but new approaches were developed to improve SIF resolution by combining OCO-2 with MODIS orbital observations, improving its resolution from 0.5° to 0.05 on a global scale. Using a high-resolution dataset and rainfall data some SIF characteristics of the satellite were studied based across 06 contrasting ecosystems in Brazil: Amazonia, Caatinga, Cerrado, Atlantic Forest, Pampa, and Pantanal, from years 2015–2018. SIF spatial variability in each biome presented significant spatial variability structures with high R2 values (>0.6, Gaussian models) in all studied years. The rainfall maps were positively and similar related to SIF spatial distribution and were able to explain more than 40% of SIF's spatial variability. The Amazon biome presented the higher SIF values (>0.4 W m−2 sr−1 μm−1) and also the higher annual rainfall precipitation (around 2000 mm), while Caatinga had the lowest SIF values and precipitations (<0.1 W m−2 sr−1 μm−1, precipitation around 500 mm). The linear relationship of SIF to rainfall across biomes was mostly significant (except in Pantanal) and presented contrasting sensitivities as in Caatinga SIF was mostly affected while in the Amazon, SIF was lesser affected by precipitation events. We believe that the features presented here indicate that SIF could be highly affected by rainfall precipitation changes in some Brazilian biomes. Combining rainfall with SIF allowed us to detect the differences and similarities across Brazil's biomes improving our understanding on how these ecosystems could be affected by climate change and severe weather conditions. • A high spatial resolution SIF dataset was used. • Ordinary kriging was used to interpolate SIF and Precipitation. • The SIF spatial distribution follows the Brazilian biomes. • The sensitiveness of SIF to precipitation changes across the biomes. • Amazon has the highest SIF and Precipitation values and Caatinga has the lowest. [ABSTRACT FROM AUTHOR]

Greenhouse gas (GHG) sources and sinks are an important global concern. Monitoring the spatiotemporal variations of GHG concentrations, particularly carbon dioxide (CO 2), is crucial for identifying potential sources and sinks and moving toward a sustainable future. Therefore, via a time-series of remote data and multispectral images, this study evaluates the CO 2 spatiotemporal dynamics and related factors during 2015–2018 in one of the world's main agricultural frontier areas, the state of Mato Grosso (SMT), Brazil, which is both experiencing continued deforestation and attempting to achieve sustainable food production. In this study, data was obtained from the measurement of column-averaged carbon dioxide (CO 2) dry air mole fraction in the atmosphere, set as X CO2 from Orbiting Carbon Observatory-2 satellite from January 2015 to December 2018. The enhanced vegetation index data were obtained from the Moderate-Resolution Imaging Spectroradiometer (MODIS) sensor, and rainfall data were obtained from the Climate Hazards Group InfraRed Precipitation with Station dataset. From a series of Landsat-8 satellite images, it was possible to distinguish land use and land cover classes and estimate the CO 2 flux in the SMT. The results showed that the temporal variability of CO 2 flux is correlated positively with rainfall, while X CO2 is negatively correlated with rainfall. Regarding spatial variability, we observed that forest areas that were converted to other land uses resulted in higher values that characterize with sources, and that the highest and lowest average concentrations of CO 2 occurred in the dry and rainy months, respectively, for X CO2 , which might be the result of differences in the vertical resolution of the CO 2 column and scale. In contrast, areas with large continuous forest areas tended to have lower values and contribute positively to the carbon balance as sinks, thereby mitigating climate change impacts. Therefore, not only X CO2 but also CO 2 flux are directly related to changes in land use and land cover (LULC) in complex systems that are affected by climatic variables and processes, such as photosynthesis and soil respiration. • X CO2 is inversely related to rainfall, with highest concentration in drier periods. • Human actions in land use and land cover change increase atmospherical CO 2. • Remote sensing to locate and understand the sources and sinks of carbon dioxide. [ABSTRACT FROM AUTHOR]

Soil CO2 efflux is the primary source of CO2 emissions from terrestrial ecosystems to the atmosphere. The rates of this flux vary in time and space producing hot moments (sudden temporal high fluxes) and hot spots (spatially defined high fluxes), but these high reaction rates are rarely studied in conjunction with each other. We studied temporal and spatial variation of soil CO2 efflux in a water-limited Mediterranean ecosystem in Baja California, Mexico. Soil CO2 efflux increased 522% during a hot moment after rewetting of soils following dry summer months. Monthly precipitation was the primary driver of the seasonal trend of soil CO2 efflux (including the hot moment) and through changes in soil volumetric water content (VWC) it influenced the relationship between CO2 efflux and soil temperature. Geostatistical analyses showed that the spatial dependence of soil CO2 efflux changed between two contrasting seasons (dry and wet). During the dry season high soil VWC was associated with high soil CO2 efflux, and during the wet season the emergence of a hot spot of soil CO2 efflux was associated with higher root biomass and leaf area index. These results suggest that sampling designs should accommodate for changes in spatial dependence of measured variables. The spatio-temporal relationships identified in this study are arguably different from temperate ecosystems where the majority of soil CO2 efflux research has been done. This study provides evidence of the complexity of the mechanisms controlling the spatio-temporal variability of soil CO2 efflux in water-limited ecosystems. [ABSTRACT FROM AUTHOR]