Implications of converting native forest areas to agricultural systems on the dynamics of CO2 emission and carbon stock in a Cerrado soil, Brazil.

The conversion of native vegetation to agricultural areas leads to a natural process of carbon loss but these systems can stabilize in terms of carbon dynamics depending on the management and conversion time, presenting potential to both store and stabilize this carbon in the soil, resulting in lower soil respiration rates. In this context, this study aimed to investigate the effect of converting native Cerrado forest areas to agricultural systems with a forest planted with Eucalyptus camaldulensis and silvopastoral systems on the dynamics of CO2 emission and carbon stock at different soil depths. The experimental sites are located in the Midwest of Brazil, in the coordinates 20°22'31″ S and 51°24'12″ W. Were evaluated soil CO2 emission (FCO2), soil organic carbon, the degree of humification of soil organic matter (HLIFS), soil temperature, soil moisture, and soil chemical and physical attributes. The soil of the area is classified as an Oxisol (Haplic Acrustox). Soil samples were collected at depths of 0.00-0.10, 0.10-0.20, 0.20-0.30, and 0.30-0.40 m. The lowest FCO2 values were found in the silvopastoral system (1.05 µmol m-2 s-1), followed by the native forest (1.65 µmol m-2 s-1) and the eucalyptus system (1.96 µmol m-2 s-1), indicating a 36% reduction in FCO2 compared to the conversion of the native forest to the silvopastoral system and an increase of 19% when converting the native forest to the eucalyptus system. The soil chemical attributes (N, K+, Ca2+, H++Al3+, CEC, and organic carbon) showed a decrease along the profile. The shallowest depths (0.00-0.10 and 0.10-0.20 m) presented no differences between systems but the subsequent depths (0.20-0.30 and 0.30-0.40 m) had a difference (95% confidence interval), relative to N, Ca2+, H++Al3, CEC, and organic carbon stock (OCS), and the soil under silvopastoral system showed a higher concentration of these attributes than the native forest. The multivariate analysis showed that the eucalyptus and silvopastoral systems did not differ from the forest in the shallowest soil layer but differed from each other. This behavior changed from the second assessed depth (0.10-0.20 m), in which the silvopastoral system stands out, differing both from the eucalyptus system and from the native forest, and this behavior is maintained at the following depths (0.20-0.30 and 0.30-0.40 m). OCS, H++Al3, CEC, and nitrogen are strongly related to land use change for silvopastoral system. Regarding the behavior/relationship of attributes as a function of depth, the silvopastoral system contributed to soil carbon accumulation and stability over consecutive years.

Artificial neural networks and adaptive neuro-fuzzy inference systems for prediction of soil respiration in forested areas southern Brazil.

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.

Machine learning for prediction of soil CO2 emission in tropical forests in the Brazilian Cerrado.

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.

Implications of CO2 emissions on the main land and forest uses in the Brazilian Amazon.

The emission of soil carbon dioxide (CO2) in agricultural areas is a process that results from the interaction of several factors such as climate, soil, and land management practices. Agricultural practices directly affect the carbon dynamics between the soil and atmosphere. Herein, we evaluated the temporal variability (2020/2021 crop season) of soil CO2 emissions and its relationship with related variables, such as the CO2 flux model, enhanced vegetation index (EVI), gross primary productivity (GPP), and leaf area index (LAI) from orbital data and soil temperature, soil moisture, and soil CO2 emissions from in situ collections from native forests, productive pastures, degraded pastures, and areas of high-yield potential soybean and low-yield potential soybean production. A significant influence (p < 0.01) was observed for all variables and between the different land uses and occupation types. September and October had lower emissions of soil CO2 and low means of soil moisture and soil temperature, and no differences were observed among the treatments. On the other hand, there was a significant effect of the CO2 flux model in productive pastures, high-yield potential soybean areas, and low-yield potential soybean areas. The months with the highest CO2 flux values in the model, regardless of land use and land cover, were October and November, which is the beginning of the rainy season. There were positive correlations between soil CO2 emissions and GPP (0.208), LAI (0.354), EVI (0.363), and soil moisture (0.280) and negative correlations between soil CO2 emissions and soil temperature (-0.240) and CO2 flux model (-0.314) values. Land use and land cover showed negative correlations with these variables, except for the CO2 flux model variable. Soil CO2 emission values were lower for high-yield potential soybean areas (averages from 0.834 to 6.835 µmol m-2 s-1) and low-yield potential soybean areas (from 0.943 to 5.686 µmol m-2 s-1) and higher for native forests (from 2.279 to 8.131 µmol m-2 s-1), whereas the opposite was true for the CO2 flux model.

High spatial resolution solar-induced chlorophyll fluorescence and its relation to rainfall precipitation across Brazilian ecosystems.

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.

Greenhouse gas emissions and carbon footprint of collard greens, spinach and chicory production systems in Southeast of Brazil.

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 "cradle-to-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.

Hot spots and anomalies of CO2 over eastern Amazonia, Brazil: A time series from 2015 to 2018.

The easternmost Amazon, located in the Maranhão State, in Brazil, has suffered massive deforestation in recent years, which has devastated almost 80% of the original vegetation. We aim to characterize hot spots, hot moments, atmospheric carbon dioxide anomalies (Xco2, ppm), and their interactions with climate and vegetation indices in eastern Amazon, using data from NASA's Orbiting Carbon Observatory-2 (OCO-2). The study covered the period from January 2015 to December 2018. The data were subjected to regression, correlation, and temporal analysis, identifying the spatial distribution of hot/cold moments and hot/cold spots. In addition, anomalies were calculated to identify potential CO2 sources and sinks. Temporal changes indicate atmospheric Xco2 in the range from 362.2 to 403.4 ppm. Higher Xco2 values (hot moments) were concentrated between May and September, with some peaks in December. The lowest values (cold moments) were concentrated from November to April. SIF 771 W m-2 sr-1 µm-1 explained the temporal changes of Xco2 in 58% (R2 adj = 0.58; p < 0.001) and precipitation in 27% (R2 adj = 0.27; p ≤ 0.001). Spatial hot spots with 90% confidence were more representative in 2016. The maximum and minimum Xco2 (ppm) anomalies were 6.19 ppm (source) and -6.29 ppm (sink), respectively. We conclude that the hot moments of Xco2 in the eastern Amazon rainforest are concentrated in the dry season of the year. Xco2 spatial hot spots and anomalies are concentrated in the southern region and close to protected areas of the Amazon rainforest.

An empirical model for estimating daily atmospheric column-averaged CO2 concentration above São Paulo state, Brazil.

BACKGROUND: The recent studies of the variations in the atmospheric column-averaged CO2 concentration ([Formula: see text]) above croplands and forests show a negative correlation between [Formula: see text]and Sun Induced Chlorophyll Fluorescence (SIF) and confirmed that photosynthesis is the main regulator of the terrestrial uptake for atmospheric CO2. The remote sensing techniques in this context are very important to observe this relation, however, there is still a time gap in orbital data, since the observation is not daily. Here we analyzed the effects of several variables related to the photosynthetic capacity of vegetation on [Formula: see text] above São Paulo state during the period from 2015 to 2019 and propose a daily model to estimate the natural changes in atmospheric CO2. RESULTS: The data retrieved from the Orbiting Carbon Observatory-2 (OCO-2), NASA-POWER and Application for Extracting and Exploring Analysis Ready Samples (AppEEARS) show that Global Radiation (Qg), Sun Induced Chlorophyll Fluorescence (SIF) and, Relative Humidity (RH) are the most significant factors for predicting the annual [Formula: see text] cycle. The daily model of [Formula: see text] estimated from Qg and RH predicts daily [Formula: see text] with root mean squared error of 0.47 ppm (the coefficient of determination is equal to 0.44, p < 0.01). CONCLUSION: The obtained results imply that a significant part of daily [Formula: see text] variations could be explained by meteorological factors and that further research should be done to quantify the effects of the atmospheric transport and anthropogenic emissions.

Avoiding burning practice and its consequences on the greenhouse gas emission in sugarcane areas southern Brazil.

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.

xCO2 temporal variability above Brazilian agroecosystems: A remote sensing approach.

Agriculture and soil management practices are closely related to CO2 emissions in crop fields. These practices directly interfere on the carbon dynamics between the land and atmosphere. In this study, we investigated the temporal variability of the column-averaged dry-air mole fraction of atmospheric CO2 (xCO2), solar-induced chlorophyll fluorescence (SIF), and the normalized difference vegetation index (NDVI) in areas with the main agroecosystems in southern-central Brazil as a way to understand if and how crops cycle and agricultural management could be associated with the temporal variability of NDVI, SIF and xCO2. The study was carried out in areas corresponding to the three agroecosystems': sugarcane (Pradópolis, State of São Paulo, Brazil), cropland with soybean-corn succession (Santo Antônio do Paraíso, State of Paraná, Brazil), and grassland (Águas Claras, State of Mato Grosso do Sul, Brazil). Air temperature, precipitation, NDVI, and SIF and xCO2 were retrieved from NASA-POWER, NASA-GIOVANNI, SATVeg-EMBRAPA, and OCO-2, respectively, during a two-year study. Trends were removed from the NDVI, SIF, and xCO2 time series applying the regression method. A negative correlation between SIF and xCO2 was found in sugarcane and cropland areas, but in grasslands, no correlation showed up. Higher SIF values were observed in grassland (2.24 W m-2 sr-1 µm-1), and lower xCO2 values were observed above grains, which varied from 396.8 to 404.2 ppm. Both xCO2 and SIF followed more a seasonal pattern in sugarcane and annual crops, but over pasture this presented an unusual pattern related to higher precipitation events. Our results indicate a potential use of SIF and xCO2 which could help identifying potential sources and sinks of the main additional greenhouse gas over agricultural areas.

Soil CO2 emission and soil attributes associated with the microbiota of a sugarcane area in southern Brazil.

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).

Crop rotation and sequence effects on temporal variation of CO2 emissions after long-term no-till application.

Production, transport, and emission of CO2 from soil to the atmosphere are directly influenced by soil temperature and moisture conditions, exhibiting a high variability over time due to the influence of climate events and soil management practices. Thus, this study aimed to investigate the effect of summer and off-season crop residues on the temporal variation of soil CO2 emission (FCO2), soil temperature (Tsoil), and soil moisture (Msoil) under a no-till system that has been managed with the same crop arrangement for >16 years. The experiment was conducted in strips with three replications. Treatments consisted of summer crop sequences maize monoculture, soybean monoculture, and soybean-maize rotation, as well as off-season crops maize, millet, pigeon pea, grain sorghum, and crotalaria. Sixteen assessments of FCO2, Tsoil, and Msoil were carried out over 51 days. A significant effect of the interaction between time and summer crop sequences (F = 1.44; p = 0.02) and between time and off-season crops (F = 2.26; p < 0.01) was observed for FCO2. Moreover, a triple interaction was observed between summer crop sequences, off-season crops, and time for Msoil (F = 1.83; p < 0.01) and Tsoil (F = 1.32; p = 0.01). The values of FCO2 and Msoil were high on days 229 and 230 due to precipitations in the study area. The relationship between FCO2 and Msoil was positive in all the assessed management, and about 60% of FCO2 variation over the study period could be explained by soil water content variation.

Fluxes of CO2, CH4, and N2O in tundra-covered and Nothofagus forest soils in the Argentinian Patagonia.

While most soils in periglacial environments present high fluxes of CO2 (FCO2), CH4 (FCH4), and N2O (FN2O), few of them have a tendency to drain greenhouse gases from the atmosphere. This study aimed to assess greenhouse gas fluxes at different sub-Antarctic sites and time periods (at the beginning of thaw and height of summer). To investigate the time of year effect on greenhouse gas emissions, FCO2, FCH4, and FN2O were measured at two sites tundra-covered (Ti and Th) and Nothofagus forest soil (Nf) on Monte Martial, at the southernmost tip of South America, Tierra del Fuego, Argentina. FCO2 ranged from 96.33 to 225.72 µg CO2 m-2 s-1 across all sites and periods, showing a positive correlation with soil temperature (Ts) (4.1 and 8.2 °C, respectively) (r2 > 0.7; p < 0.05). The highest values of FCO2 were found at Ti and Th (728.2 and 662.64 µg CO2 m-2 s-1, respectively), which were related to higher temperatures (8.2 and 8.6 °C, respectively) when compared to those of Nf. For FCH4, the capture (drain) occurred during both periods at Nf (-26 and -79 µg C-CH4 m-2 h-1) as well as Ti and Th (-21 and 12 µg C-CH4 m-2 h-1, respectively). FN2O also presented low values during both periods and showed a tendency to drain N2O from the atmosphere, especially at Nf (-2 µg N-N2O m-2 h-1). In addition, FN2O was slightly positive for Ti and Th (0.3 and 0.55 µg N-N2O m-2 h-1, respectively). Soil moisture did not show a correlation (p > 0.05) with the measured greenhouse gas fluxes. A scenario of increased temperatures might result in changes in the balance between the emissions and drains of these gases from soils, leading to higher emission values of CH4 and N2O, especially for tundra covered soils (Ti and Th), where the highest average fluxes and thermohydric variations were observed over the year.

Liming in the sugarcane burnt system and the green harvest practice affect soil bacterial community in northeastern São Paulo, Brazil.

Here we show that both liming the burnt sugarcane and the green harvest practice alter bacterial community structure, diversity and composition in sugarcane fields in northeastern São Paulo state, Brazil. Terminal restriction fragment length polymorphism fingerprinting and 16S rRNA gene cloning and sequencing were used to analyze changes in soil bacterial communities. The field experiment consisted of sugarcane-cultivated soils under different regimes: green sugarcane (GS), burnt sugarcane (BS), BS in soil amended with lime applied to increase soil pH (BSL), and native forest (NF) as control soil. The bacterial community structures revealed disparate patterns in sugarcane-cultivated soils and forest soil (R = 0.786, P = 0.002), and overlapping patterns were shown for the bacterial community structure among the different management regimes applied to sugarcane (R = 0.194, P = 0.002). The numbers of operational taxonomic units (OTUs) found in the libraries were 117, 185, 173 and 166 for NF, BS, BSL and GS, respectively. Sugarcane-cultivated soils revealed higher bacterial diversity than NF soil, with BS soil accounting for a higher richness of unique OTUs (101 unique OTUs) than NF soil (23 unique OTUs). Cluster analysis based on OTUs revealed similar bacterial communities in NF and GS soils, while the bacterial community from BS soil was most distinct from the others. Acidobacteria and Alphaproteobacteria were the most abundant bacterial phyla across the different soils with Acidobacteria Gp1 accounting for a higher abundance in NF and GS soils than burnt sugarcane-cultivated soils (BS and BSL). In turn, Acidobacteria Gp4 abundance was higher in BS soils than in other soils. These differential responses in soil bacterial community structure, diversity and composition can be associated with the agricultural management, mainly liming practices, and harvest methods in the sugarcane-cultivated soils, and they can be detected shortly after harvest.

Greenhouse gas emission associated with sugar production in southern Brazil.

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.