RESUMO

Carbon dioxide (CO2) is considered one of the main greenhouse effect gases and contributes significantly to global climate change. In Brazil, the agricultural areas offer an opportunity to mitigate this effect, especially with the sugarcane crop, since, depending on the management system, sugarcane stores large amounts of carbon, thereby removing it from the atmosphere. The CO2 production in soil and its transport to the atmosphere are the results of biochemical processes such as the decomposition of organic matter and roots and the respiration of soil organisms, a phenomenon called soil CO2 emissions (FCO2). The objective of the study was to investigate the use of neural networks with backpropagation algorithm to predict the spatial patterns of soil CO2 emission during short periods in sugarcane areas. FCO2 values were collected in three commercial crop areas in the São Paulo state, southeastern Brazil, registered through the LI-8100 system during the years 2008 (Motuca), 2010 (Guariba city), and 2012 (Pradópolis), in the period after the mechanical harvesting (green cane). A neural network multilayer perceptron with a backpropagation algorithm was applied to estimate the FCO2 in 2012, using data from 2008 and 2010 as training for the neural network. The neural network initially presented a mean absolute percentage error (MAPE) of 18.3852 and a coefficient of determination (R2) of 0.9188. Data obtained from the observed and estimated values of FCO2 present moderate spatial dependence, and it is observed from the maps of the spatial pattern of the CO2 flow that the results from the neural network show considerable similarity to the observed data. The model results identify the higher and lower characteristics in sample points of CO2 emissions and produce an overestimation of the range of spatial dependence (0.45 m) and an underestimation of the interpolated values in the field (R2 = 0.80; MAPE = 12.0591), when compared to the actual soil CO2 emission values. Therefore, the results indicate that the artificial neural network provides reliable estimates for the evaluation of FCO2 from data of the soil's physical and chemical attributes and describes the spatial variability of FCO2 in sugarcane fields, thereby contributing to the reduction of uncertainties associated with FCO2 accountings in these areas.

Assuntos

RESUMO

The Random Forest algorithm is a data mining technique used for classifying attributes in order of importance to explain the variation in an attribute-target, as soil CO2 flux. This study aimed to identify prediction of soil CO2 flux variables in management systems of sugarcane through the machine-learning algorithm called Random Forest. Two different management areas of sugarcane in the state of São Paulo, Brazil, were selected: burned and green. In each area, we assembled a sampling grid with 81 georeferenced points to assess soil CO2 flux through automated portable soil gas chamber with measuring spectroscopy in the infrared during the dry season of 2011 and the rainy season of 2012. In addition, we sampled the soil to evaluate physical, chemical, and microbiological attributes. For data interpretation, we used the Random Forest algorithm, based on the combination of predicted decision trees (machine learning algorithms) in which every tree depends on the values of a random vector sampled independently with the same distribution to all the trees of the forest. The results indicated that clay content in the soil was the most important attribute to explain the CO2 flux in the areas studied during the evaluated period. The use of the Random Forest algorithm originated a model with a good fit (R2 = 0.80) for predicted and observed values.(AU)

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RESUMO

The use of data mining is a promising alternative to predict soil respiration from correlated variables. Our objective was to build a model using variable selection and decision tree induction to predict different levels of soil respiration, taking into account physical, chemical and microbiological variables of soil as well as precipitation in renewal of sugarcane areas. The original dataset was composed of 19 variables (18 independent variables and one dependent (or response) variable). The variable-target refers to soil respiration as the target classification. Due to a large number of variables, a procedure for variable selection was conducted to remove those with low correlation with the variable-target. For that purpose, four approaches of variable selection were evaluated: no variable selection, correlation-based feature selection (CFS), chisquare method (χ2) and Wrapper. To classify soil respiration, we used the decision tree induction technique available in the Weka software package. Our results showed that data mining techniques allow the development of a model for soil respiration classification with accuracy of 81 %, resulting in a knowledge base composed of 27 rules for prediction of soil respiration. In particular, the wrapper method for variable selection identified a subset of only five variables out of 18 available in the original dataset, and they had the following order of influence in determining soil respiration: soil temperature > precipitation > macroporosity > soilmoisture > potential acidity.

Assuntos

RESUMO

The use of data mining is a promising alternative to predict soil respiration from correlated variables. Our objective was to build a model using variable selection and decision tree induction to predict different levels of soil respiration, taking into account physical, chemical and microbiological variables of soil as well as precipitation in renewal of sugarcane areas. The original dataset was composed of 19 variables (18 independent variables and one dependent (or response) variable). The variable-target refers to soil respiration as the target classification. Due to a large number of variables, a procedure for variable selection was conducted to remove those with low correlation with the variable-target. For that purpose, four approaches of variable selection were evaluated: no variable selection, correlation-based feature selection (CFS), chisquare method (χ2) and Wrapper. To classify soil respiration, we used the decision tree induction technique available in the Weka software package. Our results showed that data mining techniques allow the development of a model for soil respiration classification with accuracy of 81 %, resulting in a knowledge base composed of 27 rules for prediction of soil respiration. In particular, the wrapper method for variable selection identified a subset of only five variables out of 18 available in the original dataset, and they had the following order of influence in determining soil respiration: soil temperature > precipitation > macroporosity > soilmoisture > potential acidity.(AU)

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RESUMO

In addition to improving sustainability in cropping systems, the use of a spring and winter crop rotation system may be a viable option for mitigating soil CO2 emissions (ECO2). This study aimed to determine short-term ECO2 as affected by crop rotations and soil management over one soybean cycle in two no-till experiments, and to assess the soybean yields with the lowest ECO2. Two experiments were carried out in fall-winter as follows: i) triticale and sunflower were grown in Typic Rhodudalf (TR), and ii) ruzigrass, grain sorghum, and ruzigrass + grain sorghum were grown in Rhodic Hapludox (RH). In the spring, pearl millet, sunn hemp, and forage sorghum were grown in both experiments. In addition, in TR a fallow treatment was also applied in the spring. Soybean was grown every year in the summer, and ECO2 were recorded during the growing period. The average ECO2 was 0.58 and 0.84 g m2 h1 with accumulated ECO2 of 5,268 and 7,813 kg ha1 C-CO2 in TR and RH, respectively. Sunn hemp, when compared to pearl millet, resulted in lower ECO2 by up to 12 % and an increase in soybean yield of 9% in TR. In RH, under the winter crop Ruzigrazz+Sorghum, ECO2 were lower by 17%, although with the same soybean yield. Soil moisture and N content of crop residues are the main drivers of ECO2 and soil clay content seems to play an important role in ECO2 that is worthy of further studies. In conclusion, sunn hemp in crop rotation may be utilized to mitigate ECO2 and improve soybean yield.(AU)

Assuntos

RESUMO

In addition to improving sustainability in cropping systems, the use of a spring and winter crop rotation system may be a viable option for mitigating soil CO2 emissions (ECO2). This study aimed to determine short-term ECO2 as affected by crop rotations and soil management over one soybean cycle in two no-till experiments, and to assess the soybean yields with the lowest ECO2. Two experiments were carried out in fall-winter as follows: i) triticale and sunflower were grown in Typic Rhodudalf (TR), and ii) ruzigrass, grain sorghum, and ruzigrass + grain sorghum were grown in Rhodic Hapludox (RH). In the spring, pearl millet, sunn hemp, and forage sorghum were grown in both experiments. In addition, in TR a fallow treatment was also applied in the spring. Soybean was grown every year in the summer, and ECO2 were recorded during the growing period. The average ECO2 was 0.58 and 0.84 g m2 h1 with accumulated ECO2 of 5,268 and 7,813 kg ha1 C-CO2 in TR and RH, respectively. Sunn hemp, when compared to pearl millet, resulted in lower ECO2 by up to 12 % and an increase in soybean yield of 9% in TR. In RH, under the winter crop Ruzigrazz+Sorghum, ECO2 were lower by 17%, although with the same soybean yield. Soil moisture and N content of crop residues are the main drivers of ECO2 and soil clay content seems to play an important role in ECO2 that is worthy of further studies. In conclusion, sunn hemp in crop rotation may be utilized to mitigate ECO2 and improve soybean yield.

Assuntos

RESUMO

ABSTRACT: The Random Forest algorithm is a data mining technique used for classifying attributes in order of importance to explain the variation in an attribute-target, as soil CO2 flux. This study aimed to identify prediction of soil CO2 flux variables in management systems of sugarcane through the machine-learning algorithm called Random Forest. Two different management areas of sugarcane in the state of São Paulo, Brazil, were selected: burned and green. In each area, we assembled a sampling grid with 81 georeferenced points to assess soil CO2 flux through automated portable soil gas chamber with measuring spectroscopy in the infrared during the dry season of 2011 and the rainy season of 2012. In addition, we sampled the soil to evaluate physical, chemical, and microbiological attributes. For data interpretation, we used the Random Forest algorithm, based on the combination of predicted decision trees (machine learning algorithms) in which every tree depends on the values of a random vector sampled independently with the same distribution to all the trees of the forest. The results indicated that clay content in the soil was the most important attribute to explain the CO2 flux in the areas studied during the evaluated period. The use of the Random Forest algorithm originated a model with a good fit (R2 = 0.80) for predicted and observed values.

RESUMO

ABSTRACT: The Random Forest algorithm is a data mining technique used for classifying attributes in order of importance to explain the variation in an attribute-target, as soil CO2 flux. This study aimed to identify prediction of soil CO2 flux variables in management systems of sugarcane through the machine-learning algorithm called Random Forest. Two different management areas of sugarcane in the state of São Paulo, Brazil, were selected: burned and green. In each area, we assembled a sampling grid with 81 georeferenced points to assess soil CO2 flux through automated portable soil gas chamber with measuring spectroscopy in the infrared during the dry season of 2011 and the rainy season of 2012. In addition, we sampled the soil to evaluate physical, chemical, and microbiological attributes. For data interpretation, we used the Random Forest algorithm, based on the combination of predicted decision trees (machine learning algorithms) in which every tree depends on the values of a random vector sampled independently with the same distribution to all the trees of the forest. The results indicated that clay content in the soil was the most important attribute to explain the CO2 flux in the areas studied during the evaluated period. The use of the Random Forest algorithm originated a model with a good fit (R2 = 0.80) for predicted and observed values.

RESUMO

The harvesting system of green sugarcane, characterized by mechanized harvesting and no crop burning, affects soil quality by increasing the remaining straw left on the soil surface after harvesting, thus, contributing to the improvement of physical, chemical, and microbiological soil attributes, influencing CO2 fluxes. This study aimed to evaluate CO2 fluxes and their relation to soil properties in sugarcane crops under different harvesting managements: burned (B), Green harvesting for 5 years (G-5) and Green harvesting for ten years (G-10). For this, a 1 ha sampling grid with 30 points was installed in each area, all located in the Northeast of São Paulo State, Brazil. In each point, CO2 fluxes were measured and the soil was sampled to analyze the microbial biomass, physical (soil moisture and temperature, mean weight diameter, bulk density, clay, macroporosity and microporosity) and chemical characterization (pH, organic C, base saturation and P). The CO2 fluxes were divided into four quantitative criteria: high, moderate, low and very low from the Statistical Division (mean, first quartile, median and third quartile) and the other data were classified according this criterion. The Principal Component Analysis (PCA) was used to identify the main soil attributes that influence CO2 fluxes. The results showed that G-10 CO2 fluxes were 28 and 41 % higher than those in the G-5 and B treatments, respectively. The PCA analysis showed that macroporosity was the main soil attribute that influenced the high CO2 fluxes.

Assuntos

RESUMO

The harvesting system of green sugarcane, characterized by mechanized harvesting and no crop burning, affects soil quality by increasing the remaining straw left on the soil surface after harvesting, thus, contributing to the improvement of physical, chemical, and microbiological soil attributes, influencing CO2 fluxes. This study aimed to evaluate CO2 fluxes and their relation to soil properties in sugarcane crops under different harvesting managements: burned (B), Green harvesting for 5 years (G-5) and Green harvesting for ten years (G-10). For this, a 1 ha sampling grid with 30 points was installed in each area, all located in the Northeast of São Paulo State, Brazil. In each point, CO2 fluxes were measured and the soil was sampled to analyze the microbial biomass, physical (soil moisture and temperature, mean weight diameter, bulk density, clay, macroporosity and microporosity) and chemical characterization (pH, organic C, base saturation and P). The CO2 fluxes were divided into four quantitative criteria: high, moderate, low and very low from the Statistical Division (mean, first quartile, median and third quartile) and the other data were classified according this criterion. The Principal Component Analysis (PCA) was used to identify the main soil attributes that influence CO2 fluxes. The results showed that G-10 CO2 fluxes were 28 and 41 % higher than those in the G-5 and B treatments, respectively. The PCA analysis showed that macroporosity was the main soil attribute that influenced the high CO2 fluxes.(AU)

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Este trabalho teve por objetivo avaliar a sensibilidade de isolados dos fungos Metarhizium anisopliae (Metsch.) Sorok. e Beauveria bassiana (Bals). Vuill. ao efeito das radiações solar e ultravioleta e da temperatura. Conídios dos isolados foram expostos, por vários períodos, aos raios de um simulador solar em diversas irradiâncias e a uma lâmpada de raios ultravioleta germicida. Os conídios do isolado de M. anisopliae foram também expostos às temperaturas de 19,5; 24,2 e 31,0ºC, e os do isolado de B. bassiana a 19,4; 20,8 e 28,3ºC, e 18,7; 23,8 e 30,9ºC. Avaliou-se a germinação de conídios pelo teste de viabilidade. Os isolados dos fungos se mostraram bastantes sensíveis aos raios do simulador solar e aos raios ultravioleta. A germinação de ambos sofreu significativa redução a partir de 30 minutos de exposição à radiação do simulador solar. O efeito mais severo foi evidenciado pelo isolado de B. bassiana, com grande redução da germinação dos conídios em todas as irradiâncias testadas. A sensibilidade à radiação ultravioleta também foi grande, pois ocorreu acentuada redução da germinação dos conídios do isolado de M. anisopliae (38,2%) e de B. bassiana (65%) já aos 30 segundos de exposição. A temperatura afetou a viabilidade de ambos os fungos. Temperaturas entre 23,8 e 31ºC favoreceram a germinação dos conídios, enquanto temperaturas próximas de 20ºC dificultaram a germinação.(AU)

This study aimed to access the sensibility of isolates of the fungus Metarhizium anisopliae (Metsch.) Sorok. and Beauveria bassiana (Bals.) Vuill. to the effect of solar and ultraviolet radiation and temperature. Conidia were exposed for various periods to the rays from a solar simulator at various irradiances, and to light germicidal ultraviolet rays. Conidia of the isolate of M. anisopliae were also exposed to temperatures of 19.5, 24.2 and 31.0ºC and the isolate of B. bassiana to 19.4, 20.8 and 28.3ºC, and also to 18.7, 23.8 and 30.9ºC. The germination of conidia was evaluated by the viability test. The fungal isolates showed to be very sensitive to the solar simulator and ultraviolet rays. Germination of both was significantly decreased starting from 30 minutes of exposure to the rays of the solar simulator. The most severe effect was evidenced by the isolate of B. bassiana with great reduction in conidia germination in all the tested irradiances. Sensitivity to ultraviolet radiation was also great, showing a marked reduction in the germination of M. anisopliae (38.2%) and B. bassiana (65%) conidia after 30 seconds of exposure. The temperature affected the viability of both fungi. Temperatures ranging of 23.8 to 31ºC favor the germination of conidia while temperatures around 20ºC constrained germination.(AU)

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RESUMO

Soil CO2 emission (FCO2) is governed by the inherent properties of the soil, such as bulk density (BD). Mapping of FCO2 allows the evaluation and identification of areas with different accumulation potential of carbon. However, FCO2 mapping over larger areas is not feasible due to the period required for evaluation. This study aimed to assess the quality of FCO2 spatial estimates using values of BD as secondary information. FCO2 and BD were evaluated on a regular sampling grid of 60 m × 60 m comprising 141 points, which was established on a sugarcane area. Four scenarios were defined according to the proportion of the number of sampling points of FCO2 to those of BD. For these scenarios, 67 (F67), 87 (F87), 107 (F107) and 127 (F127) FCO2 sampling points were used in addition to 127 BD sampling points used as supplementary information. The use of additional information from the BD provided an increase in the accuracy of the estimates only in the F107, F67 and F87 scenarios, respectively. The F87 scenario, with the approximate ratio between the FCO2 and BD of 1.00:1.50, presented the best relative improvement in the quality of estimates, thereby indicating that the BD should be sampled at a density 1.5 time greater than that applied for the FCO2. This procedure avoided problems related to the high temporal variability associated with FCO2, which enabled the mapping of this variable to be elaborated in large areas.

RESUMO

Soil CO2 emission (FCO2) is governed by the inherent properties of the soil, such as bulk density (BD). Mapping of FCO2 allows the evaluation and identification of areas with different accumulation potential of carbon. However, FCO2 mapping over larger areas is not feasible due to the period required for evaluation. This study aimed to assess the quality of FCO2 spatial estimates using values of BD as secondary information. FCO2 and BD were evaluated on a regular sampling grid of 60 m × 60 m comprising 141 points, which was established on a sugarcane area. Four scenarios were defined according to the proportion of the number of sampling points of FCO2 to those of BD. For these scenarios, 67 (F67), 87 (F87), 107 (F107) and 127 (F127) FCO2 sampling points were used in addition to 127 BD sampling points used as supplementary information. The use of additional information from the BD provided an increase in the accuracy of the estimates only in the F107, F67 and F87 scenarios, respectively. The F87 scenario, with the approximate ratio between the FCO2 and BD of 1.00:1.50, presented the best relative improvement in the quality of estimates, thereby indicating that the BD should be sampled at a density 1.5 time greater than that applied for the FCO2. This procedure avoided problems related to the high temporal variability associated with FCO2, which enabled the mapping of this variable to be elaborated in large areas.

RESUMO

The irrigation application is one of the most useful techniques in tropical environments, especially during dry seasons. In this study, CO2 efflux, temperature and soil moisture were studied in a field sampled with a grid having 48 points distributed in 35 x 25 m, under irrigation promoted by a sprinkler located at the center of the area, provoking different levels of water deposition, with maximum irrigation levels of 44.4 and 62.2 mm in points closer to the sprinkler. The results show that the emissions, temperature and moisture were strongly affected by the two irrigations events, having a total water level added of 106,6 mm for the points next to the sprinkler and zero for the most distant points from it. The maps of space variation of the variables, as well as the linear correlation between them, indicate that the emissions were positively related to the soil moisture and negative correlated to the soil temperature only after the irrigations events. The special variability models of soil CO2 emission changed from exponential to spherical after the irrigations events. Such results indicate that soil moisture is among possible controlling factors of the soil CO2 emission, because even with reductions in soil temperature provoked by the wetness, emissions increased strongly.

A aplicação de lâminas de irrigação em solos é uma das práticas mais adotadas em ambientes tropicais, especialmente em épocas de seca. Neste trabalho, investigaram-se as emissões de CO2, temperatura e umidade do solo, em 48 pontos distribuídos numa área de 35 x 25 m, afetados por irrigações, promovidas com um aspersor localizado no centro da área, que provocou um molhamento com perfil triangular com lâminas máximas aplicadas de 44,4 e 62,2 mm nos pontos mais próximos do aspersor, Os resultados indicam que as emissões, temperatura e umidade do solo foram fortemente afetadas pelas duas irrigações na área, cuja lâmina total de água somou 106,6 mm para os pontos mais próximos do aspersor e aproximadamente zero para os pontos mais distantes. Os mapas de variação espacial das variáveis, bem como a correlação linear entre elas, indica que as emissões estiveram positivamente relacionadas à umidade do solo e negativamente correlacionadas à temperatura do solo, após os eventos de molhamento da área. Os modelos de variabilidade espacial da emissão de CO2 mudam de exponencial para esféricos logo após os eventos de irrigação. Tais resultados indicam que o fator limitante à emissão de CO2 do solo foi a umidade, pois, a despeito das reduções na temperatura do solo provocadas pelo molhamento, as emissões foram fortemente aumentadas.

RESUMO

The spatial and temporal variation of soil CO2 emission is influenced by several soil attributes related to CO2 production and its diffusion in the soil. However, few studies aiming to understand the effect of topography on the variability of CO2 emissions exist, especially for cropping areas of tropical regions. The objective of this study was to evaluate the spatial and temporal changes of soil CO2 emission and its relation to soil attributes in an area currently cropped with sugarcane under different relief forms and slope positions. Mean CO2 emissions in the studied period (seven months) varied between 0.23 and 0.71, 0.27 and 0.90, and 0.31 and 0.80 g m-2 h-1 of CO2 for concave (Conc), backslope (BackS) and footslope (FootS) positions, respectively. The temporal variability of CO2 emissions in each area was explained by an exponential relation between the CO2 emission and soil temperature and a linear relation between CO2 emission and soil water content. The Q10 values were 1.98 (± 0.34), 1.81 (± 0.49) and 1.71 (± 0.31) for Conc, BackS and FootS, respectively. Bulk density, macroporosity, penetration resistance, aggregation and oxidizable organic carbon content explain the changes in soil CO2 emission observed, especially when the Conc position was compared to BackS. The effect of relief form and topographic position on soil CO2 emission variation was dependent on the time of measurement.

A variação temporal e espacial da emissão de CO2 solo-atmosfera é influenciada por inúmeros atributos do solo relacionados à produção de CO2 e à difusão do gás no solo. Ainda são escassos, entretanto, estudos visando compreender o efeito da topografia na variação da emissão deste gás, especialmente em áreas agrícolas da região tropical. O objetivo deste trabalho foi estudar a variação temporal e espacial da emissão de CO2 solo-atmosfera e sua relação com atributos do solo em área de cultivo de cana-de-açúcar sob diferentes formas de relevo e posições na encosta. A média da emissão de CO2 no período de sete meses de estudo variou entre 0,23 e 0,71; 0,27 e 0,90 e 0,31 e 0.80 g CO2 m-2 h- 1, nas posições côncava (Conc), encosta superior (BackS) e encosta inferior (FootS), respectivamente. A variação temporal da emissão em cada uma das áreas foi explicada por uma relação exponencial entre emissão de CO2 e temperatura do solo, e uma relação linear da emissão deste gás com a umidade do solo. O valor de Q10 foi 1,98 (± 0,34); 1,81 (± 0,49) e 1,71 (± 0,31) para Conc, BackS e FootS, respectivamente. Densidade do solo, macroporosidade, resistência do solo à penetração, agregação e conteúdo de carbono orgânico oxidável explicaram as variações observadas na emissão de CO2, especialmente quando se compara a posição côncava com a encosta superior. O efeito do relevo e da posição topográfica sobre a variação da emissão de CO2 do solo foi dependente da época de amostragem.

RESUMO

The irrigation application is one of the most useful techniques in tropical environments, especially during dry seasons. In this study, CO2 efflux, temperature and soil moisture were studied in a field sampled with a grid having 48 points distributed in 35 x 25 m, under irrigation promoted by a sprinkler located at the center of the area, provoking different levels of water deposition, with maximum irrigation levels of 44.4 and 62.2 mm in points closer to the sprinkler. The results show that the emissions, temperature and moisture were strongly affected by the two irrigations events, having a total water level added of 106,6 mm for the points next to the sprinkler and zero for the most distant points from it. The maps of space variation of the variables, as well as the linear correlation between them, indicate that the emissions were positively related to the soil moisture and negative correlated to the soil temperature only after the irrigations events. The special variability models of soil CO2 emission changed from exponential to spherical after the irrigations events. Such results indicate that soil moisture is among possible controlling factors of the soil CO2 emission, because even with reductions in soil temperature provoked by the wetness, emissions increased strongly.

A aplicação de lâminas de irrigação em solos é uma das práticas mais adotadas em ambientes tropicais, especialmente em épocas de seca. Neste trabalho, investigaram-se as emissões de CO2, temperatura e umidade do solo, em 48 pontos distribuídos numa área de 35 x 25 m, afetados por irrigações, promovidas com um aspersor localizado no centro da área, que provocou um molhamento com perfil triangular com lâminas máximas aplicadas de 44,4 e 62,2 mm nos pontos mais próximos do aspersor, Os resultados indicam que as emissões, temperatura e umidade do solo foram fortemente afetadas pelas duas irrigações na área, cuja lâmina total de água somou 106,6 mm para os pontos mais próximos do aspersor e aproximadamente zero para os pontos mais distantes. Os mapas de variação espacial das variáveis, bem como a correlação linear entre elas, indica que as emissões estiveram positivamente relacionadas à umidade do solo e negativamente correlacionadas à temperatura do solo, após os eventos de molhamento da área. Os modelos de variabilidade espacial da emissão de CO2 mudam de exponencial para esféricos logo após os eventos de irrigação. Tais resultados indicam que o fator limitante à emissão de CO2 do solo foi a umidade, pois, a despeito das reduções na temperatura do solo provocadas pelo molhamento, as emissões foram fortemente aumentadas.

RESUMO

The spatial and temporal variation of soil CO2 emission is influenced by several soil attributes related to CO2 production and its diffusion in the soil. However, few studies aiming to understand the effect of topography on the variability of CO2 emissions exist, especially for cropping areas of tropical regions. The objective of this study was to evaluate the spatial and temporal changes of soil CO2 emission and its relation to soil attributes in an area currently cropped with sugarcane under different relief forms and slope positions. Mean CO2 emissions in the studied period (seven months) varied between 0.23 and 0.71, 0.27 and 0.90, and 0.31 and 0.80 g m-2 h-1 of CO2 for concave (Conc), backslope (BackS) and footslope (FootS) positions, respectively. The temporal variability of CO2 emissions in each area was explained by an exponential relation between the CO2 emission and soil temperature and a linear relation between CO2 emission and soil water content. The Q10 values were 1.98 (± 0.34), 1.81 (± 0.49) and 1.71 (± 0.31) for Conc, BackS and FootS, respectively. Bulk density, macroporosity, penetration resistance, aggregation and oxidizable organic carbon content explain the changes in soil CO2 emission observed, especially when the Conc position was compared to BackS. The effect of relief form and topographic position on soil CO2 emission variation was dependent on the time of measurement.

A variação temporal e espacial da emissão de CO2 solo-atmosfera é influenciada por inúmeros atributos do solo relacionados à produção de CO2 e à difusão do gás no solo. Ainda são escassos, entretanto, estudos visando compreender o efeito da topografia na variação da emissão deste gás, especialmente em áreas agrícolas da região tropical. O objetivo deste trabalho foi estudar a variação temporal e espacial da emissão de CO2 solo-atmosfera e sua relação com atributos do solo em área de cultivo de cana-de-açúcar sob diferentes formas de relevo e posições na encosta. A média da emissão de CO2 no período de sete meses de estudo variou entre 0,23 e 0,71; 0,27 e 0,90 e 0,31 e 0.80 g CO2 m-2 h- 1, nas posições côncava (Conc), encosta superior (BackS) e encosta inferior (FootS), respectivamente. A variação temporal da emissão em cada uma das áreas foi explicada por uma relação exponencial entre emissão de CO2 e temperatura do solo, e uma relação linear da emissão deste gás com a umidade do solo. O valor de Q10 foi 1,98 (± 0,34); 1,81 (± 0,49) e 1,71 (± 0,31) para Conc, BackS e FootS, respectivamente. Densidade do solo, macroporosidade, resistência do solo à penetração, agregação e conteúdo de carbono orgânico oxidável explicaram as variações observadas na emissão de CO2, especialmente quando se compara a posição côncava com a encosta superior. O efeito do relevo e da posição topográfica sobre a variação da emissão de CO2 do solo foi dependente da época de amostragem.