Variability Of Soil CO2 Emission Research Articles

Soil CO2 emissions under different land-use managements in Mato Grosso do Sul, Brazil

Understanding the variability of soil CO2 emission across several land use and cover (LULC) classes and biomes and its relationship with climate variables is important to drive strategies that contribute to meeting local and international demands for sustainable development and low carbon agriculture. The hypothesis of this research is that soil CO2 emission in situ (FCO2) is variable between LULCs across different biomes and that there may be an association between soil CO2 flux and environmental variables such as temperature and soil moisture. This study evaluated FCO2, measured by a portable EGM-5 CO2 gas analyzer, CO2Flux model (obtained by remote sensing approach), soil moisture (SM), soil temperature (ST) and relationship between these variables in different LULC classes. We identified LULCs can contribute to carbon neutralization actions over the Cerrado, Atlantic Forest and Pantanal biomes located in State of Mato Grosso do Sul (MS), Brazil. Four LULC classes were evaluated in each biome: agriculture (soybean cultivation), pasture, eucalyptus plantation, and native vegetation. A principal component analysis (PCA) was performed to verify the relationship between biomes and LULC classes with the variables evaluated, and a Pearson correlation plot was created to assess the relationship between the variables evaluated. The lowest FCO2 values were found in eucalyptus and soybean crops, regardless of biome. Our findings reveal the existence of soil CO2 flux variability between the different LULCs and biomes. Pasture in Pantanal and Atlantic Forest biomes exhibited the highest FCO2 values. Eucalyptus cultivation and native forest showed negative CO2Flux values, regardless of biome. Lower FCO2 values were also observed for soybean cultivation. Such findings reinforce that native vegetation function as carbon sinks and that, therefore, their conservation is vital for the mitigation of CO2 emissions. However, soybean and eucalyptus farming can be strategic for low carbon agriculture in MS and carbon neutralization projects by simultaneously contribute to economic and sustainable development of the regions covered by the biomes evaluated here.

Variability and controls of soil CO2 fluxes under different tillage and crop residue managements in a wheat-maize double-cropping system.

The spatial and temporal variability of soil CO2 emissions from agricultural soils is inherently high. While tillage and crop residue practices play vital roles in governing soil CO2 emission, their effects on the variability of soil CO2 fluxes across depths and seasons are still poorly understood. To address this, an experiment consisting of four treatments, namely conventional tillage with (CT+) and without crop residue application (CT-), as well as no tillage with (NT+) and without crop residue application (NT-), was conducted to investigate soil CO2 fluxes at top 40 cm soils with 10-cm depth intervals in a winter wheat-summer maize rotation system in the North China Plain. Our results showed soil CO2 fluxes increased with depth in both the wheat- and maize-growing seasons. However, the dominant factors in regulating soil CO2 fluxes changed with soil depth and seasons. In the wheat-growing season, increase in soil CO2 fluxes with depth was attributed to the increase of dissolved organic carbon-to-nitrogen ratio (DOC/DON) and a decline in soil DON concentration along the soil profile. These factors explained about 55-96% of the total variation in soil CO2 fluxes at different soil depths. In the maize-growing season, the dominant factors were soil DOC/DON ratio, soil DON concentrations, and soil moisture. These factors explained approximately 79-96% of the total variation in soil CO2 fluxes along the soil depth. Greater soil CO2 fluxes (except at 30-40 cm depth) were observed in NT- than CT- treatments. Furthermore, crop residue application enhanced soil CO2 fluxes across different depths, but the enhancement was more prominent in CT+ than NT+. Moreover, soil CO2 fluxes in the maize-growing season were greater than those in the wheat-growing season. Our results demonstrate that the effects of tillage regimes and crop residue management practices on soil CO2 emissions are not confined only to the plough layer but can extend to soils of over 30 cm depths. We also need to revisit the general conventional view that no tillage can significantly reduce soil CO2 emissions compared with conventional tillage for better climate change mitigation.

Forecasting the spatiotemporal variability of soil CO2 emissions in sugarcane areas in southeastern Brazil using artificial neural networks.

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 (0.45m) 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.

Soil carbon dioxide emission associated with soil porosity after sugarcane field reform

This study aimed to characterize soil carbon dioxide (CO2) emission associated with soil pore distribution in an Oxisol and Ultisol under chiseling in the planting row and in total area for sugarcane (Saccharum officinarum) cultivation. The experimental design was a large paired-plot design. Treatments consisted of chiseling in the planting row (CPR) and chiseling in total area (CTA) in an Oxisol and Ultisol. Soil CO2 emission, soil temperature, and soil moisture were assessed over 12 days in the Oxisol and 11 days in the Ultisol at a depth of 0–0.10 m. Organic carbon associated with minerals (OCAM) and particulate organic carbon (POC) were also assessed. OCAM, pore class C2 (0.05 ≤ ɸ < 0.1 mm), soil moisture, and soil temperature explained 72 and 53% of the variability of soil CO2 emission in CPR and CTA, respectively. In the Ultisol, pore class C1 (ɸ ≥ 0.1 mm) and OCAM explained 82% of the variability of soil CO2 emission in CPR. In CTA, soil moisture, OCAM, and POC explained 67% of the variability of soil CO2 emission. In the Oxisol, CPR and CTA affected soil structure, causing changes in both soil porosity and soil CO2 emission. In the Oxisol, the lowest average value of soil CO2 emission (2.8 μmol m−2 s−1) was observed in CPR whereas its highest value (3.4 μmol m−2 s−1) was observed in CTA. In the Ultisol, soil tillage (CPR and CTA) did not affect soil CO2 emission. These results indicate that the intensity of soil tillage in more clayey textured soils favors soil CO2 emission possibly due to a higher carbon availability for microbial activity when compared to more sandy textured soils. A less intensive soil tillage can be considered as an efficient strategy to reduce soil CO2 emission and hence soil organic carbon losses. Thus, this management strategy proved to be efficient in terms of mitigating greenhouse gas emissions, reducing the contribution of agriculture to global climate change.

Effect of clear-cutting on soil CO2 emission

An experimental study to estimate the effect of clear-cutting on CO2 emission from the soil surface was performed using the chamber method. For field measurements, several experimental plots within the clear-cut with different degrees of damage of the upper organic soil layer and different amounts of litter and logging residue on the surface were selected. Soil CO2 fluxes were simultaneously measured both on the clear-cutting plots and on two plots within the spruce forest stand located close to the clear-cut area. The results show a significant seasonal and diurnal variability of soil CO2 emission. It was found that the soil respiration rate varies significantly among plots and depends on the damage to the upper soil layer and the availability of litter and logging residue on the soil surface. It was found that the rate of CO2 emission from soil surface is strongly dependent on the air and soil temperature and moisture of the upper soil layer. Different rates of soil respiration are also revealed on the plots located at different distances from tree trunks within the control forest stand.

SPATIAL VARIABILITY OF SOIL CO2 EMISSION IN SOYBEAN AND SUGARCANE AREAS IN MATO GROSSO DO SUL CERRADO, BRAZIL

: In last years, the damage caused to the environment due to anthropic activities have become a worldwide concern. The carbon dioxide (CO2) is one of the main greenhouse gases because the increase of its concentration in the atmosphere has intensified to global warming. Therefore, to study the CO2 amounts emitted to the atmosphere has been even more important, especially in agricultural areas in Brazil. This study aimed to determine the spatial variability of soil CO2 emissions (FCO2), soil temperature (Ts) and soil moisture (Ms) in soybean and sugarcane cultivation areas in the Cerrado region in Mato Grosso do Sul. The FCO2 and Ts registration were done using a portable flow chamber and temperature sensor of the LI-8100 system. The Ms was evaluated using TDR portable system. The studies were conducted in the years of 2013, in soybean cultivation, and in 2014, in the sugarcane cultivation, during the crop growth early stages, using grids which contain 89 and 102 sampling points, respectively, spanning 1 ha area in both experiments. The spatial variability variables were characterised by geostatistics: mathematical models adjustments to the experimental variograms and spatial pattern maps construction through ordinary kriging technique. The sTe sH variables did not show significant correlations with FCO2. Despite the years of conducting the experiments, the soybean cultivation presented the higher average values of soil CO2 emission equal 2.11 µmol m-2 s-1 and higher variation in range of the spatial dependency between 8.41 and 27 m when compared to sugarcane culture, with a FCO2 average of 1.71 µmol m-2 s-1 and variation in the ranges of spatial dependency between 15.3 and 27.3 m.

Soil CO2 emission and its relationship to soil properties under different tillage systems

ABSTRACTThere is a lack of understanding as to which soil property is the most important at regulating the temporal variability of soil CO2 emissions on China’s Loess Plateau. The objective of this study was to evaluate the CO2 emissions and their relationships to certain soil properties in a winter wheat (Triticum aestivum L.) field subject to no-till (NT) and conventional tillage (CT) practices. The CO2 emissions were significantly higher in the CT (257.6 mg CO2 m−2 h−1), compared with the NT (143.8 mg CO2 m−2 h−1), treatment. Soil organic matter content and carbon stock were 8% and 14% higher, respectively, in the NT, compared with the CT, treatment. Regression analyses between the CO2 emissions and soil properties, including soil temperature and carbon stock, explained up to 88% and 60% of the temporal variability in CO2 emissions in the NT and CT treatments, respectively. Linear correlations between the soil temperature and CO2 emissions were recorded in both the NT and CT treatments. Soil temperature was the most important factor in terms of understanding the temporal variability in CO2 emissions in wheat fields of the study area.

Reducing soil CO2 emission and improving upland rice yield with no-tillage, straw mulch and nitrogen fertilization in northern Benin

To explore effective ways to decrease soil CO2 emission and increase grain yield, field experiments were conducted on two upland rice soils (Lixisols and Gleyic Luvisols) in northern Benin in West Africa. The treatments were two tillage systems (no-tillage, and manual tillage), two rice straw managements (no rice straw, and rice straw mulch at 3 Mgha−1) and three nitrogen fertilizers levels (no nitrogen, recommended level of nitrogen: 60kgha−1, and high level of nitrogen: 120kgha−1). Potassium and phosphorus fertilizers were applied to be non-limiting at 40kg K2Oha−1 and 40kg P2O5ha−1. Four replications of the twelve treatment combinations were arranged in a randomized complete block design. Soil CO2 emission, soil moisture and soil temperature were measured at 5cm depth in 6–10 days intervals during the rainy season and every two weeks during the dry season. Soil moisture was the main factor explaining the seasonal variability of soil CO2 emission. Much larger soil CO2 emissions were found in rainy than dry season. No-tillage significantly reduced soil CO2 emissions compared with manual tillage. Higher soil CO2 emissions were recorded in the mulched treatments. Soil CO2 emissions were higher in fertilized treatments compared with non-fertilized treatments. Rice biomass and yield were not significantly different as a function of tillage systems. On the contrary, rice biomass and yield significantly increased with application of rice straw mulch and nitrogen fertilizer. The highest response of rice yield to nitrogen fertilizer addition was obtained for 60kg Nha−1 in combination with 3 Mgha−1 of rice straw for the two tillage systems. Soil CO2 emission per unit grain yield was lower under no-tillage, rice straw mulch and nitrogen fertilizer treatments. No-tillage combined with rice straw mulch and 60kg Nha−1 could be used by smallholder farmers to achieve higher grain yield and lower soil CO2 emission in upland rice fields in northern Benin.

Temporal Variability of Soil CO2 Emission Contrasting Degraded and Managed Pasture in Brazil

Grazing areas represent the largest agricultural areas in Brazil, occupying more than 172 million hectares, about 20% of the agricultural land (IBGE, 2007). Despite the large areas of degraded pasture, little information exists about the temporal variation of soil properties including nutrients and soil carbon dynamic (Cerri et al., 2004). This study aimed to measure soil FCO2 from degraded and managed pasture areas, describing their temporal variability correlated to the environmental variables. Our study was carried out at two pasture areas located in Mococa city, São Paulo State Brazil. On March 04, 2013, 2 grids of 100 × 100 m with 102 samples points were installed in DP (Degraded pasture) and managed pasture (MP). Measurements of soil CO2 emissions (FCO2), soil temperature (Tsoil) and water content of soil (WCsoil) were initiated onMarch 12th, resulting 8 measuring days. Higher difference of emission between both areasoccurred during the first days, probably due to a huge precipitation influence (accumulated 80mm). In the first study day (12th), emission values were 8.73 ± 0.38 versus 4.47 ± 0.21μmol m-2 s-1, in DP and MP, respectively. In the last study day, March 19th, emissions in both areas were closer without remarkable rain influence (7.43 ± 0.54 and 5.98 ± 0.17μmol m-2 s-1). Total emission calculated by the area bellow the emission curves resulted 640.7 versus 440.0kg CO2-C ha-1 in DP and MP, respectively, corresponding to an additional emission of 200.6kg CO2-C ha-1 from DP or, 735.5kg CO2 released to the atmosphere, contributing to the Climate Change. The potential for soil C sequestration in pasture soils is strictly related to their management, and the rate and mechanism of soil C losses in pasture soils can affect this potential, with soils functioning as an atmospheric CO2 sinking or emitter.

Spatial variability of soil CO2 emission in a sugarcane area characterized by secondary information

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.

Modeling temporal variability of soil CO2 emissions from an apple orchard in the Harran Plain of southeastern Turkey

Broad interest in reducing greenhouse gas emissions requires a better understanding of controls on carbon dioxide (CO2) release under different agricultural management practices. The objective of this study was to investigate and model seasonal variation of soil CO2 emissions from an apple orchard field (Malus domestica L. 'Starkrimson'). Soil CO2 emissions from an apple orchard managed with common practices were measured weekly over a 3-year period (May 2008 to May 2011) from both under the crowns of trees (CO2-UC) and between rows (CO2-BR) using a soda lime technique and were modeled using available environmental data. The study area is located in the Harran Plain of southeastern Turkey and has a semiarid climate. The weekly soil CO2 emissions ranged from 87.8 to 1428 kg week-1 ha-1, from 74.6 to 835 kg week-1 ha-1, and from 88.6 to 1087 kg week-1 ha-1 for CO2-UC, CO2-BR, and the average of both (CO2-AVG), respectively, and showed a pronounced seasonal pattern with the lowest emissions in winter (January and February) and the highest emissions during the growing season (April to December). Relative to 2008 emissions, 2009 CO2 emissions increased by approximately 75%, and 2010 emissions increased by approximately 88%. Comparison of 3 models (multiple linear regression, principal component regression, and multivariate adaptive regression splines) showed that multivariate adaptive regression splines provided the best performance in modeling soil CO2 emissions, explaining overall variation of 64%, 56%, 76%, and 53% in CO2-AVG for the first, second, third, and all three 3 periods, respectively. In conclusion, overall findings showed that soil CO2 emissions could be modeled by available environmental data such as air and soil temperature.

Uncertainties in the prediction of spatial variability of soil CO2 emissions and related properties

The soil CO2 emission has high spatial variability because it depends strongly on soil properties. The purpose of this study was to (i) characterize the spatial variability of soil respiration and related properties, (ii) evaluate the accuracy of results of the ordinary kriging method and sequential Gaussian simulation, and (iii) evaluate the uncertainty in predicting the spatial variability of soil CO2 emission and other properties using sequential Gaussian simulations. The study was conducted in a sugarcane area, using a regular sampling grid with 141 points, where soil CO2 emission, soil temperature, air-filled pore space, soil organic matter and soil bulk density were evaluated. All variables showed spatial dependence structure. The soil CO2 emission was positively correlated with organic matter (r = 0.25, p &lt; 0.05) and air-filled pore space (r = 0.27, p &lt; 0.01) and negatively with soil bulk density (r = -0.41, p &lt; 0.01). However, when the estimated spatial values were considered, the air-filled pore space was the variable mainly responsible for the spatial characteristics of soil respiration, with a correlation of 0.26 (p &lt; 0.01). For all variables, individual simulations represented the cumulative distribution functions and variograms better than ordinary kriging and E-type estimates. The greatest uncertainties in predicting soil CO2 emission were associated with areas with the highest estimated values, which produced estimates from 0.18 to 1.85 t CO2 ha-1, according to the different scenarios considered. The knowledge of the uncertainties generated by the different scenarios can be used in inventories of greenhouse gases, to provide conservative estimates of the potential emission of these gases.

Fractal dimension and anisotropy of soil CO2 emission in a mechanically harvested sugarcane production area

Soil CO2 emissions (FCO2) are spatially dependent, and their spatial structure varies in different directions along the soil surface (anisotropy). This anisotropy, which can result from several pedological factors that are directly related to soil carbon dynamics, is affected by soil management. In this study, the anisotropies of the spatial variability of soil CO2 emissions and of other soil properties were determined for a sugarcane production area under mechanical harvest, when crop residues are left on soil surface, located in the northeastern part of the state of São Paulo, Brazil. The anisotropic characterization of variables was performed by deriving the fractal dimension (DF) from experimental semivariograms calculated at angles of 0°, 45°, 90° and 135° from the between crop line direction (0°). The mean FCO2 was 2.19μmolm−2s−1, and values were significantly lower in the 0° direction. A principal component analysis was applied to study soil properties and the first principal component was mainly related to soil physical properties and FCO2. A multiple regression analysis indicated that air-filled pore space (AFPS) was the main factor affecting the spatial variability of FCO2 in all directions. The AFPS DF values were significantly lower in the direction in which sugarcane crops were planted, indicating anisotropy of this property and greater homogeneity in this direction. Even after rainfall, there was no change in the structure of spatial variability as expressed by the values of DF. The results indicate that in sugarcane areas, several factors inherent to soil forming processes and management practices during harvest and seeding were responsible for the observed anisotropy, which affected soil CO2 emissions.

Spatial variability models of CO2 emissions from soils colonized by grass (Deschampsia antarctica) and moss (Sanionia uncinata) in Admiralty Bay, King George Island

AbstractSoil CO2 emission is an important part of the terrestrial carbon cycling and is influenced by several factors, such as type and distribution of vegetation. In this work we evaluated the spatial variability of soil CO2 emission in terrestrial ecosystems of maritime Antarctica, under two contrasting vegetation covers: 1) grass areas of Deschampsia antarctica Desv., and 2) moss carpets of Sanionia uncinata (Hedw.) Loeske. Highest mean emission was obtained for the Deschampsia (4.13 μmol m-2 s-1) developed on organic-rich soil with a strong penguin influence. The overall results indicate that soil temperature is not directly related to the spatial pattern of soil CO2 emission at the sites studied. Emission adjusted models were Gaussian and exponential with ranges varying from 1.3 to 2.8 m, depending on the studied site and vegetation cover.

Temporal variability of soil CO2 emission after conventional and reduced tillage described by an exponential decay in time model

To study Assessing the impact of tillage practices on soil carbon losses dependents it is necessary to describe the temporal variability of soil CO2 emission after tillage. It has been argued that large amounts of CO2 emitted after tillage may serve as an indicator for longer-term changes in soil carbon stocks. Here we present a two-step function model based on soil temperature and soil moisture including an exponential decay in time component that is efficient in fitting intermediate-term emission after disk plow followed by a leveling harrow (conventional), and chisel plow coupled with a roller for clod breaking (reduced) tillage. Emission after reduced tillage was described using a non-linear estimator with determination coefficient (R²) as high as 0.98. Results indicate that when emission after tillage is addressed it is important to consider an exponential decay in time in order to predict the impact of tillage in short-term emissions.

Spatial variability of soil CO2 emission in different topographic positions

The spatial variability of soil CO2 emission is controlled by several properties related to the production and transport of CO2 inside the soil. Considering that soil properties are also influenced by topography, the objective of this work was to investigate the spatial variability of soil CO2 emission in three different topographic positions in an area cultivated with sugarcane, just after mechanical harvest. One location was selected on a concave-shaped form and two others on linear-shaped form (in back-slope and foot-slope). Three grids were installed, one in each location, containing 69 points and measuring 90 x 90 m each. The spatial variability of soil CO2 emission was characterized by means of semivariance. Spatial variability models derived from soil CO2 emission were exponential in the concave location while spherical models fitted better in the linear shaped areas. The degree of spatial dependence was moderate in all cases and the range of spatial dependence for the CO2 emission in the concave area was 44.5 m, higher than the mean value obtained for the linear shaped areas (20.65 m). The spatial distribution maps of soil CO2 emission indicate a higher discontinuity of emission in the linear form when compared to the concave form.