SUPPLEMENTARY INFORMATION

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1 SUPPLEMENTARY INFORMATION DOI: /NCLIMATE2239 Payback time for soil carbon and sugar-cane ethanol Authors: Francisco F. C. Mello,* Carlos E. P. Cerri, Christian A. Davies, N. Michele Holbrook, Keith Paustian, Stoécio M. F. Maia, Marcelo V. Galdos, Martial Bernoux, Carlos C. Cerri *Corresponding author: Supplementary Information: Materials and Methods Figures S1-S4 Tables S1-S5 References (S1-S23) 1. Material and Methods 1.1 Soil carbon stock changes: Soil carbon stock changes were quantified based on methodology outlined and recommended for national or regional GHG emissions due to land use change (S1). There are three available tiers to estimate carbon removals or inputs, and for each level more detailed information is needed. Tier 1 can be performed using default information presented in the IPCC s guidelines (S1), Tier 2 requires site specific information and Tier 3 adds the need for mathematical modeling associated with sustained measurement/monitoring. Here the Tier 2 level was adopted to estimate soil carbon removal/inputs in south-central Brazil, associated with sugarcane expansion. Table S1 presents the soil C stocks information for LU and LUC presented in this study. In our paper we assume that all the soil carbon lost or gained is lost as CO 2 to the atmosphere; however some of this carbon may be lost from the boundaries of the system and be sequestered elsewhere and not released as CO 2. Leaching and losses of dissolved organic carbon (DOC), mobilization of black carbon due to historical burnt harvesting, and erosion are all mechanisms of losses that we do not quantify here and assume they are released as CO 2. Land use change from native vegetation in particular could result in significant erosion losses due to reductions in soil aggregation, decline in soil structure and reductions in the amount of residue on the soil surface (S2). NATURE CLIMATE CHANGE Macmillan Publishers Limited. All rights reserved.

2 1.2 Study sites selection: An extensive site selection process involved detailed interviews with professionals from the sugarcane industry to find appropriate study areas. This selection was based on the presence of: i) historical land use information, ii) available reference areas (pasture, annual cropping or natural vegetation) older than 20 years with similar geomorphic characteristics (topography, soil type etc.) and adjacent to the sugarcane sites (Fig. S1). This assessment identified 135 areas (75 sugarcane fields, 45 pastures, 10 cropland and 5 cerrado areas) distributed throughout south-central Brazil that were suitable for soil sampling. This gave a total of 75 comparison pairs (CP) some of the reference sites were adjacent to multiple land uses and used for comparison with multiple sugarcane fields (Table S1). This approach covered about 335,000 hectares that were evaluated in the selection process. 1.3 Soil sampling: Sampling was undertaken from nine pits distributed in a 3 x 3 grid for each study site over 100 x 100 m representing 1 hectare (Fig. S2). Six sampling pits were sampled every 10 cm from 0-30 cm, and three deeper sampling pits were sampled from 0-10, 10-20, 20-30, 40-50, 70-80, cm to determine soil carbon and bulk density. Soil texture analysis was determined from the central pit at each site. To address differences in soil carbon accumulation and bulk density that might develop in sugarcane fields over time, we sampled soil from both between and within plant rows for the cm layer. This method was performed in 63 of the 75 sugarcane fields, leading to 54 soil samples (instead 36) in each of these sites, and the average was used to determine the soil carbon stocks for the sugarcane fields. A total of ~6,000 soil samples were taken and used to evaluate the LUC impact for sugarcane conversions in south-central Brazil. 1.4 Soil carbon determination: Subsamples of the soil were sieved (2 mm), ground and sieved at 150 µm for carbon determination by dry combustion (S3). Total carbon was determined on a LECO CN elemental analyzer (furnace at 1350 o C in pure oxygen). This method provides total carbon, which is composed of inorganic (from carbonates) and organic carbon. In most Brazilian soils, the inorganic carbon content is small; therefore the total carbon content determined by dry combustion is mostly comprised of organic carbon. This methodology was used to determine soil C content in several recent studies of soils in Brazil (S4-S7). 1.5 Soil carbon regression equations: To evaluate soil carbon stocks across the full cm layer, it was necessary to derive the carbon content in the unsampled depth increments (30-40, 50-60, and cm). After finding the average carbon content per layer for each study area, regressions were applied to the carbon database to create a regression equation for soil carbon changes with depth, this was used to calculate the carbon content in the unsampled layers. The adjusted root square (R 2 adj) of these regressions are presented in the Table S2 and Fig. S Macmillan Publishers Limited. All rights reserved.

3 1.6 Deriving pedotransfer functions: The soil database includes information on soil attributes such as carbon content, bulk density and texture for one of the cm sampling pits per study area. Multiple stepwise regressions were performed to correlate soil bulk density and other soil attributes, as was found in other studies (S8, S9). The regression equations can then be used to estimate the soil bulk density using other soil attributes. We followed methods from (S10) where significant correlations were found for all evaluated land uses. The pedotransfer equations are presented in the Table S3 and the correlation between observed and predicted values, are presented in the Fig. S Soil carbon stocks: Several studies that focus on soil organic matter dynamics under different soil and residue management systems present results as carbon content, not as carbon stocks. The concept of carbon stocks is more useful than carbon content, since it is a measurement of the mass of carbon in a specific volume of soil. For each sampled soil layer the calculations for C stocks (Mg ha 1 ) were determined following (S11): M element = conc ρ b T 10,000 m 2 ha Mg kg -1 where: M element = element mass per unit area (Mg ha -1 ) conc = element concentration (kg Mg -1 ) ρ b = field bulk density (Mg m -3 ) T = thickness of soil later (m) Carbon stocks were estimated for the unsampled layers using the carbon contents derived from soil carbon regression equations and the bulk density from pedotransfer functions following (S10). 1.8 Soil mass correction: Because carbon stocks are a function of soil bulk density, factors such as vehicle traffic and soil tillage, which affect soil density, could influence the results. By correcting the density of all sites to a reference area, the stock comparison is done considering the same mass of soil (S11, S12). This correction was performed for each study site using the bulk density for each specific study area reference sample. 1.9 Land Use Change Factor: To obtain the LUC factors, the dataset was analyzed with a linear mixed-effect modeling approach (S13, S14). Linear mixed-effects models are extensions of linear regression models for data that are collected and summarized in groups. These models describe the relationship between a response variable and independent variables, with coefficients that can vary with respect to one or more grouping variables. A mixed-effects model consists of two parts, fixed effects and random effects. Fixed-effects terms are usually the conventional linear regression part and the random effects are associated with individual experimental units drawn at random Macmillan Publishers Limited. All rights reserved.

4 from a population. The random effects have prior distributions whereas fixed effects do not. Mixed-effects models can represent the covariance structure related to the grouping of data by associating the common random effects to observations that have the same level of a grouping variable (S15). Our response variable was the ratio of the mean soil organic carbon (SOC, expressed in Mg ha -1 ) observed in the sugarcane fields and the mean SOC found in the reference areas (SOC sugarcne / SOC reference ). This metric is called the response ratio, and it is equivalent to the factor values used in the IPCC method (S1). Fixed effects were used to account for the influence of soil type, depth, and time since management change. Soil type was treated as indicator variables, which are also referred to as dummy variables. Two methods were used to categorize soil data into distinct types. The first method adopted the IPCC s recommendations (S1) for soil texture, and the dataset was split in two classes: i) sandy soils, 70% of sand and <8% of clay contents; and ii) clayey soils, which were all soil profiles not included in sandy soil class. The second method considered FAO texture triangle, also known as European Soil texture triangle (S16) to organize soil data, resulting in five classes: i) coarse (sandy), ii) medium (medium), iii) medium fine (medium fine), iv) fine (clayey) and v) very fine (very clayey). Both soil texture classification lead to similar results, available in Table S4. Random effects were included in the model for site and a site-by-time interaction in order to account for spatial dependencies (S15). Specifically, the Brazilian states, municipalities, and farm sites for the sample locations were included as random effect variables. The site variable is common to all observations from the same farm (for different depth increments and sampling times), but independent across distinct farms, and therefore captures correlations among measurements from the same farm. Similarly, state and municipality variables are common to all observations from the same state or municipality, capturing correlations among observations from the same state or municipal area. The treatment of depth in the regression analysis was the same that adopted by (S13, S14). In order to aggregate the dataset to a standard set of depths (0-30, 0-50 and cm) it was used an interpolation technique, where regressors were formed from the U and L values of each increment by assuming that the SOC stock ratio declined as a quadratic function of depth, such that B 0 + B 1 D + B 2 D 2 (1) where D represents a specific depth, such as 30 cm. A quadratic function was chosen based on the assumption that agricultural management impacts would be greatest at the surface and diminish with depth in the soil profile. Note that the data do not provide direct observations of SOC stocks at specific depths, such as 15 cm, but rather stocks across increments such as cm. Therefore, the average SOC stock ratio for a single Macmillan Publishers Limited. All rights reserved.

5 increment was the integral from U to L depth positions of the quadratic function divided by thickness of the increment. The integration results in two regression variables for each depth increment. (2) (3) that is, (4) This approach allowed us to capture the changing relationship across depth between management treatments and SOC storage, without an aggregation of data and loss of information. The LUC factors were derived in a manner consistent with the IPCC soil C method (S1), which is based on the integrated effect of management for the top 30 cm of the profile after 20 years following the land use substitution to sugarcane fields, however, in order to provide a more complete information, factors were also derived for deeper layers (0-50 and cm), and with different time spans, 5, 10, 15 and 20 years. This timeline was adopted based on the complete restoration that sugarcane fields undergo every five years, when soils are ploughed, fertilizers are applied and new gems are planted (S17). Uncertainty was based on the prediction standard deviation of the factor value. These standard deviations can be used to build probability density functions for a GHG inventory analysis (S18). Statistical analyses were performed using SPLUS 8.0 software (Insightful Corporation, Seattle, WA) Payback time: The substitution of the fossil fuels with sugarcane ethanol has the potential to reduce GHG emissions (S19, S20). In this case, the payback time should be the time span that the conversion of a specific land into sugarcane would need to compensate emissions resulted from LUC considering the offset associated to the replacement of fossil fuel by sugarcane ethanol. To calculate the sugarcane payback time, the average stock of soil carbon was derived for pastures, cerrado and annual cropping areas using the data set presented in Table S1. The LUC factors (Fig. 2) were applied to soil carbon stocks for each land use, over increasing depth layers. The carbon debt in Mg CO 2 was calculated as the difference between the stocks found in sugarcane systems and the corresponding reference land use. The ethanol offset considered for sugarcane ethanol Macmillan Publishers Limited. All rights reserved.

6 was 9.8 Mg CO 2 ha -1 (S21). Payback time was calculated as the ratio between carbon debt and ethanol offset, expressed in years. Soil C stock differences for cerrado to sugarcane conversions were calculated only for the 0-30 cm depth. There were only 5 sites pairs for cerrado to sugarcane conversion and mean C stocks below 30 cm (i.e., cm interval) were not significantly different between the two land uses. The C depth and payback time for the combined data set used C stock differences for 0-30 cm only, for the cerrado conversions. To determine the historical effects of land use change into sugarcane we used data and areas converted into sugarcane from (S22), we then used average values for above and below ground biomass carbon for pasture (S1) and cerrado (S23) to determine the total biomass C loss during the conversion to sugarcane. For the crop land uses we assume that no biomass carbon is attributable to the biofuel LUC carbon debt, because these systems are under annual tillage and therefore the effects of disturbance on biomass carbon is already accounted for with the original cropland carbon stocks. We assumed that all this biomass carbon was lost as CO 2 and therefore combined with the soil carbon debt determined from the LUC-F gives the overall net carbon loss for the land that was converted. We present the calculated values for a land expansion of ~3 Mha and overall carbon balance averaged over the 20 year timeframe for the soil carbon stocks to equilibrate including the annual sugarcane ethanol offset of 9.8 Mg CO 2 ha -1 yr -1 (S21), in table S Macmillan Publishers Limited. All rights reserved.

7 Figure S1: Examples of land use conversions to sugarcane in south-central Brazil: A) cerrado, B) pasture, C) annual cropland. Pictures credit: Francisco F. C. Mello 7

8 0-100 cm 0-30 cm 0-30 cm 0-30 cm cm 0-30 cm 6 trenchs: 0-10, e trenchs: 0-10, 10-20, 20-30, e cm 50 m Total = 36 samples / site 0-30 cm 0-30 cm cm 50 m Figure S2: Sampling design for soil carbon and bulk density determinations. From 6 trenches samples are taken from the layer 0-10, and cm depths. From the remaining 3 trenches, soil is sampled from 0-10, 10-20, 20-30, 40-50, and cm layers. Thus a total of 36 samples are collected from each evaluated site. 8

9 Figure S3. Goodness of fit metrics for the carbon depth distribution regressions by land use. Numbers above the bars indicates the quantity of observed cases. The "X" axis indicates the coefficient of determination (R 2 ) results obtained from the regressions for each land use modality. 9

10 Figure S4: Observed and predicted values for soil bulk density from pedotransfer functions per land use. The ellipse corresponds to a confidence interval at 95%. 10

11 Table S1: Soil carbon stocks from different land uses in south central Brazil. Time since Soil Soil Carbon Stocks Land Soil Carbon Stocks Study Region City initial LUC last fire Texture Sugarcane (Mg ha -1 ) Use Reference (Mg ha -1 ) Pair (Fig 1) (years) (years) (IPCC) (FAO) 0-30 cm 0-50 cm cm Reference 0-30 cm 0-50 cm cm 1 Ipaussu Clayey Clayey Pasture Ipaussu Clayey V. Clayey Pasture Ipaussu Clayey V. Clayey Pasture Anhembi Sandy Sandy Pasture Anhembi Sandy Sandy Pasture Anhembi Sandy Sandy Pasture Anhembi Sandy Sandy Pasture Anhembi Clayey Sandy Pasture Anhembi Clayey Sandy Pasture Itirapina Clayey Sandy Pasture Itirapina Sandy Sandy Pasture Itirapina Sandy Sandy Pasture Iacanga Clayey Sandy Pasture Iacanga Clayey Sandy Pasture Iacanga Clayey Medium Pasture Araçatuba Sandy Sandy Pasture Araçatuba Clayey Sandy Pasture Araçatuba Clayey Sandy Pasture Araçatuba Sandy Sandy Pasture Andradina Clayey Medium Pasture

12 Table S1: Soil carbon stocks from different land uses in south central Brazil (continued). Time since Soil Soil Carbon Stocks Land Soil Carbon Stocks Study Region City initial LUC last fire Texture Sugarcane (Mg ha -1 ) Use Reference (Mg ha -1 ) Pair (Fig 1) (years) (years) (IPCC) (FAO) 0-30 cm 0-50 cm cm Reference 0-30 cm 0-50 cm cm 21 Andradina Clayey Medium Pasture Andradina Clayey Sandy Pasture Andradina Clayey Sandy Pasture Andradina Clayey Sandy Pasture Igarapava Clayey Clayey Pasture Igarapava Clayey Clayey Pasture Igarapava Clayey Clayey Pasture Igarapava Clayey Clayey Pasture Igarapava Clayey Medium Pasture Campo Florido Sandy Medium Cropland Campo Florido Clayey Clayey Cropland Campo Florido Sandy Medium Pasture Campo Florido Sandy Sandy Pasture Campo Florido Clayey Sandy Pasture Campo Florido Clayey Sandy Pasture Campo Florido Clayey Sandy Pasture Campo Florido Clayey Sandy Cerrado Campo Florido Clayey Medium Cropland Araporã Clayey Clayey Pasture Araporã Clayey V. Clayey Pasture

13 Table S1: Soil carbon stocks from different land uses in south central Brazil (continued). Time since Soil Soil Carbon Stocks Land Soil Carbon Stocks Study Region City initial LUC last fire Texture Sugarcane (Mg ha -1 ) Use Reference (Mg ha -1 ) Pair (Fig 1) (years) (years) (IPCC) (FAO) 0-30 cm 0-50 cm cm Reference 0-30 cm 0-50 cm cm 41 Araporã Clayey Clayey Cerrado Araporã Clayey V. Clayey Pasture Araporã Clayey V. Clayey Pasture Araporã Clayey V. Clayey Pasture Araporã Clayey Clayey Pasture Araporã Clayey Clayey Pasture Araporã Clayey V. Clayey Pasture Araporã Clayey V. Clayey Pasture Araporã Clayey V. Clayey Cerrado Araporã Clayey Clayey Pasture Araporã Clayey Clayey Pasture Araporã Clayey V. Clayey Pasture Araporã Clayey V. Clayey Cerrado Araporã Clayey Clayey Cropland Goiatuba Clayey Clayey Pasture Goiatuba Clayey Sandy Pasture Goiatuba Clayey Clayey Cropland Goiatuba Clayey Clayey Pasture Goiatuba Clayey Clayey Cerrado Goiatuba Clayey Clayey Pasture

14 Table S1: Soil carbon stocks from different land uses in south central Brazil (end). Time since Soil Soil Carbon Stocks Land Soil Carbon Stocks Study Region City initial LUC last fire Texture Sugarcane (Mg ha -1 ) Use Reference (Mg ha -1 ) Pair (Fig 1) (years) (years) (IPCC) (FAO) 0-30 cm 0-50 cm cm Reference 0-30 cm 0-50 cm cm 61 Goiatuba Clayey Clayey Pasture Goiatuba Clayey Clayey Pasture Goiatuba Clayey V. Clayey Cropland Maracaju Clayey Clayey Cropland Maracaju Sandy V. Clayey Cropland Maracaju Sandy Clayey Cropland Maracaju Sandy Clayey Pasture Terra Rica Sandy Sandy Pasture Terra Rica Clayey Sandy Pasture Terra Rica Clayey Sandy Pasture Terra Rica Clayey Sandy Pasture Iguatemi Clayey V. Clayey Cropland Iguatemi Clayey V. Clayey Cropland Iguatemi Clayey V. Clayey Cropland Iguatemi Clayey V. Clayey Cropland

15 Table S2: Descriptive statistics of the R 2 adj values for carbon depth distribution regressions, developed for each studied site. Land Use Observations Percentile Percentile Mean Maximum Minimum (n) (Q1) (Q2) IQR MAD Sugarcane Pasture Cropland Cerrado* *Brazilian Savannah IQR: Interquartile range MAD: Median absolute deviation 15

16 Table S3: Results of pedotransfer functions for each land use conversion. Land Use Cases (n) Pedotrasfer Funtion R 2 adjusted Sugarcane *SA *LD *C 0.75 Pasture *SA *C *LD 0.84 Cropland (MS) *CL 0.76 Cropland (MG) *LD *SA 0.73 Cerrado *SA 0.71 SA: Sand content (%) LD: Lower depth (cm) C: Carbon content (%) CL: Clay content (%) MS: areas in the State of Mato Grosso do Sul (Check Table S1 or Fig. 1) MG: areas in the State of Minas Gerais (Check Table S1 or Fig. 1) 16

17 Table S4: Land use change factors derived with soil data considering IPCC's and FAO's soil texture classification. Time Land Use Change Factors Land Use Change Factors Land Use Span IPCC FAO Change (years) 0-30 cm 0-50 cm cm 0-30 cm 0-50 cm cm (±0.03) 0.94 (±0.03) 0.98 (±0.03) 0.91 (±0.03) 0.94 (±0.03) 0.98 (±0.03) (±0.03) 0.93 (±0.02) 0.96 (±0.02) 0.91 (±0.03) 0.93 (±0.02) 0.96 (±0.02) Pasture (±0.03) 0.92 (±0.02) 0.95 (±0.02) 0.90 (±0.03) 0.92 (±0.02) 0.95 (±0.02) (±0.03) 0.91 (±0.03) 0.93 (±0.03) 0.90 (±0.03) 0.91 (±0.03) 0.93 (±0.03) Annual Cropping Cerrado (±0.04) 0.97 (±0.04) 0.97 (±0.04) 0.97 (±0.04) 0.97 (±0.04) 0.97 (±0.04) (±0.04) 1.04 (±0.04) 1.04 (±0.03) 1.04 (±0.04) 1.04 (±0.04) 1.04 (±0.03) (±0.05) 1.10 (±0.04) 1.10 (±0.04) 1.10 (±0.05) 1.10 (±0.04) 1.10 (±0.04) (±0.06) 1.17 (±0.06) 1.17 (±0.06) 1.16 (±0.06) 1.17 (±0.06) 1.17 (±0.06) (±0.04) 0.89 (±0.04) 0.98 (±0.04) 0.84 (±0.04) 0.89 (±0.04) 0.98 (±0.04) (±0.03) 0.86 (±0.03) 0.96 (±0.04) 0.81 (±0.03) 0.86 (±0.03) 0.96 (±0.04) (±0.03) 0.83 (±0.03) 0.95 (±0.04) 0.77 (±0.03) 0.83 (±0.03) 0.95 (±0.04) (±0.03) 0.80 (±0.03) 0.93 (±0.04) 0.74 (±0.03) 0.80 (±0.03) 0.93 (±0.04) IPCC soil texture classification: Soil data was split in two texture classes to generate Land Use Change Factors according to (S1). Results were generated to all land uses in different time span when regressors showed significance at 95% level of confidence (S13, S14). FAO soil texture classification: "European soil texture triangle" (S16) was used to generate Land Use Change Factors. The generated factors found were the same as pointed by the methodology that considered IPCC's soil texture classification. Factors were derived for all soil types within 95% of level of confidence (S13, S14). 17

18 Table S5: Net ecosystem carbon balance from historical LUC from 2000 to 2010 in south central part of Brazil. Land α Area β Biomass Total Biomass loss γ Soil C (20 years) Total Soil C modification (Mg CO 2 ha -1 ) (Mg CO 2 ha -1 ) Use (ha) (Mg CO 2 ha -1 ) (Mg CO 2 ) 0-30 cm cm 0-30 cm cm Native 16, ,506, ND -1,297, ,297,064.3 Pasture 1,509, ,532, ,323, ,079,963.8 Cropland 806, ,332, ,671,220.9 Net effect of above transitions 2,332,643-46,039, ,288, ,294,192.8 δ Net ecosystem emissions (Mg CO 2 ha -1 yr -1 ) α Land use conversion into sugarcane. Data from Adami et al. (S22) β Biomass losses: Native (Cerrado) (S23); Pasture (S1) γ Soil C modification based on C Debt values presented in this paper. For cm the soil C modification was considered the same as 0-30 cm. δ Net ecosystem emissions for 0-30 cm = 1.06 Mg CO 2 ha -1 yr -1 ; for cm = 0.68 Mg CO 2 ha -1 yr -1 18

19 References S1. Intergovernmental Panel on Climate Change (IPCC), 2006 IPCC Guidelines for National Greenhouse Gas Inventories: Prepared by the National Greenhouse Gas Inventories Programme, H. S. Eggleston, L. Buendia, K. Miwa, T. Ngara, K. Tanabe, Eds. (Institute for Global Environmental Strategies, Hayama, Japan, 2006). S2. R. LAL, Soil carbon sequestration to mitigate climate change. Geoderma 123, 1-22 (2004). S3. D.W. Nelson, L.E. Sommers. Total carbon, organic carbon and organic matter. In: Methods of soil analysis: Chemical methods - Part 3 (Sparks, D.L. et al, Madison: SSSA, p , 1996). S4. J. L. N. Carvalho et al., Carbon sequestration in agricultural soils in the Cerrado region of the Brazilian Amazon. Soil Tillage Res. 103, (2009). S5. S. M. F. Maia, S. M. Ogle, C. C. Cerri, C. E. P. Cerri, Changes in soil organic carbon storage under different agricultural management systems in the Southwest Amazon Region of Brazil. Soil Tillage Res 106, (2010). S6. M. Siqueira Neto et al., Soil carbon stocks under no-tillage mulch-based cropping systems in the Brazilian Cerrado: an on-farm synchronic assessment. Soil Tillage Res 110, (2010). S7. L. A. Frazão, K. Paustian, C. E. P. Cerri, C. C. Cerri, Soil carbon stocks and changes after oil palm introduction in the Brazilian Amazon. GCB Bioenergy 5(4), (2012). doi: /j x S8. M. Bernoux, D. Arrouays, C. C. Cerri, Bulk densities of Brazilian Amazon soils related to other soil properties. Soil Science Society of America Journal 62, (1998). S9. V. M. Benites et al., Pedotransfer function for estimating soil bulk density from existing soil survey reports in Brazil. Geoderma 139, (2007). S10. F. F. C. Mello, thesis, University of São Paulo (2012). S11. B. H. Ellert, J. R. Bettany Calculation of organic matter and nutrients stored in soils under contrasting management regimes. Canadian Journal of Soil Science 75, (1995). S12. J. F. L. Moraes, B. Volkoff, C. C. Cerri, M. Bernoux, Soil properties under Amazon forest and changes due to pasture installation in Rondônia. Geoderma, 70, (1996). S13. S. M. Ogle, R. T. Conant, K. Paustian, Deriving grassland management factors for a carbon accounting method developed by the intergovernmental panel on climate change. Environmental Management 33, (2004). S14. S. M. Ogle, F. J. Breidt, K. Paustian, Agricultural management impacts on soil organic carbon storage under moist and dry climatic conditions of temperate and tropical regions. Biogeochemistry 72, (2005). S15. J. C. Pinheiro, D. M. Bates. Mixed-Effects Models in S and S-PLUS. Statistics and Computing Series, Springer, Macmillan Publishers Limited. All rights reserved.

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