SUPPLEMENTARY INFORMATION

SUPPLEMENTARY INFORMATION DOI: 10.1038/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: ffcmello@gmail.com 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 www.nature.com/natureclimatechange 1 2014 Macmillan Publishers Limited. All rights reserved.

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, 90-100 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 0-100 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 0-100 cm layer, it was necessary to derive the carbon content in the unsampled depth increments (30-40, 50-60, 60-70 and 80-90 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. S3. 2 2014 Macmillan Publishers Limited. All rights reserved.

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 0-100 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. S4. 1.7 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 -1 0.001 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 3 2014 Macmillan Publishers Limited. All rights reserved.

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 0-100 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 10 20 cm. Therefore, the average SOC stock ratio for a single 4 2014 Macmillan Publishers Limited. All rights reserved.

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 0-100 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). 1.10 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 5 2014 Macmillan Publishers Limited. All rights reserved.

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., 30-100 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 2000-2010 (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 S5. 6 2014 Macmillan Publishers Limited. All rights reserved.

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

0-100 cm 0-30 cm 0-30 cm 0-30 cm 0-100 cm 0-30 cm 6 trenchs: 0-10, 10-20 e 20-30 3 trenchs: 0-10, 10-20, 20-30, 70-80 e 90-100 cm 50 m Total = 36 samples / site 0-30 cm 0-30 cm 0-100 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, 10-20 and 20-30 cm depths. From the remaining 3 trenches, soil is sampled from 0-10, 10-20, 20-30, 40-50, 70-80 and 90-100 cm layers. Thus a total of 36 samples are collected from each evaluated site. 8

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

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

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 0-100 cm Reference 0-30 cm 0-50 cm 0-100 cm 1 Ipaussu 1 15 0 Clayey Clayey 57.5 84.2 138.5 Pasture 73.4 105.4 170.8 2 Ipaussu 1 9 0 Clayey V. Clayey 62.9 92.9 143.5 Pasture 64.8 86.5 124.8 3 Ipaussu 1 7 0 Clayey V. Clayey 60.6 89.9 147.2 Pasture 64.8 86.5 124.8 4 Anhembi 2 6 0 Sandy Sandy 36.9 55.1 95.1 Pasture 45.8 65.1 95.4 5 Anhembi 2 11 0 Sandy Sandy 23.9 36.2 63.2 Pasture 45.8 65.1 95.4 6 Anhembi 2 14 0 Sandy Sandy 27.4 40.9 72.5 Pasture 24.1 35.5 58.8 7 Anhembi 2 14 0 Sandy Sandy 37.8 53.4 82.7 Pasture 34.1 46.4 70.3 8 Anhembi 2 15 0 Clayey Sandy 51.0 78.7 124.6 Pasture 50.9 75.0 114.2 9 Anhembi 2 20 0 Clayey Sandy 34.5 58.4 112.0 Pasture 79.2 109.6 161.8 10 Itirapina 3 6 0 Clayey Sandy 76.4 112.7 162.8 Pasture 55.9 84.0 127.2 11 Itirapina 3 21 0 Sandy Sandy 42.6 65.6 103.0 Pasture 55.9 84.0 127.2 12 Itirapina 3 31 0 Sandy Sandy 56.5 77.3 111.5 Pasture 55.9 84.0 127.2 13 Iacanga 4 18 0 Clayey Sandy 23.1 32.7 50.4 Pasture 33.2 48.0 73.4 14 Iacanga 4 7 0 Clayey Sandy 31.1 43.7 68.8 Pasture 33.2 48.0 73.4 15 Iacanga 4 9 0 Clayey Medium 29.3 42.6 67.4 Pasture 33.2 48.0 73.4 16 Araçatuba 5 39 9 Sandy Sandy 44.9 59.7 82.7 Pasture 35.5 49.4 71.3 17 Araçatuba 5 16 7 Clayey Sandy 25.0 35.2 52.3 Pasture 32.5 44.3 64.4 18 Araçatuba 5 39 9 Clayey Sandy 34.1 47.5 72.8 Pasture 29.1 45.9 81.0 19 Araçatuba 5 39 9 Sandy Sandy 20.0 30.0 48.7 Pasture 25.1 35.8 55.0 20 Andradina 6 5 3 Clayey Medium 32.3 46.8 74.2 Pasture 39.3 53.7 79.2 11

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 0-100 cm Reference 0-30 cm 0-50 cm 0-100 cm 21 Andradina 6 2 1 Clayey Medium 26.8 39.4 57.9 Pasture 34.6 47.8 69.4 22 Andradina 6 10 2 Clayey Sandy 22.9 36.2 58.7 Pasture 55.0 68.0 85.2 23 Andradina 6 4 1 Clayey Sandy 27.0 40.3 61.0 Pasture 30.7 41.2 56.2 24 Andradina 6 9 1 Clayey Sandy 26.8 38.2 55.3 Pasture 28.5 41.3 63.6 25 Igarapava 7 8 2 Clayey Clayey 59.8 89.1 142.5 Pasture 84.6 120.6 176.1 26 Igarapava 7 5 1 Clayey Clayey 57.9 77.7 118.9 Pasture 68.6 101.1 156.9 27 Igarapava 7 15 2 Clayey Clayey 78.0 113.5 171.3 Pasture 97.1 134.9 201.4 28 Igarapava 7 20 2 Clayey Clayey 56.5 85.0 136.3 Pasture 63.5 91.3 140.7 29 Igarapava 7 5 1 Clayey Medium 42.9 63.0 98.8 Pasture 55.8 83.5 124.2 30 Campo Florido 8 2 2 Sandy Medium 31.0 45.5 72.8 Cropland 31.6 47.4 77.5 31 Campo Florido 8 5 5 Clayey Clayey 36.7 53.1 85.2 Cropland 31.6 47.4 77.5 32 Campo Florido 8 3 3 Sandy Medium 31.1 45.2 70.2 Pasture 27.2 40.1 65.7 33 Campo Florido 8 6 3 Sandy Sandy 26.0 38.9 63.7 Pasture 28.8 41.3 63.5 34 Campo Florido 8 7 3 Clayey Sandy 27.7 41.1 66.6 Pasture 28.8 41.3 63.5 35 Campo Florido 8 7 3 Clayey Sandy 30.9 46.1 72.7 Pasture 28.6 41.1 64.0 36 Campo Florido 8 6 3 Clayey Sandy 19.9 30.3 50.6 Pasture 28.6 41.1 64.0 37 Campo Florido 8 4 0 Clayey Sandy 29.6 42.5 66.0 Cerrado 35.8 49.8 74.3 38 Campo Florido 8 7 3 Clayey Medium 31.3 46.4 73.4 Cropland 31.6 47.5 77.5 39 Araporã 9 7 2 Clayey Clayey 54.3 83.7 137.1 Pasture 54.2 78.8 121.9 40 Araporã 9 16 2 Clayey V. Clayey 43.4 64.2 100.6 Pasture 54.2 78.8 121.9 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 0-100 cm Reference 0-30 cm 0-50 cm 0-100 cm 41 Araporã 9 39 2 Clayey Clayey 62.1 94.5 152.1 Cerrado 105.6 140.9 201.8 42 Araporã 9 10 2 Clayey V. Clayey 67.7 92.8 139.4 Pasture 85.3 110.5 151.8 43 Araporã 9 20 2 Clayey V. Clayey 62.4 86.7 130.3 Pasture 85.3 110.5 151.8 44 Araporã 9 3 3 Clayey V. Clayey 62.3 93.5 150.2 Pasture 69.6 97.8 148.5 45 Araporã 9 7 2 Clayey Clayey 91.1 132.7 198.2 Pasture 70.5 98.2 145.7 46 Araporã 9 3 3 Clayey Clayey 74.6 113.5 182.7 Pasture 75.7 109.1 169.0 47 Araporã 9 7 3 Clayey V. Clayey 72.6 99.4 155.9 Pasture 67.3 95.7 153.4 48 Araporã 9 11 3 Clayey V. Clayey 66.0 96.3 149.9 Pasture 72.9 110.7 177.9 49 Araporã 9 6 6 Clayey V. Clayey 69.5 102.4 164.0 Cerrado 73.1 102.1 153.2 50 Araporã 9 13 2 Clayey Clayey 50.7 71.2 110.3 Pasture 55.5 77.1 119.4 51 Araporã 9 14 2 Clayey Clayey 59.2 91.0 143.2 Pasture 71.8 96.7 139.8 52 Araporã 9 15 2 Clayey V. Clayey 50.0 71.3 113.9 Pasture 51.4 68.8 101.8 53 Araporã 9 3 1 Clayey V. Clayey 66.6 92.8 132.3 Cerrado 86.9 113.2 155.4 54 Araporã 9 3 0 Clayey Clayey 54.7 76.9 116.5 Cropland 53.8 73.5 104.3 55 Goiatuba 10 11 8 Clayey Clayey 88.4 134.7 207.6 Pasture 111.3 159.4 234.0 56 Goiatuba 10 3 1 Clayey Sandy 51.8 79.7 129.8 Pasture 51.2 84.8 142.0 57 Goiatuba 10 3 0 Clayey Clayey 69.7 103.2 156.4 Cropland 66.4 96.9 152.7 58 Goiatuba 10 12 8 Clayey Clayey 80.3 120.2 193.2 Pasture 94.6 139.0 217.5 59 Goiatuba 10 20 3 Clayey Clayey 78.1 118.5 187.0 Cerrado 103.7 144.8 208.9 60 Goiatuba 10 17 1 Clayey Clayey 90.2 132.3 200.8 Pasture 94.6 139.0 217.5 13

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 0-100 cm Reference 0-30 cm 0-50 cm 0-100 cm 61 Goiatuba 10 16 8 Clayey Clayey 55.4 91.7 155.9 Pasture 77.5 124.7 206.2 62 Goiatuba 10 13 3 Clayey Clayey 75.0 127.0 228.8 Pasture 77.5 124.7 206.2 63 Goiatuba 10 12 0 Clayey V. Clayey 70.6 104.2 158.8 Cropland 66.4 96.9 152.7 64 Maracaju 11 3 1 Clayey Clayey 63.9 89.4 135.6 Cropland 86.9 116.6 164.4 65 Maracaju 11 1 1 Sandy V. Clayey 72.0 99.6 143.3 Cropland 74.7 104.1 152.3 66 Maracaju 11 3 1 Sandy Clayey 84.9 119.8 170.2 Cropland 94.6 132.7 193.3 67 Maracaju 11 3 3 Sandy Clayey 89.0 129.5 191.8 Pasture 79.3 112.4 163.3 68 Terra Rica 12 16 0 Sandy Sandy 50.2 73.9 118.3 Pasture 59.0 89.1 143.4 69 Terra Rica 12 5 0 Clayey Sandy 77.8 111.5 177.9 Pasture 69.6 107.6 176.6 70 Terra Rica 12 5 0 Clayey Sandy 55.8 81.1 129.0 Pasture 59.1 91.4 141.7 71 Terra Rica 12 6 1 Clayey Sandy 50.6 80.3 135.7 Pasture 44.5 68.2 113.7 72 Iguatemi 13 26 0 Clayey V. Clayey 70.6 98.4 147.0 Cropland 47.6 65.6 99.3 73 Iguatemi 13 21 0 Clayey V. Clayey 52.4 75.1 111.3 Cropland 52.7 72.8 106.3 74 Iguatemi 13 15 0 Clayey V. Clayey 51.1 69.4 102.2 Cropland 55.9 74.5 105.0 75 Iguatemi 13 15 0 Clayey V. Clayey 58.1 77.5 111.4 Cropland 56.0 78.9 111.4 14

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 75 0.932 0.997 0.042 0.929 0.974 0.045 0.022 Pasture 45 0.950 0.997 0.594 0.946 0.980 0.034 0.017 Cropland 10 0.972 0.998 0.900 0.978 0.991 0.014 0.007 Cerrado* 5 0.981 0.997 0.967 0.973 0.996 0.024 0.005 *Brazilian Savannah IQR: Interquartile range MAD: Median absolute deviation 15

Table S3: Results of pedotransfer functions for each land use conversion. Land Use Cases (n) Pedotrasfer Funtion R 2 adjusted Sugarcane 411 1.1205 + 0.0070*SA - 0.0020*LD - 0.0749*C 0.75 Pasture 205 1.0162+0.0081*SA-0.0796*C-0.0015*LD 0.84 Cropland (MS) 43 1.6408-0.0021*CL 0.76 Cropland (MG) 17 1.0283-0.0022*LD+0.0098*SA 0.73 Cerrado 10 0.8190+0.0082*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

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 0-100 cm 0-30 cm 0-50 cm 0-100 cm 5 0.91 (±0.03) 0.94 (±0.03) 0.98 (±0.03) 0.91 (±0.03) 0.94 (±0.03) 0.98 (±0.03) 10 0.91 (±0.03) 0.93 (±0.02) 0.96 (±0.02) 0.91 (±0.03) 0.93 (±0.02) 0.96 (±0.02) Pasture 15 0.90 (±0.03) 0.92 (±0.02) 0.95 (±0.02) 0.90 (±0.03) 0.92 (±0.02) 0.95 (±0.02) 20 0.90 (±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 5 0.97 (±0.04) 0.97 (±0.04) 0.97 (±0.04) 0.97 (±0.04) 0.97 (±0.04) 0.97 (±0.04) 10 1.04 (±0.04) 1.04 (±0.04) 1.04 (±0.03) 1.04 (±0.04) 1.04 (±0.04) 1.04 (±0.03) 15 1.10 (±0.05) 1.10 (±0.04) 1.10 (±0.04) 1.10 (±0.05) 1.10 (±0.04) 1.10 (±0.04) 20 1.16 (±0.06) 1.17 (±0.06) 1.17 (±0.06) 1.16 (±0.06) 1.17 (±0.06) 1.17 (±0.06) 5 0.84 (±0.04) 0.89 (±0.04) 0.98 (±0.04) 0.84 (±0.04) 0.89 (±0.04) 0.98 (±0.04) 10 0.81 (±0.03) 0.86 (±0.03) 0.96 (±0.04) 0.81 (±0.03) 0.86 (±0.03) 0.96 (±0.04) 15 0.77 (±0.03) 0.83 (±0.03) 0.95 (±0.04) 0.77 (±0.03) 0.83 (±0.03) 0.95 (±0.04) 20 0.74 (±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

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 0-100 cm 0-30 cm 0-100 cm Native 16,797 89.7 1,506,690.9-77.2 ND -1,297,064.3-1,297,064.3 Pasture 1,509,575 29.5 44,532,462.5-20.8-31.9-31,323,681.3-48,079,963.8 Cropland 806,271 - - +36.4 +79.0 +29,332,139.0 +63,671,220.9 Net effect of above transitions 2,332,643-46,039,153.4-3,288,606.6 +14,294,192.8 δ Net ecosystem emissions (Mg CO 2 ha -1 yr -1 ) -0.99-0.07 +0.31 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 0-100 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 0-100 cm = 0.68 Mg CO 2 ha -1 yr -1 18

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