payback time for soil carbon and sugar -cane ethanol · 1.7 soil carbon stocks: several studies...

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Supplementary Information for

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: [email protected]

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 CO2 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 CO2. 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 CO2. 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).

Payback time for soil carbon and sugar-cane ethanol

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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 (R2

adj) of these

regressions are presented in the Table S2 and Fig. S3.


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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):

Melement = conc × ρb × T × 10,000 m2 ha

-1 × 0.001 Mg kg

-1

where:

Melement = element mass per unit area (Mg ha-1

)

conc = element concentration (kg Mg-1

)

ρb = 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


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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 (SOCsugarcne/ SOCreference). 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

B0 + B1D + B2D2

(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


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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,

∫

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

CO2 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

(4)


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was 9.8 Mg CO2 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 CO2 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 CO2 ha-1

yr-1

(S21), in

table S5.


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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


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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.

0-100 cm

0-30 cm

0-30 cm

0-30 cm

0-100 cm

0-30 cm

0-30 cm

0-30 cm

0-100 cm

50 m

50 m

6 trenchs: 0-10, 10-20 e 20-30 cm

3 trenchs: 0-10, 10-20, 20-30, 40-50,

70-80 e 90-100 cm

Total = 36 samples / site


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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 (R2) results obtained from the

regressions for each land use modality.


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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%.


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Table S1: Soil carbon stocks from different land uses in south central Brazil.

Study City

Region Time since Soil Soil Carbon Stocks Land Soil Carbon Stocks

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




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


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Table S1: Soil carbon stocks from different land uses in south central Brazil (continued).

Study City




)


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



25 Igarapava 7 8 2 Clayey Clayey 59.8 89.1 142.5 Pasture 84.6 120.6 176.1




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



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


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Table S1: Soil carbon stocks from different land uses in south central Brazil (continued).

Study City




)


41 Araporã 9 39 2 Clayey Clayey 62.1 94.5 152.1 Cerrado 105.6 140.9 201.8








49 Araporã 9 6 6 Clayey V. Clayey 69.5 102.4 164.0 Cerrado 73.1 102.1 153.2




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


59 Goiatuba 10 20 3 Clayey Clayey 78.1 118.5 187.0 Cerrado 103.7 144.8 208.9



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Table S1: Soil carbon stocks from different land uses in south central Brazil (end).

Study City




)




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



72 Iguatemi 13 26 0 Clayey V. Clayey 70.6 98.4 147.0 Cropland 47.6 65.6 99.3





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Table S2: Descriptive statistics of the R2adj values for carbon depth distribution regressions, developed for each studied site.

Land Use Observations

Mean Maximum Minimum Percentile Percentile

IQR MAD (n) (Q1) (Q2)

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


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Table S3: Results of pedotransfer functions for each land use conversion.

Land Use Cases (n) Pedotrasfer Funtion R2

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)


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Table S4: Land use change factors derived with soil data considering IPCC's and FAO's soil texture classification.

Land Use Time Land Use Change Factors Land Use Change Factors

Span IPCC FAO

Change (years) 0-30 cm 0-50 cm 0-100 cm 0-30 cm 0-50 cm 0-100 cm

Pasture

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)

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

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)

Cerrado

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).


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Table S5: Net ecosystem carbon balance from historical LUC from 2000 to 2010 in south central part of Brazil.

Land αArea

βBiomass Total Biomass loss

γSoil C (20 years) Total Soil C modification

(Mg CO2 ha-1

) (Mg CO2 ha-1

)

Use (ha) (Mg CO2 ha-1

) (Mg CO2 ) 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 2,332,643

-46,039,153.4

-3,288,606.6 +14,294,192.8

above transitions δNet ecosystem emissions

-0.99 -0.07 +0.31

(Mg CO2 ha-1

yr-1

)

αLand use conversion into sugarcane. Data from Adami et al. (S22) βBiomass losses: Native (Cerrado) (S23); Pasture (S1) γSoil 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. δNet ecosystem emissions for 0-30 cm = 1.06 Mg CO2 ha

-1 yr

-1; for 0-100 cm = 0.68 Mg CO2 ha

-1 yr

-1


19

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