Villela et al. (2026). Soil carbon debt from land use change in Brazil

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https://doi.org/10.1038/s41467-026-68340-4

Received: 16 January 2025
Accepted: 30 December 2025

Cite this article as: Villela, J.M.,

Damian, J.M., Gonçalves, D.R.

et al.

Soil carbon debt from land use

change in Brazil.

Nat Commun (2026).

https://doi.org/10.1038/

s41467-026-68340-4

João M. Villela, Júnior M. Damian, Daniel R. P. Gonçalves, Luis G. Barioni, Maurício R.

Cherubin & Carlos E. P. Cerri

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Nature Communications

Article in Press

Soil carbon debt from land use change in Brazil

ARTICLE IN PRESS

Soil carbon debt from land use change in Brazil

João M. Villela

1

, Júnior M. Damian

2

, Daniel R. P. Gonçalves

3

, Luis G. Barioni

2

, Maurício R.

Cherubin

1,4

& Carlos E. P. Cerri

1,4

*

1

Department of Soil Science, Luiz de Queiroz College of Agriculture, University of São Paulo,

Av. Pádua Dias, 11, Piracicaba, São Paulo, 13418-260, Brazil.

2

Embrapa Digital Agriculture, Av. Dr. André Tosello, 209, Campinas, São Paulo,13083-886,

Brazil.

3

Graduate Program in Agronomy, State University of Ponta Grossa, Av. General Carlos

Cavalcanti, 4748, Ponta Grossa, Paraná, 84030-900, Brazil.

4

Center for Carbon Research in Tropical Agriculture (CCARBON), University of São Paulo,

Avenida Pádua Dias, 11, Piracicaba, São Paulo, 13418-900, Brazil.

*

Corresponding author: cepcerri@usp.br

Abstract:

Carbon farming is a fundamental strategy for mitigating climate change. Brazil, with 276 million
hectares of agricultural land, has strong potential to lead this agenda, but uncertainty  about soil
carbon (C) debt hinders understanding of its true mitigation capacity. Here, we estimate the soil
carbon gap, the difference between soil organic carbon (SOC) stocks under native vegetation and
agricultural  land  across  Brazil’s  six  biomes,  which  represents  the  theoretical  potential  for  soil
recarbonization. A meta-analysis using a comprehensive national SOC database (4,290 records,
0–30  cm)  is  used  to  estimate  an  overall  carbon  debt  of  1.40  ±  0.1  Pg  C.  The  results  show  that
sustainable  practices  such  as  crop rotation,  intercropping,  no-tillage,  and  integrated  agricultural
systems  enhance  SOC  recovery.  These  findings  highlight  Brazil’s  capacity  to  drive  global
emissions  mitigation,  guide  low-carbon  policies,  and  position  the  country  as  a  key  actor  in  the
emerging global carbon market.

Introduction

Quantifying the soil carbon (C) gap resulting from land-use change is fundamental for

understanding the magnitude of human-induced depletion and the potential for soil carbon

recovery

1,2

. Beyond defining static baselines, the carbon gap framework provides a dynamic

indicator of how far land-use systems have diverged from their natural equilibrium and how much

carbon can be regained through restoration or improved management

3–6

. By integrating current

soil organic carbon (SOC) stocks and their potential under reference conditions, this approach

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reveals spatial patterns of carbon resilience across biomes, linking soil processes to land-use

history and management intensity

7,8

.

In the context of Brazil’s Nationally Determined Contributions (NDCs, 2024) under the

Paris Agreement, which target a 59–67% reduction in greenhouse-gas emissions by 2035 and

carbon neutrality by 2050

9

, the carbon gap concept offers a practical basis for climate mitigation

and sustainable agriculture. Agriculture, responsible for about 24-35%

10,11

of national emissions,

plays a dual role as both a source and a sink of carbon. Assessing the soil carbon gap supports

national low-carbon programs such as the ABC⁺ Programme (now RenovAgro), informs the design

of credible soil-based carbon credits, and identifies priority regions for sequestration and

restoration, serving as a practical basis for evaluating and improving the Agriculture and Land Use

Change initiatives

12–14

. As such, it bridges scientific evidence, policy targets, and market

mechanisms to promote soil-centered climate action at scale.

In this study, we compiled a comprehensive database of 4,290 SOC

stocks

spanning Brazil’s

major biomes (Amazon, Caatinga, Cerrado, Atlantic Forest, Pampa, and Pantanal) to establish a

consistent baseline of SOC

stocks

in native and agricultural areas, at depths of 0–10, 0–20, 0–30, and

0–100 cm. A total of 1,247 paired observations of native vegetation (NV) and agricultural systems

(AGR) were analyzed to quantify the carbon gap and estimate the theoretical soil recarbonization

potential for the 0–30 cm layer, where most management-induced changes occur. We assessed the

variability of SOC

stocks

across biomes and the effects of land-use conversion (NV to AGR), taking

into account the main soil and climate classes, as well as the age of agricultural systems.

Furthermore, we assessed the influence of different management intensity levels by classifying

management systems into three hierarchical levels encompassing five categories: (i) single-crop

systems (annual crops, perennials, or grassland); (ii) two-crop systems (rotations or intercropping);

and (iii) multi-crop systems (integrated agricultural systems).

Results and Discussion

SOC

stocks

baseline of native vegetation and agriculture in Brazilian biomes

The Atlantic Forest (rainforest) presented the highest average values of SOC

stocks

under NV

and AGR in the four layers analyzed (0–10, 0–20, 0–30 and 0–100 cm), whereas the lowest

SOC

stocks

were found in the Pantanal (tropical wetland) and Caatinga (dryland forest) biomes (Fig.

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1, Supplementary Tables S1-3, Data1). In the 0–10 cm layer, the Atlantic Forest's NV SOC

stocks

were higher (

p

<0.001) than those of the Caatinga (86%) and Cerrado (savannah) (36%), while its

value under AGR surpassed the Pantanal’s and Caatinga’s by 154% and 62%, respectively. In the

subsequent layer, the SOC

stocks

under NV in the Atlantic Forest were 42–107% greater (

p

<0.001)

than in the Caatinga, Pantanal, Amazon (rainforest), and Pampa (native grassland), while under

AGR, they exceeded these biomes’ stocks by 12–188%. The measures in the Atlantic Forest under

AGR were also larger (10-108%) than in the aforementioned biomes in the 0–30 cm layer, while

under NV, they were greater by 29-116%, except in the Pampa. In the 0–100 cm layer, the Atlantic

Forest presented higher SOC

stocks

than the Amazon and Caatinga, both under NV (79-88%) and

AGR (54-63%).

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The SOC

stocks

in the Cerrado were also larger (

p

<0.001) than those in the Caatinga in the

first three layers under NV (34-60%) and in all layers under AGR (47-86%). The biome exhibited

greater SOC

stocks

(

p

<0.001) than the Pantanal in the first three layers under AGR, with a difference

range of 90-157%, and then the Amazon in the last three, with a range of 18-44%. The levels were

also higher (

p

<0.001) than those of the Pampa (17%) in the AGR 0–20 cm layer and of the Amazon

in the AGR 0–30 cm (18%).

The Pampa and the Amazon biomes’ SOC

stocks

also presented differences (

p

<0.001) from

values for the Caatinga under AGR's first three layers (45-68%) and from the Pampa under NV’s

0–10 cm layer (59%). The SOC

stocks

in both biomes also exceeded (

p

< 0.001) those in the Pantanal

in the 0–10 and 0–20 cm layers under AGR by 90–131%. Finally, the SOC

stocks

under AGR were

52% and 62% higher (

p

<0.001) in the 0–100 cm layer in the Pampa than those in the Amazon and

Caatinga, respectively.

The climate is an important factor controlling SOC storage at regional scales, in which

precipitation and temperature influence plant growth, the input of organic material, and its rate of

decomposition and mineralization

15,16,17

.We identified a reduction in SOC

stocks

with increasing

temperature, corroborating previous studies

18,19

. Brazil is covered by three climate zones

(Köppen’s climate classification): Tropical (A), Humid Subtropical (C), and Dry (B), distributed

across 81%, 14%, and 5% of the territory, respectively

20

. Our results confirmed higher SOC

stocks

in more humid and cold regions (i.e., Humid Subtropical>Tropical>Dry) (Supplementary Tables

S4-5; Supplementary Fig. S1). The differences (

p

<0.05) in layers 0–10, 0–20 and 0–30 cm between

SOC

stocks

of the Humid Subtropical and Dry zones were 70-91% (NV) and 58-168% (AGR), while

between Tropical and Dry, they were 31-62% (NV) and 50-131% (AGR) (Supplementary Tables

S6-9), and between Subtropical and Tropical, they were 17-40% (NV) and 12-16% (AGR). More

specifically, SOC

stocks

under NV and AGR in most soil layers of the biomes occurred in the

following climatic order: Tropical rainforest-Af, Temperate

(

no dry season/warm summer

)

-Cfb

and Temperate (dry winter/warm summer)-Cwb > Temperate (dry winter/hot summer)-Cwa,

Temperate (no dry season/hot summer)-Cfa, Tropical Monsoon-Am, Tropical Savannah (summer

rain)-Aw, Tropical Savannah (winter rain)-As > Arid (steppe/ hot)-BSh (Supplementary Tables

S10,11).

In addition to climatic effects, variations in soil bulk density (BD) further influenced SOC

distribution across biomes and land uses (Supplementary Fig. S3). Under NV, BD reflected

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intrinsic soil-forming processes, including mineralogy, clay content, and pedogenic development.

The lowest BD values were observed in Amazon and Atlantic Forest soils (1.05–1.25 Mg m⁻³),

whereas higher values occurred in the Pampa and Caatinga (1.25–1.45 Mg m⁻³). After agricultural

conversion, BD increased across all biomes, indicating compaction and structural loss. These

changes were closely associated with the magnitude of SOC

stocks

reductions, highlighting the

interaction between physical degradation and carbon depletion.

Across all evaluated biomes, SOC

stocks

under NV and AGR in topsoil (0–10, 0–20, and 0–

30 cm layers) for the five main soil classes decrease in this order: Inceptisol > Oxisol > Ultisol >

Entisol > Alfisol (see Supplementary Tables S12 and 13, Supplementary Fig. S2). Under NV,

SOC

stocks

in the first three Inceptisol soil layers were 58–142% higher (

p

<0.05) than in Alfisol and

44-127% higher than Entisol, while under AGR, the differences were 53-94% and 100-130%,

respectively. When comparing the stocks in Inceptisol with the ones in Ultisol, there was less

variation, which averaged 47% in the first two layers under NV and 43% in the topsoil under AGR.

Finally, Inceptisol SOC

stocks

were, on average, 16% greater than Oxisol SOC

stocks

in the 0–10 and

0–30 cm layers.

The SOC

stocks

of Oxisol also exceeded those of Entisol by 23-83% in the topsoil under NV

and by 72-106% under AGR. Relative to Alfisol, Oxisol SOC

stocks

were 31-67% higher in the

topsoil under AGR and, on average, 85% in the 0–20 and 0–30 cm layers under NV. Compared to

Ultisol, Oxisol SOC

stocks

in the topsoil were, on average, 25% higher under AGR and 17-30% in

the 0–20 and 0–30 cm layers under NV. Ultisol was 40% higher than Entisol in the 0–30 cm layer

under NV and 19-55% higher in the topsoil under AGR.

The highest SOC

stocks

observed in Inceptisol are due to the formation of histic O or humic

A horizons with high accumulation of organic matter, which occur in environments with rugged

relief and low temperatures throughout the year

21,22

. The high SOC

stocks

in Oxisol can be explained

by the high stability of aggregates, influenced by the strong association of SOC with Fe and Al

sesquioxides. Organo-mineral interaction is the main driver of carbon stabilization in Brazilian

tropical soils

23

. The lower SOC

stocks

observed in Alfisol can be associated with two major drivers:

i) lower annual carbon inputs by the Caatinga forest and agriculture in Brazilian drylands, and ii)

sandy texture, which reduces carbon protection and stabilization, favoring carbon losses by

mineralization and erosion processes

24,25,26,27

.

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Overall, the high proportion of areas with a subtropical climate, the presence of soils that

favor carbon accumulation and high altitudes, as identified in our analysis (Supplementary Fig.

S4ab, Supplementary Table S14), contributed to the higher SOC

stocks

observed under NV and

AGR areas within the Atlantic Forest biome, as was also observed in a previous study in this same

biome

28

. These characteristics reveal the large mitigation potential in the biome, already

demonstrated by adopting best management practices

29,30,31,32,33,34,35,36

. Only in this biome,

approximately 20Mha

37

of pastures are classified into medium and low vegetative vigor,

commonly termed “degraded”, available for recovery. In contrast, drier climates (semi-arid) and

soils highly susceptible to erosion, such as Alfisol and Entisol, led to lower SOC

stocks

for the

Caatinga and Pantanal.

In the Cerrado, the highland tropical climate in the Central Plateau combined with the

significant presence of Oxisol contributed to the high levels of SOC

stocks

in the biome

27

. On the

other hand, in the Amazon, despite presenting high rainfall rates, high temperatures reduce the

SOC storage potential due to the increased mineralization rate

38

. However, studies of the Amazon

have shown that adequate management of integrated agricultural systems

22,39

and improved

grassland

40,41,42,43

can generate accumulations equal to or greater than NV's.

Despite the extensive database on SOC in native and agricultural areas, substantial gaps

remain, particularly in the Pantanal, where only three studies are available and without any data

for the 0–100 cm layer. Similarly, the Caatinga, Amazon, and Pampa show limited data coverage

at deeper depths, and records of NV in the Pampa are absent. Future research should, therefore,

prioritize standardized sampling of deeper soil layers (0–100 cm) and long-term monitoring,

especially in underrepresented regions, such as the Pantanal, to enhance the spatial

representativeness of national SOC

stocks

assessments.

Soil carbon gap between native vegetation and agriculture

Considering all biomes, the gaps between SOC

stocks

under NV and AGR in the 0–10, 0–20,

and 0–30 cm layers were respectively -4.4 Mg ha

-1

, confidence interval (CI): [-5.0, -3.8], - 6.9 Mg

ha

-1

, CI [-7.7, -6.1], and -5.1 Mg ha

-1

, CI [-6.0, -4.3] (Fig. 2b; Supplementary Table S15 and

Data2). These results revealed that NV-to-AGR conversion resulted in the total loss of 1.4±0.1 Pg

C in the 0–30 cm layer (Fig. 2c; Supplementary Table S16.), which is equivalent to an emission

of 5.2 Pg CO

2

eq for the whole country’s agricultural area. Although the estimated value indicates

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a substantial negative effect of land-use change on SOC

stocks

, it also highlights the large carbon

sink potential and presents an excellent opportunity to recarbonize agricultural soils in Brazil.

Accumulation potential across biomes varied widely, mainly due to the agricultural management

practices, but also to the size of the gap between SOC

stocks

of NV and AGR, to the amount of

agricultural land available, and to other variables, including biophysical characteristics, climate,

soil class and mineralogy. About 72% of this potential is distributed among the Cerrado (0.53 Pg

C) and Atlantic Forest (0.48 Pg C) biomes, which hold the largest agricultural areas in Brazil,

respectively 93 Mha and 72 Mha (Fig. 2c, Supplementary Table S16). Approximately 23% of the

total potential is in the Amazon (0.19 Pg C) and Caatinga (0.14 Pg C) biomes, which have the third

and fourth largest agricultural areas (67 and 33 Mha, respectively). The lowest potentials were

found in the Pampa (0.05 Pg C) and Pantanal (0.01 Pg C) biomes, which comprise only 5% of the

total agricultural area.

Gaps in SOC

stocks

in biomes varied significantly (36-64% differences between maximum

and minimum values). The Atlantic Forest presented the largest gaps in SOC

stocks

in the three layers

(0–10 cm: -6.2 Mg ha

-1

, 0–20 cm: -7.6Mg ha

-1

, and 0–30 cm: -6.7 Mg ha

-1

). In contrast, the

Amazon and Caatinga exhibited the smallest gaps in SOC

stocks

in the three layers, with the lowest

value in the 0–10 cm layer observed in the Caatinga (-2.2 Mg ha

-1

), and in the 0–10 and 0–30 cm

layers in the Amazon (-3.7 Mg ha

-1

and -2.8 Mg ha

-1

, respectively). These contrasting patterns

suggest that climatic and pedogenic factors strongly mediate the response of SOC

stocks

to land-use

conversion.

To explore the mechanisms underlying this variability, we assessed the influence of

potential moderators (i.e., mean annual temperature, accumulated precipitation, and latitude) on

the observed differences between soil layers and biomes. The explanatory power of the moderating

variables ranged widely from 1% to 35% of the total heterogeneity across studies (Supplementary

Table S17-22). Variability was investigated in more depth through subgroup analyses, considering

climate and soil classes, system conversion age, and, at a more detailed level, temperature and

precipitation classes in each biome. Based on the average values of the 0–10, 0–20, and 0–30 cm

layers, the subtropical climate class presented the greatest reductions, from –11.8% to –22.7%

(average = –17.2%), followed by the tropical climate, from –7.5% to –19.4% (average = –13%)

(Supplementary Table S23). The arid climate showed the lowest average percentage loss in

SOC

stocks

(–11.7%) and lacked evidence of a significant effect. This result suggests that NV-to-

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AGR conversion does not substantially alter carbon dynamics, possibly due to the small initial

difference between stocks under the two land uses.

In tropical climate regions, the Atlantic Forest showed the largest losses in SOC

stocks

(–

17.4%), followed by the Caatinga and Cerrado (–12.3%), then the Amazon (–10.5%). Under

subtropical climates, similar losses were observed in the Atlantic Forest and Pampa (approximately

–17.4%), while the Cerrado showed smaller reduction (- 9.8%). In Atlantic Forest areas under the

classes Cfa, Cfb, Cwa, and Cwb, which represent approximately 66% of the biome area, losses

ranged from -4.4 to -29% and under Aw, present in about 20% of the biome, ranged from -18.1 to

-26% (Supplementary Tables S24). In the Cerrado, losses observed under the Aw class, which

covers about 75% of the biome, were smaller (-10.4% to -15.2%), while losses under the Aw

climate (about 73% of the biome) in the Pantanal exceeded those in the Atlantic Forest and

Cerrado, by an average of -22.5%. In the Amazon, losses related to the Am class, which

corresponds to about half of the biome, ranged from -7.5% to -13.6%. In the Pampa, Cfa class

losses, which encompass almost the entire biome, ranged from -11.8% to -22.3%.

The difference between carbon losses resulting from agricultural conversion under

subtropical and tropical or arid climates, ranging from 32% to 47%, is related to the greater

sensitivity of soils in cold regions to disturbances. The magnitude of carbon loss is closely linked

to initial soil carbon levels. Carbon-rich soils, though naturally stable, undergo greater absolute

losses when converted to agriculture due to enhanced organic matter decomposition from soil

disturbance and oxygen exposure

11,12

. Consequently, carbon-dense biomes such as the Atlantic

Forest and Pampa, covered by subtropical climates, tend to exhibit larger carbon gaps after land-

use conversion. In contrast, in tropical and arid climate regions, the effect of conversion is less

pronounced, since the difference in SOC

stocks

is smaller, resulting from faster decomposition and

intense carbon turnover, which may have reflected in the smaller gaps recorded in the Caatinga

and Amazon.

The analysis of the different soil classes, integrating the three evaluated layers, indicated

that average carbon losses decrease with the degree of pedogenetic development: Entisol ≈

Inceptisol (-29% and -28%) > Ultisol (-21%) > Oxisol (-11%), demonstrating the greater

susceptibility of young soils to C loss after land-use conversion (Supplementary Table S25). The

Atlantic Forest showed the greatest carbon losses in Inceptisol (-33%), followed by the Cerrado (-

28%), and the Pampa and Amazon biomes (both with -13%). The percentage loss recorded in the

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Atlantic Forest Ultisols was almost double (-24%) that observed in the Caatinga (-11%). In Oxisol,

the Atlantic Forest showed the greatest loss (-15%), followed by the Cerrado and Amazon with -

11% and -9%, respectively. The greatest carbon loss in Entisol was observed in the Pantanal (-

25%), followed by the Caatinga (-23%) and Amazon (-20%). The losses observed in the Caatinga’s

Inceptisols, the Amazon’s and Pampa’s Ultisols, the Pampa’s and Caatinga’s Oxisols, and the

Cerrado’s Entisols did not show statistical significance, possibly due to the influence of factors

such as the management system or climatic conditions.

The analysis of the average age of conversion of agricultural systems revealed that the

Pampa and Atlantic Forest have the oldest systems (≈31 years), followed by the Cerrado (26 years),

Caatinga (19 years), Amazon (18 years), and Pantanal (16 years). When evaluating the effect of

conversion age (0–15, 16–30, and >30 years) on SOC

stocks

, it was found that carbon losses occur

across biomes, being more intense in the first 15 years after conversion (Supplementary Table

S26). The Amazon showed significant losses of approximately -11% in the first 15 years after

conversion, which were reduced to -7% between 16 and 30 years, without statistical significance.

In systems older than 30 years, losses remained around -9%, indicating low recovery of SOC

stocks

over time. In the Atlantic Forest, SOC stocks decreased by approximately -15% in systems up to

30 years old and by -19% in older systems. Although the difference is modest, this pattern suggests

that SOC losses may persist over time following conversion to AGR, highlighting the biome’s

vulnerability to land use change. The Caatinga recorded a significant reduction of -17% in the first

15 years after conversion, decreasing in subsequent periods, ranging from -15% to -6%, but

without statistical significance. In the Cerrado, carbon loss during the first 15 years was lower (-

9.4%) compared to the Caatinga (-17%), Atlantic Forest (-15%), and Amazon (-11%), increasing

to -12% in the subsequent periods.

The Pampa biome was the only one to show an increase in SOC

stocks

(2%) in systems up to

15 years old, although this increase was not statistically significant. This result may reflect

transitions in management (tillage to no-tillage or to integrated agricultural systems) or the

presence of residual C from the original vegetation. Finally, the Pantanal biome recorded the

greatest C loss, averaging 24% up to 30 years, reflecting the high sensitivity of the biome's Entisol

soils to the conversion process.

The proportion of studies in certain age groups across biomes, soil and climate classes may

be reflected in the estimated C gap. The largest gaps observed in the Atlantic Forest and Pampa

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biomes may be related to the higher proportion of studies in systems over 30 years old (48% and

53%, respectively), age groups that showed the most pronounced losses compared to systems in

other age groups. In contrast, the smaller gaps in the Amazon and Caatinga may reflect the

predominance (~80%) of data from systems up to 30 years old, which exhibited a dual behavior:

while some results were close to zero, the 16–30 year group showed negative but non-significant

results, indicating high variability and low C losses.

The larger C gap in the 0–20 layer may be related to the disturbance caused by mechanical

tillage

44,45

, which accelerates the decomposition of soil organic matter (aggregates disruption,

increased microbial activity, and aeration) and, consequently, reduces the SOC contents in the soil

surface

46

. The high variability between SOC

stocks

gaps in the biomes, mainly in the 0–10 and 0–30

cm layers, can be explained by the different edaphoclimatic conditions, soil management, and land

use history in the ecoregions

47,18,23

.

Our estimate of SOC debt (1.40 ± 0.1 Pg C) in the 0-30 cm layer in agricultural lands

represents the aggregate total for Brazil compiled from all biomes and is consistent with a global

study which also estimated national SOC loss for Brazil (1.6 Pg C

4

), although without biome-scale

resolution. Thus, our estimate provides a more detailed understanding than large-scale studies,

allowing us to identify regional patterns, biome responses, and management factors associated

with SOC changes. It is important to emphasize that our estimate represents theoretical potential,

based on a hypothetical scenario assuming the restoration of original SOC

stocks

through soil

recarbonization, and should therefore be interpreted with caution. Another aspect that should be

considered and acknowledged as a methodological limitation is the use of SOC levels under NV

as a fixed reference in estimates. Although this approach is widely adopted, in contexts involving

degraded native ecosystems or highly improved agricultural lands, this assumption may not be

fully applicable, potentially resulting in an underestimation or overestimation of the SOC gap and

accumulation potential.

Despite these limitations, our results provide the best quantitative benchmark for the

achievable SOC

stocks

to this date. It allows us to estimate an order of magnitude of the opportunity:

by recarbonizing approximately

1/3

of the estimated potential, we could achieve the 59-67%

emissions reduction target for 2035 (1.51-1.71 Pg CO2eq, base year: 2005) in Brazil's NDC

2

. In

practice, the target would be achieved by harnessing approximately 40-45% of the recarbonization

potential accumulated in the Atlantic Forest and Cerrado (3.8 Pg CO2eq).

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Impact of agricultural system management on SOC

stocks

In a more detailed analysis of the SOC

stocks

gap in Brazilian agricultural lands, we assessed

the impacts of converting NV to different agricultural production systems on SOC

stocks

at combined

depths of 0–10, 0–20, and 0–30 cm (Fig. 3, Supplementary Table S3 and Supplementary Data3-

11). Considering all biomes, NV-to-monocropping conversion showed the highest percentage of

change, -22.2%, CI: [-27.0, -17.0%], followed by NV-to-crop rotation/intercropping, -13.0%, [-

19.6, -16.0%] (Fig. 3). The Atlantic Forest and Pampas showed the highest relative reductions in

SOC under monoculture (–33.0% and –32.1%, respectively), while the Atlantic Forest also had

the highest loss under crop rotation/intercropping (–23.6%) (Table S27). In contrast, the Cerrado

and Amazon recorded the smallest reductions under both systems, ranging from –15.8% to –8.1%

under monoculture and from –17.4% to –9.3% under rotation/intercropping. The Caatinga

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exhibited intermediate relative changes, with SOC

stocks

losses of -22.7% under monocropping and

-12.4% under crop rotation and intercropping.

Grasslands presented, on average, a SOC

stocks

change of -9.4%, CI [-14.3, -4.1] SOC

stocks

in comparison with NV. The greatest loss was observed in the Pantanal, with -16.3%, and the

smallest in the Cerrado (-10.0%) and Atlantic Forest (-11.4%) biomes. The Caatinga and Amazon

did not present a significant difference (

p

> 0.05).

Perennial crops showed similar mean SOC

stocks

variations (9.9%, CI [-18.1, -1.0]) to those

of grasslands (-9.4%). The Atlantic Forest had the smallest difference (-8.8%), while the Cerrado

and Caatinga recorded larger ones (-10.4% and -9.3%), without statistical significance, possibly

due to high variability between areas or partial stabilization of SOC

stocks

in systems with low soil

disturbance and continuous input of organic residues.

Integrated agricultural systems had the lowest relative change (-8.6%, IC [-10.7, -4.7]),

showing average losses lower than those observed in monoculture, crop rotation/intercropping,

and grassland, respectively 61%, 34%, and 9%. Compared to NV, integrated agricultural systems

showed the largest difference in the Amazon (-14.8%), and the smallest significant difference in

the Cerrado (-7.8%), followed by the Pampa (-10%). The differences in the Atlantic Forest (-1.2%)

and Caatinga (-8.6%) were not statistically significant (Fig. 3, Supplementary Table S27). These

results revealed the benefits of agricultural intensification, understood within the framework of

sustainable intensification, in which yields increase without adverse environmental impacts or the

conversion of additional non-agricultural land

48

, mainly by biodiversification, to preserve/restore

SOC

stocks

.

Our results also showed the positive effect of intensification through the comparison

between both crop rotation/intercropping and integrated agricultural systems with monocropping.

The crop rotation and intercropping system presented an average gain of +8.9%, IC [+13.5, +4.4],

with the Pampa increasing the most (+22%), followed by the Amazon (+14.0%), and Cerrado,

with the lowest value (+8.4%), while the Atlantic Forest’s increase (+8.6%) had no statistical

significance. Under integrated agricultural systems, the average gain related to monocropping was

+12.5%, IC [+20.5, +5.1], i.e., 40% higher than the crop rotation and intercropping value, with the

Cerrado showing a gain of 15.3%. There was no significant difference (

p

> 0.05) between the

Atlantic Forest under both systems and the Amazon under integrated agricultural systems.

ARTICLE IN PRESS

We also compared SOC

stocks

losses from NV to no-tillage and conventional tillage systems.

Our results confirmed that losses under no-tillage (-11.4%, CI [-15.7, -6.9%]) were 47% lower

than under conventional tillage (-21.4%, CI [-25.4, -17.3). The Pampa and Atlantic Forest recorded

the most significant losses under the conventional system, respectively -32.7% and -27.2%. The

loss in the Pampa was 88–110% greater than in the Cerrado (-17.2%), Caatinga (-17.4%), and

Amazon (-15.6%) biomes, while in the Atlantic Forest it exceeded these biomes by 58–74%. Under

the no-tillage system, the Atlantic Forest recorded the greatest loss of SOC

stocks

(-21.9%), followed

by the Pampa (-10.6%), while the Cerrado and Amazon presented the smallest losses of -8.0% and

-7.4%, respectively. We also evaluated the effect of the no-tillage system on different soil depths

to identify possible variations associated with the stratification induced by this management. The

results (Supplementary Fig. S5) indicated a small variation between the original mean value (-

11.4%) and those obtained in the stratified analysis (0–10 cm = -14.0%; 0–20 cm and 0–30 cm ≈

-10.0%), with a more pronounced difference in the 0–10 cm layer and slightly smaller ones in the

subsequent layers.

Soil carbon losses induced by land-use change are well-documented in the literature. Our

results, ranging from -9.4% to -26.3%, are comparable with previous studies in the tropical

49

and

global scale

50

. However, these values can be even more variable (-11.4% to -95.6%), depending

on context, when evaluated in a global perspective

51,52,53,54

. Our study showed that the magnitude

of SOC

stocks

losses in different agricultural systems decreases with the increase in their degree of

intensification and diversification, confirming the benefits induced by crop rotation,

intercropping

55,56,57,58,59,60,61

, and integrated agricultural systems

62,63,64,65,66,67,68

, reported in

different regions around the world.

For NV-to-grassland conversion (-4.1% to -14.3%), our results corroborate studies of

tropical zones

49

and of global scope (-9.8% to -14.4%)

69

, although other studies reported a positive

effect (11.5% to 8.2%)

51,52

. Best management practices (defoliation regime, plant species

manipulation, biodiversity, fertilization, improved grazing, grass-legume intercropping, and

irrigation)

70,71,72,73

and continuous grassland maintenance

74

, in addition to the effect of local soil

and climate conditions

75,1

, may explain the positive response.

The absence of significant differences observed in the Caatinga and Amazon biomes

denotes the variation in the maintenance status of Brazilian grassland areas. The greater losses

observed in the other biomes reflect the current condition of Brazilian grasslands, with ca. 60% of

ARTICLE IN PRESS

the area found at intermediate and severe levels of degradation

40

. In this sense, grassland recovery

is an important technology in Brazil, as it can boost SOC

stocks

by 14-23%

76

. It has been adopted by

national climate mitigation initiatives (ABC

+

Programme

77

and National Program for the

Conversion of Degraded Pastures into Sustainable Agricultural and Forestry Production

78

), which

project the recovery of 40 Mha as a target

77

.

Evidence from this study highlights the role of integrated agricultural systems as effective

strategies for maintaining and rebuilding SOC

stocks

, with the smallest differences observed in the

Pampa and Cerrado and statistically non-significant differences in the Atlantic Forest and

Caatinga. Compared with monocropping, using the technology in the Cerrado showed great

potential to increase SOC

stocks

, with values close to those reported in a global study (+17% to

+23%)

1

. The ABC+ Programme's portfolio of solutions includes plans to expand these systems by

4 Mha, with the potential to mitigate up to 34 million Mg CO2eq.

The benefits of no-tillage over conventional tillage revealed in this study were consistent

with estimates previously reported for Brazil (6-26%)

79,80

. Our analysis demonstrates the potential

of no-tillage as an alternative to replace conventional tillage based on differences from NV. The

area with no-tillage in Brazil increased significantly between 2006-2017 (17.9 to 33.0 Mha)

81

, (4–

16% in SOC

stocks

), replacing conventional tillage

82

. The ABC

+

Program set a goal to expand the

system by 12.5 Mha by 2030, expecting to mitigate 12 million Mg CO

2

eq.

Although our results reveal the positive effects of agricultural intensification, these benefits

depend on local conditions such as climate, soil type, and duration of management. When

supported by conservation-oriented practices, such as no-tillage, crop rotation diversification,

cover cropping, residue retention, and the use of organic amendments, intensification can enhance

SOC inputs, improve aggregation and biological activity, and foster long-term carbon

stabilization

2,83

, while promoting positive feedbacks to crop yield and climate resilience

84,85

.

It is important to note that the SOC

stocks

gap estimates presented here represent a technical

potential based on an idealized reference of NV. In reality, native ecosystems may be partially

degraded, and improved agricultural systems can already recover part of their SOC

stocks

. Thus, our

values should be interpreted as indicative of the upper limit of carbon restoration potential, rather

than as directly attainable field targets.

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In conclusion, this national-scale study provided quantitative evidence of the soil carbon

debt of 1.40 ± 0.1 Pg (0–30 cm) induced by historic land-use change of NV to AGR across the

Brazilian biomes. In addition, it revealed the positive effect of sustainable management

intensification (i.e., crop rotation and intercropping, no-tillage, and integrated agricultural

systems) on increasing SOC

stocks

,

which helps close this carbon gap over time. Finally, these results

support the formulation of regionally adapted intervention strategies, which could strengthen

Brazil’s climate policies and help mitigate global climate change.

ARTICLE IN PRESS

Methods

We performed a first-order meta-analysis to examine the impacts of land-use change from native
vegetation (NV) to agricultural (AGR) habitats on SOC

stocks

of the six Brazilian biomes. A meta-

analysis was performed using data from 372 studies (Cerrado:141, Atlantic Forest:129,
Amazon:38, Pampa:34, Caatinga:28 and Pantanal:3) from the last three decades (Supplementary
Fig. S6 and Discussion). Our analysis examined the gap between SOC

stocks

under NV and AGR,

the soil C gap, which we consider our technical potential for C accumulation. We also assessed
the magnitude of the positive and negative effects of different agricultural production and
management systems on SOC

stocks

.

Systematic literature search

The systematic review was conducted by searching the Web of Science, Scopus, and Scielo
databases (see Supplementary Fig. S6 and S7 for details) for relevant studies using data collected
in the Brazilian territory until June 2024, without language restriction. We employed the following
combinations of keywords: “soil carbon stock* OR soil carbon OR soil organic* AND CU=
(Brazil)”.
Studies were selected based on the following eligibility criteria: i) having presented SOC

stocks

data

or data that allowed the calculation of SOC

stocks

(soil organic matter and soil bulk density); ii)

studies carried out in Brazil, with data on geographic coordinates or the name of the municipality
or biome. Using these criteria, we found 4,290 data points, 776 of which pertained to primary
native vegetation and 3,514 to agriculture (Supplementary Data1).

Data collection

We collected the following data from text, tables, or figures in the selected studies (using

WebPlotDigitizer

https://automeris.io/WebPlotDigitizer):land-use classes (NV or AGR), soil

(U.S. soil taxonomy) and climate classes (in the absence of information, we used the data available
in

20

), agricultural systems (monocropping, crop-rotation and intercropping, grasslands, perennial

crops, and integrated agricultural systems), SOC

stocks

in the soil layers (0-10, 0-20, 0-30 and 0-100

cm), numbers of observations, and standard deviations. These were recorded in a data framework.
SOC

stocks

estimates for the different soil depth intervals represent independent measurements

derived from the available data and should not be interpreted as cumulative profiles. Because
complete soil profiles were not consistently reported across studies, the values for each depth layer
reflect discrete sampling intervals rather than integrated depth totals. SOC

stocks

data were

standardized by converting the values from “kg m

-2

,” “g m

-2

,” “t ha

−1

,” “kg ha

−1

,” or “percentage”

to “Mg ha

−1

”. The SOC concentration data (g kg

−1

) were converted to SOC

stock

(Mg C ha

−1

) from

the soil bulk density (BD) and specific depth (cm) using the following equation

86,52

:

𝑆𝑂𝐶 𝑠𝑡𝑜𝑐𝑘 (𝑀𝑔 𝐶 ℎ𝑎

−1

) =

𝑆𝑂𝐶 𝑥 𝐵𝐷 𝑥 𝑙𝑎𝑦𝑒𝑟 𝑡ℎ𝑖𝑐𝑘𝑛𝑒𝑠𝑠

10

(1)

Classification and comparison of management systems

The  "agriculture"  land-use  class  was  subdivided  into  agricultural  production  and  management
systems,  described  as  follows.  “Monocropping”  involves  a  single  cultivated  agricultural  crop
species.  Grassland,  eucalyptus,  pine,  and  other  perennial  fruit  crops  were  redistributed  to  other
categories.  Crop  rotation  (planned  alternation  of  crop  species  over  time)  and  intercropping
(changes between growing seasons) form a category of agricultural systems where two or more

ARTICLE IN PRESS

crops are grown sequentially in the same field, and. Such systems that included grassland in the
cycle  were  transferred  to  the  “Grassland”  category,  which  comprises  cultivated  and  native
grassland. The latter accounts for 3% of the total category data set. “Perennial crops” are planted
forest species such as Eucalyptus spp. and Pinus spp., which represent 65% of the data set, as well
as  other  perennial  fruit  species.  “Integrated  agricultural  systems”  encompass  crop–livestock
integration,  crop–forest  integration,  forest–livestock  integration,  and  crop–livestock–forest
integration.  “Conventional  tillage”  is  soil  preparation  using  techniques  that  promote  soil
disturbance,  such  as  plowing,  harrowing,  and  leveling,  whereas  “No-tillage”  systems  do  not
disturb the soil with tillage: seeds are sowed directly into straw or the previous crop’s residues.
The  study  evaluated  the  effects  of  monocropping,  crop  rotation  and  intercropping,  grasslands,
perennial  crops,  and  integrated  agricultural  systems  again  NV  baselines.  Additionally,
conventional  tillage  and  no-tillage  management  systems  were  compared  to  both  NV  and  to
monocropping agricultural production systems.

Meta-analysis

From the primary database, we selected a subset based on the following eligibility criteria for the
meta-analysis: presenting paired data on SOC

stocks

with their respective standard deviations (SD)

and number of observations. For studies lacking SD values for SOC

stocks

measurements, missing

SDs were imputed using the approach based on median coefficients of variation

87,88

, following

these steps: (1) calculated coefficients of variation (CV) from available SD and SOC

stocks

values;

(2) estimated the median CV separately for each dataset (treatment and control); and (3) computed
missing SDs by multiplying the median CV by the reported SOC

stocks

for that group. To maintain

natural variability and minimize bias, imputation was stratified by biome, land-use class, and soil
layer. We tested the effect of different imputation assumptions by conducting a sensitivity
analysis

89

, which showed only marginal change in means and 95% confidence intervals with

different imputed SD values, indicating our findings were robust (Supplementary Table S28 and
Fig. S8). The final database contains data from 2004-2024 (considering the 20-year IPCC steady-
state period), presented in 1,247 pairwise comparisons (NV vs. AGR) and grouped by layer depth
(0–10 cm, 0–20 cm, and 0–30 cm) and by biome.
We selected the weighted mean difference (MD) as a dependent variable of the meta-analysis to
measure the effect size of the NV-to-AGR transition. The MD and Natural log of response ratio
(lnRR) were calculated to measure the effect of different agricultural and management systems on
SOC

stocks

using the following equations:

MD = 𝜇

𝑒−

𝜇

𝑐

(2)

lnRR = ln

𝜇

𝑒

𝜇

𝑐

=ln ln (𝜇

𝑒

) −ln ln (𝜇

𝑐

)

(3)

where μ

c

and μ

e

correspond to the average SOC

stocks

before (control) and after (treatment) the

change in land-use/management practices

46

, respectively.

To quantify the effect size of the NV-to-AGR transition, we compared estimates obtained from
two models: a random-effects model and a linear mixed-effects model

90

. Both types of models

were fitted using restricted maximum likelihood estimation. For the mixed-effects model, we
evaluated the inclusion of nine moderator variables, both categorical and continuous, categorized
by temporal dynamics, climate and soil classes, geography, and soil management systems, nested

ARTICLE IN PRESS

by biome and soil layer. We used p-values (considered significant when <0.05, indicating a direct
effect)  and  R²  (the  proportion  of  explained  heterogeneity)  as  primary  criteria  for  including  or
excluding  moderators.  As  secondary  criteria,  we  examined  indicators  related  to  heterogeneity
diagnostics: τ² (between-study variance), I² (proportion of variance due to heterogeneity), and H²
(heterogeneity  ratio),  which  quantify  the  magnitude  of  remaining  heterogeneity

90

. In practical

terms, a significant increase (p < 0.05) in explained heterogeneity (R²), along with reductions in
heterogeneity indicators compared to the random-effects model, indicates that the variable
contributes to explaining variability. Based on these statistical indicators and on the main objective
of our meta-analysis, which was to estimate the technical potential for carbon accumulation in
agricultural soils across different biomes from the effect of NV-to-AGR transition on SOC

stocks

,

our  model  ultimately  included  only  three  moderator  variables:  latitude  (°),  mean  annual
temperature (°C), and cumulative annual precipitation (mm).
To enhance interpretability of coefficients, these continuous moderators were centered and scaled
prior to their inclusion in the models. Additionally, a subgroup meta-analysis was performed to
complement  the  global  meta-analysis  and  investigate  the  effects  of  new  moderating  variables,
which included climate and soil classes (described in Supplementary Tables S4 and S12), as well
as  the  age  of  the  agricultural  system,  considering  the  year  of  conversion  and  the  date  of  soil
sampling. The age classes were 15, 30, and over 30 years. This complementary approach allowed
for a more granular investigation into specific contextual factors that might not have been fully
captured by the global variables from the primary model, providing a nuanced understanding of
SOC

stocks

dynamics across different biomes and management contexts.

For the step of the meta-analysis procedure where we assessed the effect of different agricultural
and management systems on SOC

stocks

, the data of the three soil layers (0–10, 0–20, and 0–30 cm)

were analyzed jointly using the random-effects model (Supplementary Data3-11). To address the
non-independence of overlapping soil depth measurements within the same study site, we
employed multilevel hierarchical models

91

, using the rma.mv function. This approach explicitly

models the nested structure of our data (soil depths nested within studies) through random effects
(random = ~ 1 | study_id/depth_raw). This methodology allows us to: (1) utilize all available 0 –
rather  than discarding 60%  of observations  by selecting only one depth  per study; (2) properly
account  for  the  correlation  between  overlapping  soil  layers  from  the  same  location;  (3)  avoid
pseudoreplication  and  the  consequent  underestimation  of  variance;  and  (4)  provide  more
conservative and accurate statistical inferences. This approach is considered the gold standard for
meta-analyses dealing with non-independent observations and has been widely recommended in
the ecological meta-analysis literature. In the case of the comparison between no-tillage and NV,
in addition to the general analysis considering the three soil layers (Fig. 3), the effect of each layer
was  also  evaluated  individually  (Supplementary  Fig.  S5)  to  detect  differences  associated  with
carbon  stratification.  The  results  indicated  that  the  0–10  cm  layer  presented  greater  SOC

stocks

depletion (14.6%) than the 0–20 and 0–30 cm layers, which presented an average loss of 11.6%.

All effect size calculation procedures were performed in the R software, using the MD (raw mean
difference, e.g.

92

) and ROM (log-transformed ratio of means,

87

) functions of the R package

“Metafor”

90

, considering that the effect sizes varied randomly between the comparisons. The

effects measured to assess changes in SOC

stocks

of agricultural and management systems (ln RR)

were transformed and expressed as a percentage change using the following equation

93

:

∆ SOC stock (%) = exp [ ln (RR) − 1] x 100

(4)

ARTICLE IN PRESS

The overall effect sizes and 95% confidence intervals (CI) were also calculated using the
“Metafor” package. Statistically significant changes in SOC

stocks

were considered valid when the

95% CI value did not overlap zero. The CIs of SOC

stocks

effect sizes for NV-to-AGR transition

were shown in forest plots (Fig. 2), where negative values indicated C losses. For agricultural and
management systems, although results were not presented in forest plots (Fig. 3; Supplementary
Table S27), we also interpreted negative percentages as C losses and positive ones as C gains.

Sensitivity analyses and publication biases

We assessed the potential for publication bias (Supplementary Fig. S9 and S10) using the funnel
plot and Egger’s test

94

. The test assesses the existence of publication bias through regression

analysis using the effect size and its precision (standard error). A significant intercept (indexed by
b), i.e., with p<0.05, suggests publication bias

95

. To assess the sensitivity of the results to

publication bias, Rosenthal's fail-safe number

96

was applied, which estimates the number of

hypothetical studies with a null effect required to nullify the significance of the combined effect
(Supplementary Table S17-20). The trim-and-fill procedure was also used to adjust the mean effect
value and thus take possible missing studies into account. The findings from the combination of
the three methods confirm that the results of the meta-analyses by biome are, for the most part,
statistically robust and little affected by publication bias. The extremely high Rosenthal's fail-safe
N values and the absence of trim-and-fill adjustments reinforce the reliability of the estimates. The
Cerrado showed the greatest sensitivity to potential bias in the 0–10 cm layer, but, even in this
case, the overall effect remained significant and stable. It is worth noting that methods such as
Egger's test and the funnel plot have their own limitations, especially in contexts with high
heterogeneity of the observed effects

97

, which applies to our study.

Calculation of potential SOC

stocks

sequestration:

The technical potential of SOC

stocks

sequestration overall and for each biome was calculated by the

following equation:

SOC stock seq. = Gap

biomes

(Mg C ha

-1

) x

AGR

area

(Mha) (5)

where SOC

stocks

seq. represents the technical potential for C sequestration (Pg C). Gap

biomes

represents the gap between AGR (treatment) - NV (control) in Mg C ha-1 and AGR

area

, the

available agricultural area in the biomes in Mha.

Statistical analysis

The comparison between the SOC

stocks

(dependent variable) under NV and AGR across climate

and soil classes, as well as across biomes, was made employing the ANOVA test, the Tukey's post
hoc test for normal distribution, and the nonparametric Kruskal-Wallis (univariate) when the
normality condition was not met, all of them for a statistical significance level of 5%. The analyses
were performed using the “rstatix” package in the R software (R Core Team, 2021

98

).

Data availability

All data supporting the findings of this study are available within the paper and its Supplementary
Information files.

Code availability

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All code used for data processing, statistical analyses and figure generation has been deposited in
a

publicly

accessible

GitHub–Zenodo

repository

under

the

DOI:

https://doi.org/10.5281/zenodo.17842918.

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Acknowledgements

J.V. acknowledges support from the São Paulo Research Foundation (FAPESP, grant
#2023/09533-3). J.D. acknowledges support from the São Paulo Research Foundation (FAPESP,
grant #2022/08629-4). M.R.C. acknowledges support from the National Council for Scientific and
Technological

Development

(CNPq,

grant

#302249/2025-7).

This research was supported by: (i) the Center for Carbon Research in Tropical Agriculture
(CCARBON/USP), sponsored by FAPESP (grant #2021/10573-4); (ii) the Research Centre for
Greenhouse Gas Innovation (RCGI/USP), sponsored by FAPESP (grant #2020/15230-5) and Shell
Brasil;

and

(iii)

Bayer’s

PROCarbono

project.

The authors thank M.Sc. Julie Camolesi da Silva and Professor Fabricio Batistin Zanatta for their
assistance with statistical analyses, and Marina Pinho Oncken for language review services.

Author contributions

J.V., M.C., J.D., and C.C. designed the study. J.V. and J.D collected data. J.V. performed the meta-
analysis. J.V. wrote the article, with significant contributions provided by M.C., J.D., D.G., L.B.,
and C.C. All the authors contributed to the discussions and paper revision.

Competing interests

The authors declare no competing interests.

Figure 1.

Comparison of SOC

stocks

under agriculture and native vegetation across Brazil and its biomes.

In the

violin plots, dots (black) indicate average (mean) values for SOC

stocks

under native vegetation - NV (green dots) and

agriculture - AGR (orange dots), and the kernel density plot represents the distribution of SOC

stocks

among land uses

overall in Brazil and across its biomes for soil layers 0–10 cm, 0–20 cm, 0–30 cm, and 0–100 cm. The values for each

depth layer are independent and should not be interpreted cumulatively. *Data Absence.

Figure 2.

Soil carbon stock changes and sequestration potential across Brazilian biomes.

a) Location of studies

of SOC

stocks

under agriculture (AGR) and native vegetation (NV) and gaps in soil C stocks (Mg ha-1) of the biomes at

the depths 0–10, 0–20 and 0–30 cm, obtained from the effect size (mean difference) of land use change (NV to AGR);

b) Overall gaps in SOC

stocks

(Mg ha

-1

); c) Distribution of soil C sequestration potential (Pg C) across the Brazilian

biomes. The bars represent the confidence interval (CI: 95%) of the gap values of soil C stocks and the values next to

each mean point () represent the size of the sample evaluated (as presented in Table S1 in supporting information S1).

The values for each depth layer are independent and should not be interpreted cumulatively.

In Figure 2a, the free

biome boundary data were obtained from IBGE (https://www.ibge.gov.br/geociencias/todos-os-produtos-

geociencias.html), the data on native vegetation and agricultural areas were obtained from MapBiomas – Coleção 7

ARTICLE IN PRESS

of the Annual Land Use and Land Cover Mapping Series (https://brasil.mapbiomas.org/downloads/), and the

hydrographic network data were obtained from the ANA metadata catalogue (https://metadados.snirh.gov.br/). The

data used to map the distribution of the study sites are available in Supplementary Data 1.

Figure 3.

Percent change in SOC

stocks

resulting from land-use and management transitions in Brazilian biomes.

The bars represent the mean effect sizes expressed as percent change (±95% CI of the response ratio-lnRR),

estimated using a random-effects model. Values in parentheses indicate the number of paired observations

used to calculate changes in SOC

stocks

among land-use and management systems, whereas values in brackets

represent the confidence intervals. The notation ns indicates non-significant statistical differences at the

95% confidence interval. In the overall panel, negative effects are shown in red and positive effects in green.

Editor’s Summary

This study quantifies Brazil’s soil carbon debt (1.40 Pg C) and demonstrates that improved
agricultural practices enhance carbon recovery. The findings reveal Brazil’s significant capacity
to mitigate emissions and influence global carbon markets.

Peer review information

:

Nature Communications

thanks Ciniro Costa Junior, Filipe Behrends

Kraemer and Li Xu for their contribution to the peer review of this work. A peer review file is
available.

Villela et al. (2026). Soil carbon debt from land use change in Brazil

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