Padarian et al. (2022). Soil carbon sequestration potential in global croplands

Mann, 1986

Davidson & Ackerman, 1993

derman, Hengl & Fiske (2017)

The carbon debt from agriculture poses the challenge of reversing the process.

San-

estimated 31.2 Pg C had been lost in topsoil (0–30 cm)

How to cite this article

Padarian J, Minasny B, McBratney A, Smith P. 2022. Soil carbon sequestration potential in global croplands.

PeerJ

10

:e13740

http://doi.org/10.7717/peerj.13740

through 12,000 years of agricultural practices. SOC sequestration is considered as a viable

alternative to recoup part of this debt and to partially mitigate climate change by offsetting

part of the greenhouse gas emissions derived from anthropogenic activities (

Minasny et

al., 2017

). Researchers maintain that sustainable soil C sequestration practices need to be

scaled up globally (

Amelung et al., 2020

). However, there are still many conflicting results

on how much C can soil potentially store, especially in different regions of the world.

SOC stores and dynamics are driven by soil, environmental and human factors such as

soil type, climate, land use, and land management. However, SOC storage potential and

its controlling factors are not well understood at the global scale. Currently, SOC storage

potentials are often expressed as constant, per-year values based on management tables and

assuming a uniform time to reach a new equilibrium, which is usually the 20 years default

value used by IPCC (

Fuss et al., 2018

). Additionally, since climate exerts an essential control

on SOC and global climatic data is readily available, most studies focus on temperature

and precipitation to model SOC retention (

Jones et al., 2005

Kramer & Chadwick, 2018

Shi et al., 2020

). The effect of climate on SOC might not be well represented due to

other confounding, unmeasured variables acting as limiting factors (

Cade & Noon, 2003

Management practices are in this category, playing an essential role at the local scale, but

detailed global information about management practices is not available to date, the reason

why it is usually missing in global studies.

To partially overcome the lack of detailed management data, we create a model that

spatially predicts SOC distribution. Given a set of SOC observations from locations with

similar soil forming factors (which are accounted by the model), we hypothesise that

differences in SOC are mainly driven by management. Different parts of the statistical

distribution of SOC should represent locations with different management practices, with

‘‘good’’ practices in the higher quantiles and ‘‘bad’’ practices in the lower quantiles

Fig. 1

The model is not aware of the specific management at each location but it is capable of

representing different management levels and their impact on SOC given real world data.

This can be achieved using the quantile regression framework (

Cade & Noon, 2003

), the

framework we use in this work.

This study aims to provide an estimate of the additional SOC storage potential in

global croplands. We focused our analysis on the top 30 cm of soil since it accounts for

a large proportion of the SOC stored in soils (

Batjes, 1996

Jobbágy & Jackson, 2000

), it is

considered the depth that can be effectively managed to capture carbon (

VandenBygaart

et al., 2011

), and presents faster turnover time (

Shi et al., 2020

). Since temperature and

precipitation are important controlling factors, we also evaluate how climate change

projections might affect this additional storage potential.

METHODS

Data sources and preparation

The soil information used in this study was derived from several sources, including

the World Soil Information Service (WoSIS) (

Batjes, Ribeiro & Van Oostrum, 2020

), the

Chilean Soil Organic Carbon database (CHLSOC) (

Pfeiffer et al., 2020

), and the data

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x

SO

C

(x)

q50

q75

management

Figure 1

Diagram of a linear quantile regression fitted to the 50th, 75th percentiles to explain SOC

content based on a covariate

x

.

The points correspond to different sites, where the lower values of SOC

are due to unmeasured limiting factors (such as management). A conventional regression model is usually
adjusted to the mean (close to q50) but a quantile regression is also capable of capturing the response of
high-performing sites (q75). Since our model includes soil, climate and topography, we hypothesise that
the difference is mainly due to management practices.

DOI: 10.7717/peerj.13740/fig-1

described in

Stockmann et al. (2015)

. Since this is a collation of mostly legacy data, and

soil analytical methods change in time, part of the SOC data (

Stockmann et al., 2015

) were

harmonised to a common measuring method using pedotransfer functions (

Skjemstad,

Spouncer & Beech, 2000

). The observations correspond to soil profile data with SOC

measurements at varying depths, which were standardised to the 0–30 depth range using

the equal-area spline algorithm (

Bishop, McBratney & Laslett, 1999

). To estimate the soil C

stock, we derived a pedotransfer function to predict bulk density using samples from our

compiled dataset.

Land cover information was extracted from the MCD12Q1v6 MODIS product, generated

by the Land Processes Distributed Active Archive Center, U.S. Department of the Interior

and U.S. Geological Survey (DOI:

10.5067/MODIS/MCD12Q1.006

), specifically the IGBP

classification (

Loveland & Belward, 1997

) matching the year of each sample.

From the initial 83,416 samples, 5% were held out as a test dataset. The remaining 95%

were split into training and validation datasets using the bootstrapping routine (

Efron &

Tibshirani, 1993

) to find the optimal set of hyperparameters of the model. Hyperparameters

are the group of parameters of a machine learning algorithm which cannot be learned in

the training process (

e.g.

, learning rate, batch size, etc.). Here we report the results of the

best performing model after performing a parameter grid search.

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The covariates used as predictors included: (a) digital elevation model (GTOPO30

Padarian, Minasny & McBratney (2019)

USGS EROS, 2020

)), which is provided at 30 arc-second resolution; and (b) long term

mean annual temperature (MAT) and total annual rainfall (TAP) derived from information

provided by WorldClim (

Hijmans et al., 2005

), at 30 arc-second resolution. All data layers

were resampled to a 500 m grid and standardised using the mean and standard deviation

estimated from the training dataset.

Quantile CNN model

To model SOC distribution in space, we used a fully-connected, multi-task neural network

with three hidden layers of 20 units each and ReLu activation functions. Since we were

interested in predicting multiple percentiles of the SOC distribution, the head of the network

consisted of five branches of a single unit with linear activation, which corresponds to the

25th, 50th, 75th, 90th and 95th percentiles (

Fig. 2

). Multi-task neural networks (

i.e.,

that predict multiple targets simultaneously) have shown excellent predictive capability

compared with predicting a single target in digital soil mapping and we refer the reader

to

for a detailed introduction. Each branch can

be considered as an independent model (in reality, they share the initial layers) where the

training process tries to minimise the loss (

Eq. (1)

). In a normal regression, the loss is

usually the deviation from the mean (using mean squared error) but in this case, it is the

distance to each percentile.

The results reported here are based on the predictions at 50th, 75th and 90th percentiles

and the 25th and 95th percentiles where included for regularisation purposes (

Ruder,

2017

). The model was trained using the best set of hyperparameters obtained from a grid

search, namely 100 epochs, a batch size of 32 samples and a learning rate of 0.001. For each

percentile (branch of the multi-task neural network), the loss is estimated by:

1

n

X

i

=

1

max

τ

(

y

i

−

ˆ

y

i

)

,

(

τ

−

1)(

y

i

−

ˆ

y

i

)

(1)

as per

Koenker & Hallock (2001)

, where

τ

is the corresponding percentile and

n

is the

number of training samples. The final, total loss corresponds to the sum of the five

individual losses. We trained different models for croplands, natural/pasture and forest

land covers although here we mostly focus on global croplands. Since most global SOC

models use a central estimate, such as the mean or median, here we assume the median (50th

percentile) as the current state of SOC in the world. Higher quantiles represent situations

where better management practices are in place (

Fig. 1

). These are percentiles are defined

by real-world observations with similar climate, soil, topography, and land cover where

higher SOC content values can be observed. To avoid considering very extreme cases and

ensure that the target SOC content was reached in an important proportion of the locations,

we used a regression to the 90th percentile as a technical maximum. Acknowledging that

increasing SOC content is a challenging task and this technical maximum might not be

always achievable, we considered a regression to the 75th percentile as an intermediate, a

technically achievable storage goal.

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Figure 2

Architecture of the multi-task network.

Each branch corresponds to one of the predicted per-

centiles (25, 50, 75, 90 and 95th).

DOI: 10.7717/peerj.13740/fig-2

The model accuracy was evaluated on the test dataset by estimating the root mean

squared error (RMSE;

Eq. (2)

)

RMSE

=

v
u
u
t

1

n

X

i

=

1

y

i

−

ˆ

y

i

2

(2)

where

ˆ

y

i

corresponds to the prediction of the 50th percentile for the

i

th sample,

y

i

is the

observed

i

th value, and

n

is the total number of observations.

Model interpretation

To understand how the different covariates control SOC distribution and to corroborate

that our model is capturing sound relationships, we used an approximation of Shapley

values (

Lundberg & Lee, 2017

) (SHAP) to estimate the contribution of each covariate to the

model predictions. In brief, to estimate the marginal contribution of a covariate, ideally we

would like to train models with an without the covariate and evaluate their difference. To

correctly estimate the contribution, this process should be done for all the combinations of

covariates to consider their interactions. Since this becomes computationally prohibitive

due to the exponential complexity of the problem,

Lundberg & Lee (2017)

developed the

SHAP method as an approximation. This method has been recently introduced in soil

sciences and has been applied to large extent digital soil mapping showing potential to

interpret complex models (

Padarian, McBratney & Minasny, 2020

), or interpreting soil

spectral models (

Haghi, Pérez-Fernández & Robertson, 2021

Ojima, Galvin & Turner, 1994

Historical carbon debt

While this work focuses on croplands, the model was trained considering croplands,

pastures/natural and forest land covers. This allowed us to simulate situations where

current croplands are replaced by pastures/natural and forest, but still accounting for local

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conditions. Without considering the high complexity of land use change and its feedback
(

), land cover substitution was used as a simple proxy to

estimate the historical debt due to agriculture. Since our model is capable of predicting
different percentiles, we substituted current croplands with (a) the median (50th percentile)
of pasture/natural land cover and (b) the 90th percentile of forest land cover to get a range
of possible scenarios. As the range was quite wide, the mean was assumed as the historical
debt.

Future climate projections

To estimate the carbon stocks and additional storage capacity under future climate
projections, we ran the model using downscaled temperature and precipitation estimates
of nine General Circulation Models (BCC-CSM2-MR, CNRM-CM6-1, CNRM-ESM2-1,
CanESM5, GFDL-ESM4, IPSL-CM6A-LR, MIROC-ES2L, MIROC6, and MRI-ESM2-0)
for the moderately pessimistic SSP3

−

7.0 scenario, which considers a world that does not

enact climate policies. We present the mean estimate of the aforementioned models for the
2021–2040 period. All the estimates were retrieved from the WorldClim 2.1 database (

Fick

& Hijmans, 2017

RESULTS AND DISCUSSION

SOC and controlling factors

In terms of accuracy, the model evaluated on the test dataset (5% of the available data)
showed a good performance, with a root mean squared error of 3.14% estimated as the
deviation from the median (50th percentile). This value is lower than the error reported
by the latest release of SoilGrids which obtained an error to the median of 3.97% using a
quantile random forest regression (

Poggio et al., 2021

First, we considered croplands and pastures/natural land covers, and our model showed

an upward trend from the current state to the achievable potential and technical maximum,
with median SOC contents around 1.47% representing the current state, and 2.06 and 2.81%
for the 75th and 90th percentile in croplands. The 75th and 90th percentile in pastures were
higher with estimates of 2.43 and 3.43%, respectively. To illustrate the spatial variability
of the additional storage potential, we generated global maps of the SOC median, 75th
and 90th percentile (

Fig. 3

). The maps show the expected global SOC trend, with low SOC

contents in areas such as the Sahara, Arabian, Gobi, Australian and Atacama deserts, and
high SOC contents in the humid tropics and towards the poles. The maps also show the
increasing concentrations of SOC in the current, 75th and 90th percentiles (

and

, respectively).

The SHAP values corroborated that the model captured sensible relationships between

the environmental covariates and SOC distribution (

Figs. 4A

–

4D

). Here, we describe the

SOC dependence on environmental factors, but there are also intrinsic edaphic factors
that control soil carbon storage. Soil clay content has been recognised as a key factor in
SOC stabilisation (

Oades, 1988

DOI: 10.7717/peerj.13740/fig-3

Schimel et al., 1994

) and, ideally, our model should include

this significant relationship. We used global soil texture information (

Poggio et al., 2021

),

however, the resulting model did not show the expected spatial global patterns. This is

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Figure 3

Global maps of topsoil organic carbon predictions under (A) current condition, (B) 75th and

(C) 90th percentile.

Full-size

probably due to the current global texture maps do not capture enough local variation. In

consequence, we excluded clay content from our model but we stress the importance of it

in local models when good covariates are available.

For all the SOC percentiles, climatic variables had the greatest influence, closely followed

by elevation and land cover. All the contributions increased at higher percentiles as a

consequence of the higher predicted SOC values. To normalise these values, we calculated

the percentage change from the 50th percentile which revealed a greater influence of land

cover (64.16 and 257.95% to the 75th and 90th percentiles) when compared to other

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Figure 4

Contribution of each covariate to the final map model predictions and important interac-

tions (SHAP values).

Contributions to the 50th (A), 75th (B) and 90th (C) percentiles. The land cover co-

variate consists of three classes, namely croplands, pastures, and forest (red, purple and blue, respectively).
D–E Soil organic carbon dependency on temperature and precipitation. (D) Global distribution of the
three identified temperature groups and (E) precipitation dependency (SHAP values).

DOI: 10.7717/peerj.13740/fig-4

environmental factors (33.09 and 107.24% for MAT; 33.07 and 60.62% for TAP; 37.29

and 54.92% for elevation). The results clearly show the influence of land cover on SOC

stocks. At the median (

Fig. 4A

), the model assigned a modest negative contribution to land

cover. As we move towards higher percentiles, the negative effect of croplands (blue dots

in the land cover row) increases substantially, clearly differentiating itself from the other

two classes (pastures and forest). The contribution of MAT also increased considerably,

particularly for the observations with low temperatures, due to the decrease in carbon

turnover (

Carvalhais et al., 2014

Davidson & Janssens (2006)

The dependence of SOC on temperature and precipitation at the global scale has

been thoroughly described in the literature (

e.g.

,

). Our model

captured this dependency, showing a clear interaction between both factors. In

, using SHAP values, we can distinguish three groups of SOC responses. First, for

temperatures above 17.5

◦

C, the TAP dependency follows a logistic curve with a relatively

low plateau due to the high soil respiration rates and SOC turnover, corresponding mainly

to tropical and dry regions. A second intermediate group, with temperatures between

10.0 and 17.5

◦

C, also follows a logistic curve but with a higher plateau compared to the

previous group due to the slower turnover due to lower temperatures. A third group, with

temperatures below 10.0

◦

C, shows an unbounded and broader range of contributions

within a narrower range of precipitation, describing circumpolar and alpine regions.

Additionally, our results also show an important threshold of around 1,000 mm of annual

rainfall where the contribution of TAP becomes positive (above the mean). Areas with

precipitation above this threshold are generally located in tropical and temperate regions

where water is not a limiting factor for plant development.

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Global additional SOC storage potential

To estimate the carbon stocks, we estimated bulk density using a pedotransfer function

generated from the data used in this study (

Eq. (3)

BD

(

g cm

−

3

)

=

83

.

2687

−

0

.

011

oc

0

.

635

oc

+

52

.

847

(3)

where

oc

is the soil organic carbon content in g kg

−

1

. This global PTF had a good accuracy,

generating an unbiased prediction and a RMSE of 0.25 g cm

−

3

. The predicted bulk density

also showed the expected behaviour with decreasing values as SOC increases (

Fig. 5

Considering the difference between (a) the current and most common practices

described by the central tendency of the SOC distribution (50th percentile) and (b)

the higher ends of the SOC distribution (75th and 90th percentile) of similar soil under

similar climate and defined land cover as the additional storage potential, we estimated its

magnitude and spatial distribution at the global scale. Our results show that the soils with

the highest SOC additional storage potential are located towards the circumpolar region

Fig. 6

), which corresponds to areas with high carbon density (

Stockmann et al., 2015

Continental climates present the highest SOC additional storage potential (2.81 kg m

−

2

),

followed by Temperate and Polar/Alpine regions (both with 1.92 kg m

−

2

), Tropical regions

(1.59 kg m

−

2

) and Dry regions (1.12 kg m

−

2

).

Clearly, not all soils can be managed for SOC sequestration, thus we focussed on

the topsoil (0–30 cm) of global croplands (1,410 million hectares) which, according to

our model (50th percentile), currently contain an estimate of 83 Pg C (

Table 1

) with a

median SOC concentration of 1.37% (or 59.1 Mg C ha

−

1

). Our results are lower than the

estimate of

Zomer et al. (2021)

who estimated the global C stock of 131.81 Pg C on 1,593.5

million hectares cropland, or a 82.7 Mg C ha

−

1

. A SOC stock of 82.7 Mg C ha

−

1

would be

equivalent to 1.9–2.7%, which may be too high for global croplands.

Zomer et al. (2021)

used the SoilGrids data from

Hengl et al. (2017)

, which could be overestimated (

Tifafi,

Guenet & Hatté, 2018

). Our results show that the areas with the greatest SOC additional

storage potential are located in the ecoregions with high pre-cultivation carbon density (

e.g.

,

boreal forests/taiga, flooded grasslands and savannas, and temperate broadleaf and mixed

forests). Under the practicable scenario, the potential additional topsoil OC storage (the

difference between 75th and 50th percentiles) of global cropland is 29 Pg C. Ecoregions of

temperate broadleaf and mixed forests, and temperate grasslands, savannas and shrublands

account for 59% of the total additional storage potential with 17.2 Pg C. For the technical

maximum(difference between 90th and 50th percentiles), the total potential is 65 Pg C,

and both ecoregions account for a similar proportion (59%).

Recent years have seen increased interest in the potential of improving SOC stock in

croplands. For example, the ‘‘4 per mille’’ initiative, launched during COP21 in December

2015 (

Minasny et al., 2017

), estimates that increasing SOC stocks by 4

h

yr

−

1

could offset

some fraction of annual CO

2

emissions into the atmosphere. There has been a debate on

its actual potential as the assumption was made that all soils of the world would increase

its SOC more or less uniformly. Accumulation of SOC, regardless of the rate, can only be

achieved for a limited time as soils have a natural upper limit for carbon storage which is

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0.0

0.5

1.0

1.5

2.0

2.5

Observed BD (g cm

3

)

0.0

0.2

0.4

0.6

0.8

1.0

1.2

1.4

1.6

1.8

Pr

ed

ict

ed

B

D

(g

cm

3

)

a

10

0

10

1

10

2

Number of observations

0

100

200

300

400

500

600

700

Observed OC (g kg

1

)

0.0

0.2

0.4

0.6

0.8

1.0

1.2

1.4

1.6

1.8

Pr

ed

ict

ed

B

D

(g

cm

3

)

b

Figure 5

Pedotransfer function performance and relationship with SOC.

(A) Observed

vs.

predicted

bulk density values. (B) Predicted bulk density showing the decrease in bulk density at higher carbon con-
tents.

DOI: 10.7717/peerj.13740/fig-5

a

b

0

10

>20

SO

C

sto

ck

de

lta

(k

g/m

2

)

Figure 6

Amount of soil organic carbon required to reach the maximum soil carbon storage capacity

to the technical maximum (90th percentile; A) and to the more feasible scenario (75th percentile; B).

Full-size

DOI: 10.7717/peerj.13740/fig-6

also limited by management. Using our additional storage potential estimates, we generated

global maps simulating a 4

h

yr

−

1

accumulation rate (based on the baseline stock) from the

current condition (

Fig. 7

) and calculated the number of years to reach the practicable and

technical maximum. It is important to note that soils will accumulate carbon at different

rates, but we used the fixed rate of 4

h

yr

−

1

because it is currently being used to design

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Table 1

Total topsoil (0–30 cm) SOC stocks (in Pg C) for global croplands by ecoregions.

Current
(q50)

Intermediate
(q75)

Technical
maximum (q90)

Cropland area
(km

2

)

Boreal Forests/Taiga

1.64

2.33

3.06

174,224

Deserts and Xeric Shrublands

3.23

4.45

5.88

1,052,258

Flooded Grasslands and Savannas

0.97

1.41

1.94

145,205

Mangroves

0.20

0.27

0.35

31,880

Mediterranean forests, woodlands and scrub

4.38

5.84

7.58

859,799

Montane grasslands and shrublands

1.29

1.73

2.18

214,602

Temperate broadleaf and mixed forests

29.47

40.38

54.03

4,178,195

Temperate conifer forests

1.15

1.54

2.02

173,098

Temperate grasslands, savannas and shrublands

20.33

26.58

33.91

2,777,674

Tropical and subtropical coniferous forests

0.27

0.37

0.48

43,780

Tropical and subtropical dry broadleaf forests

4.89

6.71

9.25

1,314,051

Tropical and subtropical grasslands, savannas and
shrublands

4.92

6.91

9.53

1,262,423

Tropical and subtropical moist broadleaf forests

9.87

13.37

17.48

1,771,950

Tundra

0.05

0.07

0.09

10,712

Global

82.68

111.97

147.75

14,009,852

35

118

>200

Years under 4

Figure 7

Years to reach the 75th percentile for global croplands.

This assumes a fixed 4

h

yr

−

1

accumu-

lation rate which might not be adequate in all cases but it is currently used as an aspirational target.

DOI: 10.7717/peerj.13740/fig-7

policies in many places. Our results showed a large spatial variation of the maximum
amount of years under the four per mille initiative, with a median period of 86 and 200
years to reach the 75th and 90th percentiles, respectively. For both percentiles, the average
capture rate is around 0.34 Pg C yr

−

1

which corresponds to only 4% of the C emissions

used to estimate the 4

h

rate (8.9 Pg C yr

−

1

) (

Minasny et al., 2017

In addition to using a fixed accumulation rate, the results presented in

Fig. 7

assume

a linear accumulation. Soils behave differently with SOC accumulation diminishing
approximately exponentially in time (

Minasny et al., 2017

Franzluebbers et al., 2012

) but it

might still be a valid estimate since SOC accumulation rates, in many cases, could be greater

Padarian et al. (2022),

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than 4

h

Francaviglia et al., 2019

). Our estimates are in line with other studies that report

croplands reaching a new, higher SOC concentration equilibrium after over a century

Smith, Powlson & Glendining, 1996

Friedlingstein et al., 2022

Soussana et al., 2004

). Regardless of the accumulation

rate, the total additional carbon storage potential of the topsoil in croplands is limited. Our

total estimates of 29 and 65 Pg C for the 75th and 90th percentiles are equivalent only to

three and seven years of global emissions (37.4 Pg CO

2

eq. yr

−

1

of greenhouse gas derived

from anthropogenic activity (

)).

Compared with previous estimates, our results show a slightly higher additional carbon

storage potential for global croplands. A total of 18 to 37 Pg C, under medium and high

storage scenarios with accumulation rates of 0.9 and 1.85 Pg C yr

−

1

and the assumption

of reaching a new equilibrium after 20 years has been reported by

Zomer et al. (2017)

,

with estimates based on gridded predictions (

Hengl et al., 2017

) and considering a uniform

sequestration rate for all croplands. A slightly wider range of sequestration potential has

been reported by

Lal et al. (2018)

with a total of 7.63 to 43.25 Pg C over a period of 25 to

50 years, assuming the adoption of region-specific best management practices. According

to an extensive review by

Fuss et al. (2018)

, the best estimate of realistic technical potential

(close to the median of the minimums of their reviewed studies) is between 20.1 and 46.2

Pg C until 2050. From that year onwards, the accumulation rates could be challenged by

sink saturation. It is important to remember that our approach estimates the additional

storage potential based on real observations, within a similar climatic context, and not on

technical accumulation rates of specific management practices. Since our approach is not

based on fixed technical accumulation rates, our results are not necessarily constrained to

the 20–50 years period which most studies consider, and could be another reason for our

higher estimates.

Historical carbon debt

Our practicable potential (29 Pg C) is close to the 31.2 Pg C historical debt due to agriculture

estimated by a recent study (

Sanderman, Hengl & Fiske, 2017

). However, we believe that

their estimate is too low. In their publication,

Sanderman, Hengl & Fiske (2017)

report a

current SOC stock in global croplands of 62 Pg. If we consider that, in general, soils of

agroecosystems contain 25% to 75% less SOC than their counterparts under natural

ecosystems (

Don, Schumacher & Freibauer, 2011

Lal et al., 2018

), the pre-agriculture

content would be between 83 and 248 Pg C, which yields a debt between 21 and 186

Pg C, making their estimate very conservative.

Using our model, we estimated a historical carbon debt ranging between 10 and 160 Pg

C by replacing all croplands with a range of region-specific natural ecosystems, as described

in the methods section. If we consider a midpoint within the latter range as the historical

debt due to agriculture (85 Pg C), our practicable potentials account for only 35% of

the historical carbon debt (78% for the technical maximum). Here, we only considered

croplands as these areas have lost more SOC. There is potential for managed grasslands,

however, currently we cannot differentiate between managed and natural grassland using

satellite imagery at the global level.

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Effect of climate change

An important point to consider is that carbon the sequestration potential could vary under

the future climate. Given the high dependence of SOC on temperature, it is expected that

relatively fast global warming will shift most ecosystems toward a lower SOC equilibrium

) and that this effect will be more pronounced in areas with larger SOC

concentrations (

Figs. 4A

–

4C

). These projections have been reported in many studies

Robinson, 2007

Crowther et al., 2016

Batjes, Ribeiro & Van Oostrum, 2020

Melillo et al., 2017

) and they are likely to result in

reduced sequestration potential.

Utilising CMIP6 downscaled future climate projections for a moderate ‘‘business as

usual’’ shared socio-economic pathway (SSP3

−

7.0), we estimated a mean reduction of

14% in the total sequestration potential in croplands in the next 20 years, from 29.9 Pg C to

25.2 Pg C, and from 66.6 Pg C to 56.6 Pg C for the 75th and 90th percentiles, respectively.

That estimate does not include the drop in carbon concentration of the current state (50th

percentile) which implies an additional loss of 7.9 Pg C.

Note that here we have only included changes that affect our model (

i.e.,

temperature and

precipitation) but we have not included the potential changes in net primary productivity

(NPP), which is projected to increase in all the CMIP6 scenarios (

Gang et al., 2017

). Our

model does not explicitly consider the relationship between NPP and SOC but it is an

implicit factor since there are observations covering a gradient of climatic conditions (and

NPP) for a given combination of the rest of the covariates.

SOC sequestration is still a priority

We have shown that the total amount of additional carbon croplands can store is

relatively modest compared to the sustained emission of greenhouse gases derived from

anthropogenic activity. It is unreasonable to expect that a single sector can offset global

emissions, especially considering their increasing trend. Nevertheless, incorporating

carbon into soils by improving management practice should still be a priority to ensure

food security. According to our estimates, agriculture generates a carbon debt, so we need

to properly manage croplands to be sustainable and avoid the expansion of agricultural

land due to the loss of soil productivity.

Agricultural productivity has been directly related to SOC contents. If reduced below

some critical limits, soil condition declines and so does crop yield (

). By increasing SOC to its practicable and technical upper limits (75th and 90th

percentiles), between 224 and 418 million hectares could be taken above the critical SOC

limits of 1.1% and 2% for tropical (

Aune & Lal, 1997

) and temperate (

Loveland & Webb,

2003

) areas, respectively.

Several studies have raised concerns about the barriers to sequester SOC (

Rumpel et

al., 2020

). One of the main advantages of our study is that we use a large global database

and the estimates are based on real world observations, meaning that a group of locations

already reached the target SOC stocks for a given combination of environmental conditions.

However, a current limitation to have in mind is that our model is based on biophysical

factors but does not take into account socio-economic barriers, disproportionally affecting

developing countries, that can impede the adoption of new management practices.

Padarian et al. (2022),

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There are still many knowledge gaps that need to be filled and that could help to improve

our current model. As mentioned in previous sections, detailed texture information at

the global scale is still required and that is also applicable to other soil properties that

highly correlate with SOC (

Rasmussen et al., 2018

). This is necessary to account for local

variability. Additionally, our approach places management practices into different quantiles

of a distribution based on their SOC density, but it is not capable of identifying them. More

research is needed to identify region-specific management practices that can enhance soil

carbon. Many countries have a national registry of lands (

e.g.

, Land Parcel Identification

in Europe (

Leo & Lemoine, 2001

)), at least for the management of agricultural subsidies,

which should be integrated into soil information systems and added to this type of model.

In this work, the selection of such quantiles is illustrative of what we think can be achievable

potentials but in practice, any percentile above the median is a gain. The specific percentiles

should be adjusted to accommodate different targets, defined by different stakeholders,

as concluded by

Chen et al. (2019)

who applied a similar approach at a national scale in

France. In terms of the uncertainty of estimates from this study, they are still large in spite

of the methodological improvements. This is the case for all current global carbon stock

estimates. Further data collection is required, under different management and biophysical

conditions, to increase the confidence of such estimates. Future work should take into

account such uncertainty to provide a more robust estimation and potentially guide future

soil assessments.

CONCLUSIONS

Our results put in perspective the expectation around soil carbon sequestration in global

croplands. They indicate that, even if the additional amount of carbon croplands can

accumulate is large (30–67 Pg C), the process is slow, spanning periods up to over a

century. Regardless of the accumulation rate, the total additional storage is relatively small

compared to the sustained emission of greenhouse gases derived from anthropogenic

activity, equating to only three to seven years of emissions offsetting.

Regarding the historical impact of agriculture, our results suggest that the current

management practices close to our 75th percentile can only recoup 32% of our estimated

92 Pg C historical debt. Even considering the best current management practices (equivalent

to our 90th percentile), we would not be able to recoup that debt fully, only offsetting 72%

of them. Hence, agriculture has an intrinsic environmental cost that needs to be taken into

account for territorial planning.

Soil carbon capturing measures should be complementary to general carbon emission

reduction plans in order to tackle climate change. Moreover, given the high contribution of

climate to SOC accumulation, it is expected that the additional SOC storage potential will

be affected by climate change if this is not the case. Our results showed that the potential

could be reduced by 14% under a moderate ‘‘business as usual’’ shared socio-economic

pathway (SSP3-7.0).

The total amount of additional carbon that global croplands can store is relatively small

in the context of global carbon emissions. However, it is critical in the context of soil and

Padarian et al. (2022),

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food security. We estimate that it is possible to restore between 224 and 418 million hectares

if we implement management practices conducive to achieve 75th and 90th percentiles.

Soil carbon is directly related to agricultural productivity and any improvements in soil

condition are a step closer to more sustainable and resilient agricultural systems.

ACKNOWLEDGEMENTS

We appreciate valuable comments from the three anonymous reviewers.

ADDITIONAL INFORMATION AND DECLARATIONS

Funding

This project was funded by the ARC Discovery project DP200102542 ‘‘Forecasting soil

conditions’’. The funder had no role in study design, data collection and analysis, decision

to publish, or preparation of the manuscript.

Grant Disclosures

The following grant information was disclosed by the authors:

ARC Discovery Project DP200102542 ‘‘Forecasting soil conditions’’.

Competing Interests

The authors declare there are no competing interests. Budiman Minasny is a member of the

GLADSOILMAP research consortium supported by LE STUDIUM Institute for Advanced

Research Studies, France.

Author Contributions

•

José Padarian conceived and designed the experiments, performed the experiments,

analyzed the data, prepared figures and/or tables, authored or reviewed drafts of the

article, and approved the final draft.

•

Budiman Minasny conceived and designed the experiments, authored or reviewed drafts

of the article, and approved the final draft.

•

Alex McBratney conceived and designed the experiments, authored or reviewed drafts

of the article, and approved the final draft.

•

Pete Smith conceived and designed the experiments, authored or reviewed drafts of the

article, and approved the final draft.

Data Availability

The following information was supplied regarding data availability:

The data is available at WoSIS (

; doi:

10.5194/essd-

12-299-2020

), CHLSOC (

Pfeiffer et al., 2020

; doi:

10.5194/essd-12-457-2020

), and the data

described in

10.1016/j.gfs.2015.07.001

10.5067/MODIS/MCD12Q1.006

The land cover information was extracted from the MCD12Q1v6 MODIS product

(doi:

DOI 10.1038/s41467-019-13993-7

Padarian et al. (2022),

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REFERENCES

Amelung W, Bossio D, De Vries W, Kögel-Knabner I, Lehmann J, Amundson R, Bol R,

Collins C, Lal R, Leifeld J, Minasny B, Pan G, Paustian K, Rumpel C, Sanderman

J, van Groenigen JW, Mooney S, van Wesemael B, Wander M, Chabbi A. 2020.

Towards a global-scale soil climate mitigation strategy.

Nature Communications

11(1)

:1–10

DOI 10.1111/j.1365-2389.1996.tb01386.x

Aune J, Lal R. 1997.

Agricultural productivity in the tropics and critical limits of prop-

erties of Oxisols, Ultisols, and Alfisols.

Tropical Agriculture-London Then Trinidad

74

:96–103.

Batjes NH. 1996.

Total carbon and nitrogen in the soils of the world.

European Journal of

Soil Science

47(2)

:151–163

DOI 10.5194/essd-12-299-2020

Batjes NH, Ribeiro E, Van Oostrum A. 2020.

Standardised soil profile data to support

global mapping and modelling (WoSIS snapshot 2019).

Earth System Science Data

12(1)

:299–320

DOI 10.1016/S0016-7061(99)00003-8

Bishop T, McBratney A, Laslett G. 1999.

Modelling soil attribute depth func-

tions with equal-area quadratic smoothing splines.

Geoderma

91(1)

:27–45

DOI 10.1890/1540-9295(2003)001[0412:AGITQR]2.0.CO;2

Cade BS, Noon BR. 2003.

A gentle introduction to quantile regression for ecologists.

Frontiers in Ecology and the Environment

1(8)

:412–420

Carvalhais N, Forkel M, Khomik M, Bellarby J, Jung M, Migliavacca M, Saatchi S,

Santoro M, Thurner M, Weber U, Ahrens B, Beer C, Cescatti A, Randerson JT,

Reichstein M. 2014.

Global covariation of carbon turnover times with climate in

terrestrial ecosystems.

Nature

514(7521)

:213–217

DOI 10.1038/nature13731

DOI 10.1016/j.scitotenv.2019.02.249

Chen S, Arrouays D, Angers DA, Chenu C, Barré P, Martin MP, Saby NP, Walter C.

2019.

National estimation of soil organic carbon storage potential for arable soils:

a data-driven approach coupled with carbon-landscape zones.

Science of the Total

Environment

666

:355–367

Crowther TW, Todd-Brown KEO, Rowe CW, Wieder WR, Carey JC, Machmuller MB,

Snoek BL, Fang S, Zhou G, Allison SD, Blair JM, Bridgham SD, Burton AJ, Carrillo

Y, Reich PB, Clark JS, Classen AT, Dijkstra FA, Elberling B, Emmett BA, Estiarte

M, Frey SD, Guo J, Harte J, Jiang L, Johnson BR, Kröel-Dulay G, Larsen KS,

Laudon H, Lavallee JM, Luo Y, Lupascu M, Ma LN, Marhan S, Michelsen A, Mohan

J, Niu S, Pendall E, Peñuelas J, Pfeifer-Meister L, Poll C, Reinsch S, Reynolds LL,

Schmidt IK, Sistla S, Sokol NW, Templer PH, Treseder KK, Welker JM, Bradford

MA. 2016.

Quantifying global soil carbon losses in response to warming.

Nature

540(7631)

:104–108

DOI 10.1038/nature20150

Davidson EA, Ackerman IL. 1993.

Changes in soil carbon inventories follow-

ing cultivation of previously untilled soils.

Biogeochemistry

20(3)

:161–193

DOI 10.1007/BF00000786

Padarian et al. (2022),

PeerJ

, DOI 10.7717/peerj.13740

16/21

Davidson EA, Janssens IA. 2006.

Temperature sensitivity of soil carbon decomposition

and feedbacks to climate change.

Nature

440(7081)

:165–173

DOI 10.1038/nature04514

DOI 10.1111/j.1365-2486.2010.02336.x

Don A, Schumacher J, Freibauer A. 2011.

Impact of tropical land-use change on soil

organic carbon stocks–a meta-analysis.

Global Change Biology

17(4)

:1658–1670

Efron B, Tibshirani RJ. 1993.

An introduction to the bootstrap

. Vol. 57. New York: CRC

press, 436.

Fick SE, Hijmans RJ. 2017.

WorldClim 2: new 1-km spatial resolution climate surfaces

for global land areas.

International Journal of Climatology

37(12)

:4302–4315

DOI 10.1002/joc.5086

DOI 10.1007/s11027-018-9832-x

Francaviglia R, Di Bene C, Farina R, Salvati L, Vicente-Vicente JL. 2019.

Assessing

‘‘4 per 1000’’ soil organic carbon storage rates under Mediterranean climate: a

comprehensive data analysis.

Mitigation and Adaptation Strategies for Global Change

24(5)

:795–818

DOI 10.2489/jswc.67.4.100A

Franzluebbers AJ, Paine LK, Winsten JR, Krome M, Sanderson MA, Ogles K., Thomp-

son D. 2012.

Well-managed grazing systems: a forgotten hero of conservation.

Jour-

nal of Soil and Water Conservation

67(4)

:100A–104A

DOI 10.5194/essd-14-1917-2022

Friedlingstein P, Jones MW, O’Sullivan M, Andrew RM, Bakker DC, Hauck J, Le Quéré

C, Peters GP, Peters W, Pongratz J, Sitch S, Canadell JG, Ciais P, Jackson RB, Alin

SR, Anthoni P, Bates NR, Becker M, Bellouin N, Bopp L, Chau TTT, Chevallier F,

Chini LP, Cronin M, Currie KI, Decharme B, Djeutchouang LM, Dou X, Evans W,

Feely RA, Feng L, Gasser T, Gilfillan D, Gkritzalis T, Grassi G, Gregor L, Gruber

N, Gürses Ö, Harris I, Houghton RA, Hurtt GC, Iida Y, Ilyina T, Luijkx IT, Jain

A, Jones SD, Kato E, Kennedy D, Klein Goldewijk K, Knauer J, Korsbakken JI,

Körtzinger A, Landschützer P, Lauvset SK, Lefèvre N, Lienert S, Liu J, Marland

G, McGuire PC, Melton JR, Munro DR, Nabel JEMS, Nakaoka S-I, Niwa Y, Ono

T, Pierrot D, Poulter B, Rehder G, Resplandy L, Robertson E, Rödenbeck C, Rosan

TM, Schwinger J, Schwingshackl C, Séférian R, Sutton AJ, Sweeney C, Tanhua T,

Tans PP, Tian H, Tilbrook B, Tubiello F, van der Werf GR, Vuichard N, Wada

C, Wanninkhof R, Watson AJ, Willis D, Wiltshire AJ, Yuan W, Yue C, Yue X,

Zaehle S, Zeng J. 2022.

Global carbon budget 2021.

Earth System Science Data

14(4)

:1917–2005

DOI 10.1088/1748-9326/aabf9f

Fuss S, Lamb WF, Callaghan MW, Hilaire J, Creutzig F, Amann T, Beringer T,

De Oliveira Garcia W, Hartmann J, Khanna T, Luderer G, Nemet GF, Rogelj J,

Smith P, Vicente JLV, Wilcox J, del Mar Zamora Dominguez M, Minx JC. 2018.

Negative emissions—Part 2: costs, potentials and side effects.

Environmental Research

Letters

13(6)

:063002

DOI 10.1016/j.gloplacha.2016.12.007

Gang C, Zhang Y, Wang Z, Chen Y, Yang Y, Li J, Cheng J, Qi J, Odeh I. 2017.

Modeling

the dynamics of distribution, extent, and NPP of global terrestrial ecosystems in

response to future climate change.

Global and Planetary Change

148

:153–165

DOI 10.1016/j.geoderma.2021.115071

Padarian et al. (2022),

PeerJ

, DOI 10.7717/peerj.13740

17/21

Haghi R, Pérez-Fernández E, Robertson A. 2021.

Prediction of various soil properties

for a national spatial dataset of Scottish soils based on four different chemometric
approaches: a comparison of near infrared and mid-infrared spectroscopy.

Geoderma

396

:115071

DOI 10.1371/journal.pone.0169748

Hengl T, De Jesus JM, Heuvelink GB, Gonzalez MR, Kilibarda M, Blagotić A, Shang-

guan W, Wright MN, Geng X, Bauer-Marschallinger B, Guevara MA, Vargas R,
MacMillan RA, Batjes NH, Leenaars JGB, Ribeiro E, Wheeler I, Mantel S, Kempen
B. 2017.

SoilGrids250m: global gridded soil information based on machine learning.

PLOS ONE

12(2)

:e0169748

Hijmans RJ, Cameron SE, Parra JL, Jones PG, Jarvis A. 2005.

Very high resolution in-

terpolated climate surfaces for global land areas.

International Journal of Climatology

25(15)

:1965–1978

DOI 10.1002/joc.1276

https://www.ipcc.ch/report/srccl/

IPCC. 2019.

Intergovernmental panel on climate change (2019) Special report: climate

change and land.

Available at

DOI 10.1890/1051-0761(2000)010[0423:TVDOSO]2.0.CO;2

Jobbágy EG, Jackson RB. 2000.

The vertical distribution of soil organic carbon and

its relation to climate and vegetation.

Ecological Applications

10(2)

:423–436

DOI 10.1111/j.1365-2486.2004.00885.x

Jones C, McConnell C, Coleman K, Cox P, Falloon P, Jenkinson D, Powlson D.

2005.

Global climate change and soil carbon stocks; predictions from two con-

trasting models for the turnover of organic carbon in soil.

Global Change Biology

11(1)

:154–166

Koenker R, Hallock KF. 2001.

Quantile regression.

Journal of Economic Perspectives

15(4)

:143–156

DOI 10.1257/jep.15.4.143

DOI 10.1016/j.envint.2019.105078

Kopittke PM, Menzies NW, Wang P, McKenna BA, Lombi E. 2019.

Soil and the

intensification of agriculture for global food security.

Environment International

132

:105078

DOI 10.1038/s41558-018-0341-4

Kramer MG, Chadwick OA. 2018.

Climate-driven thresholds in reactive mineral

retention of soil carbon at the global scale.

Nature Climate Change

8(12)

:1104–1108

Lal R. 2006.

Enhancing crop yields in the developing countries through restoration of

the soil organic carbon pool in agricultural lands.

Land Degradation & Development

17(2)

:197–209

DOI 10.1002/ldr.696

DOI 10.2489/jswc.73.6.145A

Lal R, Smith P, Jungkunst HF, Mitsch WJ, Lehmann J, Nair PR, McBratney AB,

de Moraes Sá JC, Schneider J, Zinn YL, Skorupa ALA, Zhang H-L, Minasny B,
Srinivasrao C, Ravindranath NH. 2018.

The carbon sequestration potential of

terrestrial ecosystems.

Journal of Soil and Water Conservation

73(6)

:145A–152A

DOI 10.1016/S0167-1987(02)00139-3

Leo O, Lemoine G. 2001.

Land parcel identification systems in the frame of regulaton

(ec) 1593/2000 version 1.4.

E. Commission. Ispra (Italy), Joint Research Centre, Space

Application Institute

1593(2000)

:26.

Loveland P, Webb J. 2003.

Is there a critical level of organic matter in the agricul-

tural soils of temperate regions: a review.

Soil and Tillage Research

70(1)

:1–18

DOI 10.1016/S0094-5765(98)00050-2

Padarian et al. (2022),

PeerJ

, DOI 10.7717/peerj.13740

18/21

Loveland TR, Belward A. 1997.

The international geosphere biosphere programme data

and information system global land cover data set (DISCover).

Acta Astronautica

41(4–10)

:681–689

DOI 10.1097/00010694-198611000-00006

Lundberg SM, Lee S-I. 2017.

A unified approach to interpreting model predictions. In:

Advances in neural information processing systems

. 4765–4774.

Mann L. 1986.

Changes in soil carbon storage after cultivation.

Soil Science

142(5)

:279–288

DOI 10.1126/science.aan2874

Melillo JM, Frey SD, DeAngelis KM, Werner WJ, Bernard MJ, Bowles FP, Pold G,

Knorr MA, Grandy AS. 2017.

Long-term pattern and magnitude of soil carbon

feedback to the climate system in a warming world.

Science

358(6359)

:101–105

DOI 10.1016/j.geoderma.2017.01.002

Minasny B, Malone BP, Mcbratney AB, Angers DA, Arrouays D, Chambers A,

Chaplot V, Chen Z-S, Cheng K, Das BS, Field DJ, Gimona A, Hedley CB, Hong

SY, Mandal B, Marchant BP, Martin M, McConkey BG, Mulder VL, O’Rourke

S, Richer-de-Forges AC, Odeh I, Padarian J, Paustian K, Pan G, Poggio L,

Savin I, Stolbovoy V, Stockmann U, Sulaeman Y, Tsui C-C, Vågen T-G, van

Wesemael B, Winowiecki L. 2017.

Soil carbon 4 per mille.

Geoderma

292

:59–86

Oades J. 1988.

The retention of organic matter in soils.

Biogeochemistry

5(1)

:35–70

DOI 10.1007/BF02180317

Ojima D, Galvin K, Turner B. 1994.

The global impact of land-use change.

BioScience

44(5)

:300–304

DOI 10.2307/1312379

DOI 10.5194/soil-6-389-2020

Padarian J, McBratney AB, Minasny B. 2020.

Game theory interpretation of digital soil

mapping convolutional neural networks.

Soil

6(2)

:389–397

DOI 10.5194/soil-5-79-2019

Padarian J, Minasny B, McBratney AB. 2019.

Using deep learning for digital soil

mapping.

Soil

5(1)

:79–89

DOI 10.5194/essd-12-457-2020

Pfeiffer M, Padarian J, Osorio R, Bustamante N, Federico Olmedo G, Guevara MA,

Aburto FA, Albornoz F, Antilén M, Araya E, Arellano E, Barret M, Barrera J,

Boeckx P, Briceño M, Bunning S, Cabrol L, Casanova M, Cornejo P, Corradini

F, Curaqueo G, Doetterl S, Duran P, Escudey M, Espinoza A, Francke S, Fuentes

JP, Fuentes M, Gajardo G, García R, Gallaud A, Galleguillos M, Gomez A, Hi-

dalgo M, Ivelic-Sáez J, Mashalaba L, Matus F, Meza F, Mora M de la L, Mora J,

Muñoz C, Norambuena P, Olivera C, Ovalle C, Panichini M, Pauchard A, Pérez-

Quezada JF, Radic S, Ramirez J, Riveras N, Ruiz G, Salazar O, Salgado I, Seguel

O, Sepúlveda M, Sierra C, Tapia Y, Tapia F, Toledo B, Torrico JM, Valle S, Vargas

R, Wolff M, Zagal E. 2020.

CHLSOC: the Chilean Soil Organic Carbon database, a

multi-institutional collaborative effort.

Earth System Science Data

12(1)

:457–468

DOI 10.5194/soil-7-217-2021

Poggio L, De Sousa LM, Batjes NH, Heuvelink G, Kempen B, Ribeiro E, Rossiter D.

2021.

SoilGrids 2.0: producing soil information for the globe with quantified spatial

uncertainty.

Soil

7(1)

:217–240

DOI 10.1007/s10533-018-0424-3

Padarian et al. (2022),

PeerJ

, DOI 10.7717/peerj.13740

19/21

Rasmussen C, Heckman K, Wieder WR, Keiluweit M, Lawrence CR, Berhe AA, Blank-

inship JC, Crow SE, Druhan JL, Pries CEH, Marin-Spiotta E, Plante AF, Schädel

C, Schimel JP, Sierra CA, Thompson A, Wagai R. 2018.

Beyond clay: towards an

improved set of variables for predicting soil organic matter content.

Biogeochemistry

137(3)

:297–306

DOI 10.1098/rspb.2007.1012

Robinson D. 2007.

Implications of a large global root biomass for carbon sink estimates

and for soil carbon dynamics.

Proceedings of the Royal Society B: Biological Sciences

274(1626)

:2753–2759

Ruder S. 2017.

An overview of multi-task learning in deep neural networks. ArXiv

preprint.

arXiv:1706.05098

DOI 10.1007/s13280-019-01165-2

Rumpel C, Amiraslani F, Chenu C, Cardenas MG, Kaonga M, Koutika L-S, Ladha J,

Madari B, Shirato Y, Smith P, Soudi B, Soussana J-F, Whitehead D, Wollenberg

E. 2020.

The 4p1000 initiative: opportunities, limitations and challenges for imple-

menting soil organic carbon sequestration as a sustainable development strategy.

Ambio

49(1)

:350–360

DOI 10.2134/agronj1929.00021962002100100008x

Russel J, Harrison C, Wright A. 1929.

Organic matter problems under dry-farming

conditions.

Agronomy Journal

21(10)

:994–1000

DOI 10.1073/pnas.1706103114

Sanderman J, Hengl T, Fiske GJ. 2017.

Soil carbon debt of 12,000 years of human land

use.

Proceedings of the National Academy of Sciences of the United States of America

114(36)

:9575–9580

Schimel DS, Braswell B, Holland EA, McKeown R, Ojima DS, Painter TH, Parton

WJ, Townsend AR. 1994.

Climatic, edaphic, and biotic controls over storage

and turnover of carbon in soils.

Global Biogeochemical Cycles

8(3)

:279–293

DOI 10.1029/94GB00993

DOI 10.1038/s41561-020-0596-z

Shi Z, Allison SD, He Y, Levine PA, Hoyt AM, Beem-Miller J, Zhu Q, Wieder WR,

Trumbore S, Randerson JT. 2020.

The age distribution of global soil carbon

inferred from radiocarbon measurements.

Nature Geoscience

13

:555–559

DOI 10.1111/j.1475-2743.2004.tb00362.x

Skjemstad J, Spouncer LR, Beech TA. 2000.

Carbon conversion factors for historical soil

carbon data. Australian Greenhouse Office. Canberra, Australia.

Smith P, Powlson D, Glendining M. 1996.

Establishing a European GCTE soil organic

matter network (SOMNET). In: Powlson DS, Smith P, Smith JU, eds.

Evaluation of

soil organic matter models

. Berlin, Heidelberg: Springer Berlin Heidelberg, 81–97.

Soussana J-F, Loiseau P, Vuichard N, Ceschia E, Balesdent J, Chevallier T, Arrouays D.

2004.

Carbon cycling and sequestration opportunities in temperate grasslands.

Soil

Use and Management

20(2)

:219–230

DOI 10.1016/j.gfs.2015.07.001

Stockmann U, Padarian J, McBratney A, Minasny B, De Brogniez D, Montanarella L,

Hong SY, Rawlins BG, Field DJ. 2015.

Global soil organic carbon assessment.

Global

Food Security

6

:9–16

Tifafi M, Guenet B, Hatté C. 2018.

Large differences in global and regional total soil

carbon stock estimates based on SoilGrids, HWSD, and NCSCD: intercomparison

Padarian et al. (2022),

PeerJ

, DOI 10.7717/peerj.13740

20/21

and evaluation based on field data from USA, England, Wales, and France.

Global

Biogeochemical Cycles

32(1)

:42–56

DOI 10.1002/2017GB005678

DOI 10.2136/sssaj2010.0099

USGS EROS. 2020.

GTOPO30-Global 1-km digital raster data derived from a variety of

sources. (accessed on 27 May 2020).

VandenBygaart A, Bremer E, McConkey B, Ellert B, Janzen H, Angers D, Carter M,

Drury C, Lafond G, McKenzie R. 2011.

Impact of sampling depth on differences in

soil carbon stocks in long-term agroecosystem experiments.

Soil Science Society of

America Journal

75(1)

:226–234

DOI 10.1038/s41598-016-0028-x

Zomer RJ, Bossio DA, Sommer R, Verchot LV. 2017.

Global sequestration poten-

tial of increased organic carbon in cropland soils.

Scientific Reports

7(1)

:1–8

DOI 10.1038/s41598-020-79139-8

Zomer RJ, Bossio DA, Sommer R, Verchot LV. 2021.

Author correction: global seques-

tration potential of increased organic carbon in cropland soils.

Scientific Reports

11(1)

:1–2