Borrelli et al. (2017). An assessment of the global impact of 21st century land use change on soil erosion

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0

H

ealthy soil is the foundation of agriculture and an essential
resource to ensure human needs in the 21st century

such as food, feed,

ﬁ

bre, clean water and clean air. It is a

vital part of ecosystems and earth system functions that support
the delivery of primary ecosystem services

The latest reference document of the United Nations (UN) on

the status of global soil resources stresses that

‘…

the majority of

the world

’

s soil resources are in only fair, poor or very poor

condition

’

. The results of the meta-analysis reported in this

document indicate that accelerated soil erosion is a major threat
to soil. This is in line with previous assessments

6

. The harmful

impacts of accelerated soil erosion processes caused by defor-
estation, overgrazing

, tillage and unsuitable agricultural prac-

tices

are well-known and documented, as are its mechanics

Impacts can be severe, not only through land degradation and
fertility loss, but through a conspicuous number of off-site effects
(e.g., sedimentation, siltation and eutrophication of water ways or
enhanced

ﬂ

ooding

11

).

The

impact

on

climate

through

erosion-induced changes in soil carbon cycling also remains
poorly quanti

ﬁ

ed, as erosion can both increase or decrease

CO

2

emissions through enhanced mineralization and sediment

.

The fear of soil erosion, especially the associated removal of the

most fertile soil layer as a prelude to mass starvation has been
revised lately

14

. The 13% increase in production rates for the

most common crops

15

between 2001 and 2012, due to techno-

logical improvements, more rigorous land management

16

and an

increased use of fertilizer

17

, might have masked the ongoing

degradation of soils and their ecosystem service delivery capacity.
Feeding Earth

’

s growing population with increasing dietary pre-

ferences towards livestock products is undoubtedly enhancing the
pressure on fertile soils

18

thus exacerbating the erosion problem.

Sustainable governance of soil has therefore become a topic of
fundamental importance

19

The FAO led Global Soil Partnership

20

reports that 75 billion

tonnes (Pg) of soil are eroded every year from arable lands
worldwide, which equates to an estimated

ﬁ

nancial loss of US

$400 billion per year. This soil erosion estimate dates back to
1993,

ﬁ

rst reported by Myers

21

and cited by several succeeding

studies

. A lack of reliable global erosion estimates forces

the scienti

ﬁ

c community to resort to these pioneering studies

carried out during the late 1980s and early 1990s such as UNEP

’

s

project Global Assessment of Soil Degradation (GLASOD)

GLASOD and its successor GLADIS

24

provided insights into soil

erosion based on a static observation approach but did not
quantify the effects driven by changes in land use. Accelerated soil
erosion is primarily driven by modi

ﬁ

cations in land use and

management. Spatial patterns of land use and land cover change,
especially in areas susceptible to accelerated soil erosion, provide
further reason to re-evaluate former qualitative approaches,
considering the worldwide increase of croplands and pastures by
279 million hectares (ca. 16.7%) between 1985 and 2013

Following the publication of GLASOD, subsequent research

aimed to improve the ability to predict soil erosion under global
change

27

. Models placed greater emphasis on representing the

physical processes

28

. This, led to a broader understanding of the

processes and methodological improvements

29

. Scaling in space

and time, however, was a great challenge for the new mechanistic
models

30

. Nearing

observed that the disadvantage of the process-

based models is the model complexity and their substantial data
requirements. The extremely high demand for input data

gen-

erally precluded their application above

ﬁ

eld or small catchment

scales

32

.

In a context where process-based physical models and the

availability of input data are not yet mature enough for global
scale applications

, simple, physically plausible empirical

methods for predicting soil erosion such as RUSLE

, can provide

reasonably accurate estimates for most practical purposes (Sup-
plementary Note

). This applies especially to wide spatial scale

applications when prediction errors do not exceed a factor of two
or three

34

. Notwithstanding the signi

ﬁ

cant scienti

ﬁ

c contribution

of the expert-based GLASOD

approach in the 1990s and the

need to further advance process-based physical models

28

35

,

recent studies have shown the potential of RUSLE

model-based

approaches as a signi

ﬁ

cant step towards a change in global water

erosion assessment

36

.

These scoping studies

36

,

, compared to GLASOD, provided a

more detailed view of the spatial patterns of accelerated soil
erosion at a global scale. They relied, however, on coarse model
input data, particularly with respect to land use patterns and
erosive power of rainfall. The predictive power of these models is
therefore limited to assessments of the land use change effects

38

.

In this study, we provide quantitative, thorough estimates of

soil erosion at the global scale by means of a high-resolution,
spatially distributed, RUSLE-based

modelling approach. Unlike

previous studies that dealt with soil erosion as a static process,
here we shed light on the impacts of 21st century global land use
change on soil erosion. Insights into land cover and land use
change between 2001 and 2012 are achieved by combining the
extent, types and spatial distribution of global croplands and
forests measured by satellite imaging with agricultural inventory
data. The global rainfall erosivity patterns are quanti

ﬁ

ed with a

thorough methodology based on rainfall intensity instead of
volume, using a time series of sub-hourly and hourly pluvio-
graphic records (3625 stations covering 63 countries) spatialized
through a Gaussian Process Regression (GPR) geo-statistical
model.

Results

Modelling scheme

. The RUSLE-based modelling approach

(Supplementary Fig.

and Supplementary Methods) provide

estimates of the potential rates of soil displacement by water
erosion (soil erosion) (Supplementary Note

) on a 250 × 250 m

grid cell basis for the land surface of 202 countries (ca. 2.89 billion
cells; ~125 million km

2

); covering ~84.1% of the Earth

’

s land

surface. We present results for soil erosion based on data for 2001
and 2012, taking into consideration the individual land cover
type, vegetation cover dynamics and farming systems of each cell.
For cropland, we ran an additional conservation scenario spa-
tializing the information of the 54 countries that reported the
application of conservation tillage practices to the FAO.

Global perspective on soil erosion

. Our baseline model predicts

an annual average potential soil erosion amount of 35 Pg yr

−

1

for

2001, with an area-speci

ﬁ

c soil erosion average of 2.8 Mg ha

−

1

yr

−

1

.

In 2012, we estimated an overall increase of 2.5% in soil erosion
(35.9 Pg yr

−

1

), driven by spatial changes of land use. The area

which had undergone a change during the study period totalled
about 3.3% of the study area (equal to ~4 million km

), 2.9 million

km

, of which ~2.4% showed an increase in the estimates of soil

erosion of 1.74 Pg yr

−

1

. The remaining 1.1 million km

(~0.9%)

under land use change experienced a decrease in soil erosion of
0.88 Pg yr

−

1

. This results in an estimated overall increase of soil

erosion for areas with land use change of about 0.86 Pg yr

−

1

. The

soil conservation practice scenario shows a potential overall offset
of the estimated soil erosion increase of about 64% (overall increase
0.31 Pg yr

−

1

), primarily driven by the effects of the conservation

practices in North America, South America and other advanced
and transition economies. This offset is the result of heterogeneous
regional dynamics.

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The spatial pattern of soil erosion in 2012 is illustrated in

Fig.

. Areas classi

ﬁ

ed as having very low, and low erosion rates

(class 1 and class 2), represent about 71.9% and 12.7% of the total,
respectively. Moderate (class 3) and high (class 4) soil erosion
values are predicted for about 4.2% and 5.1% of the study area,

respectively. The remaining land surface (classes 5

–

7), about 7.5

million km

in total (6.1% of the land), exceeds the generic

tolerable soil erosion threshold (

T

-value) (10 Mg ha

−

1

yr

−

1

) in

2012. Descriptive statistics about the severity of soil erosion
across the continents in 2012 are provided in Table

.

160

°

E

140

°

E

120

°

E

100

°

E

80

°

E

60

°

E

40

°

E

20

°

E

0

°

20

°

W

40

°

W

60

°

W

80

°

W

100

°

W

120

°

W

140

°

W

160

°

W

180

°

70

°

N

50

°

N

30

°

N

10

°

N

10

°

S

30

°

S

50

°

S

70

°

S

180

°

0

5000

2500

km

180

°

160

°

E

140

°

E

120

°

E

100

°

E

80

°

E

60

°

E

40

°

E

20

°

E

0

°

20

°

W

40

°

W

60

°

W

80

°

W

100

°

W

120

°

W

140

°

W

160

°

W

180

°

70

°

N

50

°

N

30

°

N

10

°

N

10

°

S

30

°

S

50

°

S

70

°

S

0

5000

2500

km

0 – 1

1 – 3

3 – 5

5 –10

10 – 20

20 – 50

> 50

No data

Soil erosion 2012

Soil erosion reduction rates on cropland (conservation vs baseline scenario 2012)

0 – 0.1

0.1 – 0.5

0.5 – 1

1 – 2.5

2.5 – 5

5 – 10

> 10

No data

Baseline/conservative scenario

3.8

Mg ha

–1

yr

–1

Mg ha

–1

yr

–1

a

b

Erosion on cropland in 2012 (Pg yr

–1

)

N

E

S

W

N

E

S

W

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Continental and country-speci

ﬁ

c perspectives on soil erosion

.

Our modelling results suggest that water erosion is a common
phenomenon under all climatic conditions encompassing all
observed continents. Country-speci

ﬁ

c results and changes of the

estimated annual average soil erosion values between 2001 and
2012 are illustrated in Fig.

. According to the baseline scenario,

at a continental level, South America shows the highest prediction
of average soil erosion rate (3.53 Mg ha

−

1

yr

−

1

) in 2001, followed

by Africa (3.51 Mg ha

−

1

yr

−

1

) and Asia (3.47 Mg ha

−

1

yr

−

1

). North

America, Europe and Oceania show considerably lower predicted
values, totalling 2.23, 0.92 and 0.9 Mg ha

−

1

yr

−

1

, respectively. In

2012, the latter group of continents indicated an estimated
decreasing trend of soil erosion driven by land use change, with
the highest decrease predicted in North America (4.8%). For Asia,
we predict a slight increase of about one percent, mainly driven
by a noticeable increase in soil erosion in the Southeast Asian
countries. China (~2%) and India (~0.45%), in contrast, even
though densely populated show a decrease in soil erosion esti-
mates, while we observe a noteworthy increase in soil erosion in
South America and Africa. Africa surpasses South America with
an estimated increase of soil erosion of ~10% in 2012, thus
becoming the continent with the highest average soil erosion rate
(3.88 Mg ha

−

1

yr

−

1

). This seems to be primarily driven by a

widespread increase of erosion in the western and central African
countries. For the same period, South America showed a pre-
dicted ~8% increase in soil erosion as a consequence of defor-
estation and the large expansion of cropland areas in Argentina
(41.6%), Brazil (19.8%), Bolivia (37.8%) and Peru (5.9%).

Classi

ﬁ

ed based according to the International Monetary Fund

and United Nations classi

ﬁ

cation, the least developed economies

experienced the highest prediction of soil erosion rate in 2001
(4.81 Mg ha

−

1

yr

−

1

), equal to 4.8 Pg yr

−

1

and 13.6% of the global

soil erosion. The less developed economies have the second
highest predicted rate of soil erosion (4.74 Mg ha

−

1

yr

−

1

),

followed by the advanced economies (1.61 Mg ha

−

1

yr

−

1

) and

the transition economies (1.02 Mg ha

−

1

yr

−

1

). The less developed

economies show the highest prediction of annual total soil
erosion (20.7 Pg yr

−

1

), equal to 59.2% of the global soil erosion.

For 2012, we observed a decrease in soil erosion estimates driven
by land use change in the advanced (

−

5.3%) and transition

economies (

−

0.14%), while we estimated substantial increases in

the less developed (3%) and least developed economies (11.7%).
Notably, the least developed economies (mostly located in Sub-

Saharan Africa) show a predicted increase in soil erosion that is
three times higher than the less developed economies.

Looking for soil erosion hot-spots higher than 20 Mg ha

−

1

yr

−

1

,

the largest and most intensively eroded regions (Supplementary
Fig.

) are predicted to be in China (0.47 million km

; 6.3% of the

country), Brazil (0.32 million km

; 4.6% of the country), the

African territories located along the Equator (0.26 million km

;

3.2% of the region), India (0.20 million km

; 7.5% of the country),

South-eastern United States (0.2 million km

; 1.9% of the

country) and to a lesser extent in Central Eastern Ethiopia
(0.084 million km

; 9.5 of the country), Mexico (0.079 million

km

; 4.6% of the country), Indonesia (0.076 million km

; 5% of

the country), Peru (0.074 million km

; 7.5% of the country) and

Mediterranean Europe (0.06 million km

; 3.2% of the region).

Very high estimates of soil erosion rates are often locally
exacerbated by high annual average rainfall erosivity values,
greater than 4500 MJ mm ha

−

1

h

−

1

yr

−

1

(global average 2000 MJ

mm h

−

1

ha

−

1

yr

−

1

) which characterize regions such as sub-

Equatorial Africa, Southern China, India, South Eastern United
States, Northern Oceania, South America and Mexico (Supple-
mentary Fig.

).

Land use/cover and soil erosion

. Comparing soil erosion based

on land use types, we

ﬁ

nd a signi

ﬁ

cant decline in the estimates of

soil erosion rates from croplands to forests and other forms of
semi-natural vegetation. Cropland covers about 11% of the stu-
died land in 2001 and 11.2% in 2012, but is responsible for 49.7%
and 50.5% of the total predicted soil erosion, respectively. In
either period, the predicted average soil erosion in croplands is
more than four times higher than the overall soil erosion rate
(12.7 Mg ha

−

1

yr

−

1

compared to an average of 2.8 Mg ha

−

1

yr

−

1

).

It is estimated to be 77 times higher than in forests (0.16 Mg ha

−

1

yr

−

1

) and around seven times higher than the average of the

other natural vegetation (1.84 Mg ha

−

1

yr

−

1

). Despite the fact

that forests cover more than 28% of the observed global land,
they have, on average, the lowest soil erosion estimate with
about 0.6 Pg yr

−

1

, thus being responsible for ca. 1.7% of the total

soil erosion estimates.

Land use changes as drivers of soil erosion

. We assessed the

dynamics in land use between 2001 and 2012. The total gross
land stock changed about 3.3% (4 million km

of ~125

Fig. 1

Global rates of soil displacement by water erosion. The estimates are predicted through a RUSLE-based modelling approach integrated in a

geographic information system (GIS) environment. The model provides rates on an ~250 × 250 m cell basis for the land surface of 202 countries (ca. 2.89
billion cells; ~125 million km

2

), covering about 84.1% of the Earth

’

s land. Panel

a

illustrates the soil erosion rates divided into seven classes according to the

European Soil Bureau classi

ﬁ

cation. The colour gradation from green to red indicates the intensity of the predicted erosion rates. The grey colour indicates

the areas that were excluded from the modelling due to data unavailability (i.e., ice-covered land, terrestrial water bodies, large area of bare rock, deserts
and land with bare soil). Panel

b

illustrates the erosion reduction rates on cropland obtained from the comparison between the conservation and the

baseline scenario for the year 2012. The green gradient shows the percentage of reduction. The grey colour indicates the areas that were not modelled (no
data)

Table 1 Continental perspective on the severity of water erosion in 2012

Continent

Light (classes 1 and 2)

Moderate (class 3)

High (class 4)

Above T-value (classes 5

–

7)

Surface area (%)

Africa

80.3

6.0

7.7

Asia

80.6

4.9

6.9

7.6

Europe

94.5

2.1

1.7

1.6

North America

87.7

3.7

4.3

South America

81.9

4.6

5.2

8.3

Oceania

96.2

1.7

1.2

0.8

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million km

of observed land) (Fig.

). This resulted in a

forest decline (~1.65 million km

), an expansion of the

semi-natural vegetation (savannah, scrublands, grassland, transi-
tion forest) (~1.43 million km

) and an expansion of cropland

(~0.22 million km

).

A total of 2.26 million km

of forests are estimated to be lost

during the study period, mostly replaced by semi-natural
vegetation (2.17 million km

) and to a lesser extent by cropland

(~0.1 million km

). In the baseline scenario, the change from

forest to other land uses caused an increase of 0.61 Pg yr

−

1

of soil

18

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°

16

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0

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10°S

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70

°

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0

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km

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0

°

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0

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30

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10

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S

50

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S

70

°

S

0

5000

2500

km

–5 – –2.5 –2.5 – –0.5 –0.5 – 0

0 – 0.5

2.5 – 5

> 5

No data

Soil erosion change baseline scenario (2001–2012)

Soil erosion change conservation scenario (2001–2012)

[%]

< –5

0.5 – 2.5

a

b

N

E

S

W

N

E

S

W

Fig. 2

Country-speci

ﬁ

c changes of the annual average soil erosion. Panels

a

and

b

share the same legend. The chromatic scale represents the percentages

of increase or decrease of the annual average soil erosion rates obtained by comparing the pixel-based values in each of the 202 countries under
observation.

a

Soil erosion change between 2001 and 2012 according to the baseline scenario. The delta between the two observed periods solely depends

on the land use and land cover change outlined combining satellite-derived land use land cover information with agricultural inventory data.

b

Soil erosion

change between 2001 and 2012 according to the conservation scenario. In this case, the percentage change of erosion results from the combined effect of
land use/land cover change and the mitigating effect of soil conservation practices

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erosion (~ 2200%). The substitution of forests for cropland (4.1%
of the forest lost) is responsible for about 52% of this increase in
soil loss. At the same time, a forest area gain of about 0.61 million
km

occurred during the study period, resulting in a net loss of

~1.65 million km

. With regard to the soil erosion balance, this

forest change accounts for an estimated overall decrease of soil
erosion in forestland equal to 0.38 Pg yr

−

1

.

With regard to cropland, about 90.5% of the land classi

ﬁ

ed as

cropland did not change during the observed period. The change
in cropland (loss or gain) is equal to 1.31 million km

(32% of the

total global land use change). The cropland abandonment
amounts to 0.55 million km

(with ca. 88% and ca. 12% of

cropland recolonized by forest and semi-natural vegetation,
respectively), while 0.76 million km

2

of new cropland was

established at the expense of semi-natural vegetation (ca. 90%)
and forest (ca. 10%). This translates to an increase in soil erosion
of 0.69 billion Mg ha

−

1

yr

−

1

. Considering the overall increase in

soil erosion of about 0.86 Pg yr

−

1

, the land changes related to

cropland are responsible for about 80% of this increase.

Conservation agriculture and soil erosion

. In Fig.

, we illustrate

the variation of soil erosion modelled for a selection of the 54
countries, which reported the proportion of their cropland area
under conservation agriculture to the FAO. The conservation
agriculture covers about 15.3% of the observed cropland (1.6 of
10.3 million km

), resulting in an estimated overall soil erosion

reduction of about 7% compared to the baseline scenario in 2012
(from 10.93 to 10.15 Pg yr

−

1

). At a continental level, the highest

soil erosion reductions are estimated in South America (16%),
Oceania (15.4%), North America (12.5%), and to a lesser extent in
Europe (1.5%), Asia (1.2%) and Africa (1.1%). From a country-
speci

ﬁ

c perspective, the effects of conservation agriculture are

remarkable in South American countries like Argentina, Paraguay

and Brazil, where soil erosion is potentially reduced by 33%, 27%
and 20%, respectively.

Of these countries, 28 regularly reported the proportion of

their cropland under conservation agriculture during the last
decade for different time periods. From an analysis of the data,
different dynamics of the continental conservation pattern can be
inferred. Oceania (+21.1%), North (17.3%) and South America
(+14.2%) show the most substantial increases in areas under
conservation, while Asia (5.8%), Europe (2.4%) and Africa (1.8%)
have considerably lower values.

The bene

ﬁ

ts of reducing soil erosion due to the adoption of soil

conservation practices are notable. We estimate that if the
countries with no information about conservation agriculture
would follow the continental patterns obtained from the analysis
of the 28 countries, ca. 0.49 Pg yr

−

1

(total 34.5 Pg yr

−

1

) and 1.05

Pg yr

−

1

(total 34.8 Pg yr

−

1

) of eroded soil would be avoided in

2001 and 2012, respectively. This would redesign the global
patterns described in the baseline scenario as follows. At
continental level, Africa (3.86 Mg ha

−

1

yr

−

1

) and Asia (3.47 Mg

ha

−

1

yr

−

1

) would show the highest soil erosion rate in 2012. The

highest impact of conservation agriculture would be observed in
South America (3.38 Mg ha

−

1

yr

−

1

), North America (2.03 Mg ha

−

1

yr

−

1

) and Oceania (0.74 Mg ha

−

1

yr

−

1

), while the impact in

Europe would be limited (0.89 Mg ha

−

1

yr

−

1

). With regard to the

socio-economic prospective, the situation of the conservation
scenario does not differ from the baseline scenario. However,
while the less developed countries show the highest soil erosion
reduction in 2012 (

−

3.9% vs.

−

2.9% of the advanced economies),

the least developed countries experience a lower increase (

−

1.1%).

Discussion

Land management and the related land use changes have an effect
on the spatial patterns and magnitude of accelerated soil erosion

a

Land change

–1.65

million km

2

Soil loss

–0.02

Pg yr

–1

Soil loss

Land change

+0.22

million km

2

+0.69

Pg yr

–1

Land change

+1.43

million km

2

Soil loss

+0.20

Pg yr

–1

b

c

0

.

56

mil

li

on

km 2

0.

09

4 m

i

ll

i

on

k

m

2

0

.6

7

m

ill

io

n

km

2

Forestland

Sem

i-natural

vegetation

0

.

49

m

ill

i

o

n

k

m

2

Cropland

0

.

0

5

m

il

lio

n

km

2

2.17 million km

2

Fig. 3

Flow diagram of land use changes and their effects on the soil loss estimates. The circular forms refer to the three major land use and land cover

groups obtained from the combination of the MODIS MCD12Q1 Land Cover Type (International Geosphere Biosphere Programme (IGBP) system), Global
Forest Change maps, and cropland extend and harvested area statistics extracted from the Food and Agriculture Organization

’

s (FAO) FAOSTAT database.

The arrows indicate the amount (million km

) of land use and land cover change between 2001 and 2012. Insets

a

–

c

demonstrate the net change of the

land surface (million km

) and soil loss (Pg yr

−

1

)

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which can affect land productivity and food security

18

, biolo-

gical diversity

and carbon cycling

,

. Global soil erosion

dynamics have been previously quanti

ﬁ

ed based on scienti

ﬁ

c soil

expert judgments

, through the extrapolation of plot and river

sediment data

41

42

and RUSLE-based modelling

36

. While these

approaches range in their degree of complexity, their lumped or
coarse resolution modelling (ca. 10

–

60 km cell size) with a static

observation approach limits their predictive power to assess the
effects of frequent land use changes and to identify soil erosion
hotspots. Our study investigates the global soil erosion dynamics
by means of high-resolution spatially distributed modelling (ca.
250 × 250 m cell size). The proposed geo-statistical approach,
allows for the

ﬁ

rst time, the thoroughly incorporation of land uses

and their changes, the extent, types, spatial distribution of global
croplands, and the effects of the different regional cropping sys-
tems into a global soil erosion model. This, coupled with an
improved global assessment of the global rainfall erosivity
dynamics and the latest globally consistent dataset paved the path
towards a state-of-the-art global RUSLE-based model.

The results of this study shed light on the impacts of the 21st

century global land use change on soil erosion, providing insights
into the potential mitigating effects attributable to conservation
agriculture. The strong bond between remote sensing and
inventory statistics formed the basis for globally consistent
characterizations of soil erosion with local importance and utility
(Fig.

). The knowledge derived from this global assessment can

thus improve our understanding in both global and regional land
degradation dynamics and forms an important starting point to
develop concepts for a better management of the land and an
effective mitigation of land degradation.

The limited availability of globally consistent data on the

amount of cropland under conservation agriculture constrained
the ability of our study to comprehensively model the mitigating
effects for all the 202 countries under observation. To date, 54
counties have provided statistics about their conservation agri-
culture practices to the FAO, which cover about 73% of the global
cropland surface. In the conservation scenario, these countries
experience 7% less soil erosion than in the baseline scenario,
where no conservation practices are considered. Assuming the

average conservation practices at continental level as repre-
sentative for the remaining 27% of the global cropland, for 2012 a
total annual average soil erosion of 17

þ

1

0

:

7

Pg yr

−

1

is predicted.

The con

ﬁ

dence intervals refer to the variation between the con-

servation and baseline scenarios (superscript) and the conserva-
tion scenario assuming the maximum technical ef

ﬁ

ciency of the

employed conservation practices (subscript).

Previous global estimates of soil erosion on agricultural land

span across two orders of magnitude (23.7 and 120 Pg yr

−

1

). The

most cited estimate of global soil erosion in agricultural land by
Pimentel et al.

is equal to 73.5 Pg yr

−

1

. Citations on global soil

erosion estimates during the last three decades estimated around
75 Pg yr

−

1

of soil eroded in cropland

19

23

. Early RUSLE-based

spatially distributed modelling approaches con

ﬁ

rmed this

. However, recent studies using methods that more

closely link models to measured erosion values report smaller
global erosion rates. By means of a combined plot data and
RUSLE modelling extrapolation approach, Van Oost et al.

estimated a soil erosion amount of 33.4

±

4.4 Pg yr

−

1

for global

agricultural land (pasture and cropland). More recently, Doetterl
et al.

constrained a simulation of a coarse resolution global

application of the RUSLE model (ca. 10 × 10 km cell size) with the
soil erosion data reported for the US cropland. This resulted in an
estimated soil erosion by water in global cropland of 13.1

±

6.6

Pg yr

−

1

. Since RUSLE models do not include a description of

gully and tillage erosion processes, and also do not represent
other geomorphic processes such as landslides and river bank
erosion, it is reasonable to assume that their estimates fall into the
lower end of the 23.7 and 120 Pg yr

−

1

range of cited soil erosion

estimates. The new cropland soil erosion estimate of 17

þ

1

0

:

7

Pg yr

−

1

for the year 2012 that we present in this study is consistent

with the recent estimate of Doetterl et al.

. The good corre-

spondence of our results (without using constraining factors)
with regional estimates (US and Europe) and Doetterl et al.

’

s

,

supports the hypothesis that soil erosion due to sheet and rill
processes is smaller than previously assumed in literature.

The estimates reported in this study rest on RUSLE, a deter-

ministic and empirical-based model which was developed based
on a statistical analysis of more than 10,000 plot-years of basic

0

10

20

30

40

50

60

70

80

0

Argentina

Paraguay

Brazil

Uruguay

New Zealand

Canada

Australia

United States

Bolivia

Venezuela

Chile

Finland

Colombia

Zimbabwe

Kenya

Greece

Morocco

Lesotho

France

DPR of Korea

Ghana

Lebanon

Germany

Malawi

Ukraine

Slovakia

Republic of Moldova

Portugal

United Kingdom

Mozambique

South Africa

Switzerland

Russian Federation

Italy

China

Zambia

Spain

Kazakhstan

Syrian Arab Republic

5

10

15

20

25

30

35

Soil loss reduction (%)

Annual average soil erosion (Mg ha

–1

yr

–1

)

Soil loss threshold (

T

-value)

Fig. 4

Estimated soil loss reduced by conservation agriculture. The grey bars illustrate the estimated soil loss reduction (in percent) derived from the

implementation of conservation agriculture (40 countries show the highest reduction values). The values refer to the model application for the year 2012 adjusted
for the potential effect of conservation agriculture practices. Red dots indicate the national average of annual soil loss estimated in cropland (Mg ha

−

1

yr

−

1

). The

red error bars around the dots indicate the variation between the mean values of the conservation scenario, the baseline scenario (positive bar) and the maximum
mitigation effect of the practices (negative bar). The dotted orange line shows the soil loss tolerance threshold (

T

value

—

10 Mg ha

−

1

yr

−

1

)

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runoff and soil loss data

44

in 49 US locations covering a large

variety of landscape conditions. Although RUSLE-based models
are derived from the most comprehensive set of measurements
available

45

including universally recognized factors that affect soil

erosion by water

29

,

, they are predominantly built upon para-

meters that result from experiments conducted in the United
States

45

. The application to a non-plot-level and in areas outside

the range of the original estimates (e.g., tropics, subarctic and
tundra) may substantially reduce the accuracy of the model

45

The authors recognize that using an empirical-based prediction
tool outside the original range of environmental variables could
represent a legitimate concern

46

. Considering the proven capacity

of RUSLE-based models to overcome their empirical origin

47

, the

current lack of better performing models

, and the need for

predicting the possible impacts of global change upon soil ero-
sion

27

, the authors argue that at this stage the presented global

RUSLE application represents a legitimate approach to narrow
the current gap of knowledge and support the targeted soil
conservation efforts aiming to mitigate soil erosion.

Given the quantitative and harmonized nature of the data set,

there seemed to be no reasons to doubt the consistency between
the estimates for the two time periods as well as the reliability of
the resulting national trends. The difference obtained from the
comparison of the estimates for the two time periods was driven
by land use change and was unaffected by the predictive limits of
the empirical soil erosion model. Validity, i.e., if the model
accurately measures the amount of displaced soil, could have been
an issue as the predictive capacity of both empirical and process-
based physical models is still limited

, and because the model

was run on a global scale based on a number of data-driven
assumptions with soil erosion quantities estimated for each of the
2.9 billion cells of the global raster.

As observed by Auerswald et al.

48

, a validation sensu strictu of

USLE-based modelling at regional or larger scales is not feasible
due to the lack of long-term

ﬁ

eld-scale measurements. Therefore,

a cross-comparison of the modelling results to gain insights on
the validity of the modelling predictions was performed. This
operation shows that the modelling results are consistent with
both empirical observations and other regional soil erosion
assessments. The analysis at meta-data level con

ﬁ

rms that our

estimates fall in the range of measured data collected by Mon-
tgomery

, as well that the global model can describe the magni-

tude of soil erosion incurring between the different land cover
types. Adapting the

ﬁ

gure he created (Fig.

6

), we superimposed

the results from our global analysis for different land covers, to
which we added data on native forests and data from other meta-
analysis studies (Supplementary Note

). In addition, an exiguous

deviation was observed from the comparison between our esti-
mates and the ones provided by independent studies of the US
Department of Agriculture (USDA) for cropland in the United
States

30

(4.5%) and by the Joint Research Centre (JRC) of the

European Commission for cropland of the 28 EU countries
(1.1%). The good agreement between our estimates and the ones
provided by independent studies give con

ﬁ

dence that the

Forestland

Forestland loss 2001–2012

Other semi-natural vegetation

Cropland

0

N

100

50

Soil erosion (Mg ha

–1

yr

–1

)

0 – 1

1 – 3

3 – 5

5 – 10 10 – 20 20 – 50 > 50

a

b

Fig. 5

Examples of the local relevance and utility of the global soil erosion estimates.

a

Representation of the land use/land cover data employed in this

study. The area reported in the image is a region of Mato Grosso in Brasil. The light green indicates forest loss between 2001 and 2012.

b

Representation of

the high spatial detail of the soil erosion model predictions, capable to represent the effect of the forest loss in the Mato Grosso

0.00001

0.0001

0.001

0.01

0.1

1

10

100

Erosion rate (mm yr

–1

)

Agriculture

Alpine

Soil-mantled

Cratons

Native forest

Semi-natural vegetation

Cratons

Forest

Cropland

Highlands

Bare soil, excluding deserts

1) Haiti
2) Rwanda

Fig. 6

Comparison of measured and modelled erosion rates. Representation

of soil erosion rates measured on agricultural

ﬁ

elds under conventional

agriculture (

n

=

779), geologic erosion rates measured on alpine terrain (

n

=

44), soil-mantled landscapes (

n

=

1456), low gradient continental cratons

(

n

=

218), grassland and scrublands (

n

=

63), native forests (

n

=

46) and

averages of our predictions (indicated by an asterisk). Large parts of the
measured data come from the study of Montgomery

integrated with data

from other meta-analysis studies. The vertical red line indicates average
value of soil erosion. The red dots refer to averages soil erosion rates
modelled for two country highly susceptible to water erosion (Haiti and
Rwanda)

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quantitative estimates achieved through the global model are
reliable and valid to a level close to these higher resolution
regional assessments (Supplementary Note

). Further insights

supporting the validity of the global model were achieved com-
paring the spatial patterns of the estimates with the ones reported
by previous UN funded global assessments based on expert jud-
gement (GLASOD)

and remote sensing data (GLADIS) (Sup-

plementary Fig.

and Supplementary Note

). On the basis of a

sensitivity analysis (Supplementary Note

), the authors observed

that the soil erosion predictions of the global RUSLE-based model
were most sensitive to the cover-management factor (C-factor)
(Supplementary Figs.

and

6

). This supports the hypothesis that

a thorough de

ﬁ

nition in the C-factor of the land uses and their

changes, the extent, types and the spatial distribution of the global
croplands and cropping systems are the key to improve RUSLE-
based global assessments. In addition, the sensitivity analysis
allows us to de

ﬁ

ne the in

ﬂ

uence of the input parameters on the

global RUSLE predictions and spatially map their effects on the
model output (Supplementary Fig.

).

The uncertainty of the spatial predictions was estimated using a

Markov Chain Monte Carlo (MCMC) approach (Supplementary
Note

). The map of uncertainty is presented in Supplementary

Fig.

as the standard deviation of the MCMC simulated values.

The map gives an outline of the geographical distribution of the
prediction variance, and it can be used to compare the potential
error in different areas of the world. The error of the model esti-
mates associated with the input data assessed with a MCMC
approach is about 8 Pg yr

−

1

for the whole world. Accounting for

uncertainties in the soil erosion rates, we estimated an annual
average potential soil erosion amount of 35

þ

5

:

6

2

:

4

and 35

:

9

þ

5

:

6

2

:

4

Pg yr

−

1

for the 2001 and 2012 baseline scenarios, respectively.

It should be noted that the positive results highlighted by the

cross-check analysis do not imply that the global model captures
reality by 100%. The authors recognise that the modelling based
on data-driven assumptions has its limitations, and there is a
need for

ﬁ

eld monitoring and local scale process-based modelling.

However, in light of the useful insights gained from the opera-
tions of the model performance evaluation, we argue that the
presented RUSLE-based global approach constitutes a powerful
assessment tool for identifying hotspots and areas of concern at
the global scale. It provides the basis for a more strategic
approach in directing new monitoring/modelling efforts and
informing decision making for the development of policy.
Moreover, in the proposed new form, the global scale RUSLE-
based assessment is brought to a new level, as for the

ﬁ

rst time it

links to the key parameters required to assess the effects of global
change and support conservation planning and land manage-
ment. Hereinafter, we therefore discuss the implications of our
global modelling from a multidisciplinary perspective linking the

ﬁ

ndings of our map to GDP measures to identify potential

pressures on food production systems, risks of increased food and
feed prices due to phosphorous shortages, the global soil organic
carbon (SOC) pool that forms the basis for emission levels and
climate change analyses, the economic costs of soil erosion and
the overall implications for policy decision-making and sustain-
able development goals.

The increasing population places greater pressure on global

food production systems. The global spatial coverage of modelled
soil erosion enables us to explore the relationship between the
average soil erosion in croplands and the GDP of each country
based on World Bank

ﬁ

gures. The results presented in Fig.

also

show latitude according to size. Clearly, the wealthy countries in
temperate latitudes have the least erosion with poorest tropical
countries being the most susceptible to high levels of soil erosion.
The countries that can least afford soil protection measures are
the most vulnerable. This emphasises the importance of soil

protection in the sustainable development goals if there is any
hope of intensifying agriculture in these countries to meet the
food needs of the populations.

Along with the loss of fertile soil through erosion as quanti

ﬁ

ed

in this study goes the imminent threat of limited nutrient
resources. In 2009, clean phosphorous reserves were predicted to
run out in only 20

–

50 years

49

50

. Even though this prediction was

revised only 2 years later with previously overlooked phosphor-
ous reserves found in Morocco and Western Sarah

51

, the

ﬁ

nite

nature of resources could become a source of political tension
especially in developing-world countries where farmers cannot
afford phosphate fertilizers even at today

’

s non-monopoly prices.

With continuously rising demand due to the increasing world
population and thus higher demand of food in general and
livestock products in particular, fertilizer prices are likely to
increase. This may encourage companies to explore new reserves
from lower grade rock which is subject to a higher cadmium
pollution and exacerbate the con

ﬂ

ict with the less and least

developed countries due to food and feed shortages. Both devel-
opments could impede sustainable soil use and the application of
soil conservation management practices even further. The former
development may increase the cadmium pollution of soils and
potentially restrict soil usage. The latter, in turn, could lead to an
even more intensive land use with negative effects on soil erosion
rates. No substitutes exist for phosphates (with the exception of
organic farming using manure which is limited by livestock
availability in many countries). In this regard, one promising but
not yet widely discussed approach could be to protect phos-
phorous by reducing soil erosion rates. Cordell et al.

50

estimated

that around 36% of the total phosphorus fertilizer applied to
arable land was directly lost due to erosion. Our global soil ero-
sion assessment highlights the areas where agricultural manage-
ment based on sustainable farming practices with low soil erosion
and high phosphorous recycling rates could be most effectively
applied to help keep global food and feed prices at reasonable
levels.

The prediction of the global soil organic carbon (SOC) pool by

Earth-system models is still one of the main sources of uncer-
tainty, undermining the con

ﬁ

dence in the carbon (C) budget and

0

50

100

150

4

8

12

16

log (GDP 2015)

Soil erosion crop average

Latitude

20

40

60

4

8

12

16

log (GDP2015)

Global soil erosion and GDP

Fig. 7

Soil erosion in cropland areas on a country basis vs. the log of the

respective countries GDP according to the World Bank. The size of the
circle represents the latitude indicating the higher latitude, wealthier
countries are the least impacted by soil erosion, either through more
favourable climatic conditions, or soil erosion prevention measures

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its future projections

52

53

. Poor representation of different

mechanisms driving SOC turnover and low accuracy of soil data
inputs are among the primary causes of this uncertainty. Land-C-
atmosphere feedbacks may not be properly disentangled as long
as relevant missing processes are not implemented. Among those,
soil erosion is certainly a key process as it displaces consistent
amounts of C as lateral

ﬂ

uxes, then subjects it to different

environmental conditions that control its stabilisation and
release. The consequences of neglecting this component by large-
scale modelling and inventories are still uncertain, so that erosion
is estimated to induce a carbon sink or source up to 1 and

−

1 Pg

C yr

−

1

globally

54

, respectively. In this respect, the new global soil

erosion assessment presented in this paper has the potential to
become a reference input for integrating later C

ﬂ

uxes into large-

scale model frameworks of different complexity

40

. Combining the

global soil erosion with a recent SOC map (Supplementary
Methods), we estimated a gross SOC displacement by soil water
erosion on the order of 2.5 Pg C yr

−

1

. Thirty-six percent came

from agricultural land, although this covers only 11% of the total
area investigated. Geographically (Supplementary Fig.

), the

regions across the Tibetan plateau and China were extensively
exposed to high rates of SOC displacement, as also the north-
eastern part of Siberia.

Several on-site effects of soil erosion which occur directly at the

site where the soil is removed have been cited in the literature

Increasingly, scientists also mention offsite effects of soil erosion
in the surrounding areas

55

56

. Given these on- and offsite effects,

soil erosion assessments are highly relevant from an economic
point of view because erosion is associated with an unequal dis-
tribution of economic costs. It degrades soil and thereby reduces
soil productivity and yields of the land due to the loss of fertility
and water storage capacity

57

. Land users in economies which

can afford it, therefore have an incentive to use fertilizers or water
management practices which unfold immediate effects to com-
pensate the negative effects of soil erosion on their short-term
yields. In economies that cannot afford these measures, the price
of erosion is paid for by reduced food or forest production

. The

on-site costs of soil erosion are thus internalized by pricing them
directly into the economic decision making of the land user. As
long as cash

ﬂ

ows remain positive, land users, by themselves, thus

hardly have an incentive to adopt land conservation practices to
contain soil erosion because the costs of adopting these tech-
nologies are high and occur immediately (reducing early cash

ﬂ

ows), while the bene

ﬁ

ts spread out over a longer time horizon

55

and are discounted more heavily. From an economic perspective,
the high resolution global soil erosion assessment could help to
estimate the global costs of these on-site effects especially from a
long-term land value perspective. More precisely, the monetary
costs of fertilizers, additional water management, etc. or pro-
ductivity losses in the poorer countries could be contrasted with
the cost of soil conservation practices to reduce soil loss in the

ﬁ

rst place in order to derive appropriate political regulations for

sustainable on-site land management. Soil erosion, however, also
affects the surrounding areas with off-site costs that are external
to the future cash

ﬂ

ow calculus of the land user. Here the price

mechanism breaks down in that the offsite costs caused by
sedimentation, siltation, eutrophication,

ﬂ

ooding, etc. are not

internalized into the land user

’

s investment calculus but are borne

by society, i.e., the tax payer. While in the short-term, the
exploitation of soil resources may be economically sound for land
users, this may not be the case from a socio-economic perspective
because the society bears the offsite costs (e.g., cleaning of public
waterway infrastructure, prevention of dam bursts) while con-
sumers may be confronted with higher prices. In the case of off-
site effects, the global assessment of soil erosion is especially
bene

ﬁ

cial as it provides the long-awaited basis for an economic

assessment of the off-site costs of soil erosion on a global scale.
An economic assessment, similar to Pimentel et al.

but based on

a global assessment with high resolution soil erosion information
could assign a value to on- and off-site costs of soil erosion,
locally as well as globally, in order to calculate the net costs of soil
erosion. Placing a value on the on- and off-site effects may help
decision-makers to internalize the off-site costs into the invest-
ment calculus of the land user, e.g., in the form of obligatory soil
conservation practices in areas with high off-site costs otherwise
borne by society. This is important because fertile soils on the
planet are limited and essentially non-renewable, at least on
human time scales. It is therefore of crucial importance to protect
available soil resources from further degradation if society wants
to maintain this precious natural resource for future generations.
In this sense preserving soil quality and achieving a land degra-
dation neutral world have been explicitly recognized in the
recently approved sustainable development goals (SDG). Goal 2
explicitly mentions the relevance of maintaining soil quality for
achieving food security while Goal 15 calls for a land degradation
neutral world by 2030. These goals can only be achieved if we are
able to limit current soil erosion rates by applying sustainable soil
management practices especially in the areas mostly affected by
erosion processes. Moreover, the results of this study can also be
relevant for Goal 13 aiming at taking action to combat climate
change and its impacts

and Goal 6 to ensure availability and

sustainable management of water. The recently endorsed FAO
Voluntary Guidelines for Sustainable Soil Management

58

provide

the necessary guidance to National governments on the way
forward in order to achieve such ambitious goals by 2030. The
insights of our high resolution global modelling approach can
provide a solid starting point to support decision-making in both
ex-ante and ex-post policy evaluation, while scienti

ﬁ

cally, it can

enable better estimates of the global SOC pool including the
effects of land use change and conservation agriculture. Our

ﬁ

ndings can also provide the basis to test the possible effects of

the four per mil initiative proposed by the French authorities
during the COP21 in Paris.

Methods

Study area

. The study area includes the area of the 202 countries for which

FAOSTAT

14

provides crops statistics. The total modelled area is about 125 million

km

2

, providing living space for a population of about 7.5 billion people.

Soil erosion modelling

. The years 2001 and 2012 form the reference periods to

assess the 21st century human-induced soil erosion by water erosion at a global
scale. For 21st century human-induced soil erosion we refer to the effects caused by
land use/land cover changes. Permanent loss and gain of global croplands, forests
and semi-natural grass vegetation are considered in the modelling scheme while the
effects of grazing and the establishment of new pasturelands are implicitly re

ﬂ

ected.

Short-term effects of land use/land cover change (i.e., forest/rangeland

ﬁ

res and

wood harvesting) and overgrazing are not modelled. Climate change and human-
induced effects on climate are also not considered.

The long-term annual soil erosion rates for the two different land covers are

estimated using an improved large-scale Geographic Information System (GIS)
version of the Revised Universal Soil Loss Equation (RUSLE) model

(Supplementary Fig.

). RUSLE belongs to the so called detachment-limited model

types where the soil erosion (expressed as a mass of soil lost per unit area and time,
Mg ha

−

1

yr

−

1

) due to inter-rill and rill erosion processes is given by the

multiplication of six contributing factors. Consistently with the predictive capacity
of the model, soil displacement due to processes such as gullying and tillage erosion
is not estimated.

RUSLE-type models have demonstrated to be able to reduce a very complex

system to a quite simple one for the purposes of erosion prediction

while

maintaining a thorough representation of the main environmental and
anthropogenic factors that in

ﬂ

uence the process

. Conceptually, these models

follow the same principle of complex process-based models, with a driving force
(erosivity of the climate), a resistance term (erodibility of the soil) and the other
factors representing the farming choice, i.e. topographical conformation of the

ﬁ

eld

(LS), cropping system (C) and soil conservation practices (P). Field- and
catchment-scale experiments that compared the prediction capacity of empirical
RUSLE-type models with process-based models did not reveal a substantial

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difference between the predictive performances of the two modelling
approaches

,

. This, together with the moderate data demand of RUSLE-type

models has facilitated the process of upscaling. Today, an extensive amount of the
literature recommends the use of RUSLE-based models to provide spatially explicit
estimations of soil erosion in GIS environments.

In the original version of RUSLE

, the region of the model simulation is a

speci

ﬁ

c

ﬁ

eld plot slope with given size, rainfall pattern, soil conditions, topography,

crop system and management practices. Before the introduction of GIS-based
computational techniques, the input data employed to run the model were
generally directly measured in the

ﬁ

eld and afterwards imported in a speci

ﬁ

c

software. In this study, a simpli

ﬁ

ed application of the RUSLE model at global scale

is proposed. For this purpose, the approach paved by previous GIS-based studies
dealing with upscaling procedures to extent the applicability of the model as well as
regional studies in Europe

and China

was followed. The rates of

soil displacement by water erosion are estimated through the GIS raster scheme.
Using a GIS raster scheme applied to the USLE model means hypothesizing that
each cell is independent from the others with respect to soil erosion. Soil erosion
(synonym to RUSLE soil loss, Supplementary Note

) refers to the amount of

sediment that reaches the end of a speci

ﬁ

ed area (cell) on a hillslope that

experiences loss of soil by water erosion. The modelled area does not, in any way,
include areas of the slope that experience net deposition over the long term. This is
because the displaced soil amount is not routed downslope across each cell from
hillslopes to the sink area or the riverine systems through a transport/deposition
capacity module.

Conceptually, the global modelling presented in this study is based on the

assumption that if a catchment- or a regional-scale application of RUSLE can
predict meaningful soil loss estimates, the application of the model at larger-scale
will provide meaningful estimates as long as the scale of the input data employed is
congruent with the scale used for the estimation of the modelling factors and local
applications. If the scale of the input data meets these requisites, the global scope of
the model application represents a consistent repetition in space of the calculation
for all cells in the modelled area.

Although the input factors show a lower spatial detail in this global-scale study

compared to the recent study in Europe

59

, the data and the scale used are adequate

to ensure a spatial description of the modelling factors and are therefore within the
boundaries of the model

’

s applicability. The characterization of land use/land cover

change is based on NASA

’

s Moderate Resolution Imaging Spectroradiometer

(MODIS), which are the most consistent data currently available for soil erosion
modelling at global scale

62

. With a cell size of 250 × 250 m spatial resolution, the

dimension of each individual land unit (equal to 6.25 ha) fairly describe the general
dynamics of landscape fragmentation and can adequately represent the land unit
originally targeted by the model, i.e., the US arable

ﬁ

elds which show an average

size of about 400 acres (162 ha). For the analysis of the effect of the topography a
SRTM 3 arc-seconds (ca. 90 m) spatial resolution was used. This ca. 90 m DEM
ensured the computation of the combined topographical factor maintaining a scale
congruent with the one used during the USLE

’

s experimental measurements with

plots having a length

≤

122 m. Rainfall and soil characteristics were obtained using

the best database available to adequately describe their pattern and dynamics
during the elaboration of this study. Both have a spatial resolution of ca. 1 km.

For processing the main model components, the spatiotemporal variations of

the land cover and management (C-factor) as well as the rainfall erosivity (R-
factor) factors were assessed through new methodologies exclusively designed for
this study while the K, LS and P-factor were derived from methods reported in
literature (Supplementary Methods).

The C-factor measures the combined effect of the interrelated cover and

management variables on the soil erosion process

63

. For this global assessment

(Supplementary Fig.

10

), we followed the procedure as paved by previous

national

64

and pan-European studies

65

to assess the C-factor in a simpli

ﬁ

ed but

still meaningful way. We used steps for computing the C-factor in agricultural and
non-agricultural land. Within a

ﬁ

rst step, the global land cover dynamics were

outlined using the MODIS Land Cover Type product MCD12Q1 classi

ﬁ

ed

according to the International Geosphere Biosphere Programme (IGBP) system

66

The data were downloaded via NASA EarthData facility (reverb.echo.nasa.gov) and
pre-processed using the MODIS Reprojection Tool to obtain two rough global land
cover maps (ca. 250 × 250 m spatial resolution after applying the nearest
resampling method in ArgGIS) for the reference years 2001 and 2012. The IGBP
scheme reports seventeen land cover classes (Supplementary Table

), including

three non-vegetated land classes, three developed and mosaicked land classes and
eleven natural vegetation classes. In the following step, we spatially de

ﬁ

ned the

agricultural land according to the IGBP-12 class (croplands) and IGBP-14 class
(croplands/natural vegetation mosaic). Eleven classes were categorized as non-
agricultural land and four classes were precluded from the analysis because they
were not relevant for our soil erosion context (i.e., water, permanent wetlands,
urban and built-up, snow/ice). The MODIS MCD12Q1 Land Cover Type IGBP
was developed using a well-de

ﬁ

ned classi

ﬁ

cation scheme applied consistently at

global scale

67

. Friedl (personal communication), however, strongly advised against

mapping changes by taking simple difference across years due to the overall
accuracy across all land classes of ca. 75%

67

and the consequent presence of

mapping spurious changes. Following this advice, the subsequent step of data
processing for the MCD12Q1 global land cover maps was modi

ﬁ

ed in order to

better represent the forestland area by considering the well-known inability of the

MCD12Q1 product to accurately detect forestland in some locations

67

and align

the national cropland surface with the more accurate estimations reported by the
Food and Agriculture Organization (FAO). The global forestland area for the years
2001 and 2012 was outlined using the high-resolution global forest change maps
derived by Hansen et al.

based on Landsat imagery. Original data at 30 m spatial

resolution were downloaded from the online database of the University of
Maryland and resampled at 250 m. According to Hansen et al.

, for the forest gain

and loss a tree cover

>

50% was considered. Thus, the forestland class of the

MODIS MCD12Q1 for the year 2001 was replaced by Hansen et al.

’

s

data.

Discrepancies between Hansen et al.

http://www.fao.org/faostat/

and MODIS MCD12Q1

’

s data were

reclassi

ﬁ

ed to an additional code 21 and renamed as transitional woodland-shrub.

To delineate the cropland for each of the 202 selected countries, we capitalized on
both the spatially explicit information offered by the MODIS MCD12Q1 product
and the detailed cropland statistics reported at national level in the Food and
Agriculture Organization

’

s (FAO) FAOSTAT database (

en/#data

). Considering the FAOSTAT data as the best estimation of the real

cropland extent, a targeted removal or addition of the cropland area (IGBP-12 and
IGBP-14 class) from the MODIS 2001 (after the forest adjustment) was performed
to obtain a cropland area consistent with the FAOSTAT data for each of the 202
considered countries. The FAO-based cropland surface (arable land and permanent
crop area) for the years 2001 and 2012 was obtained by calculating the median of
the FAOSTAT data for the triennial periods 2000

–

2002 and 2011

–

2013. The

MODIS cells to be replaced were selected using the auxiliary information provided
in the MODIS MCD12Q1 product (i.e., classi

ﬁ

cation con

ﬁ

dence and second most

likely IGBP class at each cell) which allowed us to perform a targeted replacement
based on the classi

ﬁ

cation con

ﬁ

dence of each pixel. The modi

ﬁ

ed MODIS global

land cover map of 2001 was used as a baseline for the map of 2012. The

ﬁ

nal map

for the year 2012 was obtained by substituting Hansen et al.

’

s

forest data of 2001

with the one of 2012, calculating the cropland areas replaced by forestland and
modifying the cropland surface of each country consistently with the two medians
obtained from the FAOSTAT data.

For the countries that experienced a reduction of the cropland surface during

the observed period, the number of cells equal to the cropland decrease which
presented the lowest classi

ﬁ

cation con

ﬁ

dence in the IGBP-12 class of the MODIS

MCD12Q1 2012 were removed from the land cover map of 2001. The new
cropland established between 2001 and 2012 was obtained selecting the cells of the
IGBP-12 class of the MODIS MCD12Q1 2012 with the highest classi

ﬁ

cation

con

ﬁ

dence.

To outline the cropland maps for the two reference periods, twelve-year crop

statistical data (harvested area) were used to statistically describe the national crop
conditions and to estimate the C-factor (C

CROP

). A set of 170 crops were

considered

15

and subsequently grouped in 14 crop classes according to their soil

cover effectiveness (Supplementary Data

and Supplementary Table

). The

cropland data provided by FAO at a national level were regionalized for each of the
3247 FAO sub-national administrative unites (ADM1 level) (Supplementary
Fig.

11

) by means of statistical downscaling operations using the global harvested

area data of Monfreda et al.

68

to describe the local crop conditions and compute C-

factor values (C

CROP

) representative of the local farming systems (Supplementary

Methods). The C-factor for the non-agricultural land was de

ﬁ

ned for each of the

ﬁ

fteen IGBP classes by combining literature values (Supplementary Methods and

Supplementary Table

) with global vegetation metrics (MOD44B).

World patterns of rainfall erosivity (R-factor) (Supplementary Fig.

) were

spatially described by means of a relevant number of point information data and
advanced interpolation techniques

69

. We calculated the long-term average R-factor

based on high-resolution temporal rainfall data (5, 10, 15, 30 and 60 min) collected
from 3625 distributed precipitation stations across the globe

57

(Supplementary

Fig.

). The spatially continuous R-factor map (at 30 arc-seconds, ca. 1 km) was

computed using a Gaussian Process Regression (GPR) geo-statistical model.

For the remaining RUSLE factors (K-, LS- and P-) (Supplementary Methods),

the highest spatially-resolved input data available at a global scale were used. We
used the algebraic approximation reported by Wischmeier and Smith

63

and Renard

et al.

to assess the soil erodibility to water erosion (K-factor) (Supplementary

Fig.

). The soil properties (i.e., texture, organic matter, coarse fragmentation)

were downloaded from the ISRIC SoilGrids database at a 1 km spatial resolution

70

Further soil properties such as soil structure and permeability were derived
according to the methodology proposed for the soil erodibility map of Europe

71

We computed the topographical factor (LS-factor) (Supplementary Fig.

14

)

following the approach suggested by Desmet and Govers

72

, limiting the estimation

of LS to a maximum slope angle of 26.6 degrees (50%). Hole-

ﬁ

lled SRTM 3 arc-

seconds (ca. 90 m) Digital Elevation Model Version 4 were employed to represent
the land surface between 60° North and 56° South

73

. Extreme North latitudes were

covered using ASTER GDEM v2 data products

74

. In the main modelling run the P-

factor was assumed to remain constant at 1 since suitable spatial information for all
the 202 considered countries were not available.

Scenario analysis

. The soil erosion rates of the baseline scenarios were predicted

for the reference years 2001 and 2012 without considering conservation cropping
and management practices. We rested this decision on the current lack of adequate
standardised and harmonised worldwide spatial information. Besides the baseline
scenario, a conservative scenario was estimated for the 54 countries which reported
information about the implementation of conservation agriculture to the FAO.

NATURE COMMUNICATIONS | DOI: 10.1038/s41467-017-02142-7

ARTICLE

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8: 2013

|

DOI: 10.1038/s41467-017-02142-7

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11

These countries regularly submit the proportion of their cropland managed in
accordance with the three FAO conservation agriculture standards (i.e., minimum
soil disturbance, organic soil cover, crop rotation/ association). As suggested by
Wischmeier and Smith

63

, practices of improved tillage like no-till and cover crops

were considered as conservation cropping and management practices and imple-
mented in the C-factor. For these areas, we assumed a reduction of soil erosion of
45% compared to conventional tillage. A subsequent model run assuming a
reduction of soil erosion of 75% was performed

75

http://faostat3.fao.org/download/Q/QC/E

76

. This second conservation

prediction refers to the maximum technical potential reduction which we use to
represent the negative variation in our conservation scenario.

Model performance evaluation

. In order to evaluate the performance of the

global model and lay the groundwork for future studies to better identify areas
prone to soil erosion by water and their environmental and socio-economic
implications, multiple operations aiming to obtain insights about the validity of the
modelling predictions were performed. These consisted of the following: a com-
parison of the estimates with the ones provided by independent RUSLE-based
assessments in the US and Europe, an analysis at meta-data level to observe if the
estimates fell in the range of measured data, and a comparison of the spatial
patterns of soil erosion predicted by the global model for the years 2001 (baseline
scenario) with the ones reported by the expert-based Global Assessment of
Human-induced Soil Degradation (GLASOD). A further comparison was made
using the land degradation trends reported by the UN project Global Assessment of
Land Degradation and Improvement (GLADA), an assessment based on remote
sensing time series analyses of the normalised difference vegetation index (NDVI)
for the period 1981

–

2003. In a

ﬁ

nal comparison, the measured soil erosion rates

from 2500 locations across the word were superimposed to the estimates of the
global model (Supplementary Fig.

15

)

Data availability

. The authors declare that all other data supporting the

ﬁ

ndings of

this study are available within the article and its Supplementary Information

ﬁ

les,

or are available from the corresponding author upon reasonable request.

Received: 7 March 2017 Accepted: 8 November 2017

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Acknowledgements

Funding for P.B. was provided by the European Commission Joint Research Centre in
the framework of the SOIL-NACA project and by the University of Basel. We thank
Ronald Vargas (FAO) and the Intergovernmental Technical Panel on Soils (ITPS) for
their support and access to data. We also thank Dr. Tomislav Hengl and the World Soil
Information Foundation (ISRIC) for providing the global soil information and the
National Aeronautics and Space Administration (NASA) and United States Geological
Survey (USGS) who have made available the MODIS data.

Author contributions

P.B. and P.P. developed the research concepts, with L.M., D.A.R., C.A. and C.B. coor-
dinating it. P.B. conceptualized the modelling scheme and carried out its computational
steps and analyses. C.B. performed the analysis of model uncertainty and the sensitivity
analysis. P.P., P.B., K.M., K.V.O. and C.B. worked on the development of the global
rainfall erosivity map. K.M. performed the scripting for the global soil erodibility
mapping. P.B. conducted the model cross-comparison. P.B., D.A.R., L.R.F., E.L., C.B., C.
A., K.M., S.M., B.S., V.F., V.B., K.V.O., L.M. and P.P. discussed the results and con-
tributed to writing the discussions. P.B., D.A.R. and L.R.F. wrote the paper.

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NATURE COMMUNICATIONS | DOI: 10.1038/s41467-017-02142-7

ARTICLE

NATURE COMMUNICATIONS

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8: 2013

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DOI: 10.1038/s41467-017-02142-7

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