Alewell et al. (2020). Global phosphorus shortage will be aggravated by soil erosion

✉

, Bruno Ringeval

, Cristiano Ballabio

3

, David A. Robinson

Christine.alewell@unibas.ch

, Panos Panagos

3

&

Pasquale Borrelli

1,5

Soil phosphorus (P) loss from agricultural systems will limit food and feed production in the

future. Here, we combine spatially distributed global soil erosion estimates (only considering

sheet and rill erosion by water) with spatially distributed global P content for cropland soils to

assess global soil P loss. The world

’

s soils are currently being depleted in P in spite of high

chemical fertilizer input. Africa (not being able to afford the high costs of chemical fertilizer)

as well as South America (due to non-ef

ﬁ

cient organic P management) and Eastern Europe

(for a combination of the two previous reasons) have the highest P depletion rates. In a future

world, with an assumed absolute shortage of mineral P fertilizer, agricultural soils worldwide

will be depleted by between 4

–

19 kg ha

−

1

yr

−

1

, with average losses of P due to erosion by

water contributing over 50% of total P losses.

https://doi.org/10.1038/s41467-020-18326-7

OPEN

1

Environmental Geosciences, Department of Environmental Science, University of Basel, Basel, Switzerland.

2

ISPA, Bordeaux Sciences Agro, INRAE, 33140

Villenave d

’

Ornon, France.

3

European Commission, Joint Research Centre, Ispra, Italy.

4

UK Centre for Ecology & Hydrology, Environment Centre Wales,

Bangor, United Kingdom.

5

Department of Biological Environment, Kangwon National University, Chuncheon-si, Gangwon-do, Republic of Korea.

✉

email:

NATURE COMMUNICATIONS

| (2020) 11:4546 | https://doi.org/10.1038/s41467-020-18326-7 | www.nature.com/naturecommunications

123456789

0():,;

P

hosphorus (P) being a key element in DNA, RNA as well as
ATP and phospholipids is essential for the growth, func-
tioning and reproduction of all life on earth. In natural

ecosystems the P that is lost from the soil-plant cycling system
has to be replaced by the slow process of rock weathering

or

added via fertilizer in human managed systems (there is no
equivalent to the biological N

2

ﬁ

xation which is only kinetically

limited but potentially not resource limited). However, if fertili-
zation with animal waste or human excreta is not available or not
organized, P fertilizers stem from non-renewable geological
P deposits, which are an increasingly limited resource (this is
again in contrast to N, as N fertilizer can be produced as an
endless resource via the Haber Bosch process as along as energy
and natural gas is available). The one-way

ﬂ

ow of P from mineral

reserves to farms (e.g., soils), to freshwaters and

ﬁ

nally into

oceans, are already considered to be beyond the safe operating
space for sustainable human development

. The potential threats

of global P limitation due to peak phosphorus have been dis-
cussed intensively in the recent past

7

. The imminent threat of

such a P limitation has been restrained somewhat as obviously
some P deposits had been overlooked or misclassi

ﬁ

ed in the past

which will theoretically last for the next 600 years of global
P supply

8

. However, the socio economic as well as political

consequences are still dramatic with the newly discovered
P reserves being restricted to a small region of the Western Sahara
and Morocco. Recent literature is controversial as to whether or
not P supply from rock reserves in the next decades will be a real
physical scarcity

5

or will be limited by economic and technical

. Ulrich and Frossard

10

argue that the main pro-

blem is not the geological P availability, but rather socio-
economic (e.g., fertilizer access) or environmental (e.g., water
pollution) vulnerabilities, resulting from current and future
P production and consumption patterns. In parallel to the 2007-
2008 global food crisis, phosphate rock and fertilizer demand
exceeded supply, and prices increased by 400% within a 14-
month period

11

demonstrating the sensitivity of this market. The

following consequences have been instances of farmers riots and
death due to severe national shortage of fertilizers in countries
such as India, which are totally dependent on phosphate
imports

12

. The growing demand for P fertilizer globally has

caused an increase in the cost of rock phosphate from about $80
per U.S. ton in 1961 to $700 per ton in 2015 (with large year-to-
year

ﬂ

uctuations)

13

14

.

The most crucial potential P loss (P

Loss

) from ecosystems is loss

from the soil due to soil erosion by water

, and this often happens

in sudden events

15

. As the major part of soil P is tightly sorbed to

mineral particles, bound within organic matter or precipitated as
poorly soluble salts, it is mainly exported from soils to water
bodies via erosion by water. Only a very small fraction of P in
soils and bedrocks is available to plants or might leach as dis-
solved soluble phosphate

18

with the exception of excessively

fertilized soils with increasing P availability

19

20

.

Recently, global modeling approaches demonstrated that

agricultural management aiming at closing nutrient cycles such as
increasing P use ef

ﬁ

ciency and/or recycling of animal manure and

human excreta, might have a major impact on P balances

21

. In

considering principal agronomic P inputs and outputs but
neglecting soil erosion MacDonald et al.

22

calculated that 29% of

the global cropland area had P de

ﬁ

cits and 71% had surpluses

(data of the year 2000). Opportunities for recovering phosphorus
and reducing demand have been discussed extensively

However, most global, continental or regional evaluations of the
P cycle have not calculated P loss due to soil erosion, even though
erosion is acknowledged as clearly contributing to potential
P loss

, or soil erosion is very simplistically addressed

without spatially discrete data analysis

(e.g., by either

extrapolating erosion rates of Pimentel

31

or Smil

to a global

scale or using FAO average statistics (FAOSTAT

http://faostat3.

fao.org

)). Nevertheless, there is general agreement, that P losses

due to soil erosion are a crucial parameter in the global P cycle,
but that at the same time estimation of those rates are connected
to a high uncertainty

, owing to the uncertainty in spa-

tially distributed soil erosion rates and/or the limited availability
of global soil phosphorus data

15

. The neglect, or the only rough

estimates of soil erosion in evaluating P cycling in the past were
simply due to a lack of available spatially discrete data. Here, we
overcome these limitations by specifying the P loss due to soil
erosion by water in matching the global spatially discrete soil
erosion maps of Borrelli et al.

with spatially distributed global P

content of soils for croplands from Ringeval et al.

to develop

and assess global soil P loss maps and balances from arable land
(Fig.

). Our main

ﬁ

nding is that the loss of P in agricultural

systems, especially in areas with low or no future P fertilizer
input, depends on soil erosion and poses an imminent threat to
system functioning.

Results and discussion

Global P losses from soils and soil P balances

. All continents

result in negative P balances (e.g., net P losses from agricultural
systems, Table

; Fig.

) except Asia, Oceania and Australia, with

Asia having a slightly positive but near zero P balance. This is in
spite of high to very high chemical fertilizer inputs (with a range of
1.7 to 13 kg ha

−

1

yr

−

1

between the different continents, with

national values reaching up to 14 and 19 kg ha

−

1

yr

−

1

for the

European Union 15 old Member States (EU15, member states
joining before 2004 mainly in western and northern Europe)
and China, respectively). Most negative P balances are
indicated for Africa due to very low chemical fertilizer input of 1.7
kg ha

−

1

yr

−

1

paired with high losses due to soil erosion of 9.6 kg

ha

−

1

yr

−

1

. South America as well as Central and Eastern Europe

(NEU11; which are the new member states joining the EU after
2004 with the exception of Cyprus and Malta) also exhibit high P
losses but for different reasons. South America has a very high
chemical fertilizer input but also high losses due to soil erosion
paired with high P exports due to organic P management (cal-
culated as the sum of manure and residue input minus plant
uptake). In contrast, the eastern European Union New Member
States (NEU11) have rather low erosional losses but also very low
chemical fertilizer input. With the hypothetical assumption of no
replenishment due to chemical fertilizer (e.g., due to economic or
technical constraints), calculation of soil P balances results in
negative balances globally, as well as for all the continents and
regions considered (depletion between 4 and 20 kg P ha

−

1

yr

−

1

;

Figs.

and

; Table

). The latter demonstrates the vulnerability of

today

’

s global land management system and its strong dependency

on chemical P fertilizers from non-renewable mineable P deposits.

Our area related calculations result in an average P loss

for arable soils, due to erosion by water, of approximately
5

:

9

þ

1

:

17

%

0

:

79

%

kg ha

−

1

yr

−

1

globally (Fig.

, Table

). This is around

60% of the rates given by Smil

who estimated 10 kg P ha

−

1

yr

−

1

from arable

ﬁ

elds due to soil erosion by water. Our total P losses

due to soil erosion by water from arable soils globally result in
6.3 Tg yr

−

1

with 1.5 Tg yr

−

1

for organic and 4.8 Tg yr

−

1

for

inorganic P. With these values the results are at the lower end of
the range discussed in the literature (between 1

–

19 Tg yr

−

1

,

Table

).

Liu

34

in estimating net input to, and output from, cropland

systems indicated a net loss of P from the world

’

s croplands of

about 12.8 Tg yr

−

1

(calculating input from atmosphere, weath-

ering and chemical fertilizer versus output from organic
P management, soil erosion and runoff), which would be,

ARTICLE

NATURE COMMUNICATIONS | https://doi.org/10.1038/s41467-020-18326-7

2

NATURE COMMUNICATIONS

| (2020) 11:4546 | https://doi.org/10.1038/s41467-020-18326-7 | www.nature.com/naturecommunications

according to their calculations, the same order of magnitude as
synthetic fertilizer input (13.8 Tg yr

−

1

for statistical year 2003/

2004). Our calculations result in an approximate net soil loss due
to erosion of 6.3 Tg yr

−

1

with a global average chemical fertilizer

input of 9.2 Tg yr

−

1

. The

ﬂ

uxes clearly show the critical

dependence on chemical fertilizer globally, with a hypothetical
net-average-area related depletion of 10.7 kg ha

−

1

yr

−

1

globally

without the compensation due to chemical fertilizer, from which
a loss of 5.2 kg ha

−

1

yr

−

1

stems from organic P management

(sum of manure and residue input minus plant uptake) and 5.9
kg ha

−

1

yr

−

1

from soil erosion (Table

). Continental and

national erosional P losses are between 40 and 85% of total P
losses from agricultural systems with the exception of Europe and
Australia (16 and 19%, respectively). Globally, as well as for
Africa, South America and Asia, P soil losses due to erosion are
higher than losses due to organic P management.

A recent quanti

ﬁ

cation of atmospheric P dust input, based on

dust measurements in the Sierra Nevada, concluded that measured
dust

ﬂ

uxes are greater than, or equal to, modern erosional outputs

and a large fractional contribution relative to bedrock

35

. However,

even though their measured and modeled maximum atmospheric
P

ﬂ

uxes were in the same order of magnitude as the atmospheric

ﬂ

ux data of Wang et al.

used in this study (0.1 kg ha

−

1

yr

−

1

for

North America), erosion rates from sediment trap measurements
in their investigated forest ecosystems were considerably lower
(maximum of 0.06 kg ha

−

1

yr

−

35

) than might be expected in

arable lands worldwide. As such, we can clearly not agree with the
above conclusion.

Mitigation of the soils P status in the long term by decreasing

the de

ﬁ

cits of the current organic P management seems dif

ﬁ

cult

and rather unlikely in many regions of the world (see discussion
of continental balances below). As such, and considering the
expected shortage of P supply from industrial fertilizers in the
future, the evaluation of P

ﬂ

uxes clearly shows that soil erosion

has to be limited to the feasible absolute minimum in the future.

Only a small fraction of the total soil P is plant available,

because a large fraction is either bound in, adsorbed to, or made
unavailable by occlusion in minerals (apatite and occluded P)

.

Our modeling approach follows the approach of Yang et al.

that

builds on existing knowledge of soil P processes and data bases to
provide spatially explicit estimates of different forms of naturally
occurring soil P on the global scale. Yang et al.

uses data

acquired with the Hedley fractionation method

38

which splits soil

P into different fractions that are extracted sequentially with
successively stronger reagents and which are merged into various
inorganic and organic pools. There is great uncertainty to
associate these fractions with functional plant uptake

39

40

but

some recent work aims at quantifying residence times of the
different fractions

41

. We present the total P loss as well as the

plant-available pool (Figs.

and

, Table

) as the most labile P

which can participate to plant nutrition in a time scale up to
months

(i.e., the so-called labile inorganic P, inorganic P bound

to secondary minerals, labile and stable organic P

). We

contrast these more labile, short lived fractions with fractions
considered very stable which would not be plant available at short
time scales (i.e., inorganic P associated with minerals such as
apatite or occluded P

, corresponding to 52% of total soil P in

our estimates at the global scale).

Veri

ﬁ

cation of soil P loss with a comparison to riverine P

exports

. Veri

ﬁ

cation of our proposed P soil losses might be done

indirectly via a comparison to riverine P loads. However, to verify
our on-site P soil loss approach with off-site P river export data
requires two prerequisites: (i) an estimation of the agricultural
erosion and runoff contribution to total P loads in the rivers (as
the latter will be the total sum of agricultural, urban and indus-
trial runoff) and (ii) an assumption on sediment delivery rates
(e.g., the percent of sediments reaching the rivers from the total
eroded sediments) to estimate P loads to rivers from on-site P soil
loss. Regarding the

ﬁ

rst prerequisite, we searched for published

Simulated croplands

Simulated

LUHa

27%

73%

No data < 0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

N

W

E

S

Fig. 1 Modeled area and global distribution of cropland.

The chromatic scale represents the cropland ratio fraction of cropland from total land per pixel of

the modeled land. The gray color indicates the cropland areas that were excluded from the modeling due to data unavailability. The insert panel illustrates
the simulated versus total cropland area according to the harmonization of land-use LUHa.v1 database

73

NATURE COMMUNICATIONS | https://doi.org/10.1038/s41467-020-18326-7

ARTICLE

NATURE COMMUNICATIONS

| (2020) 11:4546 | https://doi.org/10.1038/s41467-020-18326-7 | www.nature.com/naturecommunications

river P export data with a separation of the agricultural erosion
and runoff contribution from total P loads in the rivers.
Regarding the second prerequisite, there is a lack of models
describing integrated sediment-delivery to rivers on a continental
or global scale, but it has been discussed that sediment delivery
ratios generally decrease as drainage area increases, ranging from
roughly 30

–

100% in small catchments (

≤

0.1 km

2

) to 2

–

20% at

large spatial scales (e.g.,

≥

1000 km

2

)

42

. For our comparison, we

used a range of average sediment delivery rates between 11

–

30%

as used in recent large scale studies from continental to global
scale

43

44

. In doing so, we would like to point out, that even if

70

–

89% of the P lost from agricultural soils might be re-deposited

within catchments, potential threats of P loss from soils are not
reduced. Erosion (and thus P loss) occurs predominantly on
agricultural soils while re-deposition will mostly occur in
depositional hollows, wetlands, riparian zones or buffer strips.
Thus, P is lost as a nutrient on food and feed production sites but
re-deposited as a potential ecological threat to biodiversity and
ecosystem health due to its eutrophication effect in less inten-
sively or unmanaged ecosystems. Last but not least we would like
to point out that RUSLE only considers soil displacement due to
rill and inter-rill erosion neither considering tillage and gully
erosion nor land sliding.

A comparison of calculated potential P export to rivers from

our on-site soil P losses is well within the range of published
riverine P exports (Table

). Beusen et al.

used total suspended

sediment measurements from the GEMS-GLORI database to
extrapolate spatially distributed sediment rates for the world

’

s

largest rivers with land use, topography, lithology and precipita-
tion as factors in a multiple linear regression approach accounting
for soil erosion as well as sediment trapping. The associated
nutrient exports for all continents, as well as global assessment
were made by calibrating nutrient export to sediment rates with
the model Global News

using established correlations between

sediment and nutrient concentrations. In comparing their P
export with suspended sediments in rivers

to our assessments,

we underestimated P export globally as well as for all continents,
with the exception of Africa (Table

), which is, however, (i)

strongly related to assumed sediment delivery rates and (ii) our
RUSLE application only considering rill and inter-rill erosional
processes with an unknown contribution of gullies, landslides and
tillage erosion.

An

analysis

of

17

large

scale

European

catchments

(250

–

11,000 km

2

) quantifying the loss of P to surface waters

from an off-site perspective (P

ﬂ

ux measurement in waters)

resulted in 0.05

–

1.5 kg ha

−

1

yr

−

1

of P loss due to soil erosion and

runoff from agricultural lands into streams and rivers (excluding
a Greek catchment with a very high P export of 6 kg ha

−

1

yr

−

1

)

46

.

Recalculating our on-site soil P losses with sediment delivery
ratios between 11

–

30% results in rates for geographic Europe

between 0.1

–

0.4 kg ha

−

1

yr

−

1

which are at the lower end of the

range of Kronvang, et al.

46

(Table

). Potential P losses as riverine

export due to agricultural runoff and erosion of 143 watersheds
across the U.S.

47

are in the same range as the calculated P loss

assessment of our study, while P loads to Lake Erie assessed in a
regional study

48

seem slightly higher (Table

). However, no

partitioning between agricultural, urban or industrial

ﬂ

uxes was

possible from the latter study. An assessment of the Yangtze River
Basin (with 1.8 Million km

2

near 20% of the whole Chinese

territory) gives modeled soil P losses (on-site perspective)
between 0

–

196 kg ha

−

1

yr

−

1

demonstrating the huge spatial

heterogeneity of on-site soil erosion rates

49

. The model output

was calibrated with total measured nutrient loads in rivers, while
the partitioning differed between dissolved point and non-point
as well as adsorbed non-point pollution which can be mostly
attributed to soil erosion of agricultural

ﬁ

elds. The average

Table

1

Main

phosphorus

(P)

statistics

in

kg

P

h

a

−

1

yr

−

1

for

all

continents

and

selected

countries.

Global

Africa

Nor

th

Americ

a

Sou

th

America

Geog

raphic

Eur

ope

EU15

a

NEU11

b

Asia

China

Oceani

a

A

ustrali

a

Atmospheric

input

0.3

0.5

0.1

0.

1

0

.1

0.1

0.2

0.5

0.1

0.

2

Chemical

fer

tilizer

8.8

1.7

10.0

11.

4

5.9

13.7

5.2

11.9

19.0

12.7

10

.4

Organ

ic

P

mana

geme

nt

−

5.2

−

2.2

−

7.1

−

8.

7

−

6.3

−

12.2

−

8.5

−

3.9

−

7.6

−

2.5

−

4.

1

Erosion

from:

Non-plant

avail

.

P

c

−

3.2

−

5.4

−

2.4

−

5.1

−

0.6

−

0.9

−

0.7

−

4.1

−

6.

4

−

0.9

−

0.

5

Plant

avail

.

inorg

anic

P

c

−

1.3

−

1.9

−

0.8

−

1.8

−

0.2

−

0.5

−

0.2

−

1.8

−

2.8

−

0.6

−

0.

2

Plant

avail

.

orga

nic

P

c

−

1.4

−

2.3

−

1.3

−

2.0

−

0.3

−

0.7

−

0.3

−

1.8

−

3.2

−

0.3

−

0.

2

Total

soil

P

−

5.9

−

9.6

−

4.6

−

8.

9

−

1.2

−

2.1

−

1.2

−

7.8

−

12.

3

−

1.8

−

0.

9

Bala

nce

d

−

1.9

−

9.7

−

1.6

−

6.

1

−

1.3

−

0.4

−

4.3

0.6

−

0.

4

8.5

5.6

Bala

nce

–

Ch

em.

Fert

e

−

10

.7

−

11.4

−

11.6

−

17.

4

−

7.3

−

14.1

−

9.5

−

11.3

−

19.5

−

4.2

−

4.

8

Erosion

loss

(%)

54

:

6

10

:

4

7

:

0

84

:

5

15

:

6

10

:

6

39

:

5

7

:

5

:

1

50

:

8

9

:

6

:

5

15

:

8

3

:

0

2

:

0

15

:

0

2

:

8

1

:

9

12

:

7

2

:

4

1

:

6

69

:

3

12

:

8

:

7

63

:

3

11

:

9

8

:

0

43

:

7

8

:

1

5

:

5

19

:

1

3

:

6

2

:

4

Organic

P

m

a

nagement

=

su

m

o

f

manu

re

and

residue

input

minus

plant

uptake.

Total

soil

P

=

tot

al

P

depletion

of

so

ils

due

to

soil

erosion

by

water

globally

.

Erosion

loss

(%)

=

Ratio

of

erosion

losses

to

soil

P

bala

nce

ex

cluding

chemic

al

fertilizer

(balanc

e

—

chemical

fertilizer)

with

non-

symmetric

to

the

mean

uncertainty

estima

tes

in

%.

Positive

numbers

give

net

retention

in

soils,

negative

depletion

of

soils.

Geographic

Eur

ope

includes

European

parts

of

the

forme

r

Soviet

Union.

a

EU15

(Austria

,

Belgium,

Denmark,

Finlan

d,

France,

Germany,

Greec

e,

Ireland,

Ita

ly,

Luxembourg,

Netherlands,

Portugal,

Spain,

Sweden,

United

Kin

gdom

)

b

NEU11:

Central

and

eastern

European

countries

joining

the

EU

after

2004:

Bulgaria,

Croatia,

Czech

Republi

c,

Estonia,

Hung

ary,

Latvia

,

Lithuania,

P

oland,

Romania,

Slovak

Republi

c,

and

Sloveni

a

c

see

the

de

ﬁ

nitio

n

o

f

plant

available

and

non-plant

available

soil

pools

in

the

methods

section

following

Hed

ley

et

al.

38

and

Helf

enstein

et

al.

41

d

Balance

=

atmospheric

input

+

chemic

al

fertilizer

+

organic

P

man

agement

+

total

soil

P

losses

due

to

soil

erosion

e

Balance

—

Chem.

Fert.

=

hypothetical

Balance

without

chemic

al

P

fertilizer

in

put

ARTICLE

NATURE COMMUNICATIONS | https://doi.org/10.1038/s41467-020-18326-7

4

NATURE COMMUNICATIONS

| (2020) 11:4546 | https://doi.org/10.1038/s41467-020-18326-7 | www.nature.com/naturecommunications

P loads due to soil erosion from agricultural

ﬁ

elds in the Yangtze

River Basin compares well with the range assessed in our study
for China (2.7 versus a range of 1.4 to 3.7 kg ha

−

1

yr

−

1

,

respectively). The same holds true for assessment comparisons
of Africa

50

and South America

51

(Table

), even though it should

be considered that especially for these latter studies scale differs
considerably from our approach.

Regional P losses and balances

. Parallel to the distribution

pattern and dynamics of global soil erosion by water

, P losses

from soils due to water erosion are most dramatic in countries
and regions with intensive agriculture and/or extreme climates
(e.g., droughts followed by signi

ﬁ

cant rain events or high fre-

quencies of heavy rain storms) due to high erosivity effects

52

. As

such, our calculations result in extremely high P losses due to
erosion (>20 kg ha

−

1

yr

−

1

) in regions such as eastern China,

many regions in Indonesia, parts of east and south-eastern Africa
(Ethiopia, Eritrea, Mozambique), Central America and parts of
South America (South-Eastern Brazil; Southern Chile, Peru
(Fig.

)). A very high P loss (10 to 20 kg ha

−

1

yr

−

1

) is estimated

for parts of Southern Africa (South Africa, Madagascar, Tanzania)
and South America (Bolivia) and a high loss (5

–

10 kg ha

−

1

yr

−

1

)

for most of India, as well as regions in Southern Africa (Angola,
Zambia) and South America (Uruguay) (Fig.

). Even though the

underlying erosion model algorithm does not calculate the net
catchment output but rather the on-site displacement of soil
sediments which might then be re-located to other parts of the

ﬁ

elds or even buried at depositional places, the considered on-site

ﬁ

eld management will clearly be confronted with substantial P

losses due to soil erosion by water. Only considering agronomic P
inputs and outputs without including P losses due to erosion by
MacDonald et al.

22

resulted in a very different global P pattern:

most widespread large de

ﬁ

cits were in South America (North-

Eastern countries, e.g., Argentina and Paraguay), the northern

0-1.1

1.1–3.2

3.2-5.4

5.4-9.7

9.7-20.5

>20.5

No data

N

W

E

S

Fig. 2 Global average phosphorus (P) losses due to soil erosion in kg ha

−

1

yr

−

1

.

The chromatic scale represents the P losses estimates, while the gray

color indicates the cropland areas that were excluded from the modeling due to data unavailability. Note that classes are not regularly scale ranked but are
divided into six classes using the quantile classi

ﬁ

cation method. Only plant available fractions were considered. For the more residual P fractions please

refer to Table

or Figs.

and

).

Atmospheric input

+0.3

Chemical fertilizer

+8.8

Organic p manag.*

–5.2

501

423

1280

Organic P:

–1.4

Non-plant P:

-3.2

Inorganic P:

–1.3

Total soil

P

lost

:

–5.9

+

1.17

–

0.79

With chemical:

–1.9

No chemical:

–10.7

Fluxes: kgP ha

–1

yr

-1

, Soil pools: kg P ha

-1

for top 30 cm soil layer

P balance

Inputs

Soil stocks

Soil P

lost

Fig. 3 Global P soil pools and depletion due to erosion.

Arrows indicate

ﬂ

uxes (positive: net input to soils, negative: depletion of soils). *Organic P

management

=

sum of manure and residue input minus plant uptake. Non-

plant P

=

non-plant available P. Inorganic and organic P give plant available

fractions. Total soil P: sum of P fractions lost from soil via erosion with
relative errors. No/with chemical

=

P balance with and without chemical

fertilizer.

NATURE COMMUNICATIONS | https://doi.org/10.1038/s41467-020-18326-7

ARTICLE

NATURE COMMUNICATIONS

| (2020) 11:4546 | https://doi.org/10.1038/s41467-020-18326-7 | www.nature.com/naturecommunications

5

United States and Eastern Europe while the largest surpluses
covered most of East Asia, Western and Southern Europe, the
coastal United States, South-Eastern Brazil and Uruguay.

With average soil depletion due to erosion of 9.6 kg ha

−

1

yr

−

1

in Africa, the overall P balance is already negative by 9.7 kg ha

−

1

yr

−

1

today (Table

, Fig.

). As the average P depletion in Africa

due to negative

ﬂ

uxes in organic P management equals the input

ﬂ

uxes from the atmosphere plus chemical fertilizer, African farmers

could decrease P losses to near zero with effective soil erosion
mitigation. Even though the system

’

s P depletion due to organic

P management is relatively low in Africa (

−

2.2 kg ha

−

1

yr

−

1

)

compared to a global average (

−

5.2 kg ha

−

1

yr

−

1

), the high

overall P losses are unlikely to be covered neither from a
mitigated and more sustainable organic P management nor from
increased chemical fertilizer input. P

ﬂ

uxes due to organic P

management are calculated here as the sum of manure and
residue input minus plant uptake (which results in biomass
export in arable systems with the exception of residues left on the

ﬁ

eld). The overall sum of plant uptake is likely to increase with

increased need for food and feed parallel to a predicted
population and livestock growth in Africa in the future. Many
soils in sub-Saharan Africa have already been characterized as
de

ﬁ

cient for levels of plant-available P for the last decades

53

Manure and residue input is simultaneously in demand in Africa
today (shortage of biomass in general, low animal production and
even if there is manure available, there are no means to transport

it to where it is needed), which results in the recommendations of
an integrated farm management with combinations of organic
and inorganic fertilizers

54

56

. With the inorganic P fertilizers

becoming increasingly scarce, the depletion due to organic P
management can be expected to increase in Africa in the future.
Simultaneously, today

’

s prices for chemical fertilizer can already

be 2

–

6 times more expensive for a farmer in Africa than in

Europe due to higher transport and storage costs

, even though

Africa itself has the highest geological P deposits in the world
(according to today

’

s estimates 80% of the global geological P

deposits are located in Morocco and the Western Sarah

8

). As

such, and if the political situation does not change dramatically
(e.g., that the P supplies are marketed within Africa instead of
being exported to US, Europe and China), the only realistic
means of reducing P depletion of African soils today, and in the
future, is to drastically reduce soil erosion.

We recognize that the values calculated in Table

and Fig.

are gross estimates over large scales and that spatial context and
scale, especially on the African continent is important. P
de

ﬁ

ciency is a country, district, farm and soil speci

ﬁ

c issue in

Africa, for example parts of east Africa and the Sahel have
substantial de

ﬁ

ciencies

. In sub Saharan Africa ~40% of soils are

considered to have low nutrient reserves (<10% weatherable
minerals) and soil degradation is enhancing the de

ﬁ

ciencies

58

.

Erosion control is important, but is only part of the solution,
which

needs to be multifaceted. Omuto and Vargas

59

assessed total soil erosion based on

ﬁ

eld measurements in Malawi

to have increased ~10% between 2010 (26 t ha

−

1

yr

−

1

) and 2017

(30 t ha

−

1

yr

−

1

, note that erosion rates from rill and interill

erosion only in our modeled assessments for Malawi based on
Borrelli et al.

are 19 t ha

−

1

yr

−

1

). Surveys of farmers indicated

that 45% were not investing in soil erosion control and many of
these were in areas where there was a high level of need.
Moreover, farmers recognized that the lack of implementation of
sustainable land management practices was a main reason for
high erosion rates, over and above the fact that the soils are often
vulnerable and fragile. Hence, erosion control offers part of the
solution.

Attempts at increasing nutrient status in Malawi since 2010

have, through blanket mineral fertilizer recommendations, not
only yielded signi

ﬁ

cantly higher production transforming the

country into a food-exporting nation, but also led to soil
acidi

ﬁ

cation in many districts

59

. As such, in addition to soil

erosion control integrated soil fertility management (ISFM) is
required that combines using mineral fertiliser and organics as
well as growing legumes (leguminous trees and/or cover crops)

.

Even though leguminous trees offer the potential to access
nutrients deep in the subsoil and deposit them to the surface via
litter fall, the time delay required to implement such a system
acted as a barrier to adoption, with farmers having to forgo one
crop

. In addition, the low P status of soils in many African

regions

means that recycling of organic materials is insuf

ﬁ

cient

to boost yields, hence the need for mineral fertiliser. Even though
leguminous plants will primarily improve N and not P status of
soils, a recent review on leguminous crops found that grain
legumes can access less accessible forms of P under P-de

ﬁ

cient

conditions (through the release of root exudates; access to more
of the labile P through a

ﬁ

ner root architecture, and enhanced

associations with mycorrhiza)

60

. Hence integrated, region and

soil speci

ﬁ

c management options are required and these

complexities have to be held in mind when interpreting our
continental scale

ﬁ

gures, which might help provide global context

for action. While the complexities of P management are well
documented and discussed

60

we would like to draw the

attention to mitigating soil erosion as one important part towards
decreasing malnutrition in Africa.

512

336

1426

628

417

1160

486

427

1328

Africa

AD

+0.5

CF

+1.7

OM

-2.2

Europe

AD

+0.1

CF

+5.9

OM

-6.3

North America

AD

+0.1

CF

+10.0

OM

-7.1

Organic P:

-2.3

Non-plant P:

-5.4

Inorganic P:

-1.9

Organic P:

-0.3

Non-plant P:

-0.6

Inorganic P:

-0.2

Organic P:

-1.3

Non-plant P:

-2.4

Inorganic P:

-0.8

-9.6

+1.

84

-

1.25

With chemical fertilizer

-9.7

-1.3

-1.6

P balance

Soil P

lost

-1.2

+

0.23

-

0.16

-4.6

+

0.88

-

0.6

No chemical fertilizer

-11.4

-7.3

-11.6

Inputs

Soil stocks

Fluxes: kgP ha

-1

yr

-1

, Soil pools: kg P ha

-1

for top 30 cm soil layer

Fig. 4 Soil P pools and depletion due to erosion in Africa, Europe and
North America.

AD

=

Atmospheric Deposition. CF

=

Chemical Fertilizer.

OM

=

Organic P management

=

sum of manure and residue input minus

plant uptake. Arrows indicate

ﬂ

uxes (positive: net input to soils, negative:

depletion of soils). Non-plant P

=

non-plant available P. Inorganic and

organic P give plant available fractions. Soil P

lost

: sum of P fractions lost

from soil via erosion with relative errors. No/with chemical

=

P balance

with and without chemical fertilizer.

ARTICLE

NATURE COMMUNICATIONS | https://doi.org/10.1038/s41467-020-18326-7

6

NATURE COMMUNICATIONS

| (2020) 11:4546 | https://doi.org/10.1038/s41467-020-18326-7 | www.nature.com/naturecommunications

With high soil depletion rates due to erosion of 8.9 kg ha

−

1

yr

−

1

in South America and losses due to organic P management of 8.7
kg ha

−

1

yr

−

1

the P balance of land under arable use results in a

negative balance of

−

6.1 kg ha

−

1

yr

−

1

in spite of the current high

chemical fertilizer input of 11.4 kg ha

−

1

yr

−

1

(Table

). As South

America has no notable geological P deposits, continuing high or
even increasing fertilizer application to balance high soil P losses
seems unrealistic in the future (and would also be unacceptable
from an ecological perspective as long as soil erosion rates and
thus P output to fresh and ocean waters is not substantially
reduced). However, with a much higher continental biomass
production capacity compared to Africa, many regions in South
America might be able to decrease P losses due to improved
organic P management (e.g., by generally applying conservation
agriculture, organic and/or other sustainable farming practices

61

)

and/or increasing use of animal waste or human excreta

, or

applying management systems with increased use of residue input.
Nevertheless, in the long-term a reduction of P losses due to
mitigating soil erosion (e.g., conservation agriculture, mulching,
increased vegetation cover, intercropping, topography adapted
land management) will be the most ef

ﬁ

cient way and will

simultaneously increase soil health, the general nutrient status of
soils and water retention capacity as well as decreasing the
ecologically negative impact on fresh and ocean waters due to high
P input and accompanied eutrophication and hypoxia.

Average P loss due to soil erosion from European croplands is,

together with Australia, the smallest of all continents (1.2 and 0.9
kg ha

−

1

yr

−

1

for Europe and Australia, respectively; Table

).

Nevertheless, the overall P losses from agricultural systems in
Europe equal losses due to soil erosion (Table

), and especially

Central and Eastern European countries (NEU11) clearly have a
negative P balance.

Csatho and Radimszky

62

in discussing land use and manage-

ment within the EU argue that the negative P balance and
worsening P status in Central- and Eastern European countries is
in sharp contrast to past practices in the former EU15 countries,
where strong positive P balances and oversupply of P led to

environmental and ecological threats. While there is evidence that
the level of oversupply in the previous EU15 countries had been
falling in the early 1990s due to declining trends in mineral
fertilizer use

63

, worsening levels of P undersupply (partly due to

the post 1990s rapid decline in fertilizer application) may result in
increasingly low yields and in economic and agronomic problems
in central and eastern European countries

62

. Even though

the central and eastern European countries (NEU11) have
lower P loss due to soil erosion compared to the former EU15
(1.2 kg ha

−

1

yr

−

1

versus 2.1 kg ha

−

1

yr

−

1

for the NEU11 com-

pared to EU15, respectively), the overall P balance is nearly
balanced in the former EU15 (0.4 kg ha

−

1

yr

−

1

) due to consider-

ably higher chemical fertilizer input in the western countries
compared to the eastern NEU11 (negative balance thus overall
soil depletion of

−

4.3 kg ha

−

1

yr

−

1

). As such, there might be a

concern of P de

ﬁ

ciency and nutrient depletion in the NEU11 in

the future, in spite of comparably lower erosional P losses from
soils to waters. With no major geological P deposits in Europe

8

,

eastern and western European countries will both be confronted
with a harsh political and economic struggle for P fertilizers in the
future, with both regions being at the higher end of negative P
balances without the addition of chemical fertilizer (

−

14 and

−

9.5 kg ha

−

1

yr

−

1

for EU15 and NEU11, respectively; Table

).

For Asia, China certainly stands out in having extensive

programs to save and re-cycle P (e.g., separating human excreta
and urine, recovery from sewage sludge, sludge ash and the
fertilizer industry

64

). Simultaneously Chinese soils experience the

highest chemical fertilizer consumption resulting in a nearly
balanced P budget (

−

0.4 kg ha

−

1

yr

−

1

). With having the highest

losses of P due to soil erosion by water (12.3 kg ha

−

1

yr

−

1

with

only considering rill and interrill erosion, Table

), a signi

ﬁ

cant

reduction in soil erosion rates would contribute tremendously to
the national struggle to save P.

Even though P demand is stagnating in some regions (mostly

Europe, North America and Australia), today overall demand is
nevertheless increasing globally due to population growth,
intensi

ﬁ

cation of agriculture and a shift from vegetarian to meat

Table 2 Global

ﬂ

uxes from soils/arable systems to waters as discussed in recent literature. For comparability, all values were

normalized to 1 billion ha of arable land (with original values given in brackets).

Area considered

Tg yr

−

1

normalized to 1

billion ha

Method to calculate soil erosion

Meybeck et al.

78

All terrestrial land

14 (20)

Based on global particulate P river export to coastal seas calculated
from global ratios of particulate carbon to phosphorus

Smil

World

’

s croplands in the mid-

1990s

9

–

10.5

(13

–

15)

Global erosion rates from Smil

79

, no further details

Mackenzie et al.

80

All terrestrial land

15.4 (22)

Terrestrial Ocean Atmosphere Ecosystem Model

Liu

34

The world

’

s croplands (1.5

billion ha)

12.8 (19.3)

Extrapolating erosion rates from Pimentel

31

to global levels

Bouwman et al.

Cropland area (1.54
billion ha)

0.7

–

1.3 (1

–

2)

Global NEWS Model based on FAO statistics

Cordell et al.

Arable soils

5.6 (8)

No information on methods or exact area given but P

ﬂ

ux due to

erosion illustrated in

ﬁ

gure

Quinton et al.

15

Agricultural land

10.2

–

18.5

(14.6

–

26.4)

Organic:
(2.1

–

3.9)

Inorganic:
(12.5

–

22.5)

Based on erosion rates by Van Oost et al.

71

considering water and

tillage erosion; no information given, how organic versus inorganic
was speci

ﬁ

ed

Chen and
Graedel

28

Agricultural land 1.1 billion ha

8.2

–

12

(9

–

13.2)

Erosion rates based on Liu

34

This study

1.04 billion ha of arable land

6.3 (6.3)
Organic:
1.5
Inorganic: 4.6

Erosion rates based on Borrelli et al.

, organic (sum of labile and

stable organic) and inorganic P (as sum of labile and inorganic P
bound to secondary minerals plus occluded and apatite P) species
according to Hedley fractionation

NATURE COMMUNICATIONS | https://doi.org/10.1038/s41467-020-18326-7

ARTICLE

NATURE COMMUNICATIONS

| (2020) 11:4546 | https://doi.org/10.1038/s41467-020-18326-7 | www.nature.com/naturecommunications

7

Table

3

On-site

P

loss

from

gross

soil

erosion

(this

study

),

calculated

potential

riverine

loads

with

sediment

delivery

ratios

between

11

–

30%

43

44

and

comparison

to

global

and

regional

riverine

P

export

studies.

All

values

in

kg

P

h

a

−

1

yr

−

1

.

Conti

nent

P

soil

loss

(this

stud

y

)

Ri

ver

loa

ds

(thi

s

study

)

River

loa

ds

global

stud

y

River

loads

region

al

val

ues

Or

igin

of

dat

a

for

re

gional

stud

ies

Referen

ce

of

region

al

stud

ies

11

%

30%

Beusse

n

et

al.

Africa

9.6

1.1

2.9

1.9

1.8

-

2.2

Ave

rage

for

sub-Sahara

n

Africa,

years

1982

and

2000;

estim

ated

from

nutrient

soil

measu

rements

and

m

easured

soil

erosion

rat

es

Stoorvoge

l

et

al.

50

North

America

4.6

0.

5

1.4

1.5

0.3

–

1

143

catchmen

ts

acr

oss

the

US;

m

odeled

P

partitioning

with

measu

red

P

ﬂ

uxes

in

rivers

Stack

poole

et

al.

47

1.6

Tot

al

P

load

s

to

Lake

Erie

including

indus

trial

and

urban

runoff,

3850

7

k

m

2

as

ave

rage

of

the

load

s

from

the

three

cat

chment

s

Maume

e,

Sandus

ky,

Cu

yaho

ga

Baker

et

al.

48

South

America

8.9

1.0

2.7

3.9

2.4

Me

asurem

ent

at

outlet

of

Guayas

B

asin,

Ecua

dor,

32026

km

2

Borbor

-

Cordov

a

e

t

al.

51

Geogr

aphic

Euro

pe

1.2

0.

1

0.4

0.6

0.05

–

1.5

Est

imated

P

expo

rt

from

agricul

tural

runoff

for

17

large

scale

Euro

pean

cat

chment

s

Kronvang

et

al.

46

Asia

7.8

0.

9

2.3

4.0

—

China

12.3

1.4

3.7

—

2.7

Ya

ngtze

Rive

r

Basin

(1.8

Million

km

2

eq

uivalent

to

18.8%

of

the

Chinese

Territor

y

),

mode

led

P

partitioning

with

m

easured

P

ﬂ

ux

es

in

rive

rs

to

assess

P

load

from

agri

cultural

ﬁ

elds

Wan

g

e

t

al.

49

Oceania

1.8

0.

2

0.5

2.6

——

Australia

0.9

0.

1

0.3

—

0.3

–

0.5

St

ream

monit

oring

in

three

cat

chment

s

between

241

and

308

km

2

Koci

et

al.

81

Global

5.9

0.

7

1.8

2.7

——

ARTICLE

NATURE COMMUNICATIONS | https://doi.org/10.1038/s41467-020-18326-7

8

NATURE COMMUNICATIONS

| (2020) 11:4546 | https://doi.org/10.1038/s41467-020-18326-7 | www.nature.com/naturecommunications

based diet

12

. It has been suggested, that a 50% reduction in food

and feed waste combined with a 50% reduction in production and
consumption of animal products, will allow a 100% conversion to
organic agriculture, thus fostering sustainable agriculture and
minimizing agricultural production related problems such as
greenhouse gas production, biodiversity loss, eutrophication of
waters and eco-toxicological related issues

65

. However, the switch

to 100% organic production globally would only be possible if
rock phosphate was used as a mineral P-fertilizer in organic
agriculture with a similar magnitude as it is used today in
conventional agriculture

65

To conclude, because P supply from geological deposits cannot

be increased but phosphorus resources will be increasingly
limited in the future globally, reducing soil erosion might be a
crucial if not the most important management option to (i) allow
decreased fertilizer application and thus save some of the precious
P resources today, (ii) stop continuous depletion of eastern
European and African soil P storages and (iii) reduce impact to
fresh and ocean waters to counteract eutrophication and hypoxia.
We would like to point out that RUSLE based erosion rates as the
basis to calculate P loss due to erosion only consider rill and
inter-rill erosion processes by water, and do not consider erosion
processes due to tillage, gullies or landslides. As such, our P loss
assessments can be expected to be conservative estimates.
Measures to reduce soil erosion will be dependent on region
speci

ﬁ

c characteristics of climate, topography, soils and harvest-

ing aims and will be dependent on economic and topographic
feasibility of management options. Adequate and adapted erosion
control might be one, or a combination of measures such as (i) no
tillage or low tillage, (ii) maintenance of a permanent soil cover
achieved by increased vegetation especially cover crops, diversi-

ﬁ

cation of plant species, mulching, and/or intercropping, as well

as (iii) topography adapted land management (e.g., terracing,
strip cropping and contour farming)

66

67

. Especially a combina-

tion of sustainable practices could make a serious impact on
reducing erosion and the associated P losses in the most
vulnerable countries, leading to positive agricultural and
environmental outcomes.

Methods

RUSLE soil loss

. Global soil loss estimates were taken from Borrelli et al.

using

the RUSLE-based Global Soil Erosion Modeling platform (GloSEM), a modi

ﬁ

ed

large-scale Geographic Information System (GIS) version (RUSLE2015

67

) of the

Revised Universal Soil Loss Equation (RUSLE

68

) model. RUSLE belongs to the so

called detachment-limited model types where the soil loss (here synonym to
RUSLE soil loss

69

) is due to inter-rill and rill erosion processes. The long-term

annual soil loss rates (Mg ha

−

1

yr

−

1

) 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 modeled 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. Borrelli
et al.

estimated soil loss from croplands as 17

þ

1

0

:

7

Pg yr

−

1

(referring to 1.43 billion

ha cropland). For a detailed description of soil loss data used in this study please
refer to Borrelli, et al.

.

To calculate the loss in soil stock of the topmost 30 cm, bulk density (BD) was

taken from the Soilgrids database at 250 m spatial resolution

70

as an average of the

four top soil layers:

Soil stock lost %

ð Þ ¼

RUSLE soil loss

ð

kg

=

ha

Þ

Soil Stock

ð

kg

=

ha

Þ

100

Soil P contents and P loss

. Soil P contents were taken from Ringeval, et al.

who

combined global data sets from

,

,

(see Supplementary Table 1) in a soil

P dynamics model to assess the contributions of the different drivers (P inherited
from natural soils, land use and land cover change, soil P input/output due to
farming practices, climatic forces, atmospheric deposition, losses though erosion,
and soil P buffering capacities, Supplementary Table 1) at the global scale and to
simulate the distribution of different P fractions in agricultural soils. The model
was run over the period 1900

–

2005 at a time scale of 1 year. Soil input/output due

to farming practices are based on data from Bouwman et al.

21

.

Phosphorus loss from soils (P

loss

) can be induced by (i) erosion/ soil loss or (ii)

leaching. We neglected leaching as this can be expected to be of only very minor
importance regarding P loss from soils. As such, P

loss

was de

ﬁ

ned for each fraction

as:

Ploss

¼

Pfraction

Soil stock lost

ð

%

Þ

with P

fractions

in kg ha

−

1

yr

−

1

de

ﬁ

ned according to Ringeval et al.

based on the

global distribution of P fractions according to Yang et al.

, which summed Hedley

fractions

,

: labile organic P (organic P extracted with resin and with

bicarbonate), stable organic P (extracted with hydroxide), labile inorganic P
(inorganic P extracted with resin and bicarbonate), inorganic P bound on
secondary minerals (inorganic P extracted with hydroxide), apatite (P extracted
with hydrochloric acid), occluded inorganic P (residual P) and total P (as sum of all
fractions). Yang et al.

built up a global database of P fractions considering the

major USDA soil taxonomy soil orders grouped in three soil weathering categories
as slightly, intermediately and highly weathered soils. However, as there is great
uncertainty to associate these fractions with functional plant uptake

39

40

we present

(Figs.

and

, Table

) the total P loss, the plant-available pool as the most labile P

which can participate to plant nutrition in a time scale up to months

(i.e., the

sum of labile inorganic P, inorganic P bound to secondary minerals, labile and
stable organic P) and the very stable pools which would not be plant available at
short time scales (i.e., inorganic P associated with minerals such as apatite or
occluded P

, corresponding to 52% of total soil P in our estimates at the global

scale). Losses of plant-available fractions are presented in Fig.

Modeled area

. As the study is based on phosphorus soil content data from

Ringeval et al.

we cover the same 1.04 billion ha of global cropland from a total of

1.43 billion ha with a resolution of 0.5° × 0.5° based on the land-use harmonization
data of Hurtt et al.

73

. The coverage of the modeled area in Ringeval et al.

was the

result of the combination of different datasets with the respective coverages.

Yearly P

ﬂ

uxes and balance

. For atmospheric input data we followed the

approach of Ringeval et al.

who based their data set on Wang et al.

. Atmo-

spheric P input via dust has also been estimated recently by Aciego et al.

35

assuming only a tenth (a maximum of 0.1 kg ha

−

1

yr

−

1

) of the

ﬂ

uxes estimated by

Wang et al.

. However, atmospheric

ﬂ

uxes are negligible relative to input from

chemical fertilizer or output via erosional

ﬂ

uxes. Nevertheless, we wanted to be sure

not to underestimate atmospheric input and thus used the global data set of Wang
et al.

Data for chemical fertilizer, manure, residue input (e.g., plant biomass that

remains on/within the soil after the harvest and include root biomass) and plant
uptake stems from Ringeval et al.

who originally based their calculations on data

from Bouwman et al.

. Bouwman et al.

,

used data of the International

Assessment of Agricultural Knowledge, Science, and Technology for
Development

76

as input for an implementation of the IMAGE model to calculate

soil input/output

ﬂ

uxes for the year 2000 corresponding to farming practices (i.e.,

chemical fertilizer, manure, P in harvest). Residue input and plant uptake used in
our study were derived from P in harvest provided by Bouwman et al.

21

,

75

following basic assumptions as described in Ringeval et al.

. Please note that P de-

occlusion (e.g., the

ﬂ

ux from occluded to secondary minerals) as a small but

continuous replenishment of the soluble P pools was not considered in the
approach of Ringeval et al.

but the soil buffering capacity driver was only

following sorption and desorption kinetics according to the Langmuir equation
with speci

ﬁ

c parameters taken from Wang et al.

24

. For all input layers to the model

of Ringeval et al.

https://esdac.jrc.ec.europa.eu/themes/global-phosphorus

as well as technical information please see Supplementary

Table 1 and

We de

ﬁ

ned the term organic P management as the sum of manure and residue

input minus plant uptake following the idea that the sum of these

ﬂ

uxes underlies

different regulating mechanisms as atmospheric deposition (diffuse input without
regulating management options) or chemical fertilizer (bound to

ﬁ

nancial,

technical and eventually geological availability of minable P).

organic P management

¼

manure

þ

residue input

plant uptake

P balance of our systems was calculated with or without chemical fertilizer input

as:

Pbalance

¼

atmospheric input

þ

organic P management

þ

chemical fertilizer

ð

Þ

total soil loss due to soil erosion

Error estimation

. For all calculated

ﬂ

uxes and balances errors were estimated as

relative, non-symmetric errors to the means in % (e.g., as

x

þ

15

:

6

%

6

:

7

%

)with error

propagation using the standard deviation of all P pool data from Ringeval et al.

and the uncertainty of

ﬂ

uxes given for the soil loss data in Borrelli et al.

(Sup-

plementary Fig. 1).

RUSLE is a purely deterministic model in which the product of physical

measures is used to derive the amount of soil loss. In such a model, uncertainty in
the model output stem from the uncertainty of input factors which then propagate
through the model. As such, a sensu stricto assessment of uncertainties of large or
global scale modeling is not feasible

77

, because of the unknown uncertainty of all

NATURE COMMUNICATIONS | https://doi.org/10.1038/s41467-020-18326-7

ARTICLE

NATURE COMMUNICATIONS

| (2020) 11:4546 | https://doi.org/10.1038/s41467-020-18326-7 | www.nature.com/naturecommunications

9

input layers. More speci

ﬁ

cally, in our approach we do have an estimate of the

uncertainty of the layer R (rainfall erosivity) and of the topography related factors
(LS factors), but we miss the uncertainty estimates due to the misclassi

ﬁ

cation of

land cover (C factor) or the uncertainty associated to the erodibility of soils (K
factor). Here, we replicated the approach followed by Borrelli et al.

which

estimates uncertainty as a probability distribution using a Bayesian modeling
technique. For this application only the cropland areas are considered. The idea is
to use the data distribution to estimate the uncertainty of the prediction. The
approach used is to approximate the magnitude of the error from the distribution
of the values in the layers. Given that RUSLE is based on a product, to simplify, all
layers were log-transformed. In other words, each of the input layers was treated as
a spatial random

ﬁ

eld. A random

ﬁ

eld is a stochastic process de

ﬁ

ned in terms of

expectation and covariance. Once these two parameters are estimated, different
simulations for each

ﬁ

eld can be created. Each simulation has the same parameters,

but differs due to the stochasticity of the process. By combining a large number of
simulations, the uncertainty is estimated and how it propagates to the model
output (soil loss).

For the practical implementation, and as a spatially continuous simulation for

each of the layers is impractical, a simulation approach based on Gibbs sampling
and an additive model was used. The model is expressed as:

z

ð

S

0

Þ ¼

z

R

ð Þ þ

z LS

ð Þ þ

z

K

ð Þ þ

z

C

ð Þ þ

e

s

ð Þ

where the z() values are realizations from each of the log-transformed model input
layers and e(s) is the spatial component of the model. The additive model uses
simulated data derived from the observed instances following a feature distance
kernel. For a given observation i, corresponding to the vector of variabes j (j

=

(S

i

,

R

i

, Ls

i

, K

i

, C

i

)), estimates of the mean (µ()

j

) and variance (

σ

2

()

j

) of all the variables

are derived from the k (k

=

100) closest observations in feature space. The

simulated vector of j is then drawn from a Gaussian distribution with mean µ()

j

and variance

σ

2

()

j

. The idea is that by selecting similar environmental conditions it

is possible to estimate the range variability of the outcome while remaining in the
domain of what is physically feasible. The main limitation of this approach is that
for numerical stability, several realizations for each simulated point must be used,
thus in

ﬂ

ating the number of data points. In order to limit the computational

burden, the simulated values are then used to

ﬁ

t a Markov Chain Monte Carlo

(MCMC) additive model to derive the posterior distribution of each parameter plus
the outcome from a random sample of 2×10

6

observation covering the spatial

extent of the layers. The MCMC additive regression model was then used to derive
realizations of

z(S

0

)

(soil loss) from which con

ﬁ

dence intervals were calculated. The

MCMC model was applied using the JAGS software through the R interface.

Regarding the uncertainty of the P soil pool data, the total soil P (P

TOT

)

simulated by Ringeval et al.

is derived from a combination of different datasets

describing the main drivers of cycle P in cropland with the soil P dynamic model
(see above). The procedure adopted by Ringeval et al.

to estimate how the

uncertainty related to each driver propagated to the simulated P

TOT

can be

synthesized as follow. Within a

ﬁ

rst step, a range of uncertainty for each driver

(Supplementary Table 1), de

ﬁ

ned by bottom and top boundaries, was de

ﬁ

ned

following available information. Since no information about the uncertainty of
some drivers (farming practices and soil buffering capacity) were available, an
uncertainty of ±30% was arbitrarily assumed. The simulation of P

TOT

was

replicated 30 times. For a given replication, the value of each driver was chosen
randomly within the range between the two estimates by assuming a uniform
distribution (i.e., assuming that all values between estimates 1 and 2 were equally
likely). This was done independently for each grid cell. The 30 replications were
used to compute for each grid-cell an average and standard deviation. For more
details please see Ringeval et al.

.

Data availability

The data presented in this study (Figs.

and

https://esdac.jrc.ec.europa.eu/themes/

) as well as all input layers to our modeling

approach are directly available at the European Soil Data Centre (ESDAC) of the
European Commission

–

Joint Research Centre:

global-phosphorus

. Access to the data is free for all public users.

Received: 13 January 2020; Accepted: 20 July 2020;

References

1.

Bouwman, A. F., Beusen, A. H. W. & Billen, G. Human alteration of the global
nitrogen and phosphorus soil balances for the period 1970-2050.

Global

Biogeochemical Cycles

23

, 1-16,

https://doi.org/10.1029/2009gb003576

(2009).

2.

Carpenter, S. R. & Bennett, E. M. Reconsideration of the planetary boundary
for phosphorus.

Environ. Res. Lett.

6

, 1

–

12

https://doi.org/10.1088/1748-9326/

6/1/014009

(2011).

3.

Cordell, D., Drangert, J. O. & White, S. The story of phosphorus: global food
security and food for thought.

Glob. Environ. Change

19

, 292

–

305 (2009).

4.

Gilbert, N. Environment: the disappearing nutrient.

Nature

461

, 716

–

718

(2009).

5.

Edixhoven, J. D., Gupta, J. & Savenije, H. H. G. Recent revisions of
phosphate rock reserves and resources: a critique.

Earth Syst. Dynam

5

,

491

–

507 (2014).

6.

Scholz, R. W. & Wellmer, F. W. Approaching a dynamic view on the
availability of mineral resources: what we may learn from the case of
phosphorus?

Glob. Environ. Change

23

, 11

–

27 (2013).

7.

Scholz, R. W. & Wellmer, F. W. Comment on:

“

Recent revisions of phosphate

rock reserves and resources: a critique

”

by Edixhoven et al. (2014) - clarifying

comments and thoughts on key conceptions, conclusions and interpretation to
allow for sustainable action.

Earth Syst. Dyn.

7

, 103

–

117 (2016).

8.

Van Kauwenbergh, S. J. World Phosphate Rock Reserves and Resources.
Technical Bulletin IFDC-T-75 (2010).

9.

Kohn, J., Zimmer, D. & Leinweber, P. Is phosphorus really a scarce resource?
Int.

J. Environ. Technol. Manag.

21

, 373

–

395 (2018).

10. Ulrich, A. E. & Frossard, E. On the history of a reoccurring concept:

phosphorus scarcity.

Sci. Total Environ.

490

, 694

–

707 (2014).

11. MinemakersLimited. Rock Phosphate Price Rockets to US$200/tonne. ASX

and Press Release Perth (2008).

12. Elser, J. & Bennett, E. A broken biogeochemical cycle.

Nature

478

, 29

–

31

(2011).

13. Amundson, R. et al. Soil and human security in the 21st century.

Science

348

,

1261071_1

–

1261076_1

https://doi.org/10.1126/science.1261071

(2015).

14. USDA. USDA Economic Research Service (2019).
15. Quinton, J. N., Govers, G., Van Oost, K. & Bardgett, R. D. The impact of

agricultural soil erosion on biogeochemical cycling.

Nat. Geosci.

3

, 311

–

314

(2010).

16. Smil, V. Phosphorus in the environment: Natural

ﬂ

ows and human

interferences.

Annu. Rev. Energy Environ.

25

, 53

–

88 (2000).

17. Helfenstein, J. et al. Combining spectroscopic and isotopic techniques gives a

dynamic view of phosphorus cycling in soil.

Nat. Commun.

9

, 1

–

9

https://doi.

org/10.1038/s41467-018-05731-2

(2018).

18. Riskin, S. H. et al. The fate of phosphorus fertilizer in Amazon soya bean

ﬁ

elds. Philos.

Trans. R. Soc. B Biol. Sci

368

, 1

–

11

https://doi.org/10.1098/

rstb.2012.0154

(2013).

19. Frossard, E., Condron, L. M., Oberson, A., Sinaj, S. & Fardeau, J. C. Processes

governing phosphorus availability in temperate soils.

J. Environ. Qual.

29

,

15

–

23 (2000).

20. Sattari, S. Z., Bouwman, A. F., Giller, K. E. & van Ittersum, M. K. Residual soil

phosphorus as the missing piece in the global phosphorus crisis puzzle.

Proc.

Natl Acad. Sci. USA

109

, 6348

–

6353 (2012).

21. Bouwman, L. et al. Exploring global changes in nitrogen and phosphorus

cycles in agriculture induced by livestock production over the 1900

–

2050

period.

Proc. Natl Acad. Sci. USA

110

, 20882

–

20887 (2013).

22. MacDonald, G. K., Bennett, E. M., Potter, P. A. & Ramankutty, N. Agronomic

phosphorus imbalances across the world

’

s croplands.

Proc. Natl Acad. Sci.

USA

108

, 3086

–

3091 (2011).

23. Drangert, J. O. Fighting the urine blindness to provide more sanitation

options.

Water SA

24

, 157

–

164 (1998).

24. Wang, Y. P., Law, R. M. & Pak, B. A global model of carbon, nitrogen and

phosphorus cycles for the terrestrial biosphere.

Biogeosciences

7

, 2261

–

2282

(2010).

25. MacDonald, G. K., Bennett, E. M. & Taranu, Z. E. The in

ﬂ

uence of time, soil

characteristics, and land-use history on soil phosphorus legacies: a global
meta-analysis.

Glob. Change Biol.

18

, 1904

–

1917 (2012).

26. Ockenden, M. C. et al. Major agricultural changes required to mitigate

phosphorus losses under climate change.

Nat. Commun.

8

, 1

–

9

https://doi.

org/10.1038/s41467-017-00232-0

(2017).

27. Liu, Y., Villalba, G., Ayres, R. U. & Schroder, H. Global phosphorus

ﬂ

ows and

environmental impacts from a consumption perspective.

J. Ind. Ecol.

12

,

229

–

247 (2008).

28. Chen, M. P. & Graedel, T. E. A half-century of global phosphorus

ﬂ

ows,

stocks, production, consumption, recycling, and environmental impacts.

Glob.

Environ. Change

36

, 139

–

152 (2016).

29. van Dijk, K. C., Lesschen, J. P. & Oenema, O. Phosphorus

ﬂ

ows and balances

of the European Union Member States.

Sci. Total Environ.

542

, 1078

–

1093

(2016).

30. Bennett, E. M., Carpenter, S. R. & Caraco, N. F. Human impact on erodable

phosphorus and eutrophication: a global perspective.

Bioscience

51

, 227

–

234

(2001).

31. Pimentel, D. Soil erosion: a food and environmental threat.

Environ. Dev.

Sustainability

8

, 119

–

137 (2006).

32. Borrelli, P. et al. An assessment of the global impact of 21st century land use

change on soil erosion.

Nat. Commun.

8

, 2013 (2017).

33. Ringeval, B. et al. Phosphorus in agricultural soils: drivers of its distribution at

the global scale.

Global Change Biol

23

, 1

–

15

https://doi.org/10.1111/

gcb.13618

(2017).

ARTICLE

NATURE COMMUNICATIONS | https://doi.org/10.1038/s41467-020-18326-7

10

NATURE COMMUNICATIONS

| (2020) 11:4546 | https://doi.org/10.1038/s41467-020-18326-7 | www.nature.com/naturecommunications

34. Liu, Y. The Human Intensi

ﬁ

ed Global Phosphorus Flows and Environmental

Impacts. Interim Report. IR-06-081, International Institute for Applied
Systems Analysis, Laxenburg, Austria, 1-30 (2006).

35. Aciego, S. M. et al. Dust outpaces bedrock in nutrient supply to montane

forest ecosystems.

Nat. Commun.

8

, 1

–

10

https://doi.org/10.1038/

ncomms14800

(2017).

36. Wang, R. et al. Signi

ﬁ

cant contribution of combustion-related emiscions to the

atmospheric phosphorus budget.

Nat. Geosci.

8

, 48

–

54 (2015).

37. Yang, X., Post, W. M., Thornton, P. E. & Jain, A. The distribution of soil

phosphorus for global biogeochemical modeling.

Biogeosciences

10

,

2525

–

2537 (2013).

38. Hedley, M. J., Stewart, J. W. B. & Chauhan, B. S. Changes in inorganic and

organic soil-phosphorus fractions induced by cultivation practices and by
laboratory incubations.

Soil Sci. Soc. Am. J.

46

, 970

–

976 (1982).

39. Tiessen, H., Stewart, J. W. B. & Cole, C. V. Pathways of phosphorus

transformations in soils of differing pedogenesis.

Soil Sci. Soc. Am. J.

48

,

853

–

858 (1984).

40. Zehetner, F., Wuenscher, R., Peticzka, R. & Unterfrauner, H. Correlation of

extractable soil phosphorus (P) with plant P uptake: 14 extraction methods
applied to 50 agricultural soils from Central Europe.

Plant Soil Environ.

64

,

192

–

201 (2018).

41. Helfenstein, J. et al. Estimates of mean residence times of phosphorus in

commonly considered inorganic soil phosphorus pools.

Biogeosciences

17

,

441

–

454 (2020).

42. Goudie, A. Encyclopedia of Geomorphology (Taylor & Francis, 2013).
43. Lugato, E. et al. Soil erosion is unlikely to drive a future carbon sink in Europe.

Sci. Adv.

4

, 1

–

7

https://doi.org/10.1126/sciadv.aau3523

(2018).

44. Lal, R. Soil erosion and the global carbon budget.

Environ. Int.

29

, 437

–

450

(2003).

45. Beusen, A. H. W., Dekkers, A. L. M., Bouwman, A. F., Ludwig, W. & Harrison,

J. Estimation of global river transport of sediments and associated particulate
C, N, and P.

Glob. Biogeochem. Cycles

19

, 19 (2005).

46. Kronvang, B., Vagstad, N., Behrendt, H., Bogestrand, J. & Larsen, S. E.

Phosphorus losses at the catchment scale within Europe: an overview.

Soil Use

Manag.

23

, 104

–

116 (2007).

47. Stackpoole, S. M., Stets, E. G. & Sprague, L. A. Variable impacts of

contemporary versus legacy agricultural phosphorus on US river water
quality.

Proc. Natl Acad. Sci. USA

116

, 20562

–

20567 (2019).

48. Baker, D. B. et al. Phosphorus loading to Lake Erie from the Maumee,

Sandusky and Cuyahoga rivers: the importance of bioavailability.

J. Gt. Lakes

Res.

40

, 502

–

517 (2014).

49. Wang, X. et al. Estimating non-point source pollutant loads for the large-

scale basin of the Yangtze River in China. Environ.

Earth Sci.

63

, 1079

–

1092

(2011).

50. Stoorvogel, J. J., Smaling, E. M. A. & Janssen, B. H. Calculating soil nutrient

balances in africa at different scales .1. Supra-national scale.

Fertilizer Res.

35

,

227

–

235 (1993).

51. Borbor-Cordova, M. J., Boyer, E. W., McDowell, W. H. & Hall, C. A. Nitrogen

and phosphorus budgets for a tropical watershed impacted by agricultural
land use: Guayas, Ecuador.

Biogeochemistry

79

, 135

–

161 (2006).

52. Panagos, P. et al. Global rainfall erosivity assessment based on high-temporal

resolution rainfall records.

Sci. Rep.

7

, 1

–

12

https://doi.org/10.1038/s41598-

017-04282-8

(2017).

53. Buresh, R. J., Smithson, P. C. & Hellums, D. T. In

Replenishing Soil Fertility in

Africa SSSA Special Publication

111

–

149 (Soil Science Society of America and

American Society of Agronomy, 1997).

54. Manyong, V. M., Makinde, K. O., Sanginga, N., Vanlauwe, B. & Diels, J.

Fertiliser use and de

ﬁ

nition of farmer domains for impact-oriented research in

the northern Guinea savanna of Nigeria.

Nutr. Cycl. Agroecosyst.

59

, 129

–

141

(2001).

55. Pypers, P. et al. Combining mineral fertilizer and green manure for increased,

pro

ﬁ

table cassava production.

Agron. J.

104

, 178

–

187 (2012).

56. Chikowo, R. et al. Nitrogen and phosphorus capture and recovery ef

ﬁ

ciencies,

and crop responses to a range of soil fertility management strategies in sub-
Saharan Africa.

Nutr. Cycl. Agroecosyst.

88

, 59

–

77 (2010).

57. Sanchez, P. A. Tripling crop yields in tropical africa.

Nat. Geosci.

3

, 299

–

300

(2010).

58. Tully, K., Sullivan, C., Weil, R. & Sanchez, P. The state of soil degradation in

Sub-Saharan Africa: baselines, trajectories, and solutions.

Sustainability

7

,

6523

–

6552 (2015).

59. Omuto, C. T. & Vargas, R. R. Soil nutrient loss assessment in Malawi.

Technical Report. FAO, UNEP and UNDP, 1

–

64 (2018).

60. Franke, A. C., van den Brand, G. J., Vanlauwe, B. & Giller, K. E. Sustainable

intensi

ﬁ

cation through rotations with grain legumes in Sub-Saharan Africa: a

review.

Agriculture Ecosyst. Environ.

261

, 172

–

185 (2018).

61. Prestele, R., Hirsch, A. L., Davin, E. L., Seneviratne, S. I. & Verburg, P. H. A

spatially explicit representation of conservation agriculture for application in
global change studies.

Global Change Biol.

00

, 1

–

16 (2018).

62. Csatho, P. & Radimszky, L. Two worlds within EU27: sharp contrasts in

organic and mineral nitrogen-phosphorus use, nitrogen-phosphorus balances,
and soil phosphorus status: widening and deepening gap between Western
and Central Europe.

Commun. Soil Sci. Plant Anal.

40

, 999

–

1019 (2009).

63. Ott, C. & Rechberger, H. The European phosphorus balance.

Resour., Conserv.

Recycling

60

, 159

–

172 (2012).

64. Zhou, K., Barjenbruch, M., Kabbe, C., Inial, G. & Remy, C. Phosphorus

recovery from municipal and fertilizer wastewater: China

’

s potential and

perspective.

J. Environ. Sci

52

, 151

–

159

https://doi.org/10.1016/j.

jes.2016.04.010

(2016).

65. Muller, A. et al. Strategies for feeding the world more sustainably with organic

agriculture.

Nat. Commun.

8

, 1290 (2017).

66. Panagos, P. et al. Estimating the soil erosion cover-management factor at the

European scale.

Land Use Policy

48

, 38

–

50 (2015).

67. Panagos, P. et al. The new assessment of soil loss by water erosion in Europe.

Environ. Sci. Policy

54

, 438

–

447 (2015).

68. Renard, K., Foster, G., Weesies, G., McCool, D. & Yoder, D. Predicting soil

erosion by water: a guide to conservation planning with the Revised Universal
Soil Loss Equation (RUSLE). Vol. 703 (Department of Agriculture,
Agricultural Research Service, 1997).

69. Nearing, M. A., Yin, S. Q., Borrelli, P. & Polyakov, V. O. Rainfall erosivity: an

historical review.

Catena

157

, 357

–

362

https://doi.org/10.1016/j.

catena.2017.06.004

(2017).

70. ISRIC. World Soil Information SoilGrids

–

an automated system for global

soil mapping. Available for download at

https://soilgrids.org

(2016).

71. Van Oost, K. et al. The impact of agricultural soil erosion on the global carbon

cycle.

Science

318

, 626

–

629 (2007).

72. Decharme, B., Martin, E. & Faroux, S. Reconciling soil thermal and

hydrological lower boundary conditions in land surface models.

J. Geophys.

Res.

118

, 7819

–

7834 (2013).

73. Hurtt, G. C. et al. Harmonization of land-use scenarios for the period 1500-

2100: 600 years of global gridded annual land-use transitions, wood harvest,
and resulting secondary lands.

Climatic Change

109

, 117

–

161 (2011).

74. Krinner, G. et al. A dynamic global vegetation model for studies of the coupled

atmosphere-biosphere system.

Global Biogeochem. Cycles

19

, 1

–

33

https://doi.

org/10.1029/2003gb002199

(2005).

75. Bouwman, A., Kram, T. & Klein Goldewijk, K. Integrated modelling of global

environmental change.

Overv. IMAGE

2

, 225

–

228 (2006).

76. McIntyre, B. D., Herren, H. R., Wakhungu, J. & Watson, R. T. International

Assessment of Agricultural Knowledge, Science and Technology for
Development (IAASTD), Global Report. 590 (Island Press. Washington, DC,
2009).

77. Auerswald, K., Kainz, M. & Fiener, P. Soil erosion potential of organic versus

conventional farming evaluated by USLE modelling of cropping statistics for
agricultural districts in Bavaria.

Soil Use Manag.

19

, 305

–

311 (2003).

78. Meybeck, M. Carbon, nitrogen, and phosphorus transport by world rivers.

Am. J. Sci.

282

, 401

–

450 (1982).

79. Smil, V. Nitrogen in crop production: an account of global

ﬂ

ows.

Glob.

Biogeochem. Cycles

13

, 647

–

662 (1999).

80. Mackenzie, F. T., Vera, L. M. & Lerman, A. Century-scale nitrogen and

phosphorus controls of the carbon cycle.

Chem. Geol.

190

, 13

–

32 (2002).

81. Koci, J. et al. Effect of reduced grazing pressure on sediment and nutrient

yields in savanna rangeland streams draining to the Great Barrier Reef.

J.

Hydrol.

582

, 14 (2020).

Acknowledgements

Funding for D.A. Robinson was provided by a NERC Knowledge Exchange fellowship
(Grant no. NE/N005309/1) and the Natural Environment Research Council award
number NE/R016429/1 as part of the UK

–

ScaPE Programme Delivering National

Capability. We thank Lex Bouwman who was at the origin of the variables related to
farming practices and Rong Wang whose data on atmospheric deposition was used in
Ringeval et al. (2017)