The Effect of Soil Erosion on Europe's Crop Yields

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The Effect of Soil Erosion on Europe‘s
Crop Yields
Martha M. Bakker,
1,
* Gerard Govers,
2
Robert A. Jones,
3
and
Mark D. A. Rounsevell
4
1
De
´partement de Ge
´ographie, Universite
´Catholique de Louvain, Place Pasteur 3, Louvain-la-Neuve, 1348, Belgium;
2
Laboratory for
Experimental Geomorphology, K.U. Leuven, Redingenstraat 16, Leuven, 3000, Belgium;
3
European Soil Bureau (CEC DG JRC),
Environment Institute, TP 280 Via Enrico Fermi, Ispra (VA), 21020, Italy;
4
School of Geosciences, University of Edinburgh, Crew
Building, King‘s Buildings, Edinburgh, EH9 3JN, UK
ABSTRACT
Soil erosion negatively affects crop yields and may
have contributed to the collapse of ancient civili-
zations. Whether erosion may have such an impact
on modern societies as well, is subject to debate. In
this paper we quantify the relationship between
crop yields and soil water available to plants, the
most important yield-determining factor affected
by erosion, at the European scale. Using informa-
tion on the spatial distribution of erosion rates we
calculate the potential threat of erosion-induced
productivity losses. We show that future reductions
in productivity in Europe as a whole are relatively
small and do not pose a substantial threat to crop
production within the coming century. However,
within Europe there is considerable variability, and
although productivity in northern Europe is not
likely to be significantly reduced by soil erosion, for
the southern countries the threat of erosion-in-
duced productivity declines is stronger.
Key words: soil erosion; crop yields; Europe; soil
available water; land degradation; wheat.
INTRODUCTION
There is little doubt that soil erosion has a detri-
mental effect on global soil resources. The depletion
of the soil has led to major instability and
undoubtedly contributed to the collapse of rela-
tively advanced societies in the past, such as the
isolated civilizations of Easter Island and Iceland
(Diamond 2004). Societies on large landmasses
were probably also affected by soil degradation.
Evidence suggests that the fall of the Mayan civi-
lization was related to problems of soil erosion
(Jacob and Hallmark 1996), whereas in ancient
China soil degradation was found to be one of the
decisive factors leading to the relocation of popu-
lation centres (Duan and others 1998). Archaeo-
logical evidence also points to severe soil erosion in
Greece during ancient Greek times (Vanandel and
others 1990), which may have contributed signifi-
cantly to the collapse of Greek civilization (Runnels
1995).
Soil erosion continues to have detrimental effects
on global soil resources. Some claim that during the
last 40 years nearly one-third of the world‘s arable
land has been lost through erosion and continues
to be lost at a rate of more than ten million hectares
per year (Pimentel and others 1995). A recent
study by Wilkinson and McElroy (2007) estimates
that soil loss from global farmlands is currently
running at a rate of more than 6 t ha
)1
y
)1
, which
is more than 15 times the estimated average rate of
erosion (0.42 t ha
)1
y
)1
) during the whole Phan-
erozoic Era, a period of 542 million years spanning
the Lower Cambrian to the Tertiary Pliocene.
Current land abandonment in some parts of Europe
Received 24 January 2007; accepted 10 July 2007; published online 14
September 2007
*Corresponding author; e-mail: martha.bakker@wur.nl
Ecosystems (2007) 10: 1209–1219
DOI: 10.1007/s10021-007-9090-3
1209

has probably been driven by soil erosion (Bakker
and others 2005a), whereby abandoned areas have
reverted to unproductive scrubland. It seems likely
that modern societies and landscapes are threa-
tened by the loss of crop yields and subsequent land
abandonment if soil erosion continues to degrade
soil resources.
Although the problem has received much
attention recently (Conway and Toenniessen 1999;
Tilman and others 2002; McNeill and Winiwarter
2004), there is no quantitative information about
the effects of past or future erosion on agricultural
productivity at either the regional or national level
(Trimble and Crosson 2000; Yaalon and Arnold
2000). Despite claims that during the past 50 years
soil loss by erosion has been a serious problem, the
current and potential future consequences of this
loss are not known. Inferences made from the
synchronicity of soil erosion events and societal
changes are not based on quantitative assessments
of the impact of soil erosion on agricultural pro-
ductivity. Likewise, the analogy between the col-
lapse of ancient societies and the risks facing
modern society is based on assumptions that have
not been tested. Hence, the extent to which soil
erosion is indeed a significant threat to the agri-
cultural productivity of modern societies remains a
subject for debate (Crosson 1997).
Soil erosion has a detrimental effect on soil
quality for agricultural production because erosion
degrades soil functions for crop growth such as the
supply of water, nutrients and rooting space. These
effects have been demonstrated through numerous
experiments conducted on plots where erosion was
either simulated by artificial desurfacing (Mbagwu
and others 1984; Dormaar and others 1986; Gol-
lany and others 1992; Malhi and others 1994; Ta-
naka 1995; Larney and others 2000), or by
comparing yield on strongly eroded areas with
yield on less eroded areas (Bramble-Brodahl and
others 1984; Busacca and others 1984; White and
others 1984; Mielke and Schepers 1986; Olson and
Carmer 1990; Kosmas and others 2001). The re-
ported results, however, show a wide variability
and a systematic overestimation of the effects due
to the use of flawed methodologies may explain a
large part of the research results (Bakker and others
2004). Nevertheless, systematic analysis of the
available data allowed some general conclusions to
be drawn: under intense, mechanized agriculture,
yield reductions at the field scale are of the order of
4% for each 0.1 m of soil loss. This finding applies
to European and North American studies, where
yield reductions could generally be attributed to a
reduction in rooting depth and/or plant available
water (for example, Olson and others 1999; Bakker
and others 2004).
The results of such field experiments cannot di-
rectly be extrapolated to a regional or national le-
vel. The effects of erosion on crop productivity may
vary per agro-ecological zone and the effects may
also depend on the scale level studied. Within lar-
ger spatial units, compensatory effects may occur:
regional productivity may be maintained by the
simple reallocation of arable land to less erosion-
prone areas with arable areas becoming permanent
grassland or forest (Bakker and others 2005a).
Furthermore, inclusion of depositional areas within
the spatial unit studied may offset the negative ef-
fect of erosion upslope, as deposition may improve
soil properties and productivity may increase.
As the extrapolation of field results to the re-
gional scale is questionable, it is appropriate to
explore other approaches to assess the relationship
between erosion and crop productivity. If a rela-
tionship between crop productivity and soil prop-
erties affected by erosion could be established
directly at the regional scale and if data on the
spatial distribution of soil erosion were available, it
would be possible to assess the effect of soil erosion
on crop productivity at the regional scale.
In this paper we attempt to quantify the effects of
erosion on the future evolution of agricultural
productivity in Europe, based on quantitative
information on: (1) the relationship between crop
productivity and soil depth and soil water avail-
ability; (2) the effect of erosion on soil depth and
soil water availability; and (3) information on the
spatial distribution of soil erosion.
METHODS
Data
Soft wheat (Triticum aestivum L.) was chosen as an
indicator crop. Wheat is widely cultivated over a
broad range of climatic conditions and its suscep-
tibility to erosion is representative of many other
common crops with similar rooting depths and
water requirements. Annual yield data were de-
rived for Germany, France and Greece from na-
tional statistical offices at the NUTS3 level
(Nomenclature of Territorial Units for Statistics le-
vel 3). This provided a total of 300 observations.
These data were averaged over the period 1991–
2000.
In the intensively cultivated agro-ecosystems of
Europe, it is reasonable to assume that fertilizer
applications compensate for erosion-induced nutri-
ent shortages and so, nutrients are not important in
1210 M. M. Bakker and others

controlling crop productivity. Previous research has
shown that under these conditions rooting space
and water availability are the main soil-related
limiting factors for crop yields (for example, Olson
and others 1999; Bakker and others 2004). We
combined available rooting space and water avail-
ability into a single variable, the total Soil Water
Available to Plants (SWAP) that is assumed to be the
most important variable controlling the effect of
erosion on crop yields. SWAP is analogous to the
available water capacity (AWC) defined by Salter
and Williams (1965) as the volume of water held
between wilting point and field capacity. SWAP was
originally defined by Thomasson (1979) and is cal-
culated as the product of soil depth and volumetric
water content for that depth, integrated down to the
normal rooting depth of a specified crop. In the case
of wheat growth in Europe, the maximum rooting
depth is considered to be 1.20 m (Thomasson 1979).
In principle, SWAP is the quantity of water (mm)
held between field capacity (5 kPa) and the per-
manent wilting point (1,500 kPa), which for a soil
growing cereals is the sum of two components: the
water held at low suctions 5–200 kPa between 0 and
1.2 m depth and the water held at higher suctions
200–1,500 kPa between 0 and 0.5 m depth (Jones
and others 2000). The assumption here is that cereal
crops with a short growing season have limited
opportunity to exploit fully the available water be-
tween field capacity and wilting point at depths
below 0.5 m because only a small proportion of the
root system extends below 0.5 m depth (Thomasson
1979). In the case of shallow soils, the integration of
available water with depth is terminated at a barrier
to rooting such as a layer of rock if this is shallower
than 1.2 m. The SWAP data used in this study were
derived from the European Soil Database (King and
others 1994,1995). These data were interpolated to
1km·1 km grid for use by the Pan European Soil
Erosion Risk Assessment project (PESERA; Kirkby
and others 2004).
The soil erosion data were derived from the
PESERA model (run at the 1 km resolution, in
October 2003, for the whole of Europe). The PE-
SERA model is a spatially distributed process-
based water erosion model that was specifically
developed to be applicable at the regional scale
using information on topography, land cover and
climate that is available at that scale (Kirkby and
others 2004). Initial validation of PESERA has
shown that the model is able to reproduce the
observed spatial variations in erosion rates within
Europe rather well, although predictions for indi-
vidual grid cells or small catchments may show
considerable deviations (Van Rompaey and others
2003a,b). It is important to note that PESERA
only predicts water erosion by rill and inter-rill,
whereas other forms of erosion, caused by wind
and tillage are not simulated even though these
may be important. However, wind erosion is
limited to certain areas within Europe and average
wind erosion rates are relatively low. Tillage ero-
sion is more widespread and is often more
important than water erosion at the field scale,
but it generally leads to soil redistribution within
the field rather than significant soil export from
the field.
Deriving the SWAP-Yield Relationship
An important complication when assessing the ef-
fects of (erosion-induced) reductions in SWAP on
crop yields is that SWAP is, at the regional scale,
correlated with climate and economic variables that
also affect yields. Finding a strong correlation be-
tween SWAP and yields may lead to a false con-
clusion that this relationship is causal. In practice, it
may be a climatic variable such as potential evapo-
transpiration that is causally related to productivity
and at the same time correlated to SWAP, because
in Europe high evapo-transpiration rates occur in
semi-arid areas that generally have slow soil-for-
mation rates and therefore thin soils (Selby 1991).
Likewise, economic variables show a similar trend
to SWAP along a north–south axis across Europe,
for which a number of explanations may exist.
Therefore, we need to assess the true relationship
between SWAP and yields, that is, the pseudo-iso-
lated effect of SWAP (Cook 1998; Morrison 2000),
controlled for important climate and economic
variables.
The following additional data were also col-
lected: annual rainfall and mean annual temper-
ature over the period 1991–2000 from the
Advanced Terrestrial Ecosystem Analysis and
Modelling (ATEAM) project database (an EU-
funded project, contract No: EVK2-2000-00075,
http://www.pik-potsdam.de/ateam/ateam.html);
annual incoming global radiation was calculated
from the empirical formula of Linacre (1969)
using only latitude as input, and; potential evapo-
transpiration was calculated from temperature
and incoming global radiation using the empirical
relationship of Jensen and Haise (1963). These
data were aggregated to the NUTS3 level. Na-
tional and local Gross Domestic Products (GDP)
were derived from the EUROSTAT database at
the NUTS3 level (local GDP) and at the country
level (national GDP) (http://www.ec.europa.eu/
eurostat).
The Effect of Soil Erosion on Europe‘s Crop Yields 1211

Interactions with economic and climatic vari-
ables, as well as controlling for confounding vari-
ables, were also taken into account. Both
confounding and interaction involve the assess-
ment of an association between two or more vari-
ables (that is, the regression coefficient) so that any
additional variables affecting this association are
accounted for (Kleinbaum and others 1998).
Thus, with respect to confounding and interac-
tion, the following hypotheses were tested:
(1) The regression coefficient of SWAP depends on
the values of annual rainfall, potential annual
evapo-transpiration and input level (local
GDP): when rainfall and input levels are high,
the coefficient is small, that is, the relationship
is less strong. When potential evapo-transpi-
ration is high, the coefficient is large, that is,
the relationship is strong. Hence, interaction is
hypothesized to occur between SWAP and
rainfall, potential evapo-transpiration and local
GDP.
(2) The regression coefficient of SWAP is con-
founded by mean annual temperature, net
incoming solar radiation and national GDP
(that is, these factors affect yields in a similar
way to SWAP). To assess the proper coefficient
of SWAP, these variables should be controlled.
Furthermore, the effect of SWAP may be non-
linear. As observed in many experiments on the
productivity response to erosion, reductions that
are caused by restrictions in soil depth and/or
water availability tend to become more severe
with incremental soil loss (Bakker and others
2004). Conversely, when SWAP increases beyond
the crop‘s water requirements, the effect is ex-
pected to be weak or even absent. Therefore, the
effect is expected to become stronger with lower
SWAP values, and weaker with higher SWAP
values.
Interaction was assessed, prior to confounding,
by examining the added value of an interaction
term (that is, the product of the interacting vari-
ables) to the model fit. Only interactions that had
significant added value at the 95% probability-
interval were taken into account. Variables for
which the hypothesis of interaction was rejected
were also tested for confounding.
Confounding was assessed by comparing the
regression coefficient of SWAP on yields when only
SWAP was in the model, with the regression coef-
ficient of SWAP when radiation, temperature and
national GDP were included. There are no statisti-
cal tests to assess confounding and a judgment
needs to be made about whether or not the dif-
ference is considered to be negligible or not
(Kleinbaum and others 1998). We considered,
therefore, changes in the regression coefficient of
SWAP of more than 5%, following the addition of
another variable, to be large enough to consider the
added variable as a confounder. The non-linearity
of SWAP was evaluated by testing the significance
(P< 0.05) of a squared SWAP term, next to SWAP.
Spatially Explicit Projection of Erosion-
Induced Yield Losses 100 Years from
Now
When assessing the effect of erosion-induced
reductions in SWAP on future productivity, con-
sideration should be given to the role of techno-
logical innovation. We know that in the past
yields have increased strongly as a result of ad-
vances in technology (Ewert and others 2005,
2007), but whether or not this trend will continue
in the future is uncertain (Ewert and others 2005;
Rounsevell and others 2005,2006). Some scien-
tists claim that trends will continue to boost yield
growth at similar rates to those of the past (Ewert
and others 2007), whereas others conclude that
yield increases will slow due to biophysical con-
straints on plant productivity (Kindal and Pimen-
tel 1994; Amthor 1998; Hafner 2003). As the role
of technology in the future is uncertain, two ex-
treme scenarios were explored in this paper to
capture the range of uncertainties. The first sce-
nario assumed that increases in yields will con-
tinue at the same rate as now, that is, a linear
extrapolation of current trends into the future.
Current trends were assumed to be an annual
increase of 1% of the yield (see Bakker and others
2005b). The second scenario assumed that yields
have peaked and that no more increase will occur.
Both scenarios are unlikely, but they indicate the
widest range of possible outcomes. A third sce-
nario, the United Nations Environmental Pro-
gramme scenario, assumed a continued increase in
productivity until 2025 (that is, a linear extrapo-
lation of current trends) and after that a decrease
in yield increase rate to about 87% of the current
value (UNEP 1997).
For Austria, the Netherlands, Belgium, Luxem-
bourg, Denmark, Ireland, Spain, Portugal and Italy,
yield data were not available. For these countries,
yields were estimated using a regression model that
was based on SWAP, potential evapo-transpiration,
mean annual temperature and local and national
GDP data (see Bakker and others 2005b for details).
Using the PESERA erosion rates, a map was
created of the relative wheat yield reductions in
1212 M. M. Bakker and others

100 years from now. First, PESERA erosion rates
(available at the 1 km
2
scale) were aggregated to
the NUTS3 level, whereby only erosion on arable
land [that is, non-irrigated arable land, irrigated
arable land and permanent crops according to the
CORINE2000 classification (EEA 2004)] was con-
sidered, resulting in average erosion rates in
tha
)1
y
)1
on arable land per NUTS3 region. To
convert these erosion rates into reductions of
SWAP, we assumed an average dry bulk density of
1,300 kg m
)3
for topsoil and an average volu-
metric water content of 150 mm m
)1
(for silty
soils this value is higher, whereas for sandy and
clayey soils it is lower). This implies that 1 t ha
)1
of erosion equals a loss in soil depth of approxi-
mately 7.7 ·10
)2
mm and a SWAP loss of
1.16 ·10
)2
mm. The resulting erosion-induced
SWAP losses were multiplied by the regression
coefficient to obtain yield loss. The yield loss was
expressed as a fraction of the future yield.
RESULTS
General Statistics
Table 1summarizes the general statistics for all
variables in the dataset containing the NUTS3 units
of France, Germany and Greece. Average yields per
NUTS3 unit vary from 1.43 t ha
)1
y
)1
in Greece to
more than 9 t ha
)1
y
)1
in northern Germany. Soil
erosion rates range from almost nothing to nearly
18 t ha
)1
y
)1
. The climatic gradient is strong, as are
the differences in the economic environment. For a
more detailed description of these variables see
Bakker and others (2005b).
Assessing the Effect of SWAP on Wheat
Yields
The initial regression equation with only SWAP as
the independent variable has a regression coeffi-
cient of 4.71 ·10
)2
(Table 2), meaning that yields
differ on average by 4.71 ·10
)2
tha
)1
y
)1
be-
tween areas that show 1 mm difference in SWAP.
The quadratic term has a significant, negative
coefficient when included in the model, meaning
that the effect of SWAP on yields grows stronger as
SWAP declines, which is to be expected.
Of the interaction terms, only the product of
local GDP with SWAP is significant next to SWAP,
meaning that the effect of SWAP reductions is less
important in regions with a higher local GDP. The
quadratic term of SWAP is no longer significant
when the interaction term is included, and is
therefore removed. Including rainfall in the
regression does not affect the regression coeffi-
cient(s) of SWAP or the interaction term, so the
hypothesis that rainfall confounds with SWAP can
be rejected, and rainfall is left out of the regres-
sion. When potential evapotranspiration was in-
cluded in the model the SWAP regression
coefficient was 2.69 ·10
)2
. As this is more than a
5% change, potential evapo-transpiration is con-
sidered to be an important confounder. The
interaction term of SWAP with local GDP becomes
non-significant when potential evapotranspiration
is introduced, and was therefore removed from
the regression.
When solar radiation is included in the model,
the coefficient of SWAP becomes even smaller
(2.24 ·10
)2
), but adding solar radiation makes
potential evapotranspiration redundant (non-sig-
nificant and uncertain parameter estimate). Thus,
solar radiation, rather than potential evapotrans-
piration, is considered to be the confounder. Po-
tential evapotranspiration is therefore excluded
from the equation.
Mean annual temperature, next to solar radia-
tion, did not affect the regression coefficient of
SWAP. Adding national GDP changes the regres-
sion coefficient of SWAP slightly, from 2.24 ·10
)2
to 2.08 ·10
)2
, which is nevertheless more than a
5% change, so the hypothesis that national GDP
confounds with SWAP is accepted. The quadratic
term of SWAP as well as the interaction term with
local GDP remained insignificant.
The final coefficient of SWAP is thus estimated at
2.08 ·10
)2
, which is the adjusted estimate when
correcting for the effect of national GDP and solar
radiation on yields.
Comparison with Field Data
The observed regression coefficient of SWAP de-
rived at the NUTS3 level can now be used to cal-
culate yield losses in response to SWAP reductions
by erosion. If we assume that an average soil has a
plant-available volumetric water content of about
150 mm m
)1
, then a soil loss of 0.1 m would cor-
respond to a SWAP reduction of 15 mm. The esti-
mated regression coefficient of 2.08 ·10
)2
for
SWAP implies that a soil loss of 0.1 m would result
in a yield loss of 0.31 t (0.1 m equals 15 mm, times
2.08 ·10
)2
tmm
)1
= 0.31 t). If we compare this
reduction to the average yield of 6.31 t ha
)1
y
)1
(Table 1) we arrive at a relative loss of 4.9%,
which is close to the average yield loss of 4.3% per
0.1 m of soil loss from a regression analysis of the
field data collected using the ‘comparing plots‘
method (Bakker and others 2004).
The Effect of Soil Erosion on Europe‘s Crop Yields 1213

Some comparisons with individual experiments
can also be made. Mokma and Sietz (1992) found
yield differences of 17.8% when comparing a soil of
0.92 m depth to a soil of 0.56 m depth in south-
central Michigan. Assuming a plant available vol-
umetric water content of 150 mm m
)1
, the two
soils correspond to SWAP values of 138 and
84 mm, respectively. The regression coefficient
derived here translates this into a reduction of
1.1 t ha
)1
, which equals a loss of 16% relative to
an assumed yield of 7 t ha
)1
y
)1
for the 0.92 m
soil. Likewise, Schumacher and others (1994)
found yield reductions of 7% when comparing a
0.75-m depth soil to a 0.59-m depth soil in the
north-central United States. Assuming again volu-
metric water content of 150 mm m
)1
, the two soils
have SWAP values of 113 and 89 mm, respectively.
The regression coefficient derived in this research
translates this in a reduction of 0.5 t ha
)1
y
)1
,
which equals a loss of 8% relative to an assumed
productivity of 6 t ha
)1
y
)1
for a 0.75 m depth soil.
To investigate the effect of very severe erosion,
Schumacher and others (1994) compared a 0.69 m
depth soil with a 0.20 m depth soil, for which they
found a 26% yield difference. Assuming a SWAP of
respectively 103 and 35 mm, we find a yield dif-
ference of 1.4 t ha
)1
y
)1
, which equals a reduction
of 25% relative to an assumed productivity of
5.5 t ha
)1
y
)1
for a 0.69 m soil.
Spatially Explicit Projection of Erosion-
Induced Yield Reductions 100 Years
from Now
The obtained regression coefficient can be used to
assess the effect of erosion on future yields in
Europe. In Table 3the effect of erosion on yields in
the future is shown for three different European
agro-ecosystems, ranging from highly productive
agriculture with relatively low erosion rates to the
marginal systems with low productivity and high
erosion rates assuming the three scenarios de-
scribed above (continuing trends, ceasing trends
and the UNEP scenario). For the three scenarios
and the three agro-ecosystems, yields were calcu-
lated with and without erosion. The percentages
shown are the difference in yields between the
erosion and the non-erosion variant, proportional
to the yield of the non-erosion variant.
In the intensive, low erosion agro-ecosystems the
impact of erosion is small, but it increases with
smaller yields, smaller trends in yields and
Table 1. Descriptive Statistics
Variable Mean SD Min. Max.
Yields (t ha
)1
y
)1
) 6.13 1.56 1.43 9.08
Erosion (t ha
)1
y
)1
) 1.37 2.73 0.01 17.97
SWAP (mm) 121 24 54 204
ET
pot
(mm y
)1
) 665 156 431 1259
Rainfall (mm y
)1
) 669 111 421 1057
Temperature (C) 10.24 1.92 5.31 16.82
Radiation (kJ m
)2
day
)1
) 13786 1365 11457 17209
National GDP (eÆperson
)1
y
)1
) 19266 2782 12912 20943
Local GDP (eÆperson
)1
y
)1
) 16424 3418 7754 29914
Sample size = 300, consisting of 94 observations in France, 160 observations in Germany (former BRD), and 46 observations in Greece.
Table 2. Assessing the Regression Coefficient of SWAP on Yields
Variables involved
next to SWAP
Coefficient
of SWAP
Interactions
with SWAP
Quadratic term
of SWAP
R
2
– 4.71 ·10
)2
0.41
– 0.14 )3.68 ·10
)4
0.44
GDP
loc
0.10 )4.14 ·10
)6
NS 0.54
ET
pot
2.69 ·10
)2
NS NS 0.73
Radiation 2.24 ·10
)2
NS NS 0.86
Radiation, GDP
nat
2.08 ·10
)2
NS NS 0.86
1214 M. M. Bakker and others

Table 3. Yields (t ha
)1
y
)1
) and the (Relative) Effect of Erosion on Yields in 2035, 2050 and 2100 for Three
Typical European Agro-Ecosystems
Yield in 2035 Yield in 2050 Yield in 2100
Intensive system with low erosion rates: current yield = 8 t ha
)1
y
)1
; trend = 0.10 t ha
)1
y
)1
; erosion = 1 t ha
)1
y
)1
Linear extrapolation of current trends Without erosion 11.50 13.00 18.00
With erosion 11.49 12.99 17.98
Relative impact of erosion 0.1% 0.1% 0.1%
UNEP scenario Without erosion 10.63 10.83 11.48
With erosion 10.62 10.81 11.45
Relative impact of erosion 0.1% 0.1% 0.2%
No further increase Without erosion 8.00 8.00 8.00
With erosion 7.99 7.99 7.98
Relative impact of erosion 0.1% 0.2% 0.3%
Intermediate system: current yield = 4 t ha
)1
y
)1
; trend = 0.04 t ha
)1
y
)1
; erosion = 5 t ha
)1
y
)1
Linear extrapolation of current trends Without erosion 5.40 6.00 8.00
With erosion 5.36 5.94 7.88
Relative impact of erosion 0.8% 1.0% 1.5%
UNEP scenario Without erosion 5.05 5.13 5.39
With erosion 5.01 5.07 5.27
Relative impact of erosion 0.8% 1.2% 2.2%
No further increase.Ceasing trends Without erosion 4.00 4.00 4.00
With erosion 3.96 3.94 3.88
Relative impact of erosion 1.1% 1.5% 3.0%
Marginal system with high erosion rates: current yield = 2 t ha
)1
y
)1
; trend = 0.02 t ha
)1
y
)1
; erosion = 10 t ha
)1
y
)1
Linear extrapolation of current trends Without erosion 2.70 3.00 4.00
With erosion 2.62 2.88 3.76
Relative impact of erosion 3.1% 4.0% 6.0%
UNEP scenario Without erosion 2.53 2.57 2.70
With erosion 2.44 2.44 2.45
Relative impact of erosion 3.3% 4.7% 8.9%
No further increase Without erosion 2.00 2.00 2.00
With erosion 1.92 1.88 1.76
Relative impact of erosion 4.2% 6.0% 12.0%
The three scenarios sketch the widest possible range. Both the extreme scenarios are unlikely, whereas the UNEP scenario is more plausible. The three systems are representative
of the European range of yields and erosion rates.
Figure 1. The relative loss in wheat
yields due to soil erosion in 100 years,
calculated for NUTS3 units and based
on the UNEP yield scenario.
The Effect of Soil Erosion on Europe‘s Crop Yields 1215

increasing erosion rates. Yield reductions are
maximum 12% and this value is only reached for a
marginal agricultural system.
In Figure 1, the projected relative productivity
losses after 100 years are shown for the countries
studied, based on the UNEP scenario. For large
parts of Northern Europe the effect of erosion on
productivity is negligible, whereas in southern
Europe the effect of erosion is clearly manifest. In
Greece, erosion-induced yield reductions some-
times exceed 15%, which is partly attributable to
the high erosion rates and partly to the low yields.
Absolute yield losses after 100 years incidentally
exceed 0.5 t ha
)1
y
)1
in France (Pyrenees-Atlan-
tiques), Italy (Sondrio, Perugia) and Portugal
(Pinhal Interior Sul).
In Figure 2the projected relative productivity
losses after 100 years are depicted per country,
based on the UNEP scenario. Greece has the
highest foreseen reduction of up to 3.8%, fol-
lowed by Portugal (3.1%), Spain (2.4%) and Italy
(2.4%).
ANALYSIS
A statistical analysis shows that the impact of SWAP
on yields confounds with the impacts of climate and
economic variables. The non-linearity of the impact
of SWAP on yields was obscured when confounding
variables were entered into the regression. Cor-
recting for this confounding has resulted in an im-
pact of SWAP on yield equal to a 20.8 kg ha
)1
y
)1
difference between units that differ 1 mm in SWAP.
If this relationship is projected along moderate yield
increases, erosion-induced yield losses in Europe
will amount to a few percentage points in the
coming century. Effects of erosion-induced losses in
soil nutrients were not taken into account, but in
the case of Europe the application of fertilizers is
likely to compensate for any loss in intrinsic soil
fertility. This may not be the case in other parts of
the world, so the results presented here cannot be
extrapolated outside of Europe.
The erosion-induced yield reductions found here
are of a similar magnitude to those derived at the
plot-scale suggesting that the hypothesized scale
effects may be absent. This implies that: (1) the
reallocation of land use within NUTS3-units to
avoid erosion prone areas or to abandon strongly
eroded areas rarely happens (due to regulations,
lack of farmers‘ perception of the problem of ero-
sion, or due to a lack of available land); and/or (2)
that the possible positive effects of deposition are
unable to offset the negative effects of erosion.
Consequently, for these two reasons erosion im-
pacts directly on agricultural productivity.
Where compensation does occur, that is, down
slope areas profiting from water and sediment
supply from upslope areas, then the in-situ effects
of erosion on productivity must be larger than the
20.8 kg ha
)1
y
)1
per 1 mm SWAP loss.
The results shown in Table 3and Figures 1and 2
indicate that erosion will not have a strong impact
on productivity in highly productive agro-ecosys-
tems. Erosion rates here are generally low, and the
erosion induced SWAP reductions result in yield
reductions that are very small compared to the
overall production. In more marginal agro-ecosys-
tems, however, the impact of erosion induced
SWAP reductions on productivity can be impor-
tant. In southern Europe, due to both severe ero-
sion in ancient times and slow soil formation rates
(typical for Mediterranean climates, because of low
rainfall and quick mineralization of carbon), soils
are stony and shallow and wheat yields are low.
Here, erosion-induced reductions from 6 to 12%
are projected to occur in the next 100 years,
depending on the contribution of technological
innovations to productivity increases. At the na-
tional level (Figure 2) agricultural productivity in
Greece in 100 years from now is likely to be re-
duced by approximately 3.5% as a result of soil
erosion. Also in Portugal, Italy and Spain, reduc-
tions are considerable, provided that erosion rates
on agricultural land in these countries are as high
as the PESERA model predicts.
Despite these regional differences, the hypothesis
that erosion poses a serious threat to Europe‘s
agricultural production is not likely to be correct
under current land use and current erosion rates.
0
1
2
3
4
eh
tNt
eeh rls
dn
a
l
erI dna
Luxembourg
m
u
i
gl
e
B
e
Grmay
n
Dne a
mrk
Austria
France
Italy
Spain
Portugal
Greece
ehtotgnidroccassoldleiyevitaleR
)%(sraey001retfaoiranecsPENU
Figure 2. The relative reduction in wheat yield due to
soil erosion in 100-years based on the UNEP yield sce-
nario, plotted per country.
1216 M. M. Bakker and others

Development of artificial fertilizers on the one
hand, and the strong increasing trends of yields are
likely to have made modern agriculture less sensi-
tive to the harmful effects of erosion, compared to
agriculture in ancient civilizations as those men-
tioned in the introduction. It should nevertheless
also be taken into account that the time scale used
in this study is relatively short. We show that the
agricultural productivity of Europe is not directly
threatened in the next 100 years, but the lifespan
of a civilization far exceeds this time period. Al-
though the effects identified are relatively minor
over 100 years, they can be of major importance at
the millennial time scale.
CONCLUSION
Although erosion may have threatened food sup-
plies in the past, it appears that for Europe tech-
nological innovations make agricultural
productivity more resistant to the detrimental im-
pacts of soil loss. From the quantitative relation-
ships presented in this paper we conclude that crop
yields in Europe are unlikely to be significantly
reduced by current levels of soil erosion within the
coming century. There is nevertheless considerable
spatial variability in the effect of erosion. For the
Mediterranean area the threat of erosion-induced
yield reductions is stronger than it is for Northern
Europe. Greece is likely to suffer the largest
reductions, followed by Portugal, Italy, Spain and
the south of France. Furthermore, the observed
yield reductions due to reductions in SWAP appear
to be consistent across scales, which implies that
soft wheat is not reallocated to more favorable
areas to maintain productivity.
These conclusions hold for (1) Europe, albeit
more for the temperate zone than for the semi-arid
zone; for (2) current erosion rates and for (3) cur-
rent agricultural land use. In regions where nutri-
ents are a potentially limiting factor, such as parts
of Sub Saharan Africa, impacts of erosion may be
much larger. If erosion rates accelerate, for exam-
ple, due to climatic change, yield reductions may
become stronger. If agriculture were to expand to
more marginal areas, yield reductions would also
become stronger.
The fact that erosion is no direct threat to agri-
cultural productivity does not imply that it should
not be controlled, as the harmful effects of soil
erosion on the environment should not be ignored.
Increasing production inputs to compensate for
nutrient losses arising from erosion has negative
effects on the sustainability of agricultural prac-
tices. This includes the increasing costs and carbon
emissions of fertilizer manufacture and the offsite
effects of fertilizer, pesticide, and herbicide runoff
on the fragile ecological balances of many terres-
trial and aquatic ecosystems. The focus of future
discussions on the impacts of erosion should
therefore include the complex relationships be-
tween agricultural needs, productivity, and the
ecological and social impacts rather than produc-
tivity alone.
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