Directory UMM :Data Elmu:jurnal:A:Agriculture, Ecosystems and Environment:Vol81.Issue3.2000:

Agriculture, Ecosystems and Environment 81 (2000) 179–190

Assessment of soil erosion vulnerability in western Europe
and potential impact on crop productivity due to loss of soil depth
using the ImpelERO model
D. de la Rosa∗ , J.A. Moreno, F. Mayol, T. Bonsón
Instituto de Recursos Naturales y Agrobiolog´ıa de Sevilla (IRNAS), Consejo Superior de Investigaciones Cient´ıficas (CSIC),
P.O. Box 1052, 41080 Sevilla, Spain
Received 22 July 1999; received in revised form 13 December 1999; accepted 28 March 2000

Abstract
Soil erosion continues to be a major concern for the development of sustainable agricultural management systems. Sustainability modelling analysis for soil erosion must include not only vulnerability prediction but also address impact and response
assessment, in an integrated way. This paper focuses on the impact of soil erosion on crop productivity and the accommodation
of agricultural use and management practices to soil protection. From the Andalucia region in Spain, soil/slope, climate and
crop/management information was used to further develop an expert-system/neural-network soil erosion predicting model
(named ImpelERO). Based on soil tolerance to water erosion, three regression equations were formulated to examine the
effects of soil depth loss on crop productivity reduction. Also, a computerised procedure was developed to find a combination
of management practices which would minimise soil loss in each field-unit. The overall approach of ImpelERO was applied
in 20 selected benchmark sites from western Europe to quantify the soil erosion vulnerability with several crops, the impact
of soil erosion on crop production, and the optimum management strategies. In the Mediterranean sites, soil losses reach
an average of almost 50 Mg ha−1 per year, the potential impact of soil erosion on the crop productivity was very important

(up to 48% reduction in the 2050 time horizon), and the farming practices can be widely modified to protect environmental
qualities. The results of this benchmark site analysis of soil erosion, however, must not be extrapolated to large geographical
areas without additional spatialisation studies. © 2000 Elsevier Science B.V. All rights reserved.
Keywords: Water erosion; Quantifying soil loss; Crop productivity; Farming by soil; Expert system; Neural network; Andalucia region;
Western Europe

1. Introduction
Although soil
as a serious and
ical distribution
roughly known.

degradation is generally recognised
wide-spread problem, its geographand total areas affected are only
This redistribution process of soil

∗ Corresponding author. Tel.: +34-954-624711;
fax: +34-954-624002.
E-mail address: diego@irnase.csic.es (D. de la Rosa).

within the landscape must be considered by land and
water resource managers in order to evaluate the consequences of their management decisions. Also, it
is necessary to separate the ‘current status’ from the
‘future risk or soil vulnerability’. According to the
GLASOD study (Oldeman et al., 1991), which reflects
the present status of human-induced soil degradation,
about 1.5 billion ha are irreversibly degraded by soil
erosion process on a world-wide basis. Soil erosion is
an increasing phenomenon in Europe (Blum, 1990).

0167-8809/00/$ – see front matter © 2000 Elsevier Science B.V. All rights reserved.
PII: S 0 1 6 7 - 8 8 0 9 ( 0 0 ) 0 0 1 6 1 - 4

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D. de la Rosa et al. / Agriculture, Ecosystems and Environment 81 (2000) 179–190

In parts of the Mediterranean region, erosion has
reached a stage of irreversibility and in some places
soil erosion has practically stopped through lack of

soil. With a very slow rate of soil formation, any soil
loss of more than 1 Mg ha−1 per year (t ha−1 per year)
can be considered irreversible within a time span of
50–100 years. Losses of 30–40 Mg ha−1 in individual storms that may happen once every 1 or 2 years
are measured regularly in the European Union, with
losses of more than 100 Mg ha−1 in extreme events
(Van Lynden, 1994). In order to assess future risks of
soil erosion, more precise and quantitative information is needed on the soil attributes, climate variables,
present land use and vegetation cover, along with new
soil erosion predicting procedures.
These predicting procedures must focus not only
on the future risk or vulnerability to soil erosion,
but also on the potential impact on crop productivity. The relative productivity of soils and its rate of
change due to erosion depend on the presence of
favourable rooting characteristics in the soil profile.
When a soil comes into production, its productivity
may remain at a sustained level for some time. If
nutrients are replaced and the soil receives good management, the soil may continue to produce crops at a
sustained level for an indefinite period. However, as
erosion removes the upper soil profile, productivity

will decline if the subsoil is limiting for crop growth.
Some land evaluation studies (e.g. Hurtado and De la
Rosa, 1982; Pierce et al., 1983) consider long-term
changes in soil productivity due to erosion, quantifying the change in productivity of different soils,
as successive increments of soil depth are lost by
erosion.
In recent years, scientists working with new
approach in agricultural production: farming by soil,
precision agriculture, plot specific management or
soil tillage research, have provided new research information on pertinent processes related to soil tillage
in order to prevent and reduce soil erosion (Robert
et al., 1993; Horn et al., 1998). Agricultural management operations according to spatially varying land
characteristics have the added difficulty of trying to
satisfy multiple, and often opposing, objectives; the
best soil conditions for plant growth (crop yield) may
not be the best for erosion concerns (natural resource
conservation) or pollution effect (environmental
impact) (Voorhees et al., 1993). In topographically

complex areas, tillage erosion rates appear to be

equal or exceed water erosion rates, and soil redistribution by tillage contributes to landscape sensitivity
to water erosion (Poesen et al., 1990). In any case,
soil survey and land evaluation must be the basic
building blocks for developing the comprehensive
data set needed to drive these specific soil farming
studies.
In this paper, an additional development of the recently proposed approach for predicting agricultural
soil erosion vulnerability in Andalucia region (ImpelERO model; De la Rosa et al., 1999) was carried
out in order to investigate the effects of soil loss
on crop yield. Alternative management practices for
diminishing soil erosion were also analysed. In addition, the overall approach was applied in 20 selected
benchmark sites from western Europe to estimate the
agricultural soil erosion vulnerability, along with the
potential impact of soil erosion on crop production,
and the accommodation of management practices for
this vulnerability reduction.

2. Materials and methods
2.1. Study sites, spatial information
The input data considered for the modelling and application analyses can be grouped in three categories:

soil/slope data, climate data and crop/management
knowledge. The ImpelERO evaluating unit is the
field-unit as a contiguous tract of land (soil/slope
and climate) with uniform land characteristics under
a specific land use (crop/management). The spatial pattern of a field-unit is a parcel, as part of a
farm, used in this work as a synonym of benchmark
site.
2.1.1. Andalucia agricultural area
In the modelling area, Andalucia (Fig. 1), natural
resources survey data and observed soil erosion and
productivity data were collected for the design, training and testing of the ImpelERO soil erosion model
(De la Rosa et al., 1999). Since 1990, the Land Evaluation Unit of the IRNAS has selected 34 observation
sites throughout the lowland Andalucia region for land
characterisation and for productivity and degradation
monitoring purposes. Dominant soils datasets, which

D. de la Rosa et al. / Agriculture, Ecosystems and Environment 81 (2000) 179–190

181

Fig. 1. Location of the modelling area: Andalucia, and the selected 20 benchmark sites for application within the three biogeographycal
regions of western Europe.

include soil descriptions and soil chemical and physical properties along with site slope characteristics,
were obtained from the SDBm–IRNAS soil database
(IRNAS, 1996a).
Representative meteorological stations were also
selected from the CDBm–IRNAS climate database
(IRNAS, 1996b), analysing monthly mean temperature and precipitation for the 1961–1990 period.
The agricultural management experience for the
field-units was captured from the Andalucia farmers
with reference to the following traditional crops: rainfed and irrigated winter wheat (Triticum sp.), rainfed
and irrigated winter sugarbeet (Beta vulgaris L.),
rainfed and irrigated spring sunflower (Helianthus
sp.) and oil and green olives (Olea sp.). The selected
field-units cover the whole range of erosion events
from what was considered very small to extreme
erosion (De la Rosa et al., 1999).

2.1.2. Western European sites

In the application area, western Europe (Fig. 1), soil
profiles dataset from the EU 12 Soil Map 1:1 000 000
(CEC, 1985) corresponding to 20 selected benchmark
sites in Europe were used (Table 1). These selected
benchmark sites correspond to three bio-geographical
regions: Mediterranean, Atlantic and Continental. The
dominant soils per region are: Luvisols and Vertisols
in the Mediterranean region, Cambisols in the Atlantic
region, and Cambisols, Phaozems and Luvisols in the
Continental region. Slope class and form from the soil
map unit of each benchmark site were considered. The
flat or almost flat and terraced conditions were the
dominant slope characteristics. The greatest variability
of slope was found in the Mediterranean region, and
the smallest in the Continental region.
Dataset files on climate data for meteorological stations closer to the selected benchmark sites (Table 1)

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D. de la Rosa et al. / Agriculture, Ecosystems and Environment 81 (2000) 179–190

were compiled from the World Weather Disc (WDA,
1994). Almost all the climatic data, monthly means
of temperature and precipitation, correspond to the
1951–1987 period. Irrigation surplus of water was not
considered in any case.
The following cropping systems were considered
on the selected application sites: winter wheat, winter
sugarbeet and spring sunflower. Table 2 summarises
for each cropping system, the agronomic practices
which were assumed for all the benchmark sites and
used as input data in the ImpelERO model application. The baseline and estimated yields were used to
derive the productivity level. The tillage implements
sequence, along with the number of times applied and
the workability consideration by the farmer, were also
established. These management practices, with special

183

reference to the tillage operations, are frequently
applied for the selected crops in rainfed conditions.
2.2. Methodology
2.2.1. Soil depth loss and crop productivity
Soil depth loss was calculated using the ImpelERO
model (De la Rosa et al., 1999). This model was
developed as an Universal Soil Loss Equation-type
model following traditional land evaluation analysis
and advanced empirical modelling techniques. Using expert-decision trees, soil survey information and
expert knowledge of the soil erosion process were
combined with land and management qualities. An
artificial neural-network approach was then applied
to capture the interactions between the land and

Table 2
Agricultural management practices for farming the testing crops, and assumed to be used in the selected European benchmark sites
Cropping
system

Crop
type

Residue
treatment

Tillage
system

Row spacing
(m)

Baseline
yielda
(Mg ha−1 )

Estimated
yieldb
(Mg ha−1 )

Tillage operation
Implement

Timec Workabilityd

Wheat crop
Rainfed
Winter crop Grazing

Band

0.15

1.5

2.0

Plow moldboard
Disk cultivator
Fertiliser applicator
Drill conventional
Fertiliser applicator
Spray implement

1
1
1
1
1
2

No
Yes
No
Yes
Yes
Yes

Sugarbeet crop
Rainfed
Winter crop Buried

Traditional 0.60

40.0

45.0

Plow moldboard
Spray implement
Disk cultivator
Harrow-roller
Fertiliser applicator
Drill conventional
Spray implement
Fertiliser applicator
Field cultivator

2
2
2
1
1
1
1
1
2

No
No
No
No
No
Yes
Yes
No
Yes

Sunflower crop
Rainfed
Spring crop Buried

Traditional 0.75

1.2

1.7

Plow moldboard
Disk cultivator
Disk cultivator
Drill conventional
Spray implement
Field cultivator
Disk 1-way

1
1
1
1
1
3
1

Yes
Yes
No
Yes
Yes
Yes
Yes

a

Mg ha−1 corresponds to megagram (t) per hectare; baseline yield is the actual value of crop production from statistical sources.
Estimated yield is the predicted value of crop production by using simulation models.
c Number of times that an implement is used.
d Workability status makes reference if (yes or no) the optimum soil water content for each tillage operation is considered by the farmer.
b

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D. de la Rosa et al. / Agriculture, Ecosystems and Environment 81 (2000) 179–190

management qualities and one output, vulnerability
index to soil erosion.
Crop productivity was calculated by application of
the MicroLEIS–ALBERO yield prediction model (De
la Rosa, 1996). This soil evaluation model is a regression equation to predict winter wheat yield from
several soil characteristics. The X-variables are useful depth of soil, depth to soil hydromorphic features,
soil carbonate content, soil salinity, sodium saturation,
cation exchange capacity and clay content.
2.2.2. Management practices accommodation
On the basis of the expert-system/neural-network
structure of the ImpelERO model (De la Rosa et al.,
1999), a computerised procedure was followed to find
an adequate combination of management practices
to minimise soil loss for each field-unit (specified
climate and soil characteristics). The procedure formulates up to 64 different management schemes specific to control erosion. Other management practices
which referred to plant growth or pollution impact
(e.g. fertiliser, seed and chemical application), were
not considered.
This computerised approach of agricultural management strategies on soil erosion reduction is summarised in Fig. 2. As a first step and for one particular
field-unit (fixed land qualities, LQs), the user can establish a percentage of vulnerability reduction (R) of
the actual vulnerability index (Va ) in order to calculate
the target vulnerability index (Vt ). As a second step,
64 applications of the neural-network (four possible
values of the three management qualities, MQs,=43 )
were made in order to calculate the vulnerability index (Vj ) which is closer to the target index. Then, the
combination of MQs which corresponds to the Vj was
selected. As a third step, the decision trees were backtracked by using the selected combination of MQs to
finally formulate the optimum management strategies.
2.2.3. Computer environment
The computer program to automate the ImpelERO
model application includes the additional development presented in this paper. This software developed
in Borland C++ builder for running on PC-platform
under WINDOWS 95 and NT environment, along
with an installation and user guide, may be obtained free by visiting the MicroLEIS website http://
www.irnase.csic.es/microlei.htm.

Fig. 2. General scheme of the automated neural-network-based
search and the decision trees backtracking to accommodate the
management practices (MQs=management qualities) to a percent
of soil erosion reduction, where Va =actual vulnerability index,
Vt =target vulnerability index, R=desired vulnerability reduction
and Vj =possible vulnerabilities for fixed land qualities (LQs).

Also, a generalisation mechanism, which is part
of the computer environment developed, allows for
better adjustment of the model to regions other than
Andalucia. It also offers the possibility to change, in
the input data step, the correspondence between the
survey parameter values and the generalisation levels considered for the Andalucia region. The future
use and validation of this generalisation mechanism,

D. de la Rosa et al. / Agriculture, Ecosystems and Environment 81 (2000) 179–190

without additional retraining of the model, would
widen the application range to different environments.
3. Results and discussion
3.1. Modelling long-term changes in crop
productivity
In this paper, the impact of soil erosion on the crop
productivity was analysed as a following step of the
prediction modelling of soil loss by water erosion already developed for the ImpelERO model (De la Rosa
et al., 1999). Following these predicting procedures,
the rate of change of the relative productivity of soils
due to erosion can be calculated on the basis that
favourable rooting characteristics are present in the
soil profile. As erosion removes the upper soil profile,
productivity will decline if the subsoil is limiting for
crop growth. The effect of diminishing soil organic
matter and nutrient contents on crop productivity is
not considered in this analysis.
The concept of soil loss tolerance was introduced
in order to give a measurement of how much erosion
a soil could tolerate before experiencing excessive
damage. In general terms, the soil loss tolerance (T
value) is defined as the maximum rate of annual soil
erosion that will permit a high level of crop productivity to be sustainable. This T value is considered to
range from 1 to 10 Mg ha−1 per year depending upon
intrinsic soil profile characteristics (Larson, 1981).
According to the profile characteristics of the dominant soils of Andalucia, the soils were grouped as
follows:
1. Soil group A (maximum tolerance): corresponds
to soils with favourable characteristics to a depth
exceeding 120 cm. The deep alluvial soils (Fluvisols) on recent Holocene plains bordering the
Guadalquivir River, along with the typical Vertisols on rolling Tertiary hills, are examples of soils
in this group.
2. Soil group B (medium tolerance): corresponds
to soils with favourable surface horizons but unfavourable subsoils. The useful depth for crop
growth is between 75 and 120 cm often due to fine
or coarse texture. Soils developed on Pleistocene
terraces (Luvisols, dominantly) exemplify the soils
in this group.

185

3. Soil group C (minimum tolerance): corresponds
to soils with favourable surface horizons and
consolidated or coarse-fragment (rock or gravel),
hydromorphic and/or extremely calcareous subsoils. The useful depth for crop growth is less
than 75 cm. Soils developed on carbonate-rich Tertiary sediments and conglomerate formations in
Plio-pleistocene surfaces (Regosols and Planosols,
dominantly) are examples of soils in this group.
Soil erosion at the rate estimated by the ImpelERO
model was simulated for four time horizons: current,
2020, 2050 and 2100. Vulnerability index, soil loss
and depth reduction rates are facilitated directly by
the model application; and the corresponding three
soil depth reductions are calculated using the depth
reduction rate.
After calculations of soil erosion, crop productivity
was calculated at each time horizon by application
of the MicroLEIS–ALBERO wheat yield prediction
model (De la Rosa, 1996). The predicted yield (PY)
was converted to productivity index (Pi) as follows:
Pi =

PY
max (PY)

(1)

where max (PY) was considered to be 5.5 Mg ha−1 .
By using this procedure for the four time horizons,
the rooting depth moved down the profile as soils
eroded, unless some limiting layer occurred in the
first 120 cm or until a limiting layer was encountered.
For such soils, that portion of the rooting depth below
the limiting layer is excluded from the productivity calculation. In other words, the depth of rooting
would be less than 120 cm. For soils with undesirable
soil characteristics in the lower portion of the profile,
productivity declines as erosion proceeds. In contrast,
erosion would not likely impair the productive capacity of soils with favourable characteristics in the
lower portion of the profile.
Fig. 3 shows productivity changes with three simulated time horizons: 2020, 2050 and 2100, for the 34
testing field-units. For 13 soils with maximum tolerance (Fig. 3, soil group A), an initial productivity index
of 0.65 was calculated. While the current erosion rate
averages 29 Mg ha−1 per year, the productivity index
dropped to 0.62, only a loss of 5%, for the time horizon 2100 of simulated erosion. Soils in this group will
continue to produce at high levels for extended periods
of time if needed plant nutrients are supplied. For the

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D. de la Rosa et al. / Agriculture, Ecosystems and Environment 81 (2000) 179–190

Fig. 3. Productivity changes with the time horizon for the 34 testing field-units in Andalucia region. The average soil loss rates were
calculated: 29 Mg ha−1 per year for 13 soils of the group A, 16 Mg ha−1 per year for 10 soils of the group B, and 37 Mg ha−1 per year
for 11 soils of the group C.

three simulated time horizons, the regression equation
of productivity reduction in percentage (%) versus soil
depth reduction in centimetre (cm) is as follows:
Y = 0.22X − 0.36 (r 2 = 0.92; n = 36)

(2)

For 10 soils with medium tolerance (Fig. 3, soil
group B), erosion initially resulted in only slight
reductions of productivity. With continued erosion,
productivity declined progressively as, for example,
roots encounter more and more of the firm, moderately permeable material. An initial productivity
index of 0.61 was calculated, with a 12% reduction
to 0.54 if the average erosion rate of 16 Mg ha−1 per
year continued for the next 100 years. Yield reductions were not uncommon as topsoil depth decreased
on soils with unfavourable subsoils. For the three
simulated time horizons, the regression equation of
productivity reduction in percentage versus soil depth
reduction in centimetre is as follows:


(3)
Y = 0.92X − 0.07,
r 2 = 0.98; n = 30
For 11 soils with minimum tolerance (Fig. 3, soil
group C), if erosion occurs, productivity was sustained
for a short time, but productivity dropped sharply with
continued erosion. It was calculated that if erosion

continued at its current average rate, 37 Mg ha−1 per
year, the productivity index would drop from 0.56 to
0.39, a loss of 30%, at the time horizon 2100 of simulated erosion. For the three simulated time horizons,
the regression equation of productivity reduction in
percentage versus soil depth reduction in centimetre
is as follows:


(4)
Y = 0.97X − 0.53,
r 2 = 0.96; n = 33

3.2. Selecting management practices
Along with the modelling of the impact of soil depth
loss by soil erosion on crop productivity, the selection
of agricultural management practices to minimise soil
erosion vulnerability was also analysed. By using the
developed computer-based approach (Fig. 2) in Andalucia, agricultural management schemes were formulated for the most representative soils and crops.
One of the most repeated schemes with more possibilities to reduce soil erosion vulnerability is as follows:
Cropping system: rainfed crops; crop type: annual
winter crops; row spacing: low (25 mm); workability consideration: yes; and soil stabilizers: any.
The predicted management schemes were generally in good agreement with results of experimental
studies (e.g. De Barros, 1997). This validation test
suggests that the management accommodation module of ImpelERO model could be used by farmers
to select the best management practices for efficient
erosion control.
3.3. Application analysis
3.3.1. Soil erosion vulnerability
The soil erosion vulnerability results of the application of ImpelERO model to the 20 selected benchmark
sites in Europe for wheat, sugarbeet and sunflower, are

Table 3
Results of the CORINE and ImpelERO soil erosion models application to the selected European benchmark sitesa
Benchmark
site

CORINE
model

ImpelERO model
Wheat

Sugarbeet

Sunflower

Vulnerability
indexb

Soil loss rate
(Mg ha−1
per year)

Vulnerability
indexb

Soil loss rate
(Mg ha−1
per year)

Vulnerability
indexb

Soil loss rate
(Mg ha−1
per year)

region
3-high
1-low
1-low
2-moderate
2-moderate
2-moderate
2-moderate

0.57
0.12
0.12
0.13
0.61
0.08
0.84

67.0
3.9
3.9
4.4
78.0
2.8
147.9

0.65
0.15
0.15
0.18
0.70
0.11
0.89

87.3
4.9
4.9
5.9
100.1
3.8
150.0

0.70
0.18
0.18
0.21
0.75
0.14
0.92

99.9
6.0
6.0
7.1
115.7
4.6
150.0

Atlantic region
08
2-moderate
09
1-low
10
1-low
11
1-low
12
1-low

0.29
0.04
0.04
0.04
0.04

9.8
1.4
1.4
1.4
1.4

0.34
0.04
0.05
0.04
0.05

18.4
1.4
1.6
1.4
1.6

0.40
0.05
0.06
0.05
0.06

29.4
1.8
2.0
1.8
2.0

Continental region
13
1-low
14
1-low
15
1-low
16
1-low
17
1-low
18
1-low
19
1-low
20
1-low

0.04
0.04
0.12
0.04
0.04
0.04
0.11
0.04

1.4
1.4
4.0
1.4
1.4
1.4
3.8
1.4

0.05
0.05
0.14
0.04
0.04
0.05
0.12
0.05

1.6
1.6
4.6
1.4
1.4
1.6
4.0
1.6

0.06
0.06
0.17
0.05
0.05
0.06
0.15
0.06

2.0
2.0
5.7
1.8
1.8
2.0
4.9
2.0

Mediterranean
01
02
03
04
05
06
07

a

Mg ha−1 per year corresponds to megagram (t) per hectare and year.
Vulnerability index ranges: 0.00–0.15=very small, 0.15–0.30=small, 0.30–0.50=moderate; 0.50–0.70=large, 0.70–0.85=very large,
0.85–1.00=extreme.
b

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D. de la Rosa et al. / Agriculture, Ecosystems and Environment 81 (2000) 179–190

Table 4
Results of long-term productivity reduction predicted by the ImpelERO model, in three simulated time horizons, for winter wheat cropa
Benchmark Vulnerability
site
index
Mediterranean region
01
0.57
02
0.12
03
0.12
04
0.13
05
0.61
06
0.08
07
0.84

Soil loss rate
(Mg ha−1 per year)

Soil depth reduction
(cm per year)

Soil loss
tolerance,
soil group

Long-term productivity reduction (%)
2020

2050

2100

67.0
3.9
3.9
4.4
78.0
2.8
147.9

0.49
0.03
0.03
0.03
0.53
0.02
1.01

Maximum, A
Medium, B
Medium, B
Maximum, A
Minimum, C
Medium, B
Minimum, C

2
0
0
0
10
0
19

5
1
1
0
25
1
48

10
3
3
0
50
2
97

Atlantic region
08
0.29
09
0.04
10
0.04
11
0.04
12
0.04

9.8
1.4
1.4
1.4
1.4

0.06
0.01
0.01
0.01
0.01

Minimum, C
Minimum, C
Maximum, A
Minimum, C
Maximum, A

1
0
0
0
0

2
0
0
0
0

5
0
0
0
0

Continental
13
14
15
16
17
18
19
20

1.4
1.4
4.0
1.4
1.4
1.4
3.8
1.4

0.01
0.01
0.03
0.01
0.01
0.01
0.03
0.01

Maximum, A
Maximum, A
Medium, B
Medium, B
Medium, B
Minimum, C
Medium, B
Minimum, C

0
0
0
0
0
0
0
0

0
0
1
0
0
0
1
0

0
0
3
0
0
0
3
0

a

region
0.04
0.04
0.12
0.04
0.04
0.04
0.11
0.04

Mg ha−1 per year corresponds to megagram (t) per hectare and year.

soils (Table 3). By comparing the two sets of model
results, ImpelERO and CORINE, it can be concluded
that the use of the more sophisticated ImpelERO
model gave more accurate and discriminate results in
the three regions and for any given crop.
3.3.2. Impact on crop productivity
Table 4 shows the application results of the ImpelERO model for each benchmark site, considering
the type of soil tolerance (soil group A, B or C) and
including the soil loss impact in terms of productivity
changes with time horizon (2020, 2050 and 2100).
The maximum impact according to the long-term
productivity reduction (97%) was shown for the
Odiaxere–Albufeira site in the Mediterranean region
and for the 2100 time horizon. According to the EEA
(1999), the EU Mediterranean countries have severe
soil erosion problems, which can reach the ultimate
stage and lead to desertification. With the present
rates of erosion, considerable areas in these countries

may reach a state of ultimate physical degradation,
beyond a point of no return within 50–75 years. Some
smaller areas have already reached this stage (Van
Lynden, 1994).
In the Atlantic and Continental benchmark sites the
potential effects of soil loss reduction on the productivity reduction was very low (0–5%), with maximum
value in Vendays–Bordeaux site. In any site as shown
in Table 4, the impact of soil erosion on the productivity was low (2–10%) for soils with maximum tolerance (soil group A).
3.3.3. Accommodation of management practices
Table 5 shows the recommended agricultural management for winter wheat to reduce or maintain soil
erosion vulnerability in the 20 European benchmark
sites using the ImpelERO model. The change of management practices to reduce soil erosion was only
feasible in 10 benchmark sites. The actual possibility to reduce the vulnerability index was maximum

D. de la Rosa et al. / Agriculture, Ecosystems and Environment 81 (2000) 179–190

189

Table 5
Recommended management for winter wheat to reduce soil erosion vulnerability by application of the ImpelERO model
Benchmark Erosion
site
vulnerability
Actual
index

Possible
reduction
(%)

Management
characteristic
Row spacing (m)

Residues
treatment

Tillage
system

Surface
roughnessa

Workability
Use of soil
considerationb stabilizers

Mediterranean region
01
0.57
9
02
0.12
17
03
0.12
17
04
0.13
8
05
0.61
10
06
0.08
13
07
0.84
5