Main authors: Lúcia Barão and Gottlieb Basch
Editor: Jane Brandt
Source document: Barão, L. and Basch, G. (2017) Identification of parameter/indicator set for testing and evaluating the impact on soil quality and crop production parameters. iSQAPER Project Milestone 6.2 29 pp


In order to evaluate soil quality in every pedo-climatic zones and under different farming systems and agricultural management practices, it is necessary to develop an index that can translate the existing soil quality (or its absence) into quantifiable classes. In iSQAPER we expedite the development of this quantification, while also respecting the conditions set previously, by evaluating the soil quality status concerning the main soil threats currently impacting on soil across a variety of pedo-climatic conditions. The evaluation of soil quality is then assessed by quantifying the level of damage caused by different threats. 

Contents table 
1. Soil quality index
2. Erosion
3. Compaction
4. Salinization
5. Soil organic matter decline
6. Soil biodiversity loss
7. Soil contamination
8. Acidification
9. Nutrient depletion/surplus
10. References

1. Soil quality index


In order to evaluate soil quality in every pedo-climatic zones and under different farming systems and agricultural management practices, it was necessary to develop an index that could translate the existing soil quality (or its absence) into quantifiable classes. Also, as discussed in »Soil quality - a critical review, several conditions should be taken into account, when developing such a soil evaluation tool:

  • The objective of the soil conceptual index should be clear, i.e. whether the soil quality assessment is meant as a basis for management recommendations, seen as an educational tool, or as part of a monitoring program;
  • The target users should be clear, taking into consideration that concepts focussing on visual soil evaluation tools are more targeted at farmers than concepts requiring quantitative laboratory measurements.
  • Soil Indicators should be based on the targeted soil function or ecosystem services associated or soil threats. Conceptually, soil functions, ecosystem services and threats are all linked and concepts focusing on either of these can thus potentially be reconciled.
  • The possibility to choose between substitute indicators/parameters would be beneficial. For example, missing analytical indicators could be substituted by visual assessments in the field.
  • The possibility to take into account non-soil indicators since it fits the idea to assess soil quality in connection with soil functions and ecosystem services.
  • Also, any soil quality assessment tool should not only give a clear interpretation, but should suggest improved management options.

One way to expedite the development of this quantification, while also respecting the conditions set previously, is to evaluate the soil quality status concerning the main soil threats currently impacting on soil across a variety of pedo-climatic conditions. The evaluation of soil quality is then assessed by quantifying the level of damage caused by different threats. The reason for choosing the soil quality evaluation based on soil threats, rather than soil ecosystem services or soil functions, is the fact that farmers – the end-users of this tool – are more aware of the problems related to their soils when compared to the scientific soil processes descriptions or the ecosystem functions. Also, the classification system established for a soil status regarding soil threats is easier, since due to the problems caused by so many threats nowadays, there is a clear knowledge about the values that translate a soil potentially damaged.

Additionally, by evaluating soil quality through the vulnerability to each soil threat it is easier to consider management options to farmers to counteract specifically the problems detected.

The implementation of such quality index includes the establishment of indicators for each soil threat and a methodology to assess them using soil chemical, physical and biological parameters. Finally, a classification system is established for every soil threat in order to evaluate the soil status.

Since the soil quality index developed during this work is clearly targeting agricultural soils, the number of soil threats considered in the index are those that are considered by the iSQAPER consortium to be the most relevant ones for this context (erosion, compaction, salinization, soil organic matter decline, biodiversity loss, contamination, nutrient depletion/surplus (Figure 1).

M6.2 fig01
Figure 1

Also, an indicator was established – described as a model/methodology/parameter – to correctly assess each threat and the input parameters necessary for its calculation and evaluation. The detailed description on the methodologies used to assess each indicator are described below. 


The calculation and merging of the individual soil quality indicators concerning the different soil threats into one single Soil Quality Index, is a subjective process that should reflect the objective of the evaluation and respond to the major concerns of the end-users. As so, we have considered two types of soil quality indexes so far:

  1. Index AVG – the average index, which considers the same weight for each soil quality indicator;
  2. Index STE – soil threats significance reported by the end-users.

In the second approach, the awareness of end-users regarding which soil threats are more significant in their specific plot/farm will determine the sensibility of the Soil Quality Index towards different threats.

M6.2 eq07

Where SoilIndicator is the value calculated for each soil threat indicator and n is the total number of soil threat indicators.

M6.2 eq08

Where SoilIndicator is the value calculated for each soil threat indicator, significance is the percentage of significance attributed by the end-user to the specific threats and n is the total number of soil threat indicators.

M6.2 fig08
Figure 8
M6.2 fig09
Figure 9

The Soil Quality Index described here was implemented in a simple Visual Basic algorithm using excel sheets. The program is able to calculate the several soil threat indicators, evaluated them and finally calculate the soil quality index (Figure 8), from a set of information, provided by the end-users (Figure 9).

2. Erosion

Erosion is divided in two categories: Erosion by water and Erosion by wind. Although important, erosion by wind is commonly difficult to estimate, since it depends on local weather conditions.

As so, in this study we have only considered Erosion by water. This soil threat level is assessed using Annual Soil Loss as an indicator. Annual Soil Loss is calculated by the overall known RUSLE equation (Panagos et al., 2015d), which calculates mean annual soil loss rates by sheet and rill erosion according to the following equation:

E = R × K × C × LS × P


E: annual average soil loss (t ha-1 yr-1);
R: rainfall erosivity factor (MJ mm ha-1 h-1 yr-1);
K: soil erodibility factor (t ha h ha-1 MJ-1 mm-1),
C: cover-management factor (dimensionless),
LS: slope length and slope steepness factor (dimensionless);
P: support practices factor (dimensionless)

Rainfall erosivity factor (R)

Note: »Access the Global Rainfall Erosivity data set from ESDAC

The erosive power of precipitation is accounted for by the rainfall erosivity factor (R-factor), which gives the combined effect of the duration, magnitude and intensity of each rainfall event. This parameter is obtained through the R-factor map, included in Global Rainfall Erosivity map (from Panagos et al., 2017). As so there is an R-factor assessed for each location (latitude/longitude) as shown in Figure 2.

M6.2 fig02
Figure 2

Soil erodibility factor (K)

Note: »Access the Soil Erodibility (K-factor) high resolution dataset for Europe from ESDAC

The K factor is a lumped parameter that represents an integrated annual value of the soil profile reaction to the process of soil detachment and transport by raindrops and surface flow (Panagos et al., 2014).

As direct measurements of K-factor on field plots are not financially sustainable at the regional or national levels, the soil erodibility nomograph is most commonly used and cited for soil erodibility calculation. An algebraic approximation of the nomograph that includes five soil parameters (texture, organic matter, coarse fragments, structure, and permeability) is proposed by (Wischmeier & Smith, 1978):

M6.2 eq01


M is the textural factor with M = (msilt+mvfs) ∗ (100 − mc);
mc [%] is the clay fraction content (<0.002 mm);
msilt [%] is the silt fraction content (0.002–0.05 mm);
mvfs [%] is the very fine sand fraction content (0.05–0.1 mm);
OM [%] is the organic matter content;
s is the soil structure class (s= 1: very fine granular, s = 2: fine granular, s = 3, medium or coarse granular, s = 4: blocky, platy or massive);
p the permeability class (p = 1: very rapid, …, p=6: very slow).

According to (Wischmeier & Smith, 1978) the equation is restricted to samples for which the silt fraction does not exceed 70%. Also, The very fine sand structure (0.05–0.1 mm as sub-factor mvfs) in the equation is usually not subject of standard soil analysis and may therefore be estimated as 20% of the sand fraction (0.05–2.0 mm).

For soil samples with organic matter content above 4%, the upper limit of 4% should be applied (Wischmeier & Smith, 1978). The application of a 4% limit to soil organic matter intends to prohibit an underestimation of soil erodibility for soils that are rich in organic matter.

Estimate Sub-soil Structure (s): In past studies soil structure was assigned based on soil types of the Food and Agriculture Organization (FAO) (Panagos et al., 2014). A pedotransfer rule for estimating soil structure when no direct measurements are available has been developed (Jones et al., 2003). In the European Soil Database, this pedotransfer rule classifies the soil structure as humic, poor, normal or good (Table 1), using pedological inputs such as the FAO soil name and soil texture.

Table 1: Classification of the subsoil structure based on soil texture

Subsoil structure (s) Description Status
1 Very fine: 1-2 mm G (good) 
2 Fine: 2-5 mm N (normal)
3 Medium or Coarse granular: 5-10 mm P (poor)
4 Blocky, platy or massive: >10 mm H (humic or peaty top soil)

Estimate Soil permeability (p): For the estimation of the soil permeability, classes were assigned according to soil texture classes (Table 2). These soil textural classes have also been employed for the estimation of the range values of saturated hydraulic conductivity (Panagos et al., 2014).

Table 2: Classification of soil permeability based on texture

Permeability class (p) Texture   Texture Saturated hydraulic conductivity, mm h-1 
1 (fast and very fast) Sand  >61.0
2 (moderate fast) Loamy sand, sandy loam 20.3–61.0 
3 (moderate) Loam, silty loam 5.1–20.3
4 (moderate low) Sandy clay loam, clay loam 2.0–5.1
5 (slow) Silty clay loam, sand clay 1.0–2.0
6 (very slow) Silty clay, clay <1.0

Cover management factor (C)

Note: »Access the Cover Management Factor (C) for the EU dataset from ESDAC

Arable Lands: Agricultural and management practices play an important role in controlling soil erosion (Panagos et al., 2015b). The C factor in arable lands (Carable) is calculated depending both on the crop type (Ccrop) growing in the soil and the management used (Cmanagement).

Carable = Ccrop × Cmanagement

(a) Ccrop – the crop factor: In the case of fallow land, Eurostat statistics consider three types of land use as fallow land: (a) bare land bearing no crops, (b) land with spontaneous natural growth which may be used as animal feed, and (c) land sown exclusively for the production of green manure (Table 3).

Table 3: Crop factor

Crop Type Ccrop
Common wheat and spelt 0.20
Durum wheat 0.20
Rye 0.20
Barley 0.21
Grain maize – corn 0.38
Rice 0.15
Dried pulses (legumes) and protein crop 0.32
Potatoes 0.34
Sugar beet 0.34
Oilseeds 0.28
Rape and turnip rape 0.30
Sunflower seed 0.32
Linseed 0.25
Soya 0.28
Cotton seed 0.50
Tobacco 0.49
Fallow land 0.50

When Erosion by water is being calculated for a plot/farm where different crop types are mixed, the Ccrop factor calculated should take into account the average weight of each crop in that farm.

M6.2 eq02


Ccrop is the factor for the farm/plot
n is the number of different crop types
i is one particular crop type
βi is the occupation of a particular crop (in area) in the total farm – value between 0 and 1

(b) Cmanagement – the management factor: The management factor quantifies the effect of management practices (tillage practices, cover crops, plant residues) on reducing soil loss from agricultural lands (Panagos et al., 2015b).

The combined effect of tillage practice (Ctillage) and plant residues (Cresidues) or cover crops (Ccover) is also taken into account for the estimation of management factor (Cmanagement).

Cmanagement = Ctillage × Cresidues × Ccover

The soil erosion by water is affected by tillage, depending on the depth, direction and timing of ploughing, the type of tillage equipment used, and the number of passages made. Generally, the less the disturbance of vegetation or residue cover at or near the surface, the more effective is the tillage practice in reducing soil erosion by water. The factors are:

Ctillage = 1 for conventional tillage;
Ctillage = 0.35 for conservation/ridge tillage;
Ctillage = 0.25 for no till practices.

In cropland, sheet and rill erosion are reduced by leaving adequate residue on the ground after the harvest. However, farmers often plow the land after harvesting, which leads to erosion. Maintaining crop residues on soil surfaces not only protects the soils from splash erosion, but also increases infiltration rates (Panagos et al., 2015b). As so the residue cover factor (Cresidues) is dependent on the percentage of land covered with plant residues (Fresidues).

Cresidues = 1 × (0.88 × Fresidues) + (1 - Fresidues)

Where Fresidues is the fraction of arable land treated with plant residues [0. . .1].

Cover crops reduce soil loss by improving soil structure and increasing infiltration, protecting the soil surface, scattering raindrop energy and reducing the velocity of the movement of water over the soil surface (Panagos et al., 2015b). As so the cover crops factor (Ccover) is dependent on the percentage of land with cover crops (F(crop-cover)).

Ccover = 1 × (0.80 × F(crop-cover)) + (1 - F(crop-cover))

Where F(crop-cover) is the fraction of arable land to which cover crops are applied during winter or spring [0. . .1].

Non-Arable Lands: C-factor estimation should take into account the combined effects of the above- and below-ground biomass, and the different environmental conditions. The C-factor was defined for each CORINE Land Cover class according to literature values (Table 4). However, the variety of values found in the literature led to the assignment of a range of values (C(land use)) to each class. The range of values has been developed based on the most cited studies covering different countries, including Italy, Belgium, Slovakia, Greece, Bulgaria, France, Switzerland, Portugal and Spain (Panagos et al., 2015b).

C(Non-arable) = Min (C(land use)) + Range (C(land use)) × (1 - Fcover)

Where Fcover describes the percentage of soil covered by any type of vegetation and the range for each type of land use is the result of maximum–minimum values.

Table 4: Land use cover factor

Group Class  Description  Cland use
Permanent crops   Vineyards  Areas planted with vines 0.15-0.45 
Fruit trees and berry plantation Parcels planted with fruit trees or shrubs: single/mixed fruit species, fruit trees associated with permanently grassed surfaces  0.1 – 0.3
Olive groves Areas planted with olive trees 0.1 – 0.3 
Pastures Pastures Dense, predominantly graminoid grass cover, of floral composition, not under a rotation system. Mainly used for grazing 0.05-0.15
Heterogeneous agricultural areas    Annual crops associated with permanent crops Non-permanent crops (arable land or pasture) associated with permanent crops on the same land parcel (non-associated annual crops represent less than 25%) 0.07-0.35
Complex cultivation patterns Juxtaposition of small parcels of diverse annual crops, pasture and/or permanent crops (arable land, pasture and orchards each occupy less than 75% of the total surface area of the land unit) 0.07-0.2
Land principally used for agriculture, with significant areas of natural vegetation Areas principally used for agriculture, interspersed with significant natural areas (agricultural land occupies between 25 and 75% of the total surface of the land unit) 0.05 – 0.2
Agro-forestry areas Annual crops or grazing land under the wooded cover of forest species 0.03-0.13
Forests   Broad-leaved forest Vegetation formation composed principally of trees, including shrub and bush understories, where broadleaved species predominate. 0.0001–0.003
Coniferous forest Vegetation formation composed principally of trees, including shrub and bush understories, where coniferous species predominate 0.0001–0.003
Mixed forest Vegetation formation composed principally of trees, including shrub and bush understories, where broadleaved and coniferous species co-dominate. 0.0001–0.003
Scrub and/or herbaceous vegetation associations    Natural grasslands Low productivity grassland. Often situated in areas of rough and uneven ground 0.01–0.08
Moors and heathland Vegetation with low and closed cover, dominated by bushes, shrubs and herbaceous plants (heath, briars, broom, gorse, laburnum) 0.01–0.1
Sclerophyllous vegetation Bushy sclerophyllous vegetation. Includes maquis (dense vegetation composed of numerous shrubs) and garrige (oak, arbutus, lavender, thyme, cistus) 0.01–0.1
Transitional woodland-shrub Bushy or herbaceous vegetation with scattered trees. Can represent either woodland degradation or forest Regeneration/colonisation. 0.003–0.05
Open spaces with little or no vegetation     Beaches, dunes, sands Beaches, dunes and expanses of sand or pebbles in coastal or continental areas 0
Bare rocks  Scree, cliffs, rocks and outcrops 0
Sparsely vegetated areas Includes steppes, tundra and badlands. Scattered high-altitude vegetation 0.1–0.45
Burnt areas Areas affected by recent fires, still mainly black 0.1–0.55
Glaciers and perpetual snow Land covered by glaciers or permanent snowfields 0

Slope length and slope steepness factor (LS)

The L-factor gives the impact of slope length while the S-factor accounts for the effect of slope steepness (Panagos et al., 2015a). The S factor is calculated based on the following equation, using the inclination (θ).

M6.2 eq03

To calculate L factor, the following equation is used:

M6.2 eq04

Where ɣ is the slope length (in meters) and m is equivalent to 0.5 for slopes steeper than 5%, 0.4 for slopes between 3%–5%, 0.3 for slopes between 1%–3% and 0.2 for slopes less than 1%.

Support practices factor (P)

Note: »Access the Support Practices factor (P) for the EU dataset from ESDAC

Of the six RUSLE/USLE input factors, values for the support practice P-factor are considered as the most uncertain. The P-factor accounts for control practices that reduce the erosion potential of runoff by their influence on drainage patterns, runoff concentration, runoff velocity and hydraulic forces exerted by the runoff on the soil surface. It is an expression of the overall effects of supporting conservation practices – such as contour farming, strip cropping, terracing, and subsurface drainage – on soil loss at a particular site, as those practices principally affect water erosion by modifying the flow pattern, grade, or direction of surface runoff and by reducing the volume and rate of runoff. The value of P-factor decreases by adopting these supporting conservation practices as they reduce runoff volume and velocity and encourage the deposition of sediment on the hill slope surface. The lower the P-factor value, the better the practice is for controlling soil erosion (Panagos et al., 2015c).

P = Pc × Psw × Pgm

where Pc is the contouring sub-factor for a given slope of a field, and Psw is the stone walls sedimentation sub-factor (known as terrace sub-factor) and Pgm is grass margins subfactor (known as strip cropping sub-factor and buffer strips).

Contour farming sub-factor: Contouring is a specific support practice applied only in croplands (CORINE land cover classes 21) which account for around 25.2% of the total European Union land area. Contour farming means that farmers apply certain field practices (ploughing, planting) along contours, perpendicular to the normal flow direction of runoff. Contour cultivation reduces runoff velocity by increasing up- and downslope surface roughness. The effectiveness of contour farming in reducing soil erosion depends on the slope gradient (Table 5) but only for slopes higher than 9%.

Table 5: Contouring sub-factor

Slope (%) Support practice factor for contouring, Pc
9-12% 0.6
13-16% 0.7
17-20% 0.8
21-25% 0.9
>25% 0.95

Stone walls sedimentation and Grass margins sub-factor: Dry stone walls are widespread landscape features in the Mediterranean and especially in the islands (Malta, Sicily, Cyprus, Isle Balearides, Aegean Islands). These stone walls were primarily used to delimit parcels being bequeathed by farmers to their children and to clean the land from stones. In the LUCAS survey, grass margins are defined as strips of mainly uncultivated land with vegetation dominated by grasses or herbs (Panagos et al., 2015c). The values of the two sub-factors are shown in Table 6.

Table 6: Stone walls sedimentation and grass margins sub-factor

No. of features (stone walls or grass margins) % of total stone walls observations  Psw  % of total grass margins observations Pgm 
0 0.9508 1 0.7299 1
1 0.0252 0.707 0.1136 0.853
2 0.011 0.577 0.0973 0.789
3 0.0053 0.5 0.0306 0.75
4 0.0032 0.448 0.017 0.724
5 0.0015 0.408 0.006  0.704

Classifying erosion threat

Note: »Access the Soil Erosion by Water (RUSLE) dataset from ESDAC 

Using the scale provided in the work of Panagos et al., (2015e) for European soil erosion, shown in Figure 3, we have established three categories to classify Annual Soil Loss (Table 7).

M6.2 fig03

Table 7: Classification of annual soil loss

Annual Soil Loss (t ha-1) Classification
0 - 2 Low
2 - 10 Moderate
>10 High

3. Compaction

Soil compaction is evaluated using the apparent compactness of the soil as an indicator (Jones et al., 2003). This indicator evaluates the soil compaction level based on its bulk density and clay content:

PD = DB + 0.009 C

Where PD (t m-3) is the apparent compactness of the soil, DB is the bulk density (t m-3) and C is the clay content (% wt).

The classification system established for soil compaction level, estimated through soil apparent compactness is shown in Table 8, as proposed by Jones et al., (2003). The classification levels are different depending on the soil texture.

Table 8: Classification of soil apparent compactness

Texture Class PD (t m-3)
Low (<1.40) Medium (1.40-1.75) High (>1.75)
Coarse High  High  Moderate
Medium High  Moderate  Moderate
Medium fine Moderate Moderate Low
Fine Moderate Low Low
Very fine Moderate Low Low
Organic High High  

4. Salinization

Plants respond to the total dissolved solids (TDS) in the soil water that surrounds the roots. The soil water TDS is influenced by irrigation practices, native salt in the soil, and by the TDS in the irrigation water. Assessing the salinity hazard of water on soil solution requires estimating the TDS but direct measurements of salt are not practical. Hence, the indicator used to estimate the level of salinization of the soil is the electrical conductivity (EC) of the water, measured in situ and expressed as decisiemens per meter – dS m-1 (Hanson et al., 2006). We will also take into account the level (thickness) of soil crusts when present in the soil due to salinization.

The classification system used to evaluate the salinity of a soil, based on the electrical conductivity of the water, is based on the individual crop thresholds for salinity. This means that instead of evaluating the soil status concerning the level of salinization, we will in fact evaluate the soil salinization concerning the most salt-sensitive crop grown on it. In order to do that, we have used the crop thresholds of Hanson et al., (2006) (Table 9), to establish the level beyond which a soil growing that specific crop is considered as saline. This approach takes into consideration the intention of the farmer in producing specific crops in their fields.

Also, to establish an intermediate category, and to reflect an electrical conductivity that, although lower than the ‘absolute’ threshold, is still high and might become a problem for crop growth, we have considered the interval between 100% and 75% of the threshold as an intermediate class (Table 9).

Table 9: Classification of soil salinization, depending on the crop and its salinity threshold.
Crops without a threshold indicated are considered not being affected by salinity (adapted from Hanson et al., 2006)

Crop Threshold Salinity (dS/m)  75% of threshold Low  Moderate  High 
Barley <6 6 - 8 >8
Bean, Common 1 0.75 <0.75 0.75-1 >1
Broad bean 1.6 1.2 <1.2 1.2-1.6  >1.6
Canola 10.4 7.8 <7.8 7.8-10.4 >10.4
Corn 1.7 1.275 <1.275 1.275-1.7 >1.7
Cotton 7.7 5.775 <5.775  5.775-7.7 >7.7
Cowpea 4.9 3.675 <3.675 3.675-4.9 >4.9
Crambe 2 1.5 <1.5 1.5-2 >2
 Flax  1.7  1.275   <1.275 1.275-1.7   >1.7
 Guar  8.8 6.6  <6.6   6.6-8.8   >8.8
 »See complete table          

5. Soil organic matter decline

Soil organic matter (SOM) decline is evaluated using the concentration of organic carbon and the soil bulk density, to calculate SOCstock as the indicator.

SOCstock = SOCconc × ρ × l

Where SOMstock is the stock of organic matter (g m-2) and SOMconc is the concentration of organic matter measured (g kg-1) in the top l meters. We are assuming the organic matter calculation in the first 30 cm.

In order to find the soil organic carbon decline of a specific soil, we used the map of SOC stocks in agricultural soils (Lugato et al., 2014; Orgiazzi et al., 2016). This map (Figure 4) provides an estimation of SOC stocks (t C ha-1) in 2010. By comparing the actual stock with that existing in 2010, it is possible to observe if the carbon stocks are increasing or decreasing.

M6.2 fig04
Figure 4

The classification is “low” when the actual SOC is higher than in 2010, “moderate” when the stock is decreasing less than 1% each year and "high" if the rate is rate of decrease is higher  (Table 10).

Table 10: Classification of soil SOM decline

 SOM decline (t C ha-1) Classification
Cactual > C2010 Low
Cactual > 1% x Years x C2010 Moderate
Cactual < 1% x Years x C2010 High

Cactual is the carbon stock for the current year and for a specific location; C2010 is the carbon stock in 2010 and for the same specific location; Years is the difference from current year and 2010

6. Soil biodiversity loss

Note: »Access the Global Soil Biodiversity Atlas Maps dataset from ESDAC

Soil biodiversity loss can be estimated by calculating the availability of a) quantification of soil microorganisms and b) diversity of soil macrofauna. The indicator to assess the biodiversity is a combination of these two measurements and can be calculated as follows:

  • Measure the biomass microbial carbon - by estimating the carbon content in the microorganism pool (g kg-1), convert it to g m-2 using the local soil bulk density and extrapolating for 1 meter of soil depth (Serna-Chavez et al., 2013).
  • Measure the number of co-occurring soil macro fauna groups - by identifying in a 25 x 25 x 25 soil sample the number of different macro fauna groups co-existing (earthworms, ants, termites, spiders, millipedes, centipedes, isopods, fly larvae, cockroaches and mantids, moth and butterfly larvae, grasshoppers and crickets, gastropods, beetles) (Orgiazzi et al., 2016).

Finally, biomass microbial carbon should be converted into a "0-1 Indicator" considering 0 as minimum and 250 g m-2 as maximum and the soil macro-fauna number of co-existing groups should be converted into a "0-1 Indicator" considering 0 as minimum and 14 as maximum number. The "soil biodiversity indicator" is the harmonized sum of the previous two indexes.

The classification of the biodiversity Indicator is based on the map of Potential Biodiversity Index from Orgiazzi et al., (2016). This map (Figure 5) shows the potential Index as calculated as above but it uses models instead of local measurements, to take into account the variations from different pedo-climatic regions. In this way, it becomes possible to compare the Soil Biodiversity Index measured in situ with the potential value for the same location. The classification system is different, therefore for each specific location, but 3 classes are always establish from 25% and 75% of the Potential Soil Biodiversity Index for a certain location.

M6.2 fig05
Figure 5


7. Soil contamination

Soil contamination is an important issue nowadays, mainly due to the presence of heavy metals (As, Cd, Cr, Cu, Ni, Pb, Zn and Hg (Lado et al., 2008)) and pesticides that remain in the soil, destroying the ecosystem and preventing healthy growth in plants/crops.

Each type of contamination should be assessed individually, since they are independent, but their final value is included in only one soil contamination . We will always adopt a conservative approach, which means that if the soil if contaminated with pesticides or heavy metals, then the soil quality Indicator for Soil Contamination should reflect that (Table 11).

Table 11: Soil contamination indicator

Soil contamination indicator Classification 
0 (Low) If soil contamination by both heavy metals and pesticides are “Low”
0.5 (Moderate) If soil contamination by heavy metals or pesticides are “Moderate” 
1 ( High) If soil contamination by heavy metals or pesticides are “High”

Heavy metals

It is often very difficult to establish threshold values for soil contamination, since toxicity and bioavailability of heavy metals are not solely dependent on the total content in soils but also on many other environmental variables. Also, the determination of natural background values is controversial because they can decrease the responsibility of human activities for the overall pollution on soils and it is often difficult to determine the background values that would correspond to a pristine situation, since the geochemistry of most of our ecosystems is greatly influenced by a long history of anthropic activities (Lado et al., 2008). However, to evaluate the soil contamination indicator it was necessary to establish a classification system that somehow could show to the end-users if there was any contamination in the soil or not.

To establish a reference classification for the heavy metal contamination, we have considered the limits from (Nicholson & Chambers, 2008). For each of the heavy metals previously established to be under control in soil contamination threat, the maximum values allowed (for each soil pH) are expressed in Table 12.

Table 12: Maximum limits established by Nicholson & Chambers (2008) for each of the heavy metals

Heavy metal (mg/kg) Soil pH
5.0 - 5.5 5.5 - 6.0 6.0-7.0 >7.0
Zn  200  200  200 300
Cu  80  100 135  200
Ni 50 60 75 110
Cd 3 3 3 3
Pb 300 300 300 300
Hg 1 1 1 1
Cr 400 400 400 400
As 50 50 50 50

In order to obtain three categories to establish the soil contamination level of a soil concerning heavy metal pollution, we have considered the intermediate category as 75% of the limit value for each heavy metal (at each soil pH). In this way the intermediate category already alerts for imminent dangerous of soil contamination by heavy metals. Below the interval consider for this category, the contamination level is considered to be "Low" (Table 13):

Table 13: Classification for soil contamination (heavy metals)

Heavy metal Low   Moderate High
Soil pH
5.0 - 5.5 5.5 - 6.0 6.0 - 7.0 >7.0 5.0 - 5.5 5.5 - 6.0 6.0-7.0 >7.0 5.0 - 5.5 5.5 - 6.0 6.0-7.0 >7.0
Zn <150  <150  <150  <225 150-200 150-200 150-200 225-300 >200 >200 >200 >300 
Cu <60  <75   <101.3  <150 60-80  75-100  101.3-135   150-200 >200  >100 >135  >200
Ni <37.5 <45 <56.25 <82.5 37.5-50 45-60 56.25-75 82.5-110 >50 >60 >75 >110
Cd <2.25 <2.25 <2.25 <2.25 2.25-3 2.25-3 2.25-3 2.25-3 >3 >3 >3 >3
Pb <225 <225 <225 <225 225-300 225-300 225-300 225-300  >300 >300 >300 >300
Hg <0.75 <0.75 <0.75 <0.75 0.75-1 0.75-1 0.75-1 0.75-1 >1 >1 >1 >1
Cr <300 <300 <300 <300 300-400 300-400 300-400 300-400 >400 >400 >400 >400
As <37.5 <37.5 <37.5 <37.5 37.5-50 37.5-50 37.5-50 37.5-50 >50 >50 >50 >50

The contamination indicator for heavy metals, is established as 0, 0.5 or 1, depending on the status regarding each pollutant (Table 14). We adopted a conservative approach and therefore consider soil contamination by heavy metals status as “High” if only one of the contaminants exceeds the limits.

Table 14: Soil contamination indicator for heavy metals

Contamination index heavy metals Classification 
0 (Low) If ALL pollutant score “Low” classification
0.5 (Moderate) IF ANY pollutant scores “Moderate” classification 
1 ( High) If ANY pollutant scores “High” classification


When considering the soil contamination with pesticides, several factors should be taken into account for an accurate evaluation of their interference and impact on the soil quality. Pesticides may be added in differing quantities (application rate), have different persistent times in soils (half life time), different adsorption capacity to other soil compounds (adsorption coefficient) and their potential to move toward groundwater will also determine if the pesticide is dangerous for soil (and human) health.

Hence, the pesticide application rate on itself cannot be regarded as a good soil contamination per pesticides indicator on its own. Here we suggest that the pesticide soil contamination indicator is a combination of two sub-indicators (Figure 6)

  • Indicator on Pesticide Persistency and Movement in soil (PPMsoil);
  • Indicator on Soil Environmental Exposure to Pesticides (EEPsoil);

M6.2 fig06
Figure 6

Each of these indicators are ranked as “High”, “Moderate” and “Low”, and we adopt a conservative approach when mixing them. This means that the Pesticide Soil contamination Indicator will only be generally classified as “Low” when both EEP and PPM have individually “Low” classifications as well (Table 15).

Table 15: Soil Contamination Indicator for pesticides

Contamination index for pesticides Classification 
0 (Low) If PPM and EEP are “Low” 
0.5 (Moderate) If PPM or EEP are “Moderate”
1 (High) If PPM or EEP are “High”

Indicator on Pesticide Persistency and Movement in soil (PPMsoil): This indicator aggregates information concerning the a) pesticide values on half-life, and b) potential to move toward groundwater. For each one of these, a classification system will also be assessed. Once more the conservative approach is used to calculate the soil aggregated PPMsoil.

a) Pesticide half-life. Based on values from OSU - Extension Pesticide Properties Database, the classification system was established as follow:

  • Low – half-life <30 days
  • Moderate – 30 < half-life < 100 days
  • High - half-life > 100 days

b) Potential to move toward groundwater. Based on the following equation, GUS (Groundwater Ubiquity Score) is calculated using both the half life time and the adsorption coefficient:

GUS = log ⁡(half-life) × (4 - log KOC)

Then, for the classification of GUS is also used the recommendation from OSU - Extension Pesticide Properties Database:

  • Low – GUS <2
  • Moderate – 2 < GUS < 3
  • High – GUS >3

Indicator on Soil Environmental Exposure to Pesticides (EEPsoil): This indicator aggregates information concerning the pesticide persistence (half life time) in soil together with the actual input of a specific pesticide (the application rate). If more than one pesticides are applied in soil, their specific half-life times should be considered. To evaluate the environmental exposure of pesticides we will use the equation described in (Wijnands, 1997):

M6.2 eq05


To establish a reference classification system to EEP, we also base ourselves in the work developed by (Wijnands, 1997):

  • Low – EEP <3
  • Moderate – 3 < GUS < 10
  • High – GUS > 10

8. Acidification

Note: »Access the Soil pH in Europe map from ESDAC

Acidification is soil enrichment by hydrogen ions. Several problems are associated with the acidification of soils, since many biogeochemical processes are enhanced at low pH, such as the mobility of plant toxic aluminium. Change in acidity is calculated by comparing the current soil pH (of a sample collected at 30 cm in CaCl2) with data from 2009 published by ESDAC (Figure 7).

 M6.2 fig07
Figure 7

If the measured pH is higher than in 2009, then no acidification is taking place. If the measured pH is lower by up to 10%, then moderate acidification is occuring; if the difference is greater than 10% then the situation is more serious (Table 16).

M6.2 eq06

Table 16: Classification of acidification

Soil pH (CaCl2) Classification 
pHatual > pH2009 Low
0.8< pHatual / pH2009 < 0.9 Moderate
pHatual / pH2009 < 0.8 High

9. Nutrient depletion/surplus

Nutrients such as Nitrogen (N), Phosphorus (P) and Potassium (K) are essential for crop growth. If soils faces nutrient depletion, the growth of crops might be compromised and soil functions will not be fully operational. Nutrient depletion are assessed by measuring total N and total P as well as extractable P and extractable K in soil samples. The results will be assessed against existing soil nutrient maps and combined into a single Index that shows if the soil is generally depleted in nutrients or not. 

In the first version of SQAPP, a  concrete reference value for nutrient depletion is missing. A combined nutrient depletion index based on the integration of the different nutrient parameters to be assessed will be developed later. 

10. References

References cited in this section of iSQAPERiS

  • Hanson, B.R., Grattan, S.R., & Fulton, A. 2006. Agricultural Salinity and Drainage. Division of Agriculture and Natural Resources Publication 3375.
  • Jones, R.J.A., Spoor, G., & Thomasson, A.J. 2003. Vulnerability of subsoils in Europe to compaction: A preliminary analysis. Soil and Tillage Research 73, 131–143.
  • Lado, L.R., Hengl, T., & Reuter, H.I. 2008. Heavy metals in European soils: A geostatistical analysis of the FOREGS Geochemical database. Geoderma 148, 189–199 Available at
  • Lugato, E., Bampa, F., Panagos, P., Montanarella, L., & Jones, A. 2014. Potential carbon sequestration of European arable soils estimated by modelling a comprehensive set of management practices. Global Change Biology 20, 3557–3567.
  • Nicholson, F.A., & Chambers, B.J. 2008. Sources and Impacts of Past, Current and Future Contamination of Soil Appendix 1 : Heavy metals.
  • Orgiazzi, A., Bardgett, R.D., Barrios, E., Behan-PelletierV., BrionesM.J.I., Chotte, J-L., De Deyn, G.B., Eggleton, P., Fierer, N., Fraser, T., HedlundK., Jeffery, S., Johnson, N.C., Jones, A., Kandeler, E., Kaneko, N., P., L., Lemanceau, P., Miko, L., Montanarella, L., Moreira, F.M.S., K.S., R., Scheu, S., Singh, B.K., Six, J., van der Putten, W.H., Wall, D.H., & (Eds.). 2016. Global Soil biodiversity atlas.
  • Panagos, P., Borrelli, P., & Meusburger, K. 2015a. A New European Slope Length and Steepness Factor (LS-Factor) for Modeling Soil Erosion by Water. Geosciences 5, 117–126 Available at
  • Panagos, P., Borrelli, P., Meusburger, K., Alewell, C., Lugato, E., & Montanarella, L. 2015b. Estimating the soil erosion cover-management factor at the European scale. Land Use Policy 48, 38–50 Available at
  • Panagos, P., Borrelli, P., Meusburger, K., Yu, B., Klik, A., Jae Lim, K., Yang, J.E., Ni, J., Miao, C., Chattopadhyay, N., Sadeghi, S.H., Hazbavi, Z., Zabihi, M., Larionov, G.A., Krasnov, S.F., Gorobets, A. V., Levi, Y., Erpul, G., Birkel, C., Hoyos, N., Naipal, V., Oliveira, P.T.S., Bonilla, C.A., Meddi, M., Nel, W., Al Dashti, H., Boni, M., Diodato, N., Van Oost, K., Nearing, M., & Ballabio, C. 2017. Global rainfall erosivity assessment based on high-temporal resolution rainfall records. Scientific Reports 7, 4175 Available at
  • Panagos, P., Borrelli, P., Meusburger, K., van der Zanden, E.H., Poesen, J., & Alewell, C. 2015c. Modelling the effect of support practices (P-factor) on the reduction of soil erosion by water at European scale. Environmental Science and Policy 51, 23–34 Available at
  • Panagos, P., Borrelli, P., Poesen, J., Ballabio, C., Lugato, E., Meusburger, K., Montanarella, L., & Alewell, C. 2015d. The new assessment of soil loss by water erosion in Europe. Environmental Science & Policy 54, 438–447 Available at
  • Panagos, P., Meusburger, K., Ballabio, C., Borrelli, P., & Alewell, C. 2014. Soil erodibility in Europe: A high-resolution dataset based on LUCAS. Science of the Total Environment 479–480, 189–200 Available at
  • Serna-Chavez, H.M., Fierer, N., & Van Bodegom, P.M. 2013. Global drivers and patterns of microbial abundance in soil. Global Ecology and Biogeography 22, 1162–1172.
  • Wijnands, F.G. 1997. Integrated crop protection and environment exposure to pesticides: methods to reduce use and impact of pesticides in arable farming. Perspectives for AgronomyAdopting Ecological Principles and Managing Resource UseProceedings of the 4th Congress of the European Society for Agronomy Volume 25, 319–328 Available at
  • Wischmeier, W.H., & Smith, D.D. 1978. Predicting rainfall erosion losses. Agriculture handbook no. 537, 285–291.


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