SQAPP data
Section 5 D4.2
Main authors: | Luuk Fleskens, Coen Ritsema, Zhanguo Bai, Violette Geissen, Jorge Mendes de Jesus, Vera da Silva, Aleid Teeuwen, Xiaomei Yang |
iSQAPERiS editor: | Jane Brandt |
Source document: | Fleskens, L et al. (2020) Tested and validated final version of SQAPP. iSQAPER Project Deliverable 4.2, 143 pp |
SQAPP enables users to access information from otherwise fragmented global data sets and information sources. If necessary, users can improve accuracy by entering their own local data. Here we list the details of the data input (maps, matrices, user input) and data output for SQAPP.
Contents table |
1. Input maps for SQAPP |
2. Matrices |
3. User input |
4. SQAPP output |
1. Input maps for SQAPP
The following data sets were used for building the final version of the SQAPP:
- Soilgrids (www.soilgrids.org), at 250 m resolution, has been used for the following soil parameters with global coverage: absolute depth to bedrock, bulk density, texture, available soil water capacity, soil organic carbon content, cation exchange capacity, soil pH and its derived soil acidification. For further information on Soilgrids see the paper by Hengl et al. (2017).
- European Soil Data Centre (ESDAC) (https://esdac.jrc.ec.europa.eu/ - Panagos et al., 2012) has been used for the following soil threat data with European or global coverage: soil erosion by water (Panagos et al., 2015), soil erosion by wind (Borrelli et al., 2017), susceptibility to soil compaction (Houšková and Van Liedekerke, 2008), soil contamination by heavy metals (Rodriguez Lado et al., 2008), except for copper for which Ballabio et al. (2018) was used. Global Soil Biodiversity Atlas Maps have been used for the following soil biological data: global estimates of soil microbial abundance and soil macrofauna (Serna-Chavez et al., 2013), and soil macrofauna data (courtesy of Dr Jerôme Mathieu of University Pierre and Marie Curie, Paris VI, manuscript in preparation).
- Global Soil Dataset for Earth System Modelling (http://globalchange.bnu.edu.cn/research/soilw) has been used for: electrical conductivity, exchangeable potassium, phosphorus using Olsen method, and total nitrogen (Shangguang et al., 2014).
More in particular, the following data layers are incorporated in the final version of SQAPP:
Global background info at 250m resolution
- Altitude (m, a.s.l.)
- Slope (%)
- Precipitation (mm/year)
- Average anual temperature (°C)
- Landscape (breaks-foothills, flat plains, high mountains-deep canyons, hills, low hills, low mountains, smooth plains)
- Land cover in 2010 at 30m resolution
- Pedoclimatic zones (n=2098)
Global soil properties in 0-30cm depth at 250m resolution
- Soil types (WRB)
- SOC (%)
- pH (H2O)
- Sand (%)
- Silt (%)
- Clay (%)
- Coarse fragments (%)
- Bulk density (kg/m3)
- CEC (cmolc/kg )
- Depth to bedrock (cm)
- Available soil water capacity (volumetric fraction) with FC = pF 2.0
- Available soil water capacity (volumetric fraction) with FC = pF 2.3
- Available soil water capacity (volumetric fraction) with FC = pF 2.5
- Non-available soil water capacity or permanent wilting point (PWP) (volumetric fraction) with FC = pF 4.2
Global soil nutrients in 0-30cm depth at 250m resolution
- Total N (g/kg)
- Total P (g/kg)
- Total K (g/kg)
- Electrical conductivity (dS/m)
- P (Olsen, ppm/weight)
- Exchangeable K (cmol/kg)
Global and regional soil threats and reclassification (low, medium, high) at 250m resolution
- Soil loss by water erosion in Europe and reclassification
- Soil loss by wind erosion in European agricultural soils and reclassification
- Global water erosion vulnerability reclassification
- Global wind erosion vulnerability reclassification
- Natural soil susceptibility to compaction in Europe and reclassification
- Arsenic in European soils
- Cadmium in European soils
- Chromium in European soils
- Copper in European soils
- Mercury in European soils
- Nickel in European soils
- Lead in European soils
- Zinc in European soils
- Global soil biodiversity index reclassification
- Global estimates of soil microbial abundance and reclassification
- Global soil macrofauna and reclassification
2. Matrices
Eight data matrices are included in the SQAPP database in its content management system. Two of these relate to respectively the applicability limitations and effects of AMPs on soil properties and soil threats, while the remaining six are the active substances’ expected PECs for respectively fungicides, herbicides and insecticides at two moments: one day and 100 days after application.
The structure of the matrices is as follows:
1. Applicability limitations of AMPs. For each AMP, applicability is defined in binary (0/1) fashion for:
- Land cover class and subtype
- Slope class: Flat (0-2%); Gentle (2-5%); Moderate (5-10%); Rolling (10-15%); Hilly (15-30%); Steep (30-60%); Very Steep (>60%)
- Annual precipitation class: 0-250 mm; 251-500 mm; 501 - 750 mm; 751 - 1000 mm; 1001 - 1500 mm; 1501 - 2000 mm; >2000 mm
- Landscape position: Breaks-foothills; Flat Plains; High Mountains-deep Canyons; Hills; Low Hills; Low Mountains; Smooth Plains
- Soil depth class: Very Shallow (0-20 cm); Shallow (20-50 cm); Moderately Deep (50-80 cm); Deep (80-120 cm); Very Deep (> 120 cm)
- Soil texture class: Coarse (sand > 50%); Fine (clay > 40%); Medium (other)
- Stoniness class: None (coarse Fragments <2%); Slightly (coarse Fragments 2-10%); Moderately (coarse Fragments 10-25%); Excessively (coarse Fragments >25%)
2. Effects of AMPs on soil properties and soil threats, defined in the following classification: negative effect: -1; no, ambiguous or unknown: 0; slight or very long-term positive effect: 1; definite positive effect: 2.
Effects can be entered for the following parameters (in bold the ones that are actually implemented in SQAPP) – note that some of these double as soil property and soil threat indicator:
- Bulk density (fine earth)
- Plant-available water storage capacity
- Soil organic carbon content (fine earth fraction)
- Soil pH
- Electrical conductivity
- CEC
- Exchangeable potassium
- Amount of phosphorus using Olsen method
- Total nitrogen
- Soil microbial abundance
- Soil macrofauna groups
- Clay
- Silt
- Sand
- Coarse fragments (volume)
- Depth to bedrock
- Global water erosion vulnerability, or soil water erosion in Europe
- Global wind erosion vulnerability, or soil wind erosion in European agricultural soils
- Natural soil susceptibility to compaction
- Contamination (linked to highest ranked heavy metal risk)
- Pesticide residues (after 1 day)
- Pesticide residues (after 100 days)
3. PECs (mg/kg) for individual active substances of fungicides expected 1 day after application:
- Different regions: Central Europe (CEU); Northern Europe (NEU); Southern Europe (SEU); Rest of the world (ROW)
- Different average annual temperature: 10°C; 20°C
- Different crop types: cereals; maize; root crops; non-permanent industrial crops; grassland; permanent crops; vineyards; dry pulses, vegetables and flowers
4. PECs (mg/kg) for individual active substances of fungicides expected 100 days after application:
- Different regions: Central Europe (CEU); Northern Europe (NEU); Southern Europe (SEU); Rest of the world (ROW)
- Different average annual temperature: 10°C; 20°C
- Different crop types: cereals; maize; root crops; non-permanent industrial crops; grassland; permanent crops; vineyards; dry pulses, vegetables and flowers
5. PECs (mg/kg) for individual active substances of insecticides expected 1 day after application:
- Different regions: Central Europe (CEU); Northern Europe (NEU); Southern Europe (SEU); Rest of the world (ROW)
- Different average annual temperature: 10°C; 20°C
- Different crop types: cereals; maize; root crops; non-permanent industrial crops; grassland; permanent crops; vineyards; dry pulses, vegetables and flowers
6. PECs (mg/kg) for individual active substances of insecticides expected 100 days after application:
- Different regions: Central Europe (CEU); Northern Europe (NEU); Southern Europe (SEU); Rest of the world (ROW)
- Different average annual temperature: 10°C; 20°C
- Different crop types: cereals; maize; root crops; non-permanent industrial crops; grassland; permanent crops; vineyards; dry pulses, vegetables and flowers
7. PECs (mg/kg) for individual active substances of herbicides expected 1 day after application:
- Different regions: Central Europe (CEU); Northern Europe (NEU); Southern Europe (SEU); Rest of the world (ROW)
- Different average annual temperature: 10°C; 20°C
- Different crop types: cereals; maize; root crops; non-permanent industrial crops; grassland; permanent crops; vineyards; dry pulses, vegetables and flowers
8. PECs (mg/kg) for individual active substances of herbicides expected 100 days after application:
- Different regions: Central Europe (CEU); Northern Europe (NEU); Southern Europe (SEU); Rest of the world (ROW)
- Different average annual temperature: 10°C; 20°C
- Different crop types: cereals; maize; root crops; non-permanent industrial crops; grassland; permanent crops; vineyards; dry pulses, vegetables and flowers
3. User input
User input in SQAPP is used to refine the projected soil threat of soil contamination by pesticides and to tailor the recommended AMPs to land use subtype selected by the user. For these inputs a selection of the user is required as global data provides only a limited specification of land cover and no information on pesticide use:
- Land cover: arable land; grazing land; permanent crops without soil cover; permanent crops with soil cover; vegetables; other
- Land cover specification depending on land cover:
- For arable land: cereals; maize; rice; root crops; oleaginous crops; leguminous crops; other
- For grazing land: pasture (intensively managed); rangeland (extensively managed)
- Permanent crops (both types): vineyards; olives/nut trees; citrus; fruit trees; other
- Vegetables: indoor vegetables; open field vegetables - Pesticide use: types used (fungicides; herbicides; insecticides), number used, specific active substances used.
Other input data used by SQAPP can be adjusted by the user if they have their own (more precise) data. This applies to all types of information:
- Field characteristics
- Soil parameters
- Soil threats
Next, there are a series of user preferences that are considered and saved for a specific location of interest:
- Specific interest in recommendation domains: terrain management; soil management; vegetation management; carbon and nutrient management; pest management; pollutant management; grazing management
- Qualifications of recommended AMPs: implemented; inappropriate; potentially interesting; definitely interesting
- Rankings of AMPs deemed potentially and definitely interesting by the user
Finally, users can provide feedback to the app development team:
- Through giving feedback on the recommended AMPs (2 evaluation questions and comment box at the end of a use cycle)
- Through the generic feedback field under ‘More information’.
4. SQAPP output
To the app user, the SQAPP provides data on:
- Field characteristics (coordinates, altitude, annual precipitation, mean annual temperature, landscape position, slope and land cover)
- Soil properties (indicator scores: for the specific location; the minimum and maximum of the selected pedoclimatic zone and broad land cover type; the cumulative frequency of scores within the zone and relative position of the specific location)
- Soil threats (indicator scores: for the specific location; the minimum and maximum of the selected pedoclimatic zone and broad land cover type; the cumulative frequency of scores within the zone and relative position of the specific location; the overall soil threat classification and local soil threat level)
- Soil improvement potential score, overall soil threat level indication and specific soil parameters and soil threats requiring attention
- Recommendations of the 10 most effective AMPs to deal with the location-specific combination of soil properties and soil threats requiring attention.
The SQAPP content management system records the following information:
- User data (email; password - for ensuring app functionality to the user only)
- Saved locations and field characteristics
- Location-specific recommendations generated and evaluated by users
- Location-specific user-suggested data (parameter, global data value, user-specified value, how this data was acquired and when)
Note: For full references to papers quoted in this article see