|Main authors:||Ana Iglesias, David Santillán, Luis Garrote and contributions from ISS (China)|
|iSQAPERiS editor:||Jane Brandt|
|Source document:||Iglesias, A. et al. (2018) Report on definition of typical combinations of farming systems and agricultural practices in Europe and China and their effects on soil quality. iSQAPER Project Deliverable 7.1, 87 pp|
|1. Sources of geo-spatial data|
|2. Data catalogue of farming systems and agricultural management practices|
This section presents the sources of information consulted to build the data catalogue. Sources of information have been classified in four categories: soil data, agriculture data, physical context data and socioeconomic context data. For each source of information, we present a description of the content and present some information related to the type of data available.
JRC Soils: European Soil Data Center (ESDAC) The Soil Geographical Database of Europe (SGDBE). The raster library provides 1kmx1km coverages of many soil attributes, listed in Table 2. Available formats are ESRI grid or Google Earth kmz. Below is an example.
Table 2. SGDBE Attributes (definition included in »Introduction to the European Soil Database)
For example, the WM1 attribute is a code for normal presence and purpose of an existing water management system on more than 50% of the STU. The following values are present:
- 0 No information
- 1 Not applicable (no agriculture)
- 2 No water management system
- 3 A water management system exists to alleviate waterlogging (drainage)
- 4 A water management system exists to alleviate drought stress (irrigation)
- 5 A water management system exists to alleviate salinity (drainage)
- 6 A water management system exists to alleviate both waterlogging and drought stress
- 7 A water management system exists to alleviate both waterlogging and salinity
SoilGrids: World Soil Data SoilGrids is designed as a globally consistent, data-driven system that predicts soil properties and classes using global covariates and globally fitted models. It provides maps a 250x250 m2 resolution with probability of each soil class (according the World Reference Base – WRB - an USDA Soil Taxonomy; TAXNWRB and TAXOUSDA databases), most probable soil class and several soil properties: Physical (Bulk density, Clay content, Coarse fragments, Silt content, Sand content) and Chemical (Cation exchange capacity, Soil organic carbon content, Soil pH in H2O, Soil pH in KCl)
GSDE: Gridded Global Soil Dataset for use in Earth System Models GSDE provides soil information including soil particle-size distribution, organic carbon, and nutrients, etc. and quality control information in terms of confidence level. GSDE is based on the Soil Map of the World and various regional and national soil databases, including soil attribute data and soil maps. It includes general information for soil profiles for 11 types of soil and 34 soil properties for 8 depths. Two versions are available with resolution 30 seconds (~1km) and 5 minutes (~10km).
Agricultural systems and management
MAPSPAM: Spatial Production Allocation Model The MapSpam Cropland dataset (You et al., 2017) provides raster information about 42 important crops. Each of the crops can be measured in terms of four variables: area harvested, physical area, production, and yield. Each crop and variable can be decomposed into two production systems: irrigated and rainfed. The maps are globally available in 5 minute (~10km ) grid resolution. The crops included in the MapSpam dataset are listed on Table 3.
Table 3. Crops included in the MapSpam dataset
|Chickpea||Cocoa||Coconut||Coffee Arabica||Coffee Robusta|
|Cotton||Cowpea||Fibers Other||Fruit Temperate||Fruit Tropical|
|Groundnut||Lentil||Maize||Millet Pearl||Millet Small|
|Oil Crops Other||Oil Palm||Pigeonpea||Plantain||Potato|
|Pulses Other||Rapeseed||Rest of Crops||Rice||Roots & Tubers Other|
|Sesame Seed||Sorghum||Soybean||Sugar Beet||Sugar Cane|
EarthStat EarthStat offers geographic data sets related to agriculture and the environment. EarthStat is a collaboration between the Global Landscapes Initiative at The University of Minnesota’s Institute on the Environment and the Ramankutty Lab at The University of British Columbia, Vancouver. They provide data on Cropland and Pasture area, Harvested Area and Yield for 175 crops, Greenhouse Gas Emissions from Croplands, Climate Variation Effects on Crop Yields for 4 major crops (Maize, Soybean, Rice and Wheat), Yield Trends and Changes for 4 major crops, Water Depletion and WaterGap3 Basins, Yield Gaps and Climate Bins for Major Crops, Nutrient Application for Major Crops, Total Nutrient Consumption for 140 Crops, Total Nutrient Balance for 140 Crops, Potential Natural Vegetation and Carbon Stocks in Potential Natural Vegetation.
Data are available in different formats and coverages. For instance, data for the 175 crops are available in Netcdf, Geoitiff or GoogleEarth forms at 5 minute (~10km) grid resolution (Monfreda et al. 2008).
Global Map of Irrigation Areas (GMIA) The GMIA is a global irrigation mapping facility developed by the Land and Water Division of FAO and the Rheinische Friedrich-Wilhelms-Universität in Bonn. They provide a world coverage raster at a resolution of 5 min (~10 km) of several variables:
- Area equipped for irrigation expressed as percentage of total area
- Area equipped for irrigation expressed in hectares per cell
- Area actually irrigated expressed as percentage of area equipped for irrigation
- Area irrigated with groundwater expressed as percentage of total area equipped for irrigation
- Area irrigated with surface water expressed as percentage of total area equipped for irrigation
- Area irrigated with water from non-conventional sources expressed as percentage of total area equipped for irrigation
This dataset is distributed by Aquastat (a FAO database).
Farm structure survey The basic farm structure survey (FSS) is conducted consistently throughout the EU with a common methodology at a regular base and provides therefore comparable and representative statistics across countries and time, at regional levels (down to NUTS 3 level). Every 3 or 4 years the FSS is carried out as a sample survey, and once in ten years as a census. The 2010 census covers the EU-27 Member States, Croatia, Iceland, Norway, Switzerland, Montenegro and Serbia.
Survey on agricultural production methods The Survey on agricultural production methods (SAPM) was a one-off survey in 2010 to collect farm level data on agri-environmental measures to support monitoring of the relevant European Union policies (e.g. the Common Agricultural Policy, Rural Development Policy, etc.) and to establish agri-environmental indicators. European Union Member States could choose whether to carry out the SAPM as a sample survey or as a census survey. Data were collected on tillage methods, soil conservation, landscape features, animal grazing, animal housing, manure application, manure storage and treatment facilities and irrigation.
Climate and hydrology
ECMWF: European Centre for Medium Range Weather Forecasts Climate ECMWF provides are several data products related to climate, including reanalysis of observations and average derived variables. The available datasets are summarized in Table 4.
Table 4. Datasets available in ECMWF climate
|cams_gfas||Global Fire Emissions and Smoke (GFAS) in the Copernicus Atmosphere Monitoring Service (CAMS)||Copernicus|
|cams_nrealtime||CAMS Near Real-time||Copernicus|
|cera20c||Coupled ECMWF Reanalysis (CERA) (Jan 1901 - Dec 2010)||general|
|era15||ECMWF Global Reanalysis Data - ERA-15 (Jan 1979 - Dec 1993)||general|
|era20c||Reanalysis of the 20th-century using surface observations only (Jan 1900 - Dec 2010)||general|
|era20cm||ERA-20CM: Ensemble of climate model integrations (Final version)||general|
|era20cmv0||ERA-20CM: Ensemble of climate model integrations (Experimental version)||general|
|era20c_ofa||ERA-20C feedback (January 1900 - December 2010), containing in situ, surface observations||general|
|era40||ECMWF Global Reanalysis Data - ERA-40 (Sep 1957 - Aug 2002)||general|
|era5_test||ERA5 Test version||era5_test|
|geff_reanalysis||GEFF Reanalysis Dataset||general|
|icoads||ICOADS v2.5.1 with interpolated 20CR feedback||research|
|interim||ECMWF Global Reanalysis Data - ERA Interim (Jan 1979 - present)||general|
|macc_nrealtime||MACC Near Real-time||Copernicus|
|s2s||Subseasonal to Seasonal||s2s|
|tigge||TIGGE (THORPEX Interactive Grand Global Ensemble)||tigge|
|uerra||Uncertainties in Ensembles of Regional ReAnalysis||uerra|
|yopp||YOPP (Year Of Polar Prediction)||general|
These data can be downloaded in netcdf format, but their time resolution is very fine (usually sub-daily) and require massive processing. The most widely used is ERA40 reanalysis data, at 6 h and 128 km resolution.
Climate Research Unit Climate The Climate Research Unit of the University of East Anglia distribute different climate datasets, both climatological averages and time series. The most relevant is the CRU CL 2.0 dataset, world raster at 10 min (~20 km) resolution of climatological averages for pre (Precipitation), wet (Wet days), tmp (Mean temperature), dtr (Mean diurnal temperature range), rhm (Relative humidity), ssh (Sunshine), frs (Ground frost), wnd (10 m wind speed)
University of New Hampshire: Global Runoff Data Centre The GRDC of the University of New Hampshire distribute the Global Composite Runoff Fields. It is a world coverage raster of 0.5 degrees (~60 km) resolution with mean annual runoff and mean monthly runoff.
CORDEX database The Coordinated Regional Downscaling Experiment (CORDEX) distributes data of Regional Climate Model (RCM) and Impact Assessment Model (IAM) simulations performed within the CORDEX framework. Data include meteorological variables (precipitation, temperature, wind speed, pressure, …) derived from RCMs and other variables (runoff, evapotranspiration, crop yield, crop water requirements,…) relevant for impact sectors derived from IAMs. These data can be adopted as the basis for future physical context data in iSQAPER upscaling model.
GRUMP: Global Rural-Urban Mapping Project The Global Rural-Urban Mapping Project, Version 1 (GRUMPv1) data collection consists of eight global data sets: population count grids, population density grids, urban settlement points, urban-extents grids, land/geographic unit area grids, national boundaries, national identifier grids, and coastlines. All grids are provided at a resolution of 30 arc-seconds (~1km), with population estimates normalized to the years 2000, 1995, and 1990. All eight data sets are available for download as global products, and the first five data sets are also available as continental, regional, and national subsets. The data are distributed by the Socio-Economic Data and Applications Center (SEDAC) of Columbia University. An additional source of information is the population database of the University of Southampton (http://www.worldpop.org.uk/).
World Bank The World Bank provides yearly country data in tabular form for many socio-economic variables. A relevant dataset is World Development Indicators, classified in several topics: Agriculture & Rural Development, Aid Effectiveness, Climate Change, Economy & Growth, Education, Energy & Mining, Environment, External Debt, Financial Sector, Gender, Health, Infrastructure, Labor & Social Protection, Poverty, Private Sector, Public Sector, Science & Technology, Social Development, Trade, Urban Development
IIASA SSP Scenario database The Shared Socioeconomic Pathways (SSP) database of the International Institute for Applied Systems Analysis (IIASA) includes quantitative projections of key variables of the SSPs scenarios. The database includes projections for population and economic development. Specifically, for the following elements quantifications are available: (a) population by age, sex, and education; (b) urbanization; and (c) economic development (GDP). For each SSP a single population and urbanization scenario, developed by the International Institute for Applied Systems Analysis (IIASA) and the National Center for Atmospheric Research (NCAR), is provided. These can be adopted as a basis for the specification of socioeconomic context in iSQAPER scenarios.
The analysis will be done at the Europe and China wide scale. However, the resolution of the data is not uniform, and some of the data are only available at local scale level and are taken from the case studies.
Most of the activity data (e.g. crop areas) are based on Eurostat data from 2008. Part of the management data may be derived from the Survey on Agricultural Production Methods (SAPM); see also Council regulation (EC) No 1166/2008, which was held together with the Farm Systems Survey (FSS) in 2010.The WOCAT technologies documentation will also be considered as an essential data source, since it gives useful information on the impacts of the AMPs on socio-economic, socio-cultural and ecological dimension.
The database includes the data provided by D2, D3, D5, and D6 complemented by regional and global data summarised in Tables 2 and 3. Some data sources provide gridded datasets that will be projected into a standard format in geographical coordinates (latitude/longitude) with spatial resolution of 5 minutes. In other cases, indicators are available by administrative units (country, region, province). If possible these data will be spatially distributed within the unit using proxy variables. A complete analysis of other data sources is given above.
We have compiled information from heterogeneous sources with different resolutions and we have structured them in a data catalogue with a unified structure and spatial resolution. This data catalogue is the basis for the dynamic model used to project model results into future scenarios. The data catalogue finally selected is included in Table 5 (see »Selecting farming systems, agricultural managment practices and soil quality indicators for upscaling and »Definition of farming systems, agricultural managment pactices and soil quality indicators for complete information and discussion). Variables are clustered in tables according to the Farming Systems, the Management Practices and the Soil Quality Indicators.
Table 5. Sources of information on agricultural systems and soil management practices
|Source of information|
|iSQAPER WP 2, 3, 5 and 6|
|EU - SmartSOIL and CATH-C projects|
|EU - Farm Systems Survey - FSS (Farm Systems Survey EU)|
|EU - CCAT survey results, as policies/subsidies are drivers for farmers to uptake the measures|
|EU - From LUCAS soil survey - Information about tillage and residues (what is seen on the field), however data are not yet available at point level, as there is discussion with the MS on the location availability at point level. DG Eurostat is the owner of this survey, and they should be asked for permission to use data|
|EU – model - Alterra provided the data on the questions at EU level, based on MITERRA and other data sources (Eurostat, FAOSTAT, JRC) to estimate changes in SOM|
|EU modelled – Calculation of data on farms and farm and soil management at EU27 NUTS-2 level. Alterra identified missing data and how to face filling|
|EU - Measures to climate change mitigation in agriculture Information on measures and activities across EU27 on basis of Smith et al. studies and IPCC AR4|
|EU - Farm Accountancy Data Network (FADN)|
Table 6. Variables used in this section of iSQAPERiS
|Farming systems considered|
|Harvested area fraction||World||Percentage||Five arc-minute|
|Management practices considered|
|Organic matter addition||Residue Management||EU-25||percentage of arable land||NUTS2|
|No tillage||Conventional tillage
|EU-25||percentage of arable land||NUTS2|
|Crop rotation||Normal winter crop cover
|EU-25||percentage of arable land||NUTS2|
|Irrigation||Total irrigated area
Percentage of irrigated area of equipped area for irrigation
Area equipped for irrigation
Percentage of area equipped for irrigation
|Organic agriculture||Area of organic farming||EU-25||percentage of utilized agricultural area||NUTS2|
|Nutrient application||N, K and P in cereal
N, K and P in rice
N, K and P in maize
N, K and P in soybean
N, K and P in potato
|Soil quality indicators linked to soil ecosystem services|
|Soil organic carbon||Organic carbon content|| t/ha
percentage in weight
|Water holding capacity||All agric areas||World||mm/m||Five arc-minute|
Note: For full references to papers quoted in this article see