This project has received funding from the European Union's Seventh Programme for research, technological development and demonstration under grant agreement No 265104


D3.1.6 Agricultural Land Use Intensity Data
PDF Print E-mail


This indicator proxies agricultural management intensity based on nitrogen input. For arable land, this was done by disaggregating nitrogen application rates from the Farm Structure Survey (FSS). For grassland, nitrogen input was estimated based on local stocking densities with cattle. As the start of the arable land methodology, the LUCAS point dataset crop information was assigned to a crop-specific nitrogen application rate based on the crop-specific application rate in the administrative unit. The assumption was that the cropping pattern serves as a proxy for the variation in nitrogen application. Nitrogen application rates are then classified in three classes: low (<50 kg/ha); medium (50-150 kg/ha) and high (>150 kg/ha). With the LUCAS locations as individual observations, the nitrogen application classes were modeled using multinominal regression and a set of environmental and socio-economic location factors. The regression models were estimated for all countries separately to account for national specific relations between agricultural intensity and location factors. The estimated regression models provided probability maps for the three arable land intensity classes, which were further allocated using a hierarchical procedure.

For grassland, LUCAS observations of grassland were linked to a nitrogen input was estimated based on the local stocking densities with cattle. Stocking densities were derived from the livestock maps of Neumann et al. (2009). We assumed a uniform quantity of 100 kg N/ha per cow per year and reclassified the observations into two classes: intensive grassland with > 50 kg N/ha and extensive grassland with < 50 kg N/ha. Similar to the procedure for arable land country-specific logistic regression models are estimated and used to downscale the areas of the different intensity classes to individual locations within the administrative units.

Time period covered: Static (2003-2006)
Extent: EU-27
Resolution: 1 km2

Input data

  • CLC 2000 land cover (www.eea.europa.eu/data-and-maps/)
  • Nitrogen input levels per crop type, Farm Structure Survey (FSS) from Eurostat (http://epp.eurostat.ec.europa.eu/portal/page/portal/farm_structure_survey/data/Database)
  • Crop types, Land Use/Cover area Frame Statistical Survey (LUCAS) (Jacques and Gallego 2010, The European land use and cover area-frame statistical survey. Agricultural survey methods. (ed. R.Benedetti, et al.), pp. 151-168. John Wiley & Sons, see also www.lucas-europa.info)
  • Livestock density maps (see Neumann et al., 2009, Land. Ecol. 24: 1207-1222 for details)
  • Eighteen biophysical and fifteen socioeconomic location factors are used in the regression analyses. The biophysical factors include topography, temperature, soil depth and environmental regions. Socioeconomic factors include accessibility, travel time, potential population and rural population density (see Temme & Verburg, 2011, Agr., Ecos. & Env. 140: 46-56 for details).

Contact information:
Peter Verburg – Institute for Environmental Studies, VU University Amsterdam
De Boelelaan 1087, 1081 HV Amsterdam, The Netherlands
+31 (20) 5983594, peter.verburg [at] vu.nl, www.ivm.vu.nl

For an application of the methodology:
Overmars, K., P., Schulp, C.J.E., Alkemade, R., Verburg, P.H., Temme, A.J.A.M., Omtzigt, N., Schaminée, J.H.J., (2014): Developing a methodology for a species-based and spatially explicit indicator for biodiversity on agricultural land in the EU. Ecological Indicators, 37(A): 186-198, ISSN 1470-160X, doi:10.1016/j.ecolind.2012.11.006.

Download data

ag01 tcm53 224030

ag02 tcm53 224031

ag03 tcm53 224032

ag04 tcm53 224033


Share this post