4.7 Article

A classification of European agricultural land using an energy-based intensity indicator and detailed crop description

Journal

LANDSCAPE AND URBAN PLANNING
Volume 198, Issue -, Pages -

Publisher

ELSEVIER
DOI: 10.1016/j.landurbplan.2020.103793

Keywords

Crop systems; EU agriculture; Agricultural management; Land-use intensity; Energy use

Funding

  1. European Union [633814]
  2. H2020 Societal Challenges Programme [633814] Funding Source: H2020 Societal Challenges Programme

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With agricultural areas covering almost half of European land, proper management of agro-ecosystems is key to achieve the European Union's environmental and climate objectives. This requires spatially explicit methods and indicators. We developed an approach for the classification of agricultural land by combining two main dimensions i) land cover, using detailed geo-spatialized census data covering 63 individual crops; ii) management intensity, measured as the anthropogenic energy required in the primary crop production. As a result we identified 10 main crop systems further classified into 30 'crop-management systems' at a spatial resolution of 5 arcminutes. The resulting maps show the spatial patterns of agricultural management intensity across Europe, both in absolute terms (total energy input per hectare) and relative to the dominant crop system in the spatial unit of analysis. The use of multiple intensity dimensions provides new, more detailed insights on agricultural intensity by which areas that were previously classified as low-medium intensive - some permanent crops systems or irrigated arable land - appear now as highly intensive. An expert-based evaluation was carried out on the intensity maps and corroborated the obtained results. The generated maps can be used to support decision-making in designing more targeted, context-specific agricultural and territorial policies. In particular, findings can be relevant in the context of the Common Agricultural Policy post 2020 and the Biodiversity Strategy towards 2030, both of which will benefit from more detailed spatially explicit information to achieve their stated objectives.

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