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Combining LULC data and agricultural statistics for A better identification and mapping of High nature value farmland: A case study in the veneto Plain, Italy

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  • Bonato, Marta
  • Cian, Fabio
  • Giupponi, Carlo

Abstract

High Nature Value farmland (HNVf) are characterised by high naturalness of land cover and low intensity of agricultural practices. They are essential for biodiversity conservation in rural environments, and their presence is an important indicator of the effectiveness of different EU policies that aim to support biodiversity in agricultural areas. Consequently, their identification, protection and implementation is of strategic importance. Previous studies have analysed the landscape along an urban-rural-natural gradient, beginning with Land Use and Land Cover (LULC) maps. Building up from this baseline, this paper integrates spatial analysis methods with statistical data related to agricultural practices and their intensity, with the aim of mapping and assessing HNVf in a portion of the Veneto Plain, north-east Italy. In particular, this paper presents a methodology for the identification of HNVf applied to two datasets: (i) the first encompassing only LULC data and (ii) the second encompassing also statistical data on agricultural practices. The aim is to demonstrate how this additional information improves the identification of HNVf.

Suggested Citation

  • Bonato, Marta & Cian, Fabio & Giupponi, Carlo, 2019. "Combining LULC data and agricultural statistics for A better identification and mapping of High nature value farmland: A case study in the veneto Plain, Italy," Land Use Policy, Elsevier, vol. 83(C), pages 488-504.
  • Handle: RePEc:eee:lauspo:v:83:y:2019:i:c:p:488-504
    DOI: 10.1016/j.landusepol.2019.02.034
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    References listed on IDEAS

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    1. Trisorio, Antonella & Borlizzi, Andrea, 2011. "Assessing the impact of rural policy on biodiversity: High Nature Value Farming in Italy," 122nd Seminar, February 17-18, 2011, Ancona, Italy 100042, European Association of Agricultural Economists.
    2. Li, Baibing & Martin, Elaine B. & Morris, A. Julian, 2002. "On principal component analysis in L1," Computational Statistics & Data Analysis, Elsevier, vol. 40(3), pages 471-474, September.
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