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

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. In the first step, a Kernel Density Estimation (KDE) technique is applied to a reclassified LULC map, in order to calculate continuous intensity indicators. A Principal Components Analysis and an ISODATA Cluster Analysis are then performed respectively to remove redundant information and to identify the different landscape structures of the study area. The second analysis follows the same steps, with the difference that LULC intensity indicators are analysed in combination with data on crop rotations, irrigation and livestock from a census survey. The first analysis returns a map of the landscape driven only by different intensities of land use. The second returns a map where the statistics on agricultural practices allow for a better characterisation of the natural value of the landscape. Agricultural statistics improved the results, since they allow the discrimination of lower intensity clusters within the cultivated areas, which are traditionally excluded from HNVf by considering only the land cover. The comparison between the results of the two analyses shows that the combined use of the agricultural statistics determines a more detailed representation of the study area, that allows a better differentiation of the agricultural areas between HNVf and non-HNVf, leading to an improvement of the HNVf identification methodology. The benefit of using additional information can be therefore of interest for territorial planning, with the ultimate aim of promoting biodiversity conservation.