Predominantly rural areas make up half of Europe and account for about 20 per cent of the population. Yet most of them are among the least privileged regions in the European Union, with a GDP per capita significantly below the European average and with an average population age higher than in urban areas, although this gap will only slowly begin to narrow in the next decade. Together with a lack of connectivity, inadequate infrastructure, lack of diversified job opportunities and limited access to services, this makes rural areas a less attractive place to live and work. At the same time, however, rural areas actively participate in the EU's green and digital transition. Achieving the EU's digital goals for 2030 can offer more opportunities for the sustainable development of rural areas in areas other than agriculture, livestock and forestry, opening up new perspectives for the growth of manufacturing and, even more, service industries, and contributing to a better geographical distribution of services and industries. In this context, the objective of this work is the construction of a synthetic index of the welfare of European nations, through the Wroclaw taxonomic method and through the use of logit models, for the identification of best practices of local realities and the interpretation in a more im-mediated way of the fair and sustainable welfare of each European nation at a rural level

Traditional soil characterization methods are time consuming, laborious and invasive and do not allow long-term repeatability of measurements. The overall aim of this paper was to assess and model spatial variability of the soil in an olive grove in south Italy by using data from two sensors of different type: a multi-spectral on-board drone radiometer and a hyperspectral visible-near infrared-shortwave infrared (VIS-NIR-SWIR) reflectance radiometer as well as sample data, to arrive at a delineation of homogeneous areas. The hyperspectral data were processed using continuum removal methodology to obtain information about the content and composition of clay. Differently, the multispectral data were firstly upscaled to the support of soil data using geostatistics and taking into account change of support. Secondly, the two-sensor data were integrated with soil granulometric properties by using the multivariate geostatistical techniques of multi-collocated cokriging and factor cokriging, in order to achieve a more exhaustive and finer-scale soil characterisation. The paper shows the impact of change of support on the uncertainty of soil prediction that can have a significant effect on decision making in Precision Agriculture. Moreover, four regionalised factors at two different scales (two per each scale) were retained and mapped. Each factor provided a different delineation of the field with areas characterised by different granulometry and clay composition. The applied method is sufficiently flexible and could be applied to any number and type of sensors.