Indicator-based tools are widely used for the assessment of farm sustainability, but analysts still face methodological and conceptual issues, including data availability, the complexity of the concept of sustainability and the heterogeneity of agricultural systems. This study contributes to this debate through the illustration of a procedure for farm sustainability assessment focussed on the case study of the South Milan Agricultural Park, Italy. The application is based on a set of environmental, social and economic indicators retrieved from the literature review. The framework is based on three main steps: (i) Data collection mainly through interviews with farmers and institutions; (ii) data elaboration through an aggregative structure; and (iii) score analysis. The latter step includes a descriptive analysis that allows a comparison among farms or groups of farms and a principal components analysis that helps to confirm the dimensions in which indicators were previously included (components). Results derived from the sampled farms show that the framework can provide easy-to-read results useful at different levels. The study highlighted the procedures for the framework construction that is compatible with the region’s context and objectives, using an analytical approach that aims at the use of balanced features of availability and reliability of data.

The present research proposes a model for the assessment of the social pillar of sustainability at the farm scale. Contrary to what is available for the environmental and economic pillars, there is a considerable lack of exhaustive approaches able to evaluate the social dimension of sustainability in rural areas. Thus, the idea was to create a mean by which a quantitative evaluation of the social characteristics of farms could be made. The study involved farms of the South Milan Agricultural Park, located in northern Italy. Thirty sampled farms were selected in order to represent the different livestock systems, land areas, economic dimensions and levels of multifunctionality of the area. The framework is based on a set of 15 indicators able to evaluate five main social “components”: (i) quality of the products and the region, (ii) short supply chain and related activities, (iii) work, (iv) ethical and human development and (v) society, culture and ecology. The work was structured using the following steps: identification of the relevant variables for the social sustainability of farms, determination of the framework of indicators, assignment of their range scores, data collection, calculation of the score for each farm, data analysis and visualization. The method allows different types of analysis in relation to the objective of the research. Three main approaches were individuated: (1) the comparison among farms is the “farms’ ranking” and the “aggregate ranking”; (2) the evaluation of single themes of sustainability is the “single indicator evaluation” approach and (3) the temporal comparison of the farm’s result is the “score evolution” approach. The method showed a high sensitivity to the multifunctionality and the type of farm production, especially organic vs conventional, while other characteristics, such as the type of livestock and the land area, seem to differentiate the sample less or to characterize it in only a few social components. The work has underlined the importance and the advancement in the study of the social dimension that, however, needs further in-depth analysis through comparison with the other two pillars and among various social states in different rural areas.

Innovation processes includes social and communicative elements. The role of innovative technology for the development of farming systems is investigated in literature, but only a few studies deal with the influence of networks on the adoption of technologies by farmers. The aim of this paper is to verify if the adoption of smartphones for professional reasons by farmers is influenced by the networks to which farmers belong, the socio-demographic characteristics of the farmers, and their farm’s size. The case study is in the Valtellina valley, a rural mountain area in the north of Italy, where 53 livestock farmers were interviewed. The methodology is based on two steps. First, social network analysis is applied to diagnose the relationships of farmers in terms of connectivity and closeness and to detect the centrality measures of farmers in three different social relationships: production networks, market networks, and information exchange networks. A multiple linear regression model is then applied to test whether centrality measures of the three networks, the farmers’ socio-demographic characters, and the farm’s features drive the adoption of smartphones for professional reasons. Results suggest that the centrality measures in production networks positively drive the adoption of smartphones, while the centrality measures of market and information exchange networks do not have the same effect. At the same time, the farmers’ age negatively affects the use of smartphones for professional reasons, while the size of the herd, and the education and gender of farmers have no significant influence. The study contributes to the debate around the Agricultural Knowledge Innovation System and supports local policies based on the inclusion of farmers in the technological development in rural areas.