Neo-Endogenous Rural Development in Favor of Organic Farming: Two Case Studies from Italian Fragile Areas

This paper analyses two case studies ascribable to neo-endogenous paradigm for rural development experimented by two small municipalities in Northern (Varese Ligure) and Central-southern (Castel del Giudice) Italian Apennines. By means of different approaches, the two towns have been able to provide local development through organic farming that in turn have boosted economic diversification or new forms of territorial aggregation. They have drawn the attention to the neighbouring communities and have stimulated emulation processes. Through a multivariate analysis this study therefore gets to a distinct partition of the two regional territories (NUTS2), where the cases belong, identifying the municipalities that show greater affinity to the cases in question, in order to offer to policy makers useful elements either for encouraging the replication of best practices or including them in future planning strategies. However, while the cluster that includes Varese Ligure (in Northern Italy) appears more clearly influenced by environmental values and a certain demographic resilience, the one to which Castel del Giudice belongs (in the Center-South) is more influenced by the aging of the population and by a greater structural dependency. Both clusters, however, could find a common way of development centred on the economic potential offered by organic farming due to the affinity shown by the proposed case studies.

Genetic diversity and structure found in samples of Eritrean bread wheat

Genetic diversity and structure plays a key role in the selection of parents for crosses in plant breeding programmes. The aim of the present study was to analyse the genetic diversity and structure of Eritrean bread wheat accessions. We analysed 284 wheat accessions from Eritrea using 30 simple sequence repeat markers. A total of 539 alleles were detected. The allele number per locus ranged from 2 to 21, with a mean allele number of 9.2. The average genetic diversity index was 0.66, with values ranging from 0.01 to 0.89. Comparing the three genomes of wheat, the B genome had the highest genetic diversity (0.66) and the D genome the lowest diversity (0.61). A STRUCTURE analysis based on the Bayesian model-based cluster analysis followed by a graphical representation of the distances by non-parametric multidimensional scaling revealed a distinct partition of the Eritrean wheat accessions into two major groups. This is the first report of the genetic diversity and structure of Eritrean bread wheat.

A MULTI-CLUSTERING FUSION SCHEME FOR DATA PARTITIONING

A multi-clustering fusion method is presented based on combining several runs of a clustering algorithm resulting in a common partition. More specifically, the results of several independent runs of the same clustering algorithm are appropriately combined to obtain a distinct partition of the data which is not affected by initialization and overcomes the instabilities of clustering methods. Subsequently, a fusion procedure is applied to the clusters generated during the previous phase to determine the optimal number of clusters in the data set according to some predefined criteria.

A Feature-Based Approach to Market Segmentation via Overlapping K-Centroids Clustering

Nonhierarchical partitioning techniques are used widely in many marketing applications, particularly in the clustering of consumers, as opposed to brands. These techniques can be extremely sensitive to the presence of outliers, which might result in misinterpretations of the segments, and subsequently to inferring incorrect relationships of segments to independently defined, actionable variables. The authors propose a general approach to market segmentation based on the concept of overlapping clusters (Shepard and Arabie 1979), wherein each pattern of overlap can be interpreted as a distinct partition. Both K-means and K-medians clustering procedures are special cases of the proposed approach. The suggested procedure can handle relatively large data sets (e.g., 2000 entities), is easily programmable, and hence can be gainfully employed in marketing research.