Agri-Food Management and Sustainable Practices: A Fuzzy Clustering Application Using the Galois Lattice

Abstract

The objective of this study was to generate groups of agri-food producers with high affinity in relation to their sustainable waste management practices. The aim of conforming these groups is the development of synergies, knowledge management, and policy- and decision-making by diverse stakeholders. A survey was conducted among the most experienced farmers in the region of Nuevo Urecho, Michoacán, Mexico, and a total of eight variables relating to sustainable waste management practices, agricultural food loss, and the waste generated at each stage of the production process were examined. The retrieved data were treated using the maximum inverse correspondence algorithm and the Galois Lattice was applied to generate clusters of highly affine producers. The results indicate 163 possible elements that generate the power set, and 31 maximum inverse correspondences were obtained. At this point, it is possible to determine the maximum number of relationships, called affinities. In general, all 15 considered farmers shared the measure of revaluation of food waste and 90% of the farmers shared affinity in measures related to ecological care and the proper management of waste. A practical implication of this study is the conformation of highly affine clusters for both policy and strategic decision-making.