Implementing Minkowski fuzzy screening, entropy, and aggregation methods for selecting agricultural sustainability indicators

ABSTRACT Owing to the irreparable damage caused by conventional agriculture and its industry, a shift to sustainable agriculture is a more strategic and essential need now than ever before. To boost the transition, this study presents a new and comprehensive tool for assessing agricultural sustainability of paddy fields in Guilan Province, Iran. Based on relevant literature review, an investigation was made into the sustainability indicators which had theoretically been adopted by researchers in recent years. Then a set of indicators was introduced for examination and prioritization. The proposed structural model included three factors (social, economic, and environmental) and 62 indicators. All of the 62 indicators were developed into a valid and reliable questionnaire, and agricultural experts and researchers familiar with agricultural sustainability concepts and the situation of Guilan’s paddy fields were surveyed about the feasibility of these factors as agricultural sustainability indicators. After that, based on the respondents’ points of view, the weight for each of the selected indicators was measured using Minkowski fuzzy screening and Shannon’s entropy method, and then final priority of indicators was determined by aggregating methods. Results show that the high priority sustainability indicators included social indicators: farmers’ satisfaction and hope for farming jobs; economic indicators: income situation, ability of supply and competition; and ecological factors: cultivation pattern and conservation of natural resources.