Assessing priorities for enhancing adaptive capacity of agricultural systems to climate change using fuzzy logic-based approaches

Abstract

This study outlines the development of a composite indicator of the adaptive capacity (ACI) toclimate change of rural communities in the Oristanese district (Sardinia, Italy). Farming systemsinclude intensive dairy cattle, rainfed dairy sheep, cereals and irrigated horticulture. Twenty-oneindicators of AC were derived from an array of several priorities, initially identified by aninterdisciplinary team of scientists and then extended and scored (on a rank from 1 to 5) by 31experts (agronomic scientists, farmers, advisors and consumers). The extended list of prioritieswas reduced to a set of indicators that could be quantified using data from different sources. Theindicators were organized into seven determinants (Infrastructure, Technology, Economicpower, Flexibility, Knowledge, Sensitivity, Social capital), in turn organized in three components:Ability, Action and Awareness. AC calculations required that 1) scores for each basic indicator benormalized and aggregated to a determinant value, 2) determinants aggregated to a componentvalue, 3) components aggregated to an AIC (best, 0≤ACI≤1, worst). A fuzzy logic inferring systemwas used based on the importance of the basic indicators and their aggregation intodeterminants and components. Favourable/unfavourable thresholds for each indicator were setfollowing expert knowledge and/or survey/census/literature data, while the priority scores wereused to assign weighting factors. Results for the Oristanese district indicate a low-medium AC(ACI=0.61) with social capital (0.27) being the strongest determinant and economic power (0.80)the weakest. These findings provide insights for enhancing effective, locally meaningful andfeasible strategies by increasing the AC of Oristanese rural communities.