Expert knowledge-based modelling approach for mapping beekeeping suitability area

It is becoming increasingly accepted that beekeeping is declining due to the damaging effect of global changes such as climate and land-use change that directly and indirectly impact Apis Melliferas. Despite numerous investigations, a comprehensive study that incorporates both global and local knowledge has yet to be conducted. For a long time, researchers have suggested that expert knowledge should be taken into account when creating decision support tools for managing activities related to natural resources, such as beekeeping. Unlike previous studies, this research seeks to tackle these questions while also introducing the concept of ecosystem service in modelling, offering a fresh perspective on sustainable land use. To achieve this goal, we combined several methods, including using literature knowledge, beekeeper knowledge, and multi-source geospatial data. These data are employed in a hierarchical fuzzy inference system in a unified way. The proposed approach was applied in the Que<acute accent>bec region and the suggested technique appears to be both reliable and effective. The validation step revealed that the landscape variable, particularly the area used for agriculture or grassland, had the greatest impact on changes in hive weight throughout the season. In addition, we demonstrated that meteorological factors such as rainfall and relative humidity are strongly correlated to beekeeping. We showed that access to data and knowledge can be a critical factor in decision-making in the beekeeping industry, and thus we suggest that wild-bees conservationists, decision-makers, farmers, beekeepers, and other stakeholders adopt a collaborative approach.