Climatic vulnerability, adoption of climate-resilient technologies, and its socioeconomic-institutional-agroecological determinants

Abstract

Climate change have been identified as the greatest challenge facing global leaders in 21st century. A major obstacle hindering the world from achieving the Sustainable Development Goals (SDGs) is climate change. Climate-resilient agriculture requires the integration of both socioeconomic and agroecological spheres with institutional investment. Numerous studies have advanced our understanding of climate change and its impact on global agriculture, but few have focused on the micro-farming situation. A geographical indicator (GI)-labelled Alphonso mango has developed a high sensitivity to climate change. It is therefore, this study intends to investigate climatic vulnerabilities; the adoption of climate-resilient technologies, and which socioeconomic-institutional-agroecological factors affect the adoption process. We adopted a multistage random sampling method to identify districts, blocks, villages, and mango farmers. A pre-tested interview schedule was employed to obtain field data. A binary logistic regression model was employed in Statistical Package for Social Sciences (SPSS). The results of this study identified extreme climatic vulnerabilities such as temperature, relative humidity, cold waves, fog, and frost that caused damage to the mango plantations around the plains and hills of the west coast India, resulting in decreased production and productivity. Therefore, mango orchardists in coastal agroecosystems have adopted a large number of climate-resilient technologies, albeit at a moderate level. We found that a set of socioeconomic, institutional, and agroecological indicators had a substantial impact on the adoption of climate-resilient technologies (CRTs). This research implied that climate change adaptation plans may be designed and implemented with socioeconomic, institutional, and agroecological indicators in mind at all levels of policy planning such as macro-scale (global), meso (regional), and landscape (local).