Determinants of smallholder farmers choice of adaptation strategies in response to the impacts of climate variability in the Ayehu watershed, Northwest Ethiopia

Abstract

Adapting to climate variability is crucial for sustainable livelihoods in developing countries like Ethiopia, where rain-fed agriculture underpins the economy. This study aims to evaluate both indigenous and introduced adaptation measures across different agroecological zones, along with their determining factors. Data was collected from 338 farm households using structured and semi-structured questionnaires. The Weighted Average Index (WAI) was used to identify the most significant adaptation methods employed by farm households in various agroecological zones, while the Problem Confrontation Index (PCI) assessed the barriers hindering the implementation of these strategies. The multinomial logit model (MNL) was utilized to investigate the factors affecting farmers' choices of adaptation strategies. The results indicated that the most popular indigenous adaptation strategies were planting local crop varieties (WAI = 2.22), crop diversification (WAI = 2.15), and adjusting planting dates (WAI = 2.14). The introduced adaptation strategies included using inorganic fertilizers (WAI = 2.64), applying improved crop varieties (WAI = 2.41), and using pesticides and herbicides (WAI = 2.24). PCI results revealed that the major barriers to adapting to climate variability were limited farm size (PCI = 694), lack of access to climate information (PCI = 641), poor soil quality (PCI = 639), lack of irrigation facilities (PCI = 623), and high input costs (PCI = 610). The logit model identified several significant factors influencing farmers' preferences for adaptation measures, such as crop failure, credit availability, recurrent drought, climate variability perception, agroecological location, and household income. The study underscores the importance of understanding local-level factors that influence farmers' adaptation strategies to enhance their resilience to climate variability.