Farmers’ perceptions and matching climate records jointly explain adaptation responses in four communities around Lake Tana, Ethiopia

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Farmers’ climate perceptions are responsible for shaping their adaptive responses and are thus essential to consider for the design of strategies to reduce vulnerability and increase resilience. In this study, we collected social data in four communities in the central Ethiopian Highlands on farmers’ climate perceptions and adaptations using group discussions and PRA tools. We related these to climate data spanning 30 years (1981 to 2010), consisting of daily minimum temperature, maximum temperature and precipitation, modelled for the four communities using global databases and regional meteorological data. We found that farmers’ climate perceptions showed considerable spatial and gender differences. Perceptions matched well with records describing climate variability, particularly in terms of the shortening and the increased variability of the rainy season, as well as the occurrence of extreme drought in recent years. Climate change, described by long-term average increases in temperature and decreases in precipitation, was perceived, but with subordinate priority. Perceived climate impacts included reduced crop yield, increased occurrence of pests and diseases and increased crop damage by extreme events and poverty. Adaptations were mainly land based and included agronomic measures, land management and ecosystem restoration. Furthermore, important gender differences in adaptation could be traced back to typical gender roles. Results highlight the risk of broadcast adaptation programs, such as the government-propagated combination of mineral fertilizers and early maturing crop varieties. Most importantly, they point to the need to consider climate variability, site- and gender-specific perceptions and priorities.
Authors: Darabant, A.; Habermann, B.; Sisay, K.; Thurnher, C.; Worku, Y.; Damtew, S.; Lindtner, M.; Burrell, L.; Abiyu, A.
Subjects: farmers, rural communities, adaptation, climate, data analysis
Publication type: ISI, Journal Article, Publication
Year: 2020
ISSN: 0165-0009

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