Six languages for a risky climate: how farmers react to weather and climate change

Kieran M. Findlater (), Terre Satterfield, Milind Kandlikar and Simon D. Donner
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Kieran M. Findlater: University of British Columbia
Terre Satterfield: University of British Columbia
Milind Kandlikar: University of British Columbia
Simon D. Donner: University of British Columbia

Climatic Change, 2018, vol. 148, issue 4, No 1, 465 pages

Abstract: Abstract How climate-sensitive actors—like commercial farmers—perceive, understand, and react to weather and climate stimuli will ultimately determine the success or failure of climate change adaptation policies. Many studies have characterized farmers’ climate risk perceptions or farming practices, but few have evaluated the in situ decision-making processes that link (or fail to link) risk perceptions to adaptive behaviors. Here, we use a novel methodology to reveal patterns in climate-sensitive decision-making by commercial grain farmers in South Africa. We structure, linguistically code, and statistically analyze causal relationships described in 30 mental models interviews. We show that farmers’ framing of weather and climate risks strongly predicts their adoption of conservation agriculture (CA)—climate-resilient best practices that reduce shorter-term financial and weather risks and longer-term agronomic risks. These farmers describe weather and climate risks using six exhaustive and mutually exclusive languages: agricultural, cognitive, economic, emotional, political, and survival. The prevalence of agricultural and economic language only weakly predicts CA practice, whereas emotional and farm survival language strongly limits CA adoption. The framing of weather risks in terms of farm survival impedes adaptations that are likely to improve such survival in the longer term. But this survival framing is not necessarily indicative of farmers’ current economic circumstances. It represents a consequential mindset rather than a financial state and it may go undetected in more conventional studies relying on direct survey or interview questions.

Date: 2018
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DOI: 10.1007/s10584-018-2217-z

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