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Articles

Applying structurational divergence theory to climate change adaptation in a localized context: understanding adaptive potential of coffee producers in Risaralda, Colombia

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Pages 651-668 | Received 18 Dec 2019, Accepted 13 Jan 2021, Published online: 01 Sep 2021
 

ABSTRACT

Climate change impacts threaten farmers’ livelihoods and global food security. Adaptation to climate change can protect both farmers and our food supply. Structurational divergence theory (SD theory) provides a holistic framework to understand the unique contexts of groups impacted by climate change and to reveal constraints and opportunities for adaptation efforts. We apply SD theory to climate change adaptation, focusing on a population of Colombian coffee farmers in Risaralda who are impacted by climate change. Our findings reveal a SD cycle blocking successful adaptation. Climate change impacts exacerbate farmers’ pre-existing financial strain and threaten their livelihoods, yet they lack the information and political and structural support needed to take action. This impedes development and creates a downward spiral of immobilization; the farmers will continue to be unprepared for climate change and practice inconsistent adaptation efforts until structures shift to provide them with the information and resources needed for successful adaptation.

Acknowledgements

Funding was provided by the Purdue University College of Liberal Arts, the Purdue University Brian Lamb School of Communication and the Purdue Climate Change Research Center. The authors would also like to thank Dr Natalie J. Lambert at Indiana University and our collaborators Diana Carolina Meza Sepulveda, John Mario Rodriguez and Sebastián Ríos Sanabria at the Universidad Tecnológica de Pereira for their support on this project.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Notes

1 Condition statements (themes that emerged from the data that are represented in ) are placed in italics for clarity.

Additional information

Funding

This work was supported by Purdue University Brian Lamb School of Communication Research Grant; Purdue University College of Liberal Arts Global Synergy Grant; Purdue University Climate Change Research Center Travel Grant.

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