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Feeding cattle under suboptimal conditions in Kenya: From emphasising technical (non-)adoption to stimulating adaptive performance

Domain:

agricultureclimate

Record type:

paper
Creator:
VerJanSieTod
Publisher:
SAG
Host:
In the climate-development interface, research for development gained a strong interest in climate-smart agriculture and sustainable forms of intensification. Moreover, the urgency to respond to climate change stimulates a strong and sometimes exclusive focus on mitigation, driven by research-based agricultural practices. In the livestock sector, this materialises in strategies to lower emissions from livestock, which centralises the adoption question: what can we do to stimulate the use of best practices by smallholder farmers? This paper flags the risk that this outlook may overlooksmallholder farmers' capacities to navigate sub-optimal conditions of drought or scarcity. The paper applies an alternative lens for understanding ‘agriculture-as-performance’ and highlights smallholder cattle owners’ agency and resourcefulness. It aims to create a conceptual space to examine how adaptive capacities are grounded in the rhythms of agriculture under suboptimal conditions. Empirically, the study focuses on the cattle-feeding practices of five farming households evolving through seasons and lifetimes in a sedentary agricultural system in Kenya. Our frame identifies diverse affordances, conceptualised as opportunities for actions, emerging and disappearing in the immediate material environment, and we portray cattle feeding as a networked response anchored in practices of giving, sharing, and receiving. We use our insights into the dynamics and performance of entangled socio-technical practices to sketch the contours of an alternative pathway for agriculture for development. We argue in favour of a shift from an exclusive focus on the adoption of predefined optimal solutions to a diagnostic and catalytic approach integrating situated adaptive performances through which farming households respond to action opportunities.

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doi.org

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https://journals.sagepub.com/page/policies/text-and-data-mining-license

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