The challenges of land degradation, climate change and food insecurity have led to the introduction of conservation agriculture (CA) aimed at enhancing yield and soil quality. Despite positive biophysical results, low adoption rates have been the focus of studies identifying constraints to wider uptake. While the adoption framework is popular for measuring agricultural innovation, objective adoption measurements remain problematic and do not recognize the contextual and dynamic decision‐making process. This study uses a technographic and participatory approach to move beyond the adoption framework and understand: (a) how agricultural decision‐making takes place including the knowledge construction, (b) how agriculture is performed in a context of project intervention and (c) how practice adaptation plays out in the context of interacting knowledge. Findings confirm that farmer decision‐making is dynamic, multidimensional and contextual. The common innovation diffusion model uses a theory of change, showcasing benefits through training lead farmers as community advocates and demonstration trials. Our study shows that the assumed model of technology transfer with reference to climate‐smart agriculture interventions is not as linear and effective as assumed previously. We introduce four lenses that contribute to better understanding complex innovation dynamics: (a) social dynamics and information transfer, (b) contextual costs and benefits, (c) experience and risk aversion, and (d) practice adaptation. Investments should build on existing knowledge and farming systems including a focus on the dynamic decision process to support the 'scaling up, scaling out and scaling deep' agenda for sustainable agricultural innovations.

中文翻译：

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为什么我们应该重新思考农业创新中的“采用”：马拉维的经验性见解

土地退化，气候变化和粮食不安全所带来的挑战已导致采用保护性农业（CA），旨在提高产量和土壤质量。尽管取得了积极的生物物理结果，但低采用率一直是确定更广泛吸收的制约因素的研究重点。尽管采用框架普遍用于衡量农业创新，但是客观采用度量仍然存在问题，并且无法识别上下文和动态决策过程。这项研究使用技术和参与性方法超越了采用框架，并了解：（a）如何进行包括知识建设在内的农业决策，（b）在项目干预的背景下如何进行农业，以及（c）如何进行实践适应在相互作用的知识中发挥作用。研究结果证实，农民的决策是动态的，多维的和背景相关的。共同的创新扩散模型采用变革理论，通过培训主要农民作为社区倡导者和示范试验来展示收益。我们的研究表明，关于气候智能农业干预的技术转让假设模型不像以前那样线性和有效。我们介绍了四个有助于更好地理解复杂创新动力的视角：（a）社会动力和信息传递，（b）背景成本和收益，（c）经验和风险规避，以及（d）适应实践。投资应建立在现有知识和耕作制度的基础上，包括关注动态决策过程，以支持“扩大规模，横向扩展和深度扩展”