Policy instruments—such as regulation, financial incentives, and agricultural extension—are commonly applied by governments to promote sustainable agricultural practices and tackle ecosystem degradation. Despite substantial investment, little data are available to gauge the impact of evolving policy mixes. We constructed a Bayesian network model to explore relationships between policy instruments, contextual factors, and adoption. Applying a series of scenarios, we present examples of how different instruments influence adoption and how their effectiveness is shaped by contextual factors. Scenarios highlight that the effect of policy instruments is often modest, and constrained by diverse practice and population characteristics. These findings allow us to reflect on the role of policy instruments, and the conditions necessary to support practice change. For example, our findings raise questions about the role of financial benefits versus financial capacity, and highlight the potential importance of concepts such as mental bandwidth in shaping both motivation and capacity to adopt.

中文翻译：

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使用贝叶斯专家模型审查政策工具对采用农业保护实践的影响

政府通常采用监管、财政激励和农业推广等政策工具来促进可持续农业实践和解决生态系统退化问题。尽管投资巨大，但很少有数据可以衡量不断变化的政策组合的影响。我们构建了一个贝叶斯网络模型来探索政策工具、背景因素和采用之间的关系。通过应用一系列场景，我们展示了不同工具如何影响采用以及它们的有效性如何受到背景因素影响的示例。情景强调，政策工具的效果往往不大，并受到不同实践和人口特征的限制。这些发现使我们能够反思政策工具的作用以及支持实践变革的必要条件。例如，我们的研究结果提出了关于经济利益与经济能力的作用的问题，并强调了心理带宽等概念在塑造采用动机和能力方面的潜在重要性。