Department of Ecological Modelling, Helmholtz Centre for Environmental Research - UFZ, Leipzig, Germany.
Department of Environmental Science and Policy, University of California Davis, Davis, CA, USA.
Environ Manage. 2022 Jun;69(6):1153-1166. doi: 10.1007/s00267-022-01635-6. Epub 2022 Apr 5.
Increasing farmers' adoption of sustainable nitrogen management practices is crucial for improving water quality. Yet, research to date provides ambiguous results about the most important farmer-level drivers of adoption, leaving high levels of uncertainty as to how to design policy interventions that are effective in motivating adoption. Among others, farmers' engagement in outreach or educational events is considered a promising leverage point for policy measures. This paper applies a Bayesian belief network (BBN) approach to explore the importance of drivers thought to influence adoption, run policy experiments to test the efficacy of different engagement-related interventions on increasing adoption rates, and evaluate heterogeneity of the effect of the interventions across different practices and different types of farms. The underlying data comes from a survey carried out in 2018 among farmers in the Central Valley in California. The analyses identify farm characteristics and income consistently as the most important drivers of adoption across management practices. The effect of policy measures strongly differs according to the nitrogen management practice. Innovative farmers respond better to engagement-related policy measures than more traditional farmers. Farmers with small farms show more potential for increasing engagement through policy measures than farmers with larger farms. Bayesian belief networks, in contrast to linear analysis methods, always account for the complex structure of the farm system with interdependencies among the drivers and allow for explicit predictions in new situations and various kinds of heterogeneity analyses. A methodological development is made by introducing a new validation measure for BBNs used for prediction.
提高农民对可持续氮管理实践的采用率对于改善水质至关重要。然而，迄今为止的研究对于农民采用的最重要驱动因素提供了模糊的结果，使得如何设计有效的政策干预措施以激励采用仍然存在高度的不确定性。其中，农民参与推广或教育活动被认为是政策措施的一个有前途的着力点。本文应用贝叶斯信念网络（BBN）方法来探讨被认为影响采用的驱动因素的重要性，运行政策实验来测试不同参与相关干预措施对提高采用率的效果，并评估干预措施对不同实践和不同类型农场的效果的异质性。基础数据来自 2018 年在加利福尼亚州中央山谷的农民中进行的一项调查。分析结果表明，在所有管理实践中，农场特征和收入一直是采用的最重要驱动因素。政策措施的效果根据氮管理实践而有很大差异。创新型农民对与参与相关的政策措施的反应优于传统型农民。与大型农场相比，小型农场的农民通过政策措施增加参与的潜力更大。与线性分析方法相比，贝叶斯信念网络始终考虑到农场系统的复杂结构，以及驱动因素之间的相互依存关系，并允许在新情况下进行明确预测和进行各种异质性分析。通过引入用于预测的 BBN 的新验证措施，实现了方法学的发展。
