Publication Details

Category Text Publication
Reference Category Journals
DOI 10.1007/s00267-022-01635-6
Licence creative commons licence
Title (Primary) Using Bayesian belief networks to investigate farmer behavior and policy interventions for improved nitrogen management
Author Jäger, F.; Rudnick, J.; Lubell, M.; Kraus, M.; Müller, B. ORCID logo
Source Titel Environmental Management
Year 2022
Department OESA
Volume 69
Issue 6
Page From 1153
Page To 1166
Language englisch
Topic T5 Future Landscapes
Keywords Bayesian belief networks; Sustainable management practices; Farmer adoption; Agricultural decision-making; Policy analysis; Nitrate pollution
Abstract 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.
Persistent UFZ Identifier
Jäger, F., Rudnick, J., Lubell, M., Kraus, M., Müller, B. (2022):
Using Bayesian belief networks to investigate farmer behavior and policy interventions for improved nitrogen management
Environ. Manage. 69 (6), 1153 - 1166 10.1007/s00267-022-01635-6