Designing an expert-led Bayesian network to understand interactions between policy instruments for adoption of eco-friendly farming practices

Mayfield, Helen J.; Eberhard, Rachel; Baker, Christopher M.; Baresi, Umberto; Bode, M. F.; Coggan, Anthea; Dean, Angela J.; Deane, Felicity; Hamman, Evan; Jarvis, Diane; Loechel, Barton; Taylor, Bruce; Stevens, Lillian; Vella, Karen; Helmstedt, Kate J.

doi:10.1016/j.envsci.2022.12.017

Cited by 3 publications

(5 citation statements)

References 78 publications

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“…This research is part of a larger project (Figure 2, full detail Supporting Information S1). Modelers facilitated participatory development of a Bayesian network model structure with a multidisciplinary research team (Mayfield et al., 2023). This conceptual model highlighted relationships between policy instruments and adoption, and included practice and population characteristics that may influence instrument effectiveness.…”

Section: Methodsmentioning

confidence: 99%

“…Input from industry stakeholders was incorporated to ensure that the model comprised all necessary components and reflected their view of the system. The model was then parameterized by the research team using conditional probability tables (Mayfield et al., 2023) (Supporting Information S2). After training, experts estimated the probability of adoption for each possible scenario relating to their area of expertise (e.g., Supporting Information S1).…”

Section: Methodsmentioning

confidence: 99%

“…Interdisciplinary nodes representing the mechanisms were completed collaboratively during facilitated workshops. Overall, processes involved in developing and parameterizing the conceptual model involved multiple rounds of structured expert elicitation (Mayfield et al., 2023). Model validation compared estimates from stakeholders with model estimates (Mayfield et al., 2023).…”

Section: Methodsmentioning

confidence: 99%

“…Overall, processes involved in developing and parameterizing the conceptual model involved multiple rounds of structured expert elicitation (Mayfield et al., 2023). Model validation compared estimates from stakeholders with model estimates (Mayfield et al., 2023). To ensure relevance to policy makers, model development focused on sugarcane farmers in the GBR catchments (and grazing to a lesser extent).…”

Section: Methodsmentioning

confidence: 99%

“…How to cite this article: Dean, A. J., Eberhard, R., Baresi, U., Coggan, A., Deane, F., Hamman, E., Helmstedt, K. J., Loechel, B., Jarvis, D., Mayfield, H., Stevens, L., Taylor, B., & Vella, K. (2023). Scrutinizing the impact of policy instruments on adoption of agricultural conservation practices using Bayesian expert models.…”

Section: S U P P O R T I N G I N F O R M At I O Nmentioning

confidence: 99%

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Scrutinizing the impact of policy instruments on adoption of agricultural conservation practices using Bayesian expert models

Dean,

Eberhard,

Baresi

et al. 2023

Conservation Letters.

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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.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: S U P P O R T I N G I N F O R M At I O Nmentioning

confidence: 99%

See 3 more Smart Citations

Scrutinizing the impact of policy instruments on adoption of agricultural conservation practices using Bayesian expert models

Dean,

Eberhard,

Baresi

et al. 2023

Conservation Letters.

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Risk Assessment of Flammable Liquid Transportation on Waterways: An Ontology-Driven Dynamic Bayesian Network Approach

Zhang,

Pu,

Huang

et al. 2024

Preprint

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Using the Implementation Centric Evolving Climate Change Adaptation Process to bridge the gap between policy and action

Wells,

Saggioro,

Petty

et al. 2023

Front. Clim.

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With climate impacts increasing in both frequency and intensity and unprecedented climate events having devastating results, the need for timely policy and action to support adaption is not in doubt. However, the gap between policy and action leaves many communities exposed to extreme events and vulnerable to loss of life and livelihoods. This is partly due to the difficulty policymakers face when confronted by climate projections with their inherent uncertainties. Competing sectoral interests and a lack of resources often compound such challenges. To address these issues, the Implementation Centric Evolving Climate Change Adaptation Process (ICECCAP) encases the climate risk assessment in an enabling framework to track resource, knowledge and regulatory needs. This process was applied as part of a UNEP project to support the National Adaptation Plan in Pakistan. A range of climate storylines, describing plausible climate futures and their potential environmental and socio-economic impacts, were developed and discussed with local stakeholders, including policy makers from across levels of governance. The process allowed us to translate complex physical science into narratives that could be communicated clearly to non-technical national stakeholders, forming a basis for subsequent negotiation and decision-making at a local level to address multiple risks and respond to adaptation needs at this finer resolution. This reflects our aim, as part of the My Climate Risk network, to amalgamate bottom-up climate risk assessment with climate projection data that remains meaningful at a local scale. We show how the integration of scientific research and local expert stakeholder views can promote buy-in to adaptation planning. Grounded in a systemic and comprehensive understanding of potential impacts of climate change, this process has implications across socio-economic, environmental and governance spheres.

show abstract

Designing an expert-led Bayesian network to understand interactions between policy instruments for adoption of eco-friendly farming practices

Cited by 3 publications

References 78 publications

Scrutinizing the impact of policy instruments on adoption of agricultural conservation practices using Bayesian expert models

Scrutinizing the impact of policy instruments on adoption of agricultural conservation practices using Bayesian expert models

Risk Assessment of Flammable Liquid Transportation on Waterways: An Ontology-Driven Dynamic Bayesian Network Approach

Using the Implementation Centric Evolving Climate Change Adaptation Process to bridge the gap between policy and action

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