A Bayesian network highlighting the linkages between landscape structure and the local economy: the case of agritourism in lowland areas of Northern Italy

Abstract

Linking landscapes to socio-economic benefits necessarily requires considering the usability of landscape structure. To do so, however, depends on the interaction between users and producers of landscape-related services. We illustrate this interaction with a Bayesian Belief Network (BBN) in a case study analysing the connection between residents' perceptions of landscape structure and agritourism restaurants in the eastern lowlands of Ferrara (Italy). We use estimates of prior and conditional probabilities from a mix of different data: land use, survey data, regional statistics, and expert judgements to show the likely effects of the landscape structure on the local economy by using intermediate forms of services (i.e. second-order services). The second-order service is highly influenced by the agritourism density and by the frequency with which customers dine at agritourism restaurants and less by landscape attractiveness, confirming the importance of the supply and demand of second-order services in the provision of landscape-related services.

Keywords:

• second-order effects
• landscape attractiveness
• residents' perception of landscapes
• landscape-related activities
• public goods
• cause–effect relations

Acknowledgements

We thank the local stakeholders who actively participated in the laboratories and data validation activities of this study and the three anonymous referees who contributed to improve the final version of this paper.

Disclosure statement

No potential conflict of interest was reported by the authors.

Supplemental data

Supplemental data for this article can be accessed here.

Notes

1. ISTAT is the National Institute of Statistics.

2. Spherical pay-off is computed as MOAC where MOAC stands for the mean probability value of a given state averaged over all cases, P_C is the probability predicted for the correct state, P_j is the probability predicted for state j, and n is the number of states (Marcot et al. 2006).

Additional information

Funding

This work was supported by the European Commission under the Seventh Framework Programme project CLAIM (Supporting the role of the Common agricultural policy in Landscape valorisation: Improving the knowledge base of the contribution of landscape Management to the rural economy, www.claimproject.eu) [grant agreement n° 289578].