Construction of a Bayesian Network for the Assessment of Agri-Environmental Measures – The Case Study of the Venice Lagoon Watershed

Abstract

A methodological framework was designed to assess the effectiveness of agri-environmental policy measures adopted by the Veneto Region to reduce diffuse water pollution of agricultural origin. Two already existing methodologies were combined in a new flexible approach for policy assessment: Conceptual Modelling and Bayesian Networks (BNs). The former supported the development of a shared conceptual model (a cognitive map) of the agro-ecosystem of the study area; while the latter allowed the development of a probabilistic model coherent with the cognitive map. BNs were selected because they allow analyses with scarce data; they can be updated when further information becomes available, and are easily understandable by layperson. The paper reports the results obtained in the Venice Lagoon Watershed (VLW) case study, where the current agri-environmental measures were assessed in order to identify their effectiveness in terms of reduction of nitrogen releases in water bodies connected to the lagoon ecosystem. Preliminary results obtained by implementing expert opinions in the BN pointed out the likely limited effects of the measures on the declared objective of safeguarding the lagoon ecosystem. Final remarks are drawn about the potentials of the proposed methodology.

Applications of Bayesian Networks as Decision Support Tools for Water Resource Management under Climate Change and Socio-Economic Stressors: A Critical Appraisal

“…This systematic quantitative literature review quantified the reviewed studies, applied decision support approaches in the management of complex and uncertain systems, and found that most of these reviewed studies applied scenario-based approaches to help decision-makers or modellers understand water-related systems, and answer the question 'what if? ', through evaluating the effectiveness of management measures under exploring different scenarios (e.g., [51,127]). Optimization-based approaches were also applied but less frequently.…”

A Bayesian Networks approach for the assessment of climate change impacts on nutrients loading

“…Corn, Soy, Winter Wheat) (Table 1I). This information was obtained from both literature (Carpani and Giupponi, 2010) and interviews with experts of Veneto Agricoltura (Bonetto, 2012;Regione Veneto, 2014).…”

Conditions for the Adoption of Conservation Agriculture in Central Morocco: An Approach Based on Bayesian Network Modelling

“…Each node of the BDN represents a system component and has a finite set of mutually exclusive and exhaustive states. The states or conditions of the nodes can be categorical, continuous, or discrete (Carpani and Giupponi, 2010) (Table 1).…”

“…( 1) where the posterior probability of an event A, P(A|B), is obtained by multiplying the prior probability of event A, P(A), with its likelihood P(B|A) normalized dividing by P(B), given that B is true (Castelletti and Soncini-Sessa, 2007;Carpani and Giupponi, 2010;Catenacci and Giupponi, 2013).…”

“…The probability of yes adoption in the different priors only varied 0.05% between the responses that led to the maximum and minimum levels of adoption rates. Hence, for the final prior that we kept for policy evaluations (Step 6), we decided to keep the average values of the different experts, for each CPTs in each node of the BDN model (Buntine, 1996;Carpani and Giupponi, 2010).…”