Evaluating the likelihood of the adoption of an animal welfare assessment system in European agri-food supply chains

Purpose – The purpose of this paper is to analyse the likelihood of adoption of a recently designed Welfare Assessment System in agri-food supply chains and the factors affecting the adoption decision. The application is carried out for pig and poultry chains. Design/methodology/approach – This research consisted of two main components: interviews with retailers in pig and poultry supply chains in eight different EU countries to explore their perceptions towards the adoption possibilities of the welfare assessment system; and a conjoint analysis designed to evaluate the perceived adoption likelihood of the assessment system by different Standards Formulating Organisations (SFOs). Findings – Stakeholders were found to be especially concerned about the costs of implementation of the system and how it could, or should, be merged with existing assurance schemes. Another conclusion of the study is that the presence of a strong third independent party supporting the implementation of the welfare assessment system would be the most important influence on the decision whether, or not, to adopt it. Originality/value – This research evaluates the adoption possibilities of a novel Welfare Assessment System and presents the views of different supply chain stakeholders on an adoption of such a system. The main factors affecting the adoption decision are identified and analysed. Contrary to expectations, the costs of adoption of a new welfare assessment system were not considered to be the most important factor affecting the decision of supply chain stakeholders about the adoption of this new welfare system.

Likelihood-free estimation of model evidence

Statistical methods of inference typically require the likelihood function to be computable in a reasonable amount of time. The class of “likelihood-free” methods termed Approximate Bayesian Computation (ABC) is able to eliminate this requirement, replacing the evaluation of the likelihood with simulation from it. Likelihood-free methods have gained in efficiency and popularity in the past few years, following their integration with Markov Chain Monte Carlo (MCMC) and Sequential Monte Carlo (SMC) in order to better explore the parameter space. They have been applied primarily to estimating the parameters of a given model, but can also be used to compare models. Here we present novel likelihood-free approaches to model comparison, based upon the independent estimation of the evidence of each model under study. Key advantages of these approaches over previous techniques are that they allow the exploitation of MCMC or SMC algorithms for exploring the parameter space, and that they do not require a sampler able to mix between models. We validate the proposed methods using a simple exponential family problem before providing a realistic problem from human population genetics: the comparison of different demographic models based upon genetic data from the Y chromosome.