Modelling Farm Animal Welfare
In this study, researchers Lisa Collins and Chérie Part evaluate the usefulness of various modeling approaches to provide for better evaluations and assessment of farm animal welfare. The paper takes into account different forms of data that numerous models collect, as well as the ways they might be able to shape or influence policy. Though the authors note that models must be “parameterised” with “real world observations,” they hope that this effort to create a comprehensive whole system approach will inspire further research in this direction.
When it comes to measuring farm animal welfare, laws are often quite subjective in both their wording and application. Modeling offers one way of better understanding how poor welfare conditions develop. “In the field of animal welfare, our aims are principally to understand why, where, how, when and who will be affected by the multitude of species-specific welfare problems, and what control strategies we can put in place to prevent these problems arising,” write Collins and Part. The problem is that there are many different kinds of models and ways of modeling, but currently no whole systems models that address animal welfare in a comprehensive way. To this end, Collins and Part looked at various model types and investigated how each might answer numerous questions. “The aim of this paper is not to provide an instruction manual on using the different modeling approaches,” they say, “but to open discussion between empirical and theoretical researchers, and to forearm empirical researchers with a set of questions that they can aim to answer with theoretical approaches.”
The authors examined a range of approaches, from Welfare Assessment, Simulation Modeling, Optimisation and Scenario Modeling, and more. They discovered that through all of these models, a more macro-analytical approach is lacking: “while considered the ‘holy grail’ in medicine, meta-analysis is rarely employed in animal welfare science but, where possible, could provide more reliable estimates of input data, and of relationships between parameters, than direct measurement in a single, all-encompassing, study.” Furthermore, they note that “as randomly controlled trials tend not to be used in animal welfare research, however, differences between studies (housing conditions, breeds, management, etc.) and study limitations must be identified and taken into consideration in the analysis.” However, through all of this, the authors recognize that even a comprehensive approach to modeling must be balanced with “real (observed) data.” Though the inclusion of real world observations does increase the risk of errors in modeling output, it is important to see beyond the data to understand what is happening on the ground.
The authors hope that by engaging in this review of available literature, they may inspire a move towards a “whole systems” approach of modeling animal welfare outcomes. “We should be working towards determining the sensitivity and specificity of individual welfare indicators and whole assessment models, and establishing non-arbitrary cut-off points for good/poor welfare.” They say that this effort “will require inter-disciplinary collaborations with systems biologists, economists, and sustainability and food security experts.” With such a combined effort, the authors hope that these more comprehensive models “may have scope to influence decision-makers and, certainly, to improve our understanding of how, and where, animal welfare improvements fit into the wider context of sustainability and food security.”
Original Abstract:
The use of models in the life sciences has greatly expanded in scope and advanced in technique in recent decades. However, the range, type and complexity of models used in farm animal welfare is comparatively poor, despite the great scope for use of modeling in this field of research. In this paper, we review the different modeling approaches used in farm animal welfare science to date, discussing the types of questions they have been used to answer, the merits and problems associated with the method, and possible future applications of each technique. We find that the most frequently published types of model used in farm animal welfare are conceptual and assessment models; two types of model that are frequently (though not exclusively) based on expert opinion. Simulation, optimization, scenario, and systems modeling approaches are rarer in animal welfare, despite being commonly used in other related fields. Finally, common issues such as a lack of quantitative data to parameterize models, and model selection and validation are discussed throughout the review, with possible solutions and alternative approaches suggested.
http://www.mdpi.com/2076-2615/3/2/416