Modelling Farm Animal Welfare

A Framework for Using Epidemiology in Animal Welfare Science

The potential advantages of using epidemiology in animal welfare research are substantial and are used with increased frequency. Collaboration between scientists of different fields, with different specific expertise is advantageous in the advancement of science. In this review, a framework to use epidemiology in animal welfare science is established. The different epidemiological study designs and analytical procedures are explored and put in an animal welfare scientific context. It is argued and demonstrated that epidemiology is used with advantage: in the identification of risk factors behind the development of maladaptation and abnormal behaviors; in the introduction of standardized procedures in research allowing comparisons between studies and facilitating the integration for evidence synthesis in systematic reviews and meta-analysis; by allowing animal welfare scientists to analyze complex settings such as farms or zoos. Mathematical modeling can also be used with advantage in risk assessment.

Computational animal welfare: towards cognitive architecture models of animal sentience, emotion and wellbeing

To understand animal wellbeing, we need to consider subjective phenomena and sentience. This is challenging, since these properties are private and cannot be observed directly. Certain motivations, emotions and related internal states can be inferred in animals through experiments that involve choice, learning, generalization and decision-making. Yet, even though there is significant progress in elucidating the neurobiology of human consciousness, animal consciousness is still a mystery. We propose that computational animal welfare science emerges at the intersection of animal behaviour, welfare and computational cognition. By using ideas from cognitive science, we develop a functional and generic definition of subjective phenomena as any process or state of the organism that exists from the first-person perspective and cannot be isolated from the animal subject. We then outline a general cognitive architecture to model simple forms of subjective processes and sentience. This includes evolutionary adaptation which contains top-down attention modulation, predictive processing and subjective simulation by re-entrant (recursive) computations. Thereafter, we show how this approach uses major characteristics of the subjective experience: elementary self-awareness, global workspace and qualia with unity and continuity. This provides a formal framework for process-based modelling of animal needs, subjective states, sentience and wellbeing.

Broiler welfare trade-off: A semi-quantitative welfare assessment for optimised welfare improvement based on an expert survey

In order to support decision making on how to most effectively improve broiler welfare an innovative expert survey was conducted based on principles derived from semantic modelling. Twenty-seven experts, mainly broiler welfare scientists (n = 20; and 7 veterinarians), responded (response rate 38%) by giving welfare scores (GWS, scale 0–10) to 14 benchmarking housing systems (HSs), and explaining these overall scores by selecting, weighing and scoring main welfare parameters, including both input and output measures. Data exploration followed by REML (Linear Mixed Model) and ALM (Automatic Linear Modelling) analyses revealed 6 clusters of HSs, sorted from high to low welfare, i.e. mean GWS (with superscripts indicating significant differences): 1. (semi-natural backyard) Flock (8.8^a); 2. Nature (7.7^ab), Label Rouge II (7.4^ab), Free range EU (7.2^ab), Better Life (7.2^ab); 3. Organic EU (7.0^bc), Freedom Food (6.2^bc); 4. Organic US (5.8^bcd), Concepts NL (5.6^abcdef), GAP 2 (4.9^bcd); 5. Conventional EU (3.7^de), Conventional US (2.9^ef), Modern cage (2.9^abcdef); 6. Battery cage (1.3^f). Mean weighting factors (WF, scale 0–10) of frequently (n> = 15) scored parameters were: Lameness (8.8), Health status (8.6), Litter (8.3), Density (8.2), Air quality (8.1), Breed (8.0), Enrichment (7.0) and Outdoor (6.6). These did not differ significantly, and did not have much added value in explaining GWS. Effects of Role (Scientist/Vet), Gender (M/F) and Region (EU/non-EU) did not significantly affect GWS or WF, except that women provided higher WF than men (7.2 vs 6.4, p<0.001). The contribution of welfare components to overall welfare has been quantified in two ways: a) using the beta-coefficients of statistical regression (ALM) analyses, and b) using a semantic-modelling type (weighted average) calculation of overall scores (CalcWS) from parameter level scores (PLS) and WF. GWS and CalcWS were highly correlated (R = ~0.85). CalcWS identified Lameness, Health status, Density, Breed, Air quality and Litter as main parameters contributing to welfare. ALM showed that the main parameters which significantly explained the variance in GWS based on all PLS, were the output parameter Health status (with a beta-coefficient of 0.38), and the input parameters (stocking) Density (0.42), Litter (0.14) and Enrichment (0.27). The beta-coefficients indicated how much GWS would improve from 1 unit improvement in PLS for each parameter, thus the potential impact on GWS ranged from 1.4 welfare points for Litter to 4.2 points for Density. When all parameters were included, 81% of the variance in GWS was explained (77% for inputs alone; 39% for outputs alone). From this, it appears that experts use both input and output parameters to explain overall welfare, and that both are important. The major conventional systems and modern cages for broilers received low welfare scores (2.9–3.7), well below scores that may be considered acceptable (5.5). Also, several alternatives like GAP 2 (4.9), Concepts NL (5.6), Organic US (5.8) and Freedom Food (6.2) are unacceptable, or at risk of being unacceptable due to individual variation between experts and farms. Thus, this expert survey provides a preliminary semi-quantified decision-support tool to help determine how to most effectively improve broiler welfare in a wide range of HSs.

A Prototype Tool to Enable Farmers to Measure and Improve the Welfare Performance of the Farm Animal Enterprise: The Unified Field Index

Schemes for the assessment of farm animal welfare and assurance of welfare standards have proliferated in recent years. An acknowledged short-coming has been the lack of impact of these schemes on the welfare standards achieved on farm due in part to sociological factors concerning their implementation. Here we propose the concept of welfare performance based on a broad set of performance attributes of an enterprise and describe a tool based on risk assessment and benchmarking methods for measuring and managing welfare performance. The tool termed the Unified Field Index is presented in a general form comprising three modules addressing animal, resource, and management factors. Domains within these modules accommodate the principle conceptual perspectives for welfare assessment: biological functioning; emotional states; and naturalness. Pan-enterprise analysis in any livestock sector could be used to benchmark welfare performance of individual enterprises and also provide statistics of welfare performance for the livestock sector. An advantage of this concept of welfare performance is its use of continuous scales of measurement rather than traditional pass/fail measures. Through the feedback provided via benchmarking, the tool should help farmers better engage in on-going improvement of farm practices that affect animal welfare.