Abstract
Simple SummaryTechnology on farms potentially brings the benefits of improved animal welfare and productivity as well as reduction in disease, waste and environmental impact. However, it also raises public concern about the welfare of individual animals, particularly when applied to large groups such as broiler (meat) chickens. We here address this issue by showing that camera technology can both provide life-long continuous monitoring of the welfare of whole flocks and also give crucial information about the individuals making up the flock. The cameras detect variation between individuals and are sensitive to birds moving abnormally. By testing samples of birds individually, we show that on average slow-moving birds came from flocks that moved slowly overall and showed large variation between individuals whereas on average fast-moving birds came from more active flocks that moved more uniformly. Properly used, camera technology can thus monitor the welfare of flocks continuously throughout their lives and is correlated with the behavior of individual birds.Group level measures of welfare flocks have been criticized on the grounds that they give only average measures and overlook the welfare of individual animals. However, we here show that the group-level optical flow patterns made by broiler flocks can be used to deliver information not just about the flock averages but also about the proportion of individuals in different movement categories. Mean optical flow provides information about the average movement of the whole flock while the variance, skew and kurtosis quantify the variation between individuals. We correlated flock optical flow patterns with the behavior and welfare of a sample of 16 birds per flock in two runway tests and a water (latency-to-lie) test. In the runway tests, there was a positive correlation between the average time taken to complete the runway and the skew and kurtosis of optical flow on day 28 of flock life (on average slow individuals came from flocks with a high skew and kurtosis). In the water test, there was a positive correlation between the average length of time the birds remained standing and the mean and variance of flock optical flow (on average, the most mobile individuals came from flocks with the highest mean). Patterns at the flock level thus contain valuable information about the activity of different proportions of the individuals within a flock.
Highlights
The use of automated methods for assessing animal welfare is a rapidly growing feature of livestock agriculture [1,2,3,4,5], but commercial poultry farming has raised particular problems because of the large numbers of animals involved
The optical flow patterns produced by the movement of broiler chicken flocks showed a positive skew (Table 1), indicating that the mode of the flock movement distribution was displaced to the left and was lower than the mean
Optical flow patterns indicate that broiler chicken flocks investigated here consisted of a majority of birds that were relatively inactive for most of the time, with a small number of very active birds
Summary
The use of automated methods for assessing animal welfare is a rapidly growing feature of livestock agriculture [1,2,3,4,5], but commercial poultry farming has raised particular problems because of the large numbers of animals involved. Flock-level analyses of visual images [6,7,8,9,10] and flock sounds [11,12] deliver useful information on the state of the flock as a whole, but not on individual animals Such group level approaches to welfare assessment have been challenged on the grounds that they overlook the most crucial element of all—the welfare of individual animals [13,14]. The aim of this paper is to show that, properly used, automated grouplevel measures of welfare can contribute to the assessment of individual bird welfare, even without identifying individuals Automated systems derive their usefulness from their capacity to collect much more detailed and more continuous information than is possible for a human observer. Their use as an extension to the work of a good stockperson has the potential to lead to an increase in the welfare of individual animals even where the automated system itself does not distinguish between individuals
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