Implementation of Inertia Sensor and Machine Learning Technologies for Analyzing the Behavior of Individual Laying Hens.

Welfare-oriented regulations cause farmers worldwide to shift towards more welfare-friendly, e.g., loose housing systems such as aviaries with litter. In contrast to the traditional cage housing systems, good technical results can only be obtained if the behavior of hens is considered. With increasing flock sizes, the automation of behavioural assessment can be beneficial. This research aims to show a proof of principle of tools for analyzing laying-hen behaviors by using wearable inertia sensor technology and a machine learning model (ML). For this aim, the behaviors of hens were classified into three classes: static, semi-dynamic, and highly dynamic behavior. The activities of hens were continuously recorded on video and synchronized with the sensor signals. Two hens were equipped with sensors, one marked green and one blue, for five days to collect the data. The training data set indicated that the ML model can accurately classify the highly dynamic behaviors with a one-second time window; a four-second time window is accurate for static and semi-dynamic behaviors. The Bagged Trees model, with an overall accuracy of 89% was the best ML model with the F1-scores of 89%, 91%, and 87% for static, semi-dynamic, and highly dynamic behaviors. The Bagged Trees model also performed well in classifying the behaviors of the hen in the validation data set with an overall F1-score of 0.92 (uniform either % or decimals). This research illustrates that the combination of wearable inertia sensors and machine learning is a viable technique for analyzing the laying-hen behaviors and supporting farmers in the management of hens in loose housing systems.

HENNOVATION: Learnings from Promoting Practice-Led Multi-Actor Innovation Networks to Address Complex Animal Welfare Challenges within the Laying Hen Industry.

The Hennovation project, an EU H2020 funded thematic network, aimed to explore the potential value of practice-led multi-actor innovation networks within the laying hen industry. The project proposed that husbandry solutions can be practice-led and effectively supported to achieve durable gains in sustainability and animal welfare. It encouraged a move away from the traditional model of science providing solutions for practice, towards a collaborative approach where expertise from science and practice were equally valued. During the 32-month project, the team facilitated 19 multi-actor networks in five countries through six critical steps in the innovation process: problem identification, generation of ideas, planning, small scale trials, implementation and sharing with others. The networks included farmers, processors, veterinarians, technical advisors, market representatives and scientists. The interaction between the farmers and the other network actors, including scientists, was essential for farmer innovation. New relationships emerged between the scientists and farmers, based on experimental learning and the co-production of knowledge for improving laying hen welfare. The project demonstrated that a practice-led approach can be a major stimulus for innovation with several networks generating novel ideas and testing them in their commercial context. The Hennovation innovation networks not only contributed to bridging the science-practice gap by application of existing scientific solutions in practice but more so by jointly finding new solutions. Successful multi-actor, practice-led innovation networks appeared to depend upon the following key factors: active participation from relevant actors, professional facilitation, moderate resource support and access to relevant expertise. Farmers and processors involved in the project were often very enthusiastic about the approach, committing significant time to the network's activities. It is suggested that the agricultural research community and funding agencies should place greater value on practice-led multi-actor innovation networks alongside technology and advisor focused initiatives to improve animal welfare and embed best practices.

Laboratory tests for controlling poultry red mites (Dermanyssus gallinae) with predatory mites in small 'laying hen' cages.

To assess their potential to control poultry red mites (Dermanyssus gallinae), we tested selected predaceous mites (Androlaelaps casalis and Stratiolaelaps scimitus) that occur naturally in wild bird nests or sometimes spontaneously invade poultry houses. This was done under laboratory conditions in cages, each with 2-3 laying hens, initially 300 poultry red mites and later the release of 1,000 predators. These small-scale tests were designed to prevent mite escape from the cages and they were carried out in three replicates at each of three temperature regimes: 26, 30 (constant day and night) and 33-25 °C (day-night cycle). After 6 weeks total population sizes of poultry red mites and predatory mites were assessed. For the temperature regimes of 26 and 33/25 °C S. scimitus reduced the poultry red mite population relative to the control experiments by a factor 3 and 30, respectively, and A. casalis by a factor of 18 and 55, respectively. At 30 °C the predators had less effect on red mites, with a reduction of 1.3-fold for S. scimitus and 5.6-fold for A. casalis. This possibly reflected hen manure condition or an effect of other invertebrates in the hen feed. Poultry red mite control was not negatively affected by temperatures as high as 33 °C and was always better in trials with A. casalis than in those with S. scimitus. In none of the experiments predators managed to eradicate the population of poultry red mites. This may be due to a prey refuge effect since most predatory mites were found in and around the manure tray at the bottom of the cage, whereas most poultry red mites were found higher up in the cage (i.e. on the walls, the cover, the perch, the nest box and the food box). The efficacy of applying predatory mites in the poultry industry may be promoted by reducing this refuge effect, boosting predatory mite populations using alternative prey and prolonged predator release devices. Biocontrol success, however, will strongly depend on how the poultry is housed in practice (free range, cage or aviary systems) and on which chemicals are applied to disinfect poultry houses and to control other pests.