Effect of an automated marking system on aggressive behavior of precision-fed broiler breeder chicks

Research note: Expert opinions of feather sucking and licking behavior in meat chicken breeder birds

Feather sucking, or feather licking, has been reported anecdotally by employees in the Australian meat chicken breeder industry, but scarcely in the scientific literature. Consequently, the causes and implications of this behavior in meat chicken breeding chickens is relatively unknown. We surveyed 17 industry experts to generate hypotheses about feather sucking behavior. We aimed to understand the frequency and when it occurs, and attempted to understand what may cause an "outbreak". The recruitment of participants was intentionally biased towards Australian perspectives; only 5 of the 17 participants were international. All participants, except 1, had seen feather sucking/licking behavior (94.1%) and most participants (80%) suggested that the behavior was most frequently observed during rearing. Participants presented varying concerns about this behavior, ranging from the perspective that it was "normal" and had no impact on welfare, to concerns about mating injuries due to damaged feathers, increased risk of feather pecking and cannibalism, and psychological stress indicated by expression of repetitive (seemingly) functionless behavior. "Feather licking," "feather sucking," "feather eating," and "feather pecking" were terms used interchangeably, leading to confusion by participants about the cause and implications of the target behavior. The most common factors reported as the cause were boredom (52.9%), nutritional deficiencies (47.1%), and feed restriction (41.2%) and more than 80% of respondents agreed that stress contributes to feather sucking. The outputs from this study reflect only a small, but expert, number of opinions on feather sucking/licking behaviors in the Australian meat chicken breeder industry. A systematic understanding of this behavior is needed to provide insight into causation and the implications for welfare.

EFSA AHAW Panel (EFSA Panel on Animal Health and Welfare), Nielsen SS, Alvarez J, Bicout DJ, Calistri P, Canali E, Drewe JA, Garin‐Bastuji B, Gonzales Rojas JL, Schmidt CG, Herskin MS, Miranda Chueca MÁ, Padalino B, Pasquali P, Roberts HC, Spoolder H, Stahl K, Velarde A, Viltrop A, Winckler C, Tiemann I, de Jong I, Gebhardt‐Henrich SG, Keeling L, Riber AB, Ashe S, Candiani D, García Matas R, Hempen M, Mosbach‐Schulz O, Rojo Gimeno C, Van der Stede Y, Vitali M, Bailly‐Caumette E, Michel V. Welfare of broilers on farm

This Scientific Opinion considers the welfare of domestic fowl (Gallus gallus) related to the production of meat (broilers) and includes the keeping of day-old chicks, broiler breeders, and broiler chickens. Currently used husbandry systems in the EU are described. Overall, 19 highly relevant welfare consequences (WCs) were identified based on severity, duration and frequency of occurrence: 'bone lesions', 'cold stress', 'gastro-enteric disorders', 'group stress', 'handling stress', 'heat stress', 'isolation stress', 'inability to perform comfort behaviour', 'inability to perform exploratory or foraging behaviour', 'inability to avoid unwanted sexual behaviour', 'locomotory disorders', 'prolonged hunger', 'prolonged thirst', 'predation stress', 'restriction of movement', 'resting problems', 'sensory under- and overstimulation', 'soft tissue and integument damage' and 'umbilical disorders'. These WCs and their animal-based measures (ABMs) that can identify them are described in detail. A variety of hazards related to the different husbandry systems were identified as well as ABMs for assessing the different WCs. Measures to prevent or correct the hazards and/or mitigate each of the WCs are listed. Recommendations are provided on quantitative or qualitative criteria to answer specific questions on the welfare of broilers and related to genetic selection, temperature, feed and water restriction, use of cages, light, air quality and mutilations in breeders such as beak trimming, de-toeing and comb dubbing. In addition, minimal requirements (e.g. stocking density, group size, nests, provision of litter, perches and platforms, drinkers and feeders, of covered veranda and outdoor range) for an enclosure for keeping broiler chickens (fast-growing, slower-growing and broiler breeders) are recommended. Finally, 'total mortality', 'wounds', 'carcass condemnation' and 'footpad dermatitis' are proposed as indicators for monitoring at slaughter the welfare of broilers on-farm.

Using an artificial neural network to predict the probability of oviposition events of precision-fed broiler breeder hens

Identifying daily oviposition events for individual broiler breeders is important for bird management. Identifying non-laying birds in a flock that might be caused by improper nutrition or diseases can guide diet changes or disease treatments for these individuals. Oviposition depends on follicle maturation and egg formation, and follicle maturation can be variable. As such, the day and time of oviposition events of individual birds in a free-run flock can be hard to predict. Based on a precision feeding (PF) system that can record the feeding activity of individual birds, a recent study reported a machine learning model to predict daily egg-laying events of broiler breeders. However, there were 2 limitations in that study: 1) It could only be used to identify daily egg-laying events on a subsequent day; 2) The prediction outputs that were binary labels were unable to indicate more details among the outputs with the same label. To improve the previous approach, the current study aimed to predict and output the probability of daily oviposition events occurring using a specific time point in 1 day. In the current study, 706 egg-laying events recorded by nest boxes with radio frequency identification of hens and 706 randomly selected no-egg-laying events were used. The anchor point was newly defined in the current study as a specific time point in 1 day, and 26 features around the anchor point were created for all events (706 egg-laying events and 706 no-egg-laying events). A feed-forward artificial neural network (ANN) model was built for prediction. The area under the receiver operating characteristic (ROC) curve was 0.9409, indicating that the model had an outstanding classification performance. The ANN model could predict oviposition events on the current day, and the output was a probability that could be informative to indicate the likelihood of an oviposition event for an individual breeder. In situations where total egg production was known for a group, the ANN model could predict the probability of daily oviposition events occurring of all individual birds and then rank them to choose those most likely to have laid an egg.