Farmers' Perceptions About Health and Welfare Issues in Turkey Production

Staaveren, Nienke van; Leishman, Emily M.; Wood, Benjamin J.; Harlander-Matauschek, Alexandra; Baes, Christine F.

doi:10.3389/fvets.2020.00332

Cited by 9 publications

(13 citation statements)

References 50 publications

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“…In poultry production, locomotion is an important health and welfare trait. Impaired locomotion is a major welfare concern (Scientific Committee on Animal Health and Animal Welfare, 2000;van Staaveren et al, 2020), and a cause of economic losses in both turkeys and broilers (Sullivan, 1994;van Staaveren et al, 2020). Impaired locomotion has been linked to high growth rate, high body weight, infection, and housing conditions (e.g., light and feeding regime) in broilers (Bradshaw et al, 2002).…”

Section: Introductionmentioning

confidence: 99%

Across-Species Pose Estimation in Poultry Based on Images Using Deep Learning

et al. 2021

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Animal pose-estimation networks enable automated estimation of key body points in images or videos. This enables animal breeders to collect pose information repeatedly on a large number of animals. However, the success of pose-estimation networks depends in part on the availability of data to learn the representation of key body points. Especially with animals, data collection is not always easy, and data annotation is laborious and time-consuming. The available data is therefore often limited, but data from other species might be useful, either by itself or in combination with the target species. In this study, the across-species performance of animal pose-estimation networks and the performance of an animal pose-estimation network trained on multi-species data (turkeys and broilers) were investigated. Broilers and turkeys were video recorded during a walkway test representative of the situation in practice. Two single-species and one multi-species model were trained by using DeepLabCut and tested on two single-species test sets. Overall, the within-species models outperformed the multi-species model, and the models applied across species, as shown by a lower raw pixel error, normalized pixel error, and higher percentage of keypoints remaining (PKR). The multi-species model had slightly higher errors with a lower PKR than the within-species models but had less than half the number of annotated frames available from each species. Compared to the single-species broiler model, the multi-species model achieved lower errors for the head, left foot, and right knee keypoints, although with a lower PKR. Across species, keypoint predictions resulted in high errors and low to moderate PKRs and are unlikely to be of direct use for pose and gait assessments. A multi-species model may reduce annotation needs without a large impact on performance for pose assessment, however, with the recommendation to only be used if the species are comparable. If a single-species model exists it could be used as a pre-trained model for training a new model, and possibly require a limited amount of new data. Future studies should investigate the accuracy needed for pose and gait assessments and estimate genetic parameters for the new phenotypes before pose-estimation networks can be applied in practice.

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Section: Introductionmentioning

confidence: 99%

Across-Species Pose Estimation in Poultry Based on Images Using Deep Learning

et al. 2021

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“…The height of bell drinkers can be individually adapted and so might help farmers manage flocks that are not very uniform in size. Variation in body size is an issue, according to the surveyed turkey farmers, and this could potentially be linked to the problem of mortality seen due to dehydration [ 37 ]. Improving flock uniformity or designing closed drinker systems that can be adapted to different heights might provide a way to counter this potential issue.…”

Section: Discussionmentioning

confidence: 99%

“…In general, nearly all farmers indicated that they had an existing relationship with a veterinarian, who typically came to the farm only as needed. Nearly 30% of the farmers reported disease within the flock at some point in time, which approximates the 40% of farmers that considered disease an issue on their farm [ 37 ]. However, it should be noted that this is a self-administered questionnaire, and it is not clear whether disease or issues indicated by farmers were supported by veterinary diagnosis, slaughterhouse reports, or were self-diagnosed by the farmer.…”

Section: Discussionmentioning

confidence: 99%

Housing and Management of Turkey Flocks in Canada

Staaveren

Leishman

Adams

et al. 2020

Animals

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An increased understanding of the turkey sector and how farmers manage flocks can help maintain and improve the health and welfare of turkeys. We conducted a cross-sectional survey among turkey farmers in Canada to gain information regarding general farm characteristics, housing aspects (incl. lighting, ventilation), litter management, feed and water management, flock characteristics, and flock health management. The survey was distributed to 500 farmers through the Turkey Farmers of Canada in April–December 2019. A total of 83 final responses (response rate approx. 20%) were used for a descriptive analysis to determine the frequency of housing and management practices (77 commercial flocks, 6 breeder flocks). Hen flocks (n = 53) had a median age of eight weeks (IQR: 7–12 weeks) and tom flocks (n = 30) had a median age of 12 weeks (IQR: 9–14 weeks). Turkey flocks within Canada are typically kept in indoor barn systems on a concrete floor (87.5%), with bedding (e.g., straw, wood shavings) provided (100%). The majority followed a brood and move growing system (68.8%), and a large proportion of farmers indicated that they raised turkeys under the ‘Raised Without Antibiotics/Antibiotic Free’ or the ‘Responsible Use of Antibiotics’ certification (70.5%). Possible room for improvement could be found in terms of litter management and biosecurity practices, however, further research is needed to make clear recommendations.

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“…During the production stage, turkeys are threatened by many factors including disease and poor environmental conditions. Turkey health can be reflected through their common behavior, and is directly related to turkey production since unhealthy turkeys are not optimal for production [1]. Sick and healthy turkeys exhibit distinct behavior, and early detection of sick turkeys through behavior analysis is critical.…”

Section: Introductionmentioning

confidence: 99%

Turkey Behavior Identification Using Video Analytics And Object Tracking

Ju¹,

Erasmus

Zhu

et al. 2021

2021 IEEE International Conference on Image Processing (ICIP)

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In this paper, we propose a method to identify behavior of experimental turkeys by automatically analyzing video recordings. Monitoring turkey health during production is crucial for improved turkey production. Turkey health can be reflected through their common behavior, and changes in the frequency and duration of their behavior can be used to detect sick turkeys early. Video recordings can be manually annotated to assist identifying turkey behaviors, but this is both time consuming and labor intensive. In this paper, we monitor and detect changes in turkey behavior using video analytics. Behaviors of interest include eating, drinking, preening, and pecking. Identifying these behaviors requires accurate estimates of turkeys' and turkey heads' locations. Reidentification of each turkey is crucial after significant shape deformation such as wing flapping and fast walking. Therefore, our system integrates a state-of-the-art turkey tracker and a head tracker with a behavior identification module to identify turkey behavior. Results demonstrate that our system is effective and accurate at estimating the spatial location of turkeys and their heads, and identifying all behaviors of interest with high recall.

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Farmers' Perceptions About Health and Welfare Issues in Turkey Production

Cited by 9 publications

References 50 publications

Across-Species Pose Estimation in Poultry Based on Images Using Deep Learning

Across-Species Pose Estimation in Poultry Based on Images Using Deep Learning

Housing and Management of Turkey Flocks in Canada

Turkey Behavior Identification Using Video Analytics And Object Tracking

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