Effects of reducing growth rate via diet dilution on bone mineralization, performance and carcass yield of coccidia-infected broilers

Coccidiosis and rapid growth rate (GR) compromise bone mineralization in modern broilers. We tested the hypothesis that reducing GR via diet dilution during peak bone development will improve bone mineralization in both infected and uninfected broilers. A total of 384 male Ross 308 chicks were allocated to a basal grower diet (3,107kcal/kg ME and 19.4% CP) diluted with 0, 5, 10, or 15% lignocellulose (n = 12 pens/treatment, 8 birds/pen) at day 10 of age. Prior to this, birds in each group received half the intended diet-dilution levels (day 8 to 10 of age) and a common starter diet (day 1 to 7 of age). At day 13 of age (day 0 post-infection, pi), birds were orally inoculated with either 7,000 sporulated Eimeria maxima oocysts (I) or water (C), forming a 4 diet-dilution level × 2 infection status factorial experiment. Performance was measured over 12 days pi and scaled to BW at infection (day 0 pi) to account for a priori BW differences. At day 12 pi (day 25 of age), 1 bird/pen (a total of 6 birds/treatment) was sampled to assess tibia and femur mineralization relative to BW, and carcass yield. There was no interaction (P > 0.05) between infection status and diet-dilution level on ADFI/BW measured over day 1 to 12 pi, or on any bone variable. ADG/BW pi decreased (P 0.05) amongst I birds. I compared to C birds had reduced breast meat (P < 0.05) and eviscerated carcass yield (P < 0.01), femur (P < 0.05) and tibia (P < 0.01) breaking strength (BS), and femur ash weight (AW) (P < 0.05). Diet dilution did not affect carcass yield, but improved femur BS (P < 0.001), and tended to improve (P < 0.1) femur and tibia AW. Overall, diet dilution significantly affected femur, more than tibia, variables: relative BS, robusticity index, and ash percentage. Reducing GR affected broiler long bone mineralization to a similar degree in the presence or absence of coccidiosis

Dynamic and Genetic Signatures of Resistance and Tolerance of pigs to PRRS<strong> </strong>

Using SNP data, we demonstrate that a previously identified QTL conferring resistance of pigs to PRRS does not confer tolerance. Similarly, variation in tolerance cannot be found at sire level. However, by analysing the dynamic relationship between both traits using individual resistance – tolerance trajectories we find significant SNP effects affecting the shape of these trajectories. Evidence of underlying genetic variation suggests it may be possible to target a specific trajectory type, or shift in trajectory, for genetic improvement. This would provide breeders with a means to simultaneously improve resistance and tolerance and their timely interactions

Toward the automated detection of behavioral changes associated with the post-weaning transition in pigs

We modified an automated method capable of quantifying behaviors which we then applied to the changes associated with the post-weaning transition in pigs. The method is data-driven and depends solely on video-captured image data without relying on sensors or additional pig markings. It was applied to video images generated from an experiment during which post-weaned piglets were subjected to treatments either containing or not containing in-feed antimicrobials (ZnO or antibiotics). These treatments were expected to affect piglet performance and health in the short-term by minimizing the risk from post-weaning enteric disorders, such as diarrhea. The method quantified total group feeding and drinking behaviors as well as posture (i.e., standing and non-standing) during the first week post-weaning, when the risk of post-weaning diarrhea is at its highest, by learning from the variations within each behavior using data manually annotated by a behavioral scientist. Automatically quantified changes in behavior were consistent with the effects of the absence of antimicrobials on pig performance and health, and manifested as reduced feed efficiency and looser feces. In these piglets both drinking and standing behaviors were increased during the first 6 days post-weaning. The correlation between fecal consistency and drinking behavior 6 days post weaning was relatively high, suggesting that these behaviors may have a diagnostic value. The presence or absence of in-feed antimicrobials had no effect on feeding behavior, which, however, increased over time. The approach developed here is capable of automatically monitoring several different behaviors of a group of pigs at the same time, and potentially this may be where its value as a diagnostic tool may lie

Consequences of timing of organic enrichment provision on pig performance, health and stress resilience after weaning and regrouping

Publication history: Accepted - 20 August 2022; Published online - 29 September 2022Most pigs in slatted systems are provided with enrichment meeting only minimum legal requirements. We aimed to explore the effects of a novel enrichment treatment consisting of daily provided fodder beet and jute bags for pigs in slatted systems, and investigate the timing of enrichment provision on performance, health and stress resilience. We used 280 weaners allocated into standard (S, meeting only legal requirements consisting of a plastic toy and softwood) or enriched (E) treatment (n = 14 groups/treatment). At regrouping during the grower to finisher transition, pigs were either kept in the same treatment (EE, SS) or switched from enriched to standard (ES) and vice versa (SE); each treatment was replicated on five groups. Pigs were weighted at the start and end of weaner, and finisher stage, and feed intake was recorded. Occurrence of scouring, respiratory problems, locomotor disorders, tail, ear, and body lesions were recorded twice a week. Ten males per treatment were sampled for saliva on days 1, 2 and 4, either postweaning or after the housing switch. Saliva samples were analysed for cortisol, alpha-amylase, haptoglobin (Hp), and adenosine deaminase. Additionally, these pigs were sampled for hair at the start and end of weaner, and end of finisher stage to analyse for hair cortisol and cortisone. We found that E weaners consumed less feed (P = 0.04), had better FCR (feed conversion ratio, P = 0.03) and less ear lesions for two weeks postweaning (P = 0.04), and tended to have lower occurrence of scouring (P = 0.07) and higher salivary cortisol concentrations (P = 0.09) than S weaners. Effects of enrichment treatment during weaner stage on performance were carried through to finisher stage, with EE and ES pigs having better FCR (P = 0.0009) and higher BW (P = 0.0001) compared to SS and SE pigs. E treatment during finisher stage decreased feed intake (P = 0.04) and tended to decrease Hp levels (P = 0.07). There was a significant interaction between enrichment treatments during weaner and finisher stages on finisher body lesions: EE finishers had less lesions than SS, ES, and SE finishers (P = 0.04). There were no other significant differences caused either by enrichment treatment during weaner/finisher stage or their interaction. We conclude that the novel enrichment applied at weaner stage had positive effects on ear lesions and performance, which were carried through to finisher stage. Body lesions were affected by its application during both stages, with finishers receiving the enrichment treatment throughout (EE) having reduced body lesions than the rest of the finishers.This research was part of the EU-China HealthyLivestock project. The authors wish to acknowledge that HealthyLivestock is funded by the European Union H2020 research and innovation programme under grant agreement number 773436. The European Commission’s support for the production of this publication does not constitute an endorsement of the contents, which reflect the views only of the authors, and the Commission cannot be held responsible for any use which may be made of the information contained therein

Automatic recognition of feeding and foraging behaviour in pigs using deep learning

Highlights• An automated detection method of pig feeding and foraging behaviour was developed.• The automated method is based on convolutional deep neural networks.• The automated method does not rely on pig tracking to estimate behaviours.• Detection of feeding behaviour is highly accurate (99.4%) and fast (0.02 sec/image).• The robust method can be applied under different husbandry/ management conditions.Automated, vision-based early warning systems have been developed to detect behavioural changes in groups of pigs to monitor their health and welfare status. In commercial settings, automatic recording of feeding behaviour remains a challenge due to problems of variation in illumination, occlusions and similar appearance of different pigs. Additionally, such systems, which rely on pig tracking, often overestimate the actual time spent feeding, due to the inability to identify and/or exclude non-nutritive visits (NNV) to the feeding area. To tackle these problems, we have developed a robust, deep learning-based feeding detection method that (a) does not rely on pig tracking and (b) is capable of distinguishing between feeding and NNV for a group of pigs. We first validated our method using video footage from a commercial pig farm, under a variety of settings. We demonstrate the ability of this automated method to identify feeding and NNV behaviour with high accuracy (99.4% ± 0.6%). We then tested the method's ability to detect changes in feeding and NNV behaviours during a planned period of food restriction. We found that the method was able to automatically quantify the expected changes in both feeding and NNV behaviours. Our method is capable of monitoring robustly and accurately the feeding behaviour of groups of commercially housed pigs, without the need for additional sensors or individual marking. This has great potential for application in the early detection of health and welfare challenges of commercial pigs

Health trajectories reveal the dynamic contributions of host genetic resistance and tolerance to infection outcome

Resistance and tolerance are two alternative strategies hosts can adopt to survive infections. Both strategies may be genetically controlled. To date, the relative contribution of resistance and tolerance to infection outcome is poorly understood. Here, we use a bioluminescent Listeria monocytogenes (Lm) infection challenge model to study the genetic determination and dynamic contributions of host resistance and tolerance to listeriosis in four genetically diverse mouse strains. Using conventional statistical analyses, we detect significant genetic variation in both resistance and tolerance, but cannot capture the time-dependent relative importance of either host strategy. We overcome these limitations through the development of novel statistical tools to analyse individual infection trajectories portraying simultaneous changes in infection severity and health. Based on these tools, early expression of resistance followed by expression of tolerance emerge as important hallmarks for surviving Lm infections. Our trajectory analysis further reveals that survivors and non-survivors follow distinct infection paths (which are also genetically determined) and provides new survival thresholds as objective endpoints in infection experiments. Future studies may use trajectories as novel traits for mapping and identifying genes that control infection dynamics and outcome. A Matlab script for user-friendly trajectory analysis is provided

Short communication:Identifying key parameters for modelling the impacts of livestock health conditions on greenhouse gas emissions

Improved animal health can reduce greenhouse gas (GHG) emissions intensity in livestock systems while increasing productivity. Integrated modelling of disease impacts on farm-scale emissions is important in identifying effective health strategies to reduce emissions. However, it requires that modellers understand the pathways linking animal health to emissions and how these might be incorporated into models. A key barrier to meeting this need has been the lack of a framework to facilitate effective exchange of knowledge and data between animal health experts and emissions modellers. Here, these two communities engaged in workshops, online exchanges and a survey to i) identify a comprehensive list of disease-related model parameters and ii) test its application to evaluating models. Fifty-six parameters were identified and proved effective in assessing the potential of farm-scale models to characterise livestock disease impacts on GHG emissions. Easy wins for the emissions models surveyed include characterising disease impacts related to feeding