Who plays with whom: farrowing environment influences isolation of foster piglets in play

Cross fostering piglets is a common management practise in the pig industry to manage large and heterogeneous litters, whereby piglets are moved from their biological litter to be reared by another sow. At present research has focused on immediate survival consequences and time of cross fostering, with little attention given to positive aspects of welfare such as social affiliations and the potential for positive interactions for these piglets such as play behaviour. The focus of our study was purely observational to record behaviour of piglets reared in either impoverished (farrowing crates) or enriched neonatal environments (PigSAFE pens) where fostering was practised as part of normal husbandry routines to promote piglet survival. We employed social network analysis to understand more about the behaviour of foster piglets in these environments and their acceptance into their new litter. In line with previous work, piglets exposed to enriched neonatal farrowing pens demonstrated more play behaviour than piglets reared in farrowing crates. We showed that pen piglets received and initiated more play invitations (higher degree centrality) than piglets in crates. We also found effects of cross fostering irrespective of neonatal environment. Non-foster piglets received and initiated more play behaviours (higher degree centrality) 2–3 weeks post-farrowing compared to piglets fostered into the litter and as such, fostered piglets remained isolated from play for the first few weeks of life. However, our data suggests this may be mitigated by neonatal environment; foster piglets reared in pens were better connected (betweenness centrality) within their foster litter than those fostered in crates. Our findings highlight the importance of the neonatal environment and its potential influence on the isolation of cross-fostered piglets and suggest that rearing in enriched neonatal environments may help mitigate against social isolation in early life of cross-fostered piglets, having obvious immediate, and long-term consequences for piglet welfare and behaviour. We also highlight the importance and application of methodologies such as social network analysis, for gaining more insight and understanding about the sociality of animal behaviour and its potential for measuring indicators of positive welfare, thus highlighting its application for veterinary science and animal behaviour and welfare research

Artificial rearing affects piglets pre-weaning behaviour, welfare and growth performance

peer-reviewedOne strategy adopted on farms to deal with managing large litters involves removing piglets from their mothers at seven days old to be reared in specialised accommodation with milk replacer. Effects on piglet behaviour, growth and some aspects of welfare were evaluated in this study by comparing 10 pairs of two litters (one sow-reared: SR, one artificially-reared: AR) selected at seven days-old at a similar weight. Piglet behaviour was recorded for 20 min following transfer of AR piglets to the artificial-rearing enclosure (D0) and for 20 min hourly between 09:00 h and 17:00 h (8 h) on D5 and D12. Hourly 5 min live observations were also undertaken. Qualitative Behavioural Assessment (QBA) was conducted on D14 to evaluate piglets’ emotional state. Survival and illness events were recorded until weaning. On D0, D1, D8 and D15 piglets were weighed and scored for tear staining, dirtiness of the face and severity of lesions on the snout, limbs, ear and tail. Survival and illness rates, as well as the rates of behaviours/min were analysed using GLMMs. Weights and QBA scores were analysed using GLM. Lesions, tear staining and dirtiness scores were averaged per litter and analysed using GLM. When AR piglets were transferred to the artificial-rearing enclosure, their behaviour was not different to SR piglets. Over the two observation days, AR piglets performed more belly-nosing (F1,76.53 = 42.25; P < 0.001), nursing-related displacements (F1,79 = 19.32, P < 0.001), visits to the milk cup (compared to nursing bouts; F1,73.8 = 38.42, P < 0.001), and oral manipulation of littermates’ ears (F1,91.95 = 12.79, P < 0.001) and tails (F1,58.54 = 15.63, P < 0.001) than SR piglets. However, SR piglets played alone (F1,88.99 = 8.29, P < 0.005) and explored their environment (F1,99.42 = 4.52, P < 0.05) more frequently than AR piglets. The QBA scores indicated a lower emotional state in AR piglets (t25.1=-3.25, P < 0.05). Survival rate and overall illness rate of piglets were similar between the treatments. AR piglets experienced a growth check following their transfer to the artificial-rearing enclosure and remained lighter than SR piglets through to weaning (6.53 ± 0.139 kg vs. 7.97 ± 0.168 kg, t256 = 9.79, P < 0.001). Overall, snout lesion scores were not different between the treatments, but AR piglets had lower limb (F1,10.1 = 5.89, P < 0.05) and ear (F1,14.5 = 24.89, P < 0.001) lesion scores and higher tail lesion scores (F1,34.5 = 15.54, P < 0.001). AR piglets were dirtier (F1,17.4 = 23.38, P < 0.001) but had lower tear staining scores (F1,19.1 = 68.40, P < 0.001) than SR piglets. In conclusion, artificial rearing impaired piglets’ behaviour, welfare and growth.This work was supported by the Irish Department of Agriculture, Food and the Marine through the FIRM/RSF/CoFoRD 2013 Research Call (project no. 13S428)

Towards on-farm pig face recognition using convolutional neural networks

© 2018 Elsevier B.V. Identification of individual livestock such as pigs and cows has become a pressing issue in recent years as intensification practices continue to be adopted and precise objective measurements are required (e.g. weight). Current best practice involves the use of RFID tags which are time-consuming for the farmer and distressing for the animal to fit. To overcome this, non-invasive biometrics are proposed by using the face of the animal. We test this in a farm environment, on 10 individual pigs using three techniques adopted from the human face recognition literature: Fisherfaces, the VGG-Face pre-trained face convolutional neural network (CNN) model and our own CNN model that we train using an artificially augmented data set. Our results show that accurate individual pig recognition is possible with accuracy rates of 96.7% on 1553 images. Class Activated Mapping using Grad-CAM is used to show the regions that our network uses to discriminate between pigs

Automatic early warning of tail biting in pigs:3D cameras can detect lowered tail posture before an outbreak

<div><p>Tail biting is a major welfare and economic problem for indoor pig producers worldwide. Low tail posture is an early warning sign which could reduce tail biting unpredictability. Taking a precision livestock farming approach, we used Time-of-flight 3D cameras, processing data with machine vision algorithms, to automate the measurement of pig tail posture. Validation of the 3D algorithm found an accuracy of 73.9% at detecting low vs. not low tails (Sensitivity 88.4%, Specificity 66.8%). Twenty-three groups of 29 pigs per group were reared with intact (not docked) tails under typical commercial conditions over 8 batches. 15 groups had tail biting outbreaks, following which enrichment was added to pens and biters and/or victims were removed and treated. 3D data from outbreak groups showed the proportion of low tail detections increased pre-outbreak and declined post-outbreak. Pre-outbreak, the increase in low tails occurred at an increasing rate over time, and the proportion of low tails was higher one week pre-outbreak (-1) than 2 weeks pre-outbreak (-2). Within each batch, an outbreak and a non-outbreak control group were identified. Outbreak groups had more 3D low tail detections in weeks -1, +1 and +2 than their matched controls. Comparing 3D tail posture and tail injury scoring data, a greater proportion of low tails was associated with more injured pigs. Low tails might indicate more than just tail biting as tail posture varied between groups and over time and the proportion of low tails increased when pigs were moved to a new pen. Our findings demonstrate the potential for a 3D machine vision system to automate tail posture detection and provide early warning of tail biting on farm.</p></div

The Sorghum QTL Atlas: a powerful tool for trait dissection, comparative genomics and crop improvement

Key message: We describe the development and application of the Sorghum QTL Atlas, a high-resolution, open-access research platform to facilitate candidate gene identification across three cereal species, sorghum, maize and rice. Abstract: The mechanisms governing the genetic control of many quantitative traits are only poorly understood and have yet to be fully exploited. Over the last two decades, over a thousand QTL and GWAS studies have been published in the major cereal crops including sorghum, maize and rice. A large body of information has been generated on the genetic basis of quantitative traits, their genomic location, allelic effects and epistatic interactions. However, such QTL information has not been widely applied by cereal improvement programs and genetic researchers worldwide. In part this is due to the heterogeneous nature of QTL studies which leads QTL reliability variation from study to study. Using approaches to adjust the QTL confidence interval, this platform provides access to the most updated sorghum QTL information than any database available, spanning 23 years of research since 1995. The QTL database provides information on the predicted gene models underlying the QTL CI, across all sorghum genome assembly gene sets and maize and rice genome assemblies and also provides information on the diversity of the underlying genes and information on signatures of selection in sorghum. The resulting high-resolution, open-access research platform facilitates candidate gene identification across 3 cereal species, sorghum, maize and rice. Using a number of trait examples, we demonstrate the power and resolution of the resource to facilitate comparative genomics approaches to provide a bridge between genomics and applied breeding. © 2018, Springer-Verlag GmbH Germany, part of Springer Nature

Nurses\u27 Alumnae Association Bulletin, June 1965

President\u27s Page Officers and Committee Chairmen Financial Report Hospital and School of Nursing Report Student Activities Annual Report Students Activities Annual Report Student Activities Annual Report Jefferson Expansion Program Psychiatric Unit Progress of the Alumnae Association Nightingale Pledge Resume of Alumnae Meetings Nursing Service Staff Association Scholarship Program Sick and Welfare Social Committee Report Bulletin Membership- WHY JOIN? Private Duty Report Annual Giving Report - 1964 PIT Alumnae Day Notes Building Fund Report - 1965 Vital Statistics IN MEMORIAM Class News Affiliated Institutions Notice