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Association between the prion protein genotype and animal performance traits in a large multibreed sheep population
Get PDFpeer-reviewedGenetic susceptibility to scrapie, a fatal disease of sheep and goats, is modulated by polymorphisms in the prion protein (PrP). Neither the frequency of the PrP genotypes nor their association with animal performance has been investigated in a large multibreed Irish sheep population. Scrapie genotypes were available on 16 416 animals; the breeds represented included purebred Belclare (733), Charollais (333), Suffolk (739), Texel (1 857), Vendeen (191), and crossbreds (12 563). Performance data on lambing, lamb and ewe performance as well as health traits were available. The association between alternative approaches of describing the PrP genotype (i.e. 15 individually called PrP genotypes, five genotype classes representing susceptibility to scrapie, or number of ARR haplotypes) and animal performance were quantified using animal linear mixed models. All 15 of the possible scrapie genotypes were detected, although the frequency differed by breed. The frequency of the five PrP haplotypes in the entire population were 0.70 (ARR), 0.15 (ARQ), 0.11 (ARH), 0.02 (AHQ) and 0.01 (VRQ); the most susceptible haplotype (VRQ) was only detected in purebred Texels and crossbreds. No association was detected between the PrP genotype of either the animal or dam and any of the lambing traits (i.e. lambing difficulty score, perinatal mortality and birth weight). With the exception of ultrasound muscle depth, no association between the PrP genotype and any of the lamb performance traits (i.e. lamb BW and carcass) was observed. Lambs carrying the category four PrP genotype (i.e. ARR/VRQ) had 1.20 (SE = 0.45) mm, 1.38 (SE = 0.12) mm, 1.47 (S = 0.25) mm shallower ultrasound muscle depth relative to lambs of the less susceptible scrapie categories of 1, 2, 3, respectively (P 0.05). For ewe mature BW, ARH/VRQ ewes differed from most other ewe PrP genotypes and were, on average, 3.79 (SE = 1.66) kg heavier than ARR/ARR genotype ewes. Lamb dag score differed by dam PrP genotype (P < 0.05), although the differences were small. Results from this study show that scrapie is segregating within the Irish sheep population, but the PrP genotype was not associated with most traits investigated and, where associations were detected, the biological significance was minimal. This suggests minimal impact of selection on PrP genotype on performance, at least for the traits investigated in the present study
An overview of Irish pig production, research and knowledge transfer since 1960
Get PDFpeer-reviewedPig production in Ireland has gone through enormous changes during the past 60 yr, from pigs being primarily produced as a sideline on dairy farms, to an industry with one of the highest average herd sizes in Europe. This happened in part due to external pressure on the industry, whereby economies of scale were needed to compete with pigs produced in other countries, but largely due to the instigation of national programmes to support the pig industry through research, education and knowledge transfer. These efforts helped producers to take advantage of genetic improvements and monitor their own performance over time, as well as allowing for benchmarking of the national herd against other countries. The research programme initiated in the 1960s continues to grow and expand, providing the pig industry with internationally renowned data and knowledge in the areas of nutrition, animal welfare, the environment and energy use. Recent initiatives such as the establishment of the Teagasc and Irish Farmers Association Pig Joint Programme, and a Pig Health Check section in Animal Health Ireland, will help to promote further cross-collaboration between stakeholders in the pig industry, and enable it to rise to the challenges of the years ahead
Identification of sperm proteins as biomarkers of field fertility in Holstein-Friesian bulls used for artificial insemination
Get PDFpeer-reviewedDespite passing stringent quality control, bulls used in artificial insemination can vary significantly in their fertility, emphasizing the need for reliable markers of sperm quality. This study aimed to identify sperm proteins acting as biomarkers of fertility in 2 different populations of dairy bulls classified based on their field fertility. Semen was collected and cryopreserved from: 54 Holstein bulls located in Ireland, classified according to fertility indexes as low fertility (LF, n = 23), medium fertility (n = 14), or high fertility (HF, n = 17); and 18 Holstein bulls located in Denmark, classified as LF (n = 8) or HF (n = 10). The proteome was measured through liquid chromatography-mass spectrometry and data were analyzed with the R software. Differentially abundant proteins between HF and LF bulls and biomarker proteins were determined through a modified t-test and random forest, respectively, selecting 301 differentially abundant proteins and 34 biomarker proteins. The predictive ability of the 34 biomarkers was evaluated employing support vector machine as the classifier, using their abundance levels in the Irish bulls to train the model and in the Danish bulls for validation. The prediction accuracy was 94.4%, with only one HF bull misclassified, corresponding to the lowest fertility index bull in the HF group. The biomarkers more abundant in sperm of HF bulls enriched axoneme assembly and sperm motility (false discovery rate <0.05), according to functional analysis. In conclusion, a robust model coupled with the application of appropriate bioinformatic tools allowed the identification of functionally relevant sperm proteins predictive of the fertility of Holstein bulls used in artificial insemination.Science Foundation Irelan
Predicting livestock behaviour using accelerometers: A systematic review of processing techniques for ruminant behaviour prediction from raw accelerometer data
Get PDFpeer-reviewedPrecision Technologies are emerging in the context of livestock farming to improve management practices and the health and welfare of livestock through monitoring individual animal behaviour. Continuously collecting information about livestock behaviour is a promising way to address several of these target areas. Wearable accelerometer sensors are currently the most promising system to capture livestock behaviour. Accelerometer data should be analysed properly to obtain reliable information on livestock behaviour. Many studies are emerging on this subject, but none to date has highlighted which techniques to recommend or avoid. In this paper, we systematically review the literature on the prediction of livestock behaviour from raw accelerometer data, with a specific focus on livestock ruminants. Our review is based on 66 surveyed articles, providing reliable evidence of a 3-step methodology common to all studies, namely (1) Data Collection, (2) Data Pre-Processing and (3) Model Development, with different techniques used at each of the 3 steps. The aim of this review is thus to (i) summarise the predictive performance of models and point out the main limitations of the 3-step methodology, (ii) make recommendations on a methodological blueprint for future studies and (iii) propose lines to explore in order to address the limitations outlined. This review shows that the 3-step methodology ensures that several major ruminant behaviours can be reliably predicted, such as grazing/eating, ruminating, moving, lying or standing. However, the areas faces two main limitations: (i) Most models are less accurate on rarely observed or transitional behaviours, behaviours may be important for assessing health, welfare and environmental issues and (ii) many models exhibit poor generalisation, that can compromise their commercial use. To overcome these limitations we recommend maximising variability in the data collected, selecting pre-processing methods that are appropriate to target behaviours being studied, and using classifiers that avoid over-fitting to improve generalisability. This review presents the current situation involving the use of sensors as valuable tools in the field of behaviour recording and contributes to the improvement of existing tools for automatically monitoring ruminant behaviour in order to address some of the issues faced by livestock farming
Exploiting genetic variability in the trajectory of lactation yield and somatic cell score with each progressing parity
Get PDFpeer-reviewedThe inclusion of reproductive performance in dairy cow breeding schemes has resulted in a cumulative improvement in genetic merit for reproductive performance; this improvement should manifest in longer productive lives through a reduced requirement for involuntary culling. Nonetheless, the average length of dairy cow productive life has not changed in most populations, suggesting that risk factors for culling, especially in older cows, are possibly more associated with lower yield or high somatic cell score (SCS) than compromised reproductive performance. The objective of the present study was to understand the dynamics of lactation yields and SCS in dairy cows across parities and, in doing so, quantify the potential to alter this trajectory through breeding. After edits, 3,470,520 305-d milk, fat, and protein yields, as well as milk fat and protein percentage and somatic cell count records from 1,162,473 dairy cows were available for analysis. Random regression animal models were used to identify the parity in which individual cows reached their maximum lactation yields, and highest average milk composition and SCS; also estimated from these models were the (co)variance components for yield, composition, and SCS per parity across parities. Estimated breeding values for all traits per parity were calculated for cows reaching ≥fifth parity. Of the cows included in the analyses, 91.0%, 92.2%, and 83.4% reached maximum milk, fat, and protein yield in fifth parity, respectively. Conversely, 95.9% of cows reached their highest average fat percentage in first parity and 62.9% of cows reached their highest average protein percentage in third parity. In contrast to both milk yield and composition traits, 98.4% of cows reached their highest average SCS in eighth parity. Individual parity estimates of heritability for milk yield traits, milk composition, and SCS ranged from 0.28 to 0.44, 0.47 to 0.69, and 0.13 to 0.23, respectively. The strength of the genetic correlations per trait among parities was inversely related to the interval between the parities compared; the weakest genetic correlation was 0.67 (standard error = 0.02) between milk yield in parities 1 and 8. Eigenvalues and eigenfunctions of the additive genetic covariance matrices for all investigated traits revealed potential to alter the trajectory of parity profiles for milk yield, milk composition, and SCS. This was further demonstrated when evaluating the trajectories of animal estimated breeding values per parity
Localization of urea transporter B in the developing bovine rumen
Get PDFpeer-reviewedUrea nitrogen secreted from blood to rumen is a crucial factor shaping the symbiotic relationship between host ruminants and their microbial populations. Passage of urea across rumen epithelia is facilitated by urea transporter B (UT-B), but the long-term regulation of these proteins remains unclear. As ruminal function develops over a period of months, the developing rumen is an excellent model with which to investigate this regulation. Using rumen epithelium samples of calves from birth to 96 d of age, this study performed immunolocalization studies to localize and semi-quantify UT-B protein development. As expected, preliminary experiments confirmed that ruminal monocarboxylate transporter 1 (MCT1) short chain fatty acid transporter protein abundance increased with age (P < 0.01, n = 4). Further investigation revealed that ruminal UT-B was present in the first few weeks of life and initially detected in the basolateral membrane of stratum basale cells. Over the next 2 months, UT-B staining spread to other epithelial layers and semi-quantification indicated that UT-B abundance significantly increased with age (P < 0.01, n = 4 or 6). These changes were in line with the development of rumen function after the advent of solid feed intake and weaning, exhibiting a similar pattern to both MCT1 transporters and papillae growth. This study therefore confirmed age-dependent changes of in situ ruminal UT-B protein, adding to our understanding of the long-term regulation of ruminal urea transporters
A model framework to investigate the role of anomalous land surface processes in the amplification of summer drought across Ireland during 2018
Get PDFpeer-reviewedDue to its latitude and ample year-round rainfall, Ireland is typically an energy-limited regime in the context of soil moisture availability and evapotranspiration. However, during the summer of 2018, regions within the country displayed significant soil moisture deficits, associated with anomalous atmospheric forcing conditions, with consequent impacts on the surface energy balance. Here, we explore the utility of a physically based land surface scheme coupled with observational, global gridded reanalysis and satellite-derived data products to analyse the spatial and temporal evolution of the 2018 summer drought event in Ireland over grassland, which represents the dominant agricultural land-cover. While the surface–air energy exchanges were initially dominated by atmospheric anomalies, soil moisture constraints became increasingly important in regulating these exchanges, as the accumulated rainfall deficit increased throughout the summer months. This was particularly evident over the freer draining soils in the east and southeast of the country. From late June 2018, we identify a strong linear coupling between soil moisture and both evapotranspiration and vegetation response, suggesting a shift from an energy-limited evapotranspiration regime into a dry or soil water-limited regime. Applying segmented regression models, the study quantifies a critical soil moisture threshold as a key determinant of the transition from wet to dry evaporative regimes. These findings are important to understand the soil moisture context under which land–atmosphere couplings are strongest in water-limited regimes across the country and should help improve the treatment of soil parameters in weather prediction models, required for subseasonal and seasonal forecasts, consequently enhancing early warning systems of summer climate extremes in the future
Stem-nesting Hymenoptera in Irish farmland: empirical evaluation of artificial trap nests as tools for fundamental research and pollinator conservation
Get PDFpeer-reviewedInsect pollinators are suffering global declines, necessitating the evaluation and development of methods for long-term monitoring and applied field research. Accordingly, this study evaluated the use of trap nests (“bee hotels”) as tools for investigating the ecology of cavity nesting Hymenoptera within Irish agricultural landscapes. Three trap nests consisting of 110 mm diameter plastic pipe containing 100 cardboard nest tubes of varying diameter were placed at eight apple orchards and eight oilseed rape sites and left in the field for five months. Sealed nest tubes occurred at 15 of the 16 sites, and in 77% of the 48 nests. However, only 7% of the 4800 individual nest tubes were sealed, and only 4% produced cavity-nesting Hymenoptera. Three cavity nesting bee species (Hylaeus communis, Osmia bicornis, Megachile versicolor) and two solitary wasp species (Ancistrocerus trifasciatus, A. parietinus) emerged from nest tubes. There were significant differences among species in terms of emergence date and the diameter of nest tubes from which they emerged, the latter allowing the calculation of niche width and niche overlap, and informing choice of tube size in future studies/conservation efforts. Trap nests, therefore, offer a valuable tool for fundamental ecological research and a model system for investigating interactions between stem-nesting species within their wider ecological networks. The ability of trap nests to actually increase farmland pollinator abundance and diversity as part of agri-environment schemes requires additional investigation. However, used in sufficient numbers, these trap nests provide valuable biogeographical data for cavity nesting Hymenoptera and offer a viable means for long term monitoring of these species in Irish farmland
Detection of Genomic Imprinting for Carcass Traits in Cattle Using Imputed High-Density Genotype Data.
No full textpeer-reviewedGenomic imprinting is an epigenetic phenomenon defined as the silencing of an allele, at least partially, at a given locus based on the sex of the transmitting parent. The objective of the present study was to detect the presence of SNP-phenotype imprinting associations for carcass weight (CW), carcass conformation (CC) and carcass fat (CF) in cattle. The data used comprised carcass data, along with imputed, high-density genotype data on 618,837 single nucleotide polymorphisms (SNPs) from 23,687 cattle; all animal genotypes were phased with respect to parent of origin. Based on the phased genotypes and a series of single-locus linear models, 24, 339, and 316 SNPs demonstrated imprinting associations with CW, CC, and CF, respectively. Regardless of the trait in question, no known imprinted gene was located within 0.5 Mb of the SNPs demonstrating imprinting associations in the present study. Since all imprinting associations detected herein were at novel loci, further investigation of these regions may be warranted. Nonetheless, knowledge of these associations might be useful for improving the accuracy of genomic evaluations for these traits, as well as mate allocations systems to exploit the effects of genomic imprinting
FitForests – Looking at how Ireland’s Forests are shaping up to climate change
Get PDFClimate change is already having impacts on the health of the world’s forests. Healthy forests
are critical to mitigate climate change. So how are Ireland’s forests shaping up?
The FitForest is a DAFM-funded project which aim to address
knowledge gaps in species and provenance selection to identify
those that may be more adapted to future climatic conditions. High
diversity among populations that may have phenotypic plasticity and/
or adaptive potential may be better adapted to capitalize on changing
environmental conditions. This adaptive capacity is commonly assessed
by studying the traits of trees which convey some inherent advantage
over other populations which allows them to prosper in certain
environments (e.g. late bud flush or water use efficiency). The project is
a collaboration between a team of researchers from Teagasc, the Agri-
Food and Biosciences Institute (NI), UCD, and climatologists from
NUI-Maynooth