Moorepark is one of the world's leading dairy research centres and specialises in pasture based systems of milk production.Research at Moorepark endeavours to anticipate the production needs of a rapidly changing industry and develop sustainable systems of milk production that will advance the competitive edge of Irish dairy farmers on the global market. Grange Animal & Grassland Research and Innovation Centre is the national Beef Research Centre which provides research information on all aspects of beef production in Ireland. Research at Grange supports the efficient production of safe, quality, healthy produce, in profitable production systems that meet stringent environmental and animal welfare standards. The Athenry Animal & Grassland Research and Innovation Centre, located in Co. Galway,provides national research services in sheep production and animal reproduction.

Recent Submissions

  • Predicting cow milk quality traits from routinely available milk spectra using statistical machine learning methods.

    Frizzarin, Maria; Gormley, I. C.; Berry, Donagh; Murphy, T. B.; Casa, A.; Lynch, A.; McParland, Sinead; Science Foundation Ireland; Department of Agriculture, Food and the Marine; 18/SIRG/5562; et al. (Elsevier for American Dairy Science Association, 2021)
    Numerous statistical machine learning methods suitable for application to highly correlated features, as exists for spectral data, could potentially improve prediction performance over the commonly used partial least squares approach. Milk samples from 622 individual cows with known detailed protein composition and technological trait data accompanied by mid-infrared spectra were available to assess the predictive ability of different regression and classification algorithms. The regression-based approaches were partial least squares regression (PLSR), ridge regression (RR), least absolute shrinkage and selection operator (LASSO), elastic net, principal component regression, projection pursuit regression, spike and slab regression, random forests, boosting decision trees, neural networks (NN) and a post-hoc approach of model averaging (MA). Several classification methods (i.e., partial least squares discriminant analysis (PLSDA), random forests, boosting decision trees, and support vector machines (SVM)) were also used after stratifying the traits of interest into categories. In the regression analyses, MA was the best prediction method for 6 of the 14 traits investigated (a60, alpha s1 CN, alpha s2 CN, kappa CN, alpha lactalbumin, and beta lactoglobulin B), while NN and RR were the best algorithms for 3 traits each (RCT, k20, and heat stability, and a30, beta CN, and beta lactoglobulin A, respectively), PLSR was best for pH and LASSO was best for CN micelle size. When traits were divided into two classes, SVM had the greatest accuracy for the majority of the traits investigated. While the well-established PLSR-based method performed competitively, the application of statistical machine learning methods for regression analyses reduced the root mean square error when compared to PLSR from between 0.18% (kappa CN) to 3.67% (heat stability). The use of modern statistical ML methods for trait prediction from MIRS may improve the prediction accuracy for some traits.
  • Improving robustness and accuracy of predicted daily methane emissions of dairy cows using milk mid‐infrared spectra

    Vanlierde, Amélie; Dehareng, Frédéric; Gengler, Nicolas; Froidmont, Eric; McParland, Sinead; Kreuzer, Michael; Bell, Matthew; Lund, Peter; Martin, Cécile; Kuhla, Björn; et al. (Wiley, 2020-11-22)
    BACKGROUND A robust proxy for estimating methane (CH4) emissions of individual dairy cows would be valuable especially for selective breeding. This study aimed to improve the robustness and accuracy of prediction models that estimate daily CH4 emissions from milk Fourier transform mid‐infrared (FT‐MIR) spectra by (i) increasing the reference dataset and (ii) adjusting for routinely recorded phenotypic information. Prediction equations for CH4 were developed using a combined dataset including daily CH4 measurements (n = 1089; g d−1) collected using the SF6 tracer technique (n = 513) and measurements using respiration chambers (RC, n = 576). Furthermore, in addition to the milk FT‐MIR spectra, the variables of milk yield (MY) on the test day, parity (P) and breed (B) of cows were included in the regression analysis as explanatory variables. RESULTS Models developed based on a combined RC and SF6 dataset predicted the expected pattern in CH4 values (in g d−1) during a lactation cycle, namely an increase during the first weeks after calving followed by a gradual decrease until the end of lactation. The model including MY, P and B information provided the best prediction results (cross‐validation statistics: R2 = 0.68 and standard error = 57 g CH4 d−1). CONCLUSIONS The models developed accounted for more of the observed variability in CH4 emissions than previously developed models and thus were considered more robust. This approach is suitable for large‐scale studies (e.g. animal genetic evaluation) where robustness is paramount for accurate predictions across a range of animal conditions. © 2020 Society of Chemical Industry
  • A comparison of 4 different machine learning algorithms to predict lactoferrin content in bovine milk from mid-infrared spectra

    Soyeurt, H.; Grelet, C.; McParland, Sinead; Calmels, M.; Coffey, M.; Tedde, A.; Delhez, P.; Dehareng, F.; Gengler, N.; European Union; et al. (American Dairy Science Association, 2020-10-22)
    Lactoferrin (LF) is a glycoprotein naturally present in milk. Its content varies throughout lactation, but also with mastitis; therefore it is a potential additional indicator of udder health beyond somatic cell count. Condequently, there is an interest in quantifying this biomolecule routinely. First prediction equations proposed in the literature to predict the content in milk using milk mid-infrared spectrometry were built using partial least square regression (PLSR) due to the limited size of the data set. Thanks to a large data set, the current study aimed to test 4 different machine learning algorithms using a large data set comprising 6,619 records collected across different herds, breeds, and countries. The first algorithm was a PLSR, as used in past investigations. The second and third algorithms used partial least square (PLS) factors combined with a linear and polynomial support vector regression (PLS + SVR). The fourth algorithm also used PLS factors, but included in an artificial neural network with 1 hidden layer (PLS + ANN). The training and validation sets comprised 5,541 and 836 records, respectively. Even if the calibration prediction performances were the best for PLS + polynomial SVR, their validation prediction performances were the worst. The 3 other algorithms had similar validation performances. Indeed, the validation root mean squared error (RMSE) ranged between 162.17 and 166.75 mg/L of milk. However, the lower standard deviation of cross-validation RMSE and the better normality of the residual distribution observed for PLS + ANN suggest that this modeling was more suitable to predict the LF content in milk from milk mid-infrared spectra (R2v = 0.60 and validation RMSE = 162.17 mg/L of milk). This PLS +ANN model was then applied to almost 6 million spectral records. The predicted LF showed the expected relationships with milk yield, somatic cell score, somatic cell count, and stage of lactation. The model tended to underestimate high LF values (higher than 600 mg/L of milk). However, if the prediction threshold was set to 500 mg/L, 82% of samples from the validation having a content of LF higher than 600 mg/L were detected. Future research should aim to increase the number of those extremely high LF records in the calibration set.
  • The Effect of Compositional Changes Due to Seasonal Variation on Milk Density and the Determination of Season-Based Density Conversion Factors for Use in the Dairy Industry

    Parmar, Puneet; Lopez-Villalobos, Nicolas; Tobin, John T.; Murphy, Eoin; McDonagh, Arleen; Crowley, Shane V.; Kelly, Alan L.; Shalloo, Laurence; Enterprise Ireland; Science Foundation Ireland; et al. (MDPI AG, 2020-07-27)
    The objective of this study was to determine the effect of seasonal variation on milk composition and establish an algorithm to predict density based on milk composition to enable the calculation of season-based density conversion calculations. A total of 1035 raw whole milk samples were collected from morning and evening milking of 60 spring-calving individual cows of different genetic groups, namely Jersey, Elite HF (Holstein–Friesian) and National Average HF, once every two weeks for a period of 9 months (March–November, 2018). The average mean and standard deviation for milk compositional traits were 4.72 ± 1.30% fat, 3.85 ± 0.61% protein and 4.69 ± 0.30% lactose and density was estimated at 1.0308 ± 0.002 g/cm3 . The density of the milk samples was evaluated using three methods: a portable density meter, DMA 35; a standard desktop version, DMA 4500M; and an Association of Official Agricultural Chemists (AOAC) method using 100-mL glass pycnometers. Statistical analysis using a linear mixed model showed a significant difference in density of milk samples (p < 0.05) across seasonal and compositional variations adjusted for the effects of days in milk, parity, the feeding treatment, the genetic group and the measurement technique. The mean density values and standard error of mean estimated for milk samples in each season, i.e., spring, summer and autumn were 1.0304 ± 0.00008 g/cm3 , 1.0314 ± 0.00005 g/cm3 and 1.0309 ± 0.00007 g/cm3 , respectively.
  • Milk adulteration with acidified rennet whey: a limitation for caseinomacropeptide detection by high-performance liquid chromatography

    de Pádua Alves, Érika; de Alcântara, Anna Laura D'Amico; Guimarães, Anselmo José Klaechim; de Santana, Elsa Helena Walter; Botaro, Bruno Garcia; Fagnani, Rafael; Coordenação de Aperfeiçoamento de Pessoal de Nível Superior; Fundação Nacional de Desenvolvimento do Ensino Superior Particular (Wiley, 2018-03-02)
    BACKGROUND High‐performance liquid chromatography (HPLC) is widely employed to determine the caseinomacropeptide (CMP) index and to detect milk tampering with rennet whey. Prior to HPLC analysis, CMP is subject to a trichloracetic acid isolation, causing further soluble proteins in the sample to precipitate. On this basis, we aimed to determine whether rennet whey acidification could adversely affect the HPLC sensitivity with respect to detecting this peptide. RESULTS As hypothesized, the CMP index from milk with added acidified rennet whey was, on average, half that quantified from milk with added rennet whey. Moreover, the quantum satis of acidified whey added to milk sufficient to demonstrate a HPLC CMP > 30 mg L–1 was 94% greater than that required for this threshold to be reached with rennet whey. CONCLUSION Milk tampering with acidified rennet whey may limit the analytical sensitivity of the reversed‐phase HPLC employed for the screening of CMP and, ultimately, disguise the fraudulent addition of whey to milk. © 2017 Society of Chemical Industry
  • Screening commercial teat disinfectants against bacteria isolated from bovine milk using disk diffusion

    Fitzpatrick, Sarah Rose; Garvey, Mary; Jordan, Kieran; Flynn, Jim; O'Brien, Bernadette; Gleeson, David; Dairy Research Ireland; Teagasc Walsh Fellowship Programme; MKLS0006; 2016054 (Veterinary World, 2019-05-06)
    Background and Aim: Teat disinfection is an important tool in reducing the incidence of bovine mastitis. Identifying the potential mastitis-causing bacterial species in milk can be the first step in choosing the correct teat disinfectant product. The objective of this study was to screen commercial teat disinfectants for inhibition against mastitis-associated bacteria isolated from various types of milk samples. Materials and Methods: Twelve commercially available teat disinfectant products were tested, against 12 mastitis-associated bacteria strains isolated from bulk tank milk samples and bacterial strains isolated from clinical (n=2) and subclinical (n=3) quarter foremilk samples using the disk diffusion method. Results: There was a significant variation (7-30 mm) in bacterial inhibition between teat disinfection products, with products containing a lactic acid combination (with chlorhexidine or salicylic acid) resulting in the greatest levels of bacterial inhibition against all tested bacteria (p<0.05). Conclusion: In this study, combined ingredients in teat disinfection products had greater levels of bacterial inhibition than when the ingredients were used individually. The disk diffusion assay is a suitable screening method to effectively differentiate the bacterial inhibition of different teat disinfectant products.
  • Choice of artificial insemination beef bulls used to mate with female dairy cattle

    Berry, Donagh; Ring, S.C.; Twomey, A.J.; Evans, R.D.; Science Foundation Ireland; Department of Agriculture, Food and the Marine; 16/RC/3835 (Elsevier for American Dairy Science Association, 2020-02)
    Understanding the preferences of dairy cattle producers when selecting beef bulls for mating can help inform beef breeding programs as well as provide default parameters in mating advice systems. The objective of the present study was to characterize the genetic merit of beef artificial insemination (AI) bulls used in dairy herds, with particular reference to traits associated with both calving performance and carcass merit. The characteristics of the beef AI bulls used were compared with those of the dairy AI bulls used on the same farms. A total of 2,733,524 AI records from 928,437 females in 5,967 Irish dairy herds were used. Sire predicted transmitting ability (PTA) values and associated reliability values for calving performance and carcass traits based on national genetic evaluations from prior to the insemination were used. Fixed effects models were used to relate both genetic merit and the associated reliability of the dairy and beef bulls used on the farm with herd size, the extent of Holstein-Friesian × Jersey crossbreeding adopted by the herd, whether the herd used a technician insemination service or do-ityourself, and the parity of the female mated. The mean direct calving difficulty PTA of the beef bulls used was 1.85 units higher than that of the dairy bulls but with over 3 times greater variability in the beef bulls. This 1.85 units equates biologically to an expectation of 1.85 more dystocia events per 100 dairy cows mated in the beef × dairy matings. The mean calving difficulty PTA of the dairy AI bulls used reduced with increasing herd size, whereas the mean calving difficulty PTA of the beef AI bulls used increased as herd size increased from 75 cows or fewer to 155 cows; the largest herds (>155 cows) used notably easier-calving beef bulls, albeit the calving difficulty PTA of the beef bulls was 3.33 units versus 1.67 units for the dairy bulls used in these herds. Although we found a general tendency for larger herds to use dairy AI bulls with lower reliability, this trend was not obvious in the beef AI bulls used. Irrespective of whether dairy or beef AI bulls were considered, herds that operated more extensive Holstein-Friesian × Jersey crossbreeding (i.e., more than 50% crossbred cows) used, on average, easier calving, shorter gestationlength bulls with lighter expected progeny carcasses of poorer conformation. Mean calving difficulty PTA of dairy bulls used increased from 1.39 in heifers to 1.79 in first-parity cows and to 1.82 in second-parity cows, remaining relatively constant thereafter. In contrast, the mean calving difficulty PTA of the beef bulls used increased consistently with cow parity. Results from the present study demonstrate a clear difference in the mean acceptable genetic merit of beef AI bulls relative to dairy AI bulls but also indicates that these acceptable limits vary by herd characteristics.
  • Associating cow characteristics with mobility scores in pasture-based dairy cows

    O'Connor, Aisling; Bokkers, E.A.M.; de Boer, I.J.M.; Hogeveen, H.; Sayers, Riona; Byrne, Nicky; Ruelle, Elodie; Shalloo, Laurence; Irish Department of Agriculture, Food and the Marine; Teagasc Walsh Fellowship Programme (Elsevier for American Dairy Science Association, 2019-07-10)
    The quality of dairy cow mobility can have significant welfare, economic, and environmental consequences that have yet to be extensively quantified for pasture-based systems. The objective of this study was to characterize mobility quality by examining associations between specific mobility scores, claw disorders (both the type and severity), body condition score (BCS), and cow parity. Data were collected for 6,927 cows from 52 pasture-based dairy herds, including mobility score (0 = optimal mobility; 1, 2, or 3 = increasing severities of suboptimal mobility), claw disorder type and severity, BCS, and cow parity. Multinomial logistic regression was used for analysis. The outcome variable was mobility score, and the predictor variables were BCS, type and severity of claw disorders, and cow parity. Three models were run, each with 1 reference category (mobility score 0, 1, or 2). Each model also included claw disorders (overgrown claw, sole hemorrhage, white line disease, sole ulcer, and digital dermatitis), BCS, and cow parity as predictor variables. The presence of most types of claw disorders had odds ratios >1, indicating an increased likelihood of a cow having suboptimal mobility. Low BCS (BCS <3.00) was associated with an increased risk of a cow having suboptimal mobility, and relatively higher parity was also associated with an increased risk of suboptimal mobility. These results confirm an association between claw disorders, BCS, cow parity, and dairy cow mobility score. Therefore, mobility score should be routinely practiced to identify cows with slight deviations from the optimal mobility pattern and to take preventive measures to keep the problem from worsening.
  • An investigation into the factors associated with ewe colostrum production

    Campion, Frank P.; Crosby, Thomas F.; Creighton, Philip; Fahey, Alan G.; Boland, Tommy M.; Teagasc Walsh Fellowship Programme (Elsevier BV, 2019-09)
    The majority of lamb mortality which occurs during the first 24 h post-partum is preventable through providing the lamb with sufficient quantities of high quality colostrum during this time. Data from seven late gestation nutrition experiments carried out at this institute between 2002 and 2014 were collated into a single data set comprising of 415 twin bearing ewes. Analysis was carried out to investigate the key drivers of ewe colostrum production excluding nutrient intake, namely body reserve mobilisation, ewe breed type, ewe age, gestation length and lamb birth weight. The volume of colostrum produced at 1 and 18 h post-partum was significantly lower than the volume recorded at 10 h post-partum (P = 0.01). Multivariate regression analysis indicated that colostrum volume during the first 18 h post-partum was influenced by lamb birth weight (P = 0.01), ewe age (P = 0.01), breed type (P = 0. 01) and gestation length (P = 0.06). Live weight change (P = 0.05) also had a significant influence on the volume of colostrum produced but BCS change did not affect colostrum production (P = 0.25). Further multivariate regression analysis indicated that IgG yield was influenced ewe breed type (P = 0.01), lamb birth weight (P = 0.02), gestation length (P = 0.05) and BCS change (P = 0.04). Live weight change (P = 0.12) and ewe age (P = 0.62) did not influence the quantity of IgG produced. Leicester ewes produced less colostrum per kg lamb birth weight at 1 h post-partum compared to all other ewe breed types (P = 0.01) and less than Suffolk ewes at 10 h post-partum (P = 0.01). The result of this analysis shows the key factors excluding ewe nutrition that drive colostrum production. Ewe breed type in particular appears to play an important role in the ability of the ewe to produce sufficient quantities of adequate quality colostrum. In conclusion the result of this analysis highlights the important factors associated with ewe colostrum volume and IgG yield excluding nutrition. In particular the overall structure of the flock such as breed type and ewe age is important when considering the ability of the flock to meet colostrum demands and hence reduce lamb mortality.
  • The effect of target postgrazing height on sward clover content, herbage yield, and dairy production from grass-white clover pasture

    Phelan, P.; Casey, I.A.; Humphreys, James; Department of Agriculture, Food and the Marine; Teagasc Walsh Fellowship Programme; RSF 07-511 (American Dairy Science Association, 2013-01-18)
    White clover (Trifolium repens) is an important legume for grazed grassland that can increase the profitability and environmental sustainability of milk production. Previous experiments on mown grass-clover plots suggest that low postgrazing heights (PGH) can increase sward clover content and herbage production. However, this has not been tested in actual strip or rotational grazing systems with dairy cows. Furthermore, lowering PGH in grass-only swards (typically perennial ryegrass without white clover) has previously been associated with reduced milk yields per cow. The objective of this experiment was to investigate the effect of PGH by dairy cows on clover content, herbage production, and milk production from strip-grazed grass-white clover swards in Ireland. Three target PGH treatments of 4, 5, and 6 cm were in place for entire grazing seasons (February to November) for 3 consecutive years (2007 to 2009). Each treatment had a mean of 21 Holstein-Friesian dairy cows that strip-grazed a mean annual area of 10.2 ha. Postgrazing height was measured twice a day with a rising plate meter, and cows were moved to the next strip once the target PGH was reached. Annual fertilizer nitrogen input was 90 kg of N/ha for each treatment. The PGH treatment did not significantly affect annual milk yield (6,202 kg/cow), solids-corrected milk yield (6,148 kg/cow), fat, protein, or lactose yields (265, 222, and 289 kg/cow, respectively), cow liveweight (592 kg) or body condition score (3.01). The PGH treatment also had no significant effect on sward white clover content (196 g/kg). However, herbage production of both grass and clover were significantly higher with the 4-cm PGH treatment compared with the 6-cm treatment. Mean annual herbage yields were 11.1, 10.2, and 9.1 t of organic matter (OM)/ha for the 4-, 5-, and 6-cm PGH treatments, respectively. The lower herbage production in the 6-cm PGH treatment resulted in lower annual silage production, greater housing requirements, and a substantially higher net silage deficit (−1,917 kg of OM/cow) compared with the 5- or 4-cm treatments (−868 and −192 kg of OM/cow, respectively). Grazing to a PGH of 4 cm is therefore recommended for grass-white clover swards.
  • PastureBase Ireland: A grassland decision support system and national database

    Hanrahan, Liam; Geoghegan, Anne; O'Donovan, Michael; Griffith, Vincent; Ruelle, Elodie; Wallace, Michael; Shalloo, Laurence (Elsevier BV, 2017-04-15)
    PastureBase Ireland (PBI) is a web-based grassland management application incorporating a dual function of grassland decision support and a centralized national database to collate commercial farm grassland data. This database facilitates the collection and storage of vast quantities of grassland data from grassland farmers. The database spans across ruminant grassland enterprises – dairy, beef and sheep. To help farmers determine appropriate actions around grassland management, we have developed this data informed decision support tool to function at the paddock level. Individual farmers enter data through the completion of regular pasture cover estimations across the farm, allowing the performance of individual paddocks to be evaluated within and across years. To evaluate the PBI system, we compared actual pasture cut experimental data (Etesia cuts) to PBI calculated outputs. We examined three comparisons, comparing PBI outputs to actual pasture cut data, for individual DM yields at defoliation (Comparison 1), for cumulative annual DM yields including silage data (Comparison 2) and, for cumulative annual DM yields excluding silage data (Comparison 3). We found an acceptable accuracy between PBI outputs and pasture cut data when statistically analyzed using relative prediction error and concordance correlation coefficients for the measurement of total annual DM yield (Comparison 2), with a relative prediction error of 15.4% and a concordance correlation coefficient of 0.85. We demonstrated an application of the PBI system through analysis of commercial farm data across two years (2014–2015) for 75 commercial farms who actively use the system. The analysis showed there was a significant increase in DM yield from 2014 to 2015. The results indicated a greater variation in pasture growth across paddocks within farms than across farms.
  • The effect of paratuberculosis on milk yield—A systematic review and meta-analysis

    McAloon, Conor G.; Whyte, Paul; More, Simon J.; Green, Martin J.; O’Grady, Luke; Garcia, AnaBelen; Doherty, Michael L.; Department of Agriculture, Food and the Marine (American Dairy Science Association, 2015-12-10)
    Bovine paratuberculosis is a disease characterized by chronic granulomatous enteritis causing protein-losing enteropathy. Adverse effects on animal productivity are key drivers in the attempt to control paratuberculosis at the farm level. Economic models require an accurate estimation of the production effects associated with paratuberculosis. The aim of this study was to conduct a systematic review and meta-analysis to investigate the effect of paratuberculosis on milk production. A total of 20 effect estimates from 15 studies were included in the final meta-analysis. Substantial between-study heterogeneity was observed. Subgroup analysis by case definition and study design was carried out to investigate heterogeneity. The majority of between-study variation was attributed to studies that defined cases on serology. Calculation of a pooled effect estimate was only appropriate for studies that defined cases by organism detection. A reduction in milk yield, corrected for lactation number and herd of origin of 1.87 kg/d, equivalent to 5.9% of yield, was associated with fecal culture or PCR positivity in individual cows.
  • Associations between paratuberculosis ELISA results and test-day records of cows enrolled in the Irish Johne's Disease Control Program

    Botaro, Bruno G.; Ruelle, Elodie; More, Simon J.; Strain, Sam; Graham, David A.; O'Flaherty, Joe; Shalloo, Laurence; Department of Agriculture, Food and the Marine (Elsevier for American Dairy Science Association, 2017-07-12)
    The effect of the Mycobacterium avium ssp. paratuberculosis (MAP) ELISA status on test-day milk performance of cows from Irish herds enrolled in the pilot national voluntary Johne’s disease control program during 2013 to 2015 was estimated. A data set comprising 92,854 cows and 592,623 complete test-day records distributed across 1,700 herds was used in this study. The resulting ELISA outcome (negative, inconclusive, and positive) of each cow within each year of the program was used to allocate the cow into different scenarios representing the MAP status. At MAPscenario1, all cows testing ELISA nonnegative (i.e., inconclusive and positive) were assigned a MAP-positive status; at MAPscenario2 only cows testing ELISA-positive were assigned a MAP-positive status; at MAPscenario3 only cows testing ELISA nonnegative (inconclusive or positive) and gathered exclusively from herds where at least 2 further ELISA nonnegative (inconclusive or positive) cows were found were assigned a MAP-positive status; at MAPscenario4 only cows testing ELISA-positive that were gathered exclusively from herds where at least 2 further ELISA-positive cows were found were assigned a MAP-positive status. Milk outputs based on test-day records were standardized for fat and protein contents (SMY) and the effect of MAP ELISA status on the SMY was estimated by a linear mixed effects model structure. The SMY mean difference recorded at test day between cows with a MAP-positive status and those with a MAP-negative status within MAPscenario1 was estimated at −0.182 kg/test day; the mean difference was −0.297 kg/test day for MAPscenario2; for MAPscenario3 mean difference between MAP-positive status and MAP test-negative cows was −0.209 kg/test day, and for MAPscenario4, the difference was −0.326 kg/ test day
  • Association of season and herd size with somatic cell count for cows in Irish, English, and Welsh dairy herds

    Archer, Simon C.; Mc Coy, Finola; Wapenaar, Wendela; Green, Martin J.; Teagasc Walsh Fellowship Programme (Elsevier BV, 2013-06-03)
    The aims of this study were to describe associations of time of year, and herd size with cow somatic cell count (SCC) for Irish, English, and Welsh dairy herds. Random samples of 497 and 493 Irish herds, and two samples of 200 English and Welsh (UK) herds were selected. Random effects models for the natural logarithm of individual cow test day SCC were developed using data from herds in one sub-dataset from each country. Data from the second sub-datasets were used for cross validation. Baseline model results showed that geometric mean cow SCC (GSCC) in Irish herds was highest from February to August, and ranged from 111,000 cells/mL in May to 61,000 cells/mL in October. For cows in UK herds, GSCC ranged from 84,000 cells/mL in February and June, to 66,000 cells/mL in October. The results highlight the importance of monitoring cow SCC during spring and summer despite low bulk milk SCC at this time for Irish herds. GSCC was lowest in Irish herds of up to 130 cows (63,000 cells/mL), and increased for larger herds, reaching 68,000 cells/mL in herds of up to 300 cows. GSCC in UK herds was lowest for herds of 130–180 cows (60,000 cells/mL) and increased to 63,000 cells/mL in herds of 30 cows, and 68,000 cells/mL in herds of 300 cows. Importantly, these results suggest expansion may be associated with increased cow SCC, highlighting the importance of appropriate management, to benefit from potential economies of scale, in terms of udder health.
  • The effect of different precooling rates and cold storage on milk microbiological quality and composition

    Paludetti, Lizandra; Kelly, Alan L.; O'Brien, Bernadette; Jordan, Kieran; Gleeson, David E (Elsevier, 2018-01-10)
    The objective of this study was to measure the effect of different milk cooling rates, before entering the bulk tank, on the microbiological load and composition of the milk, as well as on energy usage. Three milk precooling treatments were applied before milk entered 3 identical bulk milk tanks: no plate cooler (NP), single-stage plate cooler (SP), and double-stage plate cooler (DP). These precooling treatments cooled the milk to 32.0 ± 1.4°C, 17.0 ± 2.8°C, and 6.0 ± 1.1°C, respectively. Milk was added to the bulk tank twice daily for 72 h, and the tank refrigeration temperature was set at 3°C. The blend temperature within each bulk tank was reduced after each milking event as the volume of milk at 3°C increased simultaneously. The bacterial counts of the milk volumes precooled at different rates did not differ significantly at 0 h of storage or at 24-h intervals thereafter. After 72 h of storage, the total bacterial count of the NP milk was 3.90 ± 0.09 log10 cfu/mL, whereas that of the precooled milk volumes were 3.77 ± 0.09 (SP) and 3.71 ± 0.09 (DP) log10 cfu/mL. The constant storage temperature (3°C) over 72 h helped to reduce bacterial growth rates in milk; consequently, milk composition was not affected and minimal, if any, proteolysis occurred. The DP treatment had the highest energy consumption (17.6 ± 0.5 Wh/L), followed by the NP (16.8 ± 2.7 Wh/L) and SP (10.6 ± 1.3 Wh/L) treatments. This study suggests that bacterial count and composition of milk are minimally affected when milk is stored at 3°C for 72 h, regardless of whether the milk is precooled; however, milk entering the tank should have good initial microbiological quality. Considering the numerical differences between bacterial counts, however, the use of the SP or DP precooling systems is recommended to maintain low levels of bacterial counts and reduce energy consumption.
  • The effect of breed and diet type on the global transcriptome of hepatic tissue in beef cattle divergent for feed efficiency

    Higgins, Marc G; Kenny, David A.; Fitzsimons, Claire; Blackshields, Gordon; Coyle, Séan; McKenna, Clare; McGee, Mark; Morris, Derek W; Waters, Sinead M.; Department of Agriculture, Food and the Marine; et al. (Biomed Central, 2019-06-26)
    Background Feed efficiency is an important economic and environmental trait in beef production, which can be measured in terms of residual feed intake (RFI). Cattle selected for low-RFI (feed efficient) have similar production levels but decreased feed intake, while also emitting less methane. RFI is difficult and expensive to measure and is not widely adopted in beef production systems. However, development of DNA-based biomarkers for RFI may facilitate its adoption in genomic-assisted breeding programmes. Cattle have been shown to re-rank in terms of RFI across diets and age, while also RFI varies by breed. Therefore, we used RNA-Seq technology to investigate the hepatic transcriptome of RFI-divergent Charolais (CH) and Holstein-Friesian (HF) steers across three dietary phases to identify genes and biological pathways associated with RFI regardless of diet or breed. Results Residual feed intake was measured during a high-concentrate phase, a zero-grazed grass phase and a final high-concentrate phase. In total, 322 and 33 differentially expressed genes (DEGs) were identified across all diets for CH and HF steers, respectively. Three genes, GADD45G, HP and MID1IP1, were differentially expressed in CH when both the high-concentrate zero-grazed grass diet were offered. Two canonical pathways were enriched across all diets for CH steers. These canonical pathways were related to immune function. Conclusions The absence of common differentially expressed genes across all dietary phases and breeds in this study supports previous reports of the re-ranking of animals in terms of RFI when offered differing diets over their lifetime. However, we have identified biological processes such as the immune response and lipid metabolism as potentially associated with RFI divergence emphasising the previously reported roles of these biological processes with respect to RFI.
  • Short communication: Effects of changing teatcup removal and vacuum settings on milking efficiency of an automatic milking system

    Upton, John; Bolona, P. Silva; Reinemann, D. J.; Teagasc Wash Fellowship Programme; University of Wisconsin-Madison; Lely, The Netherlands (Elsevier, 2019-08-22)
    The aim of this experiment was to assess strategies to reduce milking time in a pasture-based automatic milking system (AMS). Milking time is an important factor in automatic milking because any reductions in box time can facilitate more milkings per day and hence higher production levels per AMS. This study evaluated 2 end-of-milking criteria treatments (teatcup removal at 30% and 50% of average milk flowrate at the quarter-level), 2 milking system vacuum treatments (static and dynamic, where the milking system vacuum could change during the peak milk flowrate period), and the interaction of these treatment effects on milking time in a Lely Astronaut A4 AMS (Maassluis, the Netherlands). The experiment was carried out at the research facility at Teagasc Moorepark, Cork, Ireland, and used 77 spring-calved cows, which were managed on a grass-based system. Cows were 179 DIM, with an average parity of 3. No significant differences in milk flowrate, milk yield, box time, milking time, or milking interval were found between treatments in this study on cows milked in an AMS on a pasture-based system. Average and peak milk flowrates of 2.15 kg/min and 3.48 kg/min, respectively, were observed during the experiment. Small increases in maximum milk flowrate were detected (+0.09 kg/min) due to the effect of increasing the system vacuum during the peak milk flow period. These small increases in maximum milk flowrate were not sufficient to deliver a significant reduction in milking time or box time. Furthermore, increasing the removal setting from 30% of the average milk flowrate to 50% of the average milk flowrate was not an effective means of reducing box time, because the resultant increase in removal flowrate of 0.12 kg/min was not enough to deliver practical or statistically significant decreases in milking time or box time. Hence, to make significant reductions in milking time, where cows have an average milk flow of 2 kg/min and yield per milking of 10 kg, end-of-milking criteria above 50% of average milk flowrate at the quarter level would be required.
  • Infrared thermography as a tool to detect hoof lesions in sheep

    Byrne, Daire T; Berry, Donagh; Esmonde, Harold; McGovern, Fiona; Creighton, Philip; McHugh, Noirin; Department of Agriculture, Food, and the Marine; RSF 11/S/133 (Oxford University Press (OUP), 2018-12-08)
    Lameness has a major negative impact on sheep production. The objective of this study was to 1) quantify the repeatability of sheep hoof temperatures estimated using infrared thermography (IRT); 2) determine the relationship between ambient temperature, sheep hoof temperature, and sheep hoof health status; and 3) validate the use of IRT to detect infection in sheep hooves. Three experiments (a repeatability, exploratory, and validation experiment) were conducted over 10 distinct nonconsecutive days. In the repeatability experiment, 30 replicate thermal images were captured from each of the front and back hooves of nine ewes on a single day. In the exploratory experiment, hoof lesion scores, locomotion scores, and hoof thermal images were recorded every day from the same cohort of 18 healthy ewes in addition to a group of lame ewes, which ranged from one to nine ewes on each day. Hoof lesion and locomotion scores were blindly recorded by three independent operators. In the validation experiment, all of the same procedures from the exploratory experiment were applied to a new cohort of 40 ewes across 2 d. The maximum and average temperature of each hoof was extracted from the thermal images. Repeatability of IRT measurements was assessed by partitioning the variance because of ewe and error using mixed models. The relationship between ambient temperature, hoof temperature, and hoof health status was quantified using mixed models. The percentage of hooves correctly classified as healthy (i.e., specificity) and infected (i.e., sensitivity) was calculated for a range of temperature thresholds. Results showed that a small-to-moderate proportion of the IRT-estimated temperature variability in a given hoof was due to error (1.6% to 20.7%). A large temperature difference (8.5 °C) between healthy and infected hooves was also detected. The maximum temperature of infected hooves was unaffected by ambient temperature (P > 0.05), whereas the temperature of healthy hooves was associated with ambient temperature. The best sensitivity (92%) and specificity (91%) results in the exploratory experiment were observed when infected hooves were defined as having a maximum hoof temperature ≥9 °C above the average of the five coldest hooves in the flock on that day. When the same threshold was applied to the validation dataset, a sensitivity of 77% and specificity of 78% was achieved, indicating that IRT could have the potential to detect infection in sheep hooves.
  • Residual feed intake phenotype and gender affect the expression of key genes of the lipogenesis pathway in subcutaneous adipose tissue of beef cattle

    McKenna, Clare; Porter, Richard K.; Keogh, Kate; Waters, Sinead M.; McGee, Mark; Kenny, David A.; Teagasc Walsh Fellowship Programme; RMIS: 6092 (Springer Science and Business Media LLC, 2018-09-20)
    Background Feed accounts for up to 75% of costs in beef production systems, thus any improvement in feed efficiency (FE) will benefit the profitability of this enterprise. Residual feed intake (RFI) is a measure of FE that is independent of level of production. Adipose tissue (AT) is a major endocrine organ and the primary metabolic energy reservoir. It modulates a variety of processes related to FE such as lipid metabolism and glucose homeostasis and thus measures of inter-animal variation in adiposity are frequently included in the calculation of the RFI index. The aim of this study was to determine the effect of phenotypic RFI status and gender on the expression of key candidate genes related to processes involved in energy metabolism within AT. Dry matter intake (DMI) and average daily gain (ADG) were measured over a period of 70 d for 52 purebred Simmental heifers (n = 24) and bulls (n = 28) with an initial BW±SD of 372±39.6 kg and 387±50.6 kg, respectively. Residual feed intake was calculated and animals were ranked within gender by RFI into high (inefficient; n = 9 heifers and n = 8 bulls) and low (efficient; n = 9 heifers and n = 8 bulls) groups. Results Average daily gain ±SD and daily DMI ±SD for heifers and bulls were 1.2±0.4 kg and 9.1±0.5 kg, and 1.8±0.3 kg and 9.5±1 kg respectively. High RFI heifers and bulls consumed 10% and 15% more (P < 0.05) than their low RFI counterparts, respectively. Heifers had a higher expression of all genes measured than bulls (P < 0.05). A gender × RFI interaction was detected for HMGCS2(P < 0.05) in which high RFI bulls tended to have lower expression of HMGCS2 than low RFI bulls (P < 0.1), whereas high RFI heifers had higher expression than low RFI heifers (P < 0.05) and high RFI bulls (P < 0.05). SLC2A4 expression was consistently higher in subcutaneous AT of low RFI animals across gender. Conclusion The findings of this study indicate that low RFI cattle exhibit upregulation of the molecular mechanisms governing glucose metabolism in adipose tissue, in particular, glucose clearance. The decreased expression of SLC2A4 in the inefficient cattle may result in less efficient glucose metabolism in these animals. We conclude that SLC2A4 may be a potential biomarker for RFI in cattle.
  • Variance components for bovine tuberculosis infection and multi-breed genome-wide association analysis using imputed whole genome sequence data

    Ring, S. C.; Purfield, D. C.; Good, M.; Breslin, P.; Ryan, E.; Blom, A.; Evans, R. D.; Doherty, M. L.; Bradley, D. G.; Berry, Donagh; et al. (Public Library of Science (PLoS), 2019-02-14)
    Bovine tuberculosis (bTB) is an infectious disease of cattle generally caused by Mycobacterium bovis, a bacterium that can elicit disease humans. Since the 1950s, the objective of the national bTB eradication program in Republic of Ireland was the biological extinction of bTB; that purpose has yet to be achieved. Objectives of the present study were to develop the statistical methodology and variance components to undertake routine genetic evaluations for resistance to bTB; also of interest was the detection of regions of the bovine genome putatively associated with bTB infection in dairy and beef breeds. The novelty of the present study, in terms of research on bTB infection, was the use of beef breeds in the genome-wide association and the utilization of imputed whole genome sequence data. Phenotypic bTB data on 781,270 animals together with imputed whole genome sequence data on 7,346 of these animals’ sires were available. Linear mixed models were used to quantify variance components for bTB and EBVs were validated. Within-breed and multi-breed genome-wide associations were undertaken using a single-SNP regression approach. The estimated genetic standard deviation (0.09), heritability (0.12), and repeatability (0.30) substantiate that genetic selection help to eradicate bTB. The multi-breed genome-wide association analysis identified 38 SNPs and 64 QTL regions associated with bTB infection; two QTL regions (both on BTA23) identified in the multi-breed analysis overlapped with the within-breed analyses of Charolais, Limousin, and Holstein-Friesian. Results from the association analysis, coupled with previous studies, suggest bTB is controlled by an infinitely large number of loci, each having a small effect. The methodology and results from the present study will be used to develop national genetic evaluations for bTB in the Republic of Ireland. In addition, results can also be used to help uncover the biological architecture underlying resistance to bTB infection in cattle.

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