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

  • Mitigation of greenhouse gas emissions from beef cattle production systems

    Samsonstuen, Stine; Åby, Bente A.; Crosson, Paul; Beauchemin, Karen A.; Aass, Laila; Norwegian University of Life Sciences (Informa UK Limited, 2020-08-27)
    The whole-farm model HolosNorBeef was used to estimate the efficiency of GHG emission mitigation strategies in Norwegian beef cattle herds. Various mitigation scenarios, involving female reproductive performance (i.e. calf mortality rate and the number of calves produced per cow per year), production efficiency of young bulls for slaughter (i.e. age at slaughter and carcass weight), and supplementation of an inhibitor currently reported as promising for enteric methane (CH4) inhibition (3-nitrooxypropanol; 3-NOP) was investigated in herds of British and Continental breeds. Reducing calf mortality and increasing the number of produced calves per cow per year both reduced emission intensities by 3% across breeds. Continental breeds showed greater potential of reducing emission intensities due to increased carcass production. Combining mitigation options in a best case scenario reduced the total emissions by 11.7% across breeds. The emission intensities could be further reduced by 8.3% with the use of 3-NOP.
  • Fertility of frozen sex-sorted sperm at 4 × 106 sperm per dose in lactating dairy cows in seasonal-calving pasture-based herds

    Maicas, C.; Holden, S.A.; Drake, E.; Cromie, A.R.; Lonergan, P.; Butler, S.T.; Irish Dairy Levy Trust; Munster Bovine; Meat Industry Ireland; Glanbia; et al. (American Dairy Science Association, 2020-01)
    The objective was to evaluate the reproductive performance of frozen sex-sorted sperm at 4 × 106 sperm per dose (SexedULTRA 4M, Sexing Technologies, Navasota, TX) relative to frozen conventional sperm in seasonal-calving pasture-based dairy cows. Semen from Holstein-Friesian (n = 8) and Jersey (n = 2) bulls was used. Four of the Holstein bulls used were resident at or near a sex-sorting laboratory (Cogent, UK, or ST Benelux, the Netherlands). The remaining 6 bulls were located at studs in Ireland. For these 6 bulls, ejaculates were collected, diluted with transport medium, and couriered to Cogent in parcel shippers. Transit time from ejaculation to arrival at the sorting laboratory was 6 to 7 h. For all bulls, ejaculates were split and processed to provide frozen conventional sperm (CONV) at 15 × 106 sperm per straw and frozen sex-sorted (SS) sperm at 4 × 106 sperm per straw and used to inseminate lactating dairy cows after spontaneous estrus. Pregnancy diagnosis was performed by ultrasound scanning (n = 7,246 records available for analysis). Generalized linear mixed models were used to examine effects on pregnancy per AI (P/AI) at first artificial insemination, with sperm treatment (CONV vs. SS), bull (n = 10), and treatment × bull interaction as the fixed effects, and herd (n = 142) as a random effect. Overall, P/AI was greater for cows inseminated with CONV than for those inseminated with SS (59.9% vs. 45.5%; 76.0% relative to CONV). This study was not designed to compare resident bulls vs. shipped ejaculates, but the magnitude of the difference between P/AI achieved by CONV and SS was apparently less for resident bulls (60.3% vs. 50.2%) than for shipped ejaculates (58.6% vs. 40.7%). We discovered a treatment × bull interaction for shipped ejaculates (P/AI ranged from 45 to 86% relative to CONV) but not for the resident bulls (P/AI ranged from 81 to 87% relative to CONV). Relative P/AI of SS compared with CONV was greater in cows with high or average fertility potential (76.1% and 78.3%, respectively) than in cows with low fertility potential (58.1%). In 33.1% of the enrolled herds, the P/AI achieved with SS was 90% or more of the P/AI achieved with CONV; this was mainly explained by herds in which SS performed exceptionally well but CONV performed poorly. In conclusion, SS had lower overall P/AI compared with CONV; however, P/AI achieved with SS was dependent on the bull, fertility potential of the cow, and herd. Strategies to improve the P/AI with SS in seasonal-calving pasture-based lactating dairy cows require further research.
  • Validation of an ear tag–based accelerometer system for detecting grazing behavior of dairy cows

    Pereira, G.M.; Heins, B.J.; O'Brien, Bernadette; McDonagh, A.; Lidauer, L.; Kickinger, F.; USDA National Institute of Food and Agriculture; Science Foundation Ireland; 2012-51300-20015 (Elsevier, 2020-02-20)
    The objective of the study was to develop a grazing algorithm for an ear tag–based accelerometer system (Smartbow GmbH, Weibern, Austria) and to validate the grazing algorithm with data from a noseband sensor. The ear tag has an acceleration sensor, a radio chip, and temperature sensor for calibration and it can monitor rumination and detect estrus and localization. To validate the ear tag, a noseband sensor (RumiWatch, Itin and Hoch GmbH, Liestal, Switzerland) was used. The noseband sensor detects pressure and acceleration patterns, and, with a software program specific to the noseband, pressure and acceleration patterns are used to classify data into eating, ruminating, drinking, and other activities. The study was conducted at the University of Minnesota West Central Research and Outreach Center (Morris, MN) and at Teagasc Animal and Grassland Research and Innovation Centre (Moorepark, Fermoy, Co. Cork, Ireland). During May and June 2017, observational data from Minnesota and Ireland were used to develop the grazing algorithm. During September 2018, data were collected by the ear tag and noseband sensor from 12 crossbred cows in Minnesota for a total of 248 h and from 9 Holstein-Friesian cows in Ireland for a total of 248 h. A 2-sided t-test was used to compare the percentage of grazing and nongrazing time recorded by the ear tag and the noseband sensor. Pearson correlations and concordance correlation coefficients (CCC) were used to evaluate associations between the ear tag and noseband sensor. The percentage of total grazing time recorded by the ear tag and by the noseband sensor was 37.0% [95% confidence interval (CI): 32.1 to 42.0] and 40.5% (95% CI: 35.5 to 45.6), respectively, in Minnesota, and 35.4% (95% CI: 30.6 to 40.2) and 36.9% (95% CI: 32.1 to 41.8), respectively, in Ireland. The ear tag and noseband sensor agreed strongly for monitoring grazing in Minnesota (r = 0.96; 95% CI: 0.94 to 0.97, CCC = 0.95) and in Ireland (r = 0.92; 95% CI: 0.90 to 0.94, CCC = 0.92). The results suggest that there is potential for the ear tag to be used on pasture-based dairy farms to support management decision-making.
  • An assessment of the production, reproduction, and functional traits of Holstein-Friesian, Jersey × Holstein-Friesian, and Norwegian Red × (Jersey × Holstein-Friesian) cows in pasture-based systems

    McClearn, B.; Delaby, L.; Gilliland, T.J.; Guy, C.; Dineen, M.; Coughlan, F.; Buckley, Frank; McCarthy, B.; Dairy Research Ireland; Teagasc Walsh Scholarship Programme (Elsevier, 2020-04-03)
    Pasture-based production systems typically require highly fertile, healthy, and robust genetics, with greater emphasis on milk solids (MSo; kg of fat + protein) production as opposed to milk yield. This study assessed milk production, production efficiency, reproductive performance, body weight (BW), body condition score, and functional traits in 3 different dairy cow genotypes: Holstein-Friesian (HF), Jersey × Holstein-Friesian (JEX), and Norwegian Red × (Jersey × Holstein- Friesian) (3-way). The 3 genotypes were rotationally grazed on 4 different grazing treatments after calving in spring and were stocked at a rate of 2.75 cows/ha. Holstein-Friesian cows produced higher daily and total milk yields compared with JEX and 3-way cows (5,718 vs. 5,476 and 5,365 kg/cow, respectively). However, JEX and 3-way cows had higher milk fat and protein contents (4.86 and 4.75%, respectively, for JEX and 3.87 and 3.88%, respectively, for 3-way) compared with HF (4.52 and 3.72%), resulting in similar MSo yield for JEX and HF (469 and 460 kg/cow) and slightly lower MSo yield for 3-way (453 kg/cow) compared with JEX. As parity increased, milk and MSo yield per cow increased. Reproductive performance was not significantly different between the 3 genotypes, which had similar 24-d submission rates, 6-wk pregnancy rates, and overall pregnancy rates over the 4-yr period. No difference in calving difficulty, incidence of mastitis, or incidence of lameness was observed among the 3 genotypes. Body weight was significantly different among all 3 genotypes, with HF being the heaviest followed by 3-way and JEX (530, 499, and 478 kg, respectively), and 3-way cows had a higher body condition score throughout lactation compared with HF and JEX cows. The differences in BW coupled with similar MSo production resulted in JEX cows having the highest production efficiency (4.58 kg of MSo/kg of metabolic BW), 3-way cows being intermediate (4.30 kg of MSo/kg of metabolic BW), and HF cows having the lowest (4.16 kg of MSo/kg of metabolic BW). In conclusion, HF herds with poor reproductive performance and low milk fat and protein contents are likely to benefit considerably from crossbreeding with Jersey, and all herds are likely to benefit in terms of production efficiency. However, where herd performance, particularly in relation to reproductive performance, is comparable with HF in the current study, crossbreeding with Jersey or Norwegian Red is unlikely to lead to significant improvements in overall herd performance.
  • Variability in greenhouse gas emission intensity of semi-intensive suckler cow beef production systems

    Samsonstuen, Stine; Åby, Bente A.; Crosson, Paul; Beauchemin, Karen A.; Wetlesen, Marit S.; Bonesmo, Helge; Aass, Laila; Norwegian University of Life Sciences and Department of Animal and Aquacultural Sciences; The Agriculture and Food Industry Research Funds; Geno Breeding and AI Association; et al. (Elsevier BV, 2020-09)
    Emission intensities from beef production vary both among production systems (countries) and farms within a country depending upon use of natural resources and management practices. A whole-farm model developed for Norwegian suckler cow herds, HolosNorBeef, was used to estimate GHG emissions from 27 commercial beef farms in Norway with Angus, Hereford, and Charolais cattle. HolosNorBeef considers direct emissions of methane (CH4), nitrous oxide (N2O) and carbon dioxide (CO2) from on-farm livestock production and indirect N2O and CO2 emissions associated with inputs used on the farm. The corresponding soil carbon (C) emissions are estimated using the Introductory Carbon Balance Model (ICBM). The farms were distributed across Norway with varying climate and natural resource bases. The estimated emission intensities ranged from 22.5 to 45.2 kg CO2 equivalents (eq) (kg carcass)−1. Enteric CH4 was the largest source, accounting for 44% of the total GHG emissions on average, dependent on dry matter intake (DMI). Soil C was the largest source of variation between individual farms and accounted for 6% of the emissions on average. Variation in GHG intensity among farms was reduced and farms within region East, Mid and North re-ranked in terms of emission intensities when soil C was excluded. Ignoring soil C, estimated emission intensities ranged from 21.5 to 34.1 kg CO2 eq (kg carcass)−1. High C loss from farms with high initial soil organic carbon (SOC) content warrants further examination of the C balance of permanent grasslands as a potential mitigation option for beef production systems.
  • Quality indices and sensory attributes of beef from steers offered grass silage and a concentrate supplemented with dried citrus pulp

    Salami, Saheed A.; O'Grady, Michael N.; Luciano, Giuseppe; Priolo, Alessandro; McGee, Mark; Moloney, Aidan; Kerry, Joseph P.; European Union; Department of Agriculture, Food and the Marine; 11/S/122, FEFAN (Elsevier BV, 2020-10)
    This study investigated the quality composition, oxidative stability and sensory attributes of beef (longissimus thoracis, LT) from steers offered grass silage and a concentrate supplement in which barley was replaced by 40% and 80% (as-fed basis) of dried citrus pulp (DCP). Dietary treatment did not influence the antioxidant status (α-tocopherol and total phenolic contents) and activities of LT (radical scavenging activity, ferric reducing antioxidant power and iron chelating activity). Feeding DCP significantly increased the proportion of conjugated linoleic acids and polyunsaturated fatty acids in beef. Lipid and colour stability of fresh beef patties stored in modified atmosphere packs (MAP) were unaffected by dietary treatment but feeding 40% DCP reduced (P < .05) lipid oxidation in aerobically-stored cooked beef patties. Beef patties stored in MAP for up to 7 days were assessed by sensory panellists to be juicier for those fed 40% DCP compared to 0% and 80% DCP. Results indicated that substitution of barley with DCP improved the fatty acid profiles of beef without negatively influencing the eating quality of beef.
  • Effect of thermoresistant protease of Pseudomonas fluorescens on rennet coagulation properties and proteolysis of milk

    Paludetti, Lizandra F.; Kelly, Alan L.; Gleeson, David; Teagasc Walsh Fellowship Program; Dairy Levy project (American Dairy Science Association, 2020-05)
    This study aimed to investigate the effect of different activity levels of a thermoresistant protease, produced by Pseudomonas fluorescens (ATCC 17556), on the cheesemaking properties of milk and proteolysis levels. Sterilized reconstituted skim milk powder was inoculated with the bacteria, and after incubation, centrifuged to obtain a supernatant-containing protease. Raw milk was collected and inoculated to obtain a protease activity of 0.15, 0.60, and 1.5 U/L of milk (treatments P1, P4, and P10, respectively). One sample was not inoculated (control) and noninoculated supernatant was added to a fifth sample to be used as a negative control. Samples were stored at 4°C for 72 h. After 0, 48, and 72 h, the rennet coagulation properties and proteolysis levels were assessed. The protease produced was thermoresistant, as no significant differences were observed in the activity in the pasteurized (72°C for 15 s) and nonpasteurized supernatants. The chromatograms and electrophoretograms indicated that the protease preferably hydrolyzed κ-casein and β-casein, and levels of proteolysis increased with added protease activity over storage time. The hydrolysis of αS-caseins and major whey proteins increased considerably in P10 milk samples. At 0 h, the increase in the level of protease activity decreased the rennet coagulation time (RCT, min) of the samples, possibly due to synergistic proteolysis of κ-casein into para-κ-casein. However, over prolonged storage, hydrolysis of β-casein and αS-casein increased in P4 and P10 samples. The RCT of P4 samples increased over time and the coagulum became softer, whereas P10 samples did not coagulate after 48 h of storage. In contrast, the RCT of P1 samples decreased over time and a firmer coagulum was obtained, possibly due to a lower rate of hydrolysis of β-casein and αS-casein. Increased levels of protease could result in further hydrolysis of caseins, affecting the processability of milk over storage time.
  • An economic comparison of pasture-based production systems differing in sward type and cow genotype

    McClearn, B.; Shalloo, L.; Gilliland, T.J.; Coughlan, F.; McCarthy, B.; Dairy Research Ireland; Teagasc Walsh Fellowship Programme (American Dairy Science Association, 2020-05)
    The objective of this study was to compare the economic performance of 2 sward types [perennial ryegrass (PRG; Lolium perenne L.) sown with or without white clover (Trifolium repens L.)] grazed by 3 cow genotypes. Physical performance data were collected from a 4-yr systems experiment based at Clonakilty Agricultural College, Clonakilty, Co. Cork, Ireland. The experiment compared 2 sward types (PRG-only swards and PRG–white clover swards), with each sward type being grazed by cows from 3 genotypes [Holstein-Friesian (HF), Jersey × HF (JEX), and Norwegian Red × JEX (3-way)]. All systems were stocked at 2.75 cows/ha with fixed fertilizer applications and concentrate supplementation. The data supplied 6 production systems (2 sward types × 3 cow genotypes). The production systems were modeled using the Moorepark Dairy Systems Model (stochastic budgetary simulation model) under 2 scenarios, one in which land area was fixed and one in which cow numbers were fixed. The analysis was completed across a range of milk prices, calf prices, and reseeding programs. The analysis showed that in the fixed-land scenario with a milk price of €0.29/L, adding white clover to PRG swards increased profitability by €305/ha. In the same fixed-land scenario, JEX cows were most profitable (€2,606/ha), followed by 3-way (€2,492/ha) and HF (€2,468/ha) cows. In the fixed-cow scenario, net profit per cow was €128 greater for PRG–white clover swards compared with PRG-only swards. In this scenario, JEX was the most profitable per cow (€877), followed by HF (€855) and 3-way (€831). The system that produced the highest net profit was JEX cows grazing PRG–white clover swards (€2,751/ha). Regardless of reseeding frequency or variations in calf value, JEX cows grazing PRG–white clover swards consistently produced the highest net profit per hectare.
  • Invited review: Cattle lameness detection with accelerometers

    O'Leary, N.W.; Byrne, D.T.; O'Connor, A.H.; Shalloo, Laurence; Science Foundation Ireland; 13/IA/1977 (American Dairy Science Association, 2020-05)
    Locomotion scoring is time consuming and is not commonly completed on farms. Farmers also underestimate their herds' lameness prevalence, a knowledge gap that impedes lameness management. Automation of lameness detection could address this knowledge gap and facilitate improved lameness management. The literature pertinent to adding lameness detection to accelerometers is reviewed in this paper. Options for lameness detection systems are examined including the choice of sensor, raw data collected, variables extracted, and statistical classification methods used. Two categories of variables derived from accelerometer-based systems are examined. These categories are behavior measures such as lying and measures of gait. For example, one measure of gait is the time a leg is swinging during a gait cycle. Some behavior-focused studies have reported accuracy levels of greater than 80%. Cow gait measures have been investigated to a lesser extent than behavior. However, classification accuracies as high as 91% using gait measures have been reported with hardware likely to be practical for commercial farms. The need for even higher accuracy and potential barriers to adoption are discussed. Significant progress is still required to realize a system with sufficient specificity and sensitivity. Lameness detection systems using 1 accelerometer per cow and a resolution lower than 100 Hz with gait measurement functions are suggested to balance cost and data requirements. However, gait measurement using accelerometers is rather underdeveloped. Therefore, a high priority should be given to the development of novel gait measures and testing their ability to differentiate lame from nonlame cows.
  • Effects of simulated quarter and udder teat cup removal settings on strip milk and milking duration in dairy cows

    Boloña, P. Silvia; Upton, J.; Reinemann, D. J.; Teagasc Walsh Fellowship Programme; University of Wisconsin-Madison (Elsevier, 2020-02-26)
    The aim of this study was to estimate the amount of milk left in quarters and udders and the milking duration for a variety of teat cup removal strategies. A combination of empirical data and simulated quarter and udder teat cup removal settings were used to make these estimates. Milking duration is an important factor in both automatic and conventional milking systems because it directly influences milking efficiency and hence can affect farm profitability. Strategies investigated in the literature to reduce milking duration include the application of different milk flow rate switch-points (milk flow rate at which the milking unit or teat cup is removed). Applying these milk flow rate switch-points can affect the amount of milk that is not harvested (strip milk). We are not aware of previous research analyzing strip milk yield and milking duration at the quarter level, across a range of quarter and udder milk flow rate switch-points. Quarter-level average milking duration decreased by 2 min, and strip milk increased 1.3 kg as quarter milk flow rate switch-point was increased from 0.2 kg/min to 1.0 kg/min. Using an end of milking criterion of removal of the teat cup at 50% of the quarter's rolling average milk flow rate resulted in a 0.4-min reduction in milking duration and a 0.08-kg increase in strip milk per quarter, compared with removal of the teat cup at 30% of the quarter's rolling average milk flow rate. Udder-level average milking duration decreased by 1.4 min, and strip milk increased by 0.76 kg (0.19 kg per quarter) as udder milk flow rate switch-point was increased from 0.2 kg/min to 1.0 kg/min. A 0.8-min reduction in cow milking duration and a 0.27-kg increase in strip milk at the udder level (0.08 kg per quarter) resulted when changing udder milk flow rate switch-point from 30% of the udder rolling average to 50% of the udder rolling average milk flow rate. This study provides quantitative estimates of the effect of teat cup milk flow rate switch-points on milking duration and strip milk yield.
  • 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, 2021-04-15)
    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.

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