• Labour efficiency on-farm

      O'Brien, Bernadette; Gleeson, David E; O'Donovan, K.; Ruane, D.; Kinsella, J.; Mee, John F; Boyle, Laura; McNamara, John G. (Teagasc, 2007-01-01)
      Improvements in milking efficiency have a greater influence than any other aspect of the dairy farmers work on overall farm labour inputs (Whipp, 1992). In order to facilitate the examination of milking process labour inputs, the milking process may be divided into the following three components: herding pre and post milking (transfer of cows to and from the milking parlour); milking (milking tasks / work routines within the parlour); and washing (washing of milking machine and yard). Meanwhile, within milking specifically, the number of cows milked per operator per hour is the best measure of both the performance of the operator and the milking installation (Clough, 1978). This is affected by the following three factors: the milking times of the cows, the number and arrangement of the milking units, and the operator’s work routine (Whipp, 1992). The addition of extra milking units will only increase milking performance if the operator has idle time during milking (Hansen, 1999).
    • Land Drainage - A farmer’s practical guide to draining grassland in Ireland

      Tuohy, Patrick; Fenton, Owen; O'Loughlin, James; Humphreys, James (Teagasc, 30/07/2013)
      No drainage work should be carried out before the drainage characteristics of the soil are established by a site and soil test pit investigation. • Two types of drainage system exist: a groundwater drainage system and a shallow drainage system. The design of the system depends entirely on the drainage characteristics of the soil. • Distinguishing between the two types of drainage systems essentially comes down to whether or not a permeable layer is present (at a workable depth) that will allow the flow of water with relative ease. If such a layer is evident, a piped drain system at that depth is likely to be effective. If no such layer is found during soil test pit investigations, it will be necessary to improve the drainage capacity of the soil. This involves a disruption technique such as moling, gravel moling or subsoiling in tandem with collector drains. • Drains are not effective unless they are placed in a free draining soil layer or complimentary measures (mole drainage, subsoiling) are used to improve soil drainage capacity. If water is not moving through the soil in one or other of these two ways, the water table will not be lowered. • Outfall level must not dictate the drainage system depth. If a free draining layer is present, it must be utilised. • Drain pipes should always be used for drains longer than 30 m. If these get blocked it is a drainage stone and not a drainage pipe issue. • Drainage stone should not be filled to the top of the field trench except for very limited conditions (the bottom of an obvious hollow). Otherwise it is an extremely expensive way of collecting little water. • Most of the stone being used for land drainage today is too big. Clean aggregate in the 10–40 mm (0.4 to 1.5 inch approx) grading band should be used. Generally you get what you pay for. • Subsoiling is not effective unless a shallow impermeable layer is being broken or field drains have been installed prior to the operation. Otherwise it will not have any long-term effect and may do more harm than good. • Most land drainage systems are poorly maintained. Open drains should be clean and as deep as possible and field drains feeding into them should be regularly rodded or jetted.
    • Linkage between predictive transmitting ability of a genetic index, potential milk production, and a dynamic model

      Ruelle, Elodie; Delaby, Luc; Shalloo, Laurence; Department of Agriculture, Food and the Marine; 11/S/132 (Elsevier, 2019-01-26)
      With the increased use of information and communication technology–based tools and devices across traditional desktop computers and smartphones, models and decision-support systems are becoming more accessible for farmers to improve the decision-making process at the farm level. However, despite the focus of research and industry providers to develop tools that are easy to adopt by the end user, milk-production prediction models require substantial parameterization information for accurate milk production simulations. For these models to be useful at an individual animal level, they require the potential milk yield of the individual animals (and possibly potential fat and protein yields) to be captured and parameterized within the model to allow accurate simulations of the interaction of the animal with the system. The focus of this study was to link 3 predicted transmitting ability (PTA) traits from the Economic Breeding Index (PTA for milk yield, fat, and protein) with potential index parameters for milk, fat, and protein required as inputs to a herd-based dynamic milk model. We compiled a data set of 1,904 lactations that included different experiments conducted at 2 closed sites during a 14-yr period (2003–2016). The treatments implied different stocking rates, concentrate supplementation levels, calving dates, and genetic potential. The first step, using 75% of the data randomly selected, was to link the milk, fat, and protein yields achieved within each lactation to their respective PTA value, stocking rate, parity, and concentrate supplementation level. The equations generated were transformed to correspond to inputs to the pasture-based herd dynamic milk model. The equations created were used in conjunction with the model to predict milk, fat, and protein production. Then, using the remaining 25% data of the data set, the simulations were compared against the actual milk produced during the experiments. When the model was tested, it was capable of predicting the lactation milk, fat, and protein yield with a relative prediction error of <10% at the herd level and <13% at the individual animal level.
    • Linking Hydro-Geophysics and Remote Sensing Technology for Sustainable Water and Agricultural Catchment Management

      O'Leary, Dave; Fenton, Owen; Mellander, Per-Erik; Tuohy, Patrick; Brown, C.; Daly, E. (2019-05)
      The acquisition of sub-surface data for agricultural purposes is traditionally achieved by in situ point sampling in the top 2m over limited target areas (farm scale ~ km2) and time periods. This approach is inadequate for integrated regional (water catchment ~ 100 km2) scale management strategies which require an understanding of processes varying over decadal time scales in the transition zone (~ 10’s m) from surface to bedrock. With global food demand expected to increase by 100% by 2050, there are worldwide concerns that achievement of production targets will be at the expense of water quality. In order to overcome the limitations of the traditional approach, this research programme will combine airborne and ground geophysics with remote sensing technologies to access hydrogeological and soil structure information on Irish Soils at multiple spatial scales. It will address this problem in the context of providing tools for the sustainable management of agricultural intensification envisioned in Food Harvest 2020 and Food Wise 2025 and considering the EU Habitats and Water Framework Directives (WFD), Clean Air Policy and Soil Thematic Strategies. The work will use existing ground based geophysical and hydrogeological data from Teagasc Agricultural Catchment Programme (ACP) and Heavy Soil sites co-located ground and airborne electromagnetic data. Neural Networks training and Machine learning approaches will supplement traditional geophysical workflows. Work will then focus on upscaling results from ACP to WFD catchment scale. This upscaling will require modification of traditional satellite remote sensing conceptual frameworks to analyse heterogeneous, multi-temporal data streams.
    • Live animal measurements, carcass composition and plasma hormone and metabolite concentrations in male progeny of sires differing in genetic merit for beef production

      Clarke, Anne Marie; Drennan, Michael J; McGee, Mark; Kenny, David A.; Evans, R. D.; Berry, Donagh (Cambridge University Press, 2009-07)
      In genetic improvement programmes for beef cattle, the effect of selecting for a given trait or index on other economically important traits, or their predictors, must be quantified to ensure no deleterious consequential effects go unnoticed. The objective was to compare live animal measurements, carcass composition and plasma hormone and metabolite concentrations of male progeny of sires selected on an economic index in Ireland. This beef carcass index (BCI) is expressed in euros and based on weaning weight, feed intake, carcass weight and carcass conformation and fat scores. The index is used to aid in the genetic comparison of animals for the expected profitability of their progeny at slaughter. A total of 107 progeny from beef sires of high (n = 11) or low (n = 11) genetic merit for the BCI were compared in either a bull (slaughtered at 16 months of age) or steer (slaughtered at 24 months of age) production system, following purchase after weaning (8 months of age) from commercial beef herds. Data were analysed as a 2 × 2 factorial design (two levels of genetic merit by two production systems). Progeny of high BCI sires had heavier carcasses, greater (P < 0.01) muscularity scores after weaning, greater (P < 0.05) skeletal scores and scanned muscle depth pre-slaughter, higher (P < 0.05) plasma insulin concentrations and greater (P < 0.01) animal value (obtained by multiplying carcass weight by carcass value, which was based on the weight of meat in each cut by its commercial value) than progeny of low BCI sires. Regression of progeny performance on sire genetic merit was also undertaken across the entire data set. In steers, the effect of BCI on carcass meat proportion, calculated carcass value (c/kg) and animal value was positive (P < 0.01), while a negative association was observed for scanned fat depth pre-slaughter and carcass fat proportion (P < 0.01), but there was no effect in bulls. The effect of sire expected progeny difference (EPD) for carcass weight followed the same trends as BCI. Muscularity scores, carcass meat proportion and calculated carcass value increased, whereas scanned fat depth, carcass fat and bone proportions decreased with increasing sire EPD for conformation score. The opposite association was observed for sire EPD for fat score. Results from this study show that selection using the BCI had positive effects on live animal muscularity, carcass meat proportion, proportions of high-value cuts and carcass value in steer progeny, which are desirable traits in beef production.
    • Machine learning algorithms for the prediction of conception success to a given insemination in lactating dairy cows

      Henpstalk, K.; McParland, Sinead; Berry, Donagh; European Commission (Elsevier for American Dairy Science Association, 2015-06)
      The ability to accurately predict the conception outcome for a future mating would be of considerable benefit for producers in deciding what mating plan (i.e., expensive semen or less expensive semen) to implement for a given cow. The objective of the present study was to use herd- and cow-level factors to predict the likelihood of conception success to a given insemination (i.e., conception outcome not including embryo loss); of particular interest in the present study was the usefulness of milk mid-infrared (MIR) spectral data in augmenting the accuracy of the prediction model. A total of 4,341 insemination records with conception outcome information from 2,874 lactations on 1,789 cows from 7 research herds for the years 2009 to 2014 were available. The data set was separated into a calibration data set and a validation data set using either of 2 approaches: (1) the calibration data set contained records from all 7 farms for the years 2009 to 2011, inclusive, and the validation data set included data from the 7 farms for the years 2012 to 2014, inclusive, or (2) the calibration data set contained records from 5 farms for all 6 yr and the validation data set contained information from the other 2 farms for all 6 yr. The prediction models were developed with 8 different machine learning algorithms in the calibration data set using standard 10-times 10-fold cross-validation and also by evaluating in the validation data set. The area under curve (AUC) for the receiver operating curve varied from 0.487 to 0.675 across the different algorithms and scenarios investigated. Logistic regression was generally the best-performing algorithm. The AUC was generally inferior for the external validation data sets compared with the calibration data sets. The inclusion of milk MIR in the prediction model generally did not improve the accuracy of prediction. Despite the fair AUC for predicting conception outcome under the different scenarios investigated, the model provided a reasonable prediction of the likelihood of conception success when the high predicted probability instances were considered; a conception rate of 85% was evident in the top 10% of inseminations ranked on predicted probability of conception success in the validation data set.
    • Manipulation of grass supply to meet feed demand

      French, Padraig; Hennessy, Deirdre; O’Donovan, Michael; Laidlaw, S. (Teagasc, 2006-01-01)
      Grazed grass is generally the cheapest form of feed available for beef and milk production in Ireland. Grass growth is variable during the year with a peak in May/June and a secondary peak in August. There is little net growth from December to February. Grass growth is also variable across the country with higher grass growth in the south and south-west (14 to 15 t DM/ha/year) compared with approximately 11 t DM/ha/year in the north-east (Brereton, 1995). There is poor synchrony between grass supply and feed demand on beef and dairy farms. The feed demand curve for a calf to two year old beef system shows feed demand decreasing as grass supply increases, and grass supply decreasing as feed demand increases. Similarly, the feed demand curve of a spring calving dairy herd shows poor synchrony with grass supply, with a surplus of grass from about mid-April to mid-August, and a deficit for the rest of the year. Traditionally surplus grass produced during May and June is conserved as silage or hay and fed back to cattle and dairy cows during the deficit times of the year.
    • Measuring labor input on pasture-based dairy farms using a smartphone

      Deming, J.; Gleeson, David E; O'Dwyer, T.; O'Brien, Bernadette; Kinsella, J.; Dairy Research Ireland; Teagasc Walsh Fellowship Programme (Elsevier, 2018-07-19)
      With the cessation of milk quotas in the European Union, dairy herd sizes increased in some countries, including Ireland, with an associated increase in labor requirement. Second to feed costs, labor has been identified as one of the highest costs on pasture-based dairy farms. Compared with other European Union countries, Ireland has historically had low milk production per labor unit; thus, optimization of labor efficiency on farm should be addressed before or concurrently with herd expansion. The objective of this study was to quantify current levels of labor input and labor efficiency on commercial pasture-based dairy farms and to identify the facilities and management practices associated with increased labor efficiency. Thirty-eight dairy farms of varying herd sizes, previously identified as labor-efficient farms, were enrolled on the study and data were collected over 3 consecutive days each month over a 12-mo period, starting in May 2015 and finishing in August of 2016. This was achieved through the use of a smartphone application. For analysis purposes, farms were categorized into 1 of 3 herd size categories (HSC): farms with <150 cows (HSC 1), 150–249 cows (HSC 2), or ≥250 cows (HSC 3). Overall farm labor input increased with HSC with 3,015, 4,499, and 6,023 h worked on HSC 1, 2, and 3, respectively. A higher proportion of work was carried out by hired staff as herd size increased. Labor efficiency was measured as total hours input to the dairy enterprise divided by herd size. Labor efficiency improved as herd size increased above 250 cows with 17.3 h/cow per yr observed for HSC 3; labor efficiency was similar for HSC 1 and 2, at 23.8 and 23.3 h/cow per yr, respectively. A large range of efficiency was observed within HSC. The labor requirements had a distinct seasonal pattern across the 3 HSC with the highest input observed in springtime (February to April) primarily due to calving and calf-care duties, milking, and winter feeding. The lowest input was observed in wintertime (November to January) when cows were dry. Particular facilities and management practices were associated with efficiency within certain tasks, the most notable in regard to milking and winter feeding practices. Additionally, the most efficient farms used contractors to perform a higher proportion of machinery work on farm than the least efficient farms.
    • Meat quality characteristics of high dairy genetic-merit Holstein, standard dairy genetic-merit Friesian and Charolais x Holstein-Friesian steers

      McGee, Mark; Keane, M.G.; Neilan, R.; Caffrey, P.J.; Moloney, Aidan P (Teagasc, 2020-03-13)
      The increased use of Holstein genetic material in the Irish dairy herd has consequences for beef production. In all, 42 spring-born steers [14 Holsteins (HO), 14 Friesian (FR) and 14 Charolais × Holstein-Friesian (CH)] were reared to slaughter at between 26 and 37 mo of age. Carcass weight was higher and the lipid concentration of m. longissimus thoracis et lumborum was lower (P < 0.05) for CH than the dairy breeds. Overall acceptability tended to be lower (P = 0.055) while tenderness, texture and chewiness were lower (P < 0.05) for CH compared with the dairy breeds. The proportion of C16:1 in the total lipid tended to be lower (P = 0.055) for CH than the dairy breeds. Replacing male offspring of traditional “Irish” Friesian bulls with offspring from a genetically superior (from a dairy perspective) strain of Holstein bull had no commercially important impact on beef nutritional or eating quality.
    • A mechanistic model for electricity consumption on dairy farms: Definition, validation, and demonstration

      Upton, John; Murphy, Michael D.; Shalloo, Laurence; Groot Koerkamp, Peter W.G.; De Boer, Imke J.M.; INTERREG IVB North-West Europe (Elsevier, 2014-06-07)
      Our objective was to define and demonstrate a mechanistic model that enables dairy farmers to explore the impact of a technical or managerial innovation on electricity consumption, associated CO2 emissions, and electricity costs. We, therefore, (1) defined a model for electricity consumption on dairy farms (MECD) capable of simulating total electricity consumption along with related CO2 emissions and electricity costs on dairy farms on a monthly basis; (2) validated the MECD using empirical data of 1 yr on commercial spring calving, grass-based dairy farms with 45, 88, and 195 milking cows; and (3) demonstrated the functionality of the model by applying 2 electricity tariffs to the electricity consumption data and examining the effect on total dairy farm electricity costs. The MECD was developed using a mechanistic modeling approach and required the key inputs of milk production, cow number, and details relating to the milk-cooling system, milking machine system, water-heating system, lighting systems, water pump systems, and the winter housing facilities as well as details relating to the management of the farm (e.g., season of calving). Model validation showed an overall relative prediction error (RPE) of less than 10% for total electricity consumption. More than 87% of the mean square prediction error of total electricity consumption was accounted for by random variation. The RPE values of the milk-cooling systems, water-heating systems, and milking machine systems were less than 20%. The RPE values for automatic scraper systems, lighting systems, and water pump systems varied from 18 to 113%, indicating a poor prediction for these metrics. However, automatic scrapers, lighting, and water pumps made up only 14% of total electricity consumption across all farms, reducing the overall impact of these poor predictions. Demonstration of the model showed that total farm electricity costs increased by between 29 and 38% by moving from a day and night tariff to a flat tariff.
    • Meta-analysis to investigate relationships between somatic cell count and raw milk composition, Cheddar cheese processing characteristics and cheese composition

      Geary, Una; Lopez-Villalobos, N.; O'Brien, Bernadette; Garrick, Dorian J.; Shalloo, Laurence (Teagasc (Agriculture and Food Development Authority), Ireland, 2013)
      The relationship between elevated somatic cell count (SCC) and raw milk composition, cheese processing and cheese composition, was investigated by meta-analysis using available literature representing 45 scientific articles. With respect to raw milk composition there was a significant positive relationship between SCC and the protein and fat contents and a significant negative relationship between SCC and the lactose content. In relation to cheese processing, there was a significant negative relationship between SCC and recoveries of protein and fat. As SCC increased cheese protein content declined and cheese moisture content increased.
    • A method for assessing liner performance during the peak milk flow period

      Penry, J. F.; Upton, John; Leonardi, S.; Thompson, P. D.; Reinemann, D. J. (Elsevier, 2017-11-06)
      The objective of this study was to develop a method to quantify the milking conditions under which circulatory impairment of teat tissues occurs during the peak flow period of milking. A secondary objective was to quantify the effect of the same milking conditions on milk flow rate during the peak flow rate period of milking. Additionally, the observed milk flow rate was a necessary input to the calculation of canal area, our quantitative measure of circulatory impairment. A central composite experimental design was used with 5 levels of each of 2 explanatory variables (system vacuum and pulsator ratio), creating 9 treatments including the center point. Ten liners, representing a wide range of liner compression (as indicated by overpressure), were assessed, with treatments applied using a novel quarter-milking device. Eight cows (32 cow-quarters) were used across 10 separate evening milkings, with quarter being the experimental unit. The 9 treatments, with the exception of a repeated center point, were randomly applied to all quarters within each individual milking. Analysis was confined to the peak milk flow period. Milk flow rate (MFR) and teat canal cross sectional area (CA) were normalized by dividing individual MFR, or CA, values by their within-quarter average value across all treatments. A multiple explanatory variable regression model was developed for normalized MFR and normalized CA. The methods presented in this paper provided sufficient precision to estimate the effects of vacuum (both at teat-end and in the liner mouthpiece), pulsation, and liner compression on CA, as an indicator of teat-end congestion, during the peak flow period of milking. Liner compression (as indicated by overpressure), teat-end vacuum, vacuum in the liner mouthpiece, milk-phase time, and their interactions are all important predictors of MFR and teat-end congestion during the peak milk flow period of milking. Increasing teat-end vacuum and milk-phase time increases MFR and reduces CA (indicative of increased teat-end congestion). Increasing vacuum in the liner mouthpiece also acts to reduce CA and MFR. Increasing liner compression reduces the effects of teat-end congestion, resulting in increased MFR and increased CA at high levels of teat-end vacuum and milk-phase time. These results provide a better understanding of the balance between milking speed and milking gentleness.
    • Mid-infrared prediction of lactoferrin content in bovine milk: potential indicator of mastitis

      Soyeurt, H.; Bastin, C.; Colinet, F. G.; Arnould, V.M.R; Berry, Donagh; Wall, E.; Dehareng, F.; Nguyen, H. N.; Dardenne, P.; Schefers, J.; et al. (Cambridge University Press, 2012-04-27)
      Lactoferrin (LTF) is a milk glycoprotein favorably associated with the immune system of dairy cows. Somatic cell count is often used as an indicator of mastitis in dairy cows, but knowledge on the milk LTF content could aid in mastitis detection. An inexpensive, rapid and robust method to predict milk LTF is required. The aim of this study was to develop an equation to quantify the LTF content in bovine milk using mid-infrared (MIR) spectrometry. LTF was quantified by enzyme-linked immunosorbent assay (ELISA), and all milk samples were analyzed by MIR. After discarding samples with a coefficient of variation between 2 ELISA measurements of more than 5% and the spectral outliers, the calibration set consisted of 2499 samples from Belgium (n = 110), Ireland (n = 1658) and Scotland (n = 731). Six statistical methods were evaluated to develop the LTF equation. The best method yielded a cross-validation coefficient of determination for LTF of 0.71 and a cross-validation standard error of 50.55 mg/l of milk. An external validation was undertaken using an additional dataset containing 274 Walloon samples. The validation coefficient of determination was 0.60. To assess the usefulness of the MIR predicted LTF, four logistic regressions using somatic cell score (SCS) and MIR LTF were developed to predict the presence of mastitis. The dataset used to build the logistic regressions consisted of 275 mastitis records and 13 507 MIR data collected in 18 Walloon herds. The LTF and the interaction SCS × LTF effects were significant (P < 0.001 and P = 0.02, respectively). When only the predicted LTF was included in the model, the prediction of the presence of mastitis was not accurate despite a moderate correlation between SCS and LTF (r = 0.54). The specificity and the sensitivity of models were assessed using Walloon data (i.e. internal validation) and data collected from a research herd at the University of Wisconsin – Madison (i.e. 5886 Wisconsin MIR records related to 93 mastistis events – external validation). Model specificity was better when LTF was included in the regression along with SCS when compared with SCS alone. Correct classification of non-mastitis records was 95.44% and 92.05% from Wisconsin and Walloon data, respectively. The same conclusion was formulated from the Hosmer and Lemeshow test. In conclusion, this study confirms the possibility to quantify an LTF indicator from milk MIR spectra. It suggests the usefulness of this indicator associated to SCS to detect the presence of mastitis. Moreover, the knowledge of milk LTF could also improve the milk nutritional quality.
    • Mid-infrared spectrometry of milk as a predictor of energy intake and efficiency in lactating dairy cows

      McParland, Sinead; Lewis, Eva; Kennedy, Emer; Moore, Stephen; McCarthy, Brian; O'Donovan, Michael; Butler, Stephen T.; Pryce, J. E.; Berry, Donagh; Department of Agriculture, Food and the Marine, Ireland; et al. (Elsevier for American Dairy Science Association, 2014-09)
      Interest is increasing in the feed intake complex of individual dairy cows, both for management and animal breeding. However, energy intake data on an individual-cow basis are not routinely available. The objective of the present study was to quantify the ability of routinely undertaken mid-infrared (MIR) spectroscopy analysis of individual cow milk samples to predict individual cow energy intake and efficiency. Feed efficiency in the present study was described by residual feed intake (RFI), which is the difference between actual energy intake and energy used (e.g., milk production, maintenance, and body tissue anabolism) or supplied from body tissue mobilization. A total of 1,535 records for energy intake, RFI, and milk MIR spectral data were available from an Irish research herd across 36 different test days from 535 lactations on 378 cows. Partial least squares regression analyses were used to relate the milk MIR spectral data to either energy intake or efficiency. The coefficient of correlation (REX) of models to predict RFI across lactation ranged from 0.48 to 0.60 in an external validation data set; the predictive ability was, however, strongest (REX = 0.65) in early lactation (<60 d in milk). The inclusion of milk yield as a predictor variable improved the accuracy of predicting energy intake across lactation (REX = 0.70). The correlation between measured RFI and measured energy balance across lactation was 0.85, whereas the correlation between RFI and energy balance, both predicted from the MIR spectrum, was 0.65. Milk MIR spectral data are routinely generated for individual cows throughout lactation and, therefore, the prediction equations developed in the present study can be immediately (and retrospectively where MIR spectral data have been stored) applied to predict energy intake and efficiency to aid in management and breeding decisions.
    • 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
    • Milk losses associated with somatic cell counts by parity and stage of lactation

      Gonçalves, Juliano L.; Cue, Roger I.; Botaro, Bruno G.; Horst, José A.; Valloto, Altair A.; Santos, Marcos V.; São Paulo Research Foundation; 2014/17411-6 (Elsevier, 2018-02-15)
      The reduction of milk production caused by subclinical mastitis in dairy cows was evaluated through the regression of test-day milk yield on log-transformed somatic cell counts (LnSCC). Official test-day records (n = 1,688,054) of Holstein cows (n = 87,695) were obtained from 719 herds from January 2010 to December 2015. Editing was performed to ensure both reliability and consistency for the statistical analysis, and the final data set comprised 232,937 test-day records from 31,692 Holstein cows in 243 herds. A segmented regression was fitted to estimate the cutoff point in the LnSCC scale where milk yield started to be affected by mastitis. The statistical model used to explain daily milk yield included the effect of herd as a random effect and days in milk and LnSCC as fixed effects regressions, and analyses were performed by parity and stage of lactation. The cutoff point where milk yield starts to be affected by changes in LnSCC was estimated to be around 2.52 (the average of all estimates of approximately 12,400 cells/mL) for Holsteins cows from Brazilian herds. For first-lactation cows, milk losses per unit increase of LnSCC had estimates around 0.68 kg/d in the beginning of the lactation [5 to 19 d in milk (DIM)], 0.55 kg/d in mid-lactation (110 to 124 DIM), and 0.97 kg/d at the end of the lactation (289 to 304 DIM). For second-lactation cows, milk losses per unit increase of LnSCC had estimates around 1.47 kg/d in the beginning of the lactation (5 to 19 DIM), 1.09 kg/d in mid-lactation (110 to 124 DIM), and 2.45 kg/d at the end of the lactation (289 to 304 DIM). For third-lactation cows, milk losses per unit increase of LnSCC had estimates around 2.22 kg/d in the beginning of the lactation (5 to 19 DIM), 1.13 kg/d in mid-lactation (140 to 154 DIM), and 2.65 kg/d at the end of the lactation (289 to 304 DIM). Daily milk losses caused by increased LnSCC were dependent on parity and stage of lactation, and these factors should be considered when estimating losses associated with subclinical mastitis.
    • Milk mid-infrared spectral data as a tool to predict feed intake in lactating Norwegian Red dairy cows

      Berry, Donagh; Wallen, Sini E.; Prestløkken, E.; Meuwissen, Theodorus H.E.; McParland, Sinead; the Norwegian Research Council; TINE; GENO; 225233/E40 (Elsevier, 2018-03-28)
      Mid-infrared (MIR) spectroscopy of milk was used to predict dry matter intake (DMI) and net energy intake (NEI) in 160 lactating Norwegian Red dairy cows. A total of 857 observations were used in leave-one-out cross-validation and external validation to develop and validate prediction equations using 5 different models. Predictions were performed using (multiple) linear regression, partial least squares (PLS) regression, or best linear unbiased prediction (BLUP) methods. Linear regression was implemented using just milk yield (MY) or fat, protein, and lactose concentration in milk (Mcont) or using MY together with body weight (BW) as predictors of intake. The PLS and BLUP methods were implemented using just the MIR spectral information or using the MIR together with Mcont, MY, BW, or NEI from concentrate (NEIconc). When using BLUP, the MIR spectral wavelengths were always treated as random effects, whereas Mcont, MY, BW, and NEIconc were considered to be fixed effects. Accuracy of prediction (R) was defined as the correlation between the predicted and observed feed intake test-day records. When using the linear regression method, the greatest R of predicting DMI (0.54) and NEI (0.60) in the external validation was achieved when the model included both MY and BW. When using PLS, the greatest R of predicting DMI (0.54) and NEI (0.65) in the external validation data set was achieved when using both BW and MY as predictors in combination with the MIR spectra. When using BLUP, the greatest R of predicting DMI (0.54) in the external validation was when using MY together with the MIR spectra. The greatest R of predicting NEI (0.65) in the external validation using BLUP was achieved when the model included both BW and MY in combination with the MIR spectra or when the model included both NEIconc and MY in combination with MIR spectra. However, although the linear regression coefficients of actual on predicted values for DMI and NEI were not different from unity when using PLS, they were less than unity for some of the models developed using BLUP. This study shows that MIR spectral data can be used to predict NEI as a measure of feed intake in Norwegian Red dairy cattle and that the accuracy is augmented if additional, often available data are also included in the prediction model.
    • Milk production of Holstein-Friesian cows of divergent Economic Breeding Index evaluated under seasonal pasture-based management

      O'Sullivan, Margaret; Horan, Brendan; Pierce, Karina M.; McParland, Sinead; O'Sullivan, Kathleen; Buckley, Frank (Elsevier, 2019-01-03)
      The objective of this study was to validate the effect of genetic improvement using the Irish genetic merit index, the Economic Breeding Index (EBI), on total lactation performance and lactation profiles for milk yield, milk solids yield (fat plus protein; kg), and milk fat, protein, and lactose content within 3 pasture-based feeding treatments (FT) and to investigate whether an interaction exists between genetic group (GG) of Holstein-Friesian and pasture-based FT. The 2 GG were (1) extremely high EBI representative of the top 5% nationally (referred to as the elite group) and (2) representative of the national average EBI (referred to as the NA group). Cows from each GG were randomly allocated each year to 1 of 3 pasture-based FT: control, lower grass allowance, and high concentrate. The effects of GG, FT, year, parity, and the interaction between GG and FT adjusted for calving day of year on milk and milk solids (fat plus protein; kg) production across lactation were studied using mixed models. Cow was nested within GG to account for repeated cow records across years. The overall and stage of lactation-specific responses to concentrate supplementation (high concentrate vs. control) and reduced pasture allowance (lower grass allowance vs. control) were tested. Profiles of daily milk yield, milk solids yield, and milk fat, protein, and lactose content for each week of lactation for the elite and NA groups within each FT and for each parity group within the elite and NA groups were generated. Phenotypic performance was regressed against individual cow genetic potential based on predicted transmitting ability. The NA cows produced the highest milk yield. Milk fat and protein content was higher for the elite group and consequently yield of solids-corrected milk was similar, whereas yield of milk solids tended to be higher for the elite group compared with the NA group. Milk lactose content did not differ between GG. Responses to concentrate supplementation or reduced pasture allowance did not differ between GG. Milk production profiles illustrated that elite cows maintained higher production but with lower persistency than NA cows. Regression of phenotypic performance against predicted transmitting ability illustrated that performance was broadly in line with expectation. The results illustrate that the superiority of high-EBI cattle is consistent across diverse pasture-based FT. The results also highlight the success of the EBI to deliver production performance in line with the national breeding objective: lower milk volume with higher fat and protein content.
    • Milk production per cow and per hectare of spring-calving dairy cows grazing swards differing in Lolium perenne L. ploidy and Trifolium repens L. composition

      McClearn, Bríd; Gilliland, Trevor J.; Delaby, Luc; Guy, Clare; Dineen, Michael; Coughlan, Fergal; McCarthy, Brian; Teagasc Walsh Fellowship Programme; Dairy Research Ireland (Elsevier, 2019-07-10)
      Grazed grass is the cheapest feed available for dairy cows in temperate regions; thus, to maximize profits, dairy farmers must optimize the use of this high-quality feed. Previous research has defined the benefits of including white clover (Trifolium repens L.) in grass swards for milk production, usually at reduced nitrogen usage and stocking rate. The aim of this study was to quantify the responses in milk production of dairy cows grazing tetraploid or diploid perennial ryegrass (Lolium perenne L.; PRG) sown with and without white clover but without reducing stocking rate or nitrogen usage. We compared 4 grazing treatments in this study: tetraploid PRG-only swards, diploid PRG-only swards, tetraploid with white clover swards, and diploid with white clover swards. Thirty cows were assigned to each treatment, and swards were rotationally grazed at a farm-level stocking rate of 2.75 cows/ha and a nitrogen fertilizer rate of 250 kg/ha annually. Sward white clover content was 23.6 and 22.6% for tetraploid with white clover swards and diploid with white clover swards, respectively. Milk production did not differ between the 2 ploidies during this 4-yr study, but cows grazing the PRG-white clover treatments had significantly greater milk yields (+596 kg/cow per year) and milk solid yields (+48 kg/cow per year) compared with cows grazing the PRG-only treatments. The PRG-white clover swards also produced 1,205 kg of DM/ha per year more herbage, which was available for conserving and buffer feeding in spring when these swards were less productive than PRG-only swards. Although white clover is generally combined with reduced nitrogen fertilizer use, this study provides evidence that including white clover in either tetraploid or diploid PRG swards, combined with high levels of nitrogen fertilizer, can effectively increase milk production per cow and per hectare.
    • Multiple linear regression modelling of on-farm direct water and electricity consumption on pasture based dairy farms

      Shine, Philip; Scully, Ted; Upton, John; Murphy, Michael D.; Institutes of Technology Ireland; Department of Agriculture, Food and the Marine; Sustainable Energy Authority of Ireland (Elsevier, 2018-03-05)
      An analysis into the impact of milk production, stock numbers, infrastructural equipment, managerial procedures and environmental conditions on dairy farm electricity and water consumption using multiple linear regression (MLR) modelling was carried out. Electricity and water consumption data were attained through the utilisation of a remote monitoring system installed on a study sample of 58 pasture-based, Irish commercial dairy farms between 2014 and 2016. In total, 15 and 20 dairy farm variables were analysed on their ability to predict monthly electricity and water consumption, respectively. The subsets of variables that had the greatest prediction accuracy on unseen electricity and water consumption data were selected by applying a univariate variable selection technique, all subsets regression and 10-fold cross validation. Overall, electricity consumption was more accurately predicted than water consumption with relative prediction error values of 26% and 49% for electricity and water, respectively. Milk production and the total number of dairy cows had the largest impact on electricity consumption while milk production, automatic parlour washing and whether winter building troughs were reported to be leaking had the largest impact on water consumption. A standardised regression analysis found that utilising ground water for pre-cooling milk increased electricity consumption by 0.11 standard deviations, while increasing water consumption by 0.06 standard deviations when recycled in an open loop system. Milk production had a large influence on model overprediction with large negative correlations of −0.90 and −0.82 between milk production and mean percentage error for electricity and water prediction, respectively. This suggested that overprediction was inflated when milk production was low and vice versa. Governing bodies, farmers and/or policy makers may use the developed MLR models to calculate the impact of Irish dairy farming on natural resources or as decision support tools to calculate potential impacts of on-farm mitigation practises.