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

  • Identification of possible cow grazing behaviour indicators for restricted grass availability in a pasture-based spring calving dairy system

    Werner, Jessica; Umstatter, Christina; Kennedy, Emer; Grant, Jim; Leso, Lorenzo; Geoghegan, Anne; Shalloo, Laurence; Schick, Matthias; O'Brien, Bernadette; Science Foundation Ireland; et al. (Elsevier, 2018-12-05)
    Precision livestock farming uses biosensors to measure different parameters of individual animals to support farmers in the decision making process. Although sensor development is advanced, there is still little implementation of sensor-based solutions on commercial farms. Especially on pasture-based dairy systems, the grazing management of cows is largely not supported by technology. A key factor in pasture-based milk production is the correct grass allocation to maximize the grass utilization per cow, while optimizing cow performance. Currently, grass allocation is mostly based on subjective eye measurements or calculations per herd. The aim of this study was to identify possible indicators of insufficient or sufficient grass allocation in the cow grazing behaviour measures. A total number of 30 cows were allocated a restricted pasture allowance of 60% of their intake capacity. Their behavioural characteristics were compared to those of 10 cows (control group) with pasture allowance of 100% of their intake capacity. Grazing behaviour and activity of cows were measured using the RumiWatchSystem for a complete experimental period of 10 weeks. The results demonstrated that the parameter of bite frequency was significantly different between the restricted and the control groups. There were also consistent differences observed between the groups for rumination time per day, rumination chews per bolus and frequency of cows standing or lying.
  • Comparative grazing behaviour of lactating suckler cows of contrasting genetic merit and genotype

    McCabe, S.; McHugh, Noirin; O'Connell, Niamh E.; Prendiville, Robert; Department of Agriculture, Food and the Marine; RSF 13/S4/96 (Elsevier, 2018-12-04)
    The objective of this study was to determine if differences in grazing behaviour exist between lactating suckler cows diverse in genetic merit for the national Irish Replacement index and of two contrasting genotypes. Data from 103 cows: 41 high and 62 low genetic merit, 43 beef and 60 beef x dairy (BDX) cows were available over a single grazing season in 2015. Milk yield, grass dry matter intake (GDMI), cow live weight (BW) and body condition score (BCS) were recorded during the experimental period, with subsequent measures of production efficiency extrapolated. Grazing behaviour data were recorded twice in conjunction with aforementioned measures, using Institute of Grassland and Environmental Research headset behaviour recorders. The effect of genotype and cow genetic merit during mid- and late-lactation on grazing behaviour phenotypes, milk yield, BW, BCS and GDMI were estimated using linear mixed models. Genetic merit had no significant effect on any production parameters investigated, with the exception that low genetic merit had a greater BCS than high genetic merit cows. Beef cows were heavier, had a greater BCS but produced less milk per day than BDX. The BDX cows produced more milk per 100 kg BW and per unit intake and had greater GDMI, intake per bite and rate of GDMI per 100 kg BW than beef cows. High genetic merit cows spent longer grazing and took more bites per day but had a lower rate of GDMI than low genetic merit cows, with the same trend found when expressed per unit of BW. High genetic merit cows spent longer grazing than low genetic merit cows when expressed on a per unit intake basis. Absolute rumination measures were similar across cow genotype and genetic merit. When expressed per unit BW, BDX cows spent longer ruminating per day compared to beef. However, on a per unit intake basis, beef cows ruminated longer and had more mastications than BDX. Intake per bite and rate of intake was positively correlated with GDMI per 100 kg BW. The current study implies that despite large differences in grazing behaviour between cows diverse in genetic merit, few differences were apparent in terms of production efficiency variables extrapolated. Conversely, differences in absolute grazing and ruminating behaviour measurements did not exist between beef cows of contrasting genotype. However, efficiency parameters investigated illustrate that BDX will subsequently convert herbage intake more efficiently to milk production.
  • Comparison of modelling techniques for milk-production forecasting

    Murphy, Michael D.; O’Mahony, Michéal J.; Shalloo, Laurence; French, Padraig; Upton, John (Elsevier for American Dairy Science Association, 2014-04-13)
    The objective of this study was to assess the suitability of 3 different modeling techniques for the prediction of total daily herd milk yield from a herd of 140 lactating pasture-based dairy cows over varying forecast horizons. A nonlinear auto-regressive model with exogenous input, a static artificial neural network, and a multiple linear regression model were developed using 3 yr of historical milk-production data. The models predicted the total daily herd milk yield over a full season using a 305-d forecast horizon and 50-, 30-, and 10-d moving piecewise horizons to test the accuracy of the models over long- and short-term periods. All 3 models predicted the daily production levels for a full lactation of 305 d with a percentage root mean square error (RMSE) of ≤12.03%. However, the nonlinear auto-regressive model with exogenous input was capable of increasing its prediction accuracy as the horizon was shortened from 305 to 50, 30, and 10 d [RMSE (%) = 8.59, 8.1, 6.77, 5.84], whereas the static artificial neural network [RMSE (%) = 12.03, 12.15, 11.74, 10.7] and the multiple linear regression model [RMSE (%) = 10.62, 10.68, 10.62, 10.54] were not able to reduce their forecast error over the same horizons to the same extent. For this particular application the nonlinear auto-regressive model with exogenous input can be presented as a more accurate alternative to conventional regression modeling techniques, especially for short-term milk-yield predictions.
  • 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.
  • A national methodology to quantify the diet of grazing dairy cows

    O'Brien, Donal; Moran, Brian; Shalloo, Laurence (Elsevier, 2018-07-04)
    The unique rumen of dairy cows allows them to digest fibrous forages and feedstuffs. Surprisingly, to date few attempts have been made to develop national methods to gain an understanding on the make-up of a dairy cow's diet, despite the importance of milk production. Consumer interest is growing in purchasing milk based on the composition of the cows' diet and the time they spend grazing. The goal of this research was to develop such a methodology using the national farm survey of Ireland as a data source. The analysis was completed for a 3-yr period from 2013 to 2015 on a nationally representative sample of 275 to 318 dairy farms. Trained auditors carried out economic surveys on farms 3 to 4 times per annum. The auditors collected important additional information necessary to estimate the diet of cows including the length of the grazing season, monthly concentrate feeding, type of forage(s) conserved, and milk production. Annual cow intakes were calculated to meet net energy requirements for production, maintenance, activity, pregnancy, growth, and live weight change using survey data and published literature. Our analysis showed that the average annual cow feed intake on a fresh matter basis ranged from 22.7 t in 2013 to 24.8 t in 2015 and from 4.8 to 5 t on a dry matter basis for the same period. Forage, particularly pasture, was the largest component of the Irish cow diet, typically accounting for 96% of the diet on a fresh matter basis and 82% of dry matter intake over the 3 yr. Within the cows' forage diet, grazed pasture was the dominant component and on average contributed 74 to 77% to the average annual cow fresh matter diet over the period. The proportion of pasture in the annual cow diet as fed was also identified as a good indicator of the time cows spend grazing (e.g., coefficient of determination = 0.85). Monthly, forage was typically the main component of the cow diet, but the average contribution of concentrate was substantial for the early spring months of January and February (30 to 35% of dry matter intake). Grazed pasture was the dominant source of forage from March to October and usually contributed 95 to 97% of the diet as fed in the summer period. Overall, the national farm survey from 2013 to 2015 shows that Irish dairy farms are very reliant on forage, particularly pasture, regardless of whether it is reported on a dry matter basis or as fed. There is potential to replicate this methodology in any regions or nations where representative farm surveys are conducted.
  • 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, 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.
  • Effects of fertiliser nitrogen rate to spring grass on apparent digestibility, nitrogen balance, ruminal fermentation and microbial nitrogen production in beef cattle and in vitro rumen fermentation and methane output

    O'Connor, Alan; Moloney, Aidan P.; O'Kiely, Padraig; Boland, T. M.; McGee, Mark; Teagasc Walsh Fellowship Programme; Department of Agriculture, Food and the Marine; 11/S/105 (Elsevier, 2019-06-06)
    The effects of two fertiliser nitrogen (N) application rates - 15 (LN) or 80 (HN) kg N/ha - to Lolium perenne dominant swards in spring, on grass dry matter (DM) intake, digestion, rumen fermentation, microbial N production and N-balance in beef cattle, and in vitro fermentation and methane production were studied. Sixteen Charolais steers with a mean live weight (s.d.) of 475 (18.4) kg, were used in a completely randomised block design experiment and offered zero-grazed grass harvested 21-d post N application. The same grass was incubated in an eight-vessel RUSITEC in a completely randomised block design experiment. The HN treatment had a 540 kg/ha higher grass DM yield, and a 20 g/kg DM higher crude protein (CP) concentration compared to LN. There was no difference (P > 0.05) in DM intake, or in vivo DM, organic matter (OM) and N digestibility between treatments. Rumen fermentation variables pH, lactic acid, ammonia (NH3) and total volatile fatty acid (VFA) concentration were similar (P > 0.05) for both treatments. Nitrogen intake was 19 g/d higher (P < 0.05) for HN compared to LN. Total and urine N loss was 16 and 14 g/d greater (P < 0.05), respectively, for HN compared to LN, but faecal N loss did not differ (P > 0.05) between treatments. The quantity of N retained and N-use efficiency did not differ (P > 0.05) between LN and HN. Plasma urea concentration was 1 mmol/L greater (P < 0.05) for HN compared to LN. Estimated microbial N production was greater (P < 0.05) for HN compared to LN. In vitro NH3 concentrations were higher (P < 0.05) for HN compared to LN, whereas in vitro rumen pH, lactic acid and VFA concentrations and molar proportions did not differ (P > 0.05) between HN and LN. In vitro methane and total gas output were not different (P > 0.05) between treatments. Reducing fertiliser N application rate to grass in spring reduced total and urinary N excretion, which has environmental benefits, with no effects on in vitro methane output.
  • How herd best linear unbiased estimates affect the progress achievable from gains in additive and nonadditive genetic merit

    Dunne, F. L.; McParland, Sinead; Kelleher, Margaret M.; Walsh, S.W.; Berry, Donagh P.; Science Foundation Ireland; Department of Agriculture, Food and the Marine; 16/RC/3835 (Elsevier, 2019-04-10)
    Sustainable dairy cow performance relies on coevolution in the development of breeding and management strategies. Tailoring breeding programs to herd performance metrics facilitates improved responses to breeding decisions. Although herd-level raw metrics on performance are useful, implicitly included within such statistics is the mean herd genetic merit. The objective of the present study was to quantify the expected response from selection decisions on additive and nonadditive merit by herd performance metrics independent of herd mean genetic merit. Performance traits considered in the present study were age at first calving, milk yield, calving to first service, number of services, calving interval, and survival. Herd-level best linear unbiased estimates (BLUE) for each performance trait were available on a maximum of 1,059 herds, stratified as best, average, and worst for each performance trait separately. The analyses performed included (1) the estimation of (co)variance for each trait in the 3 BLUE environments and (2) the regression of cow-level phenotypic performance on either the respective estimated breeding value (EBV) or the heterosis coefficient of the cow. A fundamental assumption of genetic evaluations is that 1 unit change in EBV equates to a 1 unit change in the respective phenotype; results from the present study, however, suggest that the realization of the change in phenotypic performance is largely dependent on the herd BLUE for that trait. Herds achieving more yield, on average, than expected from their mean genetic merit, had a 20% greater response to changes in EBV as well as 43% greater genetic standard deviation relative to herds within the worst BLUE for milk yield. Conversely, phenotypic performance in fertility traits (with the exception of calving to first service) tended to have a greater response to selection as well as a greater additive genetic standard deviation within the respective worst herd BLUE environments; this is suggested to be due to animals performing under more challenging environments leading to larger achievable gains. The attempts to exploit nonadditive genetic effects such as heterosis are often the basis of promoting cross-breeding, yet the results from the present study suggest that improvements in phenotypic performance is largely dependent on the environment. The largest gains due to heterotic effects tended to be within the most stressful (i.e., worst) BLUE environment for all traits, thus suggesting the heterosis effects can be beneficial in mitigating against poorer environments.
  • Effect of introducing weather parameters on the accuracy of milk production forecast models

    Zhang, Fan; Upton, John; Shalloo, Laurence; Shine, Philip; Murphy, Michael D. (Elsevier, 2019-04-13)
    The objective of this study was to analyze the effect of adding meteorological data to the training process of two milk production forecast models. The two models chosen were the nonlinear auto-regressive model with exogenous input (NARX) and the multiple linear regression (MLR) model. The accuracy of these models were assessed using seven different combinations of precipitation, sunshine hours and soil temperature as additional model training inputs. Lactation data (daily milk yield and days in milk) from 39 pasture-based Holstein-Friesian Irish dairy cows were selected to compare to the model outputs from a central database. The models were trained using historical milk production data from three lactation cycles and were employed to predict the total daily milk yield of a fourth lactation cycle for each individual cow over short (10-day), medium (30-day) and long-term (305-day) forecast horizons. The NARX model was found to provide a greater prediction accuracy when compared to the MLR model when predicting annual individual cow milk yield (kg), with R2 values greater than 0.7 for 95.5% and 14.7% of total predictions, respectively. The results showed that the introduction of sunshine hours, precipitation and soil temperature data improved the prediction accuracy of individual cow milk prediction for the NARX model in the short, medium and long-term forecast horizons. Sunshine hours was shown to have the largest impact on milk production with an improvement of forecast accuracy observed in 60% and 70% of all predictions (for all 39 test cows from both groups). However, the overall improvement in accuracy was small with a maximum forecast error reduction of 4.3%. Thus, the utilization of meteorological parameters in milk production forecasting did not have a substantial impact on forecast accuracy.
  • Invited review: Milk lactose—Current status and future challenges in dairy cattle

    Costa, Angela; Lopez-Villalobos, N.; Sneddon, N.W.; Shalloo, Laurence; Franzoi, Marco; de Marchi, M.; Penasa, M.; University of Padova, Italy; DOR1721792/17 (Elsevier, 2019-05-10)
    Lactose is the main carbohydrate in mammals' milk, and it is responsible for the osmotic equilibrium between blood and alveolar lumen in the mammary gland. It is the major bovine milk solid, and its synthesis and concentration in milk are affected mainly by udder health and the cow's energy balance and metabolism. Because this milk compound is related to several biological and physiological factors, information on milk lactose in the literature varies from chemical properties to heritability and genetic associations with health traits that may be exploited for breeding purposes. Moreover, lactose contributes to the energy value of milk and is an important ingredient for the food and pharmaceutical industries. Despite this, lactose has seldom been included in milk payment systems, and it has never been used as an indicator trait in selection indices. The interest in lactose has increased in recent years, and a summary of existing information about lactose in the dairy sector would be beneficial for the scientific community and the dairy industry. The present review collects and summarizes knowledge about lactose by covering and linking several aspects of this trait in bovine milk. Finally, perspectives on the use of milk lactose in dairy cattle, especially for selection purposes, are outlined.
  • Effect of using internal teat sealant with or without antibiotic therapy at dry-off on subsequent somatic cell count and milk production

    McParland, Sinead; Dillon, Pat; Flynn, James; Ryan, N.; Arkins, S.; Kennedy, Aideen E.; Dairy Research Ireland (Elsevier, 2019-03-14)
    The objective of this study was to assess the effect of treating cows with teat sealant only compared with antibiotic plus teat sealant at drying off on weekly somatic cell count, potential intramammary infection, and milk production across the entire subsequent lactation. In 3 research herds in the south of Ireland, cows with SCC that did not exceed 200,000 cells/mL in the previous lactation (LowSCC) were randomly assigned to 1 of 2 treatments at drying off: internal teat sealant alone (ITS) or antibiotic plus teat sealant (AB+ITS). Cows with SCC that exceeded 200,000 cells/mL in the previous lactation were treated with AB+ITS and included in the analyses as a separate group (HighSCC). Weekly individual animal composite SCC records were available for 654 cow lactations and were transformed to somatic cell scores (SCS) for the purpose of analysis. Data were divided into 3 data sets to represent records obtained (1) up to 35 DIM, (2) up to 120 DIM, and (3) across the lactation. Foremilk secretions were taken from all quarters at drying off, at calving, 2 wk after calving, and in mid-lactation and were cultured to detect the presence of bacteria. The LowSCC cows treated with ITS alone had higher daily milk yield (0.67 kg/d) across lactation compared with LowSCC cows treated with AB+ITS. The LowSCC cows treated with ITS alone had higher SCS in early, up to mid, and across lactation compared with LowSCC cows treated with AB+ITS. We detected no difference in weekly SCS of LowSCC cows treated with ITS alone and SCS of HighSCC cows. The least squares means back-transformed SCC across lactation of the LowSCC cows treated with ITS alone, LowSCC cows treated with AB+ITS, and HighSCC cows were 41,523, 34,001, and 38,939 cells/mL respectively. The odds of LowSCC cows treated with ITS alone having bacteria present in their foremilk across lactation was 2.7 (95% confidence interval: 1.91 to 3.85) and 1.6 (1.22 to 2.03) times the odds of LowSCC cows treated with AB+ITS and of HighSCC cows treated with AB+ITS, respectively. In this study, Staphylococcus aureus was the most prevalent pathogen isolated from the population. Recategorizing the threshold for LowSCC cows as ≤150,000 cells/mL or ≤100,000 cells/mL in the previous lactation had no effect on the results. The results indicate that herds with good mastitis control programs may use ITS alone at dry-off in cows with SCC <200,000 cells/mL across lactation with only a small effect on herd SCC.
  • 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.
  • A national methodology to quantify the diet of grazing dairy cows

    O'Brien, Donal; Moran, Brian; Shalloo, Laurence (Elsevier, 2018-07-04)
    The unique rumen of dairy cows allows them to digest fibrous forages and feedstuffs. Surprisingly, to date few attempts have been made to develop national methods to gain an understanding on the make-up of a dairy cow's diet, despite the importance of milk production. Consumer interest is growing in purchasing milk based on the composition of the cows' diet and the time they spend grazing. The goal of this research was to develop such a methodology using the national farm survey of Ireland as a data source. The analysis was completed for a 3-yr period from 2013 to 2015 on a nationally representative sample of 275 to 318 dairy farms. Trained auditors carried out economic surveys on farms 3 to 4 times per annum. The auditors collected important additional information necessary to estimate the diet of cows including the length of the grazing season, monthly concentrate feeding, type of forage(s) conserved, and milk production. Annual cow intakes were calculated to meet net energy requirements for production, maintenance, activity, pregnancy, growth, and live weight change using survey data and published literature. Our analysis showed that the average annual cow feed intake on a fresh matter basis ranged from 22.7 t in 2013 to 24.8 t in 2015 and from 4.8 to 5 t on a dry matter basis for the same period. Forage, particularly pasture, was the largest component of the Irish cow diet, typically accounting for 96% of the diet on a fresh matter basis and 82% of dry matter intake over the 3 yr. Within the cows' forage diet, grazed pasture was the dominant component and on average contributed 74 to 77% to the average annual cow fresh matter diet over the period. The proportion of pasture in the annual cow diet as fed was also identified as a good indicator of the time cows spend grazing (e.g., coefficient of determination = 0.85). Monthly, forage was typically the main component of the cow diet, but the average contribution of concentrate was substantial for the early spring months of January and February (30 to 35% of dry matter intake). Grazed pasture was the dominant source of forage from March to October and usually contributed 95 to 97% of the diet as fed in the summer period. Overall, the national farm survey from 2013 to 2015 shows that Irish dairy farms are very reliant on forage, particularly pasture, regardless of whether it is reported on a dry matter basis or as fed. There is potential to replicate this methodology in any regions or nations where representative farm surveys are conducted.
  • GWAS and eQTL analysis identifies a SNP associated with both residual feed intake and GFRA2 expression in beef cattle

    Higgins, Marc G.; Fitzsimons, Clare; McClure, Matthew C.; McKenna, Clare; Conroy, S.B.; Kenny, David A.; McGee, Mark; Waters, Sinead M.; Morris, Derek W.; Department of Agriculture, Food and the Marine; et al. (Nature Publishing Group, 2018-09-24)
    Residual feed intake (RFI), a measure of feed efficiency, is an important economic and environmental trait in beef production. Selection of low RFI (feed efficient) cattle could maintain levels of production, while decreasing feed costs and methane emissions. However, RFI is a difficult and expensive trait to measure. Identification of single nucleotide polymorphisms (SNPs) associated with RFI may enable rapid, cost effective genomic selection of feed efficient cattle. Genome-wide association studies (GWAS) were conducted in multiple breeds followed by meta-analysis to identify genetic variants associated with RFI and component traits (average daily gain (ADG) and feed intake (FI)) in Irish beef cattle (n = 1492). Expression quantitative trait loci (eQTL) analysis was conducted to identify functional effects of GWAS-identified variants. Twenty-four SNPs were associated (P < 5 × 10−5) with RFI, ADG or FI. The variant rs43555985 exhibited strongest association for RFI (P = 8.28E-06). An eQTL was identified between this variant and GFRA2 (P = 0.0038) where the allele negatively correlated with RFI was associated with increased GFRA2 expression in liver. GFRA2 influences basal metabolic rates, suggesting a mechanism by which genetic variation may contribute to RFI. This study identified SNPs that may be useful both for genomic selection of RFI and for understanding the biology of feed efficiency.
  • Effect of stocking rate and animal genotype on dry matter intake, milk production, body weight, and body condition score in spring-calving, grass-fed dairy cows

    Coffey, E. L.; Delaby, Luc; Fitzgerald, S.; Galvin, Norann; Pierce, K.M.; Horan, Brendan; Dairy Research Ireland (Elsevier, 2017-06-28)
    The objective of the experiment was to quantify the effect of stocking rate (SR) and animal genotype on milk production, dry matter intake (DMI), energy balance, and production efficiency across 2 consecutive grazing seasons (2014 and 2015). A total of 753 records from 177 dairy cows were available for analysis: 68 Holstein-Friesian and 71 Jersey × Holstein-Friesian (JxHF) cows each year of the experiment under a pasture-based seasonal production system. Animals within each breed group were randomly allocated to 1 of 3 whole-farm SR treatments defined in terms of body weight per hectare (kg of body weight/ha): low (1,200 kg of body weight/ha), medium (1,400 kg of body weight/ha), and high (1,600 kg of body weight/ha), and animals remained in the same SR treatments for the duration of the experiment. Individual animal DMI was estimated 3 times per year at grass using the n-alkane technique: March (spring), June (summer), and September (autumn), corresponding to 45, 111, and 209 d in milk, respectively. The effects of SR, animal genotype, season, and their interactions were analyzed using mixed models. Milk production, body weight, and production efficiency per cow decreased significantly as SR increased due to reduced herbage availability per cow and increased grazing severity. As a percentage of body weight, JxHF cows had higher feed conversion efficiency, higher DMI and milk solids (i.e., kg of fat + kg of protein) production, and also required less energy intake to produce 1 kg of milk solids. The increased production efficiency of JxHF cows at a similar body weight per hectare in the current analysis suggests that factors other than individual cow body weight contribute to the improved efficiency within intensive grazing systems. The results highlight the superior productive efficiency of high genetic potential crossbred dairy cows within intensive pasture-based milk production systems at higher SR where feed availability is restricted.
  • Genetic and nongenetic factors associated with milk color in dairy cows

    Scarso, S.; McParland, Sinead; Visentin, G.; Berry, Donagh P.; McDermott, A.; de Marchi, M.; European Union (Elsevier, 2017-07-12)
    Milk color is one of the sensory properties that can influence consumer choice of one product over another and it influences the quality of processed dairy products. This study aims to quantify the cow-level genetic and nongenetic factors associated with bovine milk color traits. A total of 136,807 spectra from Irish commercial and research herds (with multiple breeds and crosses) were used. Milk lightness (Lˆ*) , red-green index (aˆ*) and yellow-blue index (bˆ*) were predicted for individual milk samples using only the mid-infrared spectrum of the milk sample. Factors associated with milk color were breed, stage of lactation, parity, milking-time, udder health status, pasture grazing, and seasonal calving. (Co)variance components for Lˆ*,aˆ* , and bˆ* were estimated using random regressions on the additive genetic and within-lactation permanent environmental effects. Greater bˆ* value (i.e., more yellow color) was evident in milk from Jersey cows. Milk Lˆ* increased consistently with stage of lactation, whereas aˆ* increased until mid lactation to subsequently plateau. Milk bˆ* deteriorated until 31 to 60 DIM, but then improved thereafter until the end of lactation. Relative to multiparous cows, milk yielded by primiparae was, on average, lighter (i.e., greater Lˆ* ), more red (i.e., greater aˆ* ), and less yellow (i.e., lower bˆ* ). Milk from the morning milk session had lower Lˆ*,aˆ*, and bˆ* Heritability estimates (±SE) for milk color varied between 0.15 ± 0.02 (30 DIM) and 0.46 ± 0.02 (210 DIM) for Lˆ* , between 0.09 ± 0.01 (30 DIM) and 0.15 ± 0.02 (305 DIM) for aˆ* , and between 0.18 ± 0.02 (21 DIM) and 0.56 ± 0.03 (305 DIM) for bˆ* For all the 3 milk color features, the within-trait genetic correlations approached unity as the time intervals compared shortened and were generally <0.40 between the peripheries of the lactation. Strong positive genetic correlations existed between bˆ* value and milk fat concentration, ranging from 0.82 ± 0.19 at 5 DIM to 0.96 ± 0.01 at 305 DIM and confirming the observed phenotypic correlation (0.64, SE = 0.01). Results of the present study suggest that breeding strategies for the enhancement of milk color traits could be implemented for dairy cattle populations. Such strategies, coupled with the knowledge of milk color traits variation due to nongenetic factors, may represent a tool for the dairy processors to reduce, if not eliminate, the use of artificial pigments during milk manufacturing.
  • 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, 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.
  • Milk mid-infrared spectral data as a tool to predict feed intake in lactating Norwegian Red dairy cows

    Berry, Donagh P.; 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.
  • Factors associated with profitability in pasture-based systems of milk production

    Hanrahan, Liam; McHugh, Noirin; Hennessy, Thia; Moran, Brian; Kearney, R.; Wallace, Michael; Shalloo, Laurence (Elsevier, 2018-03-07)
    The global dairy industry needs to reappraise the systems of milk production that are operated at farm level with specific focus on enhancing technical efficiency and competitiveness of the sector. The objective of this study was to quantify the factors associated with costs of production, profitability, and pasture use, and the effects of pasture use on financial performance of dairy farms using an internationally recognized representative database over an 8-yr period (2008 to 2015) on pasture-based systems. To examine the associated effects of several farm system and management variables on specific performance measures, a series of multiple regression models were developed. Factors evaluated included pasture use [kg of dry matter/ha and stocking rate (livestock units/ha)], grazing season length, breeding season length, milk recording, herd size, dairy farm size (ha), farmer age, discussion group membership, proportion of purchased feed, protein %, fat %, kg of milk fat and protein per cow, kg of milk fat and protein per hectare, and capital investment in machinery, livestock, and buildings. Multiple regression analysis demonstrated costs of production per hectare differed by year, geographical location, soil type, level of pasture use, proportion of purchased feed, protein %, kg of fat and protein per cow, dairy farm size, breeding season length, and capital investment in machinery, livestock, and buildings per cow. The results of the analysis revealed that farm net profit per hectare was associated with pasture use per hectare, year, location, soil type, grazing season length, proportion of purchased feed, protein %, kg of fat and protein per cow, dairy farm size, and capital investment in machinery and buildings per cow. Pasture use per hectare was associated with year, location, soil type, stocking rate, dairy farm size, fat %, protein %, kg of fat and protein per cow, farmer age, capital investment in machinery and buildings per cow, breeding season length, and discussion group membership. On average, over the 8-yr period, each additional tonne of pasture dry matter used increased gross profit by €278 and net profit by €173 on dairy farms. Conversely, a 10% increase in the proportion of purchased feed in the diet resulted in a reduction in net profit per hectare by €97 and net profit by €207 per tonne of fat and protein. Results from this study, albeit in a quota limited environment, have demonstrated that the profitability of pasture-based dairy systems is significantly associated with the proportion of pasture used at the farm level, being cognizant of the levels of purchased feed.
  • 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.

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