The objective of this study was to provide comparative performance data for Holstein- Friesian (HF), Jersey (J) and Jersey×Holstein-Friesian (F1) cows under a seasonal pasture-based management system and to simulate the effect of cow breed on farm profitability. Data for a total of 329 lactations, from 162 (65 HF, 48 J and 49 F1) cows, were available. Milk yield was highest for HF, intermediate for F1 and lowest for J, while milk fat and protein concentrations were highest for J, intermediate for F1 and lowest for HF. Yield of fat plus protein was highest for F1, intermediate for HF and lowest for J. Mean bodyweight was 523, 387 and 466 kg for HF, J and F1, respectively. Body condition score was greater for the J and F1 compared to HF. Reproductive efficiency was similar for the HF and J but superior for the F1. The Moorepark Dairy Systems Model was used to simulate a 40 ha farm integrating biological data for each breed group. Milk output was highest for systems based on HF cows. Total sales of milk solids and, consequently, milk receipts were higher with J and F1 compared to HF. Total costs were lowest with F1 cows, intermediate with HF and highest with J. Overall farm profitability was highest with F1 cows, intermediate with HF and lowest with J. Sensitivity analysis of milk price, fat to protein price ratio and differences in cost of replacement heifers showed no re-ranking of the breed groups for farm profit.

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.

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.

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.

Mastitis was identified as a priority disease within the Irish dairy industry by both dairy farmers and industry animal health experts, which led to the development of the CellCheck programme. In order to support this programme it was necessary to understand the extent to which mastitis affects farm profit, processor returns and ultimately industry profitability. To this end, an analysis of the impact of mastitis on farm, processor and the overall industry profitability was carried out. The impact of mastitis on farm costs, farm receipts and farm profitability is presented across a range of bulk milk somatic cell count (SCC) categories from <100,000 to >400,000 cells/mL. A meta-analysis of the relationship between SCC and raw milk composition, cheese processing characteristics and cheese composition was carried out and utilised to establish the impact of mastitis on processor returns. As SCC increased, the impact of mastitis on the volume of product that could be produced, net processor returns, milk price and the values per kg of fat and protein were calculated. The farm and processor analysis were then combined to estimate the impact of mastitis on the Irish dairy industry returns, accounting for both farm and processor costs. The analysis suggests that as cell count reduced from >400,000 to <100,000 cells/mL, overall returns to the farm should increase by 4.8 c/L, including the farm and processor related effects. Nationally, if the cell count was reduced by 10%, it would be worth €37.6 million for the Irish dairy industry.

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.

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.

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.

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.

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.