Evaluating enteric methane emissions within a herd of genetically divergent grazing dairy cows.

Enteric methane (CH4) emissions of 3 genetic groups (GG) of dairy cows were recorded across the grazing season (early March to late October). The 3 GG were (1) high economic breeding index (EBI) Holstein-Friesian (HF) representative of the top 1% of dairy cows in Ireland at the time of the study (elite), (2) national average (NA) EBI, which were representative of the average HF dairy cow in Ireland, and (3) purebred Jersey (JE) cows. Enteric CH4 was recorded using GreenFeed technology. Seasonal variation in CH4 was observed, with the lowest daily CH4 emissions and CH4 expressed per unit of dry matter intake occurring in spring (253 g/d and 15.56 g/kg, respectively), intermediate in summer (303 g/d and 18.26 g/kg, respectively), and greatest in autumn (324 g/d and 19.80 g/kg, respectively). Seasonal variation was also observed in the proportion of gross energy intake converted to CH4 (Ym); in the spring the Ym was lowest at 0.046, increasing to 0.053 and 0.058 in the summer and autumn, respectively. There was no difference in daily CH4 between the elite and NA, whereas JE had lower CH4 emissions compared with the elite. When expressed per unit of milk solids (fat + protein yield; MS), the elite and JE produced 6.8% and 9.7% less CH4 per kilogram of MS, respectively, compared with NA. There was no difference between the GG for CH4 per unit of DMI or the Ym. This research emphasizes the variation in CH4 emissions across the grazing season and among cows of differing genetic merit for CH4 emission intensities but not for CH4 per unit of DMI or the Ym.

Animal factors that affect enteric methane production measured using the GreenFeed monitoring system in grazing dairy cows.

Similar to all dairy systems internationally, pasture-based dairy systems are under increasing pressure to reduce their greenhouse gas (GHG) emissions. Ireland and New Zealand are 2 countries operating predominantly pasture-based dairy production systems where enteric CH4 contributes 23% and 36% of total national emissions, respectively. Ireland currently has a national commitment to reduce 51% of total GHG emissions by 2030 and 25% from agriculture by 2030, as well as striving to achieve climate neutrality by 2050. New Zealand's national commitment is to reduce 10% of methane emissions by 2030 and between 24% and 47% reduction in methane emissions by 2050. To achieve these reductions, factors that affect enteric methane (CH4) production in a pasture-based system need to be investigated. The objective of this study was to assess the relationship between enteric CH4 and other animal traits (feed intake, metabolic liveweight, energy corrected milk yield, milk urea concentration, and body condition score [BCS]) in a grazing dairy system. Enteric CH4 emissions were measured on 45 late lactation (213.8 ± 29 d after calving) grazing Holstein-Friesian and Holstein-Friesian × Jersey crossbred cows (lactation number 3.01 ± 1.65, 538.64 ± 59.37 kg live weight, and 3.14 ± 0.26 BCS) using GreenFeed monitoring equipment for 10 wk. There was a training period for the cows to use the GreenFeed of 3 wk before the 10-wk study period. The average enteric CH4 produced in the study was 352 g ± 45.7 g per day with an animal to animal coefficient of variation of 13%. Dry matter intake averaged 16.6 kg ± 2.23 kg per day, while milk solids (fat plus protein) averaged 1.62 kg ± 0.29 kg per day. A multiple linear regression model indicated that each one unit increase in energy corrected milk yield, metabolic liveweight and milk urea concentration, resulted in an increase in enteric CH4 production per day by 3.9, 1.74, and 1.38 g, respectively. Although each one unit increase in BCS resulted in a decrease in 39.03 g CH4 produced per day. When combined, these factors explained 47% of the variation in CH4 production, indicating that there is a large proportion of variation not included in the model. The repeatability of the CH4 measurements was 0.66 indicating that cows are relatively consistently exhibiting the same level of CH4 throughout the study. Therefore, enteric CH4 production is suitable for phenotyping.

Predicting methane emissions of individual grazing dairy cows from spectral analyses of their milk samples.

Data on the enteric methane emissions of individual cows are useful not just in assisting management decisions and calculating herd inventories but also as inputs for animal genetic evaluations. Data generation for many animal characteristics, including enteric methane emissions, can be expensive and time consuming, so being able to extract as much information as possible from available samples or data sources is worthy of investigation. The objective of the present study was to attempt to predict individual cow methane emissions from the information contained within milk samples, specifically the spectrum of light transmittance across different wavelengths of the mid-infrared (MIR) region of the electromagnetic spectrum. A total of 93,888 individual spot measures of methane (i.e., individual samples of an animal's breath when using the GreenFeed technology) from 384 lactations on 277 grazing dairy cows were collapsed into weekly averages expressed as grams per day; each weekly average coincided with a MIR spectral analysis of a morning or evening individual cow milk sample. Associations between the spectra and enteric methane measures were performed separately using partial least squares regression or neural networks with different tuning parameters evaluated. Several alternative definitions of the enteric methane phenotype (i.e., average enteric methane in the 6 d preceding or 6 d following taking the milk sample or the average of the 6 d before and after the milk sample, all of which also included the enteric methane emitted on the day of milk sampling), the candidate model features (e.g., milk yield, milk composition, and milk MIR) as well as validation strategy (i.e., cross-validation or leave-one-experimental treatment-out) were evaluated. Irrespective of the validation method, the prediction accuracy was best when the average of the milk MIR from the morning and evening milk sample was used and the prediction model was developed using neural networks; concurrently including milk yield and days in milk in the prediction model generated superior predictions relative to just the spectral information alone. Furthermore, prediction accuracy was best when the enteric methane phenotype was the average of at least 20 methane spot measures across a 6-d period flanking each side of the milk sample with associated spectral data. Based on the strategy that achieved the best accuracy of prediction, the correlation between the actual and predicted daily methane emissions when based on 4-fold cross-validation varied per validation stratum from 0.68 to 0.75; the corresponding range when validated on each of the 8 different experimental treatments focusing on alternative pasture grazing systems represented in the dataset varied from 0.55 to 0.71. The root mean square error of prediction across the 4-folds of cross-validation was 37.46 g/d, whereas the root mean square error averaged across all folds of leave-one-treatment-out was 37.50 g/d. Results suggest that even with the likely measurement errors contained within the MIR spectrum and gold standard enteric methane phenotype, enteric methane can be reasonably well predicted from the infrared spectrum of milk samples. What is yet to be established, however, is whether (a) genetic variation exists in this predicted enteric methane phenotype and (b) selection on estimates of genetic merit for this phenotype translate to actual phenotypic differences in enteric methane emissions.

The impact of twice daily 3-nitroxypropanol supplementation on enteric methane emissions in grazing dairy cows.

Although 3-NOP has been proven to reduce enteric methane (CH4) by ∼30% in indoor systems of dairying when the additive is mixed throughout a total mixed ration (TMR), there has been very limited research to date in grazing systems in which the most convenient method of additive supplementation is at milking twice daily. To investigate the effect of twice daily 3-NOP supplementation on enteric CH4 emissions, a 12-week study was undertaken in which treatment cows (n = 26) were supplemented with 3-NOP (80 mg per kg dry matter intake; DMI) twice daily at morning and evening milking, while control cows (n = 26) received no additive supplementation. Enteric CH4, hydrogen (H2) and carbon dioxide (CO2) were measured using GreenFeed units, while milk production, body weight (BW), body condition score (BCS) and DMI were monitored to determine the effect of 3-NOP supplementation on productivity. There was no significant effect of 3-NOP supplementation on any of the aforementioned parameters with the exception of CH4 and H2 production, respectively. Cows supplemented with 3-NOP produced ∼36% more H2 (P < 0.001) across a 24-h period, with reductions in CH4 production of 28.5% recorded in the 3 h after additive consumption (P < 0.001), however, levels of CH4 production returned to that of the control group thereafter. When CH4 production was considered across the entire 24-h period, the cows offered 3-NOP produced ∼5% less CH4 than the control (P < 0.050). Future research should focus on methods to increase the efficacy of the additive throughout the day which would include the deployment of a slow-release form or an out of parlor feeding system that allows animals consume the product at additional time points.

Evaluating the effects of grass management technologies on the physical, environmental, and financial performance of Irish pasture-based dairy farms.

Grass management technologies (grass measuring devices and grassland management decision support tools) have been identified as important tools to improve the performance of pasture-based dairy farms. They have the potential to significantly improve the efficiency and sustainability of dairy systems by increasing milk production through enhanced pasture growth and utilization, which would reduce the need for supplementary feeds, along with increased output, therefore increasing farm profitability and environmental sustainability. Despite the several potential benefits of grass management technologies, there is a lack of empirical research around the effects of these technologies on the performance of pasture-based dairy systems. The current study aimed to fill this knowledge gap by using a 2018 nationally representative survey of Irish dairy farms and a propensity score matching approach to determine the effects of adopting grass management technologies on the physical, environmental, and financial performance of Irish pasture-based dairy farms. The findings showed that dairy farms utilizing grass management technologies had, on average, higher farm physical, environmental, and financial performance (in terms of grazed pasture use, total pasture use, length of the grazing season, milk yield, milk solids, greenhouse gas emissions per kilogram of fat- and protein-corrected milk, gross output, and gross margin) compared with dairy farms not utilizing these technologies. However, when controlling for selection bias, we can only attribute a positive causal effect of grass management technology adoption on the use of grazed pasture per cow, grazing season length, milk yield per cow, and milk solids per cow. This might be due to dairy farmers not yet using the technologies to their full potential, 2018 being an unusual year in terms of weather (and therefore not being able to capture the full range of farm performance benefits), or because grass management technologies need to be adopted in association with other technologies and practices to achieve their expected performance outcomes. Future research should include updated farm-level data to capture the weather and learning effects and so be able to determine the impact of grass management technologies on a wider range of performance indicators.

Modeling the economic impacts of mobility scores in dairy cows under Irish spring pasture-based management.

Moderate to severe forms of suboptimal mobility on dairy cows are associated with yield losses, whereas mild forms of suboptimal mobility are associated with elevated somatic cell count and an increased risk to be culled. Although the economic consequences of severe forms of suboptimal mobility (also referred as clinical lameness) have been studied extensively, the mild forms are generally ignored. Therefore, the aim of the current study was to determine the economic consequences associated with varying prevalence and forms of suboptimal mobility within spring calving, pasture-based dairy herds. A new submodel predicting mobility scores was developed and integrated within an existing pastured-based herd dynamic model. Using a daily timestep, this model simulates claw disorders, and the consequent mobility score of individual cows. The impact of a cow having varying forms of suboptimal mobility on production and reproduction was simulated. The economic impact was simulated including treatment costs, as well as the production and reproductive impacts of varying levels of suboptimal mobility. Furthermore, different genetic predispositions for mobility issues and their interaction with herd-level management associated with each level of suboptimal mobility were simulated. Overall, 13 scenarios were simulated, representing a typical spring calving, pasture-based dairy herd with 100 cows. The first scenario represents a perfect herd wherein 100% of the cows had mobility score 0 (optimal mobility) throughout the lactation. The remaining 12 scenarios represent a combination of (1) 3 different herd-management levels, and (2) 4 different levels of a genetic predisposition for suboptimal mobility. The analysis showed that a 17% decrease in farm net profit was achieved in the worst outcome (wherein just 5% of the herd had optimal mobility) compared with the perfect herd. This was due to reduced milk yield, increased culling, and increased treatment costs for mobility issues compared the ideal scenario.

Factors associated with intensity of technology adoption and with the adoption of 4 clusters of precision livestock farming technologies in Irish pasture-based dairy systems.

Precision livestock farming (PLF) technologies have been widely promoted as important tools to improve the sustainability of dairy systems due to perceived economic, social, and environmental benefits. However, there is still limited information about the level of adoption of PLF technologies (percentage of farms with a PLF technology) and the factors (farm and farmer characteristics) associated with PLF technology adoption in pasture-based dairy systems. The current research aimed to address this knowledge gap by using a representative survey of Irish pasture-based dairy farms from 2018. First, we established the levels of adoption of 9 PLF technologies (individual cow activity sensors, rising plate meters, automatic washers, automatic cluster removers, automatic calf feeders, automatic parlor feeders, automatic drafting gates, milk meters, and a grassland management decision-support tool) and grouped them into 4 PLF technology clusters according to the level of association with each other and the area of dairy farm management in which they are used. The PLF technology clusters were reproductive management technologies, grass management technologies, milking management technologies, and calf management technologies. Additionally, we classified farms into 3 categories of intensity of technology adoption based on the number of PLF technologies they have adopted (nonadoption, low intensity of adoption, and high intensity of adoption). Second, we determined the factors associated with the intensity of technology adoption and with the adoption of the PLF technology clusters. A multinomial logistic regression model and 4 logistic regressions were used to determine the factors associated with intensity of adoption (low and high intensity of adoption compared with nonadoption) and with the adoption of the 4 PLF technology clusters, respectively. Adoption levels varied depending on PLF technology, with the most adopted PLF technologies being those related to the milking process (e.g., automatic parlor feeders and milk meters). The results of the multinomial logistic regression suggest that herd size, proportion of hired labor, agricultural education, and discussion group membership were positively associated with a high intensity of adoption, whereas age of farmer and number of household members were negatively associated with high intensity of adoption. However, when analyzing PLF technology clusters, the magnitude and direction of the influence of the factors in technology adoption varied depending on the PLF technology cluster being investigated. By identifying the PLF technologies in which pasture-based dairy farmers are investing more and by detecting potential drivers and barriers for the adoption of PLF technologies, the current study could allow PLF technology companies, practitioners, and researchers to develop and target strategies that improve future adoption of PLF technologies in pasture-based dairy settings.

Economic impact of different strategies to use sex-sorted sperm for reproductive management in seasonal-calving, pasture-based dairy herds.

To maximize efficiency, profitability, and societal acceptance of modern dairy production, it is important to minimize the production of male dairy calves with poor beef merit. One solution involves using sex-sorted sperm (SS) to generate dairy replacements and breeding all other cows to an easy-calving, short-gestation bull with good beef merit. We used the Pasture Based Herd Dynamic Milk Model to investigate the effect of herd fertility and use of SS on farm net profit in a herd of 100 cows. This was completed by simulating herds with differing fertility performance (good, average, poor), and differing farm reproductive management [conventional semen (CONV) or SS with varying pregnancy per artificial insemination (P/AI) relative to CONV (i.e., relative P/AI 100%, 85%, and 70%)]. As an additional consideration, the method of allocating SS to cows was also examined. The first option used SS on random heifers and cows (S). The second option used SS on heifers and targeted high-fertility cows (SSel). The final option was similar to SSel, but used a fixed-time artificial insemination (AI) protocol to facilitate AI on the farm mating start date (SSync). For CONV, dairy breed semen was used for AI until 50 animals were pregnant (50% chance of a female calf), whereas for S, SSel, or SSync the target number of animals successfully conceiving with SS was set at 28 (based on assumed 90% chance of a female calf from pregnancies derived from SS). Beef breed semen was used on all other dams. The results indicated that the biggest effect on farm net profit was not based on whether or not SS was used, but instead was most affected by the overall fertility performance of the herd. Total farm profit decreased by 10% between the good and average fertility herds, and decreased by a further 12% between the average and poor fertility herds. In almost all situations, when the relative P/AI with SS was ≥85%, use of SS led to an overall increase of the farm net profit. There was an economic benefit of using either SSel or SSync compared with S for the average and poor fertility herds but not for the good fertility herd, highlighting an interaction between SS P/AI and overall herd fertility as well as management practices. If the relative P/AI with SS was <70%, the use of SS led to a decrease in profitability in all simulations except for SSync, highlighting the importance of a good management strategy for use of SS. The findings in this study indicated that SS has significant potential to help facilitate greater integration between the dairy and beef production sectors, as well as increase farm profitability when used appropriately.

Incorporation of the grazing utilization subindex and new updates to the Pasture Profit Index.

Grazing efficiency has been shown to differ between perennial ryegrass varieties. Such differences affect the utilization of grass within grazing systems, influencing the profitability of grass-based ruminant production systems. The Pasture Profit Index (PPI) is an economic merit grass variety selection tool developed to identify varieties with the greatest economic potential for grass-based dairy production systems. A new grass utilization subindex was developed and incorporated into the PPI to identify varieties with superior grazing efficiency. The subindex rewards varieties with superior grazing efficiency, measured as Residual grazed height, as these varieties allow increased amounts of herbage dry matter to be used by grazing animals. The economic values of all other traits within the PPI were reviewed and updated to ensure that the index was reflective of the current economic scenarios with appropriate assumptions included in the models, thus ensuring that varieties excelling in the agronomic traits with the greatest effect on profitability were recognized. The difference between the highest and lowest performing varieties for the grass utilization trait ranged from €23 to -€24. A range of €211 to €43 was recorded between the highest and lowest ranked varieties within the updated PPI. Spearman's rank correlation between the updated and original PPI lists was 0.96. The introduction of the utilization subindex will allow farmers to make informed variety selection decisions when reseeding pasture, particularly on their grazing platforms and it will allow a demand-based communication process between the farmer and the grass merchant or breeder, ultimately affecting trait selection for future breeding strategies.

Greenhouse gas emissions and nitrogen efficiency of dairy cows of divergent economic breeding index under seasonal pasture-based management.

Greenhouse gas (GHG) emissions and nitrogen (N) efficiencies were modeled for 2 genetic groups (GG) of Holstein-Friesian cows across 3 contrasting feeding treatments (FT). The 2 GG were (1) high economic breeding index (EBI) animals representative of the top 5% of cows nationally (elite) and (2) EBI representative of the national average (NA). The FT represented (1) generous feeding of pasture, (2) a slight restriction in pasture allowance, and (3) a high-concentrate feeding system with adequate pasture allowance. Greenhouse gas and N balance models were parameterized using outputs generated from the Moorepark Dairy Systems model, a stochastic budgetary simulation model, having integrated biological data pertaining to the 6 scenarios (2 GG × 3 FT) obtained from a 4-yr experiment conducted between 2013 and 2016. On a per hectare basis, total system GHG emissions were similar for both elite and NA across the 3 FT. Per unit of product, however, the elite group had 10% and 11% lower GHG emissions per kilogram of fat- and protein-corrected milk and per kilogram of milk solids (MSO; fat + protein kg), respectively, compared with the NA across the 3 FT. The FT incorporating high concentrate supplementation had greater absolute GHG emissions per hectare as well as GHG per kilogram of fat- and protein-corrected milk and MSO. The elite group had a slightly superior N use efficiency (N output/N input) and lower N surplus (N input - N output) compared with the NA group. The high concentrate FT had an inferior N use efficiency and a higher N surplus. The results of the current study demonstrate that breeding for increased EBI will lead to a general improvement in GHG emissions per unit of product as well as improved N efficiency. The results also illustrate that reducing concentrate supplementation will reduce GHG emissions, GHG emissions intensity, while improving N efficiency in the context of pasture-based dairy production.

An economic comparison of pasture-based production systems differing in sward type and cow genotype.

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

Economic assessment of Holstein-Friesian dairy cows of divergent Economic Breeding Index evaluated under seasonal calving pasture-based management.

The objective of this study was to investigate the economic performance of 2 genetic groups (GG) of Holstein-Friesian dairy cows of divergent Economic Breeding Index (EBI), evaluated within 3 contrasting spring-calving pasture-based feeding treatments (FT). The study was a simulated economic appraisal, using the Moorepark Dairy Systems Model, a stochastic budgetary simulation model integrating biological data obtained from a 4-yr experiment conducted from 2013 to 2016. The 2 divergent GG were (1) high EBI representative of the top 5% nationally (elite) and (2) EBI representative of the national average (NA). The 3 FT were reflective of slight restriction to generous feeding. The elite GG had the lowest replacement rate, and therefore had lower replacement costs and an older and more productive parity structure. The elite GG consistently had higher sales of milk (on average +3% or +18,370 kg of milk) and milk solids (milk fat plus protein yield; +8.7% or +4,520 kg) compared with the NA GG across the 3 FT scenarios. Milk income was consequently greater for elite versus NA (on average +9.5% or +€21,489) cows. Livestock sales were greater (on average +13.2% or +€4,715) for NA compared with elite cows. Baseline net farm profit and net profit/ha at a base milk price of 29.5 cents per liter (3.3% protein and 3.6% fat) were on average €31,156, and €772 greater for elite compared with NA cows across the 3 FT. Greater profitability achieved with elite cows in each of the FT investigated demonstrated the adaptability of high-EBI cows across different levels of feeding intensities in seasonal pasture-based feeding systems. Sensitivity analysis of varying milk price and concentrate cost did not result in a reranking of GG for farm profit. This study clearly demonstrates the power of a suitably constructed genetic-selection index together with a well-considered breeding program to deliver genetics capable of favorable change to farm physical performance and profit over a relatively short duration.

Invited review: Cattle lameness detection with accelerometers.

Locomotion scoring is time consuming and is not commonly completed on farms. Farmers also underestimate their herds' lameness prevalence, a knowledge gap that impedes lameness management. Automation of lameness detection could address this knowledge gap and facilitate improved lameness management. The literature pertinent to adding lameness detection to accelerometers is reviewed in this paper. Options for lameness detection systems are examined including the choice of sensor, raw data collected, variables extracted, and statistical classification methods used. Two categories of variables derived from accelerometer-based systems are examined. These categories are behavior measures such as lying and measures of gait. For example, one measure of gait is the time a leg is swinging during a gait cycle. Some behavior-focused studies have reported accuracy levels of greater than 80%. Cow gait measures have been investigated to a lesser extent than behavior. However, classification accuracies as high as 91% using gait measures have been reported with hardware likely to be practical for commercial farms. The need for even higher accuracy and potential barriers to adoption are discussed. Significant progress is still required to realize a system with sufficient specificity and sensitivity. Lameness detection systems using 1 accelerometer per cow and a resolution lower than 100 Hz with gait measurement functions are suggested to balance cost and data requirements. However, gait measurement using accelerometers is rather underdeveloped. Therefore, a high priority should be given to the development of novel gait measures and testing their ability to differentiate lame from nonlame cows.

Associating mobility scores with production and reproductive performance in pasture-based dairy cows.

Lameness in dairy cows can have significant effects on cow welfare, farm profitability, and the environment. To determine the economic and environmental consequences of lameness, we first need to quantify its effect on performance. The objective of this study, therefore, was to determine the associations of various production and reproductive performance measurements (including milk, fat, and protein yield, somatic cell count, calving interval, cow death, or cow slaughter), and mobility scores in spring-calving, pasture-based dairy cows. We collected mobility scores (0 = good, 1 = imperfect, 2 = impaired, and 3 = severely impaired mobility), body condition scores, and production data for 11,116 cows from 68 pasture-based dairy herds. Linear mixed modeling was used to determine the associations between specific mobility scores and milk, fat and protein yield, and somatic cell count and calving interval. Binomial logistic regression was used to determine the association between mobility score and cow death, or slaughter. Significant yield losses of up to 1.4% of the average yield were associated with mobility score 2 and yield losses of up to 4.7% were associated with mobility score 3 during the early scoring period. Elevated somatic cell count was associated with all levels of suboptimal mobility during the late scoring period. Cows with a mobility score of 2 during the early scoring period were associated with longer calving interval length, whereas only cows with a mobility score of 3 during the late scoring period were associated with longer calving interval length. Cows with a mobility score ≥1 were more likely to be culled during both scoring periods. Our study, therefore, shows an association between specific mobility scores and production and reproductive performance in spring-calving, pasture-based dairy cows scored during the summer grazing period.

Linkage between predictive transmitting ability of a genetic index, potential milk production, and a dynamic model.

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.

An examination of the effects of labor efficiency on the profitability of grass-based, seasonal-calving dairy farms.

The seasonality of grass-based, seasonal-calving dairy systems results in disproportionately higher labor demands during the spring, when cows are calving, than in the remaining seasons. This study aimed to (1) examine the relationship between labor efficiency and profitability; (2) investigate strategies to reduce the hours worked per day by the farmer, family, and farm staff in the spring by having certain tasks outsourced; and (3) quantify the economic implications of those strategies. Data from an existing labor efficiency study on Irish dairy farms were used in conjunction with economic performance data from the farms. Tasks that required the highest level of farm labor per day in the spring were identified and hypothetical strategies to reduce the farm hours worked per day were examined. A stochastic budgetary simulation model was then used to examine the economic implications of employing these strategies and the effects of their use in conjunction with a proportionate increase in cow numbers that would leave the hours worked per day unchanged. The strategies were to use contractors to perform calf rearing, machinery work, or milking. Contracting out milking resulted in the greatest reduction in hours worked per day (5.6 h/d) followed by calf rearing (2.7 h/d) and machinery work (2 h/d). Reducing the hours worked per day by removing those tasks had slight (i.e., <5%) negative effects on profitability; however, maintaining the farm hours worked per day while utilizing the same strategies and increasing herd sizes resulted in profitable options. The most profitable scenario was for farms to increase herd size while contracting out milking.

Invited review: Milk lactose-Current status and future challenges in dairy cattle.

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.

Associating cow characteristics with mobility scores in pasture-based dairy cows.

The quality of dairy cow mobility can have significant welfare, economic, and environmental consequences that have yet to be extensively quantified for pasture-based systems. The objective of this study was to characterize mobility quality by examining associations between specific mobility scores, claw disorders (both the type and severity), body condition score (BCS), and cow parity. Data were collected for 6,927 cows from 52 pasture-based dairy herds, including mobility score (0 = optimal mobility; 1, 2, or 3 = increasing severities of suboptimal mobility), claw disorder type and severity, BCS, and cow parity. Multinomial logistic regression was used for analysis. The outcome variable was mobility score, and the predictor variables were BCS, type and severity of claw disorders, and cow parity. Three models were run, each with 1 reference category (mobility score 0, 1, or 2). Each model also included claw disorders (overgrown claw, sole hemorrhage, white line disease, sole ulcer, and digital dermatitis), BCS, and cow parity as predictor variables. The presence of most types of claw disorders had odds ratios >1, indicating an increased likelihood of a cow having suboptimal mobility. Low BCS (BCS <3.00) was associated with an increased risk of a cow having suboptimal mobility, and relatively higher parity was also associated with an increased risk of suboptimal mobility. These results confirm an association between claw disorders, BCS, cow parity, and dairy cow mobility score. Therefore, mobility score should be routinely practiced to identify cows with slight deviations from the optimal mobility pattern and to take preventive measures to keep the problem from worsening.

A national methodology to quantify the diet of grazing dairy cows.

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.

Using models to establish the financially optimum strategy for Irish dairy farms.

Determining the effect of a change in management on farm with differing characteristics is a significant challenge in the evaluation of dairy systems due to the interacting components of complex biological systems. In Ireland, milk production is increasing substantially following the abolition of the European Union milk quota regime in 2015. There are 2 main ways to increase the milk production on farm (within a fixed land base): either increase the number of animals (thus increasing the stocking rate) or increase the milk production per animal through increased feeding or increased lactation length. In this study, the effect of increased concentrate feeding or an increase in grazing intensity was simulated to determine the effect on the farm system and its economic performance. Four stocking rates (2.3, 2.6, 2.9, and 3.2 cow/ha) and 5 different concentrate supplementation strategies (0, 180, 360, 600, and 900 kg of dry matter/lactation) resulting in 20 different scenarios were evaluated across different milk, concentrate, and silage purchase prices. Each simulation was run across 10 yr of meteorological data, which had been recorded over the period 2004 to 2013. Three models-the Moorepark and St Gilles grass growth model, the pasture-based herd dynamic milk model, and the Moorepark dairy systems model-were integrated and applied to simulate the different scenarios. Overall, this study has demonstrated that the most profitable scenario was a stocking rate of 2.6 cow/ha with a concentrate supplementation of 600 kg of dry matter/cow. The factor that had the greatest influence on profitability was variability of milk price.