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Both blood- and milk-based biomarkers have been analysed for decades in research settings, although often only in one herd, and without focus on the variation in the biomarkers that are specifically related to herd or diet. Biomarkers can be used to detect physiological imbalance and disease risk and may have a role in precision livestock farming (PLF). For use in PLF, it is important to quantify normal variation in specific biomarkers and the source of this variation. The objective of this study was to estimate the between- and within-herd variation in a number of blood metabolites (β -hydroxybutyrate (BHB), non-esterified fatty acids, glucose and serum IGF-1), milk metabolites (free glucose, glucose-6-phosphate, urea, isocitrate, BHB and uric acid), milk enzymes (lactate dehydrogenase and N -acetyl- β -D-glucosaminidase (NAGase)) and composite indicators for metabolic imbalances (Physiological Imbalance-index and energy balance), to help facilitate their adoption within PLF. Blood and milk were sampled from 234 Holstein dairy cows from 6 experimental herds, each in a different European country, and offered a total of 10 different diets. Blood was sampled on 2 occasions at approximately 14 days-in-milk (DIM) and 35 DIM. Milk samples were collected twice weekly (in total 2750 samples) from DIM 1 to 50. Multilevel random regression models were used to estimate the variance components and to calculate the intraclass correlations (ICCs). The ICCs for the milk metabolites, when adjusted for parity and DIM at sampling, demonstrated that between 12% (glucose-6-phosphate) and 46% (urea) of the variation in the metabolites' levels could be associated with the herd-diet combination. Intraclass Correlations related to the herd-diet combination were generally higher for blood metabolites, from 17% (cholesterol) to approximately 46% (BHB and urea). The high ICCs for urea suggest that this biomarker can be used for monitoring on herd level. The low variance within cow for NAGase indicates that few samples would be needed to describe the status and potentially a general reference value could be used. The low ICC for most of the biomarkers and larger within cow variation emphasises that multiple samples would be needed - most likely on the individual cows - for making the biomarkers useful for monitoring. The majority of biomarkers were influenced by parity and DIM which indicate that these should be accounted for if the biomarker should be used for monitoring. [ABSTRACT FROM AUTHOR]

Growth rates in pre-weaned calves influence their health, age at first calving and lifetime productivity. Many farms restrict milk rations to encourage solid feed intake and facilitate early weaning, but this can compromise growth. This study determined the milk feeding policies and associated growth rates on 11 commercial dairy farms in South East England, each following their normal management regime. Between 26 and 54 heifers were recruited per farm, providing a final cohort of 492, of which 71% were pure Holstein. Information on calf rearing practices (feeding, weaning, housing) and health was collected via questionnaires and weekly observations. Estimates of actual milk fed (kg solids) between 1 and 63 days were calculated for individual calves. Morphometric data (weight, height, length) were taken at weeks 1, 5 and 9 and at a median age of 7.5 months and growth rates were calculated. Most calves were fed milk replacer via automated feeders (four farms), teat feeder (one) or buckets (four) whereas two farms provided drums of acidified waste milk. Farms fed between 4 and 6 l/day of milk at mixing rates of 10% to 15%, providing 400 to 900 g/day of milk solids. Both skeletal growth rates and average daily weight gain (ADG) increased in the second month of life compared with the first: height growth from 0.17±0.14 to 0.25±0.16 cm/day and ADG from 0.48±0.25 to 0.71±0.28 kg/day. Post-weaning heifers up to 7.5 months had height increases of 0.16±0.035 cm/day and ADG of 0.83±0.16 kg/day. From 1 to 63 days 70% of calves had growth rates <0.7 kg/day and of these 19.6% gained <0.5 kg/day. Mean ADG before 9 weeks varied between farms from 0.52±0.30 to 0.75±0.20 kg/day. This was related to the amount of milk fed at both a farm and individual calf level. Increasing the total milk solids fed between 1 and 63 days from 20.4 to 46.3 kg (the 10th to 90th percentile observed) was associated with an increase of 0.11 kg/day ADG. All farms had a wide variation in growth rates despite single feeding policies. Higher circulating immunoglobulin G and IGF1 concentrations were associated with better growth, whereas low temperatures in month of birth, high scores for diarrhoea, respiratory and umbilical disease and large birth size reduced growth. Many commercially grown dairy heifers therefore experienced growth restriction in the pre-weaned period, potentially reducing their health, welfare and productivity. [ABSTRACT FROM AUTHOR]

Rearing quality dairy heifers is essential to maintain herds by replacing culled cows. Information on the key factors influencing the cost of rearing under different management systems is, however, limited and many farmers are unaware of their true costs. This study determined the cost of rearing heifers from birth to first calving in Great Britain including the cost of mortality, investigated the main factors influencing these costs across differing farming systems and estimated how long it took heifers to repay the cost of rearing on individual farms. Primary data on heifer management from birth to calving was collected through a survey of 101 dairy farms during 2013. Univariate followed by multivariable linear regression was used to analyse the influence of farm factors and key rearing events on costs. An Excel spreadsheet model was developed to determine the time it took for heifers to repay the rearing cost. The mean±SD ages at weaning, conception and calving were 62±13, 509±60 and 784±60 days. The mean total cost of rearing was £1819±387/heifer with a mean daily cost of £2.31±0.41. This included the opportunity cost of the heifer and the mean cost of mortality, which ranged from £103.49 to £146.19/surviving heifer. The multivariable model predicted an increase in mean cost of rearing of £2.87 for each extra day of age at first calving and a decrease in mean cost of £6.06 for each percentile increase in time spent at grass. The model also predicted a decrease in the mean cost of rearing in autumn and spring calving herds of £273.20 and £288.56, respectively, compared with that in all-year-round calving herds. Farms with herd sizes⩾100 had lower mean costs of between £301.75 and £407.83 compared with farms with <100 milking cows. The mean gross margin per heifer was £441.66±304.56 (range £367.63 to £1120.08), with 11 farms experiencing negative gross margins. Most farms repaid the cost of heifer rearing in the first two lactations (range 1 to 6 lactations) with a mean time from first calving until breaking even of 530±293 days. The results of the economic analysis suggest that management decisions on key reproduction events and grazing policy significantly influence the cost of rearing and the time it takes for heifers to start making a profit for the farm. [ABSTRACT FROM PUBLISHER]

The use of molecular genetic information in the evaluation of livestock has become more common. This study looks at the efficacy of using such information to improve the genetic evaluation of a rare breed of dual-purpose cattle. Data were available in the form of pedigree information on the Gloucester cattle breed in the United Kingdom and recorded milk and beef performance on a small number of animals. In addition, molecular genetic information in the form of multi-marker, multiple regression results converted to a 1 to 10 score (Igenity scores) and 123 single nucleotide polymorphism (SNP) genotypes for 199 non-recorded animals were available. Appropriate mixed-animal models were explored for the recorded traits and these were used to calculate estimated breeding values (EBV), and their accuracies, for 6527 animals in the breed’s pedigree file. Various ways to improve the accuracy of these EBV were explored. This involved using multivariate BLUP analyses, genomic estimated breeding values (GEBV) and combining Igenity scores with recorded traits in a series of bivariate genetic analyses. Using the milk recording traits as an example, the accuracy of a number of traits could be improved using multivariate analyses by up to 14%, depending on the combination of traits used. The level of increase in accuracy largely corresponded to the absolute difference between the genetic and residual correlations between two traits, but this was not always symmetrical. The use of GEBV did not increase the accuracy of milk trait EBV owing to the low proportion of variance explained by the 101 SNPs used. Using Igenity scores in bivariate analyses with the recorded data was more successful in increasing EBV accuracy. The largest increases were found in genotyped animals with no recorded performance (e.g. a 58% increase in fat weight in milk); however, the size of the increase depended on the level of the genetic correlation between the recorded trait and the Igenity score for that trait. Lower levels of improvements in accuracy were seen in animals that were recoded but not genotyped, and ancestors which were neither genotyped nor recorded. This study demonstrated that it was possible to improve the accuracy of EBV estimation by including Igenity score information in genetic analyses but it also concluded that increasing the level of performance recording in the breed would be beneficial. [ABSTRACT FROM PUBLISHER]

Housed pigs are often exposed to elevated concentrations of atmospheric ammonia. This aerial pollutant is widely considered to be an environmental stressor that also predisposes to reduced growth rates and poor health, although evidence to support this view is limited. Hepatic gene expression is very responsive to stress and metabolic effects. Two batches of growing pigs were therefore exposed to a nominal concentration of atmospheric ammonia of either 5 ppm (low) or 20 ppm (high) from 4 weeks of age for 15 weeks. Growth rates were monitored. Samples of liver were taken after slaughter (at ∼19 weeks of age). Samples from the second batch were analysed for global gene expression using 23 K Affymetrix GeneChip porcine genome arrays. Samples from both batches were subsequently tested for five candidate genes using quantitative real-time PCR (qPCR). The array analysis failed to detect any significant changes in hepatic gene expression following chronic exposure to atmospheric ammonia. Animals clustered into two main groups but this was not related to the experimental treatment. There was also no difference in growth rates between groups. The qPCR analyses validated the array results by showing similar fold changes in gene expression to the arrays. They revealed a significant batch effect in expression of lipin 1 (LPIN1), Chemokine (C-X-C motif) ligand 14 (CXCL14), serine dehydratase (SDS) and hepcidin antimicrobial peptide (HAMP). Only CXCL14, a chemotactic cytokine for monocytes, was significantly down-regulated in response to ammonia. As chronic exposure to atmospheric ammonia did not have a clear influence on hepatic gene expression, this finding implies that 20 ppm of atmospheric ammonia did not pose a significant material risk to the health or metabolism of housed pigs. [ABSTRACT FROM PUBLISHER]

The average dairy cow survives only three lactations, reducing the availability of replacement heifers. Prenatal losses occur due to early embryonic mortality (about 40%), later embryo loss (up to 20% in high-yielding herds) or abortion (about 5%). A recent survey of 19 UK herds showed that 7.9% of calves were born dead and 3.4% died within 1 month. During the rearing phase, 6.7% of animals were lost before reaching first service at 15 months due to disease or accident and another 2.3% failed to conceive. Many potential replacements therefore never enter the milking herd. This severely limits opportunities for on-farm selection of breeding cows in addition to presenting a welfare issue and causing economic loss. The most profitable animals once lactation is reached combine good milk production with a regular calving pattern. Some aspects of performance are related to age at first calving (AFC), which in turn is influenced by heifer growth rates. Poorly growing animals required more services to conceive, calved later and subsequently performed badly. Optimum fertility and maximum yield in the first lactation were associated with an AFC of 24 to 25 months. However, heifers calving at 22 to 23 months performed best in terms of total milk yield and survival over the first 5 years, partly because good heifer fertility was associated with better fertility later. We have investigated some possible juvenile predictors of future performance. Low-birth-weight calves were more likely to come from either primiparous mothers or older dams (3+ lactations) with higher peak milk yields, suggesting that the uterine environment may limit prenatal calf growth due to competition for nutrients with maternal growth or milk production. Linear trait classification scores for frame size show genetic correlations with longevity. The skeletal measures of height and crown rump length in 1-month-old calves was correlated to subsequent stature, and frame size was correlated to weight at 15 months. It may thus be possible to predict performance from simple size measurements as juveniles. Neither endogenous nor stimulated growth hormone (GH) release in 6-month-old calves were related to milk yield in the first three lactations, but size of a stimulated GH peak was positively related to milk energy values in the first lactation. Cows with delayed ovulation (>45 days) in the first lactation had a higher GH pulse amplitude and lower IGF-I as a juvenile. Cows that partition excess energy into milk in their first lactation may suffer reduced longevity. [ABSTRACT FROM AUTHOR]