On the use of milk composition measures to predict the energy balance of dairy cows

Genetic analysis of milk citrate predicted by milk mid-infrared spectra of Holstein cows in early lactation

Milk citrate is regarded as an early biomarker of negative energy balance in dairy cows during early lactation and serves as a suitable candidate phenotype for genomic selection due to its wide availability across a large number of cows through milk mid-infrared spectra prediction. However, its genetic background is not well known. Therefore, the objectives of this study were to (1) analyze the genetic parameters of milk citrate; (2) identify genomic regions associated with milk citrate; and (3) analyze the functional annotation of candidate genes and quantitative trait loci (QTL) related to milk citrate in Walloon Holstein cows. In total, 134,517 test-day milk-citrate phenotypes (mmol/L) collected within the first 50 d in milk on 52,198 Holstein cows were used. These milk-citrate phenotypes, predicted by milk mid-infrared spectra, were divided into 3 traits according to the first (citrate1), second (citrate2), and third to fifth parity (citrate3+). Genomic information for 566,170 SNPs was available for 4,479 animals. A multiple-trait repeatability model was used to estimate genetic parameters. A single-step GWAS was used to identify candidate genes for citrate and post-GWAS analysis was done to investigate the relationship and function of the identified candidate genes. The heritabilities estimated for citrate1, citrate2, and citrate3+ were 0.40, 0.37, and 0.35, respectively. The genetic correlations among the 3 traits ranged from 0.98 to 0.99. The genomic correlations among the 3 traits were also close to 1.00 across the genomic regions (1 Mb) in the whole genome, which means that citrate can be considered as a single trait in the first 5 parities. In total, 603 significant SNPs located on 3 genomic regions (chromosome 7, 68.569-68.575 Mb; chromosome 14, 0.15-1.90 Mb; and chromosome 20, 54.00-64.28 Mb), were identified to be associated with milk citrate. We identified 89 candidate genes including GPT, ANKH, PPP1R16A, and 32 QTL reported in the literature related to the identified significant SNPs. These identified QTL were mainly reported associated with milk fatty acids and metabolic diseases in dairy cows. This study suggests that milk citrate in Holstein cows is highly heritable and has the potential to be used as an early proxy for the negative energy balance of Holstein cows in a breeding objective.

Developmental programming of production and reproduction in dairy cows: IV. Association of maternal milk fat and protein percentage and milk fat to protein ratio with offspring's birth weight, survival, productive and reproductive performance and AMH concentration from birth to the first lactation period

Although the association of maternal milk production with developmental programming of offspring has been investigated, there is limited information available on the relationship of maternal milk components with productive and reproductive performance of the offspring. Therefore, the present study was conducted to analyze the association of maternal milk fat and protein percentage and milk fat to protein ratio with birth weight, survival, productive and reproductive performance and AMH concentration in the offspring. In study I, data of birth weight, milk yield and reproductive variables of offspring born to lactating dams (n = 14,582) and data associated with average maternal milk fat percentage (MFP), protein percentage (MPP) and fat to protein ratio (MFPR) during 305-day lactation were retrieved. Afterwards, offspring were classified in various categories of MFP, MPP and MFPR. In study II, blood samples (n = 339) were collected from offspring in various categories of MFP, MPP and MFPR for measurement of serum AMH. Maternal milk fat percentage was positively associated with birth weight and average percentage of milk fat (APMF) and protein (APMP) and milk fat to protein ratio (FPR) during the first lactation, but negatively associated with culling rate during nulliparity in the offspring (P < 0.05). Maternal milk protein percentage was positively associated with birth weight, APMF, APMP, FPR and culling rate, but negatively associated with milk yield and fertility in the offspring (P < 0.05). Maternal FPR was positively associated with APMF and FPR, but negatively associated with culling rate, APMP and fertility in the offspring (P < 0.05). However, concentration of AMH in the offspring was not associated with MFP, MPP and MFPR (P > 0.05). In conclusion, the present study revealed that maternal milk fat and protein percentage and their ratio were associated with birth weight, survival, production and reproduction of the offspring. Yet it was a preliminary research and further studies are required to elucidate the mechanisms underlying these associations.

Genetic association between fat-to-protein ratio and traits of economic interest in early lactation Holstein cows in Brazil

The aims of this study were to estimate the genetic parameters for fat-to-protein ratio (F:P) within the first 90 days of lactation and to examine their genetic associations with daily milk yield (MY), somatic cell score (SCS), and calving interval between the first and second calving (IFSC) and between the second and third calving (ISTC) during the first three lactations of Holstein cows. We utilized 200,626 production-related data officially recorded from 77,436 cows milked two or three times a day from 2012 to 2022, sourced from the Holstein Cattle Breeders Association of Paraná State, Brazil. The (co)variance components were estimated using animal models, adopting the restricted maximum likelihood (REML) method with single-trait analysis (for heritability and repeatability) and two-trait analysis (for genetic and phenotypic correlations), per lactation. Regardless of lactation number, heritability estimates were relatively low, ranging from 0.08 ± 0.005 to 0.10 ± 0.003 for F:P; 0.08 ± 0.01 to 0.18 ± 0.005 for MY; 0.04 ± 0.01 to 0.07 ± 0.004 for SCS; and 0.03 ± 0.01 for both IFSC and ISTC. Repeatability estimates within the same lactation were low for F:P (ranging from 0.17 ± 0.002 to 0.19 ± 0.03), high for MY (between 0.50 ± 0.003 and 0.53 ± 0.002), and moderate to high for SCS (between 0.39 ± 0.003 and 0.44 ± 0.004). Genetic correlations between F:P and MY ranged from -0.26 ± 0.03 to -0.15 ± 0.02; F:P and SCS, from -0.06 ± 0.03 to -0.03 ± 0.08; F:P and IFSC, 0.31 ± 0.01; F:P and ISTC, 0.20 ± 0.01; MY and IFSC, 0.24 ± 0.05; and MY and ISTC, 0.13 ± 0.08. The fat-to-protein ratio during early lactation showed low genetic variability, regardless of lactation number. Furthermore, it was genetically correlated with MY, IFSC, and ISTC, although there is an antagonistic and unfavorable correlation between traits that can limit genetic progress.

A 2-year study reveals implications of feeding management and exposure to mycotoxins on udder health, performance, and fertility in dairy herds

We recently reported the ubiquitous occurrence of mycotoxins and their secondary metabolites in dairy rations and a substantial variation in the feeding management among Austrian dairy farms. The present study aimed to characterize to which extent these factors contribute to the fertility, udder health traits, and performance of dairy herds. During 2019 and 2020, we surveyed 100 dairy farms, visiting each farm 2 times and collecting data and feed samples. Data collection involved information on the main feed ingredients, nutrient composition, and the levels of mycotoxin and other metabolites in the diet. The annual fertility and milk data of the herds were obtained from the national reporting agency. Calving interval was the target criterion for fertility performance, whereas the percentage of primiparous and multiparous cows in the herd with somatic cell counts above 200,000 cells/mL was the criterion for impaired udder health. For each criterion, herds were classified into 3 groups: high/long, mid, and low/short, with the cut-off corresponding to the <25th and >75th percentiles and the rest of the data, respectively. Accordingly, for the calving interval, the cut-offs for the long and short groups were ≥400 and ≤380 d, for the udder health in primiparous cows were ≥20% and ≤8% of the herd, and for the udder health in multiparous cows were ≥35% and ≤20% of the herd, respectively. Quantitative approaches were further performed to define potential risk factors in the herds. The high somatic cell count group had higher dietary exposure to enniatins (2.8 vs. 1.62 mg/cow per d), deoxynivalenol (4.91 vs. 2.3 mg/cow per d), culmorin (9.48 vs. 5.72 mg/cow per d), beauvericin (0.32 vs. 0.18 mg/cow per d), and siccanol (13.3 vs. 5.15 mg/cow per d), and total Fusarium metabolites (42.8 vs. 23.2 mg/cow per d) and used more corn silage in the ration (26.9% vs. 17.3% diet DM) compared with the low counterparts. Beauvericin was the most substantial contributing variable among the Fusarium metabolites, as indicated by logistic regression and modeling analyses. Logistic analysis indicated that herds with high proportions of cows with milk fat-to-protein ratio >1.5 had an increased odds for a longer calving interval, which was found to be significant for primiparous cows (odds ratio = 5.5, 95% confidence interval = 1.65-21.7). As well, herds with high proportions of multiparous cows showing levels of milk urea nitrogen >30 mg/dL had an increased odds for longer calving intervals (odds ratio = 2.96, 95% confidence interval = 1.22-7.87). In conclusion, the present findings suggest that dietary contamination of Fusarium mycotoxins (especially emerging ones), likely due to increased use of corn silage in the diet, seems to be a risk factor for impairing the udder health of primiparous cows. Mismatching dietary energy and protein supply of multiparous cows contributed to reduced herd fertility performance.

Milk as an indicator of dietary imbalance

BACKGROUND

Milk provides a readily available diagnostic fluid collected daily or more frequently on an individual animal or herd basis. Milk, as an aggregated sample in bulk tank milk (BTM) represents the status of a herd instead of a single animal. In this review, we examine the potential for milk to predict risks to efficient production, reproductive success, and health on the individual cow and herd level.

FINDINGS

For many conditions related to disorders of metabolism including hyperlipdaemia and ketonaemia, improved individual cow milk testing may allow a temporally useful detection of metabolic disorder that can target intervention. However, the extension of these tests to the BTM is made more difficult by the tight temporal clustering of disorder to early lactation and the consequent mixing of cows at even moderately different stages of lactation. Integrating herd recording demographic information with Fourier-transformed mid-infrared spectra (FT-MIR) can provide tests that are useful to identify cows with metabolic disorders. The interpretation of BTM urea and protein content provides useful indications of herd nutrition. These may provide indicators that encourage further investigations of nutritional influences on herd fertility but are unlikely to provide strong diagnostic value. The fat-to-protein ratio has a high specificity, but poor sensitivity for detection of fibre insufficiency and acidosis on an individual cow basis. Selenium, zinc, β-carotene, and vitamin E status of the herd can be determined using BTM.

CONCLUSIONS

There appears to be increasing potential for the use of milk as a diagnostic fluid as more in-parlour tests become available for individual cows. However, the BTM appears to have under-utilised potential for herd monitoring.

Nuciferine protects bovine hepatocytes against free fatty acid-induced oxidative damage by activating the transcription factor EB/peroxisome proliferator-activated receptor γ coactivator 1 alpha pathway

Excessive free fatty acid (FFA) oxidation and related metabolism are the major cause of oxidative stress and liver injury in dairy cows during the early postpartum period. In nonruminants, activation of transcription factor EB (TFEB) can improve cell damage and reduce the overproduction of mitochondrial reactive oxygen species. As a downstream target of TFEB, peroxisome proliferator-activated receptor γ coactivator 1 α (PGC-1α, gene name PPARGC1A) is a critical regulator of oxidative metabolism. Nuciferine (Nuc), a major bioactive compound isolated from the lotus leaf, has been reported to possess hepatoprotective activity. Therefore, the objective of this study was to investigate whether Nuc could protect bovine hepatocytes from FFA-induced lipotoxicity and the underlying mechanisms. A mixture of FFA was diluted in RPMI-1640 basic medium containing 2% low fatty acid bovine serum albumin to treat hepatocytes. Bovine hepatocytes were isolated from newborn calves and treated with various concentrations of FFA mixture (0, 0.3, 0.6, or 1.2 mM) or Nuc (0, 25, 50, or 100 μM), as well as co-treated with 1.2 mM FFA and different concentrations of Nuc. For the experiments of gene silencing, bovine hepatocytes were transfected with small interfering RNA targeted against TFEB or PPARGC1A for 36 h followed by treatment with 1.2 mM FFA for 12 h in presence or absence of 100 μΜ Nuc. The results revealed that FFA treatment decreased protein abundance of nuclear TFEB, cytosolic TFEB, total (t)-TFEB, lysosome-associated membrane protein 1 (LAMP1) and PGC-1α and mRNA abundance of LAMP1, but increased phosphorylated (p)-TFEB. In addition, FFA treatment increased the content of malondialdehyde (MDA) and hydrogen peroxide (H2O2) and decreased the activities of catalase (CAT) and glutathione peroxidase (GSH-Px) in bovine hepatocytes. Moreover, FFA administration enhanced the activities of alanine aminotransferase (ALT), aspartate aminotransferase (AST), and lactose dehydrogenase (LDH) in the medium of FFA-treated hepatocytes, but reduced the content of urea. In FFA-treated bovine hepatocytes, Nuc administration increased TFEB nuclear localization and the protein abundance of t-TFEB, LAMP1, and PGC-1α and mRNA abundance of LAMP1, decreased the contents of MDA and H2O2 and the protein abundance of p-TFEB, and enhanced the activities of CAT and GSH-Px in a dose-dependent manner. Consistently, Nuc administration reduced the activities of ALT, AST, and LDH and increased the content of urea in the medium of FFA-treated hepatocytes. Importantly, knockdown of TFEB reduced the protein abundance of p-TFEB, t-TFEB, LAMP1, and PGC-1α and mRNA abundance of LAMP1, and impeded the beneficial effects of Nuc on FFA-induced oxidative damage in bovine hepatocytes. In addition, PPARGC1A silencing did not alter Nuc-induced nuclear translocation of TFEB, increase of the protein abundance of t-TFEB, LAMP1, and PGC-1α and mRNA abundance of LAMP1, or decrease of the protein abundance of p-TFEB, whereas it partially reduced the beneficial effects of Nuc on FFA-caused oxidative injury. Taken together, Nuc exerts protective effects against FFA-induced oxidative damage in bovine hepatocytes through activation of the TFEB/PGC-1α signaling pathway.

Identification of Milk Adulteration in Camel Milk Using FT-Mid-Infrared Spectroscopy and Machine Learning Models

Camel milk, esteemed for its high nutritional value, has long been a subject of interest. However, the adulteration of camel milk with cow milk poses a significant threat to food quality and safety. Fourier-transform infrared spectroscopy (FT-MIR) has emerged as a rapid method for the detection and quantification of cow milk adulteration. Nevertheless, its effectiveness in conveniently detecting adulteration in camel milk remains to be determined. Camel milk samples were collected from Alxa League, Inner Mongolia, China, and were supplemented with varying concentrations of cow milk samples. Spectra were acquired using the FOSS FT6000 spectrometer, and a diverse set of machine learning models was employed to detect cow milk adulteration in camel milk. Our results demonstrate that the Linear Discriminant Analysis (LDA) model effectively distinguishes pure camel milk from adulterated samples, maintaining a 100% detection rate even at cow milk addition levels of 10 g/100 g. The neural network quantitative model for cow milk adulteration in camel milk exhibited a detection limit of 3.27 g/100 g and a quantification limit of 10.90 g/100 g. The quantitative model demonstrated excellent precision and accuracy within the range of 10-90 g/100 g of adulteration. This study highlights the potential of FT-MIR spectroscopy in conjunction with machine learning techniques for ensuring the authenticity and quality of camel milk, thus addressing concerns related to food integrity and consumer safety.

Correlation between breeding values for milk fatty acids and Nordic Total Merit index traits for Danish Holstein and Danish Jersey

Milk fatty acid composition is gaining interest in the Danish dairy industry both to develop new dairy products and as a management tool. To be able to implement milk fatty acid (FA) composition in the breeding program, it is important to know the correlations with the traits in the breeding goal. To estimate these correlations, we measured milk fat composition in Danish Holstein (DH) and Danish Jersey (DJ) cattle breeds using mid-infrared spectroscopy. Breeding values were estimated for specific FA and for groups of FA. Correlations with the estimated breeding values (EBV) underlying the Nordic Total Merit index (NTM) were calculated within breed. For both DH and DJ, we showed that FA EBV had moderate correlations with the NTM and production traits. For both DH and DJ, the correlation of FA EBV and NTM were in the same direction, except for C16:0 (0 in DH, 0.23 in DJ). A few correlations differed between DH and DJ. The correlation between claw health index and C18:0 was negative in DH (-0.09) but positive in DJ (0.12). In addition, some correlations were not significant in DH but were significant in DJ. The correlations between udder health index and long-chain FA, trans FA, C16:0, and C18:0 were not significant in DH (-0.05 to 0.02), but were significant in DJ (-0.17, -0.15, 0.14, and -0.16, respectively). For both DH and DJ, the correlations between FA EBV and nonproduction traits were low. This implies that it is possible to breed for a different fat composition in the milk without affecting the nonproduction traits in the breeding goal.

Implications of Organic Dairy Management on Herd Performance and Milk Fatty Acid Profiles and Interactions with Season

Interest in organic cows' milk has increased due to the perceived superior nutritional quality and improved sustainability and animal welfare. However, there is a lack of simultaneous assessments on the influence of organic dairy practices and dietary and breed drivers on productivity, feed efficiency, health parameters, and nutritional milk quality at the herd level. This work aimed to assess the impact of organic vs. conventional management and month on milk yield and basic composition, herd feed efficiency, health parameters, and milk fatty acid (FA) composition. Milk samples (n = 800) were collected monthly from the bulk tanks of 67 dairy farms (26 organic and 41 conventional) between January and December 2019. Data on breed and feeding practices were gathered via farm questionnaires. The samples were analyzed for their basic composition and FA profile using Fourier transform infrared spectroscopy (FTIR) and gas chromatography (GC), respectively. The data were analyzed using a linear mixed model, repeated measures design and multivariate redundancy analysis (RDA). The conventional farms had higher yields (kg/cow per day) of milk (+7.3 kg), fat (+0.27 kg), and protein (+0.25 kg) and higher contents (g/kg milk) of protein, casein, lactose, and urea. The conventional farms produced more milk (+0.22 kg), fat (+8.6 g), and protein (+8.1 g) per kg offered dry matter (DM). The organic farms produced more milk per kg of offered non-grazing and concentrate DM offered, respectively (+0.5 kg and +1.23 kg), and fat (+20.1 g and +51 g) and protein (+17 g and +42 g). The organic milk had a higher concentration of saturated fatty acid (SFA; +14 g/kg total FA), polyunsaturated fatty acid (PUFA; +2.4 g/kg total FA), and nutritionally beneficial FA alpha linolenic acid (ALNA; +14 g/kg total FA), rumenic acid (RA; +14 g/kg total FA), and eicosapentaenoic acid (EPA; +14 g/kg total FA); the conventional milk had higher concentrations of monounsaturated FA (MUFA; +16 g/kg total FA). Although the conventional farms were more efficient in converting the overall diet into milk, fat, and protein, the organic farms showed better efficiency in converting conserved forages and concentrates into milk, fat, and protein as a result of reduced concentrate feeding. Considering the relatively small differences in the FA profiles between the systems, increased pasture intake can benefit farm sustainability without negatively impacting consumer nutrition and health.

Heritabilities and genetic and phenotypic correlations for milk production and fertility traits of spring-calved once-daily or twice-daily milking cows in New Zealand

The objectives of this study were to estimate the genetic and phenotypic correlations and heritabilities for milk production and fertility traits in spring-calved once-daily (OAD) milking cows for the whole season in New Zealand and compare those estimates with twice-daily (TAD) milking cows. Data used in the study consisted of 69,252 first parity cows from the calving seasons 2015-2016 to 2017-2018 in 113 OAD and 531 TAD milking herds. Heritability estimates for production and fertility traits were obtained through single-trait animal models, and estimates of genetic and phenotypic correlations were obtained through bivariate animal models. Heritability estimates of production traits varied from 0.26 to 0.61 in OAD and from 0.13 to 0.63 in TAD. Heritability estimates for fertility traits were low in both OAD and TAD milking cow populations, and estimates were consistent (OAD: 0.01 to 0.10 and TAD: 0.01 to 0.08) across milking regimens. Estimates of phenotypic and genetic correlations among production traits were consistent across populations. In both populations, phenotypic correlations between milk production and fertility traits were close to zero, and most of the genetic correlations were antagonistic. In OAD milking cows, genetic correlations of milk and lactose yields with the start of mating to conception, 6-wk in-calf, not-in-calf, and 6-wk calving rate were close to zero. Interval from first service to conception was negatively genetically correlated with milk and lactose yields in OAD milking cows. Protein percentage was positively genetically correlated with 3-wk and 6-wk submission, 3-wk in-calf, 6-wk in-calf, first service to conception, 3-wk calving, and 6-wk calving rate in the TAD milking cow population, but these correlations were low in the OAD milking cow population. Further studies are needed to understand the relationship of protein percentage and fertility traits in the OAD milking system. The phenotypic correlations between fertility traits were similar in OAD and TAD milking populations. Genetic correlations between fertility traits were strong (≥0.70) in cows milked TAD, but genetic correlations varied from weak to strong in cows milked OAD. Further research is required to evaluate the interaction between genotype by milking regimen for fertility traits in terms of sire selection in the OAD milking cow population.

Revisiting the Relationships between Fat-to-Protein Ratio in Milk and Energy Balance in Dairy Cows of Different Parities, and at Different Stages of Lactation

Simple Summary

Data from 840 Holstein-Friesian cows (1321 lactations) were used to evaluate trends in fat-to-protein ratios in milk (FPR), and the use of FPR as an indicator of energy balance (EB). The fat-to-protein ratio was negatively related to EB, and this relationship became more negative with increased parity. Regression slopes describing linear relationships between FPR and EB differed over time, although trends were inconsistent. Similarly, ‘High’ FPR scores in milk (≥1.5) were consistently associated with a greater negative energy balance, milk yields, body weight loss, and plasma non-esterified fatty acid concentrations; however, their relationships with dry matter intake did not follow a clear trend. Although FPR can provide an indication of EB at a herd level, this analysis suggests that FPR cannot accurately predict the EB of individual cows.

Abstract

A statistical re-assessment of aggregated individual cow data was conducted to examine trends in fat-to-protein ratio in milk (FPR), and relationships between FPR and energy balance (EB, MJ of ME/day) in Holstein-Friesian dairy cows of different parities, and at different stages of lactation. The data were collected from 27 long-term production trials conducted between 1996 and 2016 at the Agri-Food and Biosciences Institute (AFBI) in Hillsborough, Northern Ireland. In total, 1321 lactations (1 to 20 weeks in milk; WIM), derived from 840 individual cows fed mainly grass silage-based diets, were included in the analysis. The energy balance was calculated daily and then averaged weekly for statistical analyses. Data were further split in 4 wk. intervals, namely, 1–4, 5–8, 9–12, 13–16, and 17–20 WIM, and both partial correlations and linear regressions (mixed models) established between the mean FPR and EB during these periods. Three FPR score categories (‘Low’ FPR, <1.0; ‘Normal’ FPR, 1.0–1.5; ‘High’ FPR, >1.5) were adopted and the performance and EB indicators within each category were compared. As expected, multiparous cows experienced a greater negative EB compared to primiparous cows, due to their higher milk production relative to DMI. Relatively minor differences in milk fat and protein content resulted in large differences in FPR curves. Second lactation cows displayed the lowest weekly FPR, and this trend was aligned with smaller BW losses and lower concentrations of non-esterified fatty acids (NEFA) until at least 8 WIM. Partial correlations between FPR and EB were negative, and ‘greatest’ in early lactation (1–4 WIM; r = −0.38 on average), and gradually decreased as lactation progressed across all parities (17–20 WIM; r = −0.14 on average). With increasing parity, daily EB values tended to become more negative per unit of FPR. In primiparous cows, regression slopes between FPR and EB differed between 1–4 and 5–8 WIM (−54.6 vs. −47.5 MJ of ME/day), while differences in second lactation cows tended towards significance (−57.2 vs. −64.4 MJ of ME/day). Irrespective of the lactation number, after 9–12 WIM, there was a consistent trend for the slope of the linear relationships between FPR and EB to decrease as lactation progressed, with this likely reflecting the decreasing milk nutrient demands of the growing calf. The incidence of ‘High’ FPR scores was greatest during 1–4 WIM, and decreased as lactation progressed. ‘High’ FPR scores were associated with increased energy-corrected milk (ECM) yields across all parities and stages of lactation, and with smaller BW gains and increasing concentrations (log transformed) of blood metabolites (non-esterified fatty acid, NEFA; beta-hydroxybutyrate, BHB) until 8 WIM. Results from the present study highlight the strong relationships between FPR in milk, physiological changes, and EB profiles during early lactation. However, while FPR can provide an indication of EB at a herd level, the large cow-to-cow variation indicates that FPR cannot be used as a robust indicator of EB at an individual cow level.

Milk metabolites and fatty acids as noninvasive biomarkers of metabolic status and energy balance in early-lactation cows

The objective was to study the effects of week of lactation (WOL) and experimental nutrient restriction on concentrations of selected milk metabolites and fatty acids (FA), and assess their potential as biomarkers of energy status in early-lactation cows. To study WOL effects, 17 multiparous Holstein cows were phenotyped from calving until 7 WOL while allowed ad libitum intake of a lactation diet. Further, to study the effects of nutrient restriction, 8 of these cows received a diet containing 48% straw (high-straw) for 4 d starting at 24 ± 3 days in milk (mean ± SD), and 8 cows maintained on the lactation diet were sampled to serve as controls. Blood and milk samples were collected weekly for the WOL data set, and daily from d -1 to 3 of nutrient restriction (or control) for the nutritional challenge data set. Milk β-hydroxybutyrate (BHB), isocitrate, glucose, glucose-6-phosphate (glucose-6P), galactose, glutamate, creatinine, uric acid, and N-acetyl-β-d-glucosaminidase activity (NAGase) were analyzed in p.m. and a.m. samples, and milk FA were analyzed in pooled p.m. and a.m. samples. Average energy balance (EB) per day ranged from -27 MJ/d to neutral when cows received the lactation total mixed ration, and from -109 to -87 ± 7 MJ/d for high-straw (least squares means ± standard error of the mean). Plasma nonesterified FA concentration was 1.67 ± 0.13 mM and BHB was 2.96 ± 0.39 mM on the d 3 of high-straw (least squares means ± standard error of the mean). Milk concentrations of BHB, glucose, glucose-6P, glutamate, and uric acid differed significantly between p.m. and a.m. milkings. Milk isocitrate, glucose-6P, creatinine, and NAGase decreased, whereas milk glucose and galactose increased with WOL. Changes in milk BHB, isocitrate, glucose, glucose-6P, and creatinine were concordant during early lactation and in response to nutrient restriction. Milk galactose and NAGase were modulated by WOL only, whereas glutamate and uric acid concentrations responded to nutrient restriction only. The high-straw increased milk concentrations of FA potentially mobilized from adipose tissue (e.g., C18:0 and cis-9 C18:1 and sum of odd- and branched-chain FA (OBCFA) with carbon chain greater than 16; ∑ OBCFA >C16), and decreased concentrations of FA synthesized de novo by the mammary gland (e.g., sum of FA with 6 to 15 carbons; ∑ C6:0 to C15:0). Similar observations were made during early lactation. Plasma nonesterified FA concentrations had the best single linear regression with EB (R² = 0.62). Milk isocitrate, Σ C6:0 to C15:0. and cis-9 C18:1 had the best single linear regressions with EB (R² ≥ 0.44). Milk BHB, isocitrate, galactose, glutamate, and creatinine explained up to 64% of the EB variation observed in the current study using multiple linear regression. Milk concentrations of ∑ C6:0 to C15:0, C18:0, cis-9 C18:1, and ∑ OBCFA >C16 presented some of the best correlations and regressions with other indicators of metabolic status, lipomobilization, and EB, and their responses were concordant during early lactation and during experimental nutrient restriction. Metabolites and FA secreted in milk may serve as noninvasive indicators of metabolic status and EB of early-lactation cows.

Ufbp1, a Key Player of Ufm1 Conjugation System, Protects Against Ketosis-Induced Liver Injury via Suppressing Smad3 Activation

The dairy cattle suffer from severe liver dysfunction during the pathogenesis of ketosis. The Ufm1 conjugation system is crucial for liver development and homeostasis. Ufm1 binding protein (Ufbp1) is a putative Ufm1 target and an integral component, but its role in ketosis-induced liver injury is unclear so far. The purpose of this study is to explore the key role of Ufbp1 in liver fibrosis caused by ketosis in vivo and in vitro. Liver tissues were collected from ketotic cows and Ufbp1 conditional knockout (CKO) mice in vivo. However, Ufbp1^–/– mouse embryonic fibroblast cells and Hela cells were used for in vitro validation. Subsequently, various assays were performed to reveal the underlying molecular mechanisms of the Ufbp1 protective effect. In this study, hepatic fibrosis, endoplasmic reticulum (ER) stress, and apoptosis were reported in the liver of ketotic cows, fibrotic markers (alpha-smooth muscle actin, Collagen1) and ER stress markers (glucose-regulated protein 78, CEBP homologous protein) were upregulated remarkably, and the apoptosis-related genes (Bcl2, Bax) were in line with expectations. Interestingly, Ufbp1 expression was almost disappeared, and Smad2/Smad3 protein was largely phosphorylated in the liver of ketotic cows, but Ufbp1 deletion caused Smad3 phosphorylation apparently, rather than Smad2, and elevated ER stress was observed in the CKO mice model. At the cellular level, Ufbp1 deficiency led to serious fibrotic and ER stress response, Smad3 was activated by phosphorylation significantly and then was translocated into the nucleus, whereas p-Smad2 was largely unaffected in embryonic fibroblast cells. Ufbp1 overexpression obviously suppressed Smad3 phosphorylation in Hela cells. Ufbp1 was found to be in full combination with Smad3 using endogenous immunoprecipitation. Taken together, our findings suggest that downregulation or ablation of Ufbp1 leads to Smad3 activation, elevated ER stress, and hepatocyte apoptosis, which in turn causes liver fibrosis. Ufbp1 plays a protective role in ketosis-induced liver injury.

Milk fatty acids as indicators of negative energy balance of dairy cows in early lactation

Most dairy cows experience negative energy balance (NEB) in early lactation because energy demand for milk synthesis is not met by energy intake. Excessive NEB may lead to metabolic disorders and impaired fertility. To optimize herd management, it is useful to detect cows in NEB in early lactation, but direct calculation of NEB is not feasible in commercial herds. Alternative methods rely on fat-to-protein ratio in milk or on concentrations of non-esterified fatty acids (NEFA) and β-hydroxybutyrate (BHB) in blood. Here, we considered methods to assess energy balance (EB) of dairy cows based on the fatty acid (FA) composition in milk. Short- and medium-chain FAs (primarily, C14:0) are typically synthesized de novo in the mammary gland and their proportions in milk fat decrease during NEB. Long-chain FAs C18:0 and C18:1 cis-9 are typically released from body fat depots during NEB, and their proportions increase. In this study, these FAs were routinely determined by Fourier-transform infrared spectroscopy (FTIR) of individual milk samples. We performed an experiment on 85 dairy cows in early lactation, fed the same concentrate ration of up to 5 kg per day and forage ad libitum. Daily milk yield and feed intake were automatically recorded. During lactation weeks 2, 4, and 6 after calving, two milk samples were collected for FTIR spectroscopy, Tuesday evening and Wednesday morning, blood plasma samples were collected Thursday morning. Net energy content in feed and net energy required for maintenance and lactation were estimated to derive EB, which was used to compare alternative indicators of severe NEB. Linear univariate models for EB based on NEFA concentration (deviance explained = 0.13) and other metabolites in blood plasma were outperformed by models based on concentrations of metabolites in milk: fat (0.27), fat-to-protein ratio (0.18), BHB (0.20), and especially C18:0 (0.28) and C18:1 cis-9 (0.39). Analysis of generalized additive models (GAM) revealed that models based on milk variables performed better than those based on blood plasma (deviance explained 0.46 vs. 0.21). C18:0 and C18:1 cis-9 also performed better in severe NEB prediction for EB cut-off values ranging from -50 to 0 MJ NEL/d. Overall, concentrations of C18:0 and C18:1 cis-9 in milk, milk fat, and milk BHB were the best variables for early detection of cows in severe NEB. Thus, milk FA concentrations in whole milk can be useful to identify NEB in early-lactation cows.

Invited review: Disentangling residual feed intake-Insights and approaches to make it more fit for purpose in the modern context

Residual feed intake (RFI) is an increasingly used trait to analyze feed efficiency in livestock, and in some sectors such as dairy cattle, it is one of the most frequently used traits. Although the principle for calculating RFI is always the same (i.e., using the residual of a regression of intake on performance predictors), a wide range of models are found in the literature, with different predictors, different ways of considering intake, and more recently, different statistical approaches. Consequently, the results are not easily comparable from one study to another as they reflect different biological variabilities, and the relationship between the residual (i.e., RFI) and the underlying true efficiency also differs. In this review, the components of the RFI equation are explored with respect to the underlying biological processes. The aim of this decomposition is to provide a better understanding of which of the processes in this complex trait contribute significantly to the individual variability in efficiency. The intricacies associated with the residual term, as well as the energy sinks and the intake term, are broken down and discussed. Based on this exploration as well as on some recent literature, new forms of the RFI equation are proposed to better separate the efficiency terms from errors and inaccuracies. The review also considers the time period of measurement of RFI. This is a key consideration for the accuracy of the RFI estimation itself, and also for understanding the relationships between short-term efficiency, animal resilience, and long-term efficiency. As livestock production moves toward sustainable efficiency, these considerations are increasingly important to bring to bear in RFI estimations.

Precision dairy farming: Opportunities and challenges for India

Effective management of a dairy farm has to focus on individual animal apart from group or herd management since 'smallest production unit in the dairy is the individual animal’. In this context, precision dairy farming (PDF) aims to manage the basic production unit in order to exploit its maximal production capacity. PDF is the use of information and technology based farm management system to measure physiological, behavioural and production indicators of individual animals to improve management strategies, profitability and farm performance. PDF applications are finding their way on dairy farms, although there seem to be differences in the uptake of PDF applications between dairy systems. The authors have attempted to identify different PDF tools utilized across the globe and have highlighted the status of adoption in Indian scenario by highlighting about few farms/organizations involved in its utilization and uptake over the years. In this direction, the authors have also focused on several benefits and challenges faced by developing countries including India since the benefits are often not immediately apparent and they require more management expertise along with an investment of time and money to realize. In addition, the adoption rate depends on various factors like farmer education, farm size, perceptions of risk, ownership of a non-farm business etc. Addressing these issues is very essential for the uptake of technologies and hence, an effort has been made to propose strategies for adoption and operationalization of PDF in India and other developing countries where the similar scenario exists. The study also highlights that PDF in many developing countries including India is in its infancy, but there are tremendous opportunities for improvements in individual animal and herd management in dairy farms. The progressive farmers or the farmers’ groups, with guidance from the public and private sectors, and professional associations, can adopt it on a limited scale as the technology shows potential for raising yields and economic returns on fields with significant variability, and for minimizing environmental degradation. Additional research needs to be undertaken to examine the adoption process for not only successful adoption of technology, but also to solve the issues associated with the technology adoption. Further, right extension approaches and advisory services for the farmers interested in PDF needs to be undertaken for its effective application under different socio-economic and ecological conditions.

Predicting the likelihood of conception to first insemination of dairy cows using milk mid-infrared spectroscopy

The objective of this study was to examine the ability of milk mid-infrared (MIR) spectroscopy and other on-farm data, such as milk yield, milk composition, stage of lactation, calving age, days in milk at insemination, and somatic cell count, to identify cows that were most or least likely to conceive to first insemination. A total of 16,628 spectral and milk production records of 7,040 cows from 29 commercial dairy herds across 3 Australian states were used. Three models, comprising different explanatory variables, were tested. Model 1 included features that are readily available on farms participating in milk recording, such as milk yield, milk composition, somatic cell count, days from calving to insemination, and calving season. Days in milk and age at calving were incorporated into model 1 to form model 2. In model 3, MIR was added to model 2, but to avoid double counting, milk composition traits of model 2 were removed. The models were first trained on extreme data [i.e., including cows that (1) conceived to first insemination and (2) cows with no conception event recorded and with only 1 insemination]. Then, the models were validated in a fresh data set with all cows regardless of conception outcomes present to test for their ability to identify cows that conceived or did not conceive to first insemination. To do this, we ranked the predicted probability of all cows in the validation set and then selected the top and bottom records in varying proportions from 5 to 40% (i.e., where the model predicted the highest versus lowest likelihood of conception to first insemination, respectively) and compared with the actual values. The model's performance was evaluated through herd-year by herd-year external validation and measured as the proportion of selected records being correct. The results show that when more cows are selected (i.e., descending confidence), the accuracy of the models was reduced, and selecting the 10% of cows with the highest confidence of predictions produces optimal accuracy. Irrespective of the proportions, none of the models could predict cows that conceived to first insemination, with an accuracy around 0.48. When attempting to predict the bottom 10% of cows, which had the least likelihood of conception to first insemination, model 1 had prediction accuracy around 0.64. Compared with model 1, the addition of days in milk and calving age (model 2) resulted in a negligible improvement in prediction accuracy (0.01 to 0.03). Model 3 had the highest prediction accuracy (0.76), which implies that in the models tested, MIR is of primary importance in the prediction of fertility of dairy cows. In conclusion, this study indicates that MIR and other milk recording data could be used to identify cows with potential difficulty in getting pregnant to first insemination with promising accuracy.

Associations between milk fat, protein and fat-to-protein ratio with some reproductive indices in dairy cows

Postpartum induced lipolysis by negative energy balance (NEB) causing an increase in milk fat, a decrease in milk protein, and as a consequence an increase in milk fat to protein ratio (FPR). The aim of this study was to evaluate the relationship between milk FPR and first service conception risk (FSCR), days to first service (DFS) and calving to conception interval at first service (CCIFS). Therefore, milk and reproduction data of 1,375 primiparous and multiparous Holstein dairy cows from 10 commercial dairy farms located on sub-tropical region were collected on days 30 and 60 of days in milk (DIM) and near the first service. The Pearson correlation test of milk compositions revealed only a significant correlation between milk protein at day 30 DIM and DFS, but in Logistic regression analysis it did not have a constant effect on reproductive indices. On the other hand, the effect of previous dry-off duration and AI season on FSCR were significant. Based on the result of the present study, it is concluded that milk compositions such as fat, protein and FPR had no correlation with the result of the first AI.

Infrared Spectrometry as a High-Throughput Phenotyping Technology to Predict Complex Traits in Livestock Systems

High-throughput phenotyping technologies are growing in importance in livestock systems due to their ability to generate real-time, non-invasive, and accurate animal-level information. Collecting such individual-level information can generate novel traits and potentially improve animal selection and management decisions in livestock operations. One of the most relevant tools used in the dairy and beef industry to predict complex traits is infrared spectrometry, which is based on the analysis of the interaction between electromagnetic radiation and matter. The infrared electromagnetic radiation spans an enormous range of wavelengths and frequencies known as the electromagnetic spectrum. The spectrum is divided into different regions, with near- and mid-infrared regions being the main spectral regions used in livestock applications. The advantage of using infrared spectrometry includes speed, non-destructive measurement, and great potential for on-line analysis. This paper aims to review the use of mid- and near-infrared spectrometry techniques as tools to predict complex dairy and beef phenotypes, such as milk composition, feed efficiency, methane emission, fertility, energy balance, health status, and meat quality traits. Although several research studies have used these technologies to predict a wide range of phenotypes, most of them are based on Partial Least Squares (PLS) and did not considered other machine learning (ML) techniques to improve prediction quality. Therefore, we will discuss the role of analytical methods employed on spectral data to improve the predictive ability for complex traits in livestock operations. Furthermore, we will discuss different approaches to reduce data dimensionality and the impact of validation strategies on predictive quality.

The use of milk Fourier transform mid-infrared spectra and milk yield to estimate heat production as a measure of efficiency of dairy cows

Background

Transformation of feed energy ingested by ruminants into milk is accompanied by energy losses via fecal and urine excretions, fermentation gases and heat. Heat production may differ among dairy cows despite comparable milk yield and body weight. Therefore, heat production can be considered an indicator of metabolic efficiency and directly measured in respiration chambers. The latter is an accurate but time-consuming technique. In contrast, milk Fourier transform mid-infrared (FTIR) spectroscopy is an inexpensive high-throughput method and used to estimate different physiological traits in cows. Thus, this study aimed to develop a heat production prediction model using heat production measurements in respiration chambers, milk FTIR spectra and milk yield measurements from dairy cows.

Methods

Heat production was computed based on the animal’s consumed oxygen, and produced carbon dioxide and methane in respiration chambers. Heat production data included 168 24-h-observations from 64 German Holstein and 20 dual-purpose Simmental cows. Animals were milked twice daily at 07:00 and 16:30 h in the respiration chambers. Milk yield was determined to predict heat production using a linear regression. Milk samples were collected from each milking and FTIR spectra were obtained with MilkoScan FT 6000. The average or milk yield-weighted average of the absorption spectra from the morning and afternoon milking were calculated to obtain a computed spectrum. A total of 288 wavenumbers per spectrum and the corresponding milk yield were used to develop the heat production model using partial least squares (PLS) regression.

Results

Measured heat production of studied animals ranged between 712 and 1470 kJ/kg BW^0.75. The coefficient of determination for the linear regression between milk yield and heat production was 0.46, whereas it was 0.23 for the FTIR spectra-based PLS model. The PLS prediction model using weighted average spectra and milk yield resulted in a cross-validation variance of 57% and a root mean square error of prediction of 86.5 kJ/kg BW^0.75. The ratio of performance to deviation (RPD) was 1.56.

Conclusion

The PLS model using weighted average FTIR spectra and milk yield has higher potential to predict heat production of dairy cows than models applying FTIR spectra or milk yield only.

Sirtuin 3 improves fatty acid metabolism in response to high nonesterified fatty acids in calf hepatocytes by modulating gene expression

Sirtuin 3 (SIRT3), a mitochondrial deacetylase, is a key regulator of energy metabolism in the liver. In nonruminants, the hepatic abundance of SIRT3 is decreased in individuals with nonalcoholic fatty liver diseases, and recovery of SIRT3 alleviates hepatic triacylglycerol (TG) deposition. However, the level of SIRT3 expression and its effects on lipid metabolism in dairy cows have not been characterized. Here we studied the hepatic expression of SIRT3 in cows with fatty liver and the role of SIRT3 in fatty acid metabolism in bovine hepatocytes. This in vivo study involved 10 healthy cows and 10 cows with fatty liver, from which we collected samples of liver tissue and blood. Primary hepatocytes were isolated from Holstein calves and treated with 0, 0.5, or 1.0 mM nonesterified fatty acids (NEFA) for 24 h or transinfected with SIRT3 overexpression adenovirus (Ad-SIRT3)/SIRT3-short interfering (si)RNA for 48 h. Cows with fatty liver displayed lower serum glucose concentrations but higher serum NEFA and β-hydroxybutyrate concentrations relative to healthy cows. Cows with fatty liver also had significant lower mRNA and protein abundance of hepatic SIRT3. Incubation of primary hepatocytes with NEFA reduced SIRT3 abundance in primary hepatocytes in a dose-dependent manner. Fatty acid (1 mM) treatment also markedly increased the abundance of acetyl-CoA carboxylase 1 (ACC1) and fatty acid synthase (FAS) but significantly decreased the abundance of carnitine palmitoyltransferase I (CPT1A), carnitine palmitoyltransferase II (CPT2), and acyl-CoA oxidase (ACO). Knockdown of SIRT3 by SIRT3-siRNA downregulated the mRNA abundance of CPT1A, CPT2, and ACO. In contrast, overexpression of SIRT3 by Ad-SIRT3 upregulated the mRNA abundance of CPT1A, CPT2, and ACO; downregulated the mRNA abundance of ACC1 and FAS; and consequently, decreased intracellular TG concentrations. Overexpression of SIRT3 ameliorated exogenous NEFA-induced TG accumulation by downregulating the abundance of ACC1 and FAS and upregulating the abundance of CPT1A, CPT2, and ACO in calf hepatocytes. Our data demonstrated that cows with fatty liver had lower hepatic SIRT3 contents, and an increase in SIRT3 abundance by overexpression mitigated TG deposition by modulating the expression of lipid metabolism genes in bovine hepatocytes. These data suggest a possible role of SIRT3 as a therapeutic target for fatty liver disease prevention in periparturient dairy cattle.

Genome-wide association study for genotype by lactation stage interaction of milk production traits in dairy cattle

Substantial evidence demonstrates that the genetic background of milk production traits changes during lactation. However, most GWAS for milk production traits assume that genetic effects are constant during lactation and therefore might miss those quantitative trait loci (QTL) whose effects change during lactation. The GWAS for genotype by lactation stage interaction are aimed at explicitly detecting the QTL whose effects change during lactation. The purpose of this study was to perform GWAS for genotype by lactation stage interaction for milk yield, lactose yield, lactose content, fat yield, fat content, protein yield, and somatic cell score to detect QTL with changing effects during lactation. For this study, 19,286 test-day records of 1,800 first-parity Dutch Holstein cows were available and cows were genotyped using a 50K SNP panel. A total of 7 genomic regions with effects that change during lactation were detected in the GWAS for genotype by lactation stage interaction. Two regions on Bos taurus autosome (BTA)14 and BTA19 were also significant based on a GWAS that assumed constant genetic effects during lactation. Five regions on BTA4, BTA10, BTA11, BTA16, and BTA23 were only significant in the GWAS for genotype by lactation stage interaction. The biological mechanisms that cause these changes in genetic effects are still unknown, but negative energy balance and effects of pregnancy may play a role. These findings increase our understanding of the genetic background of lactation and may contribute to the development of better management indicators based on milk composition.

Combined use of milk infrared spectra and genotypes can improve prediction of milk fat composition

It has been shown that milk infrared (IR) spectroscopy can be used to predict detailed milk fat composition. In addition, polymorphisms with substantial effects on milk fat composition have been identified. In this study, we investigated the combined use of milk IR spectroscopy and genotypes of dairy cows on the accuracy of predicting milk fat composition. Milk fat composition data based on gas chromatography and milk IR spectra were available for 1,456 Dutch Holstein Friesian cows. In addition, genotypes for the diacylglycerol acyltransferase 1 (DGAT1) K232A and stearoyl-CoA desaturase 1 (SCD1) A293V polymorphisms and a SNP located in an intron of the fatty acid synthase (FASN) gene were available. Adding SCD1 genotypes to the milk IR spectra resulted in a considerable improvement of the prediction accuracy for the unsaturated fatty acids C10:1, C12:1, C14:1 cis-9, and C16:1 cis-9 and their corresponding unsaturation indices. Adding DGAT1 genotypes to the milk IR spectra resulted in an improvement of the prediction accuracy for C16:1 cis-9 and C16 index. Adding genotypes of the FASN SNP to the IR spectra did not improve prediction of milk fat composition. This study demonstrated the potential of combining milk IR spectra with genotypic information from 3 polymorphisms to predict milk fat composition. We hypothesize that prediction accuracy of milk fat composition can be further improved by combining milk IR spectra with genomic breeding values.

Relative effect of milk constituents on fertility performance of milk-recorded, spring-calving dairy cows in Ireland

Several studies have reported associations between milk composition data and fertility performance. However, no work to date has estimated the effect of milk constituents on fertility performance in cows with low milk constituent concentrations. The objective of this study was to assess the association between milk constituents, animal characteristics, and time from mating start date (MSD) to conception using survival analysis. Furthermore, we aimed to investigate the relative effect of each variable by predicting median times to conception for animals with different combinations of characteristics and milk compositions. The final data set consisted of 87,227 cow lactation records from 64,519 cows in 2,049 herds with calving dates from 2010 to 2013. Milk recording data from each lactation were used, including test day recordings at 0 to 30, 31 to 60, and 61 to 90 d in milk (DIM). The analysis was limited to spring-calving cows (i.e., animals calving from January to May inclusive). Mating start date was determined for each unique herd in each year. A cow-specific MSD (MSD_cow) was defined taking into consideration the MSD for each herd and the calving date and a minimum calving to insemination interval of each herd year. The conception date for each cow was estimated using the subsequent calving date. Cows with no subsequent calving date were assumed not to have conceived. Time from MSD_cow to approximate conception date was analyzed using survival analysis. Cox proportional hazard models were constructed for each of the 3 recording windows: 0 to 30, 31 to 60, and 61 to 90 DIM. A fourth model was used to assess the dynamics of milk composition over the 3 windows. To investigate the effect of these variables, model outputs were used to create parametric accelerated failure time models to predict median survival times for animals at the 10th and 90th percentiles of the variable of interest but otherwise identical across the rest of the variables. Results demonstrated that fertility breeding subindex had the largest effect on time from MSD_cow to conception, with an additional 62 d open for those in the 10th percentile versus those in the 90th percentile. Of the milk constituents, milk lactose concentration had the greatest effect on MSD to conception, particularly when measured from 0 to 30 DIM. An additional 10 d open resulted from comparing those in the 10th and 90th percentiles. Milk protein concentration, although statistically significant, had a lower effect on fertility outcome when comparing cows in the 10th and 90th percentiles for this exposure variable. The greatest effect was found in the 61 to 90 DIM recording window, where cows in the 10th percentile had an additional 9 d open at the subsequent breeding season compared with those in the 90th percentile. Overall, our study shows that although the associations between milk constituents and fertility are statistically significant, their overall influence in determining MSD to conception in this study population is relatively modest, particularly compared with fertility breeding subindex, when comparing cows at the 10th and 90th percentiles. Of the milk constituents measured, milk lactose concentration measured at 0 to 30 DIM had the greatest effect in determining fertility outcome when comparing cows at the 10th and 90th percentiles. The predictive value of early-lactation test day milk composition data on hazard of pregnancy during the following breeding period, within a spring-calving context, appears to be relatively modest at the individual-cow level. Further work is required to test the usefulness of these associations at the herd level.

Multi-breed genome-wide association studies across countries for electronically recorded behavior traits in local dual-purpose cows

Basic bovine behavior is a crucial parameter influencing cattle domestication. In addition, behavior has an impact on cattle productivity, welfare and adaptation. The aim of the present study was to infer quantitative genetic and genomic mechanisms contributing to natural dual-purpose cow behavior in grazing systems. In this regard, we genotyped five dual-purpose breeds for a dense SNP marker panel from four different European countries. All cows from the across-country study were equipped with the same electronic recording devices. In this regard, we analyzed 97,049 longitudinal sensor behavior observations from 319 local dual-purpose cows for rumination, feeding, basic activity, high active, not active and ear temperature. According to the specific sensor behaviors and following a welfare protocol, we computed two different welfare indices. For genomic breed characterizations and multi-breed genome-wide association studies, sensor traits and test-day production records were merged with 35,826 SNP markers per cow. For the estimation of variance components, we used the pedigree relationship matrix and a combined similarity matrix that simultaneously included both pedigree and genotypes. Heritabilities for feeding, high active and not active were in a moderate range from 0.16 to 0.20. Estimates were very similar from both relationship matrix-modeling approaches and had quite small standard errors. Heritabilities for the remaining sensor traits (feeding, basic activity, ear temperature) and welfare indices were lower than 0.09. Five significant SNPs on chromosomes 11, 17, 27 and 29 were associated with rumination, and two different SNPs significantly influenced the sensor traits “not active” (chromosome 13) and “feeding” (chromosome 23). Gene annotation analyses inferred 22 potential candidate genes with a false discovery rate lower than 20%, mostly associated with rumination (13 genes) and feeding (8 genes). Mendelian randomization based on genomic variants (i.e., the instrumental variables) was used to infer causal inference between an exposure and an outcome. Significant regression coefficients among behavior traits indicate that all specific behavioral mechanisms contribute to similar physiological processes. The regression coefficients of rumination and feeding on milk yield were 0.10 kg/% and 0.12 kg/%, respectively, indicating their positive influence on dual-purpose cow productivity. Genomically, an improved welfare behavior of grazing cattle, i.e., a higher score for welfare indices, was significantly associated with increased fat and protein percentages.

Energy profiling of dairy cows from routine milk mid-infrared analysis

The balance of body energy within and across lactations can have health and fertility consequences for the dairy cow. This study aimed to create a large calibration data set of dairy cow body energy traits across the cow's productive life, with concurrent milk mid-infrared (MIR) spectral data, to generate a prediction tool for use in commercial dairy herds. Detailed phenotypic data from 1,101 Holstein Friesian cows from the Langhill research herd (SRUC, Scotland) were used to generate energy balance (EB) and effective energy intake (EI), both in megajoules per day. Pretreatment of spectral data involved standardization to account for drift over time and machine. Body energy estimates were aligned with their spectral data to generate a prediction of these traits based on milk MIR spectroscopy. After data edits, partial least squares analysis generated prediction equations with a coefficient of determination from split sample 10-fold cross validation of 0.77 and 0.75 for EB and EI, respectively. These prediction equations were applied to national milk MIR spectra on over 11 million animal test dates (January 2013 to December 2016) from 4,453 farms. The predictions generated from these were subject to phenotypic analyses with a fixed regression model highlighting differences between the main dairy breeds in terms of energy traits. Genetic analyses generated heritability estimates for EB and EI ranging from 0.12 to 0.17 and 0.13 to 0.15, respectively. This study shows that MIR-based predictions from routinely collected national data can be used to generate predictions of dairy cow energy turnover profiles for both animal management and genetic improvement of such difficult and expensive-to-record traits.

Prediction of metabolic status of dairy cows in early lactation with on-farm cow data and machine learning algorithms

Metabolic status of dairy cows in early lactation can be evaluated using the concentrations of plasma β-hydroxybutyrate (BHB), free fatty acids (FFA), glucose, insulin, and insulin-like growth factor 1 (IGF-1). These plasma metabolites and metabolic hormones, however, are difficult to measure on farm. Instead, easily obtained on-farm cow data, such as milk production traits, have the potential to predict metabolic status. Here we aimed (1) to investigate whether metabolic status of individual cows in early lactation could be clustered based on their plasma values and (2) to evaluate machine learning algorithms to predict metabolic status using on-farm cow data. Through lactation wk 1 to 7, plasma metabolites and metabolic hormones of 334 cows were measured weekly and used to cluster each cow into 1 of 3 clusters per week. The cluster with the greatest plasma BHB and FFA and the lowest plasma glucose, insulin, and IGF-1 was defined as poor metabolic status; the cluster with the lowest plasma BHB and FFA and the greatest plasma glucose, insulin, and IGF-1 was defined as good metabolic status; and the intermediate cluster was defined as average metabolic status. Most dairy cows were classified as having average or good metabolic status, and a limited number of cows had poor metabolic status (10-50 cows per lactation week). On-farm cow data, including dry period length, parity, milk production traits, and body weight, were used to predict good or average metabolic status with 8 machine learning algorithms. Random Forest (error rate ranging from 12.4 to 22.6%) and Support Vector Machine (SVM; error rate ranging from 12.4 to 20.9%) were the top 2 best-performing algorithms to predict metabolic status using on-farm cow data. Random Forest had a higher sensitivity (range: 67.8-82.9% during wk 1 to 7) and negative predictive value (range: 89.5-93.8%) but lower specificity (range: 76.7-88.5%) and positive predictive value (range: 58.1-78.4%) than SVM. In Random Forest, milk yield, fat yield, protein percentage, and lactose yield had important roles in prediction, but their rank of importance differed across lactation weeks. In conclusion, dairy cows could be clustered for metabolic status based on plasma metabolites and metabolic hormones. Moreover, on-farm cow data can predict cows in good or average metabolic status, with Random Forest and SVM performing best of all algorithms.

Polymorphism association and expression analysis of alpha-lactalbumin (LALBA) gene during lactation in Nili Ravi buffalo

Alpha-lactalbumin has been reported as a highly polymorphic gene that potentially alters the gene expression and is associated with milk composition in dairy breeds. Current study was conducted in two phases. In the first phase, polymorphisms identification in alpha-lactalbumin (LALBA) gene and its association with milk composition was performed. To identify the genetic polymorphism, Nili Ravi buffaloes at their second lactation were selected from Government livestock farm (Buffalo Research Institute, Pattoki). Genomic DNA was extracted from blood samples. After PCR amplification, products were sequenced, and data was analyzed. Results showed that the identified polymorphisms at chromosomal position 34310940 were found associated with major whey protein. In the second phase of study, milk samples were collected from five healthy mastitis-free Nili Ravi buffaloes in their second lactation for expression analysis of alpha-lactalbumin gene at their transition (day 15), mid (day 90), and late (day 250) lactation. Gene expression was observed highest in transition phase with a gradual decrease of expression in mid and late phase of lactation. Further studies are needed to explore the regulation of milk production genes and their translational efficiency during the course of lactation.

Body and milk traits as indicators of dairy cow energy status in early lactation

The inclusion of feed intake and efficiency traits in dairy cow breeding goals can lead to increased risk of metabolic stress. An easy and inexpensive way to monitor postpartum energy status (ES) of cows is therefore needed. Cows' ES can be estimated by calculating the energy balance from energy intake and output and predicted by indicator traits such as change in body weight (ΔBW), change in body condition score (ΔBCS), milk fat:protein ratio (FPR), or milk fatty acid (FA) composition. In this study, we used blood plasma nonesterified fatty acids (NEFA) concentration as a biomarker for ES. We determined associations between NEFA concentration and ES indicators and evaluated the usefulness of body and milk traits alone, or together, in predicting ES of the cow. Data were collected from 2 research herds during 2013 to 2016 and included 137 Nordic Red dairy cows, all of which had a first lactation and 59 of which also had a second lactation. The data included daily body weight, milk yield, and feed intake and monthly BCS. Plasma samples for NEFA were collected twice in lactation wk 2 and 3 and once in wk 20. Milk samples for analysis of fat, protein, lactose, and FA concentrations were taken on the blood sampling days. Plasma NEFA concentration was higher in lactation wk 2 and 3 than in wk 20 (0.56 ± 0.30, 0.43 ± 0.22, and 0.13 ± 0.06 mmol/L, respectively; all means ± standard deviation). Among individual indicators, C18:1 cis-9 and the sum of C18:1 in milk had the highest correlations (r = 0.73) with NEFA. Seven multiple linear regression models for NEFA prediction were developed using stepwise selection. Of the models that included milk traits (other than milk FA) as well as body traits, the best fit was achieved by a model with milk yield, FPR, ΔBW, ΔBCS, FPR × ΔBW, and days in milk. The model resulted in a cross-validation coefficient of determination (R²cv) of 0.51 and a root mean squared error (RMSE) of 0.196 mmol/L. When only milk FA concentrations were considered in the model, NEFA prediction was more accurate using measurements from evening milk than from morning milk (R²cv = 0.61 vs. 0.53). The best model with milk traits contained FPR, C10:0, C14:0, C18:1 cis-9, C18:1 cis-9 × C14:0, and days in milk (R²cv = 0.62; RMSE = 0.177 mmol/L). The most advanced model using both milk and body traits gave a slightly better fit than the model with only milk traits (R²cv = 0.63; RMSE = 0.176 mmol/L). Our findings indicate that ES of cows in early lactation can be monitored with moderately high accuracy by routine milk measurements.

Using quantile regression for fitting lactation curve in dairy cows

The main objective of this study was to compare the performance of different 'nonlinear quantile regression' models evaluated at the τth quantile (0·25, 0·50, and 0·75) of milk production traits and somatic cell score (SCS) in Iranian Holstein dairy cows. Data were collected by the Animal Breeding Center of Iran from 1991 to 2011, comprising 101 051 monthly milk production traits and SCS records of 13 977 cows in 183 herds. Incomplete gamma (Wood), exponential (Wilmink), Dijkstra and polynomial (Ali &amp; Schaeffer) functions were implemented in the quantile regression. Residual mean square, Akaike information criterion and log-likelihood from different models and quantiles indicated that in the same quantile, the best models were Wilmink for milk yield, Dijkstra for fat percentage and Ali &amp; Schaeffer for protein percentage. Over all models the best model fit occurred at quantile 0·50 for milk yield, fat and protein percentage, whereas, for SCS the 0·25th quantile was best. The best model to describe SCS was Dijkstra at quantiles 0·25 and 0·50, and Ali &amp; Schaeffer at quantile 0·75. Wood function had the worst performance amongst all traits. Quantile regression is specifically appropriate for SCS which has a mixed multimodal distribution.

Milk Metabolomics Data Reveal the Energy Balance of Individual Dairy Cows in Early Lactation

In early lactation, dairy cows typically have a negative energy balance which has been related to metabolic disorders, compromised health and fertility, and reduced productive lifespan. Assessment of the energy balance, however, is not easy on the farm. Our aims were to investigate the milk metabolic profiles of dairy cows in early lactation, and to obtain models to estimate energy balance from milk metabolomics data and milk production traits. Milk samples were collected in week 2 and 7 after calving from 31 dairy cows. For each cow, the energy balance was calculated from energy intake, milk production traits and body weight. A total of 52 milk metabolites were detected using LC-QQQ-MS. Data from different lactation weeks was analysed by partial least squares analysis, the top 15 most relevant variables from the metabolomics data related to energy balance were used to develop reduced linear models to estimate energy balance by forward selection regression. Milk fat yield, glycine, choline and carnitine were important variables to estimate energy balance (adjusted R²: 0.53 to 0.87, depending on the model). The relationship of these milk metabolites with energy balance is proposed to be related to their roles in cell renewal.

Association between left-displaced abomasum corrected with 2-step laparoscopic abomasopexy and milk production in a commercial dairy farm in Italy

Background

Left displacement of the abomasum (LDA) is a condition of dairy cows that causes huge economic losses. The aim of the study was to evaluate the effect of LDA after on-farm correction by the 2-step laparoscopic abomasopexy on milk production based on 305-d milk yield on a commercial dairy farm in Italy.The study was performed between January 2011 and January 2014 on 58 Holstein Friesian cattle with left displacement of the abomasum in a commercial dairy farm in the farmland of Ozieri, Sardinia (Italy). Each cow underwent a 2-step laparoscopic abomasopexy performed by the same veterinarian. Each case was matched with a control herdmate by age, parity and calving date. Cows with LDA and healthy control cows also had a similar 305-d milk yield in the previous lactation. Data on milk production were collected using a dairy herd management software programme (Afimilk®, Afimilk Ltd., Israel). The 305-d lactation yield was obtained from the sum of daily milk yields for each cow. An unpaired Student’s t-test was used to compare changes in milk production, mean fat and protein percentage of cases and controls before and after surgical procedure.

Results

Data from 4 cows were excluded from the analysis due to post-surgical complications. 54 cases and 54 control cows participated in the study. We found that milk production significantly decreased from a baseline of 12,295 ± 1690 kg to 11,165 ± 1989 kg in the affected lactation. Conversely, a significant increase was observed for mean fat and protein percentage during lactation in case cows.

Conclusions

In the present study cows with left displacement of the abomasum corrected with 2-step laparoscopic abomasopexy produced less milk than their control herdmates. Each case and control pair in the present study came from the same farm in order to eliminate farm to farm differences in management, housing, season, etc. However, this limits the validity of our data to the specific situation described here.

Genetic parameters for body weight from birth to calving and associations between weights with test-day, health, and female fertility traits

A data set including 57,868 records for calf birth weight (CABW) and 9,462 records for weight at first insemination (IBW) were used for the estimation of direct and maternal genetic effects in Holstein Friesian dairy cattle. Furthermore, CABW and IBW were correlated with test-day production records and health traits in first-lactation cows, and with nonreturn rates in heifers. Health traits considered overall disease categories from the International Committee for Animal Recording diagnosis key, including the general disease status, diarrhea, respiratory diseases, mastitis, claw disorders, female fertility disorders, and metabolic disorders. For single-trait measurements of CABW and IBW, animal models with maternal genetic effects were applied. The direct heritability was 0.47 for CABW and 0.20 for IBW, and the direct genetic correlation between CABW and IBW was 0.31. A moderate maternal heritability (0.19) was identified for CABW, but the maternal genetic effect was close to zero for IBW. The correlation between direct and maternal genetic effects was antagonistic for CABW (-0.39) and for IBW (-0.24). In bivariate animal models, only weak genetic and phenotypic correlations were identified between CABW and IBW with either test-day production or health traits in early lactation. Apart from metabolic disorders, there was a general tendency for increasing disease susceptibilities with increasing CABW. The genetic correlation between IBW and nonreturn rates in heifers after 56 d and after 90 d was slightly positive (0.18), but close to zero when correlating nonreturn rates with CABW. For the longitudinal BW structure from birth to the age of 24 mo, random regression models with the time-dependent covariate "age in months" were applied. Evaluation criteria (Bayesian information criterion and residual variances) suggested Legendre polynomials of order 3 to modeling the longitudinal body weight (BW) structure. Direct heritabilities around birth and insemination dates were slightly larger than estimates for CABW and IBW from the single-trait models, but maternal heritabilities were exactly the same from both models. Genetic correlations between BW were close to 1 for neighboring age classes, but decreased with increasing time spans. The genetic correlation between BW at d 0 (birth date) and at 24 mo was even negative (-0.20). Random regression model estimates confirmed the antagonistic relationship between direct and maternal genetic effects, especially during calfhood. This study based on a large data set in dairy cattle confirmed genetic parameters and (co)variance components for BW as identified in beef cattle populations. However, BW records from an early stage of life were inappropriate early predictors for dairy cow health and productivity.

Minor milk constituents are affected by protein concentration and forage digestibility in the feed ration

The present study was conducted in order to investigate if selected minor milk components would be indicative for the nutritional situation of the cow. Forty-eight dairy cows were offered a high digestible ration vs. a lower digestible ration combined with 2 protein levels in a 4 × 4 Latin square design. Milk glucose, glucose-6-phosphate, cholesterol, triacylglycerides (TAG), uric acid and β-hydroxybutyrate (BHBA) were measured and correlated mutually and towards other milking parameters (yield, h since last milking, days in milk (DIM), urea, etc). The variation range of the suggested variables were broad, a fact that may support their utilisation as predictive parameters. The content of milk metabolites was significantly affected by the change in rations as milk glucose, glucose-6-phosphate, uric acid, and the ratio cholesterol: triacylglycerides increased with higher energy intake while BHBA and TAG decreased. The content of some of the milk metabolites changed during 24 h day/night periods: BHBA, cholesterol, uric acid and TAG increased whereas free glucose decreased in the night period. Certain associations between milk metabolites and calculated energy parameters like ECM, body condition score (BCS), and body weight gain were found, however, these associations were to some extent explained by an interaction with DIM, just as changes in milk metabolites during a 24 h period seems to interfere. It is concluded that the practical use of the suggested milk variables should be based on more than one metabolite and that stage of lactation and possibly time of the day where the milk is collected should be incorporated in predictive models.

Capitalizing on fine milk composition for breeding and management of dairy cows

The challenge of managing and breeding dairy cows is permanently adapting to changing production circumstances under socio-economic constraints. If managing and breeding address different timeframes of action, both need relevant phenotypes that allow for precise monitoring of the status of the cows, and their health, behavior, and well-being as well as their environmental impact and the quality of their products (i.e., milk and subsequently dairy products). Milk composition has been identified as an important source of information because it could reflect, at least partially, all these elements. Major conventional milk components such as fat, protein, urea, and lactose contents are routinely predicted by mid-infrared (MIR) spectrometry and have been widely used for these purposes. But, milk composition is much more complex and other nonconventional milk components, potentially predicted by MIR, might be informative. Such new milk-based phenotypes should be considered given that they are cheap, rapidly obtained, usable on a large scale, robust, and reliable. In a first approach, new phenotypes can be predicted from MIR spectra using techniques based on classical prediction equations. This method was used successfully for many novel traits (e.g., fatty acids, lactoferrin, minerals, milk technological properties, citrate) that can be then useful for management and breeding purposes. An innovation was to consider the longitudinal nature of the relationship between the trait of interest and the MIR spectra (e.g., to predict methane from MIR). By avoiding intermediate steps, prediction errors can be minimized when traits of interest (e.g., methane, energy balance, ketosis) are predicted directly from MIR spectra. In a second approach, research is ongoing to detect and exploit patterns in an innovative manner, by comparing observed with expected MIR spectra directly (e.g., pregnancy). All of these traits can then be used to define best practices, adjust feeding and health management, improve animal welfare, improve milk quality, and mitigate environmental impact. Under the condition that MIR data are available on a large scale, phenotypes for these traits will allow genetic and genomic evaluations. Introduction of novel traits into the breeding objectives will need additional research to clarify socio-economic weights and genetic correlations with other traits of interest.

SREBP-1c overactivates ROS-mediated hepatic NF-κB inflammatory pathway in dairy cows with fatty liver

Dairy cows with fatty liver are characterized by hepatic lipid accumulation and a severe inflammatory response. Sterol receptor element binding protein-1c (SREBP-1c) and nuclear factor κB (NF-κB) are components of the main pathways for controlling triglyceride (TG) accumulation and inflammatory levels, respectively. A previous study demonstrated that hepatic inflammatory levels are positively correlated with hepatic TG content. We therefore speculated that SREBP-1c might play an important role in the overactivation of the hepatic NF-κB inflammatory pathway in cows with fatty liver. Compared with healthy cows, cows with fatty liver exhibited severe hepatic injury and high blood concentrations of the inflammatory cytokines TNF-α, IL-6 and IL-1β. Hepatic SREBP-1c-mediated lipid synthesis and the NF-κB inflammatory pathway were both overinduced in cows with fatty liver. In vitro, treatment with non-esterified fatty acids (NEFA) further increased SREBP-1c expression and NF-κB pathway activation, which then promoted TG and inflammatory cytokine synthesis. SREBP-1c overexpression overactivated the NF-κB inflammatory pathway in hepatocytes by increasing ROS content and not through TLR4. Furthermore, SREBP-1c silencing decreased ROS content and further attenuated the activation of the NEFA-induced NF-κB pathway, thereby decreasing TNF-α, IL-6 and IL-1β synthesis. SREBP-1c-overexpressing mice exhibited hepatic steatosis and an overinduced hepatic NF-κB pathway. Taken together, these results indicate that SREBP-1c enhances the NEFA-induced overactivation of the NF-κB inflammatory pathway by increasing ROS in cow hepatocytes, thereby further increasing hepatic inflammatory injury in cows with fatty liver.