Genetic and phenotypic variation and consistency in cow preference and circadian use of robotic milking units.

Many dairy herds use automatic milking stations (AMS), with cows in large herds often having access to 2 or more AMS, and must choose between them when they go for milking. Individual cows acquire routines of either consistently using a specific milking box or consistently using any available milking box. Here, we hypothesized that the degree of use of the same milking box was an expression of preference, and quantified it as preference consistency score (PCS). The PCS was calculated as a ratio between the excess frequencies of the first choice over the base frequency of "not first choice" over 15-d segments of lactation. This ratio was 0 if all choices were taken equally, and became 1.0 if only the first choice was taken in all events. We investigated the consistency of milking box preference in 2 cohorts (one Holstein and one Jersey) across 6 commercial dairy herds in Denmark (n = 4,665 cows total). In addition to PCS, we recorded and analyzed associated milking and behavior traits, including a time profile index showing use of specific clock hours when cows were milked (Time_profile, based on excess use of specific clock hours), milking frequency, time spent in the milking box, and milk yield. Records from each milking event were condensed into 15-d segments based on days in milk. The data were analyzed using a linear mixed model, with random genetic and individual cow effects, to estimate heritability (h2), repeatability (t), and individual level correlations (ri) between traits. The average PCS was 0.43 and 0.41 in Holstein and Jersey, respectively, showing that cows developed routines for consistently using the same milking box; however, some cows had lower preference (i.e., greater flexibility in use). The Time_profile indicated that some cows were milked in a few hour-bins, whereas others were more flexible. The PCS and Time_profile traits had low heritability (h2, PCS/Time_profile = 0.07 ± 0.02/0.11 ± 0.02 Holstein, 0.13 ± 0.03/0.04 ± 0.02 Jersey) and moderate repeatability (t, PCS/Time_profile = 0.47/0.40 Holstein, 0.50/0.42 Jersey). The 2 traits were weakly correlated with each other (ri = 0.18 and 0.17), and were weakly correlated with milk yield (ri range: 0.0 to -0.10). However, the time profile was strongly correlated with milking frequency (ri range: -0.81 to -0.73), and was moderately correlated with daily box time (ri range: -0.43 to -0.35). In general, Holstein and Jersey parameter estimates were of similar size, and thus in good agreement. Overall, individual cows covered a broad spectrum of preference consistency, both regarding the use of specific milking boxes and time profiles, with these 2 traits representing different aspects or dimensions of milking behavior. The findings that some cows have strong preferences for specific AMS may be most useful in herd management and farm design. The weak correlation to milk yield indicated that yield minimally affected these 2 milking associated behavior traits. In conclusion, although the traits were repeatable, heritability was low; thus, genetic selection for milk yield might minimally affect these 2 traits.

Genome-wide association study for methane emission traits in Danish Holstein cattle.

Selecting for lower methane emitting cows requires insight into the most biologically relevant phenotypes for methane emission, which are close to the breeding goal. Several methane phenotypes have been suggested over the last decade. However, the (dis)similarity of their underlying genetic architecture and correlation structures are poorly understood. Therefore, the objective of this study was to test association of SNP and genomic regions through GWAS on 8 CH4 emission traits in Danish Holstein cattle. The traits studied were methane concentration (MeC; ppm), methane production (MeP ; g/d), 2 definitions of residual methane (RMETc and RMETp: MeC and MeP regressed on metabolic body weight and energy-corrected milk, respectively), 2 definitions of methane intensity (MeI; MeIc = MeC/ECM and MeIp = MeP/ECM); 2 definitions of methane yield per kilogram of dry matter intake (MeY; MeYc = MeC/dry matter intake and MeYp = MeP/dry matter intake). A total of 1,962 cows with genotypes (Illumina BovineSNP50 Chip or Eurogenomic custom SNP chip) and repeated records of the above-mentioned 8 methane traits were analyzed. Strong associations were found with 3 traits (MeC, MeP, and MeYc) on chromosome 13 and with 5 traits (MeC, MeP, MeIp, MeYp, and MeYc) on chromosome 26. For MeIc, MeIp, RMETc, MeYc, and MeYp, some suggestive association signals were identified on chromosome 1. Genomic segments of 1 Mbp (n = 2,525) were tested for their association with these traits, which identified between 33 to 54 significantly associated regions. In a pairwise comparison, MeC and MeP were the traits that shared the highest number of significant segments (17). The same trend was observed when comparing SNP significantly associated with the traits MeC and MeP shared from 23 to 25 SNP (most of which were located in chromosomes 11, 13, and 26). Based on our results on GWAS and genetic correlations, we conclude that MeC is (genetically) more closely linked to MeP than any of the other methane traits analyzed.

Genetic (co-)variation of methane emissions, efficiency, and production traits in Danish Holstein cattle along and across lactations.

Methane emissions in ruminant livestock has become a hot topic, given the pressure to reduce greenhouse gas emissions drastically in the European Union over the next 10 to 30 yr. During the 2021 United Nations Climate Change conference, countries also made collective commitments to curb methane emissions by 2050. Genetic selection for low-methane-emitting animals, particularly dairy cows, is one possible strategy for mitigation. However, it is essential to understand how methane emissions in lactating animals vary along lactation and across lactations. This understanding is useful when making decisions for future phenotyping strategies, such as the frequency and duration of phenotyping within and across lactations. Therefore, the objectives of this study were to estimate (1) genetic parameters for 2 methane traits: methane concentration (MeC) and methane production (MeP) at 2 parity levels in Danish Holstein cows across the entire lactation using random regression models; (2) genetic correlations within and between methane traits across the entire lactation; and (3) genetic correlations between the methane traits and economically important traits throughout first lactation. Methane concentration (n = 19,639) records of 575 Danish Holstein cows from a research farm measured between 2013 and 2020 were available. Subsequently, CH4 production in grams/day (MeP; n = 13,866) was calculated; MeP and MeC for first and second lactation (L1 and L2) were analyzed as separate traits: MeC_L1, MeP_L1, MeC_L2, and MeP_L2. Heritabilities, variance components, and genetic correlations within and between the 4 CH4 traits were estimated using random regression models with Legendre polynomials. The additive genetic and permanent environmental effects were modeled using second-order Legendre polynomial for lactation weeks. Estimated heritabilities for MeP_L1 ranged between 0.11 and 0.49, for MeC_L1 between 0.10 and 0.28, for MeP_L2 between 0.14 and 0.36, and for MeC_L2 between 0.13 and 0.29. In general, heritability estimates of MeC traits were lower and more stable throughout lactation and were similar between lactations compared with MeP. Genetic correlations (within trait) at different lactation weeks were generally highly positive (0.7) for most of the first lactation, except for the correlation of early lactation (<10 wk) with late lactation (>40 wk) where the correlation was the lowest (<0.5). Genetic correlations between methane traits were moderate to highly correlated during early and mid lactation. Finally, MeP_L1 has stronger genetic correlations with energy-corrected milk and dry matter intake compared with MeC_L1. In conclusion, both traits are different along (and across) lactation(s) and they correlated differently with production, maintenance, and intake traits, which is important to consider when including one of them in a future breeding objective.

Changes to steps, lying, and eating behavior during lactation in Jersey and Holstein cows and the relationship to feed intake, yield, and weight.

Devices that record behavior automatically have made it possible to accurately measure the lying and eating behavior of large numbers of dairy cows. During lactation, weight, feed intake, and production of cows change; however, longitudinal studies of how the behavior of dairy cows is correlated with production traits during lactation are limited. This study describes changes in duration of lying and eating behavior throughout lactation and how these variables are related to changes in milk yield, live weight, and feed intake in lactating primi- and multiparous Holstein and Jersey cows. Data were from 255 cow lactations (43 primi- and 80 multiparous Jersey cows, and 56 primi- and 76 multiparous Holstein cows) from 5 to 200 d in milk. Leg-mounted tags were used to record lying time and steps; ad libitum feed intake (of a partial mixed ration) variables were recorded from feed bins on weight cells; and milk yield and live weight were recorded during automatic milking, all on a daily basis. The lactation trajectory was split into 4 segments. Data were analyzed using mixed effects linear models. Holstein cows spent more time lying and eating than Jersey cows, whereas Jersey cows had a greater number of steps (25-37%). First-lactation cows spent less time eating and had more steps than older cows. Average daily lying time was approximately 1 h longer during February than the shortest lying time, which was observed in August. Both Holstein and Jersey multiparous cows had longer lying times than cows in first parity after parturition; however, the lying time of multiparous cows decreased, whereas that of primiparous cows increased in the beginning of lactation. Later in lactation, older cows tended to increase duration of lying more than younger cows did. The daily change in behavior (lying, eating, and steps) and milk yield, live weight, and dry matter intake, characterized as slopes in the lactation period for each cow, were not strongly correlated. However, we found a moderate correlation between changes in milk yield and dry matter intake, and between changes in eating time and rate of eating. An increase in eating rate in multiparous Holstein cows was correlated with increasing lying time. In conclusion, the use of automated behavior recording enabled thorough investigations of relationships between a range of behavior traits and frequently recorded production traits, and revealed that patterns of change during lactation are strongly affected by breed and parity.

Phenotypic modeling of residual feed intake using physical activity and methane production as energy sinks.

Residual feed intake (RFI) is a measure of feed efficiency in dairy cattle. This study modeled phenotypic RFI of first- and second-parity Holstein and Jersey dairy cows within 9 lactation segments (consecutive segments of 4 wk each) covering the first 36 lactation weeks. We aimed to evaluate physical activity and daily methane production as additional energy sinks in the estimation of RFI, to examine the correlations of RFI among the first 36 wk of lactation (WOL), and to evaluate whether parities and breeds show similar results. Records for first-parity Holstein (n = 449), second-parity Holstein (n = 298), first-parity Jersey (n = 195), and second-parity Jersey cows (n = 146) were used. Model 1 included the following energy sinks: energy-corrected milk yield, metabolic body weight (BW), body condition score (BCS), daily changes in BW (ΔBW) and BCS (ΔBCS), and physical activity. Model 2 was based on a subset of the data and only for Holstein cows, and included the same energy sinks as Model 1, plus daily methane production. The trajectories of segment-specific partial regression coefficients (PRC) of DMI on activity were similar across parities but differed slightly between breeds. For daily methane production, the trajectory in PRC decreased over lactation segments for first- and second-parity Holstein cows. The trajectories in PRC of DMI on energy-corrected milk yield, metabolic BW, BCS, and ΔBW were generally similar across parities, except for ΔBCS. Activity accounted for on average 7.3, 6.8, 7.2, and 6.4% of DMI for first-parity Holsteins, second-parity Holsteins, first-parity Jerseys, and second-parity Jerseys, respectively. Methane losses accounted for 8.7% and 8.5% of DMI for first- and second-parity Holstein cows, respectively. Repeatability estimates for RFI over 36 WOL for Model 1 were 0.63 for first-parity Holsteins, 0.65 for second-parity Holsteins, 0.76 for first-parity Jerseys, and 0.80 for second-parity Jerseys. For Model 2, the estimates were 0.59 and 0.61 for first- and second-parity Holstein cows, respectively. Correlations of RFI between WOL varied in strength, with weak correlations for the first 2 to 3 WOL with other WOL. In conclusion, physical activity and daily methane production accounted for part of DMI, and RFI of dairy cattle is not the same trait throughout lactation.

Bayesian estimation of genetic variance and response to selection on linear or ratio traits of feed efficiency in dairy cattle.

This study aimed to estimate genetic parameters of the linear trait genetic residual feed intake (RFI) and the ratio traits feed conversion ratio (FCR) and feed conversion efficiency (FCE) along with dry matter intake (DMI) and energy sink traits such as energy-corrected milk (ECM), body weight (BW), body condition score (BCS), and BW change (BWC) across different weeks in the first lactation of Danish Holstein cows. A second objective was to conduct a Bayesian analysis of direct and correlated superiority of the selected group when selecting on genetic RFI, FCR, or FCE. Feed intake and energy sink traits were recorded during wk 1 to 44 of lactation on 847 primiparous Danish Holstein cows. A Bayesian multivariate random regression animal model was used to analyze DMI, ECM, BW, and BCS in different weeks of lactation. Genetic RFI was obtained by conditioning DMI on ECM, BW, BCS, and BWC using genetic partial regression coefficients. The posterior distribution of the breeding values for FCR and FCE was derived from the posterior distribution of functions of "fixed" environmental effects and random additive genetic effects on DMI and ECM. Genetic superiority of the selected group was defined as the difference in additive genetic mean of the selected top individuals expected to be potential parents, and the total population after integrating genetic trends out of the posterior distribution of selection responses. Posterior means of heritability of genetic RFI ranged from 0.10 to 0.15, genetic variance of FCR and FCE ranged from 2.13 × 10-3 to 3.2 × 10-3 (kg2 DMI/kg2 ECM) and 6.11 × 10-3 to 2.4 × 10-2 (kg2 ECM/kg2 DMI), respectively. Selection against RFI showed a direct response of -1.01 to -2.23 kg/d RFI and correlated responses of -0.031 to -0.056 kg/kg for FCR, 0.104 to 0.160 kg/kg for FCE, and -0.316 to -1.057 kg/d for DMI in different weeks of lactation. Selection against RFI had no significant effect on production traits but selection for ratio traits reduced BW and BCS. Posterior means of genetic correlation between DMI and ratio traits were low. In conclusion, the Bayesian procedure allowed us to estimate genetic RFI without the need for separate multiple regression analysis and considered the non-normal posterior distribution of ratio traits. Selection against genetic RFI might be an effective means to improve feed efficiency compared with ratio traits for feed efficiency in dairy cattle.

Multitrait genomic prediction of methane emissions in Danish Holstein cattle.

In dairy cattle, selecting for lower methane-emitting animals is one of the new challenges of this decade. However, genetic selection requires a large number of animals with records to get accurate estimated breeding values (EBV). Given that CH4 records are scarce, the use of information on routinely recorded and highly correlated traits with CH4 has been suggested to increase the accuracy of genomic EBV (GEBV) through multitrait (genomic) prediction. Therefore, the objective of this study was to evaluate accuracies of prediction of GEBV for CH4 by including or omitting CH4, energy-corrected milk (ECM), and body weight (BW) as well as genotypic information in multitrait analyses across 2 methods: BLUP and single-step genomic BLUP (SSGBLUP). A total of 2,725 cows with CH4 concentration in breath (14,125 records), BW (61,667 records), and ECM (61,610 records) were included in the analyses. Approximately 2,000 of these cows were genotyped or imputed to 50K. Ten cross-validation groups were formed by randomly grouping paternal half-sibs. Five scenarios were performed: (1) base scenario with only CH4 information; (2) without CH4, but with information from BW, ECM, or BW+ECM only in reference population; (3) without CH4, but with information from BW, ECM, or BW+ECM in both validation and reference population; (4) with CH4 information and BW, ECM, or BW+ECM information only in the reference population; and (5) with CH4 information and BW, ECM, or BW+ECM information in both validation and reference population. As a result, for each method (BLUP, SSGBLUP), 13 sub-scenarios were performed, 1 from scenario 1, and 3 for each of the subsequent 4 scenarios. The average accuracy of GEBV for CH4 in the base scenario was 0.32 for BLUP and 0.42 for SSGBLUP, and it ranged from 0.10 in scenario 2 to 0.78 in scenario 5 across methods. In terms of bias, the base scenario 1 was unbiased for SSGBLUP; similar results were achieved with scenario 5. Including information on ECM increased the accuracy of GEBV for CH4 by up to 61%, whereas adding information on both traits (BW and ECM) increased the accuracy by up to 90%. Scenarios that did not include CH4 in the reference population had the lowest correlations (0.17-0.33) with single-trait CH4 GEBV, and scenarios with CH4 in the reference population had the highest correlations (0.41-0.81). Thus, failure to include CH4 in future reference populations results in predicted CH4 GEBV, which cannot be used in practical selection. Therefore, recording CH4 in more animals remains a priority. Finally, multiple-trait genomic prediction using routinely recorded BW and ECM leads to higher prediction accuracies than traditional single-trait genomic prediction for CH4 and is a viable solution for increasing the accuracies of GEBV for scarcely recorded CH4 in practice.

Can greenhouse gases in breath be used to genetically improve feed efficiency of dairy cows?

There is considerable interest in improving feed utilization of dairy cattle while limiting losses to the environment (i.e., greenhouse gases, GHG). To breed for feed-efficient or climate-friendly cattle, it is first necessary to obtain accurate estimates of genetic parameters and correlations of feed intake, greenhouse gases, and production traits. Reducing dry matter take (DMI) requirements while maintaining production has high economic value to farmers, but DMI is costly to record and thus limited to small research or nucleus herds. Conversely, enteric methane (CH4) currently has no economic value, is also costly to record, and is limited to small experimental trials. However, breath gas concentrations of methane (CH4c) and carbon dioxide (CO2c) are relatively cheap to measure at high throughput under commercial conditions by installing sniffers in automated milking stations. The objective of this study was to assess the genetic correlations between DMI, body weight (BW), fat- and protein-corrected milk yield (FPCM), and GHG-related traits: CH4c and CO2c from Denmark (DNK) and the Netherlands (NLD). A second objective was to assess the genetic potential for improving feed efficiency and the added benefits of using CH4c and CO2c as indicators. Feed intake data were available on 703 primiparous cows in DNK and 524 in NLD; CH4c and CO2c records were available on 434 primiparous cows in DNK and 656 in NLD. The GHG-related traits were heritable (e.g., CH4c h2: DNK = 0.26, NLD = 0.15) but were differentially genetically correlated with DMI and feed efficiency in both magnitude and sign, depending on the population and the definition of feed efficiency. Across feed efficiency traits and DMI, having bulls with 100 daughters with FPCM, BW, and GHG traits resulted in sufficiently high accuracy to almost negate the need for DMI records. Despite differences in genetic correlation structure, the relatively cheap GHG-related traits showed considerable potential for improving the accuracy of breeding values of highly valuable feed intake and feed efficiency traits.

Effects of an individual cow concentrate strategy on production and behavior.

The aim of this study was to investigate the effects of a concentrate strategy based on the individual cow partial mixed ration (PMR) intake compared with a flat rate. Half of the cows were fed the individual concentrate strategy, whereas the other half were fed the control strategy. The individually fed cows were offered a concentrate proportion equal to 30% of the ad libitum intake of the PMR in the automatic milking system, and the control cows were offered 3 kg of concentrate/d in the automatic milking system. The cows (83 Holstein and 64 Jersey), kept in 2 separate groups, were blocked between the treatments according to expected calving date, breed, and parity and were randomly divided between treatments. Lactation was divided into 3 periods (early, mid, and late lactation), and the MIXED and GLIMMIX procedures in SAS (SAS Institute Inc., Cary, NC) were used to analyze the production and behavioral responses. The response trajectories during lactation were analyzed with a MIXED procedure fitted by Wilmink parameters. The individually fed cows had a higher concentrate intake and a lower PMR intake than control cows. Moreover, the total dry matter intake and energy-corrected milk yield did not differ between concentrate strategies. The actual average concentrate intake reached 19% of the PMR intake during mid lactation and not 30% of the PMR intake, as intended with the strategy. This was due to leftovers and lower allocation than intended. The cow behavior did not differ between concentrate strategies. However, variation in PMR intake and eating rate between cows fed individually was lower during mid lactation and lower for lying bouts during early lactation compared with control cows. This may indicate that an individual cow concentrate strategy results in a more stable PMR intake and time budget, which potentially could improve welfare, but this needs further investigation. The overall conclusion is that the cows were robust to adjustments in the concentrate allowance.

Reliability of breeding values for feed intake and feed efficiency traits in dairy cattle: When dry matter intake recordings are sparse under different scenarios.

Currently, routine recordings of dry matter intake (DMI) in commercial herds are practically nonexistent. Recording DMI from commercial herds is a prerequisite for the inclusion of feed efficiency (FE) traits in dairy cattle breeding goals. To develop future on-farm phenotyping strategies, recording strategies that are low cost and less demanding logistically and that give relatively accurate estimates of the animal's genetic merit are therefore needed. The objectives of this study were (1) to estimate genetic parameters for daily DMI and FE traits and use the estimated parameters to simulate daily DMI phenotypes under different DMI recording scenarios (SCN) and (2) to use the simulated data to estimate for different scenarios the associated reliability of estimated breeding value and accuracies of genomic prediction for varying sizes of reference populations. Five on-farm daily DMI recording scenarios were simulated: once weekly (SCN1), once monthly (SCN2), every 2 mo (SCN3), every 3 mo (SCN4), and every 4 mo (SCN5). To estimate reliability of estimated breeding values, DMI and FE observations and true breeding values were simulated based on variance components estimated for daily observations of Nordic Red cows. To emulate realistic on-farm recording, 5 data set replicates, each with 36,037 DMI and FE records, were simulated for real pedigree and data structure of 789 Holstein cows. Observations for the 5 DMI recording scenarios were generated by discarding data in a step-wise manner from the full simulated data per the scenario's definitions. For each of these scenarios, reliabilities were calculated as correlation between the true and estimated breeding values. Variance components and genetic parameters were estimated for daily DMI, residual feed intake (RFI), and energy conversion efficiency (ECE) fitting the random regression model. Data for variance components were from 227 primiparous Nordic Red dairy cows covering 8 to 280 d in milk. Lactation-wise heritability for DMI, RFI, and ECE was 0.33, 0.12, and 0.32, respectively, and daily heritability estimates during lactation ranged from 0.18 to 0.45, 0.08 to 0.32, and 0.08 to 0.45 for DMI, RFI, and ECE, respectively. Genetic correlations for DMI between different stages of lactation ranged from -0.50 to 0.99. The comparison of different on-farm DMI recording scenarios indicated that adopting a less-frequent recording scenario (SCN3) gave a similar level of accuracy as SCN1 when 17 more daughters are recorded per sire over the 46 needed for SCN1. Such a strategy is less demanding logistically and is low cost because fewer observations need to be collected per animal. The accuracy of genomic predictions associated with the 5 recording scenarios indicated that setting up a relatively larger reference population and adopting a less-frequent DMI sampling scenario (e.g., SCN3) is promising. When the same reference population size was considered, the genomic prediction accuracy of SCN3 was only 5.0 to 7.0 percentage points lower than that for the most expensive DMI recording strategy (SCN1). We concluded that DMI recording strategies that are sparse in terms of records per cow but with slightly more cows recorded per sire are advantageous both in genomic selection and in traditional progeny testing schemes when accuracy, logistics, and cost implications are considered.

Effect of dried oregano (Origanum vulgare L.) plant material in feed on methane production, rumen fermentation, nutrient digestibility, and milk fatty acid composition in dairy cows.

Essential oils (EO) from oregano may have antimicrobial properties, potentially representing a methane mitigation strategy suitable for organic production. This study aimed to (1) examine the potential of oregano in lowering enteric methane production of dairy cows fed differing levels of dried oregano (Origanum vulgare ssp. hirtum) plant material containing high levels of EO; (2) determine whether differing levels of dried oregano plant material of another subspecies (Origanum vulgare ssp. vulgare) with naturally low levels of EO in feed affected enteric methane production; and (3) evaluate the effect of various levels of the 2 oregano subspecies (containing high or low levels of EO) in feed on rumen fermentation, nutrient digestibility, and milk fatty acids. Each experiment had a 4 × 4 Latin square design using 4 lactating Danish Holstein dairy cows that had rumen, duodenal, and ileal cannulas and were fed 4 different levels of oregano. Experiment 1 used low EO oregano [0.12% EO of oregano dry matter (DM)] and evaluated a control (C) diet with no oregano and 3 oregano diets with 18 (low; L), 36 (medium; M), and 53 g of oregano DM/kg of dietary DM (high; H). Experiment 2 used high EO oregano (4.21% EO of oregano DM) with 0, 7, 14, and 21 g of oregano DM/kg of dietary DM for C, L, M, and H, respectively. Oregano was added to the diets by substituting grass/clover silage on a DM basis. Low or high EO oregano in feed did not affect dry matter intake (DMI) or methane production (grams per day, grams per kilogram of DMI, grams per kilogram of energy-corrected milk, and percentage of gross energy intake). Rumen fermentation was slightly affected by diet in experiment 1, but was not affected by diet in experiment 2. In both experiments, the apparent total-tract digestibility of DM, organic matter, and neutral detergent fiber decreased linearly and cubically (a cubic response was not observed for neutral detergent fiber) with increasing dietary oregano content, while milk fatty acids were slightly affected. In conclusion, dried oregano plant material with either high or low levels of EO did not lower the methane production of dairy cows over 4 consecutive days, and no substantial effects were observed on rumen fermentation or nutrient digestibility. This conclusion regarding methane production is in contrast with literature and requires further study.

Herd factors influencing free fatty acid concentrations in bulk tank milk.

Free fatty acid (FFA) concentrations can be elevated in raw milk due to improper handling and management at the dairy farm, and high concentrations of FFA can lead to off flavors in milk. This study aimed to describe how the herd production system, milking system, feeding and technological factors impact on FFA concentrations in bulk tank milk. FFA concentrations in bulk milk samples from 259 organic and 3326 conventional herds were analyzed by FT-IR during one year. The FFA content was significantly lower in bulk milk from organic than conventional herds. This was most evident during the summer half-year when the organic cows graze pasture. Bulk milk from automatic milking systems (AMS) and tie-stalls contained greater concentrations of FFA than any other milking parlor systems. In AMS, high milking frequency was found to be the most significant contributor to elevated FFA content in milk. Moreover, a strong interaction was found between milking interval and production system (organic vs. conventional). The technical factors, pre-cooling, onset time for cooling after milk inlet, contact between milk and agitation also impacted on the FFA concentration, whereas other technical factors including centrifugal pump type, length and height of pumping line and type of AMS manufacturer were found to be without significant effect on FFA. Feeding variables, based on feeding plans and evaluation, only explained a small part of the variation in bulk milk FFA. Overall, this study demonstrated that AMS compared to other milking system contributes significantly to increased FFA concentration in bulk tank milk, and within AMS high milking frequency contributes to increased FFA concentration.

Gene co-expression networks from RNA sequencing of dairy cattle identifies genes and pathways affecting feed efficiency.

BACKGROUND: Selection for feed efficiency is crucial for overall profitability and sustainability in dairy cattle production. Key regulator genes and genetic markers derived from co-expression networks underlying feed efficiency could be included in the genomic selection of the best cows. The present study identified co-expression networks associated with high and low feed efficiency and their regulator genes in Danish Holstein and Jersey cows. RNA-sequencing data from Holstein and Jersey cows with high and low residual feed intake (RFI) and treated with two diets (low and high concentrate) were used. Approximately 26 million and 25 million pair reads were mapped to bovine reference genome for Jersey and Holstein breed, respectively. Subsequently, the gene count expressions data were analysed using a Weighted Gene Co-expression Network Analysis (WGCNA) approach. Functional enrichment analysis from Ingenuity® Pathway Analysis (IPA®), ClueGO application and STRING of these modules was performed to identify relevant biological pathways and regulatory genes. RESULTS: WGCNA identified two groups of co-expressed genes (modules) significantly associated with RFI and one module significantly associated with diet. In Holstein cows, the salmon module with module trait relationship (MTR) = 0.7 and the top upstream regulators ATP7B were involved in cholesterol biosynthesis, steroid biosynthesis, lipid biosynthesis and fatty acid metabolism. The magenta module has been significantly associated (MTR = 0.51) with the treatment diet involved in the triglyceride homeostasis. In Jersey cows, the lightsteelblue1 (MTR = - 0.57) module controlled by IFNG and IL10RA was involved in the positive regulation of interferon-gamma production, lymphocyte differentiation, natural killer cell-mediated cytotoxicity and primary immunodeficiency. CONCLUSION: The present study provides new information on the biological functions in liver that are potentially involved in controlling feed efficiency. The hub genes and upstream regulators (ATP7b, IFNG and IL10RA) involved in these functions are potential candidate genes for the development of new biomarkers. However, the hub genes, upstream regulators and pathways involved in the co-expressed networks were different in both breeds. Hence, additional studies are required to investigate and confirm these findings prior to their use as candidate genes.

Is rumination time an indicator of methane production in dairy cows?

As long as large-scale recording of expensive-to-measure and labor-consuming traits, such as dry matter intake (DMI) and CH4 production (CH4P), continues to be challenging in practical conditions, alternative traits that are already routinely recorded in dairy herds should be investigated. An ideal indicator trait must, in addition to expressing genetic variation, have a strong correlation with the trait of interest. Our aim was to estimate individual level and phenotypic correlations between rumination time (RT), CH4P, and DMI to determine if RT could be used as an indicator trait for CH4P and DMI. Data from 343 Danish Holstein cows were collected at the Danish Cattle Research Centre for a period of approximately 3 yr. The data set consisted of 14,890 records for DMI, 15,835 for RT, and 6,693 for CH4P. Data were divided in primiparous cows only (PC) and all cows (MC), and then divided in lactation stage (early, mid, late, and whole lactation) to analyze the changes over lactation. Linear mixed models, including an animal effect but no pedigree, were used to estimate the correlations among traits. Phenotypic and individual level correlations between RT and both CH4P and DMI were close to zero, regardless of lactation stage and data set (PC or MC). However, CH4P and DMI were highly correlated, both across lactation stages and data sets. In conclusion, RT is unsuitable to be used as an indicator trait for either CH4P or DMI. Our study failed to validate RT as a useful indicator trait for both CH4P and DMI, but more studies with novel phenotypes can offer different approaches to select and incorporate important yet difficult to record traits into breeding goals and selection indexes.

Genetic heterogeneity of feed intake, energy-corrected milk, and body weight across lactation in primiparous Holstein, Nordic Red, and Jersey cows.

In this study, we aimed to estimate and compare the genetic parameters of dry matter intake (DMI), energy-corrected milk (ECM), and body weight (BW) as 3 feed efficiency-related traits across lactation in 3 dairy cattle breeds (Holstein, Nordic Red, and Jersey). The analyses were based on weekly records of DMI, ECM, and BW per cow across lactation for 842 primiparous Holstein cows, 746 primiparous Nordic Red cows, and 378 primiparous Jersey cows. A random regression model was applied to estimate variance components and genetic parameters for DMI, ECM, and BW in each lactation week within each breed. Phenotypic means of DMI, ECM, and BW observations across lactation showed to be in very similar patterns between breeds, whereas breed differences lay in the average level of DMI, ECM, and BW. Generally, for all studied breeds, the heritability for DMI ranged from 0.2 to 0.4 across lactation and was in a range similar to the heritability for ECM. The heritability for BW ranged from 0.4 to 0.6 across lactation, higher than the heritability for DMI or ECM. Among the studied breeds, the heritability estimates for DMI shared a very similar range between breeds, whereas the heritability estimates for ECM tended to be different between breeds. For BW, the heritability estimates also tended to follow a similar range between breeds. Among the studied traits, the genetic variance and heritability for DMI varied across lactation, and the genetic correlations between DMI at different lactation stages were less than unity, indicating a genetic heterogeneity of feed intake across lactation in dairy cattle. In contrast, BW was the most genetically consistent trait across lactation, where BW among all lactation weeks was highly correlated. Genetic correlations between DMI, ECM, and BW changed across lactation, especially in early lactation. Energy-corrected milk had a low genetic correlation with both DMI and BW at the beginning of lactation, whereas ECM was highly correlated with DMI in mid and late lactation. Based on our results, genetic heterogeneity of DMI, ECM, and BW across lactation generally was observed in all studied dairy breeds, especially for DMI, which should be carefully considered for the recording strategy of these traits. The genetic correlations between DMI, ECM, and BW changed across lactation and followed similar patterns between breeds.

Methane production, rumen fermentation, and diet digestibility of Holstein and Jersey dairy cows being divergent in residual feed intake and fed at 2 forage-to-concentrate ratios.

Improving feed efficiency of dairy cows through breeding is expected to reduce enteric methane production per unit of milk produced. This study examined the effect of 2 forage-to-concentrate ratios on methane production, rumen fermentation, and nutrient digestibility in Holstein and Jersey dairy cows divergent in residual feed intake (RFI). Before experimental onset, RFI was estimated using a random regression model on phenotypic herd data. Ten lactating Holstein and 10 lactating Jersey cows were extracted from the herd and allocated to a high or low pre-experimental RFI group of 5 animals each within breed. Cows were fed ad libitum with total mixed rations either low (LC) or high (HC) in concentrates during 3 periods in a crossover design with a back-cross and staggered approach. Forage-to-concentrate ratio was 68:32 for LC and 39:61 for HC. Cows adapted to the diets in 12 to 24 d and feces were subsequently collected on 2 d. Afterward, gas exchange was measured in respiration chambers and rumen liquid was collected once after cows exited the chambers. Pre-experimental RFI was included in the statistical analysis as a class (low and high RFI) or continuous variable. Methane per kilogram of dry matter intake (DMI) was lower for Holsteins than Jerseys and the response to increased concentrate level was more pronounced for Holsteins than Jerseys (27.2 vs.13.8%); a similar pattern was found for the acetate:propionate ratio. However, methane production per kilogram of energy-corrected milk (ECM) was unaffected by breed. Further, total-tract digestibility of neutral detergent fiber was higher for Jerseys than Holsteins. For RFI as a class variable, DMI, methane production regardless of the expression, and digestibility were unaffected by RFI. For RFI as a continuous variable, DMI was lower and methane per kilogram of DMI was higher for cows with negative (efficient) than positive (inefficient) RFI values, and neutral detergent fiber digestibility was higher for Holsteins with negative than positive RFI values, but not for Jerseys. Daily methane production and methane per kilogram of ECM were unaffected by RFI. In conclusion, methane per kilogram of DMI of Jerseys was lowered to a smaller extent in response to the HC diet than of Holsteins. When pre-experimental RFI was used as a continuous variable, higher methane per kilogram of DMI was found for cows with negative RFI than positive RFI values, but not for methane per kilogram of ECM. These findings call for validation in larger studies.

RNA-Seq transcriptomics and pathway analyses reveal potential regulatory genes and molecular mechanisms in high- and low-residual feed intake in Nordic dairy cattle.

BACKGROUND: The selective breeding of cattle with high-feed efficiencies (FE) is an important goal of beef and dairy cattle producers. Global gene expression patterns in relevant tissues can be used to study the functions of genes that are potentially involved in regulating FE. In the present study, high-throughput RNA sequencing data of liver biopsies from 19 dairy cows were used to identify differentially expressed genes (DEGs) between high- and low-FE groups of cows (based on Residual Feed Intake or RFI). Subsequently, a profile of the pathways connecting the DEGs to FE was generated, and a list of candidate genes and biomarkers was derived for their potential inclusion in breeding programmes to improve FE. RESULTS: The bovine RNA-Seq gene expression data from the liver was analysed to identify DEGs and, subsequently, identify the molecular mechanisms, pathways and possible candidate biomarkers of feed efficiency. On average, 57 million reads (short reads or short mRNA sequences < ~200 bases) were sequenced, 52 million reads were mapped, and 24,616 known transcripts were quantified according to the bovine reference genome. A comparison of the high- and low-RFI groups revealed 70 and 19 significantly DEGs in Holstein and Jersey cows, respectively. The interaction analysis (high vs. low RFI x control vs. high concentrate diet) showed no interaction effects in the Holstein cows, while two genes showed interaction effects in the Jersey cows. The analyses showed that DEGs act through certain pathways to affect or regulate FE, including steroid hormone biosynthesis, retinol metabolism, starch and sucrose metabolism, ether lipid metabolism, arachidonic acid metabolism and drug metabolism cytochrome P450. CONCLUSION: We used RNA-Seq-based liver transcriptomic profiling of high- and low-RFI dairy cows in two breeds and identified significantly DEGs, their molecular mechanisms, their interactions with other genes and functional enrichments of different molecular pathways. The DEGs that were identified were the CYP's and GIMAP genes for the Holstein and Jersey cows, respectively, which are related to the primary immunodeficiency pathway and play a major role in feed utilization and the metabolism of lipids, sugars and proteins.

Genetic parameters of rumination time and feed efficiency traits in primiparous Holstein cows under research and commercial conditions.

Feed efficiency has the potential to be improved both through feeding, management, and breeding. Including feed efficiency in a selection index is limited by the fact that dry matter intake (DMI) recording is only feasible under research facilities, resulting in small data sets and, consequently, uncertain genetic parameter estimates. As a result, the need to record DMI indicator traits on a larger scale exists. Rumination time (RT), which is already recorded in commercial dairy herds by a sensor-based system, has been suggested as a potential DMI indicator. However, RT can only be a DMI indicator if it is heritable, correlates with DMI, and if the genetic parameters of RT in commercial herd settings are similar to those in research facilities. Therefore, the objective of our study was to estimate genetic parameters for RT and the related traits of DMI in primiparous Holstein cows, and to compare genetic parameters of rumination data between a research herd and 72 commercial herds. The estimated heritability values were all moderate for DMI (0.32-0.49), residual feed intake (0.23-0.36), energy-corrected milk (ECM) yield (0.49-0.70), and RT (0.14-0.44) found in the research herd. The estimated heritability values for ECM were lower for the commercial herds (0.08-0.35) than that for the research herd. The estimated heritability values for RT were similar for the 2 herd types (0.28-0.32). For the research herd, we found negative individual level correlations between RT and DMI (-0.24 to -0.09) and between RT and RFI (-0.34 to -0.03), and we found both positive and negative correlations between RT and ECM (-0.08 to 0.09). For the commercial herds, genetic correlations between RT and ECM were both positive and negative (-0.27 to 0.10). In conclusion, RT was not found to be a suitable indicator trait for feed intake and only a weak indicator of feed efficiency.

Prediction and validation of residual feed intake and dry matter intake in Danish lactating dairy cows using mid-infrared spectroscopy of milk.

The present study explored the effectiveness of Fourier transform mid-infrared (FT-IR) spectral profiles as a predictor for dry matter intake (DMI) and residual feed intake (RFI). The partial least squares regression method was used to develop the prediction models. The models were validated using different external test sets, one randomly leaving out 20% of the records (validation A), the second randomly leaving out 20% of cows (validation B), and a third (for DMI prediction models) randomly leaving out one cow (validation C). The data included 1,044 records from 140 cows; 97 were Danish Holstein and 43 Danish Jersey. Results showed better accuracies for validation A compared with other validation methods. Milk yield (MY) contributed largely to DMI prediction; MY explained 59% of the variation and the validated model error root mean square error of prediction (RMSEP) was 2.24kg. The model was improved by adding live weight (LW) as an additional predictor trait, where the accuracy R2 increased from 0.59 to 0.72 and error RMSEP decreased from 2.24 to 1.83kg. When only the milk FT-IR spectral profile was used in DMI prediction, a lower prediction ability was obtained, with R2=0.30 and RMSEP=2.91kg. However, once the spectral information was added, along with MY and LW as predictors, model accuracy improved and R2 increased to 0.81 and RMSEP decreased to 1.49kg. Prediction accuracies of RFI changed throughout lactation. The RFI prediction model for the early-lactation stage was better compared with across lactation or mid- and late-lactation stages, with R2=0.46 and RMSEP=1.70. The most important spectral wavenumbers that contributed to DMI and RFI prediction models included fat, protein, and lactose peaks. Comparable prediction results were obtained when using infrared-predicted fat, protein, and lactose instead of full spectra, indicating that FT-IR spectral data do not add significant new information to improve DMI and RFI prediction models. Therefore, in practice, if full FT-IR spectral data are not stored, it is possible to achieve similar DMI or RFI prediction results based on standard milk control data. For DMI, the milk fat region was responsible for the major variation in milk spectra; for RFI, the major variation in milk spectra was within the milk protein region.

Predicting methane emissions of lactating Danish Holstein cows using Fourier transform mid-infrared spectroscopy of milk.

Enteric methane (CH4), a potent greenhouse gas, is among the main targets of mitigation practices for the dairy industry. A measurement technique that is rapid, inexpensive, easy to use, and applicable at the population level is desired to estimate CH4 emission from dairy cows. In the present study, feasibility of milk Fourier transform mid-infrared (FT-IR) spectral profiles as a predictor for CH4:CO2 ratio and CH4 production (L/d) is explained. The partial least squares regression method was used to develop the prediction models. The models were validated using different random test sets, which are independent from the training set by leaving out records of 20% cows for validation and keeping records of 80% of cows for training the model. The data set consisted of 3,623 records from 500 Danish Holstein cows from both experimental and commercial farms. For both CH4:CO2 ratio and CH4 production, low prediction accuracies were found when models were obtained using FT-IR spectra. Validated coefficient of determination (R2Val) = 0.21 with validated model error root mean squared error of prediction (RMSEP) = 0.0114 L/d for CH4:CO2 ratio, and R2Val = 0.13 with RMSEP = 111 L/d for CH4 production. The important spectral wavenumbers selected using the recursive partial least squares method represented major milk components fat, protein, and lactose regions of the spectra. When fat and protein predicted by FT-IR were used instead of full spectra, a low R2Val of 0.07 was obtained for both CH4:CO2 ratio and CH4 production prediction. Other spectral wavenumbers related to lactose (carbohydrate) or additional wavenumbers related to fat or protein (amide II) are providing additional variation when using the full spectral profile. For CH4:CO2 ratio prediction, integration of FT-IR with other factors such as milk yield, herd, and lactation stage showed improvement in the prediction accuracy. However, overall prediction accuracy remained modest; R2Val increased to 0.31 with RMSEP = 0.0105. For prediction of CH4 production, the added value of FT-IR along with the aforementioned traits was marginal. These results indicated that for CH4 production prediction, FT-IR profiles reflect primarily information related to milk yield, herd, and lactation stage rather than individual milk fatty acids related to CH4 emission. Thus, it is not feasible to predict CH4 emission based on FT-IR spectra alone.