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.
It may be possible for dairy farms to improve profitability and reduce environmental impacts by selecting for higher feed efficiency and lower methane (CH4) emission traits. It remains to be ...clarified how CH4 emission and feed efficiency traits are related to each other, which will require direct and accurate measurements of both of these traits in large numbers of animals under the conditions in which they are expected to perform. The ranking of animals for feed efficiency and CH4 emission traits can differ depending upon the type and duration of measurement used, the trait definitions and calculations used, the period in lactation examined and the production system, as well as interactions among these factors. Because the correlation values obtained between feed efficiency and CH4 emission data are likely to be biased when either or both are expressed as ratios, therefore researchers would be well advised to maintain weighted components of the ratios in the selection index. Nutrition studies indicate that selecting low emitting animals may result in reduced efficiency of cell wall digestion, that is NDF, a key ruminant characteristic in human food production. Moreover, many interacting biological factors that are not measured directly, including digestion rate, passage rate, the rumen microbiome and rumen fermentation, may influence feed efficiency and CH4 emission. Elucidating these mechanisms may improve dairy farmers ability to select for feed efficiency and reduced CH4 emission.
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.
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.
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.
Detection of estrus in dairy cattle is effectively aided by electronic activity tags or pedometers. Characterization of estrus intensity and duration is also possible from activity data. This study ...aimed to develop an algorithm to detect and characterize behavioral estrus from hourly recorded activity data and to apply the algorithm to activity data from an experimental herd. The herd comprised of Holstein (n=211), Jersey (n=126), and Red Dane (n=178) cattle, with virgin heifers (n=132) and lactating cows in the first 4 parities; n=895 cow-parities, with a total of 3,674 activity episodes. The algorithm was based on deviations from exponentially smoothed hourly activity counts and was used to identify onset, duration, and intensity of estrus. Learning data included 461 successful inseminations with activity records over a 2-wk period before and after the artificial insemination. Rates of estrus detection and error rate depended on the chosen threshold level. At a threshold giving 74.6% detection rate, daily error rate was 1.3%. When applied to a subset of the complete data where milk progesterone was also available, concordance of days to first activity-detected estrus with the similar trait based on progesterone was also dependent on the chosen threshold so that, with stricter thresholds, the agreement was closer. A single-trait mixed model was used to determine the effects of systematic factors on the estrus activity traits. In general, an activity episode lasted 9.24h in heifers and 8.12h in cows, with the average strength of 1.03 ln units (equivalent to a 2.8-fold increase) in both age groups. Red Danes had significantly fewer days to first episode of high activity than Holsteins and Jerseys (29.4, 33.1, and 33.9 d, respectively). However, Jerseys had significantly shorter duration and less strength of estrus than both Red Danes and Holsteins of comparable age. The random effect of cow affected days to first episode of high activity and strength as well as estrus duration. Days from calving to first episode of high activity correlated negatively with body condition scores in early lactation. The results suggest that data from activity monitors could supply valuable information about fertility traits and could thereby be helpful in management of herd fertility. To establish the complementarities or interdependence between progesterone and activity measurements, further studies with more information from different sources of measuring estrus are needed.
Electrical conductivity (EC) of milk has been introduced as an indicator trait for mastitis over the last decade, and it may be considered as a potential trait in a breeding program where selection ...for improved udder health is included. In this study, various EC traits were investigated for their association with udder health. In total, 322 cows with 549 lactations were included in the study. Cows were classified as healthy or clinically or subclinically infected, and EC was measured repeatedly during milking on each quarter. Four EC traits were defined; the inter-quarter ratio (IQR) between the highest and lowest quarter EC values, the maximum EC level for a cow, IQR between the highest and lowest quarter EC variation, and the maximum EC variation for a cow. Values for the traits were calculated for every milking throughout the entire lactation. All EC traits increased significantly (P<0.001) when cows were subclinically or clinically infected. A simple threshold test and discriminant function analysis was used to validate the ability of the EC traits to distinguish between cows in different health groups. Traits reflecting the level rather than variation of EC, and in particular the IQR, performed best to classify cows correctly. By using this trait, 80.6% of clinical and 45.0% of subclinical cases were classified correctly. Of the cows classified as healthy, 74.8% were classified correctly. However, some extra information about udder health status was obtained when a combination of EC traits was used.
Individual methane (CH4) production was recorded repeatedly on 93 dairy cows during milking in an automatic milking system (AMS), with the aim of estimating individual cow differences in CH4 ...production. Methane and CO2 were measured with a portable air sampler and analyzer unit based on Fourier transform infrared (FTIR) detection. The cows were 50 Holsteins and 43 Jerseys from mixed parities and at all stages of lactation (mean=156 d in milk). Breath was captured by the FTIR unit inlet nozzle, which was placed in front of the cow's head in each of the 2 AMS as an admixture to normal barn air. The FTIR unit was running continuously for 3 d in each of 2 AMS units, 1 with Holstein and another with Jersey cows. Air was analyzed every 20s. From each visit of a cow to the AMS, CH4 and CO2 records were summarized into the mean, median, 75, and 90% quantiles. Furthermore, the ratio between CH4 and CO2 was used as a derived measure with the idea of using CO2 in breath as a tracer gas to quantify the production of methane. Methane production records were analyzed with a mixed model, containing cow as random effect. Fixed effects of milk yield and daily intake of the total mixed ration and concentrates were also estimated. The repeatability of the CH4-to-CO2 ratio was 0.39 for Holsteins and 0.34 for Jerseys. Both concentrate intake and total mixed ration intake were positively related to CH4 production, whereas milk production level was not correlated with CH4 production. In conclusion, the results from this study suggest that the CH4-to-CO2 ratio measured using the noninvasive method is an asset of the individual cow and may be useful in both management and genetic evaluations.
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.
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.
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