Impact on prime animal beef merit from breeding solely for lighter dairy cows

As the proportion of prime carcasses originating from dairy herds increases, the focus is shifting to the beef merit of the progeny from dairy herds. Several dairy cow total merit indexes include a negative weight on measures of cow size. However, there is a lack of knowledge on the impact of genetic selection, solely for lighter or smaller-sized dairy cows, on the beef performance of their progeny. Therefore, the objective of this study was to quantify the genetic correlations among cow size traits (i.e., cow body weight (BW), cow carcass weight (CW)), cow body condition score (BCS), cow carcass conformation (CC), and cow carcass fat cover (CF), as well as the correlations between these cow traits and a series of beef performance slaughter-related traits (i.e., CW, CC, CF, and age at slaughter (AS)) in their progeny. After data editing, there were 52,950 cow BW and BCS records, along with 57,509 cow carcass traits (i.e., CW, CC, and CF); carcass records from 346,350 prime animals along with AS records from 316,073 prime animals were also used. Heritability estimates ranged from moderate to high (0.18 to 0.62) for all cow and prime animal traits. The same carcass trait in cows and prime animals were strongly genetically correlated with each other (0.76 to 0.85), implying that they are influenced by very similar genomic variants. Selecting exclusively for cows with higher BCS (i.e., fatter) will, on average, produce more conformed prime animals carcasses, owing to a moderate genetic correlation (0.30) between both traits. Genetic correlations revealed that selecting exclusively for lighter BW or CW cows will, on average, result in lighter prime animal carcasses of poor CC, while also delaying slaughter age. Nonetheless, selective breeding through total merit indexes should be successful in breeding for smaller dairy cows, and desirable prime animal carcass traits concurrently, because of the non-unity genetic correlations between the cow and prime animal traits; this will help to achieve a more ethical, environmentally sustainable, and economically viable dairy-beef industry.

An artificial intelligence approach of feature engineering and ensemble methods depicts the rumen microbiome contribution to feed efficiency in dairy cows

Genetic selection has remarkably helped U.S. dairy farms to decrease their carbon footprint by more than doubling milk production per cow over time. Despite the environmental and economic benefits of improved feed and milk production efficiency, there is a critical need to explore phenotypical variance for feed utilization to advance the long-term sustainability of dairy farms. Feed is a major expense in dairy operations, and their enteric fermentation is a major source of greenhouse gases in agriculture. The challenges to expanding the phenotypic database, especially for feed efficiency predictions, and the lack of understanding of its drivers limit its utilization. Herein, we leveraged an artificial intelligence approach with feature engineering and ensemble methods to explore the predictive power of the rumen microbiome for feed and milk production efficiency traits, as rumen microbes play a central role in physiological responses in dairy cows. The novel ensemble method allowed to further identify key microbes linked to the efficiency measures. We used a population of 454 genotyped Holstein cows in the U.S. and Canada with individually measured feed and milk production efficiency phenotypes. The study underscored that the rumen microbiome is a major driver of residual feed intake (RFI), the most robust feed efficiency measure evaluated in the study, accounting for 36% of its variation. Further analyses showed that several alpha-diversity metrics were lower in more feed-efficient cows. For RFI, [Ruminococcus] gauvreauii group was the only genus positively associated with an improved feed efficiency status while seven other taxa were associated with inefficiency. The study also highlights that the rumen microbiome is pivotal for the unexplained variance in milk fat and protein production efficiency. Estimation of the carbon footprint of these cows shows that selection for better RFI could reduce up to 5 kg of diet consumed per cow daily, potentially reducing up to 37.5% of CH4. These findings shed light that the integration of artificial intelligence approaches, microbiology, and ruminant nutrition can be a path to further advance our understanding of the rumen microbiome on nutrient requirements and lactation performance of dairy cows to support the long-term sustainability of the dairy community.

Genetic parameters for feed intake and body weight in dairy cattle using high-throughput 3-dimensional cameras in Danish commercial farms

Recording complex phenotypes on a large scale is becoming possible with the incorporation of recently developed new technologies. One of these new technologies is the use of 3-dimensional (3D) cameras on commercial farms to measure feed intake and body weight (BW) daily. Residual feed intake (RFI) has been proposed as a proxy for feed efficiency in several species, including cattle, pigs, and poultry. Dry matter intake (DMI) and BW records are required to calculate RFI, and the use of this new technology will help increase the number of individual records more efficiently. The aim of this study was to estimate genetic parameters (including genetic correlations) for DMI and BW obtained by 3D cameras from 6,000 cows in commercial farms from the breeds Danish Holstein, Jersey, and Nordic Red. Additionally, heritabilities per parity and genetic correlations among parities were estimated for DMI and BW in the 3 breeds. Data included 158,000 weekly records of DMI and BW obtained between 2019 and 2022 on 17 commercial farms. Estimated heritability for DMI ranged from 0.17 to 0.25, whereas for BW they ranged from 0.44 to 0.58. The genetic correlations between DMI and BW were moderately positive (0.58-0.65). Genetic correlations among parities in both traits were highly correlated in the 3 breeds, except for DMI between first parity and late parities in Holstein where they were down to 0.62. Based on these results, we conclude that DMI and BW phenotypes measured by 3D cameras are heritable for the 3 dairy breeds and their heritabilities are comparable to those obtained by traditional methods (scales and feed bins). The high heritabilities and correlations of 3D measurements with the true trait in previous studies demonstrate the potential of this new technology for measuring feed intake and BW in real time. In conclusion, 3D camera technology has the potential to become a valuable tool for automatic and continuous recording of feed intake and BW on commercial farms.

Implementation of Feed Efficiency in Iranian Holstein Breeding Program

This study aimed to evaluate the economic impact of improving feed efficiency on breeding objectives for Iranian Holsteins. Production and economic data from seven dairy herds were used to estimate the economic values of different traits, and a meta-analysis was conducted to analyze the genetic relationships between feed efficiency and other traits. Economic weights were calculated for various traits, with mean values per cow and per year across herds estimated at USD 0.34/kg for milk yield, USD 6.93/kg for fat yield, USD 5.53/kg for protein yield, USD −1.68/kg for dry matter intake, USD −1.70/kg for residual feed intake, USD 0.47/month for productive life, and USD −2.71/day for days open. The Iranian selection index was revised to improve feed efficiency, and the feed efficiency sub-index (FE$) introduced by the Holstein Association of the United States of America was adopted to reflect Iran’s economic and production systems. However, there were discrepancies between Iranian and US genetic coefficients in the sub-index, which could be attributed to differences in genetic and phenotypic parameters, as well as the economic value of each trait. More accurate estimates of economic values for each trait in FE$ could be obtained by collecting dry matter intake from Iranian herds and conducting genetic evaluations for residual feed intake.

Estimation of Genetic Parameters for Conformation Traits and Milk Production Traits in Chinese Holsteins

The objective of this study was to explore the genetic parameters of conformation traits and milk production traits in Chinese Holstein cattle and to provide a reference for dairy cattle breeding. We collected the phenotypic data of 23 conformation traits and five milk production traits of Chinese Holsteins and used animal models to estimate the genetic parameters of conformation traits and milk production traits. The estimated heritability of conformation traits ranged from 0.11 (angularity) to 0.37 (heel depth) and the genetic correlation between conformation traits ranged from -0.73 (bone quality and rear leg-rear view) to 0.76 (chest width and loin strength). The heritability of milk production traits ranged from 0.23 (somatic cell score) to 0.50 (305-d milk yield). The estimated values of genetic correlation between conformation traits and milk production traits ranged from -0.56 (heel depth and 305-d milk yield) to 0.57 (udder texture and milk fat percentage). There was a positive genetic correlation between most conformation traits and milk fat percentage, but a weak negative genetic correlation with milk yield. Strengthening the moderately and highly heritable milk production and conformation traits, especially the selection of rear udder traits and body shape total score, will be beneficial in improving the performance of dairy cows.

Emphasis on resilience in dairy cattle breeding: Possibilities and consequences

This study aimed to investigate dairy cattle breeding goals with more emphasis on resilience. We simulated the consequences of increasing weight on resilience indicators and an assumed true resilience trait (TR). Two environments with different breeding goals were simulated to represent the variability of production systems across Europe. Ten different scenarios were stochastically simulated in a so-called pseudogenomic simulation approach. We showed that many modern dairy cattle breeding goals most likely have negative genetic gain for TR and promising resilience indicators such as the log-transformed, daily deviation from the lactation curve (LnVAR). In addition, there were many ways of improving TR by increasing the breeding goal weight of different resilience indicators. The results showed that adding breeding goal weight to resilience indicators, such as body condition score and LnVAR, could reverse the negative trend observed for resilience indicators. Loss in the aggregate genotype calculated with only current breeding goal traits was 12 to 76%. This loss was mainly due to a reduction in genetic gain in milk production. We observed higher genetic gain in beef production, fertility, and udder health when breeding for more resilience, but from an economical point of view, this was not high enough to compensate for the reduction in genetic gain in milk production. The highest genetic gain in TR was obtained when adding the highest breeding goal weight to LnVAR or TR, both with 0.29 genetic standard deviation units. The indicators we used, body condition score and LnVAR, can be measured on a large scale today with relatively cheap methods, which is crucial if we want to improve these traits through breeding. Economic values for resilience have to be estimated to find the most optimal breeding goal for a more resilient dairy cow in the future.

Genetic parameters for body weight and milk production of dairy Gyr herds

The aim of this study was to estimate genetic parameters for cow weight at calving (CW) and cumulative 305-day milk yield (MY305) in dairy Gyr cattle by two-trait analysis. The study used 1,847 CW records, in which 418 females presented more than one measure, and 4,048 MY305 records, wherein 1068 females provided repeated measures, from 2,339 females belonging to three herds, which calved between 1986 and 2019. Variance components were estimated by the restricted maximum likelihood method (REML) using a two-trait animal model. The model included direct additive genetic, permanent environmental and residual effects as random effects and the fixed effects of contemporary group, formed by animals that had calved on the same farm year and season, and age of cow at calving as covariates (fitted as linear and quadratic effect). The heritability estimates for CW and MY305 were 0.21 ± 0.06 and 0.29 ± 0.04, respectively, and repeatability estimates were 0.49 ± 0.03 and 0.49 ± 0.02. The genetic correlation between CW and MY305 was positive and of low magnitude (0.33 ± 0.18), indicating that selection for MY305 will cause little genetic change in the weights of dairy Gyr animals. The genetic trends of breeding values of analyzed traits showed marked genetic gains in MY305, with little changes in CW of dairy Gyr cows over the years in the herds studied, which is an important result considering the production systems adopted in the tropics.

The response to genetic merit for milk production in dairy cows differs by cow body weight

•

Heavier cows yield more milk, fat, and protein.

•

The association between genetic merit for milk yield and actual yield differs by BW.

•

The association between genetic merit for milk composition and actual composition does not differ by BW.

Attention is increasing on both cow size and body weight (BW) as energy sinks and thus as contributors to differences in production efficiency among cows. What is not currently clear, however, is how cow BW affects the increase in yield per cow per unit increase in genetic merit for milk production. This void in knowledge was filled in the present study using BW data from 20,470 lactations on 16,980 Holstein-Friesian dairy cows stratified into 4 groups on BW adjusted for differences in parity, days in milk, and body condition score. Using linear mixed models that adjusted for nuisance factors, cow phenotypic milk production variables were regressed on estimates of parental average genetic merit for the respective trait within each stratum of BW defined within contemporary group; estimates of genetic merit were from the national genetic evaluations. Both the intercept and linear regression coefficients on genetic merit were compared across BW strata. The intercepts representing the mean phenotypic yield at a genetic merit of zero differed among BW strata; irrespective of yield trait, the least squares means yield per BW stratum increased numerically as cows got heavier, although not every stepwise increase in BW stratum was associated with significantly greater yield compared with the previous (lighter) stratum. Nonetheless, the yield of the cows in the lightest of the 4 strata was always less than that of the heaviest 2 strata; relative to the lightest stratum, cows in the heaviest BW stratum produced only 3 to 4% more yield. Furthermore, the association between phenotypic yield and its respective measures of genetic merit differed by BW stratum; the response to selection for each of the yield traits was 15 to 23% greater for the heaviest stratum of cows compared with their contemporaries in the lightest stratum. Although BW stratum was associated with mean fat and protein concentration after adjusting for differences in genetic merit for fat and protein concentration, the association did not differ by BW stratum for either fat or protein concentration. The effect of BW on efficiency should consider the association between BW and not only mean phenotypic yield at a given genetic merit, but also how the differences in yield diverge as genetic merit increases.

Genomic prediction of crossbred dairy cattle in Tanzania: A route to productivity gains in smallholder dairy systems

Selection based on genomic predictions has become the method of choice for genetic improvement in dairy cattle. This offers huge opportunity for developing countries with little or no pedigree data, and preliminary studies have shown promising results. The African Dairy Genetic Gains (ADGG) project initiated a digital system of dairy performance data collection, accompanied by genotyping in Tanzania in 2016. Currently, ADGG has the largest body of dairy performance data generated in East Africa from a smallholder dairy system. This study examines the use of genomic best linear unbiased prediction (GBLUP) and single-step (ss)GBLUP for the estimation of genetic parameters and accuracy of genomic prediction for daily milk yield and body weight in Tanzania. The estimates of heritability for daily milk yield from GBLUP and ssGBLUP were essentially the same, at 0.12 ± 0.03. The heritability estimates for daily milk yield averaged over the whole lactation from random regression model (RRM) GBLUP or ssGBLUP were 0.22 and 0.24, respectively. The heritability of body weight from GBLUP was 0.24 ± 04 but was 0.22 ± 04 from the ssGBLUP analysis. Accuracy of genomic prediction for milk yield from a forward validation was 0.57 for GBLUP based on fixed regression model or 0.55 from an RRM. Corresponding estimates from ssGBLUP were 0.59 and 0.53, respectively. Accuracy for body weight, however, was much higher at 0.83 from GBLUP and 0.77 for ssGBLUP. The moderate to high levels of accuracy of genomic prediction (0.53-0.83) obtained for milk yield and body weight indicate that selection on the basis of genomic prediction is feasible in smallholder dairy systems and most probably the only initial possible pathway to implementing sustained genetic improvement programs in such systems.

Prediction of genetic merit for live weight and body condition score in dairy cows using routinely available linear type and carcass data

Accurate estimates of genetic merit for both live weight and body condition score (BCS) could be useful additions to both national- and herd-breeding programs. Although recording live weight and BCS is not technologically arduous, data available for use in routine genetic evaluations are generally lacking. The objective of the present study was to explore the usefulness of routinely recorded data, namely linear type traits (which also included BCS but only assessed visually) and carcass traits in the pursuit of genetic evaluations for both live weight and BCS in dairy cows. The data consisted of on-farm records of live weight and BCS (assessed using both visual and tactile cues) from 33,242 dairy cows in 201 commercial Irish herds. These data were complemented with information on 6 body-related linear type traits (i.e., stature, angularity, chest width, body depth, BCS, and rump width) and 3 cull cow carcass measures (i.e., carcass weight, conformation, and fat cover) on a selection of these animals plus close relatives. (Co)variance components were estimated using animal linear mixed models. The genetic correlation between the type traits stature, angularity, body depth, chest width, rump width, and visually-assessed BCS with live weight was 0.68, -0.28, 0.43, 0.64, 0.61, and 0.44, respectively. The genetic correlation between angularity and BCS measured on farm (based on both visual and tactile appraisal) was -0.79; the genetic and phenotypic correlation between BCS assessed visually as part of the linear assessment with BCS assessed by producers using both tactile and visual cues was 0.90 and 0.27, respectively. The genetic (phenotypic) correlation between cull cow carcass weight and live weight was 0.81 (0.21), and the genetic (phenotypic) correlation between cull cow carcass fat cover and BCS assessed on live cows was 0.44 (0.12). Estimated breeding values (EBV) for live weight and BCS in a validation population of cows were generated using a multitrait evaluation with observations for just the type traits, just the carcass traits, and both the type traits and carcass traits; the EBV were compared with the respective live weight and BCS phenotypic observations. The regression of phenotypic live weight on its EBV from the multitrait evaluations was 1.00 (i.e., the expectation) when the EBV was generated using just linear type trait data, but less than 1 (0.83) when using just carcass data. However, the regression changed across parities and stages of lactation. The partial correlation (after adjusting for contemporary group, parity by stage of lactation, heterosis, and recombination loss) between phenotypic live weight and EBV for live weight estimated using the 3 different scenarios (i.e., type only, carcass only, type plus carcass) ranged from 0.38 to 0.43. Although the prediction of phenotypic BCS from its respective EBV was relatively good when using just the linear type trait data (regression coefficient of 0.83 with a partial correlation of 0.22), the predictive ability of BCS EBV based on just carcass data was poor and should not be used. Overall, linear type trait data are a useful source of information to predict live weight and BCS with minimal additional predictive value from also including carcass data. Nonetheless, in the absence of linear type trait data, information on carcass traits can be useful in predicting genetic merit for mature cow live weight. Prediction of cow BCS from cow carcass data is not recommended.

Assessment of Performance and Some Welfare Indicators of Cows in Vietnamese Smallholder Dairy Farms

Smallholder dairy farms (SDFs) are distributed widely across lowland and highland regions in Vietnam, but data on the productivity and welfare status of these cows remains limited. This cross-sectional study was conducted to describe and compare the productivity and welfare status of SDF cows across contrasting regions. It was conducted in autumn 2017 on 32 SDFs randomly selected from four typical but contrasting dairy regions (eight SDFs per region); a south lowland, a south highland, a north lowland, and a north highland region. Each farm was visited over a 24-h period (an afternoon followed by a morning milking and adjacent husbandry activities) to collect data of individual lactating cows (n = 345) and dry cows (n = 123), which included: milk yield and concentrations, body weight (BW), body condition score (BCS, 5-point scale, 5 = very fat), inseminations per conception, and level of heat stress experienced (panting score, 4.5-point scale, 0 = no stress). The high level of heat stress (96% of lactating cows were moderate to highly heat-stressed in the afternoon), low energy corrected milk yield (15.7 kg/cow/d), low percentage of lactating cows (37.3% herd), low BW (498 and 521 kg in lactating and dry cows, respectively), and low BCS of lactating cows (2.8) were the most important productivity and welfare concerns determined and these were most serious in the south lowland. By contrast, cows in the north lowland, a relatively hot but new dairying region, performed similarly to those in the south highland; a region historically considered to be one of the most suitable for dairy cows in Vietnam due to its cool environment. This indicates the potential to mitigate heat stress through new husbandry strategies. Cows in the north highland had the highest BW (535 and 569 kg in lactating and dry cows, respectively) and the highest energy corrected milk yield (19.2 kg/cow/d). Cows in all regions were heat-stressed during the daytime, although less so in the highlands compared to the lowlands. Opportunities for research into improving the productivity and welfare of Vietnamese SDF cows are discussed.

Genetic parameters of forage dry matter intake and milk produced from forage in Swedish Red and Holstein dairy cows

High-yielding dairy cows are often fed high proportions of cereal grain and pulses. For several reasons, it would be desirable to replace these feed sources with forage, which is not suitable for human consumption. Feeding large amounts of forage to dairy cows could also make dairy production more publicly acceptable in the future. In this study, we estimated genetic parameters for total dry matter intake (DMI), DMI from forage (DMI_For), energy-corrected milk (ECM), and ECM produced from forage (ECM_For). A total of 1,177 lactations from 575 cows of Swedish Red (SR) and Holstein (HOL) dairy breeds were included in the study. Mixed linear animal random regression models were used, with fixed effect of calving season and lactation week nested within parity 1 and 2+, fixed effect of calving year, and random regression coefficients for breeding value (up to linear) and permanent environmental effect (up to quadratic) of the cow. Heritability for DMI and DMI_For was generally higher for HOL than for SR in all-parity data and in later parities; however, the opposite was true for first parity. Heritability for DMI and DMI_For during the first 8 wk averaged 0.11 and 0.15, respectively, in all-parity data for the 2 breeds. Corresponding values for ECM_For and ECM were 0.21 and 0.29, respectively. In first parity, values were 0.32, 0.36, 0.28, and 0.51, respectively. The genetic correlation between DMI and DMI_For was high, above 0.83, and fairly constant across the lactation. The genetic correlation between ECM_For and ECM was close to unity in the later part of lactation for both breeds, but was around 0.8 in the early lactation for both breeds; it decreased for HOL to 0.54 in wk 17. The genetic correlations between DMI and ECM_For and between DMI_For and ECM_For were low and negative for HOL (absolute value ∼0.2-0.3), but changed for SR from weakly positive in early lactation to negative values and back to positive toward the end of lactation. For most traits, the correlation between wk 1 and wk 8 into the lactation was very high; the lowest value was for DMI in HOL at 0.81. The genetic correlation between parities was rather high in the first part of the lactation. During the first 8 wk, the correlation was lower for HOL than for SR, except for ECM. We found that DMI_For and ECM_For showed reasonably large heritability, and future work should explore the possibility of genomic evaluations.

A new method to estimate residual feed intake in dairy cattle using time series data

In dairy, the usual way to measure feed efficiency is through the residual feed intake (RFI) method. However, this method is, in its classical form, a linear regression, which, by construction, does not take into account the evolution of the RFI components across time, inducing approximations in the results. We present here a new approach that incorporates the dynamic dimension of the data. Using a multitrait random regression model, the correlations between milk, live weight, DM intake (DMI) and body condition score (BCS) were investigated across the lactation. In addition, at each time point, by a matrix regression on the variance-covariance matrix and on the animal effects from the three predictor traits, a predicted animal effect for intake was estimated, which, by difference with the actual animal effect for intake, gave a RFI estimation. This model was tested on historical data from the Aarhus University experimental farm (1 469 lactations out of 740 cows). Correlations between animal effects were positive and high for milk and DMI and for weight and DMI, with a maximum mid-lactation, stable across time at around 0.4 for weight and BCS, and slowly decreasing along the lactation for milk and weight, DMI and BCS, and milk and BCS. At the Legendre polynomial coefficient scale, the correlations were estimated with a high accuracy (averaged SE of 0.04, min = 0.02, max = 0.05). The predicted animal effect for intake was always extremely highly correlated with the milk production and highly correlated with BW for the most part of the lactation, but only slightly correlated with BCS, with the correlation becoming negative in the second half of the lactation. The estimated RFI possessed all the characteristics of a classical RFI, with a mean at zero at each time point and a phenotypic independence from its predictors. The correlation between the averaged RFI over the lactation and RFI at each time point was always positive and above 0.5, and maximum mid-lactation (>0.9). The model performed reasonably well in the presence of missing data. This approach allows a dynamic estimation of the traits, free from all time-related issues inherent to the traditional RFI methodology, and can easily be adapted and used in a genetic or genomic selection context.

Genetic mechanisms underlying feed utilization and implementation of genomic selection for improved feed efficiency in dairy cattle

The economic importance of genetically improving feed efficiency has been recognized by cattle producers worldwide. It has the potential to considerably reduce costs, minimize environmental impact, optimize land and resource use efficiency, and improve the overall cattle industry’s profitability. Feed efficiency is a genetically complex trait that can be described as units of product output (e.g., milk yield) per unit of feed input. The main objective of this review paper is to present an overview of the main genetic and physiological mechanisms underlying feed utilization in ruminants and the process towards implementation of genomic selection for feed efficiency in dairy cattle. In summary, feed efficiency can be improved via numerous metabolic pathways and biological mechanisms through genetic selection. Various studies have indicated that feed efficiency is heritable, and genomic selection can be successfully implemented in dairy cattle with a large enough training population. In this context, some organizations have worked collaboratively to do research and develop training populations for successful implementation of joint international genomic evaluations. The integration of “-omics” technologies, further investments in high-throughput phenotyping, and identification of novel indicator traits will also be paramount in maximizing the rates of genetic progress for feed efficiency in dairy cattle worldwide.

Exploration of Long Non-coding RNAs and Circular RNAs in Porcine Milk Exosomes

RNA in milk exosomes can be absorbed in the mammalian intestinal tract and function in gene expression regulations. Our previous work demonstrated that porcine milk exosomes (PME) contain large amounts of miRNAs and mRNAs. Increasing evidence suggests that long non-coding RNAs (lncRNAs) and circular RNAs (circRNAs) are of particular interest, given their key role in diverse biological processes of animals. However, the expression profiles and the potential functions of lncRNAs and circRNAs in PME are still unknown. In the present study, we isolated PME by ultracentrifugation and performed a comprehensive analysis of lncRNA and circRNA in PME by using RNA sequencing. As a result, 2,466 novel lncRNAs, 809 annotated lncRNAs, and 61 circRNAs were identified in PME. The lncRNAs shared similar characteristics with other mammals in terms of length, exon number, and open reading frames. However, lncRNAs showed a higher level compared with mRNAs. Eight lncRNAs and five circRNAs in PME were selected for PCR identification. A functional enrichment analysis on the target genes of lncRNAs indicated that these genes were involved in cellular macromolecule metabolic, RNA metabolic, and immune processes. The circRNAs host genes were enriched in nucleic acid binding and adherence junction. We also evaluated the potential interaction targets between miRNAs and PME lncRNAs or circRNAs, and the results showed that the PME lncRNAs and the circRNAs have a high density of miRNA target sites. The top 20 highly expressed lncRNAs were found to interact with the proliferation-related miRNAs, and the circRNAs potentially targeted many miRNAs that are associated with the intestinal barrier. This study is the first to provide a resource for lncRNA and circRNA research of porcine milk. Moreover, the potential interaction between lncRNA/circRNA and miRNA is revealed. The present study expands our knowledge of non-coding RNAs in milk, and additional research is necessary to confirm their exactly physiological functions.

Short communication: Diet digestibility measured from fecal samples and associations with phenotypic and genetic merit for milk yield and composition

Selection for improved feed utilization is of high interest globally but is limited by the high cost of obtaining feed intake for individual cows and relies on indirect measures of feed efficiency. Supplementing selection with mechanistic measures of feed use could make selection for feed utilization more direct and effective. The objectives of this study were to evaluate fecal sampling as a method of determining digestive efficiency of individual cows and to evaluate associations of digestive efficiency with genetic and phenotypic merit for milk yield and composition. Fecal samples were obtained manually from the rectum of 90 Holstein cows in the morning, afternoon, and evening on a single date and composited across the day. The fecal samples were dried, ground, and stored. Diet and fecal neutral detergent fiber (NDF) were determined using the filter bag method, and indigestible NDF was determined in situ with a 12-d rumen incubation. Fecal NDF (60.1%) and indigestible NDF (41.9%) were higher than that from feed samples (14.2 and 35.9%, respectively). Total-tract digestibility was calculated using the marker ratio method. Total-tract dry matter (DM) digestibility averaged 66.0 ± 2.4% and total-tract NDF digestibility averaged 42.8 ± 3.0%. Higher milk fat percent and genetic merit for milk fat percent were associated with greater NDF and DM digestibility. Milk yield was negatively associated with NDF and DM digestibility. Fecal sampling is a feasible method to directly measure digestive efficiency, and substantial variation was observed among cows. Given significant between-cow variation and associations with milk fat percent and genetic merit for milk fat percent, potential selection for total-tract NDF digestibility estimated via fecal sampling warrants further exploration.

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.

Annual rhythms of milk and milk fat and protein production in dairy cattle in the United States

An annual pattern of milk composition has been well recognized in dairy cattle, with the highest milk fat and protein concentration observed during the winter and lowest occurring in the summer; however, rhythms of milk yield and composition have not been well quantified. Cosinor rhythmometry is commonly used to model repeating daily and annual rhythms and allows determination of the amplitude (peak to mean), acrophase (time at peak), and period (time between peaks) of the rhythm. The objective of this study was to use cosinor rhythmometry to characterize the annual rhythms of milk yield and milk fat and protein concentration and yield using both national milk market and cow-level data. First, 10 yr of monthly average milk butterfat and protein concentration for each Federal Milk Marketing Order were obtained from the US Department of Agriculture Agricultural Marketing Service database. Fat and protein concentration fit a cosine function with a 12-mo period in all milk markets. We noted an interaction between milk marketing order and milk fat and protein concentration. The acrophase (time at peak) of the fat concentration rhythm ranged from December 4 to January 19 in all regions, whereas the rhythm of protein concentration peaked between December 27 and January 6. The amplitude (peak to mean) of the annual rhythm ranged from 0.07 to 0.14 percentage points for milk fat and from 0.08 to 0.12 percentage points for milk protein. The amplitude of the milk fat rhythm generally was lower in southern markets and higher in northern markets. Second, the annual rhythm of milk yield and milk fat and protein yield and concentration were analyzed in monthly test day data from 1,684 cows from 11 tiestall herds in Pennsylvania. Fat and protein concentration fit an annual rhythm in all herds, whereas milk and milk fat and protein yield only fit rhythms in 8 of the 11 herds. On average, milk yield peaked in April, fat and protein yield peaked in February, fat concentration peaked in January, and protein concentration peaked in December. Amplitudes of milk, fat, and protein yield averaged 0.82 kg, 55.3 g, and 30.4 g, respectively. Milk fat and protein concentration had average amplitudes of 0.12 and 0.07, respectively, similar to the results of the milk market data. Generally, milk yield and milk components fit annual rhythm regardless of parity or diacylglycerol O-acyltransferase 1 (DGAT1) K232A polymorphism, with only cows of the low-frequency AA genotype (5.2% of total cows) failing to fit rhythm of milk yield. In conclusion, the yearly rhythms of milk yield and fat and protein concentration and yield consistently occur regardless of region, herd, parity, or DGAT1 genotype and supports generation by a conserved endogenous annual rhythm.

Development of mathematical models to predict calcium, magnesium and selenium excretion from lactating Holstein cows

The aim of this study was to develop and evaluate mathematical models that predict mineral excretion, particularly calcium (Ca), magnesium (Mg) and selenium (Se), from lactating dairy cows. Mineral excretion can be affected by several dietary factors. A deficiency in Ca or Mg application to pasture, among other factors, can contribute to grass tetany or wheat pasture poisoning in cows, whereas an excess can cause runoff into water supplies. Manure application with high Se concentration can also result in runoff, causing the bioaccumulation of selenium in aquatic ecosystems, wetland habitats and estuaries, leading to toxic levels in fish. A database composed of studies relating to mineral utilisation in lactating dairy cows conducted after and including the year 2000 was compiled. A meta-analysis was conducted with the aim of creating multiple empirical equations to predict Ca, Mg and Se excretion from lactating dairy cows. Calcium intake, feed Ca content, milk yield, milk protein content and acid detergent fibre content in diet were positively and linearly related to Ca excretion. Dietary crude protein content and milk fat content were negatively related to Ca excretion. Magnesium intake, feed Mg content and milk yield were positively and linearly related to Mg excretion. Selenium content of diet and dry matter intake were linearly and positively related to Se excretion. Two sets of models were developed using or excluding the intake variable and both sets of models were evaluated with independent data originating from commercial herd or individual animals. In general, intake measurements improved prediction when evaluated with independent datasets (root mean square prediction error = 8% to 19% vs 14% to 26% of the average observed value). There were substantial mean biases, particularly those evaluated with data from a commercial farm, perhaps due to inaccurate feed intake measurements. Although there was generally good agreement between predicted and observed mineral excretion, model development and evaluation would benefit from an expanded database.

Genetic models for breed quality and navel development scores and its associations with growth traits in beef cattle

Estimation and prediction ability of linear and threshold models for yearling breed quality score (BQ) and navel development score at weaning (WN) and yearling (YN), considering variances, heritabilities, and rank correlations based on the breeding values predicted for bulls, were compared. Furthermore, it was determined whether BQ, WN, and YN are genetically associated with growth traits (BWG: birth to weaning weight gain, WH: weaning height, WYG: weaning to yearling weight gain, YH: yearling height) to field data of Nelore cattle. For BQ, similar heritabilities were estimated using linear (0.14 ± 0.01) and threshold (0.15 ± 0.02) models. For navel development scores, higher heritability was estimated with threshold (WN 0.22 ± 0.03; YN 0.42 ± 0.03) rather than linear (WN 0.16 ± 0.01; YN 0.29 ± 0.01) models. Rank correlations between sires breeding values predicted for visual scores with linear and threshold models ranging from 0.53 to 0.98, indicating that different sires would be selected using these models, mainly for higher selection intensities. The BQ showed little genetic variability and was not associated with WH and YH. However, low and positive genetic correlations were obtained between BQ with BWG (0.27 ± 0.02) and WYG (0.25 ± 0.02). In general, they are expected low genetic gains for BQ as correlated response to selection based on any of the growth traits studied. The WN showed higher genetic correlation with BWG (0.63 ± 0.02) and WH (0.53 ± 0.02) rather than WYG (-0.06 ± 0.02) and YH (0.26 ± 0.02), indicating that selection for increased growth at weaning (height and weight gain) should lead to longer and most pendulous navels at this age. Weak genetic correlations were obtained between yearling navel and growth traits.

Genetic and phenotypic associations of type traits and body condition score with dry matter intake, milk yield, and number of breedings in first lactation Canadian Holstein cows

The objective of the present study was to estimate genetic parameters of milk yield (MY), intake traits, type traits, body condition score (BCS), and number of breedings (NOB) in first lactation Canadian Holsteins with a focus on the possibility of using type traits as an indicator of feed intake. Data were obtained from the Canadian Dairy Network and Valacta. A mixed linear model was fitted under REML for the statistical analysis. The multivariate (five traits) model included the fixed effects of age at calving, stage of lactation, and herd-round-classifier for type traits; age at calving, stage of lactation, and herd–year–season of calving (HYS) for BCS; age at calving and HYS for MY, feed intake, and NOB. Animal and residual effects were fitted as random effects for all traits. Estimates of heritabilities for MY, dry matter intake (DMI), angularity, body depth, stature, dairy strength, final score, BCS, and NOB were 0.41, 0.13, 0.24, 0.30, 0.50, 0.30, 0.22, 0.20, and 0.02, respectively. Genetic correlations between type traits and DMI ranged from 0.16 to 0.60. Results indicate that type traits appear to have the potential to predict DMI as a combination/index of two or more traits.

Genetic modeling of feed intake

With the development of automatic self-feeders and electronic identification, automated, repeated measurements of individual feed intake (FI) and BW are becoming available in more species. Consequently, genetic models for longitudinal data need to be applied to study FI or related traits. To handle this type of data, several flexible mixed-model approaches exist such as character process (CPr), structured antedependence (SAD), or random regression (RR) models. The objective of this study was to compare how these different approaches estimate both the covariance structure between successive measurements of FI and genetic parameters and their ability to predict future performances in 3 species (rabbits, ducks, and pigs). Results were consistent between species. It was found that the SAD and CPr models fit the data better than the RR models. Estimations of genetic and phenotypic correlation matrices were quite consistent between SAD and CPr models, whereas correlations estimated with the RR model were not. Structured antedependence and CPr models provided, as expected and in accordance with previous studies, a decrease of the correlations with the time interval between measurements. The changes in heritability with time showed the same trend for the SAD and RR models but not for the CPr model. Our results show that, in comparison with the CPr model, the SAD and RR models have the advantage of providing stable predictions of future phenotypes 1 wk forward whatever the number of observations used to estimate the parameters. Therefore, to study repeated measurements of FI, the SAD approach seems to be very appropriate in terms of genetic selection and real-time managements of animals.

Genetic and phenotypic correlations among feed efficiency, production and selected conformation traits in dairy cows

The difficulties and costs of measuring individual feed intake in dairy cattle are the primary factors limiting the genetic study of feed intake and utilisation, and hence the potential of their subsequent industry-wide applications. However, indirect selection based on heritable, easily measurable, and genetically correlated traits, such as conformation traits, may be an alternative approach to improve feed efficiency. The aim of this study was to estimate genetic and phenotypic correlations among feed intake, production, and feed efficiency traits (particularly residual feed intake; RFI) with routinely recorded conformation traits. A total of 496 repeated records from 260 Holstein dairy cows in different lactations (260, 159 and 77 from first, second and third lactation, respectively) were considered in this study. Individual daily feed intake and monthly BW and body condition scores of these animals were recorded from 5 to 305 days in milk within each lactation from June 2007 to July 2013. Milk yield and composition data of all animals within each lactation were retrieved, and the first lactation conformation traits for primiparous animals were extracted from databases. Individual RFI over 301 days was estimated using linear regression of total 301 days actual energy intake on a total of 301 days estimated traits of metabolic BW, milk production energy requirement, and empty BW change. Pair-wise bivariate animal models were used to estimate genetic and phenotypic parameters among the studied traits. Estimated heritabilities of total intake and production traits ranged from 0.27±0.07 for lactation actual energy intake to 0.45±0.08 for average body condition score over 301 days of the lactation period. RFI showed a moderate heritability estimate (0.20±0.03) and non-significant phenotypic and genetic correlations with lactation 3.5 % fat-corrected milk and average BW over lactation. Among the conformation traits, dairy strength, stature, rear attachment width, chest width and pin width had significant (P<0.05) moderate to strong genetic correlations with RFI. Combinations of these conformation traits could be used as RFI indicators in the dairy genetic improvement programmes to increase the accuracy of the genetic evaluation of feed intake and utilisation included in the index.

Genomic selection using indicator traits to reduce the environmental impact of milk production

The aim of this simulation study was to test the hypothesis that phenotype information of specific indicator traits of environmental importance recorded on a small-scale can be implemented in breeding schemes with genomic selection to reduce the environmental impact of milk production. A stochastic simulation was undertaken to test alternative breeding strategies. The breeding goal consisted of milk production, a functional trait, and environmental impact (EI). The indicator traits (IT) for EI were categorized as large-, medium-, or small-scale, depending on how the traits were recorded. The large-scale traits were stayability and stature; the medium-scale traits were live weight and methane in the breath of the cow measured during milking; and the small-scale traits were residual feed intake and methane recorded in a respiration chamber. Simulated scenarios considered information for just one IT in addition to information for milk production and functional traits. The annual monetary genetic gain was highest in the large-scale scenario that included stayability as IT. The annual monetary gain in the scenarios with medium- or small-scale IT varied from €50.5 to 47.5. The genetic gain improvement in EI was, however, best in the scenarios where the genetic correlation between IT and EI was ≥0.30 and the accuracy of direct genomic value was ≥0.40. The genetic gain in EI was 26 to 34% higher when indicator traits such as greenhouse gases in the breath of the cow and methane recorded in respiration chamber were used compared with a scenario where no indicator trait was included. It is possible to achieve increased genetic gain in EI by using a highly correlated indicator trait, but it requires that the established reference population for the indicator trait is large enough so that the accuracy of direct genomic values will be reasonably high.

Genetic parameters for energy balance, feed efficiency, and related traits in Holstein cattle

Objectives of the current study were to estimate genetic parameters in Holstein cows for energy balance (EB) and related traits including dry matter intake (DMI), body weight (BW), body condition score (BCS), energy-corrected milk (ECM) production, and gross feed efficiency (GFE), defined as the ratio of total ECM yield to total DMI over the first 150 d of lactation. Data were recorded for the first half of lactation on 227 and 175 cows in their first or later lactation, respectively. Random regression models were fitted to longitudinal data. Also, each trait was averaged over monthly intervals and analyzed by single and multivariate animal models. Heritability estimates ranged from 0.27 to 0.63, 0.12 to 0.62, 0.12 to 0.49, 0.63 to 0.72, and 0.49 to 0.53 for DMI, ECM yield, EB, BW, and BCS, respectively, averaged over monthly intervals. Daily heritability estimates ranged from 0.18 to 0.30, 0.10 to 0.26, 0.07 to 0.22, 0.43 to 0.67, and 0.25 to 0.38 for DMI, ECM yield, EB, BW, and BCS, respectively. Estimated heritability for GFE was 0.32. The genetic correlation of EB at 10d in milk (DIM) with EB at 150 DIM was -0.19, suggesting the genetic regulation of this trait differs by stage of lactation. Positive genetic correlations were found among DMI, ECM yield, and BW averaged over monthly intervals, whereas correlations of these traits with BCS depended upon stage of lactation. Total ECM yield for the lactation was positively correlated with DMI, but a negative genetic correlation between total ECM yield and EB was found. However, the genetic correlation between total ECM yield and EB in the first month of lactation was -0.02, indicating that total production is not genetically correlated with EB during the first month of lactation, when negative EB is most closely associated with diminished fitness. The genetic correlation between GFE and EB ranged from -0.73 to -0.99, indicating that selection for more efficient cows would favor a lower energy status. However, the genetic correlation between EB in the first month of lactation and GFE calculated from 75 to 150 DIM was not significant, indicating that the unfavorable correlation between GFE and EB in early lactation may be minimized with alternative definitions of efficiency. Thus, EB, GFE and related traits will likely respond to genetic selection in Holstein cows. However, the impact of selection for improved feed efficiency on EB must be carefully considered to avoid potential negative consequences of further reductions in EB at the onset of lactation.