Genetic analysis of lactation consistency in US Holsteins using temporal variation in daily milk weights.

The ability of a dairy cow to perform reliably over time is an interesting trait to include in dairy cattle breeding programs aimed at improving dairy cow resilience. Consistency, defined as the quality of performing as expected each day of the lactation, could be highly associated with resilience, defined as animal's ability to maintain health and performance in the presence of environmental challenges, including pathogens, heat waves, and nutritional changes. A total of 51,415,022 daily milk weights collected from 2018 to 2023 were provided for 255,191 multiparous Holstein cows milked 3 times daily in conventional parlor systems on farms in 32 states. The temporal variance (TEMPVAR) of milk yield from 5 to 305 d postpartum was computed as the log-transformed variance of daily deviations between observed and expected individual milk weights. Lower values of TEMPVAR imply smaller day-to-day deviations from expectations, indicating consistent performance, whereas larger values indicate inconsistent performance. Expected daily milk weights were computed using 3 nonparametric and parametric regression models: (1) loceally estimated scatterplot smoothing regression with a 0.75 span; (2) polynomial quantile regression using the median (0.5 quantile), and (3) polynomial quantile regression using a 0.7 quantile. The univariate statistical model included age at first calving and herd-year-season as fixed effects and cow as a random effect. Heritability estimates (standard errors) of TEMPVAR phenotypes calculated over the entire lactation ranged between 0.227 (0.011) and 0.237 (0.011), demonstrating that cows are genetically predisposed to display consistent or inconsistent performance. Estimated genetic correlations calculated using a multiple-trait model between TEMPVAR traits and between lactations were high (>0.95), indicating TEMPVAR is repeatable across lactations and robust to the model used to compute expected daily milk yield. Higher TEMPVAR phenotypes reflect more variation in performance, hence greater inconsistency, which is undesirable. Therefore, correlations between predicted transmitting abilities (PTA) for TEMPVAR and milk yield of 0.57 indicate that high-producing cows exhibit more day-to-day variation in performance. Correlations with productive life and livability were -0.38 and -0.48, respectively. Correlations between PTA for TEMPVAR and those of postpartum health traits were also negative, ranging from -0.41 to -0.08. Given that health traits are derived from disease resistance measurements, and higher health trait PTA are preferred, our results indicate that more consistent cows tend to have fewer health problems and greater longevity. Overall, our findings suggest that temporal variation in daily milk weights can be used to identify consistent animals that maintain expected performance throughout the lactation, which will enable selection for greater resilience to management and environmental perturbations.

Preference for feed bins shared with cows of the same or different parity and relationships with feeding behavior and feed efficiency.

Our objectives were to (1) evaluate cows' preferences for visiting feed bins limited to either same- versus mixed-parity social interactions, depending on their parity; (2) examine the effect of parity and bin social dynamic type on competition behavior and feeding patterns, and (3) investigate cow-level relationships between feed bunk competition behavior, feeding patterns, and feed efficiency. Twenty-eight primiparous and 28 multiparous (2.4 ± 0.6 lactations) lactating Holstein cows (127.8 ± 30.1 and 145.3. ± 10.4 DIM, respectively) were housed in a freestall pen with 28 roughage intake control bins (2:1 stocking density). Each cow was assigned to 2 bins, including 1 shared with 3 other cows of the same parity (SM) and 1 with 3 cows of mixed parities (MX, 50% primiparous and 50% multiparous). Feed bunk competition was recorded via video in the first hour after morning feed delivery for 2 d, and feeding patterns were recorded from 24-h roughage intake control data. Residual feed intake was calculated as the difference between predicted and observed dry matter intake after accounting for known energy sinks. Based on the first visit to the feed bunk after fresh feed delivery, multiparous cows tended to prefer the MX bin compared with the SM one; cows showed no other overall preference for bin type based on number of visits. Over time, multiparous cows remained consistent in their magnitude of preference for visiting each bin type, but involvement in competition was not consistent over time. Primiparous cows tended to be involved in more total competitive contacts and ate faster at the SM bin compared with the MX one. Those primiparous cows who visited the SM bin more often within the first hour after morning feed delivery tended to be less feed efficient. Multiparous cows initiated more successful replacements after a displacement at the MX versus SM bin, with no difference in feeding patterns between bin types. Regardless of parity or bin type, visiting the bunk sooner after feed delivery was correlated with involvement in more competitive interactions and more time eating within the first 30 min. Consuming more feed during a longer first visit to the bunk after fresh feed delivery was correlated with being less feed efficient. Overall, when given the choice of feeding from bins shared with cows of the same or mixed parities at a 2:1 stocking density, primiparous cows showed differences in behavior between those bin types, with implications for feed efficiency; these effects are perhaps an unintended consequence of compensatory strategies to avoid direct competition with multiparous cows.

Long-term growth, feed efficiency, enteric methane emission, and blood metabolite responses to in utero hyperthermia in Holstein heifers.

Dairy producers are experiencing production and animal welfare pressures from the increasing frequency and severity of heat stress events due to global climate change. Offspring performance during the pre-weaning and lactating periods is compromised when exposed to heat stress during late gestation (in utero). However, knowledge of the lingering impacts of in utero heat stress on yearling dairy heifers is limited. Herein, we investigated the long-term effects of in utero heat stress on heifer growth, feed efficiency, and enteric methane emissions in post-pubertal heifers. During the last 56 d of gestation, 38 pregnant cows carrying heifer calves were exposed to either heat stress (IUHT; n = 17) or artificial cooling (IUCL; n = 21). At 18 ± 1 mo of age, the resulting IUCL and IUHT heifers were enrolled in the present 63-d study. Heifers were blocked by weight and randomly assigned to 3 pens with Calan gates. Body weights (BW) were recorded on 3 consecutive days at the start and end of the trial and used to calculate average daily gain (ADG). Body condition score (BCS), hip width, body length, and chest girth were measured at the start and end of the study. All heifers were fed a TMR comprised of 46.6% oatlage, 44.6% grass/alfalfa haylage, 7.7% male-sterile corn silage, 0.3% urea, and 0.8% mineral/vitamin supplement (DM basis). The TMR and refusal samples were obtained daily, composited weekly, and dried to calculate DMI. During the study, each pen had access to a GreenFeed unit for 8 ± 1d to measure CH4 and CO2 gas fluxes. During the last 3 d of measuring CH4 and CO2 fluxes, fecal samples were collected, composited by animal, dried, and analyzed to calculate NDF, OM, and DM digestibility. On the last day of fecal sampling, blood samples were also collected via coccygeal venipuncture, and gas chromatography time-of-flight mass spectrometry analysis was performed Residual feed intake (RFI; predicted DMI - observed DMI) and feed conversion efficiency (FCE; DMI/ADG) were calculated to estimate feed efficiency. No differences were found in initial or final BW, hip width, chest girth, or BCS; however, IUCL heifers were longer in body length compared with IUHT heifers. Dry matter intake, ADG, RFI, and FCE were similar between IUHT and IUCL heifers. In utero heat stressed and IUCL heifers produced similar amounts of CH4 and CO2, and no differences were found in the number of GreenFeed visits or latency to approach the GreenFeed. The concentrations of 6 blood metabolites involved in lipogenic pathways were different between in utero treatments. In conclusion, in utero heat stress does not seem to have long-term effects on feed efficiency or methane emissions during the post-pubertal growing phase; however, IUCL heifers maintained a body length advantage over their IUHT counterparts and differed in concentrations of several candidate metabolites that encourage further exploration of their potential function in key organs, such as the liver and mammary gland.

Consistency of dry matter intake in Holstein cows: Heritability estimates and associations with feed efficiency.

Resilience can be defined as the capacity to maintain performance or bounce back to normal functioning after a perturbation, and studying fluctuations in daily feed intake may be an effective way to identify resilient dairy cows. Our goal was to develop new phenotypes based on daily dry matter intake (DMI) consistency in Holstein cows, estimate genetic parameters and genetic correlations with feed efficiency and milk yield consistency, and evaluate their relationships with production, longevity, health, and reproduction traits. Data consisted of 397,334 daily DMI records of 6,238 lactating Holstein cows collected from 2007 to 2022 at 6 research stations across the United States. Consistency phenotypes were calculated based on the deviations from expected daily DMI for individual cows during their respective feeding trials, which ranged from 27 to 151 d in duration. Expected values were derived from different models, including simple average, quadratic and cubic quantile regression with a 0.5 quantile, and locally estimated scatterplot smoothing (LOESS) regression with span parameters 0.5 and 0.7. We then calculated the log of variance (log-Var-DMI) of daily deviations for each model as the consistency phenotype. Consistency of milk yield was also calculated, as a reference, using the same methods (log-Var-Milk). Genetic parameters were estimated using an animal model, including lactation, days in milk and cohort as fixed effects, and animal as random effect. Relationships between log-Var-DMI and traits currently considered in the US national genetic evaluation were evaluated using Spearman's rank correlations between sires' breeding values. Heritability estimates for log-Var-DMI ranged from 0.11 ± 0.02 to 0.14 ± 0.02 across models. Different methods (simple average, quantile regressions, and LOESS regressions) used to calculate log-Var-DMI yielded very similar results, with genetic correlations ranging from 0.94 to 0.99. Estimated genetic correlations between log-Var-DMI and log-Var-Milk ranged from 0.51 to 0.62. Estimated genetic correlations between log-Var-DMI and feed efficiency ranged from 0.55 to 0.60 with secreted milk energy, from 0.59 to 0.63 with metabolic body weight, and from 0.26 to 0.31 with residual feed intake (RFI). Relationships between log-Var-DMI and the traits in the national genetic evaluation were moderate and positive correlations with milk yield (0.20 to 0.21), moderate and negative correlations with female fertility (-0.07 to -0.20), no significant correlations with health and longevity, and favorable correlations with feed efficiency (-0.23 to -0.25 with feed saved and 0.21 to 0.26 with RFI). We concluded that DMI consistency is heritable and may be an indicator of resilience. Cows with lower variation in the difference between actual and expected daily DMI (more consistency) may be more effective in maintaining performance in the face of challenges or perturbations, whereas cows with greater variation in observed versus expected daily DMI (less consistency) are less feed efficient and may be less resilient.

Investigating relationships between the host genome, rumen microbiome, and dairy cow feed efficiency using mediation analysis with structural equation modeling.

The rumen microbiome is crucial for converting feed into absorbable nutrients used for milk synthesis, and the efficiency of this process directly impacts the profitability and sustainability of the dairy industry. Recent studies have found that the rumen microbial composition explains part of the variation in feed efficiency traits, including dry matter intake, milk energy, and residual feed intake. The main goal of this study was to reveal relationships between the host genome, rumen microbiome, and dairy cow feed efficiency using structural equation models. Our specific objectives were to (i) infer the mediation effects of the rumen microbiome on feed efficiency traits, (ii) estimate the direct and total heritability of feed efficiency traits, and (iii) calculate the direct and total breeding values of feed efficiency traits. Data consisted of dry matter intake, milk energy, and residual feed intake records, SNP genotype data, and 16S rRNA rumen microbial abundances from 448 mid-lactation Holstein cows from 2 research farms. We implemented structural equation models such that the host genome directly affects the phenotype (GP → P) and the rumen microbiome (GM → P), while the microbiome affects the phenotype (M → P), partially mediating the effect of the host genome on the phenotype (G → M → P). We found that 7 to 30% of microbes within the rumen microbial community had structural coefficients different from zero. We classified these microbes into 3 groups that could have different uses in dairy farming. Microbes with heritability <0.10 but significant causal effects on feed efficiency are attractive for external interventions. On the other hand, 2 groups of microbes with heritability ≥0.10, significant causal effects, and genetic covariances and causal effects with the same or opposite sign to feed efficiency are attractive for selective breeding, improving or decreasing the trait heritability and response to selection, respectively. In general, the inclusion of the different microbes in genomic models tends to decrease the trait heritability rather than increase it, ranging from -15% to +5%, depending on the microbial group and phenotypic trait. Our findings provide more understanding to target rumen microbes that can be manipulated, either through selection or management interventions, to improve feed efficiency traits.

Host and rumen microbiome contributions to feed efficiency traits in Holstein cows.

It is now widely accepted that dairy cow performance is influenced by both the host genome and rumen microbiome composition. The contributions of the genome and the microbiome to the phenotypes of interest are quantified by heritability (h2) and microbiability (m2), respectively. However, if the genome and microbiome are included in the model, then the h2 reflects only the contribution of the direct genetic effects quantified as direct heritability (hd2), and the holobiont effect reflects the joint action of the genome and the microbiome, quantified as the holobiability (ho2). The objectives of this study were to estimate h2, hd2,m2, and ho2 for dry matter intake, milk energy, and residual feed intake; and to evaluate the predictive ability of different models, including genome, microbiome, and their interaction. Data consisted of feed efficiency records, SNP genotype data, and 16S rRNA rumen microbial abundances from 448 mid-lactation Holstein cows from 2 research farms. Three kernel models were fit to each trait: one with only the genomic effect (model G), one with the genomic and microbiome effects (model GM), and one with the genomic, microbiome, and interaction effects (model GMO). The model GMO, or holobiont model, showed the best goodness-of-fit. The hd2 estimates were always 10% to 15% lower than h2 estimates for all traits, suggesting a mediated genetic effect through the rumen microbiome, and m2 estimates were moderate for all traits, and up to 26% for milk energy. The ho2 was greater than the sum of hd2 and m2, suggesting that the genome-by-microbiome interaction had a sizable effect on feed efficiency. Kernel models fitting the rumen microbiome (i.e., models GM and GMO) showed larger predictive correlations and smaller prediction bias than the model G. These findings reveal a moderate contribution of the rumen microbiome to feed efficiency traits in lactating Holstein cows and strongly suggest that the rumen microbiome mediates part of the host genetic effect.

Dry matter intake in US Holstein cows: exploring the genomic and phenotypic impact of milk components and body weight composite.

Large data sets allow estimating feed required for individual milk components or body maintenance. Phenotypic regressions are useful for nutrition management, but genetic regressions are more useful in breeding programs. Dry matter intake (DMI) records from 8,513 lactations of 6,621 Holstein cows were predicted from phenotypes or genomic evaluations for milk components and body size traits. The mixed models also included days in milk, age-parity subclass, trial date, management group, and body weight change during 28- and 42-d feeding trials in mid-lactation. Phenotypic regressions of DMI on milk (0.014 ± 0.006), fat (3.06 ± 0.01), and protein (4.79 ± 0.25) were much less than corresponding genomic regressions (0.08 ± 0.03, 11.30 ± 0.47, and 9.35 ± 0.87) or sire genomic regressions multiplied by 2 (0.048 ± 0.04, 6.73 ± 0.94, and 4.98 ± 1.75). Thus, marginal feed costs as fractions of marginal milk revenue were higher from genetic than phenotypic regressions. According to the energy-corrected milk formula, fat production requires 69% more DMI than protein production. In the phenotypic regression, it was estimated that protein production requires 56% more DMI than fat. However, the genomic regression for the animal showed a difference of only 21% more DMI for protein compared with fat, while the sire genomic regressions indicated approximately 35% more DMI for fat than protein. Estimates of annual maintenance in kg DMI / kg body weight/lactation were similar from phenotypic regression (5.9 ± 0.14), genomic regression (5.8 ± 0.31), and sire genomic regression multiplied by 2 (5.3 ± 0.55) and are larger than those estimated by NASEM (2021) based on NEL equations. Multiple regressions on genomic evaluations for the 5 type traits in body weight composite (BWC) showed that strength was the type trait most associated with body weight and DMI, agreeing with the current BWC formula, whereas other traits were less useful predictors, especially for DMI. The Net Merit formula used to weight different genetic traits to achieve an economically optimal overall selection response was revised in 2021 to better account for these estimated regressions. To improve profitability, breeding programs should select smaller cows with negative residual feed intake that produce more milk, fat, and protein.

Genetic relationships between behavioral traits and feed efficiency traits in lactating Holstein cows.

The evaluation of dairy cow feed efficiency using residual feed intake accounts for known energy sinks. However, behavioral traits may also contribute to the variation in feed efficiency. Our objective was to estimate the heritability and repeatability of behavioral traits and their genetic correlations with feed efficiency and its components in lactating Holstein cows. The first data set consisted of 36,075 daily rumination and lying time records collected using a SMARTBOW ear tag accelerometer (Zoetis, Parsippany, NJ) and 6,371 weekly feed efficiency records of 728 cows from the University of Wisconsin-Madison. The second data set consisted of 59,155 daily activity records, measured as number of steps, recorded by pedometers (AfiAct; S.A.E. Afikim, Kibbutz Afikim, Israel), and 8,626 weekly feed efficiency records of 635 cows from the University of Florida. Feed efficiency and its components included dry matter intake, change in body weight, metabolic body weight, secreted milk energy, and residual feed intake. The statistical models included the fixed effect of cohort, lactation number, and days in milk, and the random effects of animal and permanent environment. Heritability estimates for behavioral traits using daily records were 0.19 ± 0.06 for rumination and activity, and 0.37 ± 0.07 for lying time. Repeatability estimates for behavioral traits using daily data ranged from 0.56 ± 0.02 for activity to 0.62 ± 0.01 for lying time. Both heritability and repeatability estimates were larger when weekly records instead of daily records were used. Rumination and activity had positive genetic correlations with residual feed intake (0.40 ± 0.19 and 0.31 ± 0.22, respectively) while lying time had a negative genetic correlation with this residual feed intake (-0.27 ± 0.11). These results indicate that more efficient cows tend to spend more time lying and less time active. Additionally, less efficient cows tend to eat more and therefore also tend to ruminate longer. Overall, sensor-based behavioral traits are heritable and genetically correlated with feed efficiency and its components and, therefore, they could be used as indicators to identify feed efficient cows within the herd.

Decision-support tool for global allocation of dairy sire semen based on regional demand, supply constraints, and genetic profiles.

An artificial insemination (AI) company seeks to allocate semen units globally by balancing perceived demand with uncertain product supply, in what is an arduous subjective process. This study aimed to objectivize this process by providing a user-friendly linear programming model to allocate bulls' semen units to regions for the next trimester sales period based on maximum revenue, and to describe the features and outcomes of this model when applied to a sample bull herd and global demand scenario reflective of a leading AI company. The objective function of maximizing revenue was calculated by summing the product of units allocated by bull and region with purchase prices assigned by bull and region. Constraints considered were regional demand for overall units, regional preferences for specific genetic traits, bulls' production capacity, and percentage of bulls' units allocated to a single region. A sensitivity analysis was performed to identify the effects of variables and constraints on total revenue. Production, sales, and bull demographic data from 2018 to 2021 from a leading AI company were used to establish base values and build a sample herd of 61 bulls and 5 global regions. The case study provided a maximum revenue of $8,287,197 in semen sales per trimester, with 634,700 units allocated. Of the 61 bulls in the case study, 9 were not allocated to any region. The most limiting constraint was regional demand, which resulted in a surplus of 274,564 units not allocated. A sensitivity analysis confirmed this finding, with the largest shadow prices assigned to regional demands, and indicated that a single unit increase in regional demand would add up to $14.84 in total revenue.

Effects of type traits, inbreeding, and production on survival in US Jersey cattle.

Greater longevity is associated with lower replacement costs, higher average milk production, and fewer replacement heifers needed. Longevity data are obtained late in life, and for this reason stayability, defined as the probability of survival from birth until a certain age, can be used as an alternative measure. The objective of this study was to evaluate the effects of different type traits, inbreeding, and production level on the stayability of Jersey cows to various ages, and to assess trends over time. Data consisted of 460,172 to 204,658 stayability records, depending on length of the opportunity period, for survival from birth until 36, 48, 60, 72, or 84 mo of age. Threshold models were used to analyze the stayability traits, including different type traits, inbreeding coefficient, and within-herd production level as explanatory variables. Heritability estimates for stayability traits ranged from 0.05 (36 mo) to 0.22 (84 mo). As expected, the probability of survival decreased as age increased. Highly productive cows were more likely to survive than their poor-producing contemporaries regardless of age and the type trait evaluated. Our data indicate that farmers' selection decisions tend to punish poor production at early ages and reward high production at later stages. Inbreeding negatively affected the probability of survival, especially when inbreeding coefficients exceeded 10%, and this impact was most noticeable at 48 mo of age or later. Some type traits, such as stature and foot angle, had little effect on the probability of survival. Other type traits, such as strength, dairy form, rump width, and rear legs, showed higher probability of survival at intermediate scores, whereas other type traits, such as fore udder attachment, rear udder height, udder depth, and final score, showed higher probability of survival at higher scores. Finally, our results indicate that the probability of survival has decreased in the last decade, probably due to a greater number of heifers available and, therefore, higher culling rates.

Social interactions, feeding patterns, and feed efficiency of same- and mixed-parity groups of lactating cows.

Social dynamics in group-housed animals can have important effects on their welfare, feed efficiency, and production potential. Our objectives were to: (1) evaluate the effects of parity and social grouping on competition behavior, feeding patterns, and feed efficiency, and (2) investigate cow-level relationships between competition and feeding behavior, production, and feed efficiency. Fifty-nine Holstein cows (144.5 ± 21.8 starting days in milk, mean ± SD) were housed in a freestall pen with 30 Roughage Intake Control (RIC) bins. We evaluated the effects of parity (primiparous [PR, n = 29] vs. multiparous [MU, n = 30]) and group composition at the feed bunk (same-parity [SM, n = 39] vs. mixed-parity [MX, n = 20, 50% of each parity]) with a 2 × 2 factorial design (SM-MU: n = 20; SM-PR: n = 19; MX-MU: n = 10; MX-PR: n = 10) on competition behavior, feeding patterns, and feed efficiency. Within the pen, groups of 9 to 10 cows were considered subgroups and assigned to treatments defined by sets of 5 assigned bins (2:1 stocking density). Feed bunk competition and feeding patterns were recorded via continuous video in the first hour after morning feed delivery and 24-h RIC data, respectively. Residual feed intake (RFI) was calculated as the difference between predicted and observed dry matter intake (DMI) after accounting for known energy sinks. Linear models were used to evaluate the effects and interactions of parity and group composition on competition, feeding behavior, and feed efficiency. Within-cow correlations were performed between competition, feeding behavior, and RFI. Cows in MX, compared with SM, were involved in more competitive interactions [mean (95% CI): competitive contacts: 11.5 (8.1, 16.3) vs. 7.2 (5.5, 9.3) events; displacements: 4.0 (3.0, 5.3) vs. 2.1 (1.7, 2.7) events, and replacements: 3.5 (2.6, 4.7) vs. 1.9 (1.5, 2.5) events]. Cows in MX vs. those in SM had more bunk visits/meal ( 4.3 [3.9, 4.8] vs. 3.7 [3.4, 3.9] visits/meal) and longer meals (31.2 vs. 27.4 ± 0.9 min/meal) and tended to have higher RFI (0.41 ± 0.3 vs. -0.21 ± 0.2) and were therefore less feed efficient. Multiparous versus PR cows had greater DMI per day (29.3 ± 0.6 vs. 25.5 ± 0.4 kg/d) and per meal (4.2 [4.0, 4.4] vs. 3.4 [3.2, 3.6] kg/meal), faster eating rates (0.14 [0.13, 0.15] vs. 0.12 [0.11, 0.13] kg/min), and fewer bunk visits/d (26.6 [24.0, 29.4] vs. 32.8 [29.7, 35.9]). Regardless of grouping or parity, cows with shorter latencies to first visit the bunk after feed delivery were involved in more competition and tended to be less feed efficient. Overall, individual cow- and group-level relationships among competition, feeding behavior, and feed efficiency play an important role in feed bunk social dynamics. At a competitive 2:1 stocking density, mixed-parity groups for lactating cows may have potentially negative animal welfare and feed efficiency implications that should be considered when selecting grouping strategies on the farm.

Estimates of genetic parameters for feeding behavior traits and their associations with feed efficiency in Holstein cows.

Residual feed intake (RFI) is commonly used to measure feed efficiency but individual intake recording systems are needed. Feeding behavior may be used as an indicator trait for feed efficiency using less expensive precision livestock farming technologies. Our goal was to estimate genetic parameters for feeding behavior and the genetic correlations with feed efficiency in Holstein cows. Data consisted of 75,877 daily feeding behavior records of 1,328 mid-lactation Holstein cows in 31 experiments conducted from 2009 to 2020 with an automated intake recording system. Feeding behavior traits included number of feeder visits per day, number of meals per day, duration of each feeder visit, duration of each meal, total duration of feeder visits, intake per visit, intake per meal [kg of dry matter (DM)], feeding rate per visit, and feeding rate per meal (kg of DM per min). The meal criterion was estimated as 26.4 min, which means that any pair of feeder visits separated by less than 26.4 min were considered part of the same meal. The statistical model included lactation and days in milk as fixed effects, and experiment-treatment, animal, and permanent environment as random effects. Genetic parameters for feeding behavior traits were estimated using daily records and weekly averages. Estimates of heritability for daily feeding behavior traits ranged from 0.09 ± 0.02 (number of meals; mean ± standard error) to 0.23 ± 0.03 (feeding rate per meal), with repeatability estimates ranging from 0.23 ± 0.01 (number of meals) to 0.52 ± 0.02 (number of feeder visits). Estimates of heritability for weekly averages of feeding behavior traits ranged from 0.19 ± 0.04 (number of meals) to 0.32 ± 0.04 (feeding rate per visit), with repeatability estimates ranging from 0.46 ± 0.02 (duration of each meal) to 0.62 ± 0.02 (feeding rate per visit and per meal). Most of the feeding behavior measures were strongly genetically correlated, showing that with more visits or meals per day, cows spend less time in each feeder visit or meal with lower intake per visit or meal. Weekly averages for feeding behavior traits were analyzed jointly with RFI and its components. Number of meals was genetically correlated with milk energy (0.48), metabolic body weight (-0.27), and RFI (0.19). Duration of each feeder visit and meal were genetically correlated with milk energy (0.43 and 0.44, respectively). Total duration of feeder visits per day was genetically correlated with DM intake (0.29), milk energy (0.62), metabolic body weight (-0.37), and RFI (0.20). Intake per visit and meal were genetically correlated with DM intake (0.63 and 0.87), milk energy (0.47 and 0.69), metabolic body weight (0.47 and 0.68), and RFI (0.31 and 0.65). Feeding rate was genetically correlated with DM intake (0.69), metabolic body weight (0.67), RFI (0.47), and milk energy (0.21). We conclude that measures of feeding behavior could be useful indicators of dairy cow feed efficiency, and individual cows that eat at a slower rate may be more feed efficient.

Predicting sperm production of young dairy bulls using collection history and management factors.

Selection of elite young dairy bulls by using genomic data shortened the generation interval and increased pressure to collect and market germplasm at an early age. The objectives of this study were (1) develop prediction models for daily, weekly, and monthly total sperm (TSp) production from collection history, health status, and management factors, and (2) assess the ability of these models to forecast future TSp production, as well as differences in prediction accuracy by seasonality or age of bull. Data consisted of 43,918 daily processing records from 1,037 Holstein and Jersey bulls between 10 and 28 mo of age at collection. Potential explanatory variables included year and season of collection, barn location, collection frequency, breed, scrotal circumference, TSp in previous months, health events, and age at arrival, first collection, and current collection. Linear regression, random forest (RF), Bayesian regularized neural network, model tree, multilayer perceptron neural network with multiple layers, and extreme learning machine were used to predict daily, weekly, and monthly TSp (R v3.5.1, https://www.r-project.org/). In the additive approach, all prior data were used for training; however, in the fixed-window approach, records from 3 previous months were used for age-based prediction, records from 4 previous months or 1 yr were used for the monthly date-based analyses, and records from 1 previous month or year were used for the weekly date-based analyses. Model performance was measured by root mean squared error (RMSE) and the correlation (r) between actual and predicted TSp in testing sets. In monthly analyses, RF with additive training performed best in age-based (RMSE = 13.6 billion cells, r = 0.93) and date-based (RMSE = 11.9, r = 0.94) prediction, compared with linear regression (age-based RMSE = 16.6, r = 0.89; date-based RMSE = 15.5, r = 0.90) and Bayesian regularized neural network (age-based RMSE = 14.1, r = 0.92). On average, RMSE was 0.93 or 0.14 billion cells greater with fixed 4-mo or 1-yr training windows, respectively, than in the additive analyses. The most important management variables affecting TSp were collection frequency, TSp in previous months, and age at collection. Results indicate RF models with additive training can predict TSp output of individual bulls with ≥85% accuracy up to 4 mo into the future. Spikes in accuracy were associated with sire summary times and company processing changes, and accuracy tended to stabilize when bulls reached 19 to 20 mo of age.

Hyperketonemia GWAS and parity-dependent SNP associations in Holstein dairy cows intensively sampled for blood ß-hydroxybutyrate concentration.

Hyperketonemia (HYK) is a metabolic disorder that affects early postpartum dairy cows; however, there has been limited success in identifying genomic variants contributing to HYK susceptibility. We conducted a genome-wide association study (GWAS) using HYK phenotypes based on an intensive screening protocol, interrogated genotype interactions with parity group (GWIS), and evaluated the enrichment of annotated metabolic pathways. Holstein cows were enrolled into the experiment after parturition, and blood samples were collected at four timepoints between 5 and 18 days postpartum. Concentration of blood ß-hydroxybutyrate (BHB) was quantified cow-side via a handheld BHB meter. Cows were labeled as a HYK case when at least one blood sample had BHB ≥ 1.2 mmol/L, and all other cows were considered non-HYK controls. After quality control procedures, 1,710 cows and 58,699 genotypes were available for further analysis. The GWAS and GWIS were performed using the forward feature select linear mixed model method. There was evidence for an association between ARS-BFGL-NGS-91238 and HYK susceptibility, as well as parity-dependent associations to HYK for BovineHD0600024247 and BovineHD1400023753. Candidate genes annotated to these single nuclear polymorphism associations have been previously associated with obesity, diabetes, insulin resistance, and fatty liver in humans and rodent models. Enrichment analysis revealed focal adhesion and axon guidance as metabolic pathways contributing to HYK etiology, while genetic variation in pathways related to insulin secretion and sensitivity may affect HYK susceptibility in a parity-dependent matter. In conclusion, the present work proposes several novel marker associations and metabolic pathways contributing to genetic risk for HYK susceptibility.

Genomic analysis of bovine respiratory disease and lung consolidation in preweaned Holstein calves using clinical scoring and lung ultrasound.

Bovine respiratory disease (BRD) is a leading cause of morbidity and mortality in dairy calves, with detrimental long-term effects that include stunted growth, increased age at first calving, and decreased milk production in first lactation. The objectives of this study were to establish a protocol for objective and efficient assessment of BRD phenotypes in preweaned dairy calves, develop a genomic reference population with well-defined clinical and subclinical phenotypes, identify chromosomal regions associated with BRD in a genome-wide association study, estimate genetic parameters of BRD, and predict genomic breeding values of dairy calves. A total of 1,107 Holstein calves from 6 dairy farms in southern Wisconsin were examined using clinical respiratory scoring and lung ultrasound at 3 and 6 wk of age. The clinical respiratory score was based on visual appraisal of eyes, nose, ears, cough, and temperature. Lung ultrasound scores were assigned based on the amount of consolidation present. Calves were genotyped with a commercially available SNP array and after quality control and imputation to higher density, 690,291 SNP markers and 1,014 individuals remained. Single-step genome-wide association study and single-step genomic best linear unbiased prediction were applied to binary 3- and 6-wk phenotypes considered as overall respiratory healthy versus affected (RESP3, RESP6) or as presence or absence of lung consolidation (CON3, CON6). Lung ultrasound combined with a clinical scoring system allowed for efficient and objective assessment for the prevalence of BRD. Proportions of variance attributed to 1-Mb non-overlapping windows suggested genomic regions that may contain putative candidate genes, most notably regions on Bos taurus autosomes 1, 6, 7, 10, 11, 12, 15, 17, 18, 27, and 28 that explained 0.70 to 1.45% of the genetic variance. Heritability estimates were higher at 3 wk (0.214 and 0.241 for CON3 and RESP3, respectively) than 6 wk (0.084 and 0.111 for CON6 and RESP6, respectively), and mean reliabilities of genomic estimated breeding vales for calves with genotypes and phenotypes ranged from 0.12 for CON6 to 0.30 for RESP3.

Assessing genomic prediction accuracy for Holstein sires using bootstrap aggregation sampling and leave-one-out cross validation.

Since the introduction of genome-enabled prediction for dairy cattle in 2009, genomic selection has markedly changed many aspects of the dairy genetics industry and enhanced the rate of response to selection for most economically important traits. Young dairy bulls are genotyped to obtain their genomic predicted transmitting ability (GPTA) and reliability (REL) values. These GPTA are a main factor in most purchasing, marketing, and culling decisions until bulls reach 5 yr of age and their milk-recorded offspring become available. At that time, daughter yield deviations (DYD) can be compared with the GPTA computed several years earlier. For most bulls, the DYD align well with the initial predictions. However, for some bulls, the difference between DYD and corresponding GPTA is quite large, and published REL are of limited value in identifying such bulls. A method of bootstrap aggregation sampling (bagging) using genomic BLUP (GBLUP) was applied to predict the GPTA of 2,963, 2,963, and 2,803 young Holstein bulls for protein yield, somatic cell score, and daughter pregnancy rate (DPR), respectively. For each trait, 50 bootstrap samples from a reference population comprising 2011 DYD of 8,610, 8,405, and 7,945 older Holstein bulls were used. Leave-one-out cross validation was also performed to assess prediction accuracy when removing specific bulls from the reference population. The main objectives of this study were (1) to assess the extent to which current REL values and alternative measures of variability, such as the bootstrap standard deviation (SD) of predictions, could detect bulls whose daughter performance deviates significantly from early genomic predictions, and (2) to identify factors associated with the reference population that inform about inaccurate genomic predictions. The SD of bootstrap predictions was a mildly useful metric for identifying bulls whose future daughter performance may deviate significantly from early GPTA for protein and DPR. Leave-one-out cross validation allowed us to identify groups of reference population bulls that were influential on other reference population bulls for protein yield and observe their effects on predictions of testing set bulls, as a whole and individually.

Relationships between body condition score change, prior mid-lactation phenotypic residual feed intake, and hyperketonemia onset in transition dairy cows.

Extensive efforts have been made to identify more feed-efficient dairy cows, yet it is unclear how selection for feed efficiency will influence metabolic health. The objectives of this research were to determine the relationships between residual feed intake (RFI), a measure of feed efficiency, body condition score (BCS) change, and hyperketonemia (HYK) incidence. Blood and milk samples were collected twice weekly from cows 5 to 18 d postcalving for a total of 4 samples. Hyperketonemia was diagnosed at a blood ß-hydroxybutyrate (BHB) ≥1.2 mmol/L and cows were treated upon diagnosis. Dry period, calving, and final blood sampling BCS was recorded. Prior mid-lactation production, body weight, body weight change, and dry matter intake (DMI) data were used to determine RFI phenotype, calculated as the difference between observed DMI and predicted DMI. The maximum BHB concentration (BHBmax) for each cow was used to group cows into HYK or not hyperketonemic. Lactation number, BCS, and RFI data were analyzed with linear and quadratic orthogonal contrasts. Of the 570 cows sampled, 19.7% were diagnosed with HYK. The first positive HYK test occurred at 9 ± 0.9 d postpartum and the average BHB concentration at the first positive HYK test was 1.53 ± 0.14 mmol/L. In the first 30 d postpartum, HYK-positive cows had increased milk yield and fat concentration, decreased milk protein concentration, and decreased somatic cell count. Cows with a dry BCS ≥4.0, or that lost 1 or more BCS unit across the transition to lactation period, had greater BHBmax than cows with lower BCS. Prior-lactation RFI did not alter BHBmax. Avoiding over conditioning of dry cows and subsequent excessive fat mobilization during the transition period may decrease HYK incidence; however, RFI during a prior lactation does not appear to be associated with HYK onset.

Semi-supervised learning for genomic prediction of novel traits with small reference populations: an application to residual feed intake in dairy cattle.

BACKGROUND: Genomic prediction for novel traits, which can be costly and labor-intensive to measure, is often hampered by low accuracy due to the limited size of the reference population. As an option to improve prediction accuracy, we introduced a semi-supervised learning strategy known as the self-training model, and applied this method to genomic prediction of residual feed intake (RFI) in dairy cattle. METHODS: We describe a self-training model that is wrapped around a support vector machine (SVM) algorithm, which enables it to use data from animals with and without measured phenotypes. Initially, a SVM model was trained using data from 792 animals with measured RFI phenotypes. Then, the resulting SVM was used to generate self-trained phenotypes for 3000 animals for which RFI measurements were not available. Finally, the SVM model was re-trained using data from up to 3792 animals, including those with measured and self-trained RFI phenotypes. RESULTS: Incorporation of additional animals with self-trained phenotypes enhanced the accuracy of genomic predictions compared to that of predictions that were derived from the subset of animals with measured phenotypes. The optimal ratio of animals with self-trained phenotypes to animals with measured phenotypes (2.5, 2.0, and 1.8) and the maximum increase achieved in prediction accuracy measured as the correlation between predicted and actual RFI phenotypes (5.9, 4.1, and 2.4%) decreased as the size of the initial training set (300, 400, and 500 animals with measured phenotypes) increased. The optimal number of animals with self-trained phenotypes may be smaller when prediction accuracy is measured as the mean squared error rather than the correlation between predicted and actual RFI phenotypes. CONCLUSIONS: Our results demonstrate that semi-supervised learning models that incorporate self-trained phenotypes can achieve genomic prediction accuracies that are comparable to those obtained with models using larger training sets that include only animals with measured phenotypes. Semi-supervised learning can be helpful for genomic prediction of novel traits, such as RFI, for which the size of reference population is limited, in particular, when the animals to be predicted and the animals in the reference population originate from the same herd-environment.

Improving reliability of genomic predictions for Jersey sires using bootstrap aggregation sampling.

Genomic selection has revolutionized the dairy genetics industry and enhanced the rate of response to selection for most economically important traits. All young bulls are now genotyped using commercially available single nucleotide polymorphism arrays to compute genomic predicted transmitting ability (GPTA) and reliability (REL) values. Decisions regarding the purchasing, marketing, and culling of dairy bulls are based on GPTA until roughly 5 yr of age, when milk-recorded offspring become available. At that time, daughter yield deviations (DYD) can be used to assess the accuracy of the GPTA computed several years earlier. Although agreement between predictions and DYD is often good, the DYD of some bulls differ widely from corresponding GPTA, and published REL are of limited value in identifying such bulls. A method of bootstrap aggregation sampling (bagging) using genomic BLUP (GBLUP) was implemented to predict the GPTA of 379, 379, and 342 young Jersey bulls for protein yield, somatic cell score, and daughter pregnancy rate, respectively. For each trait, 50 bootstrap samples from a reference population consisting of 2011 DYD of 1,738, 1,616, and 1,551 older Jersey bulls were used, and correlations between bagged GBLUP predictions and 2014 DYD were lower than GBLUP predictions derived from the full reference population. Although the bagged GBLUP approach did not improve the predictive correlations, it allowed computation of bootstrap predictive reliabilities across random samples of the reference population. The bootstrap predictive reliabilities could be a useful diagnostic tool for assessing genome-enabled prediction systems or evaluating the composition of a reference population. Our main objective was to determine if bagging GBLUP of young Jersey bulls could lead to measures of reliability that would be a useful alternative to published REL values. The standard deviations of bagged GBLUP predictions were found to weakly improve our ability to identify bulls whose future daughter performance may deviate significantly from early GPTA for protein, but not for somatic cell score or daughter pregnancy rate.

Optimization of reproductive management programs using lift chart analysis and cost-sensitive evaluation of classification errors.

The common practice on most commercial dairy farms is to inseminate all cows that are eligible for breeding, while ignoring (or absorbing) the costs associated with semen and labor directed toward low-fertility cows that are unlikely to conceive. Modern analytical methods, such as machine learning algorithms, can be applied to cow-specific explanatory variables for the purpose of computing probabilities of success or failure associated with upcoming insemination events. Lift chart analysis can identify subsets of high fertility cows that are likely to conceive and are therefore appropriate targets for insemination (e.g., with conventional artificial insemination semen or expensive sex-enhanced semen), as well as subsets of low-fertility cows that are unlikely to conceive and should therefore be passed over at that point in time. Although such a strategy might be economically viable, the management, environmental, and financial conditions on one farm might differ widely from conditions on the next, and hence the reproductive management recommendations derived from such a tool may be suboptimal for specific farms. When coupled with cost-sensitive evaluation of misclassified and correctly classified insemination events, the strategy can be a potentially powerful tool for optimizing the reproductive management of individual farms. In the present study, lift chart analysis and cost-sensitive evaluation were applied to a data set consisting of 54,806 insemination events of primiparous Holstein cows on 26 Wisconsin farms, as well as a data set with 17,197 insemination events of primiparous Holstein cows on 3 Wisconsin farms, where the latter had more detailed information regarding health events of individual cows. In the first data set, the gains in profit associated with limiting inseminations to subsets of 79 to 97% of the most fertile eligible cows ranged from $0.44 to $2.18 per eligible cow in a monthly breeding period, depending on days in milk at breeding and milk yield relative to contemporaries. In the second data set, the strategy of inseminating only a subset consisting of 59% of the most fertile cows conferred a gain in profit of $5.21 per eligible cow in a monthly breeding period. These results suggest that, when used with a powerful classification algorithm, lift chart analysis and cost-sensitive evaluation of correctly classified and misclassified insemination events can enhance the performance and profitability of reproductive management programs on commercial dairy farms.