Greenhouse gases produced by livestock are important contributors to climate change. The ability to measure large-scale exhaled metabolic gases from cattle using GreenFeed systems will help farmers to reduce enteric emissions while maintaining or increasing cow productivity. GreenFeed units are portable chamber systems that measure individual animal gas production in real time. Thus, the machines generate large amounts of daily data that can be overwhelming for users to process. This challenge motivated us to develop an R package named greenfeedr that offers functions for downloading, processing, and reporting GreenFeed data. Herein, we describe all functions implemented in the greenfeedr R package and present examples based on dairy cow data. The R package has functions for downloading GreenFeed data (get_gfdata), for generating daily and final reports (report_gfdata), for processing daily and final records (process_gfdata), and extra functions that help to extract information regarding pellet intakes and daily visits (pellin and viseat). Using our example data with 32 lactating dairy cows, we demonstrated the capabilities of the different functions to generate easy-to-read reports and process large amount of data. Also, we included in the function process_gfdata some parameters that will help users to define the best criteria to process their own GreenFeed data. Overall, greenfeedr represents an important advancement in the management and analysis of GreenFeed data, offering an efficient tool tailored to the needs of the user.

Resilience is an animal's capacity to recover after a perturbation or maintain specific functions under stress. The increasing frequency of extreme weather events and labor shortages highlight the need to identify and select animals that can maintain production in unpredictable environments. The US dairy herds group cows into pens based on factors such as parity, lactation stage, reproductive status, and milk production. By coupling daily milk yield data with day-to-day pen location information, we can model management and environmental stressors affecting groups of cows and detect perturbations at the pen level. It also allows us to detect management and environmental perturbations that may occur each day at the pen level and subsequently measure the responses of individual cows to these stressors over a particular period. Our entire dataset included 62,580,945 daily milk weights and pen locations of 227,633 cows in parity 1, 2, or 3 from 204 herds representing 30 US states from 2018 to 2024. Individual lactation curves were fitted using polynomial quantile regression (0.5 quantile) to derive expected lactation curves. Perturbations were identified using a data-driven detection method and were based on residuals between mean expected and mean observed daily milk weights at the pen level. The initial dataset was stratified based on the severity and duration of the perturbation periods, considering 40 combinations that included severity levels from ≥3% to ≥7% milk yield loss and durations ranging from ≥3 to ≥10 d. Our resilience phenotype, delta milk yield (ΔMY) measured the change in a cow's mean daily milk production relative to her mean expected daily milk yield during an identified perturbation period. Variance components, heritabilities, and repeatabilities were estimated using a model with fixed effects for calving age, DIM, parity, and herd-year-season, and random effects for perturbation event, additive genetics, and permanent environment. Sire PTA correlations with TempVar (milk consistency across the full lactation) were calculated using the Calo's method to assess the relationship between resilience and consistency traits. Additionally, sire PTA Pearson correlations were estimated within comparable severity thresholds to determine the genetic correlations between sire PTA during perturbations with similar severities. Estimated h2 of ΔMY during perturbations ranged from 0.01 (0.00) to 0.20 (0.08), depending on the severity and duration of the perturbation, whereas sire PTA correlations between ΔMY and TempVar ranged from -0.51 (0.01) to -0.16 (0.03), indicating that more consistent cows have lower milk loss during perturbations. Our findings suggest that animals differ in their response to perturbations at the pen level in comparison to their contemporaries within the pen, and this measure of resilience using daily milk data is heritable. Identifying perturbations of varying severity and duration at the pen level can more effectively capture the management and environmental conditions affecting an individual cow, and resilience can be measured by comparing how her response differs from that of her contemporaries when exposed to stressful conditions. This enables the selection and management of more adaptable and sustainable cows capable of handling diverse challenges through a data-driven approach to detecting perturbations.

The availability of daily milk weights and pen location information provides an interesting opportunity to capture additional data and review how contemporary groups are defined for dairy cattle genetic evaluations. In the United States, dairy cows in larger herds are grouped into pens according to various characteristics such as parity, production level, reproductive status, lactation stage, and health status. Our dataset included pen location information for each daily milk weight, so instead of using herd-year-season of calving to form contemporary groups, we used herd-pen-milking date to more precisely model the environmental effects cows experience at the pen level on a given day. Our dataset included 21,000,951 aggregated daily milk records from 114,243 first-parity Holstein cows milked 3 times daily in conventional parlor systems in 157 herds representing 29 US states. Our phenotype of interest was daily milk weight, and alternative repeatability animal models were used to estimate genetic parameters and predict breeding values. Age at first calving (6 levels) and DIM (10 levels) were included as fixed effects and cow (114,243 levels) was included as a random effect. Contemporary group effects included a fixed or random herd-year-season of calving effect (1,492 levels) and a fixed or random herd-pen-milking date effect (285,592 levels). Genetic parameters (kg2; posterior SD) were estimated using GIBBSF90+ software. The additive genetic variance ranged from 10.48 (0.60) to 24.12 (0.66), herd-year-season variance was 10.34 (0.40), herd-pen-milking date variance ranged from 4.91 (0.02) to 4.96 (0.02), permanent environmental variance ranged from 10.65 (0.44) to 16.94 (0.30), and residual variance ranged from 11.81 (0.01) to 14.60 (0.01). Heritability estimates ranged from 0.21 (0.01) to 0.47 (0.01), and repeatability estimates ranged from 0.51 (0.01) to 0.71 (0.01), and mean reliability of sires' breeding value predictions ranged from 0.81 to 0.89. Although caution is needed when disentangling associations between genetic effects, permanent environmental effects, and herd-pen-milking date contemporary groups, our results suggest that using daily milk weights and pen locations may improve the precision of genetic evaluations through increased sire PTA reliabilities for milk production traits in dairy cattle.

Differences in feed efficiency among dairy cows could be partially explained by changes in body temperature due to the heat increment. This heat represents chemical energy lost, and theoretically, it should be lower in more efficient cows. The aim of this study was to investigate the associations between 3 body temperature traits, namely average body temperature, consistency of body temperature, and change in body temperature after the largest meal of the day, with 4 feed efficiency traits: DMI, milk energy, metabolic BW, and residual feed intake (RFI) in lactating Holstein cows. Data were collected from 1,068 mid-lactation Holstein cows enrolled in 36 feed efficiency trials from 2020 to 2023 at 5 research stations across the United States. Temperature records were obtained using an automatic temperature logger placed vaginally for 2 wk. Average body temperature was calculated as the individual mean, daily and over the 2-wk period, and consistency of body temperature was calculated as the log-transformed variance of the deviations of individual records from the cow's mean. Change in body temperature was calculated for a subset of cows that used the roughage intake control system (n = 278) and was defined as the difference in temperature after and before the largest meal of the day. Data for DMI, milk energy, metabolic BW, and RFI were collected from the same cows for 6 to 7 wk during the feeding trials. The associations between body temperature traits and feed efficiency traits were assessed using univariate linear regression models including cohort (trial-treatment), temperature-humidity index (THI), and the interaction between THI and research station as independent variables. Partial correlations between body temperature and feed efficiency traits controlling for cohort were calculated. Additionally, the importance of body temperature traits on RFI calculation was also evaluated. Increased body temperature was associated with cow that ate less and were lighter. Consistency of body temperature was negatively associated with DMI and milk energy, showing that greater variance in body temperature is linked to higher DMI and more milk energy. Smaller changes in body temperature after the largest meal were associated with lower DMI and milk energy. Adding body temperature traits to the RFI model did not change the coefficient of determination (adjusted R2), which remained ∼0.86. We found that cows that eat less have higher body temperatures, with less fluctuation throughout the day, compared with cows that consume more feed and produce more milk. Overall, our results suggest that vaginal temperature did not capture the heat increment expected in high RFI cows.

The rise in global temperature is affecting livestock production by limiting crop yields and exposing animals to more frequent heat-stress events. Heat-abatement technologies are widely used but increase production costs. Therefore, genetic selection for thermotolerance is an interesting tool to overcome the effects of heat stress. The goal of this study was to evaluate the effect of heat stress on both DMI and residual feed intake in lactating Holstein cows. Data consisted of 388,629 daily DMI and 54,353 weekly residual feed intake records from 6,333 cows collected on 6 research stations in Wisconsin, Michigan, and Iowa between 2007 and 2024. Heat stress was assessed using temperature-humidity index (THI), based on the measurements taken at weather stations. Average THI per day and per week were used to analyze DMI and residual feed intake, respectively. The effect of heat stress was also evaluated as the number of hours a cow was exposed to heat stress for DMI, and the number of days within a week a cow was exposed to heat stress for residual feed intake. Multitrait random regression models were used to estimate variance components for daily DMI and weekly residual feed intake, considering the first 3 lactations as different traits. The models included cohort (trial-treatment) and DIM effects as fixed, and general (intercept) and thermotolerance (slope) additive genetic and permanent environmental effects as random. Heritability estimates at the heat-stress level ranged from 0.16 to 0.33 for DMI and from 0.15 to 0.21 for residual feed intake. These results suggest substantial genetic variability underlying DMI and residual feed intake when cows are exposed to heat-stress conditions. Estimated genetic correlations between thermoneutral (intercept) and thermotolerant (slope) additive effects ranged from -0.06 to -0.36 for average THI and from 0.10 to -0.43 for hours exposed to heat stress. For residual feed intake, estimated genetic correlations between thermoneutral (intercept) and thermotolerant (slope) additive effects were negative and ranged from -0.17 to -0.48. The Pearson correlations among EBVs calculated in thermoneutral and thermal-stress conditions in different lactations were generally high. The Pearson correlation between the first and second lactations were higher than correlations between the first and third or second and third lactations. Note that most cows did not have records in multiple lactations, and the third group included animals in later lactations, which may have contributed to the lower correlations. In conclusion, DMI and residual feed intake are traits susceptible to heat stress. The negative genetic correlations observed between thermoneutral conditions and thermal-stress conditions suggest cows that consume more feed and are less efficient are more susceptible to heat stress.

Feeding behaviour traits, such as number, duration or intake per feeder visit, have been associated with feed efficiency in dairy cattle. Those traits, however, do not fully capture cows' feeding patterns throughout the day. The goal of this study was to propose a new phenotype for characterising within-day feeding patterns and estimate its heritability and genetic correlations with dry matter intake (DMI), secreted milk energy, metabolic body weight and residual feed intake. Feeding patterns were evaluated using 4.8 million bunk visits from 1684 midlactation Holstein cows collected from 2009 to 2023 with an Insentec system. Feed efficiency traits were available from 6099 lactating Holstein cows at six research stations across the United States. Daily bunk visits were ordered, with Time 0 designated as the time of first feed delivery. Intake proportions were calculated by visit for each cow by dividing feed intake per visit by the total intake of the cow for that day. Feeding patterns were characterised by the area under the curve of cumulative feed intake proportions for each cow throughout the day. The feeding pattern phenotype per cow was defined as the average of areas under the curve across days, whereas consistency of feeding pattern was calculated as the natural logarithm of variance of daily area under the curve values. Estimates of heritability and genetic correlations were performed using Bayesian inference with an animal model, considering lactation, days in milk and cohort (trial-treatment) as fixed effects and animal as a random effect. Heritability estimates for average area under the curve and variance of daily area under the curve were 0.35 ± 0.05 and 0.16 ± 0.05, respectively. The genetic correlation between average area under the curve and secreted milk energy was -0.30 ± 0.14. Genetic correlations between average area under the curve and DMI, metabolic body weight and residual feed intake were not statistically significant. Variance of daily area under the curve was genetically correlated with DMI (0.47 ± 0.15), secreted milk energy (0.40 ± 0.17) and metabolic body weight (0.28 ± 0.13). The genetic correlation between variance of daily area under the curve and residual feed intake was not significant. Overall, we provided a reliable method to truly characterise feeding patterns in midlactation dairy cows. Feeding pattern and its consistency were heritable, indicating that a significant proportion of phenotypic variation is explained by additive genetic effects. Genetic correlation estimates indicate that cows with more consistent daily feeding patterns have lower DMI, lower secreted milk energy and lower metabolic body weight.

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.

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.

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.

Genomic selection has revolutionized the dairy cattle breeding industry, with ripple effects that have greatly impacted dairy herd management. Rate of genetic progress has increased markedly, especially in Holstein and Jersey breeds, for production, health, and fertility traits. Genomic testing of young bulls and heifers provides greater accuracy of selection decisions involving traditional fertility traits, such as daughter pregnancy rate, while creating the opportunity to improve novel traits, such as fetal loss. Cameras, wearable sensors, and other precision livestock farming technologies will allow selection for traits such as estrus duration and intensity that require high frequency phenotyping. At the same time, synergies between genomic testing and advanced reproductive technologies have led to rapid and widespread adoption of sexed semen, coupled with mating of females whose offspring are not needed as herd replacements to beef sires. This strategy produces added-value crossbred calves for the beef supply chain, while allowing genetically inferior mature cows that are still producing at a high level to remain in the herd for additional lactations.

Feed efficiency is important for economic profitability of dairy farms; however, recording daily DMI is expensive. Our objective was to investigate the potential use of milk mid-infrared (MIR) spectral data to predict proxy phenotypes for DMI based on different cross-validation schemes. We were specifically interested in comparisons between a model that included only MIR data (model M1); a model that incorporated different energy sink predictors, such as body weight, body weight change, and milk energy (model M2); and an extended model that incorporated both energy sinks and MIR data (model M3). Models M2 and M3 also included various cow-level variables (stage of lactation, age at calving, parity) such that any improvement in model performance from M2 to M3, whether through a smaller root mean squared error (RMSE) or a greater squared predictive correlation (R-2), could indicate a potential benefit of MIR to predict residual feed intake. The data used in our study originated from a multi-institutional project on the genetics of feed efficiency in US Holsteins. Analyses were conducted on 2 different trait definitions based on different period lengths: averaged across weeks versus averaged across 28 d. Specifically, there were 19,942 weekly records on 1,812 cows across 46 experiments or cohorts and 3,724 28-d records on 1,700 cows across 43 different cohorts. The cross-validation analyses involved 3 different k-fold schemes. First, a 10-fold cow-independent cross-validation was conducted whereby all records from any one cow were kept together in either training or test sets. Similarly, a 10-fold experiment-independent cross-validation kept entire experiments together, whereas a 4-fold herd-independent cross-validation kept entire herds together in either training or test sets. Based on cow-independent cross-validation for both weekly and 28-d DMI, adding MIR predictors to energy sinks (model M3 vs. M2) significantly (P < 10(-10)) reduced average RMSE to 1.59 kg and increased average R-2 to 0.89. However, adding MIR to energy sinks (M3) to predict DMI either within an experiment-independent or herd-independent cross-validation scheme seemed to demonstrate no merit (P > 0.05) compared with an energy sink model (M2) for either R-2 or RMSE (respectively, 0.68 and 2.55 kg for M2 in herd-independent scheme). We further noted that with broader cross-validation schemes (i.e., from cow-independent to experiment-independent to herd-independent schemes), the mean and slope bias increased. Given that proxy DMI phenotypes for cows would need to be almost entirely generated in herds having no DMI or training data of their own, herd-independent cross-validation assessments of predictive performance should be emphasized. Hence, more research on predictive algorithms suitable for broader cross-validation schemes and a more earnest effort on calibration of spectrophotometers against each other should be considered.

Subclinical ketosis (SCK) is a prevalent metabolic disorder that occurs during the transition to lactation period. It is defined as a high blood concentration of ketone bodies (beta-hydroxybutyric acid f ≥ 1.2 mmol/L) within the first few weeks of lactation, and often presents without clinical signs. SCK is mainly caused by negative energy balance (NEB). The objective of this study is to identify single nucleotide polymorphisms (SNPs) associated with SCK using genome-wide association studies (GWAS), and to predict the biological functions of proximal genes using gene-set enrichment analysis (GSEA). Blood samples were collected from 112 Holstein cows between 5 and 18 days postpartum to determine the incidence of SCK. Genomic DNA extracted from both SCK and healthy cows was examined using the Illumina Bovine SNP50K BeadChip for genotyping. GWAS revealed 194 putative SNPs and 163 genes associated with those SNPs. Additionally, GSEA showed that the genes retrieved by Database for Annotation, Visualization, and Integrated Discovery (DAVID) belonged to calcium signaling, starch and sucrose, immune network, and metabolic pathways. Furthermore, the proximal genes were found to be related to germ cell and early embryo development. In summary, this study proposes several feasible SNPs and genes associated with SCK through GWAS and GSEA. These candidates can be utilized in selective breeding programs to reduce the genetic risk for SCK and subfertility in high-performance dairy cows.

IntroductionHigh feed bunk stocking densities can differentially impact individual dairy cows’ competitive behaviors, feeding patterns, and feed efficiency. Our objective was to manipulate feed bunk stocking densities to evaluate intra-individual behavioral consistency across stocking densities and quantify associations with feed efficiency and production.MethodsThirty-two primiparous (130.7 ± 29.0 days in milk, DIM) and 32 multiparous (111.3 ± 38.3 DIM) lactating Holstein cows were housed with 32 roughage intake control (RIC) bins. Each cow was assigned to share 8 bins with others of the same parity and similar body weight (16 cows/block; 2:1 feed bunk stocking density except during tests). Competition and feeding patterns were evaluated via video and RIC data, respectively, at 3 stocking densities (1:1, 2:1, 4:1 cows/bin) during 1-h tests (2 tests/stocking density; 6 tests/cow) following 2 h feed deprivation. Residual feed intake (RFI) was calculated across the 45-d study as the difference between observed and predicted dry matter intake (DMI) after accounting for known energy sinks. Linear mixed models were used to evaluate the overall impact of test stocking density on competition and feeding patterns. To evaluate intra-individual consistency between stocking densities, individual stability statistic (ISS) scores were computed. Correlational relationships were determined between RFI and ISS scores.Results and dicsussionCows displayed the most competitive behaviors at 2:1 stocking density (p < 0.0001) but experienced the highest rate of contacts per minute of eating time at 4:1 (1:1 vs. 2:1 vs. 4:1: 0.09 vs. 0.95 vs. 1.60 contacts/min; p < 0.0001). Feeding patterns were modulated as stocking density increased; eating rate increased (0.16 vs. 0.18 vs. 0.22 kg/min; p < 0.001) as eating time (40.3 vs. 28.2 vs. 14.6 min; p < 0.001) and DMI decreased (6.3 vs. 5.1 vs. 3.0 kg; p < 0.001). As stocking density doubled, individuals remained consistent (p = 0.018) in time spent near others actively eating and tended to remain consistent in competition behavior and feeding patterns (0.053 ≤ p ≤ 0.094). Between 2:1 and 4:1, cows with higher DMI and milk production were more consistent in first-visit DMI and duration. Feed efficiency was not associated with behavioral consistency across the tests (p ≥ 0.14). Nonetheless, feed bunk stocking density has behavioral implications which should be considered in nutritional management decisions.

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 preweaning and lactating periods is compromised when exposed to heat stress during late gestation (in utero). However, knowledge of the lingering effects of in utero heat stress on yearling dairy heifers is limited. Herein, we investigated the longterm effects of in utero heat stress on heifer growth, feed efficiency, and enteric methane emissions in postpubertal 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 were recorded on 3 consecutive days at the start and end of the trial and used to calculate ADG. Body condition score, 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 (on a 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 4 and CO2 2 gas fluxes. During the last 3 d of measuring CH4 4 and CO2 2 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 GC time-of-flight MS 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 4 and CO2, 2 , 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 postpubertal 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.

Ruminants have the ability to digest human-inedible plant materials, due to the symbiotic relationship with the rumen microbiota. Rumen microbes supply short chain fatty acids, amino acids, and vitamins to dairy cows that are used for maintenance, growth, and lactation functions. The main goal of this study was to investigate gene-microbiome networks underlying feed efficiency traits by integrating genotypic, microbial, and phenotypic data from lactating dairy cows. Data consisted of dry matter intake (DMI), net energy secreted in milk, and residual feed intake (RFI) records, SNP genotype, and 16S rRNA rumen microbial abundances from 448 mid-lactation Holstein cows. We first assessed marginal associations between genotypes and phenotypic and microbial traits through genomic scans, and then, in regions with multiple significant hits, we assessed gene-microbiome-phenotype networks using causal structural learning algorithms. We found significant regions co-localizing the rumen microbiome and feed efficiency traits. Interestingly, we found three types of network relationships: (1) the cow genome directly affects both rumen microbial abundances and feed efficiency traits; (2) the cow genome (Chr3: 116.5 Mb) indirectly affects RFI, mediated by the abundance of Syntrophococcus, Prevotella, and an unknown genus of Class Bacilli; and (3) the cow genome (Chr7: 52.8 Mb and Chr11: 6.1-6.2 Mb) affects the abundance of Rikenellaceae RC9 gut group mediated by DMI. Our findings shed light on how the host genome acts directly and indirectly on the rumen microbiome and feed efficiency traits and the potential benefits of the inclusion of specific microbes in selection indexes or as correlated traits in breeding programs. Overall, the multistep approach described here, combining whole-genome scans and causal network reconstruction, allows us to reveal the relationship between genome and microbiome underlying dairy cow feed efficiency.

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) LOESS 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 (PTAs) 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 PTAs 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 PTAs 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.

Large datasets allow estimation of 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 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 DIM, age- parity subclass, trial date, management group, and BW change during 28- and 42-d feeding trials in mid lactation. Phenotypic regressions of DMI on milk (0.014 f 0.006), fat (3.06 f 0.01), and protein (4.79 f 0.25) were much less than corresponding genomic regressions (0.08 f 0.03, 11.30 f 0.47, and 9.35 f 0.87, respectively) or sire genomic regressions multiplied by 2 (0.048 f 0.04, 6.73 f 0.94, and 4.98 f 1.75). Thus, marginal feed costs as fractions of marginal milk revenue were higher from genetic than phenotypic regressions. According to the ECM 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, whereas the sire genomic regressions indicated approximately 35% more DMI for fat than protein. Estimates of annual maintenance in kilograms DMI/kilograms BW per lactation were similar from phenotypic regression (5.9 f 0.14), genomic regression (5.8 f 0.31), and sire genomic regression multiplied by 2 (5.3 f 0.55) and are larger than those estimated by the National Academies for Science, Engineering, and Medicine 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 BW 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.

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.

Our objective in this exploratory study was to evaluate the long-term impacts of pre-weaning social isolation vs. contact on subsequent growth and feed efficiency of Holstein heifers. As pre-weaned calves, 41 heifers were housed individually (n = 15 heifers) or in pairs (n = 13 pairs; 26 heifers). At 18 months of age, heifers were blocked by body weight and randomly assigned to one of three pens within a block (six to eight heifers per pen; six pens total), with original pairs maintained. Body weight (BW), hip height and width, and chest girth were measured at the start and end of the study. Each pen was given 3 days of access to a GreenFeed greenhouse gas emissions monitor to assess potential physiological differences between treatments in enteric methane emissions or behavioral differences in propensity to approach a novel object. During the 9-week study, heifers were fed a common diet containing 62.3% male-sterile corn silage, 36.0% haylage, 0.7% urea, and 1.0% mineral (DM basis). To calculate daily feed intake, as-fed weights and refusals were recorded for individual heifers using Calan gates. Feed samples were collected daily, composited by week, and dried to calculate dry matter intake (DMI). Feed refusal and fecal samples were collected on 3 consecutive days at 3 timepoints, composited by heifer, dried, and analyzed to calculate neutral detergent fiber (NDF), organic matter (OM), and DM digestibility. Feed efficiency was calculated as feed conversion efficiency (FCE; DMI/average daily gain [ADG]) and residual feed intake (RFI; observed DMI-predicted DMI). Paired and individually housed heifers did not differ in DMI, ADG, FCE, or RFI. Although no differences were found in initial or final hip height, hip width, or chest girth, heifers which had been pair-housed maintained a greater BW than individually housed heifers during the trial. Methane production, intensity, and yield were similar between treatments. Pre-weaning paired or individual housing did not impact the number of visits or latency to approach the GreenFeed; approximately 50% of heifers in each treatment visited the GreenFeed within 8 h of exposure. Digestibility of OM, DM, and NDF were also similar between housing treatments. In conclusion, pre-weaning pair housing had no adverse effects on growth, feed efficiency, or methane emissions at 18 to 20 months of age.

The Resilient Dairy Genome Project (RDGP) is an international large-scale applied research project that aims to generate genomic tools to breed more resilient dairy cows. In this context, improving feed efficiency and reducing greenhouse gases from dairy is a high priority. The inclusion of traits related to feed efficiency (e.g., dry matter intake [DMI]) or greenhouse gases (e.g., methane emissions [CH4]) relies on available genotypes as well as high quality phenotypes. Currently, 7 countries, i.e., Australia [AUS], Canada [CAN], Denmark [DNK], Germany [DEU], Spain [ESP], Switzerland [CHE], and United States of America [USA] contribute with genotypes and phenotypes including DMI and CH4. However, combining data is challenging due to differences in recording protocols, measurement technology, genotyping, and animal management across sources. In this study, we provide an overview of how the RDGP partners address these issues to advance international collaboration to generate genomic tools for resilient dairy. Specifically, we describe the current state of the RDGP database, data collection protocols in each country, and the strategies used for managing the shared data. As of February 2022, the database contains 1,289,593 DMI records from 12,687 cows and 17,403 CH4 records from 3,093 cows and continues to grow as countries upload new data over the coming years. No strong genomic differentiation between the populations was identified in this study, which may be beneficial for eventual across-country genomic predictions. Moreover, our results reinforce the need to account for the heterogeneity in the DMI and CH4 phenotypes in genomic analysis.