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.

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.

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.

Inorganic sources of Mg are commonly used in dairy cow diets, but their availability varies significantly. This study assessed the relative availability of 4 commonly used inorganic Mg sources and a novel alkalinizing proprietary mineral blend [PMB; Multesium (GLC Minerals, LLC, Green Bay, WI, USA)]. The study was a duplicated 6 × 6 Latin square, with 12 nonlactating, non-pregnant Holstein dairy cows assigned to a square based on BW and parity. Cows were fed 90% of their voluntary DMI (diet contained 0.21% Mg). Each experimental period lasted 7 d. On d 2 of each period, urinary catheters were fitted. Total urine collection started on d 3 for 48 h with samples collected and measured every 12 h. On d 4, 30 g of Mg were administered as boluses with gelatin capsules: negative control (one empty capsule), magnesium oxide (MgO), magnesium sulfate (MgSO4), calcium magnesium hydroxide [CaMg(OH)4], calcium magnesium carbonate [CaMg(CO3)2], and PMB [a blend of Ca and Mg sources that includes CaMg(CO3)2, CaMg(OH)4, and MgO]. Blood samples were collected at 0, 1, 2, 3, 12, and 24 h after treatment administration on d 4 of each treatment period. Urine and blood samples were analyzed for Mg and Ca concentration. Statistical analyses were conducted with PROC GLIMMIX including treatment, time, period, square, treatment × time, treatment × period, and time × period as fixed effects, and cow nested within square as a random effect in the model. Urinary Mg excretion for 4 of the Mg sources studied [PMB, MgO, CaMg(OH)4, and MgSO4] increased significantly, representing an increase of at least 40.8% relative to control. The supplementation of CaMg(CO3)2 did not significantly increase relative to control. There were no significant changes in blood Mg concentration with treatment; but, a significant treatment × time effect was observed. Calcium-rich sources [PMB, CaMg(OH)4, CaMg(CO3)2] had lower blood Mg concentrations at 12 or 24 h after treatment than control and CaMg(CO3)2. Based on urinary Mg excretion 24 h after treatment, 4 of the Mg sources evaluated (including PMB) showed a similar availability, however, the availability of the commercial CaMg(CO3)2 source included in our study was similar to the negative control (no-supplemented cows).

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.

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.

Meta-analyses have become increasingly common, providing meaningful summaries of cumulative knowledge in the dairy science literature. Some of the corresponding meta-analytic techniques have been developed by knowledgeable dairy scientists, some of which pre-date tractable likelihood-based random or mixed effects model meta-analytic techniques and associated software developed by statisticians. This review compares various meta-analytic techniques on aggregate data (i.e., study-specific treatment or slope estimates and their standard errors) generated from simulated data involving regression, completely randomized designs (CRD), and Latin square design scenarios. In all cases, meta-estimates generated from the analysis of individual performance data (IPD), using the same statistical model as that used to simulate the data, were considered to be gold standard references for meta-estimates derived from various meta-analysis strategies on aggregate data. In all cases, likelihood-based techniques outperformed techniques developed by dairy scientists for meta-estimate proximity to corresponding IPD estimates. An extensive simulation study comparing meta-analytic techniques within a CRD framework indicated that these advantages widen with increasing study heterogeneity in effect sizes, smaller number of experimental replicates (i.e., cows) per treatment per study, and lower within study variability; nevertheless, the impact of meta-analytic methods on estimated standard errors of these meta-estimates were rather trivial. To best utilize aggregate data from Latin square studies in meta-analyses, a concerted effort is required to recover standard errors of mean differences rather than the standard errors of the means themselves. Perhaps the most compelling reason for choosing likelihood-based methods for meta-analysis is their ability to provide reliable prediction intervals on effect sizes noting that these intervals are currently under-reported in the dairy science literature. Compared with the reporting of meta-estimates and their standard errors, prediction intervals provide a far more appropriate indication of uncertainty on treatment effects in future studies and should be greater emphasized in extension or outreach efforts. Although R software packages like metafor are readily available for likelihood-based methods, both SAS and R code for linear mixed models can be readily modified to facilitate these analyses as demonstrated extensively in the supplementary materials of this review.

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.

Producers are moving toward cage-free systems to house laying hens. These include aviary styles with multilevel wire enclosures and litter areas on the floor. In aviaries with doors hens can be confined within the tiered enclosure, which can be done to promote oviposition in nests and prevent hens from laying eggs in litter. However, there are multiple genetic strains of laying hen used in the egg industry, and some show different temporal patterns for key behaviors that could affect when they want to be on litter. For example, though dust bathing by laying hens is typically considered to peak in early afternoon, there may be variation in timing of motivation to dust bathe among strains. Differences in hens’ temporal patterns, coupled with aviary configurations or management practices, may restricts birds’ ability to perform important behaviors, such as dust bathing (DB), when they would most prefer to do them. Our objective was to determine if there were strain differences in the temporal pattern of DB. We examined the timing of DB in 4 strains of laying hen (Hy-Line Brown [HB], Bovans Brown [BB], DeKalb White [DW], and Hy-Line W36 [W36]) housed in aviaries using 144 hens of each strain per aviary unit (4 units/strain). We recorded the number of hens DB and on litter using instantaneous scan sampling every 5 min using video collected at 26 and 28 wk of age beginning at 11:35 (when litter access began each day) to 20:00 (lights off). Brown strains acclimated to litter access more slowly than white strains. Hens of all strains DB most often soon after gaining access to litter, and more white hens (DW and W36) DB simultaneously and in the presence of more conspecifics. Further examination of diurnal rhythm of behaviors, such as dust bathing, under unconstrained conditions by a range of genetic strains of laying hens is needed to design management practices and aviary styles that best meet hens’ needs.

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.

Inorganic sources of Mg are commonly used in dairy cow diets, but their availability varies significantly. This study assessed the relative availability of 4 commonly used inorganic Mg sources and a novel alkalinizing proprietary mineral blend (PMB; Multesium; GLC Minerals LLC, Green Bay, WI). The study was a duplicated 6 x 6 Latin square, with 12 nonlactating, nonpregnant Holstein dairy cows assigned to a square based on BW and parity. Cows were fed 90% of their voluntary DMI (diet contained 0.21% Mg). Each experimental period lasted 7 d. On d 2 of each period, urinary catheters were fitted. Total urine collection started on d 3 for 48 h with samples collected and measured every 12 h. On d 4, 30 g of Mg were administered as boluses with gelatin capsules: negative control (one empty capsule), magnesium oxide (MgO), magnesium sulfate (MgSO4), calcium magnesium hydroxide [CaMg(OH)4], calcium magnesium carbonate [CaMg(CO3)2], and PMB [a blend of Ca and Mg sources that includes CaMg(CO3)2, CaMg(OH)4, and MgO]. Blood samples were collected at 0, 1, 2, 3, 12, and 24 h after treatment administration on d 4 of each treatment period. Urine and blood samples were analyzed for Mg and Ca concentration. Statistical analyses were conducted with PROC GLIMMIX including treatment, time, period, square, treatment x time, treatment x period, and time x period as fixed effects, and cow nested within square as a random effect in the model. Urinary Mg excretion for 4 of the Mg sources studied [PMB, MgO, CaMg(OH)4, and MgSO4] increased significantly, representing an increase of at least 40.8% relative to control. The supplementation of CaMg(CO3)2 did not significantly increase relative to control. There were no significant changes in blood Mg concentration with treatment, but a significant treatment x time effect was observed. Calcium-rich sources [PMB, CaMg(OH)4, CaMg(CO3)2] had lower blood Mg concentrations at 12 or 24 h after treatment than control and CaMg(CO3)2. Based on urinary Mg excretion 24 h after treatment, 4 of the Mg sources evaluated (including PMB) showed a similar availability, however, the availability of the commercial CaMg(CO3)2 source included in our study was similar to the negative control (non- supplemented cows).

Improving production efficiency and minimizing the environmental impact of dairy farming are 2 important goals of the dairy industry. Achieving these objectives requires improving the feed-to-milk conversion efficiency. One way to achieve this goal is through genetic selection. However, measuring feed efficiency in commercial herds is currently not feasible. As such, we conducted a study to evaluate the genetic accuracy of various selection indices derived from Fourier transform mid-infrared (FTIR)-spectra or milk composition. We use 7,793 weekly records on 537 genotyped cows (78,964 SNPs), with information on residual feed intake (RFI), and FTIR-spectra. We fitted various types of selection indexes using the complete FTIR-spectra of milk samples. The estimated heritability of RFI was 0.12 ± 0.02. The accuracy of indirect selection using the FTIR-spectra was maximized using a principal components selection index (0.16 ± 0.07), followed by a Lasso-type penalized selection index (0.14 ± 0.06). We determined that an index based on milk spectral data recorded on ~25 daughters produced a progeny average with an accuracy comparable to direct phenotypic selection for RFI. We conclude that indirect selection for RFI using FTIR-spectra data can be effective for sires with progeny; however, future studies with a larger sample size are needed to validate these results.

Excessive FSH doses during ovarian stimulation in the small ovarian reserve heifer (SORH) cause premature cumulus expansion and follicular hyperstimulation dysgenesis (FHD) in nearly all ovulatory-size follicles with predicted disruptions in cell-signaling pathways in cumulus cells and oocytes (before ovulatory hCG stimulation). These observations support the hypothesis that excessive FSH dysregulates cumulus cell function and oocyte maturation. To test this hypothesis, we determined whether excessive FSH-induced differentially expressed genes (DEGs) in cumulus cells identified in our previously published transcriptome analysis were altered independent of extreme phenotypic differences observed amongst ovulatory-size follicles, and assessed predicted roles of these DEGs in cumulus and oocyte biology. We also determined if excessive FSH alters cumulus cell morphology, and oocyte nuclear maturation before (premature) or after an ovulatory hCG stimulus or during IVM. Excessive FSH doses increased expression of 17 cumulus DEGs with known roles in cumulus cell and oocyte functions (responsiveness to gonadotrophins, survival, expansion, and oocyte maturation). Excessive FSH also induced premature cumulus expansion and oocyte maturation but inhibited cumulus expansion and oocyte maturation post-hCG and diminished the ability of oocytes with prematurely expanded cumulus cells to undergo IVF or nuclear maturation during IVM. Ovarian stimulation with excessive FSH is concluded to disrupt cumulus cell and oocyte functions by inducing premature cumulus expansion and dysregulating oocyte maturation without an ovulatory hCG stimulus yielding poor-quality cumulus-oocyte complexes that may be incorrectly judged morphologically as suitable for IVF during ART.

Residual feed intake is viewed as an important trait in breeding programs that could be used to enhance genetic progress in feed efficiency. In particular, improving feed efficiency could improve both economic and environmental sustainability in the dairy cattle industry. However, data remain sparse, limiting the development of reliable genomic evaluations across lactation and parity for residual feed intake. Here, we estimated novel genetic parameters for genetic residual feed intake (gRFI) across the first, second, and third parity, using a random regression model. Research data on the measured feed intake, milk production, and body weight of 7,379 cows (271,080 records) from 6 countries in 2 continents were shared through the Horizon 2020 project Genomic Management Tools to Optimise Resilience and Efficiency, and the Resilient Dairy Genome Project. The countries included Canada (1,053 cows with 47,130 weekly records), Denmark (1,045 cows with 72,760 weekly records), France (329 cows with 16,888 weekly records), Germany (938 cows with 32,614 weekly records), the Netherlands (2,051 cows with 57,830 weekly records), and United States (1,963 cows with 43,858 weekly records). Each trait had variance components estimated from first to third parity, using a random regression model across countries. Genetic residual feed intake was found to be heritable in all 3 parities, with first parity being predominant (range: 22-34%). Genetic residual feed intake was highly correlated across parities for mid- to late lactation; however, genetic correlation across parities was lower during early lactation, especially when comparing first and third parity. We estimated a genetic correlation of 0.77 ± 0.37 between North America and Europe for dry matter intake at first parity. Published literature on genetic correlations between high input countries/continents for dry matter intake support a high genetic correlation for dry matter intake. In conclusion, our results demonstrate the feasibility of estimating variance components for gRFI across parities, and the value of sharing data on scarce phenotypes across countries. These results can potentially be implemented in genetic evaluations for gRFI in dairy cattle.

Residual feed intake (RFI) has been used as a measure of feed efficiency in farm animals. In lactating dairy cattle, RFI is typically obtained as the difference between dry matter intake observations and predictions from regression on known energy sinks, and effects of parity, days in milk, and cohort. The impact of parity (lactation number) on the estimation of RFI is not well understood, so the objectives of this study were to (1) evaluate alternative RFI models in which the energy sinks (metabolic body weight, body weight change, and secreted milk energy) were nested or not nested within parity, and (2) estimate variance components and genetic correlations for RFI across parities. Data consisted of 72,474 weekly RFI records of 5,813 lactating Holstein cows collected from 2007 to 2022 in 5 research stations across the United States. Estimates of heritability, repeatability, and genetic correlations between weekly RFI for parities 1, 2, and 3 were obtained using bivariate repeatability animal models. The nested RFI model showed better goodness of fit than the nonnested model, and some partial regression coefficients of dry matter intake on energy sinks were heterogeneous between parities. However, the Spearman's rank correlation between RFI values calculated from nested and nonnested models was equal to 0.99. Similarly, Spearman's rank correlation between the RFI breeding values from these 2 models was equal to 0.98. Heritability estimates for RFI were equal to 0.16 for parity 1, 0.19 for parity 2, and 0.22 for parity 3. Repeatability estimates for RFI across weeks within parities were high, ranging from 0.51 to 0.57. Spearman's rank correlations of sires' breeding values were 0.99 between parities 1 and 2, 0.91 between parities 1 and 3, and 0.92 between parities 2 and 3. We conclude that nesting energy sinks within parity when computing RFI improves model goodness of fit, but the impact on the estimated breading values appears to be minimal.

Feed efficiency has become an increasingly important research topic in recent years. As feed costs rise and the environmental impacts of agriculture become more apparent, improving the efficiency with which dairy cows convert feed to milk is increasingly important. However, feed intake is expensive to measure accurately on large populations, making the inclusion of this trait in breeding programs difficult. Understanding how the genetic parameters of feed efficiency and traits related to feed efficiency vary throughout the lactation period is valuable to gain understanding into the genetic nature of feed efficiency. This study used 121,226 dry matter intake (DMI) records, 120,500 energy -corrected milk (ECM) records, and 98,975 metabolic body weight (MBW) records, collected on 7,440 first -lactation Holstein cows from 6 countries (Canada, Denmark, Germany, Spain, Switzerland, and the United States), from January 2003 to February 2022. Genetic parameters were estimated using a multiple -trait random regression model with a fourth -order Legendre polynomial for all traits. Weekly phenotypes for DMI were re -parameterized using linear regressions of DMI on ECM and MBW, creating a measure of feed efficiency that was genetically corrected for ECM and MBW, referred to as genomic residual feed intake (gRFI). Heritability (SE) estimates varied from 0.15 (0.03) to 0.29 (0.02) for DMI, 0.24 (0.01) to 0.29 (0.03) for ECM, 0.55 (0.03) to 0.83 (0.05) for MBW, and 0.12 (0.03) to 0.22 (0.06) for gRFI. In general, heritability estimates were lower in the first stage of lactation compared with the later stages of lactation. Additive genetic correlations between weeks of lactation varied, with stronger correlations between weeks of lactation that were close together. The results of this study contribute to a better understanding of the change in genetic parameters across the first lactation, providing insight into potential selection strategies to include feed efficiency in breeding programs.

The impact of genomic epistasis effects on the accuracy of predicting the phenotypic values of residual feed intake (RFI) in U.S. Holstein cows was evaluated using 6215 Holstein cows and 78,964 SNPs. Two SNP models and seven epistasis models were initially evaluated. Heritability estimates and the accuracy of predicting the RFI phenotypic values from 10-fold cross-validation studies identified the model with SNP additive effects and additive × additive (A×A) epistasis effects (A + A×A model) to be the best prediction model. Under the A + A×A model, additive heritability was 0.141, and A×A heritability was 0.263 that consisted of 0.260 inter-chromosome A×A heritability and 0.003 intra-chromosome A×A heritability, showing that inter-chromosome A×A effects were responsible for the accuracy increases due to A×A. Under the SNP additive model (A-only model), the additive heritability was 0.171. In the 10 validation populations, the average accuracy for predicting the RFI phenotypic values was 0.246 (with range 0.197–0.333) under A + A×A model and was 0.231 (with range of 0.188–0.319) under the A-only model. The average increase in the accuracy of predicting the RFI phenotypic values by the A + A×A model over the A-only model was 6.49% (with range of 3.02–14.29%). Results in this study showed A×A epistasis effects had a positive impact on the accuracy of predicting the RFI phenotypic values when combined with additive effects in the prediction model.

Residual feed intake (RFI) and feed saved (FS) are important feed efficiency traits that have been increasingly considered in genetic improvement programs. Future sustainability of these genetic evaluations will depend upon greater flexibility to accommodate sparsely recorded dry matter intake (DMI) records on many more cows, especially from commercial environments. Recent multiple-trait random regression (MTRR) modeling developments have facilitated days in milk (DIM)-specific inferences on RFI and FS, particularly in modeling the effect of change in metabolic body weight (MBW). The MTRR analyses, using daily data on the core traits of DMI, MBW, and milk energy (MilkE), were conducted separately for 2,532 primiparous and 2,379 multiparous US Holstein cows from 50 to 200 DIM. Estimated MTRR variance components were used to derive genetic RFI and FS and DIM-specific genetic partial regressions of DMI on MBW, MilkE, and change in MBW. Estimated daily heritabilities of RFI and FS varied across lactation for both primiparous (0.05-0.07 and 0.11-0.17, respectively) and multiparous (0.03-0.13 and 0.10-0.17, respectively) cows. Genetic correlations of RFI across DIM varied (>0.05) widely compared with FS (>0.54) within either parity class. Heritability estimates based on average lactation-wise measures were substantially larger than daily heritabilities, ranging from 0.17 to 0.25 for RFI and from 0.35 to 0.41 for FS. The partial genetic regression coefficients of DMI on MBW (0.11 to 0.16 kg/kg0.75 for primiparous and 0.12 to 0.14 kg/kg0.75 for multiparous cows) and of DMI on MilkE (0.45 to 0.68 kg/Mcal for primiparous and 0.36 to 0.61 kg/Mcal for multiparous cows) also varied across lactation. In spite of the computational challenges encountered with MTRR, the model potentially facilitates an efficient strategy for harnessing more data involving a wide variety of data recording scenarios for genetic evaluations on feed efficiency.