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The aims of this study were to: (1) estimate genetic correlation for milk production traits (milk, fat and protein yields and fat and protein contents) and fatty acids (FA: C16:0, C18:1 cis-9, LCFA, SFA, and UFA) over days in milk, (2) investigate the performance of genomic predictions using single-step GBLUP (ssGBLUP) based on random regression models (RRM), and (3) identify the optimal scaling and weighting factors to be used in the construction of the H matrix. A total of 302 684 test-day records of 63.875 first lactation Walloon Holstein cows were used. Positive genetic correlations were found between milk yield and fat and protein yield (rg from 0.46 to 0.85) and between fat yield and milk FA (rg from 0.17 to 0.47). On the other hand, negative correlations were estimated between fat and protein contents (rg from −0.22 to −0.59), between milk yield and milk FA (rg from −0.22 to −0.62), and between protein yield and milk FA (rg from −0.11 to −0.19). The selection for high fat content increases milk FA throughout lactation (rg from 0.61 to 0.98). The test-day ssGBLUP approach showed considerably higher prediction reliability than the parent average for all milk production and FA traits, even when no scaling and weighting factors were used in the H matrix. The highest validation reliabilities (r2 from 0.09 to 0.38) and less biased predictions (b1 from 0.76 to 0.92) were obtained using the optimal parameters (i.e., ω = 0.7 and α = 0.6) for the genomic evaluation of milk production traits. For milk FA, the optimal parameters were ω = 0.6 and α = 0.6. However, biased predictions were still observed (b1 from 0.32 to 0.81). The findings suggest that using ssGBLUP based on RRM is feasible for the genomic prediction of daily milk production and FA traits in Walloon Holstein dairy cattle. [ABSTRACT FROM AUTHOR]

Simple Summary: Mid-infrared (MIR) spectroscopy is the method of choice to determine milk components like fat, protein and urea. We examined the potential of MIR spectra analyses for the prediction of clinical mastitis events of dairy cows additionally, or alternatively, to somatic cell count, which is routinely used as an indicator for mastitis monitoring. Prediction models based on MIR spectra and a somatic cell count-derived score (SCS) were developed and compared. A model based on MIR spectra and SCS proved more accurate at predicting mastitis than models based on either indicator alone. Consequently, MIR spectra analyses add extra value in the prediction of clinical mastitis, making them potentially useful for dairy farm management and as an auxiliary trait for the genetic evaluation of udder health. Monitoring for mastitis on dairy farms is of particular importance, as it is one of the most prevalent bovine diseases. A commonly used indicator for mastitis monitoring is somatic cell count. A supplementary tool to predict mastitis risk may be mid-infrared (MIR) spectroscopy of milk. Because bovine health status can affect milk composition, this technique is already routinely used to determine standard milk components. The aim of the present study was to compare the performance of models to predict clinical mastitis based on MIR spectral data and/or somatic cell count score (SCS), and to explore differences of prediction accuracies for acute and chronic clinical mastitis diagnoses. Test-day data of the routine Austrian milk recording system and diagnosis data of its health monitoring, from 59,002 cows of the breeds Fleckvieh (dual purpose Simmental), Holstein Friesian and Brown Swiss, were used. Test-day records within 21 days before and 21 days after a mastitis diagnosis were defined as mastitis cases. Three different models (MIR, SCS, MIR + SCS) were compared, applying Partial Least Squares Discriminant Analysis. Results of external validation in the overall time window (−/+21 days) showed area under receiver operating characteristic curves (AUC) of 0.70 when based only on MIR, 0.72 when based only on SCS, and 0.76 when based on both. Considering as mastitis cases only the test-day records within 7 days after mastitis diagnosis, the corresponding areas under the curve were 0.77, 0.83 and 0.85. Hence, the model combining MIR spectral data and SCS was performing best. Mastitis probabilities derived from the prediction models are potentially valuable for routine mastitis monitoring for farmers, as well as for the genetic evaluation of the trait udder health. [ABSTRACT FROM AUTHOR]

We investigated the use of different Legendre polynomial orders to estimate genetic parameters for milk production and fatty acid (FA) traits in the first lactation Walloon Holstein cows. The data set comprised 302,684 test‐day records of milk yield, fat and protein contents, and FAs generated by mid‐infrared (MIR) spectroscopy, C16:0 (palmitic acid), C18:1 cis‐9 (oleic acid), LCFAs (long‐chain FAs), SFAs (saturated FAs) and UFAs (unsaturated FAs) were studied. The models included random regression coefficients for herd‐year of calving (h), additive genetic (a) and permanent environment (p) effects. The selection of the best random regression model (RRM) was based on the deviance information criterion (DIC), and genetic parameters were estimated via a Bayesian approach. For all analysed random effects, DIC values decreased as the order of the Legendre polynomials increased. Best‐fit models had fifth‐order (degree 4) for the p effect and ranged from second‐ to fifth‐order (degree 1–4) for the a and h effects (LEGhap: LEG555 for milk yield and protein content; LEG335 for fat content and SFA; LEG545 for C16:0 and UFA; and LEG535 for C18:1 cis‐9 and LCFA). Based on the best‐fit models, an effect of overcorrection was observed in early lactation (5–35 days in milk [DIM]). On the contrary, third‐order (LEG333; degree 2) models showed flat residual trajectories throughout lactation. In general, the estimates of genetic variance tended to increase over DIM, for all traits. Heritabilities for milk production traits ranged from 0.11 to 0.58. Milk FA heritabilities ranged from low‐to‐high magnitude (0.03–0.56). High Spearman correlations (>0.90 for all bulls and >0.97 for top 100) were found among breeding values for 155 and 305 DIM between the best RRM and LEG333 model. Therefore, third‐order Legendre polynomials seem to be most parsimonious and sufficient to describe milk production and FA traits in Walloon Holstein cows. [ABSTRACT FROM AUTHOR]

BACKGROUND: A robust proxy for estimating methane (CH4) emissions of individual dairy cows would be valuable especially for selective breeding. This study aimed to improve the robustness and accuracy of prediction models that estimate daily CH4 emissions from milk Fourier transform mid‐infrared (FT‐MIR) spectra by (i) increasing the reference dataset and (ii) adjusting for routinely recorded phenotypic information. Prediction equations for CH4 were developed using a combined dataset including daily CH4 measurements (n = 1089; g d−1) collected using the SF6 tracer technique (n = 513) and measurements using respiration chambers (RC, n = 576). Furthermore, in addition to the milk FT‐MIR spectra, the variables of milk yield (MY) on the test day, parity (P) and breed (B) of cows were included in the regression analysis as explanatory variables. RESULTS: Models developed based on a combined RC and SF6 dataset predicted the expected pattern in CH4 values (in g d−1) during a lactation cycle, namely an increase during the first weeks after calving followed by a gradual decrease until the end of lactation. The model including MY, P and B information provided the best prediction results (cross‐validation statistics: R2 = 0.68 and standard error = 57 g CH4 d−1). CONCLUSIONS: The models developed accounted for more of the observed variability in CH4 emissions than previously developed models and thus were considered more robust. This approach is suitable for large‐scale studies (e.g. animal genetic evaluation) where robustness is paramount for accurate predictions across a range of animal conditions. © 2020 Society of Chemical Industry [ABSTRACT FROM AUTHOR]

Evaluating genetically heat stress (HS) of cattle is still a major challenge, also because of the need to have access to HS sensitive phenotypes relating their reaction to temperature and humidity as described by the temperature-humidity index (THI). A solution could be to add the information of data from sensors. However, activity (ACT) and rumination (RUM) sensors reflect no "production" [i.e., milk yields (MY), milk composition, but also intake or growth] related aspects which is suboptimal. Therefore, the objectives of this study were thus to assess the possible usefulness for adding sensors data for HS genetic evaluation based on a dairy situation. But the context described can be easily enlarged and include beef cattle and pigs. Strategy was illustrated using daily SenseHub collar records that were obtained from October 2019 to July 2022 in six dairy herds in Wallonia in Southern Belgium. A total of 453 Walloon Holstein cows were followed during this period for activity time. Meteorological data and a HS sensitive "production" trait (here MY) were also obtained from 2015 to 2022 for 1,740 cows from the same herds. In total 32,154 MY, 130,867 ACT and 130,848 RUM records were available. The thresholds at which the different traits start to be affected by HS were estimated at a THI of 63, 64 and 66, respectively, for MY, ACT and RUM. A three-trait random regression reaction norm model was fitted with these different thresholds. Estimated heritability values were 0.19 ± 0.03, 0.14 ± 0.06 and 0.21 ± 0.05 respectively for MY, ACT and RUM using AIREML. Ratios of variance associated to THI and constant genetic effect were 0.002, 0.02 and 0.04 indicating a stronger reaction by RUM but also ACT than MY to THI. However, it is uncertain if the reactions of these sensor traits are reflected in the production traits. Estimated genetic correlations of regressions on THI for ACT and RUM showed values of 0.51 ± 0.70 and 0.08 ± 0.54 with MY and 0.77 ± 0.69 between ACT and RUM. The first eigenvector of the correlation matrix explained 65% of the total variability and standardized coefficients of this eigenvector were 0.42, 0.69 and 0.59 for MY, ACT and RUM. A total of 50% of the variance associated with the regression on THI for MY could be explained by the regressions on THI for ACT and RUM. Adding the constant genetic effects for ACT and RUM only marginally increased to 51% the ability to explain the effect of THI on MY. Based on these results, reactions of different indicator traits, here sensor data, may reflect a different reaction than production and therefore economically important traits. Additional research, also beyond dairy cattle, should continue to address this topic that is very important for the validation of sensor data detecting HS. [ABSTRACT FROM AUTHOR]

Genetic parameters were estimated for first lactation survival defined as a binary trait (alive or dead to second calving) and the curve shape traits of milk yield, fat and protein percentages using information from 25 981 primiparous Tunisian Holsteins. For each trait, shape curves (i.e. peak lactation, persistency), level of production adjusted to 305 days in milk (DIMs) for total milk yield (TMY), and average fat (TF %) and protein (TP %) percentages were defined. Variance components were estimated with a linear random regression model under three bivariate animal models. Production traits were modelled by fixed herd × test-day (TD) interaction effects, fixed classes of 25 DIMs × age of calving × season of calving interaction effects, fixed classes of pregnancy, random environment effects and random additive genetic effects. Survival was modelled by fixed herd × year of calving interaction effects and age of calving × season of calving interaction effects, random permanent environment effects, and random additive genetic effects. Heritability (h2) estimates were 0.03 (±0.01) for survival and 0.23 (±0.01), 0.31 (±0.01) and 0.31 (±0.01) for TMY, TF % and TP %, respectively. Genetic correlations between survival and TMY, TF % and TP % were 0.26 (±0.08), -0.24 (±0.06) and -0.13 (±0.06), respectively. Genetic correlations between survival and persistency for fat and protein percentages were -0.35 (±0.09) and -0.19 (±0.09), respectively. Cows that had higher persistencies for fat and protein percentages were more likely not to survive. [ABSTRACT FROM AUTHOR]

Copyright of Biotechnologie, Agronomie, Societe et Environnement is the property of Les Presses Agronomiques de Gembloux and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Biotechnologie, Agronomie, Societe et Environnement is the property of Les Presses Agronomiques de Gembloux and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Simple Summary: Dry matter intake, related to the number of nutrients available to an animal to meet its production and health needs, is crucial for the economic, environmental, and welfare management of dairy herds. Because the equipment required to weigh the ingested food at an individual level is not broadly available, we propose some new ways to approach the actual dry matter consumed by a dairy cow for a given day. To do so, we used regression models using parity (number of lactations), week of lactation, milk yield, milk mid-infrared spectrum, and prediction of bodyweight, fat, protein, lactose, and fatty acids content in milk. We chose these elements to predict individual dry matter intake because they are either easily accessible or routinely provided by regional dairy organizations (often called "dairy herd improvement" associations). We succeeded in producing a model whose dry matter intake predictions were moderately related to the actual values. We predicted dry matter intake of dairy cows using parity, week of lactation, milk yield, milk mid-infrared (MIR) spectrum, and MIR-based predictions of bodyweight, fat, protein, lactose, and fatty acids content in milk. The dataset comprised 10,711 samples of 534 dairy cows with a geographical diversity (Australia, Canada, Denmark, and Ireland). We set up partial least square (PLS) regressions with different constructs and a one-hidden-layer artificial neural network (ANN) using the highest contribution variables. In the ANN, we replaced the spectra with their projections to the 25 first PLS factors explaining 99% of the spectral variability to reduce the model complexity. Cow-independent 10 × 10-fold cross-validation (CV) achieved the best performance with root mean square errors (RMSECV) of 3.27 ± 0.08 kg for the PLS regression and 3.25 ± 0.13 kg for ANN. Although the available data were significantly different, we also performed a country-independent validation (CIV) to measure the models' performance fairly. We found RMSECIV varying from 3.73 to 6.03 kg for PLS and 3.69 to 5.08 kg for ANN. Ultimately, based on the country-independent validation, we discussed the developed models' performance with those achieved by the National Research Council's equation. [ABSTRACT FROM AUTHOR]

Simple Summary: First, the current work consisted of validating the feasibility of large-scale dairy cow bodyweight prediction from models involving the day in milk, milk yield, parity, and milk mid-infrared spectrum. Second, it aimed to improve the accuracy of predictive models by using feature selection algorithms to decrease the number of predictors to limit overfitting. The models, using accessible and low-cost measurements, provided highly reproducible predictions. These could be easily obtained on an individual basis throughout a cow's productive life by dairy herd improvement organizations, thus providing potentially relevant information for the dairy farmer at three levels: economics (reproductive performance), animal welfare (disease detection), and environment (methane production). Knowing the body weight (BW) of a cow at a specific moment or measuring its changes through time is of interest for management purposes. The current work aimed to validate the feasibility of predicting BW using the day in milk, parity, milk yield, and milk mid-infrared (MIR) spectrum from a multiple-country dataset and reduce the number of predictors to limit the risk of over-fitting and potentially improve its accuracy. The BW modeling procedure involved feature selections and herd-independent validation in identifying the most interesting subsets of predictors and then external validation of the models. From 1849 records collected in 9 herds from 360 Holstein cows, the best performing models achieved a root mean square error (RMSE) for the herd-independent validation between 52 ± 2.34 kg to 56 ± 3.16 kg, including from 5 to 62 predictors. Among these models, three performed remarkably well in external validation using an independent dataset (N = 4067), resulting in RMSE ranging from 52 to 56 kg. The results suggest that multiple optimal BW predictive models coexist due to the high correlations between adjacent spectral points. [ABSTRACT FROM AUTHOR]

Simple Summary: Cows are social animals, therefore understanding the ways that they interact can help improve their management and welfare. We used social network analysis (SNA) to data on voluntary cow movement through a sort gate in an automatic milking system to identify pairs of cows that repeatedly passed through a sort gate in close succession (affinity pairs). Results from this exploratory study showed that when cows were separated from their affinity-pair cow the day-day variability in milk production increased by a factor of 3, a possible indicator of stress. The results of this exploratory study suggest that SNA could be used as a tool to better understand the social dynamics of dairy cows and inform group and regrouping process to produce positive outcomes. We have applied social network analysis (SNA) to data on voluntary cow movement through a sort gate in an automatic milking system to identify pairs of cows that repeatedly passed through a sort gate in close succession (affinity pairs). The SNA was applied to social groups defined by four pens on a dairy farm, each served by an automatic milking system (AMS). Each pen was equipped with an automatic sorting gate that identified when cows voluntarily moved from the resting area to either milking or feeding areas. The aim of this study was two-fold: to determine if SNA could identify affinity pairs and to determine if milk production was affected when affinity pairs where broken. Cow traffic and milking performance data from a commercial guided-flow AMS dairy farm were used. Average number of milked cows was 214 ± 34, distributed in four AMS over 1 year. The SNA was able to identify clear affinity pairs and showed when these pairings were formed and broken as cows entered and left the social group (pen). The trend in all four pens was toward higher-than-expected milk production during periods of affinity. Moreover, we found that when affinities were broken (separation of cow pairs) the day-to-day variability in milk production was three times higher than for cows in an affinity pair. The results of this exploratory study suggest that SNA could be potentially used as a tool to reduce milk yield variation and better understand the social dynamics of dairy cows supporting management and welfare decisions. [ABSTRACT FROM AUTHOR]

In the next decencies, dairy production will be under even more pressure to provide healthy milk from well-managed and robust cows producing under economically and ecologically sustainable conditions. Performance recording has been for years linked to genetic improvement and to basic farm management. For yield traits, efforts to model, still in a genetic perspective, this type of longitudinal data focused on random regression, also called random coefficient, models. Other data from performance recording is available as linear data, body condition scores, health (i.e., somatic cell count), fertility and longevity data. In this presentation we will show how extensive data recording, combined with statistical modeling is the basis for the development of advanced management and selection tools for dairy cattle. [ABSTRACT FROM AUTHOR]

The aims of this study were to estimate genetic parameters and to identify genomic regions associated with eating time (ET) and rumination time (RUT) in Holstein dairy cows. Genetic correlations among ET, RUT, and milk yield traits were also estimated. The data were collected from 2019 to 2022 in 6 dairy herds located in the Walloon Region of Belgium. The dataset consisted of daily ET and RUT records on 284 Holstein cows, from which 41 cows had records only for the first parity (P1), 101 cows had records from both the first and second parities, and 142 cows had records only for the second parity (P2). The number of daily ET and RUT records in the P1 and P2 cows were 18,569 (on 142 cows) and 34,464 (on 243 cows), respectively. Data on 28,994 SNPs located on 29 Bos taurus autosomes (BTA) of 747 animals (435 males) were used. Random regression test-day models were used to estimate genetic parameters through the Bayesian Gibbs sampling method. The SNP solutions were estimated using a single-step genomic best linear unbiased prediction approach. The proportion of genetic variance explained by each 20-SNP sliding window (with an average size of 1.52 Mb) was calculated, and regions accounting for at least 1.0% of the total additive genetic variance were used to search for candidate genes. Mean (standard deviation; SD) averaged daily ET and RUT were 327.0 (85.66) and 559.4 (77.69) min/d for cows in P1 and 316.0 (82.24) and 574.2 (75.42) min/d for cows in P2, respectively. Mean (standard deviation; SD) heritability estimates for daily ET and RUT were 0.42 (0.09) and 0.45 (0.06) for cows in P1 and 0.45 (0.04) and 0.43 (0.02) for cows in P2, respectively. Mean (SD) daily genetic correlations between daily ET and RUT were 0.27 (0.07) for P1 and 0.34 (0.08) for P2. Genome-wide association analyses identified 6 genomic regions distributed over 5 chromosomes (BTA1, BTA4, BTA11, 2 regions of BTA14, and BTA17) associated with ET or RUT. The findings of this study increase our preliminary understanding of the genetic background of feeding behavior in dairy cows; however, larger datasets are needed to determine whether ET and RUT might have the potential to be used in selection programs. [ABSTRACT FROM AUTHOR]

Copyright of Biotechnologie, Agronomie, Societe et Environnement is the property of Les Presses Agronomiques de Gembloux and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Direct measurements of methane (CH 4) from individual animals are difficult and expensive. Predictions based on proxies for CH 4 are a viable alternative. Most prediction models are based on multiple linear regressions (MLR) and predictor variables that are not routinely available in commercial farms, such as dry matter intake (DMI) and diet composition. The use of machine learning (ML) algorithms to predict CH 4 emissions from across-country heterogeneous data sets has not been reported. The objectives were to compare performances of ML ensemble algorithm random forest (RF) and MLR models in predicting CH 4 emissions from proxies in dairy cows, and assess effects of imputing missing data points on prediction accuracy. Data on CH 4 emissions and proxies for CH 4 from 20 herds were provided by 10 countries. The integrated data set contained 43,519 records from 3,483 cows, with 18.7% missing data points imputed using k-nearest neighbor imputation. Three data sets were created, 3k (no missing records), 21k (missing DMI imputed from milk, fat, protein, body weight), and 41k (missing DMI, milk fat, and protein records imputed). These data sets were used to test scenarios (with or without DMI, imputed vs. nonimputed DMI, milk fat, and protein), and prediction models (RF vs. MLR). Model predictive ability was evaluated within and between herds through 10-fold cross-validation. Prediction accuracy was measured as correlation between observed and predicted CH 4 , root mean squared error (RMSE) and mean normalized discounted cumulative gain (NDCG). Inclusion of DMI in the model improved within and between-herd prediction accuracy to 0.77 (RMSE = 23.3%) and 0.58 (RMSE = 31.9%) in RF and to 0.50 (RMSE = 0.327) and 0.13 (RMSE = 42.71) in MLR, respectively than when DMI was not included in the predictive model. When missing DMI records were imputed, within and between-herd accuracy increased to 0.84 (RMSE = 18.5%) and 0.63 (RMSE = 29.9%), respectively. In all scenarios, RF models out-performed MLR models. Results suggest routinely measured variables from dairy farms can be used in developing globally robust prediction models for CH 4 if coupled with state-of-the-art techniques for imputation and advanced ML algorithms for predictive modeling. [ABSTRACT FROM AUTHOR]

• The most informative variables were identified to predict dairy cow's liveweight; • Robust and accurate prediction models were developed to predict liveweight of dairy cows; • The developed models were sufficient for the on farm use; • The developed models presented good transferability between different brand of spectrometers. Body weight (BW) of dairy cows is relevant for breeding programs and farm management to assess the maintenance requirements, reproduction performance, or health status of cow. Currently, it is still difficult to follow BW changes of individual cows routinely in large herds. Combined with animal characteristics, milk mid-infrared (MIR) spectrum was proposed as an additional source of information to predict BW under the framework of dairy herd improvement (DHI) programs. However, the presence of less informative variables in the prediction equation could impact negatively its robustness. This research aims to improve the robustness of BW regression models by applying a feature selection before modeling. A total of 5,920 BW records composed of animal characteristics and milk MIR spectrum were collected from Holstein cows. Three feature selection algorithms were applied to select the most informative variables: partial least squares regression (PLS) combined with sum of ranking difference (PLS-SRD), PLS combined with uninformative variables elimination (PLS-UVE), and the output of Elastic Net regression (EN). Four herd independent validation sets and the corresponding remained calibration datasets having on average 163 and 1,708 records, respectively, were used to develop models using PLS or EN approaches. Ten-fold cross-validation was conducted to parametrize each model. Parity, days in milk (DIM), milk yield (MY), and two MIR spectral points were selected as relevant variables to predict BW. PLS (root mean square error of validation, RMSEp = 60 kg) and EN (RMSEp = 60 kg) regressions employing these 5 predictors were more robust than the models developed without MIR or using MIR without feature selection. The EN models had a cross-validation root mean square error of around 53 kg. The 2 MIR points explained up to 4.20% variation in predicting BW. The RMSE of validation sets using another brand of spectrometer were around 64 kg. This study confirms the possibility to predict an indicator of BW from animal characteristics and MIR variables. The variable selection procedures improved the model's robustness and transferability. The accuracy of BW prediction seems to be sufficient to provide useful information for breeding program and farm management decisions under a DHI framework. [ABSTRACT FROM AUTHOR]