Lactoferrin (LF) is a glycoprotein naturally present in milk. Its content varies throughout lactation, but also with mastitis; therefore it is a potential additional indicator of udder health beyond somatic cell count. Condequently, there is an interest in quantifying this biomolecule routinely. First prediction equations proposed in the literature to predict the content in milk using milk mid-infrared spectrometry were built using partial least square regression (PLSR) due to the limited size of the data set. Thanks to a large data set, the current study aimed to test 4 different machine learning algorithms using a large data set comprising 6,619 records collected across different herds, breeds, and countries. The first algorithm was a PLSR, as used in past investigations. The second and third algorithms used partial least square (PLS) factors combined with a linear and polynomial support vector regression (PLS + SVR). The fourth algorithm also used PLS factors, but included in an artificial neural network with 1 hidden layer (PLS + ANN). The training and validation sets comprised 5,541 and 836 records, respectively. Even if the calibration prediction performances were the best for PLS + polynomial SVR, their validation prediction performances were the worst. The 3 other algorithms had similar validation performances. Indeed, the validation root mean squared error (RMSE) ranged between 162.17 and 166.75 mg/L of milk. However, the lower standard deviation of cross-validation RMSE and the better normality of the residual distribution observed for PLS + ANN suggest that this modeling was more suitable to predict the LF content in milk from milk mid-infrared spectra (R2v = 0.60 and validation RMSE = 162.17 mg/L of milk). This PLS +ANN model was then applied to almost 6 million spectral records. The predicted LF showed the expected relationships with milk yield, somatic cell score, somatic cell count, and stage of lactation. The model tended to underestimate high LF values (higher than 600 mg/L of milk). However, if the prediction threshold was set to 500 mg/L, 82% of samples from the validation having a content of LF higher than 600 mg/L were detected. Future research should aim to increase the number of those extremely high LF records in the calibration set.

Pregnancy diagnosis is an essential part of successful breeding programs on dairy farms. Milk composition alters with pregnancy, and this is well documented. Fourier-transform mid-infrared (MIR) spectroscopy is a rapid and cost-effective method for providing milk spectra that reflect the detailed composition of milk samples. Therefore, the aim of this study was to assess the ability of MIR spectroscopy to predict the pregnancy status of dairy cows. The MIR spectra and insemination records were available from 8,064 Holstein cows of 19 commercial dairy farms in Australia. Three strategies were studied to classify cows as open or pregnant using partial least squares discriminant analysis models with random cow-independent 10-fold cross-validation and external validation on a cow-independent test set. The first strategy considered 6,754 MIR spectra after insemination used as independent variables in the model. The results showed little ability to detect the pregnancy status as the area under the receiver operating characteristic curve was 0.63 and 0.65 for cross-validation and testing, respectively. The second strategy, involving 1,664 records, aimed to reduce noise in the MIR spectra used as predictors by subtracting a spectrum before insemination (i.e., open spectrum) from the spectrum after insemination. The accuracy was comparable with the first approach, showing no superiority of the method. Given the limited results for these models when using combined data from all stages after insemination, the third strategy explored separate models at 7 stages after insemination comprising 348 to 1,566 records each (i.e., progressively greater gestation) with single MIR spectra after insemination as predictors. The models developed using data recorded after 150 d of pregnancy showed promising prediction accuracy with the average value of area under the receiver operating characteristic curve of 0.78 and 0.76 obtained through cross-validation and testing, respectively. If this can be confirmed on a larger data set and extended to somewhat earlier stages after insemination, the model could be used as a complementary tool to detect fetal abortion.

Contexte : La température moyenne à la surface du globe ne cesse d’augmenter. Elle entraîne notamment une augmentation de la fréquence des vagues de chaleur c’est-à-dire de périodes d’une durée de 3 à 6 jours durant lesquelles la température de l’air est supérieure à la température moyenne de la zone géographique concernée. Cette situation s’accompagne de multiples conséquences pour les populations humaines, végétales et animales. Objectif : Cette revue de littérature a pour objectif de récapituler les concepts fondamentaux régissant la régulation thermique chez les bovins. Méthode : Une recherche bibliographique a été réalisée dans PubMed. Elle s’est concentrée dans un premier temps sur les articles de synthèse puis a été complétée par les références des articles identifiés. Résultats : La régulation thermique implique une série de processus tels que la radiation, la convection, la conduction et l’évaporation. Ces processus vont, en fonction des caractéristiques de l’animal et de son environnement, contribuer à réduire ou à augmenter les effets du stress thermique. Le stress thermique se définit comme l’état de déséquilibre entre, d’une part, les facteurs qui contribuent à augmenter la température corporelle et d’autre part, ceux qui au contraire, contribuent à la diminuer. Cette situation de stress thermique se rencontre quand l’animal se trouve en-dehors de sa zone dite de neutralité thermique définie par des températures minimale et maximale critiques dont les valeurs dépendent de l’âge, la race, le niveau de production, l’état corporel, le stade de lactation, les bâtiments d’élevage, la couleur du poil et de la peau. Les effets de la température extérieure dépendent du degré d’humidité de l’environnement. Ils peuvent être évalués au moyen de divers index THI (Temperature Humidity Index). De plus en plus, des technologies émergent (imagerie en profondeur, thermographie par infrarouge, vidéosurveillance, accéléromètres, podomètres) et permettent de suivre les données météorologiques, l’environnement thermique des animaux, voire de détecter leurs signes de stress. Conclusions : Il est essentiel de sensibiliser les responsables de la santé animale aux effets du stress thermique sur la capacité des animaux à réguler leur température corporelle. Cette capacité peut être évaluée de manière toujours plus précise grâce à des index et des technologies qui permettent de mesurer et de comprendre les effets du stress thermique sur les animaux.

The use of test-day models to model milk mid-infrared (MIR) spectra for genetic purposes has already been explored; however, little attention has been given to their use to predict milk MIR spectra for management purposes. The aim of this paper was to study the ability of a test-day mixed model to predict milk MIR spectra for management purposes. A data set containing 467,496 test-day observations from 53,781 Holstein dairy cows in first lactation was used for model building. Principal component analysis was implemented on the selected 311 MIR spectral wavenumbers to reduce the number of traits for modeling; 12 principal components (PC) were retained, explaining approximately 96% of the total spectral variation. Each of the retained PC was modeled using a single trait test-day mixed model. The model solutions were used to compute the predicted scores of each PC, followed by a back-transformation to obtain the 311 predicted MIR spectral wavenumbers. Four new data sets, containing altogether 122,032 records, were used to test the ability of the model to predict milk MIR spectra in 4 distinct scenarios with different levels of information about the cows. The average correlation between observed and predicted values of each spectral wavenumber was 0.85 for the modeling data set and ranged from 0.36 to 0.62 for the scenarios. Correlations between milk fat, protein, and lactose contents predicted from the observed spectra and from the modeled spectra ranged from 0.83 to 0.89 for the modeling set and from 0.32 to 0.73 for the scenarios. Our results demonstrated a moderate but promising ability to predict milk MIR spectra using a test-day mixed model. Current and future MIR traits prediction equations could be applied on the modeled spectra to predict all MIR traits in different situations instead of developing one test-day model separately for each trait. Modeling MIR spectra would benefit farmers for cow and herd management, for instance through prediction of future records or comparison between observed and expected wavenumbers or MIR traits for the detection of health and management problems. Potential resulting tools could be incorporated into milk recording systems.

Calves are born agammaglobulinemic and they rely on transfer of passive immunity (TPI) through ingestion of colostrum from the dam. Ensuring the effectiveness of TPI through blood serum immunoglobulins (IgG) quantification is of critical importance for the prevention of calf diseases. The main objective of this study was to assess the performance of a novel on-farm immunochromatographic quick assay (SmartStrips^TM, Bio-X Diagnostics, Rochefort, Belgium) compared to the ELISA reference method to directly measure serum IgG concentration and assess TPI status in beef and dairy calves. Additional comparison was made with the commonly used Brix refractometer. Jugular blood samples were collected from beef (n = 71) and dairy (n = 26) calves in Belgium within 7 days post-birth. Quantitative (Pearson correlation coefficients, Bland-Altman plots) and qualitative (diagnostic test characteristics, weighted kappa for classification into 4 categories of TPI) analyses were performed to evaluate the performances of the quick test and the refractometer compared to ELISA. The quick test showed a correlation of 0.83 and a classification agreement (weighted kappa) of 0.79 with the reference method (average values for two types of blood anticoagulants). Performances were better for low IgG concentrations and the assessment of poor TPI status and they outperformed those of the Brix refractometer. Results suggested that the immunochromatographic quick test can be considered as a suitable on-farm method for direct serum IgG measurement and the assessment of TPI status in calves, contributing to timely interventions in the management of calves with inadequate TPI.

Contexte : L’augmentation régulière depuis plusieurs décennies de la température environnementale s’est accompagnée dans l’espèce bovine, notamment d’une diminution de la fertilité dans différentes régions du monde. Objectifs : Cette revue de littérature passe en revue les divers effets et leurs mécanismes sur les divers aspects de la reproduction du mâle et de la femelle bovine. Méthode : À partir de la base PubMed, cette revue de littérature s’est concentrée dans un premier temps sur les articles de synthèse puis a été complétée par les références des articles identifiés. Résultats : La diminution de la fertilité résulte des effets du stress thermique sur la croissance folliculaire ovarienne, avec une incidence plus marquée sur les follicules pré-antraux que sur les follicules antraux, l’ovocyte et le corps jaune. Ces effets sont la conséquence d’une réduction de la synthèse d’oestradiol, de la progestérone, de l’inhibine et de l’hormone lutéotrope (LH) ainsi que de l’augmentation de celle de l’hormone folliculostimulante (FSH), une modification des gradients de température au niveau du système génital n’étant pas non plus à exclure. Le stress thermique est également responsable d’une augmentation de la fréquence de la mortalité embryonnaire particulièrement durant la première semaine de la gestation. L’exposition des vaches à un stress thermique durant la gestation en raccourcit la durée, affecte le placenta, modifie le métabolisme de la vache lors de sa lactation, altère son immunité et exerce des effets négatifs sur la croissance pondérale, le développement mammaire et folliculaire, la longévité et les performances de reproduction du veau. Chez le mâle, le stress thermique se traduit par une réduction de la motilité et une augmentation des anomalies morphologiques des spermatozoïdes. Conclusions : La reconnaissance croissante des effets du stress thermique sur les performances reproductives de bovins souligne la nécessité pour les responsables de la santé animale de les intégrer dans leurs approches. Il est essentiel de mettre en place des stratégies visant à en atténuer les impacts sur la rentabilité des élevages, d’autant plus que son influence épigénétique sur les performances de la descendance est de mieux en mieux documentée.