Residual analysis for the identification of potential MIR-derived biomarkers of heat stress in dairy cattle.

Numerous prediction equations have been developed based on mid-infrared (MIR) spectra and some could be potentially used as biomarkers of heat stress. However, practical experience shows that confusion between the effect of heat stress and other effects like lactation stage or feeding variation over the year can easily occur. On this basis, the objective of this study was to identify potential milk components predicted by MIR as biomarkers of heat stress based on a 2-step approach allowing to correct for those effects.

Rapid selection of milk mid-infrared spectra for creating a dairy cow population world representative spectral database.

The advantage of employing mid-infrared spectrometry for milk analysis in breeding lies in its ability to quickly generate millions of records. However, these records may be biased if the calibration process does not account for their spectral variability when constructing the predictive model. Therefore, this study introduces a novel method for developing a world representative spectral database (WRSD) to reduce the risks of spectral extrapolation when predicting dairy traits in new samples.

Exploring a Bayesian sparse factor model-based strategy for the genetic analysis of thousands of mid-infrared spectra traits for animal breeding.

With the rapid development of animal phenomics and deep phenotyping, we can obtain thousands of traditional (but also molecular) phenotypes per individual. However, there is still a lack of exploration regarding how to handle this huge amount of data in the context of animal breeding, presenting a challenge that we are likely to encounter more and more in the future. This study aimed to (1) explore the use of the mega-scale linear mixed model (MegaLMM), a factor model-based approach that is able to simultaneously estimate (co)variance components and genetic parameters in the context of thousands of milk traits, hereafter called thousand-trait (TT) models; (2) compare the phenotype values and genomic breeding value (u) predictions for focal traits (i.

Prediction of key milk biomarkers in dairy cows through milk mid-infrared spectra and international collaborations.

At the individual cow level, suboptimum fertility, mastitis, negative energy balance, and ketosis are major issues in dairy farming. These problems are widespread on dairy farms and have an important economic impact. The objectives of this study were (1) to assess the potential of milk mid-infrared (MIR) spectra to predict key biomarkers of energy deficit (citrate, isocitrate, glucose-6 phosphate [glucose-6P], free glucose), ketosis (β-hydroxybutyrate [BHB] and acetone), mastitis (N-acetyl-β-d-glucosaminidase activity [NAGase] and lactate dehydrogenase), and fertility (progesterone); (2) to test alternative methodologies to partial least squares (PLS) regression to better account for the specific asymmetric distribution of the biomarkers; and (3) to create robust models by merging large datasets from 5 international or national projects.

Imputation of missing milk Fourier transform mid-infrared spectra using existing milk spectral databases: A strategy to improve the reliability of breeding values and predictive models.

The use of milk Fourier transform mid-infrared (FT-MIR) spectrometry to develop management and breeding tools for dairy farmers and industry is growing and supported by the availability of numerous new predicted phenotypes to assess the nutritional quality of milk and its technological properties, but also the animal health and welfare status and its environmental fingerprint. For genetic evaluations, having a long-term and representative spectral dairy herd improvement (DHI) database improves the reliabilities of estimated breeding values (EBV) from these phenotypes. Unfortunately, most of the time, the raw spectral data used to generate these estimations are not stored.

Single-step genome-wide association for selected milk fatty acids in Dual-Purpose Belgian Blue cows.

The aim of this study was to estimate genetic parameters and identify genomic regions associated with selected individual and groups of milk fatty acids (FA) predicted by milk mid-infrared spectrometry in Dual-Purpose Belgian Blue cows. The used data were 69,349 test-day records of milk yield, fat percentage, and protein percentage along with selected individual and groups FA of milk (g/dL milk) collected from 2007 to 2020 on 7,392 first-parity (40,903 test-day records), and 5,185 second-parity (28,446 test-day records) cows distributed in 104 herds in the Walloon Region of Belgium. Data of 28,466 SNPs, located on 29 Bos taurus autosomes (BTA), of 1,699 animals (639 males and 1,060 females) were used.

The use of a genomic relationship matrix for breed assignment of cattle breeds: comparison and combination with a machine learning method.

To develop a breed assignment model, three main steps are generally followed: 1) The selection of breed informative single nucleotide polymorphism (SNP); 2) The training of a model, based on a reference population, that allows to classify animals to their breed of origin; and 3) The validation of the developed model on external animals i.e., that were not used in previous steps.

Fourier transform mid-infrared milk screening to improve milk production and processing.

Milk mid-infrared spectrometry has been used for many years to quantify major milk compounds. Recently, much research has been conducted to extend the use of this technology to predict new, relevant phenotypes to assess the animals' welfare and the nutritional quality of milk, as well as its technological quality and environmental footprint. The transition from the research stage to field implementation is not easy, due to intrinsic and extrinsic constraints, but some developments can be considered to address these issues.

Prediction of Indirect Indicators of a Grass-Based Diet by Milk Fourier Transform Mid-Infrared Spectroscopy to Assess the Feeding Typologies of Dairy Farms.

This research aims to develop a predictive model to discriminate milk produced from a cattle diet either based on grass or not using milk mid-infrared spectrometry and the month of testing (an indirect indicator of the feeding ration). The dataset contained 3,377,715 spectra collected between 2011 and 2021 from 2449 farms and 3 grazing traits defined following the month of testing. Records from 30% of the randomly selected farms were kept in the calibration set, and the remaining records were used to validate the models.

Identification of chronic stress biomarkers in dairy cows.

Stress in dairy herds can occur from multiple sources. When stress becomes chronic because of a long duration and inability of animals to adapt, it is likely to deeply affect the emotional state, health, immunity, fertility and milk production of cows. While assessing chronic stress in herds would be beneficial, no real consensus has emerged from the literature regarding the indicators of interest.

Integrating heterogeneous across-country data for proxy-based random forest prediction of enteric methane in dairy cattle.

Direct measurements of methane (CH) from individual animals are difficult and expensive. Predictions based on proxies for CH 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.

Volatile Organic Compounds Emitted by Strains Producing or Not Aflatoxin B1.

is a phytopathogenic fungus able to produce aflatoxin B1 (AFB1), a carcinogenic mycotoxin that can contaminate several crops and food commodities. In , two different kinds of strains can co-exist: toxigenic and non-toxigenic strains. Microbial-derived volatile organic compounds (mVOCs) emitted by toxigenic and non-toxigenic strains of were analyzed by solid phase microextraction (SPME) coupled with gas chromatography-mass spectrometry (GC-MS) in a time-lapse experiment after inoculation.

Multiple Breeds and Countries' Predictions of Mineral Contents in Milk from Milk Mid-Infrared Spectrometry.

Measuring the mineral composition of milk is of major interest in the dairy sector. This study aims to develop and validate robust multi-breed and multi-country models predicting the major minerals through milk mid-infrared spectrometry using partial least square regressions. A total of 1281 samples coming from five countries were analyzed to obtain spectra and in ICP-AES to measure the mineral reference contents.

Genetic analysis of milk urea concentration and its genetic relationship with selected traits of interest in dairy cows.

The aim of this study was to estimate genetic parameters of milk urea concentration (MU) and its genetic correlations with milk production traits, longevity, and functional traits in the first 3 parities in dairy cows. The edited data set consisted in 9,107,349 MU test-day records from the first 3 parities of 560,739 cows in 2,356 herds collected during the years 1994 to 2020. To estimate the genetic parameters of MU, data of 109 randomly selected herds, with a total of 770,016 MU test-day records, were used.

Development of a genomic tool for breed assignment by comparison of different classification models: Application to three local cattle breeds.

Assignment of individual cattle to a specific breed can often not rely on pedigree information. This is especially the case for local breeds for which the development of genomic assignment tools is required to allow individuals of unknown origin to be included to their herd books. A breed assignment model can be based on two specific stages: (a) the selection of breed-informative markers and (b) the assignment of individuals to a breed with a classification method.

Validation of Dairy Cow Bodyweight Prediction Using Traits Easily Recorded by Dairy Herd Improvement Organizations and Its Potential Improvement Using Feature Selection Algorithms.

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.

Appropriate Data Quality Checks Improve the Reliability of Values Predicted from Milk Mid-Infrared Spectra.

The use of abnormal milk mid-infrared (MIR) spectrum strongly affects prediction quality, even if the prediction equations used are accurate. So, this record must be detected after or before the prediction process to avoid erroneous spectral extrapolation or the use of poor-quality spectral data by dairy herd improvement (DHI) organizations. For financial or practical reasons, adapting the quality protocol currently used to improve the accuracy of fat and protein contents is unfeasible.

The Walloon farmers position differently their ideal dairy production system between a global-based intensive and a local-based extensive model of farm.

Dairy farming systems are evolving. This study presents dairy producers' perceptions of their ideal future farm (IFF) to ensure revenue, and attempts to determine the reasons for this choice, the environmental aspects related to this choice, the proximity between the current farm and the IFF and the requirements for reaching this IFF. Just before the end of the European milk quota, a total of 245 Walloon dairy producers answered a survey about the characteristics of their IFF and other socio-environmental-economic information.

A comparison of 4 different machine learning algorithms to predict lactoferrin content in bovine milk from mid-infrared spectra.

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.

Improving robustness and accuracy of predicted daily methane emissions of dairy cows using milk mid-infrared spectra.

Background: A robust proxy for estimating methane (CH ) 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 CH 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 CH were developed using a combined dataset including daily CH measurements (n = 1089; g d ) collected using the SF tracer technique (n = 513) and measurements using respiration chambers (RC, n = 576).

Effects of Wheat Bran Applied to Maternal Diet on the Intestinal Architecture and Immune Gene Expression in Suckling Piglets.

The strategy of improving the growth and health of piglets through maternal fiber diet intervention has attracted increasing attention. Therefore, 15 sows were conducted to a wheat bran (WB) group, in which the sows' diets included 25% of WB in gestation and 14% in lactation, and a control (CON) group, in which the sows' diets at all stages of reproduction did not contain WB. The results show that maternal high WB intervention seems not to have an impact on the growth of the offspring or the villus height of the duodenum, and the ratio of villi/crypts in the duodenum and jejunum were all higher in piglets born from WB sows, which may indicate that WB piglets had a larger absorption area and capacity for nutrients.

Large-scale phenotyping in dairy sector using milk MIR spectra: Key factors affecting the quality of predictions.

Methods and technologies enabling the estimation at large scale of important traits for the dairy sector are of great interest. Those phenotypes are necessary to improve herd management, animal genetic evaluation, and milk quality control. In the recent years, the research was very active to predict new phenotypes from the mid-infrared (MIR) analysis of milk.

Short communication: Potential prediction of vitamin B concentration based on mid-infrared spectral data using Holstein Dairy Herd Improvement milk samples.

The purpose of this study was (1) to predict the quantitative concentration of vitamin B in milk using mid-infrared (MIR) spectrometry, and (2) to evaluate the potential of MIR spectra to discriminate different clusters of records based on their B concentration. Milk samples were collected from 4,340 Holstein cows between 3 and 592 d in milk and located in 100 herds. Samples were taken using in-line milk meters and divided into 2 aliquots: one for MIR spectrometry and the other for B concentration reference analyses by radioassay.