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

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. So, 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. Utilizing a 2-phase selection procedure that is both efficient and minimizes memory usage, we first generate a decomposition matrix via Principal Component Analysis (PCA) on a data set of 2,324,443 records. The next phase iterates spectral selection based on a location index from PCA scores, calculating spectra occurrence frequency for refined barycenter estimations. The chosen spectra's barycenter closely aligns with the entire data set, proving the efficacy of using just 3 principal components (PCs). Applied to 4 varied data sets, totaling over 21 million records, we select 583,440 spectra to represent spectral diversity, with selection rates between 2.00% and 14.88%. This selection illustrates the spectral variability across different dairy populations and data providers. Demonstrated through a hypothetical calibration set of 71 samples, the WRSD's utility for algorithm developers becomes apparent. This calibration set covers between 91.42 to 98.50% of the WRSD variability, except for the Irish data set (3.50%), indicating a need for additional samples to accurately represent Irish variability and minimize spectral extrapolation. This study offers valuable insights into the representativeness of training sets for capturing spectral variability within targeted dairy populations. While the current WRSD does not fully encompass global milk spectral diversity, its development underscores the importance of gathering more data and standardizing spectral information across spectrometer brands. Ultimately, the WRSD proves crucial not just for trait prediction but also for identifying abnormal milk samples, also marking a significant relevance in dairy science technology.

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 (ß-hydroxybutyrate [BHB] and acetone), mastitis (N-acetyl-ß-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. Benefiting from this international collaboration, the dataset comprised a total of 9,143 milk samples from 3,758 cows located in 589 herds across 10 countries and represented 7 breeds. The samples were analyzed by reference chemistry for biomarker contents, whereas the MIR analyses were performed on 30 instruments from different models and brands, with spectra harmonized into a common format. Four quantitative methodologies were evaluated to address the strongly skewed distribution of some biomarkers. Partial least squares regression was used as the reference basis, and compared with a random modification of distribution associated with PLS (random-downsampling-PLS), an optimized modification of distribution associated with PLS (KennardStone-downsampling-PLS), and support vector machine (SVM). When the ability of MIR to predict biomarkers was too low for quantification, different qualitative methodologies were tested to discriminate low versus high values of biomarkers. For each biomarker, 20% of the herds were randomly removed within all countries to be used as the validation dataset. The remaining 80% of herds were used as the calibration dataset. In calibration, the 3 alternative methodologies outperform the PLS performances for the majority of biomarkers. However, in the external herd validation, PLS provided the best results for isocitrate, glucose-6P, free glucose, and lactate dehydrogenase (coefficient of determination in external herd validation [R2v] = 0.48, 0.58, 0.28, and 0.24, respectively). For other molecules, PLS-random-downsampling and PLS-KennardStone-downsampling outperformed PLS in the majority of cases, but the best results were provided by SVM for citrate, BHB, acetone, NAGase, and progesterone (R2v = 0.94, 0.58, 0.76, 0.68, and 0.15, respectively). Hence, PLS and SVM based on the entire dataset provided the best results for normal and skewed distributions, respectively. Complementary to the quantitative methods, the qualitative discriminant models enabled the discrimination of high and low values for BHB, acetone, and NAGase with a global accuracy around 90%, and glucose-6P with an accuracy of 83%. In conclusion, MIR spectra of milk can enable quantitative screening of citrate as a biomarker of energy deficit and discrimination of low and high values of BHB, acetone, and NAGase, as biomarkers of ketosis and mastitis. Finally, progesterone could not be predicted with sufficient accuracy from milk MIR spectra to be further considered. Consequently, MIR spectrometry can bring valuable information regarding the occurrence of energy deficit, ketosis, and mastitis in dairy cows, which in turn have major influences on their fertility and survival.

Can unsupervised learning methods applied to milk recording big data provide new insights into dairy cow health?

Among the dairy sector's current concerns, the assessment of global animal health status is a complex challenge. Its multidimensionality means that global monitoring tools are rarely considered. Instead, specific disease detection is often studied separately and, due to financial and ethical issues, uses small-scale data sets focusing on few biomarkers. Several studies have already been conducted using milk Fourier transform mid-infrared (FT-MIR) spectroscopy to detect mastitis and lameness or to quantify health-related biomarkers in milk or blood. Those studies are relevant but they focus mainly on one biomarker or disease. To solve this issue and the small-scale data set, in this study, we proposed a holistic approach using big data obtained from milk recording, including milk yield, somatic cell count, and 27 FT-MIR-based predictors related to milk composition and animal health status. Using 740,454 records collected from 114,536 first-parity Holstein cows in southern Belgium, we performed repeated unsupervised learning algorithms based on Ward's agglomerative hierarchical clustering method to find potential interesting patterns. A divide-and-conquer approach was used to overcome the limitation of computational resources in clustering a relatively large data set. Five groups of records were identified. Differences observed in the fourth group suggested a relationship to metabolic disorders. The fifth group seemed to be related to mastitis. In a second step, we performed a partial least squares discriminant analysis (PLS-DA) to predict the probability of belonging to those specific groups for the entire data set. The obtained global accuracy was 0.77 and the balanced accuracy (i.e., the mean between sensitivity and specificity) of discriminating the fourth and fifth groups was 0.88 and 0.96, respectively. Then, a validation of the interpretation of those groups was performed using 204 milk and blood reference records. The predicted probability associated with the metabolic disorders issue had significant correlations of 0.54 with blood ß-hydroxybutyrate, 0.44 with blood nonesterified fatty acids, -0.32 with blood glucose, -0.23 with milk glucose-6-phosphate, and 0.38 with milk isocitrate. In contrast, the predicted probability of belonging to the mastitis group had correlations of 0.69 with milk lactate dehydrogenase, 0.46 with milk N-acetyl-ß-d-glucosaminidase, -0.18 with milk free glucose, and 0.16 with milk glucose-6-phosphate. Consequently, these results suggest that the obtained quantitative traits indirectly reflect some of the main health disorders in dairy farming and could be used to monitor dairy cows on a large scale. By using unsupervised learning on large-scale milk recording data and then validating the pattern using reference laboratory measures, we propose a new approach to quickly assess dairy cow health status.

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. The goal of this study was to compare and evaluate potential biomarkers for chronic stress after inducing stress over a 4-week period through severe overstocking, restricted access to feed and isolated unusual events. A total of 30 cows were involved in the experiment and two similar groups were constituted. Over a 4-week period, the 15 cows of the stress group were housed in overstocked conditions, with 4.6 m2 per cow, including resting and feeding areas. In this area, only seven individual places at the feeding area were available for the 15 cows to generate competition for feed access. Twice during the trial and over a period of 2 h, an additional stress was induced by moving cows to an unfamiliar barn and diffusion of stressing noises (dog barking). Meanwhile, the 15 cows of the control group stayed in the original barn, with more than 10 m2 per cow and more individual places at the feeding area than cow number. On a weekly basis, several variables considered as potential biomarkers for chronic stress were recorded. Collected data were analysed using single trait linear repeated mixed models. No differences were observed regarding milk yield, BW of cows or body condition score but the milk loss was more pronounced in the stress group. The activity was more heterogeneous and the rumination of cows was lower in the stress group. The heart rate was lower in the stress group and showed more heterogeneity at the end of the stress period. No differences were observed regarding salivary cortisol, blood glucose, ß-endorphin, thyroxine and leucocyte profile. A higher level of hair cortisol and blood fructosamine were observed in the stress group at the end of the stress period. Regarding the practical use of the highlighted biomarkers, milk loss may be an effective and easy way to detect general problems, including stress. The blood fructosamine and the hair cortisol concentrations are promising indicators to assess chronic stress in commercial farms.

Multigenerational effects of a reduced balanced protein diet during the rearing and laying period of broiler breeders. 2. Zootechnical performance of the F1 broiler offspring.

Several studies in mammals focused on the maternal programming of the metabolism by epigenetic mechanisms, while currently, the consequences of a maternal dietary treatment on the offspring performance of farm animals are of particular interest for commercial purpose. In the present study, we investigated if the zootechnical performance of the progeny was altered by a maternal dietary treatment, being a lower dietary crude protein (CP) of the grandparent and/or parent generation. The multigenerational effects of a reduced maternal CP content were investigated by reducing the dietary CP level by 25% in rearing and laying diets of pure line A breeders. The F0 generation breeders were fed either control (C) or reduced balanced protein (RP) diets. The F1 breeder generation was constructed by dividing the F0 female progeny again over a C or RP diet, resulting in 4 dietary treatments in the F1 generation: C/C, C/RP, RP/C, and RP/RP (letters indicating the diets in, respectively, F0 and F1 generations). The offspring performance was evaluated by a zootechnical and nitrogen retention trial on C and low-protein (LP) broiler diets. For the C broiler diet, the C/RP and RP/RP offspring were characterized by a higher BW from d 35 until d 42 compared to the C/C progeny, whereas the RP/C offspring had an intermediate BW that did not differ from the other groups. A tendency (P = 0.067) towards a better nitrogen retention was observed for the offspring of breeders that received the RP diets in F0 and/or F1 generation compared to the C/C progeny. For the LP broiler diet, the C/RP (P = 0.021) and RP/C (P = 0.001) offspring had a higher BW compared to the C/C progeny during the entire grow-out period. In addition, the C/RP offspring were characterized by a lower FCR from d 28 onwards (P = 0.021). In conclusion, dietary treatments imposed on mother hens can have direct effects on the next generation, as well as indirect effects on multiple generations.

Feeding broiler breeders a reduced balanced protein diet during the rearing and laying period impairs reproductive performance but enhances broiler offspring performance.

Mammalian studies have shown that nutritional constraints during the perinatal period are able to program the progeny (metabolism, performance). The presented research aimed to investigate if broiler breeders and their offspring performance could be influenced by reducing the dietary crude protein (CP) level with 25%. A total of 160 day-old pure line A breeder females were randomly divided over 2 dietary treatments. The control group was fed commercial diets, whereas the reduced balanced protein (RP) breeders received an isoenergetic diet that was decreased with 25% in dietary CP and amino acid during their entire lifespan. The RP birds required an increased feed allowance, varying between 3 and 15%, to meet the same BW goals as their control fed counterparts. The difference in feed allocations and reduction of the dietary CP level resulted in a net protein reduction varying between 14 and 23%. At wk 27 and 40, the body composition of the breeders was changed as a result of the dietary treatment. At both ages, the proportional abdominal fat pad weight of the RP breeders was increased (P < 0.001), whereas the proportional breast muscle weight was only higher at wk 27 in the control group compared to the RP group (P < 0.001). Egg weight (P < 0.001) and egg production (P < 0.001) was decreased for the RP fed birds. The lower dietary CP level reduced the proportional albumen weight of the RP eggs (P = 0.006). Male offspring from RP breeders were characterized by an increase in BW from 28 d until 35 d of age (P = 0.015). Moreover, female progeny of RP breeders showed a reduced FCR (P = 0.025), whereas male progeny showed a tendency (P = 0.052) towards a lower FCR at 5 wk of age. In conclusion, lowering dietary CP levels in rearing and laying phase of breeders had a negative effect on breeder performance but enhanced live performance of the offspring.