Estimation of the scope of milk prediction equations for new breeds by selection of representative spectra and comparison of spectral variability

editorial reviewedThe objective of this study was to estimate if the use of milk prediction equations based on mid-infrared (MIR) spectrum could be extended to other breeds than Holstein (HOL). MIR-based equations are most of the time developed on HOL datasets which can prevent their use for other breeds. However, if the milk spectral variability of other breeds is included in the HOL calibration dataset, the spectral extrapolation is limited. We compared spectral variability between Walloon (Southern Belgium) HOL (n = 6,867,134 spectra) and two other Belgian breeds: the Dual-Purpose Blue (DPB, n = 292,106 spectra) and the East Belgian Red and White (EBRW, n = 42,418 spectra), with records from 2007 to 2023. To achieve this purpose, for each breed, we projected the standardized spectra on the first three principal components (PC) estimated from a sample of one million Walloon spectra. Localization index of each spectrum, computed from the scores of the first three PC, were used to select representative spectra: n = 165,507 for HOL, n = 53,561 for DPB and n = 21,752 for EBRW. Finally, to estimate if the spectral variability of the DPB and the EBRW was included in the spectral variability of HOL, we used PC scores to compute the barycenter of selected HOL spectra weighted by their density. We then computed the GH distance of each DPB and EBRW selected spectrum to this HOL barycenter. If the GH distance of the DPB or EBRW spectrum was higher than 3, this spectrum was considered as outside the spectral variability of HOL. It was found that 98.85% of DPB and 99.53% of EBRW selected spectra were included in the spectral variability of HOL. To conclude, the results suggest that milk MIR prediction equations developed on HOL spectra can be applied to DPB and EBRW without spectral extrapolation

A new approach for data processing of Milk Recording data using unsupervised learning

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Development of a monitoring tool for metabolic imbalance issues based on Milk Recording data

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Peut-on utiliser des méthodes d'apprentissages non-supervisés sur des données massives issues du contrôle laitier pour détecter des problèmes de santé?

peer reviewedAmong 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 β-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-β-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

limpca: An R package for the linear modeling of high- dimensional designed data based on ASCA/APCA family of methods

Many modern analytical methods are used to analyze samples issued from an experimental design, for example, in medical, biological, chemical, or agronomic fields. Those methods generate most of the time, highly multivariate data like spectra or images, where the number of variables (descriptor responses) tends to be much larger than the number of experimental units. Therefore, multivariate statistical tools are necessary to identify variables that are consistently affected by experimental factors. In this context, two recent methods combining ANOVA and PCA, namely, ASCA (ANOVA-Simultaneous Component Analysis) and APCA (ANOVA-Principal Component Analysis), were developed. They provide powerful visualization tools for multivariate structures in the space of each effect of the statistical model linked to the experimental design. Their main limitation is that they produce biased estimators of the factor effects when the design of experiment is unbalanced. This article presents the R package limpca (for linear models with principal com- ponent effects analysis) that implements ASCA+ and APCA+, an enhanced version of ASCA and APCA methods based on several principles from the theory of general linear models (GLM). In this paper, the methodology is reviewed, the package structure and functions are presented, and a met- abolomics data set is used to clearly demonstrate the potential of ASCA+ and APCA+ methods to highlight true biomarkers corresponding to effects of interest in unbalanced designs

Assessing animal welfare: Deriving individual welfare phenotypes from existing milk recording data

Animal welfare is an increasing concern in dairy production. Consumers want an ethical production while farmers want to ensure the health of the animals. Animal welfare measurements at the herd level such as the Welfare Quality® (WQ®) Protocol already exist but are time-consuming and costly. Moreover, assessing the overall well-being at the animal level becomes a challenge as herd measures for welfare can not be directly translated to the animal level. Two projects, active in the Walloon Region of Belgium, HappyMoo (Interreg NWE) and ScorWelCow, are trying to define individual welfare scores (IWS) and their prediction from routinely measured milk recording data, including mid-infrared spectral data representing fine milk composition. Data from WQ® Protocol and routine milk recording was collected during the same timeframe in 18 dairy farms with 1386 cows, the majority being genotyped. Two approaches to assess and to predict individual animal welfare were developed. The first approach consisted of two steps: translating the WQ® principles into IWS and predicting these from milk recording data. The variation observed in the first step while regressing WQ® animal measures on WQ® principles was considered representative of the biological variation between cows. IWS prediction Partial Least Square regression for the 4 principles of the welfare quality scores have R2 between 0.65 and 0.77. Moreover, results from this first approach showed a significant welfare assessor effect suggesting that welfare measurements were strongly human interpretation-dependent. This suggested the need for an alternative approach. The second approach directly used milk recording data such as spectral data to cluster cows in different groups, bypassing a priori definition of welfare by WQ®. Those groups were compared to results from the first approach and showed possible discrimination for herds with enhanced WQ® score ( Specificity = 1.00 but Sensitivity = 0.10) thus suggesting further unsupervised analysis. Based on this research, novel individual welfare traits could be developed allowing future genomic selection for improved welfare.HappyMo

Developing new quantitative traits related to animal health status using a holistic Big Data Approach

Among the dairy sector's current concerns, the early detection of animal health disorders is a complex challenge as it includes different diseases. This multidimensionality explains why disease detection is often studied separately and, due to financial and ethical issues, using small-scale datasets. Several studies were conducted in the past using the milk mid-infrared (MIR) spectra, for instance, to detect mastitis, lameness or to quantify the contents of citrate, β-hydroxybutyrate (BHB) or acetone in milk. To solve this issue and the small scale data size, we considered a holistic approach using traits obtained from milk recording to detect animal health disorders: milk yield, the somatic cell count and 27 MIR predictions related to the milk composition and animal health status. From 740,054 records collected from first parity Holstein cows in the Southern part of Belgium, we performed repeated unsupervised learnings. The obtained clustering divided the records into five groups. Significant differences of feature means were found between groups, suggesting that one group was related to mastitis and a second group to metabolic disorders. A validation from 87 milk and blood reference records obtained through the Interreg European project GplusE confirmed this interpretation. Moreover, after using a principal components analysis performed on the used features, it appeared that the first and fourth principal components (PC) were strongly related to the two discovered groups of sick animals. From reference values, the first PC had correlations of -0.68 with blood BHB, -0.70 with blood Non-Esterified Fatty Acids, 0.61 with blood Glucose and -0.46 with milk isocitrate. On the other hand, the fourth PC had correlations of 0.51 with milk N-acetyl-β-D-glucosaminidase and 0.55 with milk lactate dehydrogenase. Those results suggest that the obtained PCs reflect directly main health disorders and could be used to monitor dairy farms on large scale data.HappyMo

Not all roads are barriers: Large mammals use logging roads in a timber concession of south-eastern Cameroon

peer reviewedIn the literature, roads are often considered to be barriers for large vertebrates. In central Africa, the creation of roads and skid trails by logging operations leads to changes in the structure of forest landscapes that could influence wildlife movements. To assess the use of logging roads by six emblematic species of the central African forests, we conducted a camera trap (CT) survey on three types of tracks (secondary roads, skid trails, and elephant paths as control) in a logging concession of south-eastern Cameroon. The relative abundance indices (RAI) of each species derived from the CT data were used in a mixed linear model to test the effects of four factors (type of track; time: day vs. night; gregariousness: alone vs. group; and time after logging: less than one year vs. between one and two years). The results showed no preference for any type of track for gorillas (Gorilla gorilla) and chimpanzees (Pan troglodytes). In contrast, significantly higher RAI on secondary roads were observed for buffalos (Syncerus caffer), forest elephants (Loxodonta cyclotis), and bongos (Tragelaphus eurycerus). For the last two, the difference was only significant if they were detected in the most recently logged areas, at night (for elephants), or alone (for bongos). We could not test for leopard (Panthera pardus) as we captured only two events. Although none of the species appear to avoid roads and skid trails, nor do they perceive them as a barrier, further studies should be conducted to increase sampling efforts over time and space to consider seasonality, vegetation growth after logging, geographical variability, and other anthropogenic influence. However, these first results reveal the importance of closing roads after logging to limit encounters between wildlife and humans and highlight the relevance of characterizing roads (type of road, width, surfacing, and canopy structure over the road) when studying their impact on wildlife.Dans la littérature, les routes sont souvent considérées comme des barrières pour les grands vertébrés. En Afrique centrale, la création de routes et de pistes de débardage par l’exploitation forestière entraîne des changements dans la structure des paysages forestiers qui pourraient influencer les déplacements de la faune. Afin d’évaluer l’utilisation des routes et pistes d’exploitation par six espèces emblématiques des forêts d’Afrique centrale, nous avons installé des pièges photographiques (PP) sur trois types de pistes (routes secondaires, pistes de débardage et pistes d’éléphants comme contrôle) dans une concession forestière du sud-est du Cameroun. Les indices d’abondance relative (RAI) de chaque espèce dérivés des données des PP ont été utilisés dans un modèle linéaire mixte pour tester les effets de quatre facteurs (type de piste; heure: jour vs nuit; grégarité: seul vs groupe; et temps après l’exploitation forestière: moins d’un an vs entre un et deux ans). Les résultats n’ont montré aucune préférence pour un type de piste donné chez les gorilles (Gorilla gorilla) et les chimpanzés (Pan troglodytes). En revanche, des RAI significativement plus élevés sur les routes secondaires ont été observés pour les buffles (Syncerus caffer), les éléphants de forêt (Loxodonta cyclotis) et les bongos (Tragelaphus eurycerus). Pour les deux derniers, la différence n’était significative que s’ils étaient détectés dans les zones les plus récemment exploitées, la nuit (pour les éléphants) ou seuls (pour les bongos). Nous n’avons pas pu tester pour le léopard (Panthera pardus) car nous n’avons enregistré que deux événements pour cette espèce. Bien qu’aucune des espèces ne semble éviter les routes et les pistes de débardage, ni les percevoir comme une barrière, d’autres études devraient être menées pour augmenter l’effort d’échantillonnage dans le temps et l’espace afin de prendre en compte la saisonnalité, la croissance de la végétation après l’exploitation forestière, la variabilité géographique et d’autres influences anthropogéniques. Cependant, ces premiers résultats révèlent l’importance de la fermeture des routes après l’exploitation forestière pour limiter les rencontres entre la faune et l’homme et soulignent la pertinence de caractériser les routes (type de route, largeur, revêtement et structure de la canopée au-dessus de la route) lors de l’étude de leur impact sur la faune