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Between- and within-herd variation in blood and milk biomarkers in Holstein cows in early lactation
- M. A. Krogh, M. Hostens, M. Salavati, C. Grelet, M. T. Sorensen, D. C. Wathes, C. P. Ferris, C. Marchitelli, F. Signorelli, F. Napolitano, F. Becker, T. Larsen, E. Matthews, F. Carter, A. Vanlierde, G. Opsomer, N. Gengler, F. Dehareng, M. A. Crowe, K. L. Ingvartsen, L. Foldager
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Both blood- and milk-based biomarkers have been analysed for decades in research settings, although often only in one herd, and without focus on the variation in the biomarkers that are specifically related to herd or diet. Biomarkers can be used to detect physiological imbalance and disease risk and may have a role in precision livestock farming (PLF). For use in PLF, it is important to quantify normal variation in specific biomarkers and the source of this variation. The objective of this study was to estimate the between- and within-herd variation in a number of blood metabolites (β-hydroxybutyrate (BHB), non-esterified fatty acids, glucose and serum IGF-1), milk metabolites (free glucose, glucose-6-phosphate, urea, isocitrate, BHB and uric acid), milk enzymes (lactate dehydrogenase and N-acetyl-β-D-glucosaminidase (NAGase)) and composite indicators for metabolic imbalances (Physiological Imbalance-index and energy balance), to help facilitate their adoption within PLF. Blood and milk were sampled from 234 Holstein dairy cows from 6 experimental herds, each in a different European country, and offered a total of 10 different diets. Blood was sampled on 2 occasions at approximately 14 days-in-milk (DIM) and 35 DIM. Milk samples were collected twice weekly (in total 2750 samples) from DIM 1 to 50. Multilevel random regression models were used to estimate the variance components and to calculate the intraclass correlations (ICCs). The ICCs for the milk metabolites, when adjusted for parity and DIM at sampling, demonstrated that between 12% (glucose-6-phosphate) and 46% (urea) of the variation in the metabolites’ levels could be associated with the herd-diet combination. Intraclass Correlations related to the herd-diet combination were generally higher for blood metabolites, from 17% (cholesterol) to approximately 46% (BHB and urea). The high ICCs for urea suggest that this biomarker can be used for monitoring on herd level. The low variance within cow for NAGase indicates that few samples would be needed to describe the status and potentially a general reference value could be used. The low ICC for most of the biomarkers and larger within cow variation emphasises that multiple samples would be needed - most likely on the individual cows - for making the biomarkers useful for monitoring. The majority of biomarkers were influenced by parity and DIM which indicate that these should be accounted for if the biomarker should be used for monitoring.
Potential of milk mid-IR spectra to predict metabolic status of cows through blood components and an innovative clustering approach
- C. Grelet, A. Vanlierde, M. Hostens, L. Foldager, M. Salavati, K. L. Ingvartsen, M. Crowe, M. T. Sorensen, E. Froidmont, C. P. Ferris, C. Marchitelli, F. Becker, T. Larsen, F. Carter, GplusE Consortium, F. Dehareng
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Unbalanced metabolic status in the weeks after calving predisposes dairy cows to metabolic and infectious diseases. Blood glucose, IGF-I, non-esterified fatty acids (NEFA) and β-hydroxybutyrate (BHB) are used as indicators of the metabolic status of cows. This work aims to (1) evaluate the potential of milk mid-IR spectra to predict these blood components individually and (2) to evaluate the possibility of predicting the metabolic status of cows based on the clustering of these blood components. Blood samples were collected from 241 Holstein cows on six experimental farms, at days 14 and 35 after calving. Blood samples were analyzed by reference analysis and metabolic status was defined by k-means clustering (k=3) based on the four blood components. Milk mid-IR analyses were undertaken on different instruments and the spectra were harmonized into a common standardized format. Quantitative models predicting blood components were developed using partial least squares regression and discriminant models aiming to differentiate the metabolic status were developed with partial least squares discriminant analysis. Cross-validations were performed for both quantitative and discriminant models using four subsets randomly constituted. Blood glucose, IGF-I, NEFA and BHB were predicted with respective R2 of calibration of 0.55, 0.69, 0.49 and 0.77, and R2 of cross-validation of 0.44, 0.61, 0.39 and 0.70. Although these models were not able to provide precise quantitative values, they allow for screening of individual milk samples for high or low values. The clustering methodology led to the sharing out of the data set into three groups of cows representing healthy, moderately impacted and imbalanced metabolic status. The discriminant models allow to fairly classify the three groups, with a global percentage of correct classification up to 74%. When discriminating the cows with imbalanced metabolic status from cows with healthy and moderately impacted metabolic status, the models were able to distinguish imbalanced group with a global percentage of correct classification up to 92%. The performances were satisfactory considering the variables are not present in milk, and consequently predicted indirectly. This work showed the potential of milk mid-IR analysis to provide new metabolic status indicators based on individual blood components or a combination of these variables into a global status. Models have been developed within a standardized spectral format, and although robustness should preferably be improved with additional data integrating different geographic regions, diets and breeds, they constitute rapid, cost-effective and large-scale tools for management and breeding of dairy cows.
Spatial patterns and seasonality in the epibenthic communities of the Westerschelde (Southern Bight of the North Sea)
- K. Hostens
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- Journal:
- Journal of the Marine Biological Association of the United Kingdom / Volume 80 / Issue 1 / February 2000
- Published online by Cambridge University Press:
- 01 February 2000, pp. 27-36
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Beamtrawl samples were taken monthly between January 1988 and December 1989 in 14 subtidal stations along the longitudinal axis of the Westerschelde Estuary. The estuary harboured high densities of epibenthic species (total average of 2250 ind 1000 m−2). Only 37 fish species were recorded and three mobile macroinvertebrate species were quantified. Seventeen epibenthic species were common, only eight of which were very abundant. An overall dominance of the brown shrimp Crangon crangon was noted. The near absence of anadromous and freshwater species was correlated with the low oxygen concentrations upstream the Dutch–Belgian border. The epibenthic species could be divided into a polyhaline and a mesohaline community along the main salinity–oxygen–turbidity gradients of the estuary. The mesohaline zone was characterized by higher densities and biomasses for shrimps, gobies and flatfish. The spatial structure in the epibenthic community of the polyhaline zone was more influenced by the watermasses from the sea and inflowing channels, and was correlated with the degree of exposure (current velocities and tides). A classification is proposed, based on the season in which the epibenthic species occur in the estuary, and on their life stages and abundances during that period. The monthly data demonstrate that while the eight abundant epibenthic species were generally present throughout the year (summer or winter resident), the majority tended to appear in the Westerschelde at varying times (summer, winter or bimodal) and often remained in large numbers (nine common species) for only a short time. The fact that mainly juveniles were recorded in the higher density-classes, suggests that the Westerschelde Estuary still acts as a nursery area for many epibenthic species.