Can FT-Mid-Infrared Spectroscopy of Milk Samples Discriminate Different Dietary Regimens of Sheep Grazing With Restricted Access Time?

Investigation of phenotypic, genetic and genomic background of Milk spectra in Sarda dairy sheep

Aim of this study was to analyse the genetic background of milk Fourier transform infrared (FTIR) spectra in dairy sheep. Individual milk FTIR spectra, with 1060 wavenumbers each, were available for 793 adult Sarda breed ewes genotyped at 45,813 SNP. The absorbance values of each wavenumber was analysed using a linear mixed model that included dim class, parity and lambing month as fixed effects and flock-test date and animal as random effects. The model was applied to estimate variance components and heritability and to perform a genome-wide association study for each wavenumber. Average h2 of wavenumbers absorbance was 0.13 ± 0.08, with the largest values observed in the regions associated with the characteristic bonds of carbonylic and methylenic groups of milk fat (h2 = 0.57 at 1724-1728 cm-1; and h2 = 0.34 at 2811-2834 cm-1, respectively). The absorbance values of wavenumbers were moderately correlated with the estimated heritabilities. After the Bonferroni correction, a total of nine markers were found to be significantly associated with 32 different wavenumbers. Of particular interest was the SNP s63269.1, mapped on chromosome 2, that was found to be associated with 27 wavenumbers. Genes previously found to be related to traits of interest (e.g. disease resistance, milk yield and quality, cheese firmness) are located close to the significant SNP. As expected, the heritability estimated for the absorbance of each wavenumbers seems to be associated with the related milk components.

Dairy Sheep Grazing Management and Pasture Botanical Composition Affect Milk Macro and Micro Components: A Methodological Approach to Assess the Main Managerial Factors at Farm Level

Simple Summary

Studies on the management factors that affect milk components at the farm level are important for understanding how to transfer the results from experimental study. Plant phenological stages and partially fresh herbage intakes affect the lactose and milk fatty acid profile. The botanical composition of the grassland partially affects the milk’s phenol content. A few small relationships between plant phenols and milk colour could be of interest to explain the changes in milk colour parameters.

Abstract

The fatty acid profile, vitamins A and E, cholesterol, antioxidant power colour and the phenols profile of Sarda sheep milk from 11 commercial sheep flocks managed under permanent grassland were investigated. In each farm, the structural and managerial data and milk samples were collected during four periods (sampling dates, SD): January, March, May, and July. Data from the milk composition (fat, protein, casein, lactose, and somatic cell count), 68 fatty acids, 7 phenols, 1 total gallocatechin equivalent, ferric reducing antioxidant power, vitamins A and E, cholesterol, degree of antioxidant protection, and the colour (b *, a * and L *) were analyzed by multivariate factorial analysis using a principal component analysis approach. A proc mixed model for repeated measurement to point out the studied factors affecting significant macro and micro milk composition was also used. Only the first five components were detailed in this paper, with approximately 70% of the explained variance detected. PC1 presented the highest positive loadings for milk lactose, de novo FA synthesis and the BH intermediate, whereas OBCFA had negative loadings values. The PC2, LCFA, UFA, MUFA, vitamins E, and DAP showed positive loadings values, while SFA had a negative value. The PC3 showed a high positive loading for total phenols and non-flavonoids. PC4 presented a high positive loading for the milk macro-composition and negative values for n-3 FAs. The PC5 is characterized by high positive loadings for the a * and L * colour parameters whereas negative loadings were detected for the milk flavonoids content. These preliminary results could help to establish future threshold values for the biomarkers in milk sourced from grazing dairy sheep in natural, permanent pasture-based diets.

Use of Milk Infrared Spectral Data as Environmental Covariates in Genomic Prediction Models for Production Traits in Canadian Holstein

The purpose of this study was to provide a procedure for the inclusion of milk spectral information into genomic prediction models. Spectral data were considered a set of covariates, in addition to genomic covariates. Milk yield and somatic cell score were used as traits to investigate. A cross-validation was employed, making a distinction for predicting new individuals' performance under known environments, known individuals' performance under new environments, and new individuals' performance under new environments. We found an advantage of including spectral data as environmental covariates when the genomic predictions had to be extrapolated to new environments. This was valid for both observed and, even more, unobserved families (genotypes). Overall, prediction accuracy was larger for milk yield than somatic cell score. Fourier-transformed infrared spectral data can be used as a source of information for the calculation of the 'environmental coordinates' of a given farm in a given time, extrapolating predictions to new environments. This procedure could serve as an example of integration of genomic and phenomic data. This could help using spectral data for traits that present poor predictability at the phenotypic level, such as disease incidence and behavior traits. The strength of the model is the ability to couple genomic with high-throughput phenomic information.