The use of mid-infrared spectrometry to estimate the ration composition of lactating dairy cows

The composition of cow milk is strongly affected by the feeding regimen. Because milk components are routinely determined using mid-infrared (MIR) spectrometry, MIR spectra could also be used to estimate an animal’s ration composition. The objective of this study was to determine whether and how well amounts of dry matter intake and the proportions of concentrates, hay, grass silage, maize silage, and pasture in the total ration can be estimated using MIR spectra at an individual animal level. A total of 10,200 milk samples and sets of feed intake data were collected from 90 dairy cows at 2 experimental farms of the Agricultural Research and Education Centre in Raumberg-Gumpenstein, Austria. For each run of analysis, the data set was split into a calibration and a validation data set in a 40:60 ratio. Estimated ration compositions were calculated using a partial least squares regression and then compared with the respective observed ration compositions. In separate analyses, the factors milk yield and concentrate intake were included as additional predictors. To evaluate accuracy, the coefficient of determination (R2) and ratio to performance deviation were used. The highest R2 values (for kg of dry matter intake/ for % of ration) for the individual feedstuffs were as follows: pasture, 0.63/0.66; grass silage, 0.32/0.43; concentrate intake, 0.39/0.34; maize silage, 0.32/0.33; and hay, 0.15/0.16. Estimation of groups of feedstuffs (forages, energy-dense feedstuffs) mostly resulted in R2 values >0.50. Including the parameters milk yield or concentrate intake improved R2 values by up to 0.21, with an average improvement of 0.04. The results of this study indicate that not all ration components may be estimated equally accurately. Even if some estimates are good on average, there may be strong deviations between estimated and observed values in individual data sets, and therefore individual estimates should not be overemphasized. Further research including pooled samples (e.g., bulk milk, farm samples) or variations in ration composition is called for

Potenzial der Mid-Infrarot-Spektrometrie bei Kuhmilchproben zur Abschätzung der Rationszusammensetzung

Milk composition of dairy animals is influenced by the composition of the ration fed. The objective of this study was to determine if the percentages and absolute amounts of hay, grass silage, pasture, maize silage and concentrate in the feed ration can be estimated using MIR spectrometry of milk. A total of 10200 milk samples from 90 dairy cows were collected, and the intakes of all ration components were measured. Using partial least squares regression (PLS), equations were developed to estimate ration compositions corresponding to each milk sample. To evaluate accuracy, the correlation between observed and estimated values (R) and ratio to performance (RPD) were used. Notable R values (for kg/for %) were observed for the ration proportion of pasture (0.85/0.87), maize silage (0.74/0.75) and concentrate intake (0.75/0.73). Estimation of groups of feedstuffs (all forages, energy-dense feedstuffs) resulted in R values of >0.8. Including the parameters milk yield and/or concentrate intake into PLS improved R values by up to 0.08. The results indicate a potential use of MIR spectra as a promising predictor for ration composition of dairy cows

Inbreeding, Microsatellite Heterozygosity, and Morphological Traits in Lipizzan Horses

While the negative effects of inbreeding and reduced heterozygosity on fecundity and survival are well established, only a few investigations have been carried out concerning their influence on morphological traits. This topic is of particular interest for a small and closed population such as the Lipizzan horse. Thus, 27 morphological traits were measured in 360 Lipizzan mares and were regressed on the individual inbreeding coefficients, as well as on the individual heterozygosity and mean squared distances (mean d2) between microsatellite alleles within an individual. Both individual heterozygosity and mean d2 were based on 17 microsatellite loci dispersed over 14 chromosomes. The results obtained by multivariate analysis reveal significant effects of stud (P <.0001), age at measurement (P <.0001), and mean d2 (P =.0143). In univariate analyses, significant associations were obtained between length of pastern-hindlimbs and inbreeding coefficient (P <.01), length of cannons-hindlimb and mean d2 (P <.01), and length of neck and mean d2 (P <.001). After adjustment of single-test P values for multiple tests (Hochberg's step-up Bonferroni method), only the association of the length of neck and mean d2 remained significant (P =.0213). Thus, no overall large effects of inbreeding, microsatellite heterozygosity, and mean d2 on morphological traits were observed in the Lipizzan hors

Inbreeding, Microsatellite Heterozygosity, and Morphological Traits in Lipizzan Horses

While the negative effects of inbreeding and reduced heterozygosity on fecundity and survival are well established, only a few investigations have been carried out concerning their influence on morphological traits. This topic is of particular interest for a small and closed population such as the Lipizzan horse. Thus, 27 morphological traits were measured in 360 Lipizzan mares and were regressed on the individual inbreeding coefficients, as well as on the individual heterozygosity and mean squared distances (mean d2) between microsatellite alleles within an individual. Both individual heterozygosity and mean d2 were based on 17 microsatellite loci dispersed over 14 chromosomes. The results obtained by multivariate analysis reveal significant effects of stud (P <.0001), age at measurement (P <.0001), and mean d2 (P =.0143). In univariate analyses, significant associations were obtained between length of pastern-hindlimbs and inbreeding coefficient (P <.01), length of cannons-hindlimb and mean d2 (P <.01), and length of neck and mean d2 (P <.001). After adjustment of single-test P values for multiple tests (Hochberg's step-up Bonferroni method), only the association of the length of neck and mean d2 remained significant (P =.0213). Thus, no overall large effects of inbreeding, microsatellite heterozygosity, and mean d2 on morphological traits were observed in the Lipizzan horse

Editorial: Why livestock genomics for developing countries offers opportunities for success.

Universidad Nacional Agraria La Molina. Escuela de Posgrado. Maestría en Producción AnimalThe Research Topic yielded 23 articles that are either review (five papers) or original research articles (18 papers) covering major livestock species kept in developing countries including cattle (seven papers), sheep (five papers), goats (three papers), and chickens (three papers). The manuscripts cover a broad range of genomic applications such as genomic selection/assisted breeding, genome-wide association analysis, diversity studies with a particular emphasis on adaptive genetic variation and signatures of selection analysis, and some elements of functional genomics using RNA sequencing and differential gene expression profiling. Whilst a broad range of genomic applications are covered, there is a bias toward genomic diversity studies, indicating the limited utility of other genomic applications due to inherent limitations to data collection and funding that characterize most developing countries, and are highlighted in some of the review article

Genomic analysis for managing small and endangered populations: a case study in Tyrol Grey cattle.

Analysis of genomic data is increasingly becoming part of the livestock industry. Therefore, the routine collection of genomic information would be an invaluable resource for effective management of breeding programs in small, endangered populations. The objective of the paper was to demonstrate how genomic data could be used to analyse (1) linkage disequlibrium (LD), LD decay and the effective population size (NeLD); (2) Inbreeding level and effective population size (NeROH) based on runs of homozygosity (ROH); (3) Prediction of genomic breeding values (GEBV) using small within-breed and genomic information from other breeds. The Tyrol Grey population was used as an example, with the goal to highlight the potential of genomic analyses for small breeds. In addition to our own results we discuss additional use of genomics to assess relatedness, admixture proportions, and inheritance of harmful variants. The example data set consisted of 218 Tyrol Grey bull genotypes, which were all available AI bulls in the population. After standard quality control restrictions 34,581 SNPs remained for the analysis. A separate quality control was applied to determine ROH levels based on Illumina GenCall and Illumina GenTrain scores, resulting into 211 bulls and 33,604 SNPs. LD was computed as the squared correlation coefficient between SNPs within a 10 mega base pair (Mb) region. ROHs were derived based on regions covering at least 4, 8, and 16 Mb, suggesting that animals had common ancestors approximately 12, 6, and 3 generations ago, respectively. The corresponding mean inbreeding coefficients (F ROH) were 4.0% for 4 Mb, 2.9% for 8 Mb and 1.6% for 16 Mb runs. With an average generation interval of 5.66 years, estimated NeROH was 125 (NeROH>16 Mb), 186 (NeROH>8 Mb) and 370 (NeROH>4 Mb) indicating strict avoidance of close inbreeding in the population. The LD was used as an alternative method to infer the population history and the Ne. The results show a continuous decrease in NeLD, to 780, 120, and 80 for 100, 10, and 5 generations ago, respectively. Genomic selection was developed for and is working well in large breeds. The same methodology was applied in Tyrol Grey cattle, using different reference populations. Contrary to the expectations, the accuracy of GEBVs with very small within breed reference populations were very high, between 0.13-0.91 and 0.12-0.63, when estimated breeding values and deregressed breeding values were used as pseudo-phenotypes, respectively. Subsequent analyses confirmed the high accuracies being a consequence of low reliabilities of pseudo-phenotypes in the validation set, thus being heavily influenced by parent averages. Multi-breed and across breed reference sets gave inconsistent and lower accuracies. Genomic information may have a crucial role in management of small breeds, even if its primary usage differs from that of large breeds. It allows to assess relatedness between individuals, trends in inbreeding and to take decisions accordingly. These decisions would be based on the real genome architecture, rather than conventional pedigree information, which can be missing or incomplete. We strongly suggest the routine genotyping of all individuals that belong to a small breed in order to facilitate the effective management of endangered livestock populations.Article 173