Grazing season and forage type influence goat milk composition and rennet coagulation properties

Two different types of pasture (cultivated and rangeland) and 2 different hay qualities (high and low quality) were examined for their effects on goat milk composition and rennet coagulation properties. Furthermore, the effect of dietary treatments in both the early and late grazing season was studied. As lactation stage is known to influence milk composition, the goats in the early and late grazing season were in the same lactation stage at the start of the experiment. The milk composition was influenced both by dietary treatment and season. Milk from goats on pasture was superior to those on hay by containing a higher content of protein and casein, and the goats on cultivated pasture had the highest milk yield. Casein composition was significantly influenced by forage treatment. Goats grazing on cultivated pasture had higher contents of αs1-casein and also of κ-casein compared with the other treatments, whereas goats grazing on rangeland had the highest content of β-casein. Factors such as milk yield, casein micelle size, αs2-casein, and calcium content were reduced in late compared with early season. More favorable rennet coagulation properties were achieved in milk from the early grazing season, with shorter firming time and higher curd firmness compared with milk from the late grazing season, but the firming time and curd firmness were not prominently influenced by forage treatment. The content of αs2-casein and calcium in the milk affected the firming time and the curd firmness positively. The influence of season and forage treatment on especially milk yield, casein content, and rennet coagulation properties is of economic importance for both the dairy industry and goat milk farmers

Casein SNP in Norwegian goats: additive and dominance effects on milk composition and quality

<p>Abstract</p> <p>Background</p> <p>The four casein proteins in goat milk are encoded by four closely linked casein loci (<it>CSN1S1</it>, <it>CSN2</it>, <it>CSN1S2 </it>and <it>CSN3</it>) within 250 kb on caprine chromosome 6. A deletion in exon 12 of <it>CSN1S1</it>, so far reported only in Norwegian goats, has been found at high frequency (0.73). Such a high frequency is difficult to explain because the national breeding goal selects against the variant's effect.</p> <p>Methods</p> <p>In this study, 575 goats were genotyped for 38 Single Nucleotide Polymorphisms (SNP) located within the four casein genes. Milk production records of these goats were obtained from the Norwegian Dairy Goat Control. Test-day mixed models with additive and dominance fixed effects of single SNP were fitted in a model including polygenic effects.</p> <p>Results</p> <p>Significant additive effects of single SNP within <it>CSN1S1 </it>and <it>CSN3 </it>were found for fat % and protein %, milk yield and milk taste. The allele with the deletion showed additive and dominance effects on protein % and fat %, and overdominance effects on milk quantity (kg) and lactose %. At its current frequency, the observed dominance (overdominance) effects of the deletion allele reduced its substitution effect (and additive genetic variance available for selection) in the population substantially.</p> <p>Conclusions</p> <p>The selection pressure of conventional breeding on the allele with the deletion is limited due to the observed dominance (overdominance) effects. Inclusion of molecular information in the national breeding scheme will reduce the frequency of this deletion in the population.</p

A fast Newton–Raphson based iterative algorithm for large scale optimal contribution selection

International audienceAbstractBackgroundThe management of genetic variation in a breeding scheme relies very much on the control of the average relationship between selected parents. Optimum contribution selection is a method that seeks the optimum way to select for genetic improvement while controlling the rate of inbreeding.MethodsA novel iterative algorithm, Gencont2, for calculating optimum genetic contributions was developed. It was validated by comparing it with a previous program, Gencont, on three datasets that were obtained from practical breeding programs in three species (cattle, pig and sheep). The number of selection candidates was 2929, 3907 and 6875 for the pig, cattle and sheep datasets, respectively.ResultsIn most cases, both algorithms selected the same candidates and led to very similar results with respect to genetic gain for the cattle and pig datasets. In cases, where the number of animals to select varied, the contributions of the additional selected candidates ranged from 0.006 to 0.08 %. The correlations between assigned contributions were very close to 1 in all cases; however, the iterative algorithm decreased the computation time considerably by 90 to 93 % (13 to 22 times faster) compared to Gencont. For the sheep dataset, only results from the iterative algorithm are reported because Gencont could not handle a large number of selection candidates.ConclusionsThus, the new iterative algorithm provides an interesting alternative for the practical implementation of optimal contribution selection on a large scale in order to manage inbreeding and increase the sustainability of animal breeding programs