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      Factors associated with milk processing characteristics predicted by mid-infrared spectroscopy in a large database of dairy cows

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      Journal of Dairy Science
      American Dairy Science Association

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          Invited review: milk protein polymorphisms in cattle: effect on animal breeding and human nutrition.

          The 6 main milk proteins in cattle are encoded by highly polymorphic genes characterized by several nonsynonymous and synonymous mutations, with up to 47 protein variants identified. Such an extensive variation was used for linkage analysis with the description of the casein cluster more than 30 yr ago and has been applied to animal breeding for several years. Casein haplotype effects on productive traits have been investigated considering information on the whole casein complex. Moreover, mutations within the noncoding sequences have been shown to affect the specific protein expression and, as a consequence, milk composition and cheesemaking. Milk protein variants are also a useful tool for breed characterization, diversity, and phylogenetic studies. In addition, they are involved in various aspects of human nutrition. First, the occurrence of alleles associated with a reduced content of different caseins might be exploited for the production of milk with particular nutritional qualities; that is, hypoallergenic milk. On the other hand, the frequency of these alleles can be decreased by selection of sires using simple DNA tests, thereby increasing the casein content in milk used for cheesemaking. Furthermore, the biological activity of peptides released from milk protein digestion can be affected by amino acid exchanges or deletions resulting from gene mutations. Finally, the gene-culture coevolution between cattle milk protein genes and human lactase genes, which has been recently highlighted, is impressive proof of the nonrandom occurrence of milk protein genetic variation over the centuries.
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            Invited review: Mid-infrared spectroscopy as phenotyping tool for milk traits.

            Interest in methods that routinely and accurately measure and predict animal characteristics is growing in importance, both for quality characterization of livestock products and for genetic purposes. Mid-infrared spectroscopy (MIRS) is a rapid and cost-effective tool for recording phenotypes at the population level. Mid-infrared spectroscopy is based on crossing matter by electromagnetic radiation and on the subsequent measure of energy absorption, and it is commonly used to determine traditional milk quality traits in official milk laboratories. The aim of this review was to focus on the use of MIRS to predict new milk phenotypes of economic relevance such as fatty acid and protein composition, coagulation properties, acidity, mineral composition, ketone bodies, body energy status, and methane emissions. Analysis of the literature demonstrated the feasibility of MIRS to predict these traits, with different accuracies and with margins of improvement of prediction equations. In general, the reviewed papers underlined the influence of data variability, reference method, and unit of measurement on the development of robust models. A crucial point in favor of the application of MIRS is to stimulate the exchange of data among countries to develop equations that take into account the biological variability of the studied traits under different conditions. Due to the large variability of reference methods used for MIRS calibration, it is essential to standardize the methods used within and across countries.
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              Economic merit of crossbred and purebred US dairy cattle.

              Heterosis and breed differences were estimated for milk yield traits, somatic cell score (SCS), and productive life (PL), a measure of longevity. Yield trait data were from 10,442 crossbreds and 140,421 purebreds born since 1990 in 572 herds. Productive life data were from 41,131 crossbred cows and 726,344 purebreds born from 1960 through 1991. The model for test-day yields and SCS included effects of herd-year-season, age, lactation stage, regression on sire's predicted transmitting ability, additive breed effects, heterosis, and recombination. The model for PL included herd-year-season, breed effects, and general heterosis. All effects were assumed to be additive, but estimates of heterosis were converted to a percentage of the parent breed average for reporting. Estimates of general heterosis were 3.4% for milk yield, 4.4% for fat yield, and 4.1% for protein yield. A coefficient of general recombination was derived for multiple-breed crosses, but recombination effects were not well estimated and small gains, not losses, were observed for yield traits in later generations. Heterosis for SCS was not significant. Estimated heterosis for PL was 1.2% of mean productive life and remained constant across the range of birth years. Protein yield of Brown Swiss x Holstein crossbreds (0.94 kg/d) equaled protein yield of purebred Holsteins. Fat yields of Jersey x Holstein and Brown Swiss x Holstein crossbreds (1.14 and 1.13 kg/d, respectively) slightly exceeded that of Holsteins (1.12 kg/d). With cheese yield pricing and with all traits considered, profit from these crosses exceeded that of Holsteins for matings at breed bases. For elite matings, Holsteins were favored because the range of evaluations is smaller and genetic progress is slower in breeds other than Holstein, in part because fewer bulls are sampled. A combined national evaluation of data for all breeds and crossbreds may be desirable but would require an extensive programming effort. Animals should receive credit for heterosis when considered as mates for another breed.
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                Author and article information

                Journal
                Journal of Dairy Science
                Journal of Dairy Science
                American Dairy Science Association
                00220302
                April 2017
                April 2017
                : 100
                : 4
                : 3293-3304
                Article
                10.3168/jds.2016-12028
                cfa98bb9-6990-4b44-96dc-5d2fde315e10
                © 2017

                https://www.elsevier.com/tdm/userlicense/1.0/

                http://www.elsevier.com/open-access/userlicense/1.0/

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