Impact of milk products on weight and body composition among children and teens

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A research project recently completed under the Dairy Research Cluster 2 found that children and adolescents who consume milk products are more likely to have a lean body type. Dr. Hope Weiler of the University of McGill and her team performed a meta-analysis (a statistical analysis of multiple existing studies) of 17 randomized control trials (RCTs) that included children and teens aged 6-18 years old. This is the first meta-analysis summarizing results from RCTs for the effects of milk and milk product consumption on weight and body composition in children and adolescents.

Their analysis showed that milk and milk-product consumption resulted in an increase in lean mass and a lower gain in percent body fat, concluding that children and adolescents who consume milk and milk products are more likely to achieve a lean body type.

The results provide very high-level evidence to support dairy’s beneficial impact on weight and body composition.

A copy of the published results can be accessed here: https://academic.oup.com/advances/article-abstract/10/2/250/5370011?utm_campaign=511018_20190515__NutriNews_Weight_Children&utm_medium=email&utm_source=Nutri_News-All_Users

Key findings from the Industrial Research Chair in Infectious Diseases of Dairy Cattle

Results from a five-year NSERC Industrial Research Chair on infectious diseases of dairy cattle led by Dr. Herman Barkema, University of Calgary, will help farmers improve the management of dairy animal health to prevent, manage and treat dairy cattle for Johne’s Disease (JD) and mastitis for a more profitable and sustainable dairy sector. The Chair is supported in partnership with dairy sector organizations and Dairy Farmers of Canada.

Mastitis and JD are costly diseases to the dairy sector, impacting animal health and farm profitability. The economic impact of mastitis in Canadian herds is calculated at $665 million[i] per year in Canada and for JD, another $90 million is estimated.

Some key findings image006.png

  • Experiments indicated that each Mycobacterium avium paratuberculosis (MAP)-infected calf infected an average of about 3 non-infected pen mates in a group-housed setting. Also, calves had fecal shedding of MAP in the first months of life, exposing them and others potentially to early infection. Calf-to-calf transmission of Johne’s Disease (JD) needs to be a key area of focus and should be part of future control programs for early identification and evaluation of MAP.
  • Better communications and exchange between a farmer and their veterinarian improved the likelihood of adoption of management practices and control programs by farmers, not only for JD, but for other diseases that can be found in dairy farms.
  • A better understanding of non-aureus staphylococci (NAS) species, the most common group of bacteria isolated from the bovine udder, and other bacteria species in milk production, may ultimately lead to the discovery of bacteriocins with the potential for control of S. aureus mastitis.
  • Identified and tested a method to better record and quantify antimicrobial use – a method that can be applied in future surveillance programs.

Research Chair provides opportunity to hire a new scientist in the dairy area

Dr. Eduardo Cobo was recruited for the position of assistant professor at the University of Calgary as a result of this Chair. He is a veterinary immunologist and studies alternatives to antimicrobials. Dr. Cobo will be investigating the role of immunology in bovine mastitis, MAP infection, and digital dermatitis.

 

UnknownDr. Herman Barkema is a Professor in Epidemiology of Infectious Diseases at the University of Calgary’s Faculty of Veterinary Medicine and the NSERC Industrial Research Chair in Infectious Diseases of Dairy Cattle, with a joint appointment in the Dept. of Community Health Sciences of the Cumming School of Medicine. He is also a Guest Professor at Ghent Univ. (Belgium) and Foreign Expert at the China Agricultural Univ. in Beijing. Dr. Barkema’s research program focuses on the prevention and control of diseases in cattle herds, including antimicrobial resistance. He has published > 300 scientific manuscripts and has lectured all over the world.

[i]Mahjoob Aghamohammadi, Denis Haine, David F. Kelton, Herman W. BarkemaHenk Hogeveen, Gregory P. Keefe and Simon Dufour. “Herd-Level Mastitis-Associated Costs on Canadian Dairy Farms”. Frontiers in Veterinary Science (May 2018)14;5:100.https://www.ncbi.nlm.nih.gov/pubmed/29868620. 

 

 

 

 

Is “Phenotype King” in the era of genomics?

{The following is an extract from the Canadian Dairy Network’s extension article entitled, “Value of Type Classification in the Era of Genomics” published April 30, 2019. To access the full article, click here:  https://www.cdn.ca/document.php?id=524.}

CowsEating_2017.jpgIt has been said by many researchers around the world that “Phenotype is King!” in this era of genomics. What does this really mean? In Canada, there has been a recent surge of discussion on this topic, especially as it relates to the value of type classification data.  The Canadian Dairy Network took a closer look at the key questions being asked by breeders to help clarify the value of genotypes versus phenotypes (i.e.: performance data) in today’s environment of dairy cattle selection.

…If a bull dam’s classification data has such a minor impact on the accuracy of their son’s genomic evaluation, why is type classification important at all?  Why do researchers claim that “Phenotype is King!”? 

This question can be answered in two ways.

First, in a general way, the accuracy of any genomic evaluation system is dependent upon the continued collection of good quality data (i.e.: phenotypes) on an ongoing basis.  Even once a genomic evaluation system is built and established, such phenotypic data is required year after year to keep the genomic predictions relevant.

 Secondly, the reason to collect phenotypes is more specific to each breeder at their herd level.  Every heifer calf born on a farm starts with a Parent Average as the first estimate of its genetic potential.  This estimate of an animal’s genetic merit serves as a predictor of those that are expected to have the highest level of performance in the milking herd.  After birth, there are two ways to improve the accuracy of this first estimate.  By genomic testing a heifer calf, the Parent Average (PA) can be replaced by its Genomic Parent Average (GPA).  Later in life, however, measuring each animal’s own performance also contributes to their estimate of genetic merit, whether they have been genotyped or not.  As an example, classifying all first lactation animals in your herd results in changes to their Conformation index as they go from being a Parent Average (PA) to an Estimated Breeding Value (EBV).  The figure below shows the distribution of changes that occur for Conformation when a heifer PA becomes an EBV after being classified in the first lactation.  Half of all heifers change by at least 1 point up or down once they are classified with some changing as much as ±8 points for their Conformation genetic evaluation. Classifying cows in your own herd will re-rank your cows and cow families, which can have a significant impact on your heifer replacement and culling decisions.

Distribution of the Change in Conformation Index by Adding an Animal’s Own Classification (Without Genomics)

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As expected, if heifer calves are genotyped and the PA is replaced by a GPA, then adding their classification phenotype in the first lactation has less impact than the distribution shown in the Figure.  That said, there are still 20% of heifers that experience a change in their Conformation index that is 2 points or more.

Genomics has changed so many things associated with dairy cattle selection schemes. Genetics offered by A.I. companies through their genomic young sires has reached incredible heights resulting in (a) a focus on reduced generation intervals, (b) doubling of the semen market share occupied by young bulls, and (c) more than doubling the annual rate of genetic progress.  These significant changes have also led to less complete information being available on young sire pedigrees compared to a decade ago, especially the performance data on bull dams. While this trend is undesirable from the perspective of pedigree completeness for the resulting daughters, the impact on the accuracy of selection decision is minor.  On the other hand, herd owners must realize the benefits and value of a continued collection of performance data, such as production and classification, for their milking herd.  Such data serves to validate and/or improve the genetic evaluation predictions used to make important selection and mating decisions.