Monthly Archives: September 2021

Alberta Innovates announces funding for Gentec’s “Arm-Chair Rancher” project

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Livestock Gentec, partnered with the University of Alberta and Beefbooster, was awarded $481,000 to develop an app to harness on-farm and industry data and use machine learning to generate guiding scenarios and predictions for optimal herd and farm management. Funding was awarded through Alberta Innovates “Smart Agriculture and Food Digitization and Automation Challenge”.

Read the full announcement here.

Improvement of Feed Efficiency, Carcass Traits, Fertility and Profitability in Commercial Beef Cattle

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Acceleration of genetic improvement of Canadian seedstock though increased use of genomic technologies and development of multi-trait indexes that perform in commercial crossbred cattle

Genomic tools can help the beef industry address challenges in global competitiveness, production efficiency, and sustainability. This project bought together international leaders in beef genomics to leverage vast amounts of genomic data and deliver commercial value to producers. The first ‘made in Canada’ genomic tool was developed to assess hybrid vigour (degree of cross-breeding). High hybrid resulted in a net return of $160/cow/year and was associated with improved fertility, stayability, feed efficiency and health resilience. Additionally, two multi-trait indices are being developed for commercial crossbred cattle in Alberta. 1) Feeder Profit Index to improve growth, feed efficiency, carcass quality and profitability in feeder cattle and 2) Replacement Heifer Profit Index to improve hybrid vigour, feed efficiency, fertility and lifetime return for cows in the herd.

Download the full project summary here.

For more information on this project or genomic testing and indexes, please contact Livestock Gentec:

Phone: 780.248.1740
lsgentec@ualberta.ca

Institution: Alberta Agriculture and Forestry

Primary Investigator: John Basarab

Co-Primary Investigator: Donagh Berry (AGRIC), John Crowley (CBBC), Changxi Li (AAFC)

Term: 2015 - 2020

Funding: $849,251 from Genome Alberta

Genetic Analyses of Feed Intake, Feed Efficiency, Female Fertility, and Cow Lifetime Productivity in Beef Cattle Raised in Two Environments

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Optimization of a multiple trait selection index for replacement heifers to reduce production costs and increase sustainability of beef production

Feed efficiency, feed intake, production performance, and fertility are major determinants of sustainable beef production. Understanding genetic correlations among these traits is crucial for optimizing multiple trait selection indices that improve calf crop percentage and sustainability. This project aims to develop a genomic selection tool for improved feed efficiency while maintaining or improving heifer/cow reproductive performance. An accurate and reliable multi-trait selection index for heifers and cows will improve feed efficiency and sustainability, as well as profitability and competitiveness. Production of more efficient cows with improved performance will reduce production cost and carbon intensity.

Download the full project summary here.

For more information on this project or genomic testing and indexes, please contact Livestock Gentec:

Phone: 780.248.1740
lsgentec@ualberta.ca

Institution: University of Alberta and Agriculture and Agri-Food Canada

Primary Investigator: John Basarab (UAlberta

Co-Primary Investigator: Changxi Li (AAFC)

Term: 2018 - 2023

Funding: $1,047,314 from BCRC

Development of a Functional Genomic Prediction Platform for Industry Application

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Development of a platform to increase genomic prediction accuracy and promote the use of genomic tools in commercial cattle producers

Genetic improvement of beef production efficiency and carcass quality is a key strategy to enhance national and international competitiveness and sustainability of beef production. However, the rate of genetic improvement using traditional phenotype and/or pedigree based genetic evaluation and selection has been slow for important beef performance traits that are difficult/expensive to measure, such as feed efficiency. In recent years, researchers at Livestock Gentec (AAFC, AAF, UAlberta) developed a number of genomic prediction tools for commercial producers who do not have access to herd improvement tools from a breed association and who want to select the best replacement animals from their own herd. This project aims to refine those genomic tools and to improve prediction accuracy for multiple beef breeds. The genomic prediction platform with improved accuracy will help service providers to deliver genomic decision support tools to their customers, which will allow the beef industry to improve beef production efficiency and quality via selection and management of genetics in their herd.

Download the full project summary here.

This project has led to the development of a Genomic-Enhanced Whole Herd Genetic Management Platform that is now ready for demonstration in the beef industry. For more information or to participate in the new project contact Michael Vinsky:

michael.vinsky@ualberta.ca
https://www.beefgenomicprediction.ca/

Institution: Agriculture and Agri-Food Canada

Primary Investigator: Changxi Li

Co-Primary Investigator: John Basarab (UAlberta)

Term: 2019 - 2021

Funding: $230,328 from Genome Alberta

Development and Deployment of a Computation Tool for Efficient Whole-Genome Sequence Association and Prediction Analysis

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A more statistically powerful and computationally efficient tool is needed to improve the efficiency and accuracy of whole-genome sequence analysis and prediction 

A vast amount of genetic information has been generated from the 1000 bull genome project across 171 cattle breeds. This information can potentially be used to facilitate the discovery of causal mutations and to greatly improve the accuracy of genomic prediction for economically important traits in beef cattle.  Recently, Livestock Gentec has imputed its legacy genotypes on about 25,000 beef individuals to whole-genome sequence data. This project aims to develop a powerful and efficient computing algorithm for whole-genome sequence association and prediction analyses. Successful development of this tool will provide the Alberta beef industry and research institutions with a powerful tool for fast integration of sequence information into genomic research and applications.

Download the full project summary here.

For more information on this project contact Livestock Gentec:

Phone: (780) 248-1740
lsgentec@ualberta.ca

Institution: University of Alberta

Primary Investigator: Graham Plastow

Term: 2020 - 2021

Funding: $120,000 from Genome Alberta

Remote Monitoring of Cattle Performance: A Path Forward to Long Term Sustainability

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Multispectral cameras may improve remote monitoring of cattle and measurement of performance traits in both drylot and extensively managed cattle herds

Feed intake, growth, carcass yield and fatness, methane production and cattle behaviour can be measured by a range of technologies to identify the best animals for breeding or production, or to identify those animals which are sick and require treatment. Currently this requires specialist equipment that are relatively invasive and require significant handling and labour. A new generation of monitoring technologies are based on imaging. Imaging systems offer a number of potential advantages: reduced labour, increased accuracy of measurement or prediction, new phenotypes, and improved animal welfare. This project aims to validate the remote monitoring of cattle using multispectral cameras to determine health, growth and production efficiency. Successful remote monitoring and collection of data on cattle will support the competitiveness and development of precision beef production in Alberta.

Download the full project summary here.

For more information on this project contact Livestock Gentec:

Phone: (780) 248-1740
lsgentec@ualberta.ca

Institution: University of Alberta

Primary Investigator: Graham Plastow

Term: 2021 - 2023

Funding: $196,000 from RDAR

Development and Demonstration of a Genomic-Enhanced Whole Herd Genetic Management Platform to Improve Beef Production Efficiency and Quality

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A platform to aid producers in herd genetic management will increase genomic tool adoption and improve beef production efficiency and quality

Constant improvement of beef production efficiency and quality is essential to enhance the competitiveness of the beef industry. A key strategy to improve beef production efficiency and quality is to manage the genetics of the whole cattle herd to achieve optimal beef production performance. This project aims to refine and demonstrate the Genomics Whole Herd Management Platform to the beef industry. The platform will allow beef producers to easily access information on the genomic profile of their herd including status or ranking of genetic merit for production traits and hybrid vigour. Based on the genomic profile, producers will be able to select breeding stock (sire and dam) that will optimize genetic gain and improve efficiency and profitability.

Download the full project summary here.

For more information or to participate in the project contact Michael Vinsky:

michael.vinsky@ualberta.ca
https://www.beefgenomicprediction.ca/

Institution: Agriculture and Agri-Food Canada

Primary Investigator: Changxi Li

Co-Primary Investigator: John Basarab (UAlberta)

Term: 2021 - 2024

Funding: $318,900 from BCRC

Demonstrating the Impact of Genomics-Enhanced Whole Herd Genetic Management Platform on Reducing Beef GHG Emissions

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Reducing beef cattle greenhouse gas emissions can be achieved through an effective and accessible genomic selection and whole herd genetic management program

More efficient cattle consume less feed and produce less GHG emissions than inefficient cattle. Additionally, cattle with enhanced retained hybrid vigour (more cross-breeding) have improved reproductive performance and reduced GHG emissions. Beef cattle GHG emissions can be reduced through genetic selection, but the industry lacks effective and science based tools to select and breed more efficient cattle with maximum hybrid vigour. This project aims to demonstrate the genomics enhanced whole herd management platform to the beef industry. The adoption of this tool can help the beef industry make genomic decisions for their herd more easily and contribute towards improved efficiency and sustainability.

Download the full project summary here.

For more information or to participate in the project contact Michael Vinsky:

michael.vinsky@ualberta.ca
https://www.beefgenomicprediction.ca/

Institution: Agriculture and Agri-Food Canada

Primary Investigator: Changxi Li

Co-Primary Investigator: John Basarab (UAlberta), Graham Plastow (UAlberta)

Term: 2021 - 2024

Funding: $487,370 Emissions Reduction Alberta (ERA)