An estimated 50% of U.S. dairy cows will be affected by lameness during their productive life, resulting in economic losses, poor health, and suboptimal animal welfare. Hoof horn lesions are the second leading cause of lameness. CDCB has initiated a project to provide dairy producers with state-of-the-art knowledge and actionable information to enhance hoof health, promote productivity and enhance dairy sustainability.
Current Research
CDCB and several collaborators have diverse research projects underway, leveraging new methods and information to continue the U.S. legacy of dairy cattle improvement through genetic selection.
Current CDCB Research Collaborations
| Project | Deliverables | Institutions | Start Date | End Date | Status |
|---|---|---|---|---|---|
| Mobility and hoof health data pipeline | Data pipeline that captures mobility and hoof health phenotypes Mobility and hoof health genetic evaluation(s) Hoof health management tools for dairy farms | CDCB University of Minnesota USDA Animal Genomics and Improvement Laboratory (AGIL) | Oct. 1, 2020 | Dec. 1, 2026 | Ongoing |
| Improved feed efficiency, sustainability and profitability through genetic selection | Genomic evaluation for feed efficiency in U.S. Holsteins Dairy management tools | Iowa State University Michigan State University University of Florida University of Wisconsin-Madison USDA AGIL | April 1, 2019 | March 31, 2024 | Complete |
| Genomic prediction for Johne's disease resistance via data pipeline for milk ELISA tests | Genetic evaluation for resistance to Johne's disease in U.S. Holsteins | CDCB USDA AGIL | May 1, 2019 | Dec 31, 2023 | Ongoing |
| Framework for predicting milk, fat and protein yields from heterogeneous data sources | Updated milk yield and component trait predictions Comparison of current milk yield and component prediction methodologies | CDCB USDA AGIL National Dairy Herd Improvement Association | Oct. 10, 2020 | Dec. 31, 2024 | Ongoing |
| Adoption of single-step genomic methodology | CDCB genetic and genomic selection evaluations via Single Step GBLUP methodology | CDCB USDA AGIL University of Georgia | May 23, 2022 | Sept 2, 2024 | Ongoing |
Mobility and Hoof Health Data Pipeline
Develop, implement and maintain a hoof health data pipeline that will:
- Contribute phenotypes to create a U.S. genetic evaluation for hoof health traits
- Facilitate hoof health management tools for dairy cattle
- Support research and development in hoof health and locomotion
- Data pipeline that captures mobility and hoof health phenotypes
- Mobility and hoof health genetic evaluation(s)
- Hoof health management tools for dairy farms
CDCB
University of Minnesota
USDA Animal Genomics and Improvement Laboratory
Dr. Kristen Gaddis (CDCB); Dr. Gerard Cramer (University of Minnesota); Dr. Asha Miles (USDA AGIL); Dr. Duane Norman (CDCB); Dr Nick Wu (CDCB)
Improved Feed Efficiency through Genetic Selection
Enhanced feed efficiency will improve dairy farming profitability and sustainability due to reduced use of feed and land resources while potentially reducing emissions of greenhouse gas per unit of milk. Selection of animals that are genetically superior for feed efficiency requires precise measurements of feed intake and milk output from enough cows to enable genetic merit predictions of reasonable reliability. Data collection is underway at 5 research institutions, acquiring data for feed intake, body weight, and milk yield and composition (via mid-infrared spectral profiles), in 3600 mid-lactation cows over a 5-year period. A subset of cows will be fitted with sensors to monitor body temperature, feeding behavior, and locomotion. Methane emission will be measured in 300 cows.
This project launched in 2019 builds on previous results to implement genetic selection for feed efficiency and address concerns about greenhouse gas emissions. Previously, collaborators from North America and Europe created a data pool of 5,000 cows genotyped and phenotyped for feed intake and related traits. Using this database, researchers showed that dry matter intake (DMI) and residual feed intake (RFI) had sufficient heritability to advance genetic progress for feed efficiency. Data from that study published in Journal of Dairy Science projected the U.S. dairy sector could save $540 million per year with maintained milk production by breeding for more feed-efficient cows.
- Increase the reliability of genomic evaluations for RFI and Feed Saved
- Develop a feed intake index that uses sensors to predict feed intake on individual cows
- Initiate a long-term program to update genomic predictions
- Determine if genomic predictions of feed efficiency can decrease methane emissions
- Genomic evaluation for feed efficiency in U.S. Holsteins
- Dairy management tools
Iowa State University
Michigan State University
University of Florida
University of Wisconsin-Madison
USDA Animal Genomics and Improvement Laboratory
Research support from Foundation for Food and Agriculture Research
Kristen Gaddis, (CDCB); P. Van Raden (USDA AGIL); R. Baldwin (USDA AGIL); M. J. VandeHaar, (MSU); R.J. Tempelman, (MSU); F. Peñagaricano (UW); H.M. White, (UW); K.A. Weigel (UW); J. Santos (UF); J.E. Koltes, (ISU).
Genomic Prediction for Johne’s Disease Resistance via Data Pipeline for Milk ELISA Tests
Genetic selection may be a tool to reduce infections with Mycobacterium avium ssp. paratuberculosis (MAP), which challenges dairy animal health and herd profitability. Expenses to dairy farms include decreased milk yield, early culling, reduced salvage value, and added health and veterinary costs. Johne’s disease is caused by infection with MAP and is characterized clinically by granulomatous inflammation of the small intestine that fatally obstructs nutrient absorption and utilization. Estimates indicate that 68% of U.S. dairy herds are infected and that MAP infection is continuing to proliferate (NAHMS, 2007).
- Characterize milk ELISA scores for MAP collected through the Dairy Herd Improvement (DHI) system
- Estimate the genetic and environmental effects on ELISA milk scores for MAP
- Estimate the impact of MAP infection on milk and fitness traits
- Estimate bull breeding values for MAP resistance
- Determine relationships of breeding values for MAP resistance with other traits
Genetic evaluation for resistance to Johne’s disease in U.S. Holsteins
CDCB
USDA Animal Genomics and Improvement Laboratory
Dr. Kristen Gaddis (CDCB); Dr. Nick Wu (CDCB); Dr. Asha Miles (AGIL-USDA); Dr. Mahesh Neupane (AGIL-USDA); Dr. Duane Norman (CDCB)
Framework for Predicting Milk, Fat and Protein Yields from Heterogeneous Data Sources
Reliable recording of lactation yields is essential for genetic improvement and herd management in dairy cattle. Historically, most cows enrolled in a milk recording program in the US have had milk weights recorded monthly. However, the practices for collecting milk and component data (fat and protein) have changed rapidly toward less labor for recording milk weights and collecting component samples (fat and protein), thereby reducing the cost to the producers. In the current system, a cow is milked two or more times each day during her lactation, but often only a few of those milkings are weighed or sampled.
There exist several methods to estimate milk and component yields that are not measured. The variation in estimates obtained by these methods led to a series of discussions between AGIL, USDA, NDHIA, CDCB, and other stakeholders, motivating the need to update or develop new prediction factors for milk yield to enhance day-to-day dairy management practices in a timely manner. Current factors used to predict milk, fat, and protein yields were developed 30 years ago.
- Develop prediction or projection factors to estimate mature equivalent (ME) and daily milk yield, based on a new set of representative milk sampling and production records collected in commercial and experimental herds in the U.S.
- Review the “Best Prediction” approach for estimating ME and daily milk yield, compared with various linear and non-linear prediction methods (including linear and non-linear regression models and neural network)
- Updated milk yield and component trait predictions
- Comparison of current milk yield and component prediction methodologies
CDCB
USDA Animal Genomics and Improvement Laboratory
National Dairy Herd Information Association
Dr. Nick Wu (CDCB); Dr. Asha Miles (USDA AGIL); Dr. Randy Baldwin (USDA AGIL); Mr. Jay Mattison (NDHIA); Mr. Steve Sievert (NDHIA)
Adoption of Single-Step Genomic Methodology
A high priority for CDCB is the implementation of single-step genomic (ssBLUP) methodology in U.S. genomic evaluations for dairy cattle. Collaboration among CDCB, USDA AGIL and the University of Georgia (UGA) began in January 2019 to assess the advantages and feasibility of implementing single-step methods in the U.S. evaluations, considering both the theoretical improvements and the scalability needed. While ssBLUP has been successfully applied to evaluations for beef cattle and other animals, the major challenge for dairy application is the huge volume of dataset.
Develop genetic/genomic selection using the Single Step GBLUP methodology
CDCB genetic and genomic selection evaluations via Single Step GBLUP methodology
CDCB
USDA Animal Genomics and Improvement Laboratory
University of Georgia
Dr. Nick Wu (CDCB); Dr. Kristen Gaddis (CDCB); Dr. Paul Vanraden (USDA AGIL); Dr. Andrés Lagarra (INRAE); Dr. Daniela Lourenco (UGA)