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

ProjectDeliverablesInstitutionsStart DateEnd DateStatus
Mobility and hoof health data pipelineData 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, 2020Dec. 1, 2023Ongoing
Improved feed efficiency, sustainability and profitability through genetic selectionGenomic 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, 2019March 31, 2024Ongoing
Genomic prediction for Johne's disease resistance via data pipeline for milk ELISA testsGenetic evaluation for resistance to Johne's disease in U.S. HolsteinsCDCB

USDA AGIL
May 1, 2019Dec 31, 2023Ongoing
Framework for predicting milk, fat and protein yields from heterogeneous data sourcesUpdated 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, 2020Dec. 31, 2024Ongoing
Adoption of single-step genomic methodologyCDCB genetic and genomic selection evaluations via Single Step GBLUP methodologyCDCB

USDA AGIL

University of Georgia
May 23, 2022Sept 2, 2024Ongoing

Mobility and Hoof Health Data Pipeline

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.

Develop, implement and maintain a hoof health data pipeline that will:

  1. Contribute phenotypes to create a U.S. genetic evaluation for hoof health traits
  2. Facilitate hoof health management tools for dairy cattle
  3. Support research and development in hoof health and locomotion
  1. Data pipeline that captures mobility and hoof health phenotypes
  2. Mobility and hoof health genetic evaluation(s)
  3. 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.

  1. Increase the reliability of genomic evaluations for RFI and Feed Saved
  2. Develop a feed intake index that uses sensors to predict feed intake on individual cows
  3. Initiate a long-term program to update genomic predictions
  4. Determine if genomic predictions of feed efficiency can decrease methane emissions
  1. Genomic evaluation for feed efficiency in U.S. Holsteins
  2. 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).

  1. Characterize milk ELISA scores for MAP collected through the Dairy Herd Improvement (DHI) system
  2. Estimate the genetic and environmental effects on ELISA milk scores for MAP
  3. Estimate the impact of MAP infection on milk and fitness traits
  4. Estimate bull breeding values for MAP resistance
  5. 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.

  1. 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.
  2. 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)
  1. Updated milk yield and component trait predictions
  2. 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)