Research

CDCB and several collaborating institutions have diverse research projects underway, leveraging new methods and information to continue the U.S. legacy of dairy cattle improvement through genetic selection.

As the steward of the National Cooperator Database, the Council on Dairy Cattle Breeding has a responsibility to provide the U.S. dairy industry with transparent research on economically-important genetic selection traits. Below are current and recent research projects made possible because of the industry-wide collaboration that supports the National Cooperator Database.

CDCB Research and Development Mission: To strengthen the scientific foundation needed to drive dairy cattle improvement using state-of-the-art genetic evaluations and precision management tools.

CDCB Research and Development Vision: To be the leading research center for genetic improvement of dairy cattle in the world.

Current Research

Background:
Hoof disorders resulting in lameness are a considerable expense for dairy producers, including the cost of treatment and indirect expenses from nonsaleable milk, reduced milk production, poor reproductive performance, and deteriorated welfare, which affects the sustainability of the dairy enterprise. A single incidence of a hoof lesion such as a sole ulcer, white line separation, or digital dermatitis can cost between $232 and $622 in direct and indirect costs. While hoof health and mobility can be impacted by many factors, research has shown that genetic selection is an effective strategy to make consistent, cumulative progress in these traits. However, there is no official national evaluation related to hoof health or mobility currently available with the largest hurdle to accomplishing this being the lack of accurate phenotypes.

Timeline:
Beginning November 2021; First stage implementation targeting late 2025

Objectives:

  1. Develop a data pipeline to collect novel phenotypes related to hoof health and mobility
  2. Develop a better understanding of cow mobility, hoof health, and their interaction
  3. Implement a new genetic evaluation that will aid producers in selecting animals with genetically superior hoof health and mobility
  4. Determination of the economic impact of these traits and their incorporation into the lifetime net merit selection index


Deliverables:

  1. Genetic evaluation for hoof health and mobility
  2. Enhanced net merit that includes direct selection for improved hoof health and mobility


Partner Institutions and Investigators:

Council on Dairy Cattle Breeding
Dr. Kristen Parker Gaddis
Dr. Javier Burchard
Dr. John Cole
Dr. Ashley Ling

University of Minnesota
Dr. Gerard Cramer

More information coming soon.

Background:
The global demand for animal products, such as milk and meat, is expected to increase by more than 60% in the coming decades as the global population expands. The expansion of livestock production to meet these demands will exacerbate already rising atmospheric concentrations of greenhouse gases and the dire consequences of climate change. Enteric methane from livestock is the second largest source of methane emissions in the U.S., with dairy cattle accounting for 26% of enteric emissions. Therefore, there is an urgent need to develop cost-effective strategies to reduce methane emissions while meeting consumer demand for high-quality, safe, and affordable food from healthy cows. Our goal is the effective mitigation of enteric methane emissions from dairy cattle. We will first develop a reference population for methane emission traits, and then tackle selective breeding, diet formulation, and microbiome manipulation.

This project will address several challenges and knowledge gaps for reducing methane production in cattle, such as a lack of genetic tools to select against methane emissions directly, limited understanding of commonalities between different dietary interventions, and incomplete characterization of the rumen microbiome, particularly the archaeal community. The long-term goal of this project is to develop an integrated platform to mitigate enteric methane emissions from dairy cattle by combining selective breeding, effective dietary manipulations, and innovative rumen microbiome interventions.

This project is a part of the Greener Cattle Initiative and have received additional funding from CDCB beyond the collaboration.

Timeline:
January 2023 to December 2025

Objectives:

  1. Mitigate methane emissions through genomic selection
  2. Mitigate methane emissions through dietary manipulations
  3. Decipher how the rumen microbiome affects methane emissions


Deliverables:

  1. A foundation for the development of routine genetic evaluations for methane emissions in U.S. dairy cattle
  2. Milk spectra-based solutions: If milk spectra data is identified as a good proxy for methane emissions, high-intensity methane data collection could be completed on a few research farms, and these direct observations combined with correlated low-cost, high-volume milk spectra data on many commercial farms
  3. Identification of feed additives and manipulations that effectively inhibit methane emissions without negative impacts on nutrient utilization and cow performance
  4. Microbial solutions: A better understanding of how the rumen microbiome composition and activity affect methane production


Partner Institutions and Investigators:

Council on Dairy Cattle Breeding
Dr. Kristen Parker Gaddis
Dr. Javier Burchard
Dr. John Cole

University of Wisconsin
Dr. Francisco Peñagaricano
Dr. Heather White

Dr. Hilario Mantovani
Dr. Kent Weigel

Animal Genomics and Improvement Laboratory, ARS, USDA
Dr. Paul VanRaden
Dr. Ransom Baldwin

Dr. Asha Miles

Iowa State University
Dr. James. Koltes
Dr. Ranga Appuhamy

Michigan State University
Dr. Michael VandeHaar
Dr. Rob Tempelman

University of Florida
Dr. José Santos
Dr. Kwang Jeong

Dairy Forage Research Center, ARS, USDA
Dr. Elizabeth French
Dr. Kenneth Kalscheur


Additional Information:
Greener Cattle Initiative

Background:
The current factors used to estimate daily yields of milk, fat, and protein from single milking sampling were developed more than 30 years ago. Following decades of genetic selection and evolving management, especially during the transition period, modern cows are quite different from their ancestors. Key economic decisions are made during the first 120 days in milk and improving early lactation yield projections will benefit dairy herds. The impact of this work includes enhanced genetic selection, but most importantly, an immediate return on investment by providing herd managers with better performance predictions for individual cows to facilitate informed decision-making about their herds.

The lactation factors project will enroll approximately 15 Holstein and Jersey herds milking 2 or 3 times daily from across the major dairy regions of the United States. Participating herds will be sampled weekly at all milkings within a day for 16 weeks and then followed on their usual DHI testing schedule for a period of approximately 8 months after. Participating herds will generate the immense dataset needed to validate the new factors developed and contribute to a number of secondary objectives and future research projects.


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.

Timeline:
The first milk samples were collected for the project in May 2023 and the final samples are projected to be collected in the summer of 2026. Additional time will be required to analyze the data and produce updated lactation factors after the data collection is complete.

Objectives:

  1. Evaluate the projection factors to update daily yield predictions of milk, fat, and protein. This will also continue to improve the 305-AA standardized lactation model that was introduced to the industry in April 2024.
  2. Develop additional management tools using milk fatty acid data.
  3. Establish a framework to move, store, and standardize milk infrared spectra data for future research projects.


Deliverables:

  1. Updated factors for daily yield predictions of milk, fat, and protein.
  2. More accurate daily and 305-day yields for day-to-day management of dairy cattle.


Partner Institutions and Investigators:

Council on Dairy Cattle Breeding
Dr. Malia Caputo
Dr. Nick Wu
Dr. Javier Burchard
Dr. João Dürr
Mr. Chip Donatone

National Dairy Herd Information Association
Mr. Jay Mattison
Mr. Steve Sievert

USDA Animal Genomics and Improvement Laboratory
Dr. Asha Miles
Dr. Ransom Baldwin


Additional Information:
Paper in Frontiers in Genetics
Progressive Dairy Article

Background:
A high priority for CDCB is the implementation of single-step genomic (ssBLUP) methodology in U.S. genomic evaluations for dairy cattle. This priority status is due in part because the current multi-step evaluations don’t use all available data. Phenotypes and pedigrees are preprocessed first, then genomics are added to the blend; this results in some loss of information. In particular, genetic trends tend to be underestimated. Single-step methods use all information available resulting in better estimates of genetic trends, are conceptually easier to understand, and are expected to be more stable from run to run.

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 globally, the major challenge for dairy application in the United States is the huge volume of the dataset. The algebra required for this system is “easy,” but the operations are challenging as it requires a different organization of computations.

Timeline:
Started January 2023. Currently targeting by the end of 2025, a well-tested prototype of the system is complete for evaluation and decisions related to implementation.

Objectives:
Develop genetic/genomic selection tools using the Single Step GBLUP methodology.


Deliverables:
A prototype of a ssBLUP system and the results of the tests.


Partner Institutions and Investigators:

Council on Dairy Cattle Breeding
Dr. Andrés Lagarra
Dr. Nick Wu
Dr. Kristen Gaddis

Dr. John B. Cole

University of Georgia
Joe Tabet (PhD Student)
Dr. Daniela Lourenco
Dr. Matias Bermann
Dr. Ignacy Misztal

USDA Animal Genomics and Improvement Laboratory
Dr. Paul VanRaden


Background:
McWhorter et al. (2023) reported that selection for general (overall) genetic merit without consideration of heat tolerance may result in less-favorable performance in hot and humid climates. PTA for milk, fat, and protein production can be computed for general performance, as well as performance under heat stress, and these values can be combined into a single PTA for a specific heat load. This allows us to compute heat stress evaluations which better account for genotype-by-environment interactions.

Timeline:
Research ongoing with December 31, 2026 as the target completion date.

Objectives:

  1. Develop a genomic evaluation for heat tolerance in dairy cattle
  2. Update estimates of heat stress effects and production losses
  3. Determine the best mathematical models to calculate PTA accounting for heat tolerance
  4. Evaluate the strength of genotype-by-environment interactions

Deliverables:

  1. Populate the National Cooperator Database with herd location so that farms can be assigned to the nearest weather-recording station
  2. Genetic evaluations for heat stress for milk, fat, and protein yields for Holsteins and Jerseys


Partner Institutions and Investigators:

Council on Dairy Cattle Breeding
Dr. Andrés Lagarra
Dr. Nick Wu
Dr. Kristen Gaddis

University of Connecticut
Dr. Breno Fragomeni

USDA Animal Genomics and Improvement Laboratory
Dr. Paul VanRaden


Recent Research

Background:
As dairy producers seek new and additional strategies to improve production efficiency, feed costs are a clear focus point, as feed averages 51% of total dairy farm expenses (USDA Economic Research Services, 2018). Historically, improved production efficiency has also improved feed efficiency, but producers need new tools to continue this progress. Genetic selection of animals with improved feed efficiency is an attractive tool, however, the impediment remains that these data are very cost- and labor-intensive to collect.

Earlier research created the foundational database of phenotypes and genotypes related to feed efficiency for approximately 5,000 cows. In order to increase the reliability of genetic predictions and continue the relevance of these data, this project will continue to collect essential phenotypes associated with feed efficiency and continue critical research to better understand the foundations of feed efficiency in dairy cattle.

Timeline:
May 2019 to July 2024.
Phenotyping and research continue through the Enteric Methane Emissions initiative.

Objectives:

  1. Increase the reliability of genomic predictions for feed efficiency so that efficiency is included in the lifetime Net Merit Index.
  2. Identify proxies for predicting feed intake on individual cows
  3. Implement a long-term strategy for adding animals to the feed efficiency reference population at a reasonable price so that reliable selection can continue into the future
  4. Determine the relationship of methane emissions to feed efficiency and if genomics can be used to predict emissions.


Deliverables:

  1. Increased database of individual daily feed intake records and associated data to be used for improved genetic evaluations
  2. Official genetic evaluation for feed efficiency published by CDCB
  3. Incorporation of feed efficiency trait into the lifetime net merit index


Partner Institutions and Investigators:

Council on Dairy Cattle Breeding
Dr. Kristen Parker Gaddis
Dr. Javier Burchard

University of Wisconsin
Dr. Kent Weigel
Dr. Heather White

Dr. Francisco Peñagaricano

USDA Animal Genomics and Improvement Laboratory
Dr. Paul VanRaden
Dr. Ransom Baldwin

Iowa State University
Dr.  James Koltes

Michigan State University
Dr. Michael VandeHaar
Dr. Rob Tempelman

University of Florida
Dr. José Santos


Additional Information:
JDS Symposium Review
JDS Paper: Residual Feed Intake Evaluation

Background:

Background:

Background:
CDCB genetic evaluations are provided for six direct health traits for the resistance to Milk Fever (MFEV), Displaced Abomasum (DA), Ketosis (KETO), Mastitis (MAST), Metritis (METR) and Retained Placenta (RETP) to help producers select animals more resistant to health events. The health evaluations were first incorporated for Holstein in April 2018, Jersey in April 2020, and Brown Swiss in August 2022. Variance components were originally estimated in 2018, when Holstein records available for each trait ranged from 1.2 to 2.2 million. Since the 2018 debut of CDCB evaluations for disease resistance, the number of health records in the National Cooperator Database has tripled or quadrupled – depending on the trait. Record counts now include Holstein, Jersey, and Brown Swiss animals and range from 4.3 to 7.7 million depending on the trait. With this data surge, the evaluation model has been adjusted with new variance component estimates and adjusted weights. This follows a typical progression and evolution of newer traits.

Weights applied to health traits were also updated from 0 or 1 to be a value estimated from the variance components. These variance-adjusted weights are used to standardize genetic variance across differing parities that have differing heritabilities. The new weights are a function of repeatability, h2, and h2-by-lactation. The previous weights of 0 or 1 are now 0 or 0.25-1.46 depending on lactation and trait. These updates will be more representative of the available data, providing more accurate health evaluations for producers.

Timeline:
March 2023 to implementation in April 2024

Objectives:

  1. Re-estimate variance components and heritability for each health trait and by parity
  2. Implement weights based on variance component estimations

Deliverables:

  1. Improved genetic evaluations for health traits that better reflect the current dairy population
  2. Updated heritabilities for all currently-evaluated health traits
  3. Updated weighting procedure for all health traits moving from a 0/1 value to a value estimated from the variance components

Partner Institutions and Investigators:

Council on Dairy Cattle Breeding
Dr. Kristen Parker Gaddis
Dr. Taylor McWhorter

Dr. Ezequiel Nicolazzi

USDA Animal Genomics and Improvement Laboratory
Dr. Paul VanRaden


Additional Information:
April 2024 What’s New

Background:
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).

Timeline:
Started May 1, 2019. Research completed December 31, 2023.

Objectives:

  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

Deliverables:
The deliverable of this project was a genetic evaluation for resistance to Johne’s disease in U.S. Holsteins. Research has been completed and the service can be added to CDCB’s genetic evaluation portfolio when the need arises in the industry.

Partner Institutions and Investigators:

Council on Dairy Cattle Breeding
Dr. Kristen Gaddis
Dr. Nick Wu

Dr. Duane Norman

USDA Animal Genomics and Improvement Laboratory
Dr. Asha Miles
Dr. Mahesh Neupane

Meet the Research and Innovation Team

For speaking inquiries, please contact Katie Schmitt, CDCB Outreach Specialist, katie.schmitt@uscdcb.com.

Javier

Ph.D. MSc Animal Science (McGill.CA), DVM (UACH.CL) Javier Burchard, Ph.D., leads the development and execution of CDCB innovation strategy. Prior expertise includes: Developed and Implemented short-term and long-term technology transfer cooperation projects with the private sector in the area of herd health, milk recording, livestock nutrition, and quality systems in Argentina, Brazil, China and Mexico. Directed a 10-year research program on the toxicology of Electric and Magnetic fields on dairy livestock to support policy development of rural electric networks in Quebec, Canada. Provided technical assistance to the pharmaceutical industry on efficacy trials for livestock production. Lectured undergraduate and graduate students in Animal Health and Mammalian Physiology. Trained veterinarians and agronomists on animal health and production, herd health and animal nutrition including sanitary and phytosanitary (SPS) measures.

John B. Cole

John serves CDCB as the Chief Research and Development Officer. Before coming to CDCB, he spent three years as the Senior Vice President, Research and Development, for PEAK Genetics, where he oversaw genetics and reproductive biology research for the largest producer of cattle genetics in the world. Prior to joining PEAK, he spent 17 years as a Research Geneticist (Animals) and Acting Research Leader for USDA’s Animal Genomics and Improvement Laboratory (formerly the Animal Improvement Programs Laboratory). John has authored more than 150 peer-reviewed research articles, mentored many postdoctoral scientists and graduate students, and is a frequent speaker at industry and scientific meetings. His research has been recognized with the Jay L. Lush Award in Breeding and Genetics from the American Dairy Science Association, the Distinguished Service Award from the National Dairy Herd Information Association, and the National Association of Animal Breeders Peer Research Award.

A native of South Louisiana, he holds a PhD in animal breeding and genetics from Louisiana State University and is a graduate of the Louisiana School for Math, Science, and the Arts. His research interests include genetic improvement of fertility, health, and fitness traits in dairy cattle; development of economic selection indices; pedigree analysis; biological mechanisms underlying fertility; and recessive genetic defects. He’s also a Life Member of the Bowie Volunteer Fire Department and is a founding member of the Lethal Recessives. John, his wife Misty, and their sons Ellery and Henry live in Sun Prairie, WI, with two cats of dubious origin.

Malia Caputo

Malia (Martin) Caputo, Ph.D., began a postdoctoral appointment at CDCB in May 2023 before transitioning to her current role as Associate Research Scientist. Malia received her bachelor’s degree in animal science with a minor in agronomy from the University of Minnesota-Crookston in 2018 and her doctorate degree in dairy science from the University of Wisconsin-Madison in June of 2023. Her graduate research addressed two areas of dairy cattle feed efficiency: predictive modeling of feed intake and feed efficiency, and establishing post-absorptive nutrient metabolism as a source of variation in residual feed intake.

Kristen Parker Gaddis

Kristen Parker Gaddis, Ph.D., has served as Geneticist at CDCB since October 2016. Kristen studied at North Carolina State University, where she received her Bachelors and Doctorate degree in animal science and quantitative genetics. Her Ph.D. research focused on utilization of producer-recorded cow health information to improve understanding of the genetics behind disease resistance, analysis of the health data, and estimation of traditional and genomic breeding values of dairy animals for common health traits. Kristen conducted Post-doctoral research at the University of Florida and USDA AGIL (Animal Genomics and Improvement Laboratory) involving fertility and reproductive technology traits, along with continued work on development of health trait evaluations.

Andres Legarra

Andres Legarra joined CDCB as Senior Geneticist, effective January 1, 2023. He works on genetic improvement of livestock with strong emphasis in genetic evaluation with phenotypes, pedigree and markers, and he enjoys making incursions in pure Quantitative Genetics. Andres has vast experience in quantitative genetics and animal breeding in Spain, the U.S. and France, where he most recently served as Research Director in Animal Genetics at INRA-Toulouse. Within a large network of collaborators, he actively develops the Single Step GBLUP method, which is becoming the standard in genomic prediction. Lately, he has been working in extensions of Single Step GBLUP to crosses, dominance and epistasis. Andres is also Senior Editor, Genetics and Genomics section for Journal of Dairy Science®. Speaking fluently three languages, he believes in the great value of international collaborations. He also believes that key ingredients to science are rigor, enthusiasm, fun and usefulness.

Ashley Ling

Ashley Ling joined CDCB on June 3 as Support Scientist, a new position to provide scientific and technical assistance in various research projects and ensure successful execution, reporting and implementation of priorities in the CDCB Strategic Plan.
 
Ashley has worked the past two years as Research Geneticist at the United States Department of Agriculture (USDA). Her work has included building efficient pipelines for analysis of genomic data, investigation of genetic variation and genotype-by-environment interactions for body condition score in beef cattle, evaluation of statistical models for analysis of longevity data, and a variety of additional projects.
 
Ashley’s training is from the University of Georgia (UGA), where she earned a Ph.D. in animal breeding and genetics under Dr. Romdhane Rekaya and a B.S.A. in animal science. While a graduate research assistant at UGA, she was engaged in research on statistical challenges to preselection of SNP markers, data collection and BLUP analysis to characterize heritability of hornfly tolerance in beef cattle, collection of beef and poultry phenotype data, and research documentation in manuscripts and peer-reviewed journals.

Xiao-Lin (Nick) Wu

Xiao-Lin (Nick) Wu is product development manager at CDCB, with primary role to evaluate existing tools and develop new statistical methods and products beneficial to U.S. dairy. He holds an adjunct faculty position as associate professor in animal science at University of Wisconsin-Madison (UWM). Previously, Dr. Wu was computational scientist at UWM, a principal scientist at Bayer CropScience, and director of Bioinformatics and Biostatistics at Neogen GeneSeek. He has advanced education in China and Denmark and research experience in China, South Korea, and the U.S. His post-doc experiences were at multiple universities, including Iowa State, Washington State, University of Idaho, and UWM. Dr. Wu has published over 120 peer-reviewed papers and 5 books and book chapters. His research in the past 20 years has covered several major fields in molecular genetics, quantitative genetics and genomics, and bioinformatics.

The research work done at CDCB is heavily complimented by long-term partners at the USDA’s Animal Genomics and Improvement Laboratory in Beltsville, Maryland.