Duration: 10/01/2024 to 09/30/2029

Administrative Advisor(s):

NIFA Reps:

Non-Technical Summary

The project provides a comprehensive and coordinated plan for enhancing the performance, efficiency, and resiliency of dairy cattle in the United States. It addresses the need for innovative breeding strategies in the dairy industry, which ranks as the fourth largest agricultural commodity in the U.S., generating $41 billion in receipts. The dairy industry faces challenges such as inbreeding, health challenges in cows and calves, and the necessity for improved production efficiency and longevity amidst dwindling resources.  The proposed solution involves developing novel genetic and genomic breeding strategies and embracing technologies to bolster performance and resilience. This includes an interdisciplinary approach, fostering collaboration among researchers, and training a new generation of students in quantitative genetics to lead these initiatives. The key aspects of the project include: 1) Enhancing cow and calf health through genomic predictions and evaluating the impact of breed, 2) Investigating genetic parameters related to diseases, fertility, and other traits to improve longevity and efficiency, 3) Exploring the genetic basis for behavior and disease resistance in different environments and production systems, 4) Developing strategies to incorporate functional -omics data into breeding schemes to optimize genetic variation and economic indices.  The project also emphasizes creating educational opportunities to develop a pipeline of diverse graduate students skilled in quantitative and functional genetics and bioinformatics. This initiative aims to prepare the next generation of researchers and industry leaders to sustainably improve the dairy industry.  Through collaborative research, it aims to enhance the well-being of dairy cattle and the growing demand for dairy.

Statement of Issues and Justification

Behind cattle, corn, and soybeans, the dairy industry represents the 4th greatest commodity in the U.S. with $41 billion is receipts (USDA-ERS, 2021). Consumers have made it abundantly clear that dairy production is important and, therefore, research and outreach efforts must be concentrated to address key challenges in the dairy industry. Currently, the dairy industry struggles with the effects of inbreeding, dairy cow and calf health (e.g., mastitis, ketosis, scours, respiratory diseases, fertility), production efficiency and longevity. Furthermore, as land, labor, and resources continue to evolve, the dairy industry will be challenged to increase production with less assets available.  To continue to meet protein demands both domestically and internationally, it is imperative that novel genetic and genomic breeding strategies be developed to improve production, efficiency, and longevity. Furthermore, the dairy industry will be required to embrace technologies to improve dairy cattle performance, efficiency, and resiliency.

To do this, we must incorporate an interdisciplinary approach to address these complex problems and continue to build a pipeline of students in the fields of quantitative genetics.  Academia continues to fall behind in training students in quantitative genetics; therefore, for the dairy industry to continue to make strides in genetic improvement, it is critical that we create learning opportunities for students to be trained in quantitative genetics and develop a pipeline of students to lead and manage these industries. At our recent annual meeting of SCC84, the participants (Purdue Univ, Virginia Tech, University of Florida, Penn State, Univ of Minnesota, Cornell, Iowa State, Massey Univ, Univ of Maryland, Univ of Georgia, and Michigan State) have decided to move to a “S” project due to increase research collaboration across the group.

The primary stakeholders for this project are dairy producers, the Council on Dairy Cattle Breeding, artificial insemination companies, and dairy breed association personnel. The success of this project will result in a more efficient and resilient dairy industry that will benefit dairy producers, consumers, and the environment. The well-being of dairy cows will also be protected as we continue to select for enhanced efficiency and resiliency through precision livestock technologies.

This Southern Multistate research group is composed of land-grant, federal government (USDA - NAGP, and AGIL), and private sector (CDCB) quantitative geneticists throughout the United States with expertise in genetic parameter analyses (e.g., heritability and genetic correlations), selection indices and breeding value development, heterosis and cross-breeding, modeling, genome-wide association studies, and precision technology for a variety of performance and health traits. Therefore, we are well-positioned to integrate –omics technologies with traditional genetic predictions to develop tools to make significant improvements in the dairy cattle industry.

Related, Current and Previous Work

The recently completed project "SCC84: Selection and mating strategies to improve dairy cattle performance, efficiency, and longevity" focused on addressing key genetic challenges in the dairy industry. The primary aim of this project was to develop novel genetic and genomic breeding strategies to enhance the performance, efficiency, and longevity of dairy cattle. The project recommended breeding strategies to optimally use breed resources and crossbreeding to maintain or exploit within-breed genetic variation. The various project members captured phenotypic data for novel and economically important traits to understand their genetic regulation and potential for genomic selection.

Furthermore, the project members collaborated with various organizations like the National Animal Germplasm Program Dairy Committee, USDA AGIL, and the CDCB to improve genetic variation in dairy and optimize economic merit indices. The outcomes and impacts of the project included working with USDA-AGIL to develop an updated Lifetime Net Merit formula and related genetic selection indexes which are widely used in the dairy industry.

The SCC84 project members also started work on optimal mating strategies for commercial dairy producers that incorporate crossbreeding and genomic evaluations. Furthermore, the project coordinated with the Dairy Committee of the National Animal Germplasm Program to optimize its dairy collection and monitor the genetic diversity represented in the repository.

To summarize, the past 10 years have been pivotal in advancing the use of genetic selection and crossbreeding in enhancing the health, fertility, and survival of US dairy cattle. The efforts of SCC84 project members have been instrumental in creating and applying a multi-breed genetic evaluation system, aiding US dairy farmers in augmenting fitness traits through crossbreeding, and training future dairy cattle geneticists. This committee's work also played a key role in establishing national genetic evaluation systems for various traits, such as health traits and feed saved. Furthermore, significant enhancements were made to the genetic evaluation system for determining fertility and productive life of dairy cows and heifers. Notably, substantial changes to the Lifetime Net Merit, an economic index widely used by US dairy farmers for selecting sires, were also rooted in this group’s long-standing research and collaboration. As a result of these efforts, US dairy farmers have been better equipped with an extensive range of genomic tools to improve the health, fertility, production, and longevity of their cattle. However, more work needs to be done.

Objectives

1. Recommend breeding strategies for optimal use of breed resources, maintenance and(or) exploitation of within-breed (additive and non-additive) genetic variation
   
2. Derive novel traits based on longitudinal datasets from precision livestock farming to improve sustainability, welfare, and resilience.
   
3. Develop variant discovery strategies to incorporate functional –omics data into breeding schemes to improve genetic variation of dairy and optimize economic merit indices
   
4. Create a pipeline of diverse graduate students in the fields of quantitative and functional genetics and bioinformatics via outreach and educational opportunities
   

Methods

Methods

1. Recommend breeding strategies for optimal use of breed resources, maintenance and(or) exploitation of within-breed (additive and non-additive) genetic variation.

 1.1 Breed conservation with the National Animal Germplasm Program and Cooperative Dairy DNA Repository

In recent years voluntary submission of samples from the industry to the National Animal Germplasm Program has declined. In this multi-state project, project members will compare genomic data of Jersey bulls in the NAGP collection with Jersey bulls in the Council on Dairy Cattle Breeding and Cooperative Dairy DNA Repository database, to ascertain their inbreeding status, relatedness, sample origin and genetic diversity. Project members will work with NAGP to assess breed collection completeness and identify collection gaps, thereby allowing NAGP to target specific modern genetics to augment their collection.  We will work the Cooperative Dairy DNA Repository (CDDR) to characterize QTLs and SNPs to estimate allelic effects across families.

 1.2 Evaluate genomic and crossbreeding effects of cow health in an organic environment

Minnesota and Penn State have genotyped approximately 3000 organic dairy cattle on 13 organic farms in 8 states. Cows have also been visually assessed for measures such as body condition score and fly counts, whereas calves have been evaluated for respiratory health and scours. These project members have also obtained farmer recorded disease events. With this data, they will develop genomic predictions of cow health using single step methodologies that incorporate DNA marker genotypes (61K markers) with pedigree data. These predictions will be compared with national evaluation data from conventional dairy herds. They will also conduct crossbred genetic analyses to determine the effect of breed on cow performance and health. Optimal genetic selection and crossbreeding strategies to enhance herd profitability levels will be established. Genome wide association studies will help them uncover underlying genetic variants that are associated with cow health. Optimal methodologies developed in their evaluation of cattle will be applied to other important animal species.

 1.3 Determine genetic parameters for telomere length and associations with health and survival

Blood samples have been collected from ~1000 dairy animals at multiple time points at Penn State. Telomere length will be determined from these samples and whenever an animal presents a health problem, is culled, or dies, events will be recorded. Associations of telomere length with diseases, culling risk, milk traits, and reproductive fitness will be determined. They will also investigate how specific disease events impact telomeres in short (at diagnosis and +2 weeks) and longer terms. Telomere length within specific white blood cell lineages in calves will be measured to identify the exact location of telomere length change. Associations of telomere length with welfare measures such as locomotion score and hair cortisol will be examined. These animals have been genotyped (61K DNA markers) and will also be used for genetic and genomic studies of telomere length. Single-step genetic/genomic analyses will provide heritability estimates, genetic relationships with other traits, and an initial genome scan to identify quantitative trait loci affecting telomere length. Genomic predictions of telomere length will be provided to industry partners upon completion of the study based on our DNA marker effect estimates.  Furtheremore, health trait EBVs will be provided to industry partners. They will use the general principles developed in this research trial to spur development for other important animal species.

 1.4 Breed composition on behavior and fly infestation

The influences of genotype and breed composition on behavior and fly infestation are minimally known and could have important implications for both the health of cows and pasture use recommendations. The goals of this joint Minnesota and Penn State work are to enhance disease resistance in pastured dairy cattle through the adoption of optimal genomic selection and crossbreeding strategies and to understand the relationship of genotype to grazing behavior. Genomic predictions for resistance to calfhood pneumonia, mastitis and other cow disease will be determined for Holsteins and crossbreds. Also, they will determine A2 beta casein genotype, and obtain genomic predictions for yield traits, productive life, and conformation. Health events and disease will be recorded and associated with genomic predictions.

2. Derive novel traits based on longitudinal datasets from precision livestock farming to improve sustainability, welfare, and resilience.

 2.1 Endoparasite and ectoparasite load and effects on dairy genetics systems

Project members will quantify the effects endoparasite and ectoparasite load at different life stages on current and future performance. They will determine how parasites in calves, heifers and cows affect growth, milk production traits, digestive efficiency, fertility, body condition score, somatic cell score, and susceptibility to future parasite infection. This information will help determine the economic consequence of parasites on organic farms which will inform organic producers about the importance of control efforts. They will determine how breed, crossbreeding, and within-breed genetic effects influence parasite load. They will estimate heritability for parasite resistance, determine genetic correlations between parasite resistance and commonly recorded traits such as milk yield, determine genomic regions that are associated with variation in parasite resistance, determine breed differences for parasite level and effect, and determine if crossbreeding confers resistance to parasites.

 2.2 Genetic selection for reducing greenhouse gas emissions of Holstein and crossbred dairy cattle

Decreasing the emissions of enteric methane from dairy cattle is strategic to reduce the carbon footprint of dairy production systems. This project will evaluate Holstein and crossbred dairy cattle for methane emissions. The goal of the project will be to develop novel nutritional strategies to reduce enteric methane emissions of conventional and grazing dairy cattle. On this multi-state project, project members will collect baseline emissions with the GreenFeed System with cows from university research herds as well as participating farms. Variables that will be determined are methane production (grams CH4 per day), methane yield (grams CH4 per kg of dry matter intake), methane intensity (grams CH4 per kg of energy-corrected milk), and residual methane (grams CH4 regressed on DMI, BW, and MilkE). Crossbreds, Holstein, and 1964 genetic control Holsteins will be compared for methane emissions, and for the proportions of energy used for maintenance, milk production, and emissions. These projects will be the first step to lower emissions on US dairy farms and to develop resilient dairy farms. Through this project, project members will understand how genetic selection has improved sustainability of dairy cows and help improve our estimates of the energy requirements of dairy cows.

2.3 Fertility of bulls and cows’ impact of dairy farm resiliency

There is a critical need to improve dairy cow fertility in an environmentally sustainable manner. One approach is focusing on estrous (or reproductive) behavior. This project will conduct extensive research into the estrous cycle with the end goal of delivering a novel genomic prediction tool. This tool can be used to identify, rank, and select cows with improved estrous expression. Selecting cows with increased estrous expression will help increase pregnancy rates without relying on hormonal interventions.

Fertility is an economically important trait in dairy cattle. Despite recent advances, there is room for improvement in reproductive performance for most herds, still resulting in significant economic losses for dairy farmers. This project will investigate male fertility in the Jersey breed, the second most important breed in the US. This research team has already identified genomic regions that can impact Jersey bull fertility. The objective of this project is to identify the cause of differing conception rates using targeted DNA-sequencing. This project will deliver new genomic tools to assist dairy farmers, breeders, and artificial insemination companies responsible for accurate management and selection decisions on bull fertility. Improving reproductive performance through advancements in service sire fertility has a direct impact on farm profitability and animal health and welfare while also ensuring the long-term sustainability of the dairy industry.

This project seeks to improve the health and welfare of dairy cows and the sustainability of dairy farms by genetic selection for resistance to, and rapid recovery from, environmental and management disturbances. Project members will use data from approximately 220 commercial dairy farms to develop and evaluate measures of resilience based on deviations in daily milk yield from expected performance. Project members will validate the ability of resilience traits that can be measured on commercial farms to reflect underlying changes in feeding behavior, feed consumption, and energy balance using dry matter intake and residual feed intake data from nearly 10,000 cows at research stations throughout North America, Europe, and Australia. Lastly, project members will use pedigree, genome, and performance data from the research stations and cooperating dairy farms to develop a prototype for routine genetic evaluation of resilience in U.S. Dairy cattle, with the aim of enhancing their ability to cope with challenges and preform at a higher level under variable management and environmental conditions.

2.4 Precision technology and dairy genomics

Precision technology and dairy genomics enhance the health and welfare of dairy cattle, as well as the operational and economic efficiency of dairy farms, by focusing on selecting cattle that are resistant to, and can quickly recover from, various disturbances. These disturbances, becoming more common due to a warming climate, include extreme weather events, labor shortages, disease outbreaks, and supply chain disruptions. Precision technology research utilizes high-frequency phenotypes to measure daily deviations in milk yield, feed intake, and behavior at the individual cow level, compared to their expected norms. High-frequency phenotypes are used to devise methods for detecting environmental and management disturbances that affect milk yield, feed intake, and behavior at the cohort level and implement causal inference models to identify factors contributing to cow resilience. Project members will examine the genetic variability in individual animals' ability to withstand and bounce back from negative impacts caused by environmental and management disturbances. Aims is to develop a prototype system for routine genetic evaluation of resilience in U.S. dairy cattle.

            Project members will evaluate and develop computational and statistical algorithms and tools for processing high-throughput datasets. These will aid in identifying innovative indicators and predictors of animal welfare. Project members plan to analyze extensive datasets encompassing over 50,000 dairy cattle, including pedigree, phenotypic, and genomic information. This analysis will employ machine learning algorithms like support vector machines (SVMs), randomized forests, and deep learning models using neural networks. Project members will also conduct data mining analyses to discover new indicators of animal welfare from the wealth of high-throughput datasets collected in precision farming environments. This objective will focus on mining data related to animal resilience, including metrics like feed intake, growth rate, behavioral normality, and the speed of learning to use automated feeding or milking systems.

 

3. Develop variant discovery strategies to incorporate functional –omics data into breeding schemes to improve genetic variation of dairy and optimize economic merit indices.

3.1 Impact of genetics on animal replacement and insemination decisions

Genetic data plays a key role in animal replacement and insemination decisions. Despite the growing volume of data collected on dairy farms through sensors and genetic testing, there's a gap in integrating these data into practical, decision-support tools that address these practical issues that dairy farmers face daily. Project members aim to develop and implement an algorithm that provides dairy farmers with enhanced support for making informed replacement and insemination decisions for heifers and cows. The approach to resolving these challenges encompasses developing app-based tools that aid in making optimal replacement and insemination decisions for heifers and cows. The effectiveness of these tools increases with more accurate phenotypic predictions based, in part, on genetic data. Collaborating, project members will advance the prediction of future fertility and milk production in dairy heifers and cows by incorporating genetic, health, and sensor data through data science techniques.

3.2 Optimizing selection indices

Project members will advise CDCB on potential changes to national economic merit indices and strategize with USDA-AGIL to incorporate new economically relevant traits into current national selection indices. Project members will work with USDA-AGIL to develop an updated Lifetime Net Merit formula and related genetic selection indexes, based on recommnded trait addtions. Project members will coordinate with the Dairy Committee of the National Animal Germplasm Program to optimize its dairy collection and monitor the genetic diversity represented in the repository.

3.3 Genomic prediction models

Project members will enhance ssGBLUP predictive models to handle extensive datasets featuring genotyped animals with incomplete pedigrees, potentially from various breeds. Integrate alternative methods for handling Unknown Parent Groups and the metafounders approach into ssGBLUP. This integration aims to produce more precise and unbiased genomic predictions in intricate datasets and models across different species, ultimately boosting genetic progress rates. Project members will implement a solid method for estimating individual theoretical accuracy in very large, genotyped populations using APY. This new accuracy estimation approach will be designed for easy calculation across any dataset size and model, ensuring reliability and acceptance within the dairy industry. Further, project members will expand ssGBLUP's functionality to calculate p-values for selecting sequence variants in genomic prediction for sizable, genotyped populations. This enhancement will enable ssGBLUP to accurately select sequence variants for genomic selection (GS) without the need for data reduction or projections, such as deregressions.

3.4 Metatranscriptome analysis

Project members will explore the microbial composition and functions in the rumen of dairy cows using shotgun metatranscriptome analysis. Animals will be genotyped as well. They will utilize newly discovered variants to enhance genome annotation and delve into the study of epigenetic inheritance and its links with development, disease, and performance in dairy cattle. Project members will examine the patterns of DNA methylation inheritance from both the sire and dam, as well as among half-siblings. They will track the normal fluctuations in methylation throughout the stages of calf and heifer development, including the first lactation period. They will also identify regions of the genome with differential methylation that are linked to increased risk of diseases such as scours and transition disease. Further, project members will investigate changes in methylation that occur in an individual following disease episode. They will assess the relationship between methylation patterns, disease occurrence, and future performance in dairy cattle. They will also explore differentially methylated regions in offspring that may be associated with a dam's history of transition disease. Finally, project members will determine how the history of disease in the dam may influence the methylation, performance, and disease susceptibility of their offspring.

3.5 Genome-wide association studies with field data

The primary objective of this project is to unravel the genetic basis of resistance to bovine mastitis and leverage these genomic insights to enhance cattle health and the profitability of the dairy industry. The specific goals include identifying key genomic regions and potential genes linked to mastitis through comprehensive genome-wide association studies (GWAS) that utilize large-scale data, focusing on mastitis and other immune-related diseases. Project members will integrate detailed GWAS data at the sequence level with transcriptome analyses and functional validations of immune cells. This step aims to pinpoint health-related SNPs (single nucleotide polymorphisms) and apply these findings to refine genomic selection strategies for disease traits. Project members will examine the transcriptome and functionality of immune cells in cows that are either genetically resistant or susceptible to mastitis. This approach aligns computational and experimental methods, where computational analyses suggest candidate SNPs for experimental testing, and experimental results provide context and validation for computational predictions.

            With the advancement of genomic selection in the dairy industry, the U.S. Dairy Genomics Database now boasts an extensive collection of cattle genotypes and economically significant phenotypes. The collaborative efforts of the SCC84 group and the wider animal genomics community have produced a wealth of functional genomics data and annotations for cattle. By integrating this genome, phenome, and functional genomics information, project members have an exceptional opportunity to deepen the understanding of genome-phenome relationships and enhance genomic selection, ultimately improving livestock production.

            The overarching aim is to expedite the genomic enhancement of economically vital traits in cattle. This will be achieved by combining functional genomics, genome annotations, genome-wide association studies, and genomic selection analyses. The specific objectives of this project are: 1) to process, compile, and validate existing cattle functional genomics and annotation data; 2) extract valuable functional annotation insights using the U.S. dairy genomics and phenomics database, and 3) integrate functional genomics and annotation data to augment cattle GWAS and genomic selection processes.

 

4. Create a pipeline of diverse graduate students in the fields of quantitative and functional genetics, genomics and bioinformatics via outreach and educational opportunities.

Many station leaders recall the time when they were graduate students and how they benefitted and participated in earlier versions of this long-standing genetics multi-state project. Therefore, the specific objectives for this initiative are to foster communication and training within the project members’ dairy research groups. This will encourage community discussions, raise awareness of communal needs, keep members informed about current events, available resources, and other relevant topics. Collaborations initiated or enhanced though this multi-state project will contribute to the education of students and postdoctoral researchers, grooming them to become future leaders in agriculture-focused computational science. The educational component of this program will be dedicated to preparing the next generation of graduates with a comprehensive understanding of dairy genetics and genomics research. It's crucial to equip these students with the skills required to merge data science and analytics to enhance dairy production in the U.S. Participation in this project will also demonstrate collaborative science to our rising students.  The HBCU community is an under-utilized resource and outreach efforts will be pursued with these students.

Measurement of Progress and Results

Outputs

• An annual meeting will be held to exchange information on research at individual stations, to identify areas of collaboration among project members, and to discuss the most pressing research and outreach needs Comments: Project members and their graduate students will discuss strategies to capture novel phenotypic data in order to facilitate genomic selection for economically important traits, strategies to improve non-additive genetic merit, coordinate the development of optimal mating strategies for commercial dairy producers, and develop strategies to incorporate alternative –omics data into current breeding schemes. Project members will advise CDCB on potential changes to national economic merit indices and strategize with USDA-AGIL and CDCB to incorporate new economically relevant traits into current national selection indices. Project members will serve on the Dairy Species committee of the National Animal Germplasm Program Dairy Committee. Project members will evaluate the status of the dairy collection and advise the National Animal Germplasm Program to optimize its dairy collection. Project members will work with other disciplines to develop and host a Discover Conference through the American Dairy Science Association (ADSA). Committee members will collaborate with Historically Black Colleges and Universities (HBCU) to recruit underserved graduate students and build partnerships.
• Project members at multiple stations are gathering the same types of phenotypic data. Individually, each station's dataset may be too small for robust conclusions, but pooling these data can greatly enhance resource utilization Comments: This collaboration will significantly boost statistical power and enable exploration of critical scientific inquiries, such as genotype-environment interactions. The extensive phenotyping involved in this project promises to deepen our understanding of the genetic factors underlying key aspects of dairy cattle health, fertility, lifespan, and other economically important traits. Despite the use of genomic information, challenges persist in improving these traits, partly due to their low heritability. This issue stems from the variability in the phenotypes collected. Through combined analyses and meta-analyses, it is anticipated that innovative methods for genetic assessment and new economic valuation models for dairy cattle may emerge.

Outcomes or Projected Impacts

• Project members will work with USDA-AGIL and CDCB to develop an updated Lifetime Net Merit formula and related genetic selection indexes. Project members will develop and recommend optimal mating strategies for commercial dairy producers that incorporate the use of crossbreeding and genomic evaluations for both bulls and cows. Project members will coordinate with the Dairy Committee of the National Animal Germplasm Program to optimize its dairy collection and monitor the genetic diversity represented in the repository. Project members will develop selection tools to enhance reproduction and survival using field data. Project members will evaluate the biological and economic impact of crossbreeding on lifetime performance of dairy cattle and facilitate the use of crossbreeding to optimize lifetime performance of dairy cattle. Last but not least, project members will continue to create a pipeline of graduate students trained in dairy cattle genetics and genomics.

Milestones

(2025):Evaluate the quality of field data used in genetic assessments concerning reproduction, health, and survival in dairy cattle. Project members will engage in yearly communication and undertake annual data collection and reporting activities.

(2026):Estimate genetic parameters associated with reproduction, health and survival, and methane emissions of dairy cattle. Project members will engage in yearly communication and undertake annual data collection and reporting activities.

(2027):Assess and contrast purebred and crossbred calves in terms of birth ease, survival during early calfhood, disease resistance, and growth rates. Update selection indices to reflect lifetime economic value. Project members will engage in yearly communication and undertake annual data collection and reporting activities.

(2028):Evaluate the differences between purebred and crossbred groups in terms of reproduction, milk production, and the interval between rebreeding during their first lactation period as cows. Project members will engage in yearly communication and undertake annual data collection and reporting activities.

(2029):(2025-2029): Project members will engage in yearly communication and undertake annual data collection and reporting activities.

Projected Participation

View Appendix E: Participation

Outreach Plan

• The group will disseminate research results through professional society meetings, multi-state research groups, peer reviewed publications, articles in popular press, social media, and workshops.


• The group will coordinate efforts to improve access to dairy genetics/genomics curricula through participation in short courses and academies.


• Representatives of related groups like CDCB and the University of Guelph will be invited to attend and participate in annual multi-state meetings.


• The group will serve as advisors to breed associations and related industry groups.

Organization/Governance

The recommended Standard Governance for multistate research activities include the election of a Chair, a Chair-elect, and a Secretary. All officers are to be elected for at least two-year terms to provide continuity. Administrative guidance will be provided by an assigned Administrative Advisor and a USDA-NIFA Representative. The group has already elected a Chair, Chair-elect and Secretary for its anticipated next meeting to be held in the fall of 2024.

Literature Cited

USDA-ERS, 2022. Cash receipts by state, commodity ranking and share of U.S. total, 2022

Nominal (current dollars). https://data.ers.usda.gov/reports.aspx?ID=17843#P492c23616dbc46a185d774620b785fd7_2_17iT0R0x0   Accessed 3/12/2024

Attachments

Land Grant Participating States/Institutions

FL, MD, MI, MN, NC, NY, PA, VA, WI

Non Land Grant Participating States/Institutions

Council on Dairy Cattle Breeding, USDA ARS