Brief Summary of Minutes of Annual Meeting

George Smith (NC2040 program administrative advisor) shared an overview of the group’s progress meeting objectives required under the multistate program and emphasized that annual reports should focus on collaborative efforts across research stations. Steve Smith (USDA-NIFA national program leader) provided an overview of current and potential funding opportunities relevant to the NC2040 membership. Many funding opportunities are dependent on the passage of a new Farm Bill. Both Steve and George highlighted potential federal legislation for research station facility renovation and improvements, which is predicated on successful passage of a new Farm Bill.

The group discussed collaborative efforts to deliver an ADSA symposium or platform session related to the NC2040 mission and membership. Over the next several years, the group will work to bring a topic of interest to the forefront at the ADSA annual meeting. The sessions will include a presentation about advancements and key strategies for consistent data generation across institutions, coupled with abstracts from the general ADSA membership related to the overarching topic. Kirby Krogstad and Anne Laarman agreed to lead the effort for the 2025 annual meeting. 

The NC2040 members shared research updates occurring on their respective experiment stations and received input and feedback on collaborative opportunities. Annual reports of collaborative efforts across research stations are due within 60 days of the meeting.

For new business, Billy Brown will assume the role of chair for 2025, and Kelly Nichols was elected secretary. The 2025 meeting dates are slated for October 7-8 in East Lansing, Michigan.

Objective 1: To quantify factors that impact supply and availability of nutrients utilized for efficient milk production while reducing environmental impact

An existing inter-disciplinary, multi-disciplinary collaboration between UW (H. White), MSU (M. VandeHaar and Z. Zhou), U of Florida, Iowa State, and USDA-Beltsville continues to explore feed efficiency of dairy cattle. This research determines the feed intake and feed efficiency of several hundred cows each year in order to improve genomic selection for feed efficiency. Additionally, we interrogate the impact of nutritional and managerial interventions to improve feed efficiency, and recently, methane emissions. The collaborators that are a part of NC2040 also have a USDA grant (PI: White; co-PIs: VandeHaar and Zhou) specifically determining the influence of post-absorptive nutrient use efficiency on the individual animal variance in feed efficiency.

• Phenotypes collected for feed intake, feed efficiency, and methane production
• Grant funded, and another pending, to expand this research to include methane emissions and mitigation
• Several studies completed interrogating tissue specific nutrient use at MSU and UW.

A collaboration between University of Delaware (T. Gressley) and Purina (V. Daley) evaluated hindgut buffers under high-starch diet conditions in lactating Holstein cows to alter fecal measures of intestinal fermentation and systemic inflammation due to hindgut acidosis.

Objective 2: To identify and quantify molecular, cellular, and organismal signals that regulate intake, partitioning and efficient utilization of nutrients

A collaboration between MSU (Z. Zhou and M. VandeHaar), University of Minnesota Twin Cities (C. Chen), and University of Wisconsin (H. White) has a grant (PI: Z. Zhou; co-PIs: C. Chen, M. VandeHaar, and H. White) funded by Michigan State University AgBioResearch to assess metabolic adaptations in more efficient cows and to determine the differences in maintenance requirement of efficient and inefficient cows and the corresponding functional implications.

Collaborative work between MSU (B. Bradford) and KSU (E. Titgemeyer) explored the relative importance of the G-protein coupled receptor HCAR2 for mediating impacts of the B-vitamin niacin and the metabolite beta-hydroxybutrate (BHB) on function of bovine immune cells.

Objective 3: To use this knowledge of feed properties and metabolic and molecular quantitative relationships to challenge and refine nutrient requirement models leading to more accurate feeding systems for dairy cattle

Using data supplied by members of NC2040 (past and present); Dhiman, T.R. (WI station); Donkin, S.S. (IN station); Ferguson, J.D., Varga, G.A. and Hristov, A. N. (PA station), and Huyler, M.T. (WA station); Dr. Heidi Rossow from the CA station created a ration formulation program that determines the impact on the ration solution when using each DMI prediction model. Ration solutions using the pen distribution shape for the DMI constraint were not different from the true DMI ration solution and most closely matched the true distribution of DMI. The model of DMI by pen based on machine learning techniques was tested against the NASEM dry matter intake equation for the average cow in a pen, the sum of DMI for each cow within in a pen to predict pen DMI and its distribution and the true pen DMI and distribution for fresh pens, high milking pens and low milking pens.

Collaborative work between Purina and VA station is ongoing to assess the existing framework for prediction of milk protein production and to develop and assess alternative, static, deterministic predictions of milk protein production using predicted absorbed nutrient supplies and observed animal characteristics as independent variables.

Outputs and Outcomes

Objective 1: To quantify factors that impact supply and availability of nutrients utilized for efficient milk production while reducing environmental impact

Joint work between WI, MI, IA, FL, and USDA-Beltsville has collected phenotypes for feed intake, feed efficiency, and methane production. MI and WI stations also have completed several studies interrogating tissue specific nutrient use as part of a joint USDA grant.

Collaborative research between Purina and the DE Station showed that abomasal infusion of corn starch without supplemental buffer (IS) did not elevate acute phase proteins compared to the control, indicating that the hindgut challenge did not elicit an inflammatory response. Buffer supplementation increased fecal pH while simultaneously increasing fecal VFA and purines, pointing to enhanced hindgut fermentation.

Objective 2: To identify and quantify molecular, cellular, and organismal signals that regulate intake, partitioning and efficient utilization of nutrients

Collaborative work among WI, MI, and MN stations had demonstrated that lidocaine can be detected in blood immediately after administration of subcutaneous injected lidocaine but thus far, no lidocaine has been detected (less than background and less than the pre-surgery sample) in milk samples (n > 250) collected after a range of biopsies or displaced abomasum surgery. A new collaboration between H. White and C. Chen has been initiated and has already expanded to other topics of potential collaboration.

Collaborative research between MI and KS stations observed expression of HCAR2 at the protein level within lymphocytes, monocytes, and granulocytes. The proportion of cells expressing HCAR2 tended to be greater in mid-lactation compared to early lactation cows; the increase was a result of increased proportion of T and B cells expressing HCAR2. Stimulation of HCAR2 with niacin or BHB promoted calcium mobilization in neutrophils and mononuclear cells. Niacin also increased cyclic AMP concentrations in neutrophils, although BHB did not. These findings demonstrate activation of second messenger systems by these nutrients, and siRNA knockdown of HCAR2 reduced these responses, demonstrating mediation by this receptor.

Objective 3: To use this knowledge of feed properties and metabolic and molecular quantitative relationships to challenge and refine nutrient requirement models leading to more accurate feeding systems for dairy cattle

In the past year, collaborative effort among various stations has developed pen dry matter intake that can be used to formulate a diet for a pen of cows that have a wide range of days in milk and non-normal intake distributions. This model can be used to simulate pen intake and milk production from commercial dairies that could be used to examine commercial dairy herd practices, manipulate N excretion from dietary sources and formulate diets to reduce methane production using computer models and could be a template for converting individual cow data such as nutrient requirements to pen nutrient requirements.

Collaboration between Purina and VA station showed that additive and independent effects of the individual AA and energy providing nutrients-based equations performed better than equations based on 1^st limiting nutrients for predicting milk protein production. Specifically, DE, Ile, Lys, and Met showed good precision, His and the NEAA showed moderate precision, whereas Leu and Thr showed poor precision for predicting milk protein production.

Abstracts:

1. Kendall, S.J., K.M. Kennedy, S.J. Johnson, U. Arshad, M. VandeHaar, Z. Zhou, and H.M. White. 2024. Hepatic mitochondrial maximal respiration capacity rate, but not oxidative gene expression, may differ in mid-lactation Holstein dairy cows of divergent feed efficiency. ISRP. Accepted. (UW, MSU)
2. Kendall, S.J., K.M. Kennedy, S.J. Johnson, A. Bosch, G.F. Praisler, and H.M. White. 2024. Glucose metabolism may differ in mid-lactation cows of high or low feed efficient status. ISRP. Accepted. (UW, MSU)
3. Daddam, J.R., M. Sura, C. Collings, G, Ahmad, S.R. Naughton, M.J. VandeHaar, H.M. White, and Z. Zhou. 2024. Altered abundance of proteins in amino acid, fatty acid, and carbohydrate metabolism pathways contribute to variance in residual feed intake. ISRP. Accepted. (UW, MSU)
4. Sura, M., J.R. Daddam, C. Collings, G. Ahmad, S.R. Naughton, M.J. VandeHaar, H.M. White, and Z. Zhou. 2024. Differences in post-absorptive fatty acid oxidation and mitochondrial uncoupling may contribute to variation in feed efficiency in dairy cows. ISRP. Accepted. (UW, MSU)
5. Cavani, L., K.L. Parker Gaddis, R.L. Baldwin, J.E.P. Santos, J.E. Koltes, R.J. Tempelman, M.J. VandeHaar, H.M. White, F. Peñagaricano, and K.A. Weigel. 2024. Genetic characterization of daily feeding pattern in lactating Holstein cows and its association with feed efficiency. J. Dairy Sci. 107(Suppl. 1). Accepted. (UW, MSU)
6. Baes, C., D. Hailemariam, G. Kistemaker, F. Miglior, F. Schenkel, A. Butty, E. Abdala, J. Lassen, O. Gonzáles-Recio, K. Parker-Gaddis, J. Koltes, F. Peñagaricano, M. VandeHaar, K. Weigel, H. White. 2024. The Resilient Dairy Genome Project Synthesis—Overview of feed efficiency and methane emissions. J. Dairy Sci. 107(Suppl. 1). Accepted. (UW, MSU)
7. Martinez Boggio, G., H. Mantovani, H.M. White, K.A. Weigel, and F. Peñagaricano. 2024. Preliminary analysis of methane emissions traits in US Holstein cows. J. Dairy Sci. 107 (Suppl. 1). Accepted. (UW, MSU)
8. Nascimento, B.M., L. Cavani, K.L. Parker Gaddis, R.L. Baldwin, J.E.P. Santos, J.E. Koltes, R.J. Tempelman, M.J. VandeHaar, H.M. White, F. Peñagaricano, and K.A. Weigel. 2024. Impact of heat stress on dry matter intake in mid-lactation Holstein cows. J. Dairy Sci. 107 (Suppl. 1). Accepted. (UW, MSU)
9. Koltes, J.E., L.M. James, M.S. Mayes, C.J. Cooper, K.L. Parker Gaddis, P. VanRaden, R.L. Baldwin, J.E.P. Santos, R.J. Tempelman, H.M. White, F. Peñagaricano, K.A. Weigel, and M.J. VandeHaar. 2024. Deriving novel traits based on data from sensors and other technologies. J. Dairy Sci. 107(Suppl. 1). Accepted. (UW, MSU)
10. Novo, L.C., L. Cavani, F.S. Reyes, H.M. White, K.A. Weigel, and F. Peñagaricano. 2024. Associations between body temperature and feed efficiency traits in Holstein cows. J. Dairy Sci. 107(Suppl. 1). Accepted. (UW, MSU)
11. Sheybani, N., A. Peres Marques Assumpção, G. Martinez Boggio, H. White, Weigel, H. Mantovani, F. Peñagaricano. 2024. Associations between fecal methanogens and methane emission traits in Holstein cows. J. Dairy Sci. 107(Suppl. 1). Accepted. (UW, MSU)
12. Lucey PM, Rossow HA. Predicting the dry matter intake of dairy cow pens with machine learning 2024. Presentation and Abstract at ADSA annual meeting, West Palm Beach, FL. Dairy Sci. 107(Suppl. 1). Accepted. (CA, WI, IN, PA, WA)

Publications:

1. Cavani, L., K., Parker Gaddis, R. Baldwin, J. Santos, J. Koltes, R. Tempelman, M. VandeHaar, H. White, F. Peñagaricano, and K. Weigel. 2024. Genetic characterisation of feeding patterns in lactating Holstein cows and their association with feed efficiency traits. J. Anim. Breed. Genetics. Accepted. (UW, MSU)
2. Nascimento, B., L. Cavani, M. Caputo, M. Marinho, M. Borchers, R. Wallace, J. Santos, H. White, F. Peñagaricano, K. Weigel. 2023. Genetic relationships between behavioral traits and feed efficiency traits in lactating Holstein cows. J. Dairy Sci. Accepted. (UW, MSU)
3. Yilmaz Adkinson, A., M. Abouhawwash, K. Parker Gaddis, J. Burchard, F. Penagaricano, H. White, K. Weigel, R. Baldwin, J. Santos, M. VandeHaar, J. Koltes, and R. Templeman. 2024. Assessing different cross-validation schemes for predicting novel traits using sensor data: an application to dry matter intake and residual feed intake using milk spectral data. J. Dairy Sci. Accepted. (UW, MSU)
4. Toghiani, S., P. VanRaden, R. Baldwin, K. Weigel, H. White, F. Peñagaricano, J. Koltes, J. Santos, K. Parker Gaddis, M. VandeHaar, and R. Tempelman. 2023. Dry matter intake in US Holstein cows: exploring the genomic and phenotypic impact of milk components and body weight composite. J. Dairy Sci. Accepted. (UW, MSU)
5. Hanigan, M. D., V. C. Souza, R. Martineau, H. Lapierre, X. Feng, and V. L. Daley. 2024. A meta-analysis of the relationship between milk protein production and absorbed amino acids and digested energy in dairy cattle. J Dairy Sci 107(8):5587-5615. (Purina, VA)
6. Cronin, S., Smith, M., Bradley, C., Veridiana, D., Gadeyne, F., Bustos, M., and T. Gressley. 2025. J. Dairy Sci. Submitted. (Purina, DE)
7. Mamedova, L.K., K.C. Krogstad, P.O. McDonald, L. Pokhrel, D.H. Hua, E.C. Titgemeyer, and B.J. Bradford. 2024. Investigation of HCAR2 antagonists as a potential strategy to modulate bovine leukocytes. J Anim Sci Biotechnol. 15:38. doi: 10.1186/s40104-024-00999-5. (KS, MI)