Brief Summary of Minutes of Annual Meeting

Conference calls are scheduled monthly throughout the year.  Minutes from conference calls and meetings are included in the attachment.

Following a complete overhaul of the underlying infrastructure for the NANP website, the group continued the process of updating and improving the content and research tools made available through it.  With the new content management system in place, the NANP staff were trained on its use to facilitate routine updates and adjustments much more rapidly than was possible on the previous website.  The next phases of the redesign were completed and focused on database infrastructure integration with the new platform for both the feed ingredient and modeling databases. The resulting website layout is now flexible for use on mobile devices with content load speeds of less than 3 seconds.

The Feed Composition and Modeling committees continued their work in populating the new website with expanded and refined tools available through the website. The groups also continued to focus on meeting stakeholder needs and improving the utility of the tools being developed through the delivery of symposia and workshops. 

The NANP continued to serve key roles in the updates to the National Academy of Science, Engineering and Medicine’s Animal Nutrition Committee, creating feed composition and modeling resources for the “Nutrient Requirements of Dairy Cattle 8^th Edition”.  

Major accomplishments toward meeting the project’s objectives and anticipated outcomes include:

• The consolidation of the species-specific feed composition databases into a single database was completed resulting in a transparent database with uniform ingredient names and descriptions.  
• In support of the NASEM Dairy publication update, datasets containing 2.1 million feed samples provided by four commercial laboratories were standardized for feed and nutrient names. Subsequently, the standardized dataset was screened using a univariate procedure which deleted values above and below 3.5 standard deviations from the average for each nutrient followed by a principal component analysis and a hierarchical clustering procedure. This procedure was useful for eliminating outlier data points and allowed identification of disparate feedstuffs within a large dataset, both important actions aimed at increasing the accuracy of true variability of nutrient composition. The final dataset and tables contained more than 1.4 million feed samples comprising 174 ingredients and 37 nutrients.
• Additional data were added to the observed performance database for the NASEM Dairy publication update and used to derive digestion estimates for individual fat sources, and equations to predict milk fat and milk protein production. Calf growth data were also compiled and used to derive predictions of calf grain intake and calf growth rates.
• To address the variability in techniques used to analyze carbohydrate composition of feed ingredients and the lack of carbohydrate fractions accounted for in the previous feed composition database, an NANP postdoctoral associate has developed a meta-analysis of the literature relative to the feed ingredient carbohydrate composition.
• A second nutrition models workshop was held at the 2018 American Dairy Science Society meeting in Knoxville, TN. This full-day workshop was geared toward animal researchers with little or no modeling experience and included both lectures and hands-on exercises. The maximum capacity of 100 was reached and evaluations of the workshop indicated a strong desire for additional content in future workshops.
• Held a NANP Symposium at the 2018 ASAS-CSAS Annual Meeting and Trade Show in Vancouver, Canada titled ”Future of Data Analytics in Nutrition: Knowledge Gaps, Data Collection and Quality, and the Role of Supporting Tools for Sustainable Development." A toal of 83 attendees were engaged with the goal of raising awareness of how big data and modeling techniques can be applied to research applications in animal nutrition and animal production. The symposium was supported through a competitive NIFA grant (Grant Number: 2018-67021-28558; PI: Tedeschi, L.) for $24,813.

Presentations and other materials from workshops and symposia can be found on the NANP website (https://animalnutrition.org/). Additional detail on accomplishments and activities can be found in the attached meeting and conference call minutes.

Daley, V. L., Dye, C., Bogers, S. H., Akers, R. M., Rodriguez, F. C., Cant, J. P., Doelman, J., Yoder, P., Kumar, K., Webster, D., Hanigan, M. D. Bovine Mammary Gland Biopsy Techniques. J. Vis. Exp. (142), e58602, doi:10.3791/58602 (2018).

Daley, V.L, J.K. Drackley, C.M.M. Bittar, L.O. Tedeschi, S.Y. Morrison, P.A. LaPierre, M.D. Hanigan. 2018. Estimation of starter intake in young dairy calves during the preweaning phase. J. Dairy Sci. 101, Suppl. 2, p. 260.

Daley, V.L., L.E. Armentano, P.J. Kononoff, J.M. Prestegaard, M.D. Hanigan. 2018. Estimation of total fatty acid content and composition of feedstuffs for dairy cattle. J. Dairy Sci. 101, Suppl. 2, p. 295.

Daley, V.L., L.E. Armentano, P.J. Kononoff, J.M. Prestegaard, M.D. Hanigan. 2018. Fatty acid digestion in dairy cows fed different fat sources: A meta-analytic approach. J. Dairy Sci. 101, Suppl. 2, p. 304.

Daley, V.L., L.E. Armentano, M.D. Hanigan. 2018. Modeling fatty acids for dairy cattle: digestion and milk fat secretion. 2018 Annual Meeting of the Animal Science Modeling Group, June 23, Knoxville, Tennessee.  

Hackmann, T., M.D. Hanigan, V.L. Daley. 2018. Workshop: National Animal Nutrition Program (NANP) Models. J. Dairy Sci. Vol. 101, Suppl. 2, p.1-2.

Hanigan, M. D., V. L. Daley, T. J Hackmann. 2018.  Introduction and model construction: Part II (exercises). J. Dairy Sci. Vol. 101, Suppl. 2, p.1-2.

Kebreab, E.. 2018. Model evaluation: Part I (lecture). J. Dairy Sci. Vol. 101, Suppl. 2, p.1-2.

Kebreab, E.. 2018. Model evaluation: Part II (exercises). J. Dairy Sci. Vol. 101, Suppl. 2, p.1-2.

White, R. R.. 2018. Meta-analysis: Part I (lecture). J. Dairy Sci. Vol. 101, Suppl. 2, p.1-2.

Liebe, D. M., R. R. White. 2018. Meta-analysis: Part II (exercises). J. Dairy Sci. Vol. 101, Suppl. 2, p.1-2.

Smith, S. I.,  M. A. Mirando, 2018. Opportunities for federal funding of modeling research. J. Dairy Sci. Vol. 101, Suppl. 2, p.1-2.