2026
- I. J. Martinez-Moyano. 2026. ASAS-NANP Symposium: Mathematical Modeling in Animal Nutrition: Modeling the dynamics of complex systems: Incorporating AI and HPC into dynamic modeling. J. Anim. Sci. skag310. doi:10.1093/jas/skag310
- Menendez, H. M., III, J. Adams, K. Kaniyamattam, and L. O. Tedeschi. 2026. ASAS-NANP SYMPOSIUM: MATHEMATICAL MODELING IN ANIMAL NUTRITION: Model Transfer and Application in Livestock Production Systems. J. Anim. Sci. skag308. doi:10.1093/jas/skag308
- Pi, Y., K. Kaniyamattam, A. Adekunle, A. Adiga, L. O. Tedeschi, N. Duffield, and C. Zhang. 2026. ASAS-NANP SYMPOSIUM: MATHEMATICAL MODELING IN ANIMAL NUTRITION: Data-efficient Computer Vision for Precision Livestock Farming. J. Anim. Sci. 101:skad316. doi:10.1093/jas/skag316
- Ellis, J. L., J. Bode, M. Sahar, J. You, D. Tulpan, N. Aldenhoven, and L. O. Tedeschi. 2026. ASAS-NANP SYMPOSIUM: MATHEMATICAL MODELING IN ANIMAL NUTRITION: Modelling in the Agriculture 4.0 Era and Beyond: Merging Mechanistic Knowledge and Artificial Intelligence with Hybrid Modelling. J. Anim. Sci. skad311. doi:10.1093/jas/skag311
- L. O. Tedeschi. 2026. ASAS-NANP SYMPOSIUM: MATHEMATICAL MODELING IN ANIMAL NUTRITION: The Evolution of Large Language Models and Their Impact on Animal Sciences. J. Anim. Sci. skad306. doi:10.1093/jas/skag306
2025
- Tedeschi, L. O. and H. M. Menendez, III. 2025. ASAS-NANP symposium: mathematical modeling in animal nutrition: training the future generation in data and predictive analytics for sustainable development. A summary of the 2024 symposium. J. Anim. Sci. 104:skaf452. doi:10.1093/jas/skaf452
- Menendez, H. M., III, Y. Chang, E. Palmer, F. La Manna, E. R. V. Moreno, I. Parsons, B. L. Turner, H. Moradi Rekabdarkolaee, A. L. Husmann, and L. O. Tedeschi. 2025. ASAS-NANP SYMPOSIUM: mathematical modeling in animal nutrition: the importance of animal science flight simulators to enhance the competitiveness and sustainability of livestock production. J. Anim. Sci. 104:skaf440. doi:10.1093/jas/skaf440
- Peter, J., M. Li, J. Brennan, H. Menendez, and H. Moradi Rekabdarkolaee. 2025. ASAS-NANP symposium: mathematical modeling in animal nutrition: review of time series techniques and machine learning models for advancing livestock production. J. Anim. Sci. 104:skaf439. doi:10.1093/jas/skaf439
- Condotta, I. C. F. S., and L. O. Tedeschi. 2025. ASAS-NANP Symposium: Mathematical modeling in animal nutrition: revolutionizing animal farming with artificial intelligence: trends, challenges, and opportunities. J. Anim. Sci. 104:skaf441 doi:10.1093/jas/skaf441
- Cadéro, A., Tedeschi, L. O., and F. Garcia-Launay. 2025. ASAS-NANP symposium: mathematical modeling in animal nutrition: contributions of mathematical modeling to life cycle assessment to support environmental sustainability of animal production. J. Anim. Sci. 104:skaf442. doi:10.1093/jas/skaf442
- Tedeschi, L. O. and H. M. Menendez, III. 2025. ASAS-NANP SYMPOSIUM: Mathematical Modeling in Animal Nutrition: Training the Future Generation in Data and Predictive Analytics for Sustainable Development. A Summary of the 2023 Symposium. J. Anim. Sci. 103:skaf141. doi:10.1093/jas/skaf141
- E. O. Oviedo-Rondón. 2025. ASAS-NANP symposium: mathematical modeling in animal nutrition: overview of poultry nutrition modeling. J. Anim. Sci. 103:skaf140. doi:10.1093/jas/skaf140
- L. O. Tedeschi. 2025. ASAS-NANP symposium: mathematical modeling in animal nutrition: synthetic database generation for non-normal multivariate distributions: a rank-based method with application to ruminant methane emissions. J. Anim. Sci. 103:skaf136. doi:10.1093/jas/skaf136
- Lee, M., D. H. Kim, S. Seo, and L. O. Tedeschi. 2025. Development of machine learning-based sub-models for predicting net protein requirements in lactating dairy cows. In Animals. 15(14):2127. doi:10.3390/ani15142127
- Lee, M., D. H. Kim, S. Seo, and L. O. Tedeschi. 2025. Development of machine learning models for estimating metabolizable protein supply from feed in lactating dairy cows. In Animals. 15(5):687. doi:10.3390/ani15050687
- Miller, P. S. 2025. The National Animal Nutrition Program: Digital – editable – perennial – accessible", Open Access Government January 2025, pp.464-465. doi:10.56367/OAG-045-11775
2023
- Brennan, J., H. M. Menendez, III, K. Ehlert, and L. O. Tedeschi. 2023. ASAS-NANP symposium: mathematical modeling in animal nutrition—Making sense of big data and machine learning: how open-source code can advance training of animal scientists. J. Anim. Sci. 101:skad317. doi: 10.1093/jas/skad317
- Kaniyamattam, K., and L. O. Tedeschi. 2023. ASAS-NANP symposium: mathematical modeling in animal nutrition: Agent-based modeling for livestock systems: the mechanics of development and application. J. Anim. Sci. 101:skad321. doi: 10.1093/jas/skad321
- Muñoz-Tamayo, R., and L. O. Tedeschi. 2023. ASAS-NANP symposium: Mathematical modeling in animal nutrition: the power of identifiability analysis for dynamic modeling in animal science - a practitioner approach. J. Anim. Sci. 101:skad320. doi: 10.1093/jas/skad320
- Tedeschi, L. O., H. M. Menendez, III, and A. Remus. 2023. ASAS-NANP SYMPOSIUM: Mathematical modeling in animal nutrition: Training the future generation in data and predictive analytics for sustainable development. A summary of the 2021 and 2022 symposia. J. Anim. Sci. 101:skad318. doi: 10.1093/jas/skad318
2022
- Jacobs, M., A. Remus, C. Gaillard, H. M. Menendez, III, L. O. Tedeschi, S. Neethirajan, and J. L. Ellis. 2022. ASAS-NANP SYMPOSIUM: MATHEMATICAL MODELING IN ANIMAL NUTRITION: Limitations and potential next steps for modeling and modelers in the animal sciences. J. Anim. Sci. 100 (6):1-15. doi: 10.1093/jas/skac132
- Menendez, H. M., III, J. R. Brennan, C. Gaillard, K. Ehlert, J. Quintana, S. Neethirajan, A. Remus, M. Jacobs, I. A. M. A. Teixeira, B. L. Turner, et al. 2022. ASAS-NANP SYMPOSIUM: MATHEMATICAL MODELING IN ANIMAL NUTRITION: Opportunities and challenges of confined and extensive precision livestock production. J. Anim. Sci. 100 (6):1-19. doi: 10.1093/jas/skac160
- Tedeschi, L. O. 2022. ASAS-NANP SYMPOSIUM: MATHEMATICAL MODELING IN ANIMAL NUTRITION: The progression of data analytics and artificial intelligence in support of sustainable development in animal science. J. Anim. Sci. 100 (6):1-11. doi: 10.1093/jas/skac111
2021
- Gerrits, W., M. Schop, S. de Vries, and J. Dijkstra. 2021. ASAS-NANP symposium: digestion kinetics in pigs: The next step in feed evaluation and a ready-to-use modeling exercise. J. Anim. Sci. 99 (2):1-8. doi: 10.1093/jas/skab020
- Morota, G., H. Cheng, D. Cook, and E. Tanaka. 2021. ASAS-NANP SYMPOSIUM: prospects for interactive and dynamic graphics in the era of data-rich animal science. J. Anim. Sci. 99 (2):1-17. doi: 10.1093/jas/skaa402
- Stephens, E. C. 2021. ASAS-NANP SYMPOSIUM: Review of systems thinking concepts and their potential value in animal science research J. Anim. Sci. 99 (2):1-7. doi: 10.1093/jas/skab021
- Tedeschi, L. O., D. P. Bureau, P. R. Ferket, and N. L. Trottier. 2021. ASAS-NANP SYMPOSIUM: Mathematical modeling in animal nutrition: training the future generation in data and predictive analytics for sustainable development. A Summary. J. Anim. Sci. 99 (2):1-3. doi: 10.1093/jas/skab023
- Wang, Z., S. Shadpour, E. Chan, V. Rotondo, K. Wood, and D. Tulpan. 2021. ASAS-NANP SYMPOSIUM: Applications of machine learning for livestock body weight prediction from digital images. J. Anim. Sci. 99 (2):1-15. doi: 10.1093/jas/skab022
2020
- Tran, H., A. Schlageter-Tello, A. Caprez, P. S. Millerm M. B. Hall, W. P. Weiss, and P. J. Kononoff. 2020. Development of feed composition tables using a statistical screening procedure. J. Dairy Sci. 103:P3786-3803. doi:10.3168/jds.2019-16702
- Schlageter-Tello, A., G. C. Fahey, T. Freel, L. Koutsos, P. S. Miller, and W. P. Weiss. 2020. ASAS-NANP Symposium: Ruminant/Nonruminant Feed Composition: Challenges and opportunities associated with creating large feed ingredient composition tables. J. Anim. Sci. 98. doi:10.1093/jas/skaa240
- Menendez III, H. M. and L. O. Tedeschi. 2020. The characterization of the cow-calf, stocker and feedlot cattle industry water footprint to assess the impact of livestock water use sustainability. J. Agric. Sci. doi:10.1017/S0021859620000672
- Mark D. Hanigan and Veridiana L. Daley. 2020. Use of Mechanistic Nutrition Models to Identify Sustainable Food Animal Production. Annu. Rev. Anim. Biosci. 8:355-376. doi:10.1146/annurev-animal-021419-083913
2019
- Daley, V. L., L. E. Armentano, P. J. Kononoff, and M. D. Hanigan. 2019. Modeling fatty acids for dairy cattle: models to predict total fatty acid concentration and fatty acid digestion of feedstuffs. J. Dairy Sci. 103:6982-6999. doi:10.3168/jds.2019-17407
- C.F Nicholson, A.R.P. Simões, P.A. LaPierre, M.E. Van Amburgh. 2019. ASN-ASAS Symposium: Future of Data Analytics in Nutrition: Modeling complex problems with system dynamics: applications in animal agriculture. J. Anim. Sci. 97:1903–1920. doi:10.1093/jas/skz105
- L.O. Tedeschi. ASN-ASAS Symposium: Future of Data Analytics in Nutrition: Mathematical modeling in ruminant nutrition: approaches and paradigms, extant models, and thoughts for upcoming predictive analytics. J. Anim. Sci. 7:1921–1944. doi:10.1093/jas/skz092
2018
2017
- R. R. White, Y. Roman-Garcia, J. L. Firkins, P. Kononoff, M.J. VandeHaar, H. Tran, T. McGill, R. Garnett, M.D. Hanigan. 2017. Evaluation of the National Research Council (2001) dairy model and derivation of new prediction equations. 2. Rumen degradable and undegradable protein. J. Dairy Sci. 100:3611-3627. doi:10.3168/jds.2015-10801
- R. R. White, Y. Roman-Garcia, J. L. Firkins, M.J. VandeHaar, L.E. Armentano, W.P. Weiss, T. McGill, R. Garnett, M.D. Hanigan. 2017. Evaluation of the National Research Council (2001) dairy model and derivation of new prediction equations. 1. Digestibility of fiber, fat, protein, and nonfiber carbohydrate. J. Dairy Sci. 100:3591-3610. doi:10.3168/jds.2015-10800
2016
- J. P. McNamara, M. D. Hanigan, R. R. White. 2016. Invited review: Experimental design, data reporting, and sharing in support of animal systems modeling research. J. Dairy Sci. 99:9355–9371. doi:10.3168/jds.2015-10303
- R. R. White, Y. Roman-Garcia, J. L. Firkins. 2016. Meta-analysis of postruminal microbial nitrogen flows in dairy cattle. II. Approaches to and implications of more mechanistic prediction. J. Dairy Sci. 99:7932-7944. doi:10.3168/jds.2015-10662
- Y. Roman-Garcia, R. R. White, J. L. Firkins. 2016. Meta-analysis of postruminal microbial nitrogen flows in dairy cattle. I. Derivation of equations. J. Dairy Sci. 99:7918-7931. doi:10.3168/jds.2015-10661
- R.R. White, M.D. Hanigan. 2016. Modeling cross species intake responses to environmental stress. J. Agric. Sci. 154:136-150. doi:10.1017/S0021859615001033
2015