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Peer-Reviewed Papers
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
2018
- 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. 2018. Bovine mammary gland biopsy techniques. J. Vis. Exp. 142:e58602. doi:10.3791/58602.
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
2015
