2021 review

ASAS-NANP SYMPOSIUM: Mathematical modeling in animal nutrition: training the future generation in data and predictive analytics for sustainable development. A Summary

[Review of ]. JOURNAL OF ANIMAL SCIENCE, 99(2).

By: L. Tedeschi *, D. Bureau *, P. Ferket n  & N. Trottier *

MeSH headings : Animal Nutritional Physiological Phenomena; Animals; Models, Theoretical; Social Responsibility; Sustainable Development
TL;DR: The main goals of the Modeling Committee of the National Animal Nutrition Program (NANP) are to raise awareness of the needs and methods for quantitative MM approaches for data and predictive analytics and develop MM skills for future generations in animal science programs. (via Semantic Scholar)
UN Sustainable Development Goal Categories
13. Climate Action (Web of Science)
Source: Web Of Science
Added: June 10, 2021

Data analytics and mathematical modeling (MM) are essential to understand complex systems related to science and society (National Academies of Sciences, Engineering, and Medicine, 2019). Mathematical modeling can be defined as an abstraction and simplification of reality to capture and integrate interactions within a system. It has been a vital tool in animal nutrition for over 100 yr (France and Kebreab, 2008). In animal nutrition, MM is essential to make decisions that can be applied in the real world, such as balancing diets, dietary supplementation responses, and excretion of nutrients given a specific diet (Tedeschi and Fox, 2020). However, despite MM’s importance, there are few opportunities for students and researchers to receive training in modeling principles. Recent advancements in data and predictive analytics (Tedeschi, 2019a), including artificial intelligence (AI), make this lack of training an even more daunting challenge for further developing MM. Hence, the main goals of the Modeling Committee of the National Animal Nutrition Program (NANP; https://animalnutrition. org) are to 1) raise awareness of the needs and methods for quantitative MM approaches for data and predictive analytics and 2) develop MM skills for future generations in animal science programs. Symposium Overview