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.

中文翻译：

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ASAS-NANP研讨会：动物营养中的数学建模：培训下一代数据和可持续发展的预测分析。总结

数据分析和数学建模（MM）对于理解与科学和社会相关的复杂系统至关重要（美国国家科学，工程和医学研究院，2019年）。数学建模可以定义为对现实的抽象和简化，以捕获和集成系统内的交互。100多年来，它一直是动物营养的重要工具（法国和Kebreab，2008年）。在动物营养中，MM对于做出可以在现实世界中应用的决策至关重要，例如平衡饮食，补充饮食反应以及特定饮食下营养的排泄（Tedeschi和Fox，2020年）。但是，尽管MM非常重要，但学生和研究人员几乎没有机会接受有关建模原理的培训。数据和预测分析的最新进展（Tedeschi，2019a）（包括人工智能（AI））使缺乏培训成为进一步发展MM的更大挑战。因此，国家动物营养计划建模委员会（NANP； https://animalnutrition.org）的主要目标是：1）提高对数据和预测分析的定量MM方法的需求和方法的认识，以及2）开发子孙后代在动物科学计划中的MM技能。