Interpretive Summary: ASAS-NANP symposium: mathematical modeling in animal nutrition: the progression of data analytics and artificial intelligence in support of sustainable development in animal science

By: Luis O. Tedeschi

Data analytics have evolved remarkably. This paper discussed the evolution of data analytics from a competitive advantage perspective within academia and illustrated the combination of different advanced technological systems in developing hybrid intelligent mechanistic models (HIMM). Data analytics tools are divided into 3 stages. The first stage (collect and respond) ensures that data are correct and free of influential data points, and it represents the data and information phases for which data are cataloged and organized. The second stage (predict and prescribe) results in gained knowledge from the data and comprises most predictive modeling paradigms, and optimization and risk assessment tools are used to prescribe future decision-making opportunities. The third stage (smart learning and policy making) aims to apply the information obtained in the previous stages to foment knowledge and use it for rational decisions. Although still incipient, HIMM form the forthcoming stage of competitive advantage. HIMM may not increase our ability to understand the underlying mechanisms controlling the outcomes of a system, but it may increase the predictive ability of existing models by helping the analyst explain more of the data variation. The scientific community needs to resolve the lack of transparency and reporting of artificial intelligence for code reproducibility.

Read the full article on the Journal of Animal Science.