Asas-Nanp Symposium: Mathematical Modeling in Animal Nutrition: Limitations and potential next steps for modeling and modelers in the Animal Sciences
Abstract
The field of animal science, and especially animal nutrition, relies heavily on modeling to accomplish its day-to-day objectives. New data streams ('big data') and the exponential increase in computing power have allowed the appearance of 'new' modelling methodologies, under the umbrella of artificial intelligence (AI). However, many of these modelling methodologies have been around for decades. According to Gardner, technological innovation follows five distinct phases: technology trigger, peak of inflated expectations, trough of disillusionment, slope of enlightenment, and plateau of productivity. The appearance of AI certainly elicited much hype within agriculture leading to over-promised plug-and-play solutions in a field heavily dependent on custom solutions. The threat of failure can become real when advertising a disruptive innovation as sustainable. This does not mean that we need to abandon AI models. What is most necessary is to demystify the field and placing a lesser emphasis on the technology and more on business application. As AI becomes increasingly more powerful and applications start to diverge, new research fields are introduced, and opportunities arise to combine 'old' and 'new' modelling technologies into hybrids. However, sustainable application is still many years away, and companies and universities alike do well to remain at the forefront. This requires investment in hardware, software, and analytical talent. It also requires a strong connection to…
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