I started coming to UNE and the PHA in 2018 to help develop teaching materials from a book I helped wright and Mingan Choct edited. Retirement from the University of Georgia gave me time to study aspects of teaching poultry nutrition I had never found time for. I have stayed busy reading and writing papers and spreadsheets to simplify tedious calculations and help students and researchers understand some mathematical applications to animal nutrition.

Project 1. I updated my feed formulation program that was developed for teaching to include the Australian Feed Ingredient database (AFiD) that was put together by Amy Moss. It is unique because it included amino acids and more detailed carbohydrate information than other databases. Mingan Choct and his colleagues at UNE had been working to identify the various carbohydrates in feed ingredients. My students and I had been working on true protein and how it should be used in feed formulation. Combining the concepts resulted in a new Microsoft Excel-based feed formulation program for teaching. It can be downloaded free from the PHA web pages. It includes ingredient composition data based on what I proposed be called the Armidale method.

The Armidale method is the first overhaul to the Proximate Analysis method in about 165 years. Proximate Analysis, also called the Weende method, was developed in the Hanover Kingdom before the true nature of proteins, lipids and carbohydrates was even known. This is important because a paradigm shift in feed analysis is necessary to make the transition to real “precision” nutrition.

FURTHER READING

Poultry Hub Australia Web Page

User-Friendly Feed Formulation with data from Australia (UffdAu)

Poultry Science Paper https://www.sciencedirect.com/science/article/pii/S0032579124002153

Animal Production Science Paper

DOI: 10.1071/AN24176

CABI Animal Science Case Studies

https://www.cabidigitallibrary.org/doi/10.1079/animalsciencecases.2025.0016

Project 2. I helped adapt analytical chemistry techniques to animal bioassays. Bob Swick and his student, Anna Nyugen, ran a bioavailability trial on a zinc source that had an excellent, if unusual, design. I remembered that analytical chemists do their calculations differently than typical biologists: Chemistry students are taught in beginning classes to run standard, or “calibration” curves intuitively. They are not taught to calculate them correctly until analytical chemistry class. In a nutshell, we are first taught how a response relates to a dose. We assume that the same equations apply to how the dose relates to the responses (the bioavailability problem). Unfortunately, the same equations do not always apply. We developed an Excel spreadsheet to make all the appropriate calculations. It is available from the PHA web pages, and we published a paper about it in the Animal Nutrition journal. It is important because by using the correct equations, researchers can be more accurate and confident in their results than by using the intuitive method.

FURTHER READING

Poultry Hub Australia Web Page

Calibration Curve Confidence Calculator

Animal Nutrition Paper

https://www.sciencedirect.com/science/article/pii/S2405654522000506

Project 3. I demonstrated the effects of ratios on statistical conclusions. I first worked on bioenergetics at UNE with David Farrell in the 1980’s. It has become dogma in many fields of science to analyze response variables in proportion to “Metabolic body size” or BW^0.75. BW^0.75 is based on the resting metabolic rate, (measured in heat production). It is represented by the body weight of an animal taken to the ¾ power. It is applied to all manner of biological responses, for both resting and active animals without first checking to see if there even is any relationship between the response and body weight. It is generally assumed that there is some sort of natural law that says resting metabolic rate is related to BW^0.75. It follows that animal responses should automatically be adjusted for BW^0.75. Actually, there is just a slight curve in the relationship between resting heat production and body weight for animals between mice and whales.

The exponential function (BW^¾ power) was derived because the pioneers of animal bioenergetics did not have computers in the 1920’s. The tool they had to represent curvilinear relationships was log-linear graph paper. They used it and found, the slope came out to about 0.75. If they were fitting such data today, they would likely just fit a quadratic curve and find that it fits just as well or even better. Karl Pearson, a major founder of modern statistical science, warned about making new variables by dividing one normally distributed variable by another (like body weight^0.75). He determined that they introduced spurious (false) effects. Unfortunately, Pearson did not have a laptop computer that could do simulations to prove his points. Lynne Billard and I were able to run some simulations and confirm what Pearson hypothesized in 1896 was true. This is important because it shows that researchers should always consider each variable by itself before making any sort of ratios or assuming any curve without testing for it.

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