Behind the scenes with Ph.D. candidate Melissa Williams

Research Roundup Fall 2023

Two steers in a feedlot

Tell us about your background and what brought you to the University of Guelph! 

I am currently a Ph.D. Candidate at the University of Guelph, where I began my post-secondary academic career 10 years ago, in the Animal Biology program. Since then, I have been able to bring together my love for cattle, learning new things, and problem-solving in my graduate education. I completed my Masters degree with a focus on beef cattle nutrition in 2019, and plan to finish my Doctorate in a similar area of research at the end of 2023.

Can you tell us about what a meta-analysis is and what your meta-analysis focuses on?

A meta-analysis is a form of research where you gather data from similar studies and then analyze it to answer specific questions. This type of research allows for the utilization of data from around the world, with small variations in the experimental design or focus on finding broad themes or the overall trends of a specific question. Essentially, it is trying to paint a picture with the data that is currently out there across multiple similar experiments.

My meta-analysis focused on the ability to predict the performance and carcass outcomes of feedlot beef cattle using a dietary fibre characteristic called undigestible neutral detergent fibre (uNDF) as a driving variable. Additionally, I assessed the prediction capabilities of the digestible portion of NDF, versus the undigestible portion (uNDF) on outcomes of interest like feed intake, gain, feed conversion, hot carcass weight, and dressing percentage.

What is the take-home message of this paper?

The take-home message of this project was that the undigestible portion of NDF, uNDF, is a better predictor of the performance and carcass characteristics of feedlot cattle than the digestible portion of NDF. Although both of these variables are not as strong predictors in general of the outcomes like overall feed intake, the energy density of the diet, or forage inclusion, uNDF is a better predictor than NDF. Therefore, my co-authors and I believe that improved characterization of uNDF would be a beneficial area for continued research. Additionally, we recommend measuring and reporting uNDF in academia and industry.

I understand that this meta-analysis has various limitations; could you elaborate on that for us? 

Firstly, at the time of data collection, only one published research article assessed uNDF and feedlot performance outcomes. Therefore, our next step was to look for experiments where the amount or type of forage was the varying treatment, which would typically change the amount of uNDF within each treatment diet. The project's most prominent limitation was the overall lack of reporting of uNDF in current published research nutrition tables. This limitation was circumvented by using a nutritional database at Cumberland Valley Analytical Services, where the uNDF value of the diets was estimated for each experiment in the meta-analysis. We, the authors, understand that with uNDF being an estimated value, there is increased variation within the data; however, the conclusions still point at dietary uNDF being a promising variable for performance prediction in the feedlot. What remains here in this project is a great starting point for an updated meta-analysis when more research is published with reported uNDF values.

What are your plans after wrapping up your Ph.D?

After I have completed my Doctorate, I hope to find work in my field that allows me to continue researching and helping producers meet the needs of consumers.

Did you have any funding sources or anyone working on the project with you?

This project did not have any funding associated with it. I want to thank Cumberland Valley Analytical Services for access to their database, which allowed us to estimate the uNDF values of the diets used in this meta-analysis. I would also like to thank my co-authors, Dr. Jennifer Ellis, Dr. Katharine Wood, and Dr. Greg Penner, for their support and Jordan Johnson for aid in data collection.

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