Modelling, AI open new avenues
By Lilian Schaer for Livestock Research Innovation Corporation
Poultry research around the world is evolving, giving farmers more tools to address complex issues, and improve the health and welfare of their flocks.
Reducing the carbon footprint of poultry production, avoiding the next global pandemic or finding alternatives to antimicrobial use in production are among a slew of issues facing the industry, and increasingly, science is turning to artificial intelligence and modelling in the search for practical, workable solutions for the livestock and poultry sectors.
Enter Jennifer Ellis, assistant professor in Animal Systems Modelling in the University of Guelph’s Department of Animal Bioscience with a particular specialty in poultry and dairy cattle nutrition modelling.
Her research uses models to identify patterns in data, increase understanding of how biological systems work to better predict outcomes, and build links between complex nutrition, health, genetics, and management data to help farmers with on-farm decision making.
“We have a big data wave coming at us, but it is worthless if we don’t know what to do with it,” she explains. “We have to harness the tools we do have for better opportunity analysis and decision-making on the farm. “
That’s where artificial intelligence can help. It’s an entire knowledge field that includes computer systems that can perform tasks that normally require human intelligence, like speech or image-recognition or decision-making.
According to Ellis, machine learning is a sub-group of that field that focuses on the development of algorithms to predict outcomes based on detecting patterns in data.
For poultry and livestock producers, this means a greater ability to track individual birds and animals instead of simply a pen, a herd, or a flock, and to do so without intensive labour involvement. It opens up new avenues for precision management, nutrition and feeding that can ultimately lead to healthier, more productive animals.
Examples include activity sensors that monitor and analyze behaviour and can alert producers to possible health problems before an animal shows actual clinical signs of illness or image analysis that can count livestock or estimate their body weight.
“Precision feeding is one area where we are already seeing some prototypes come to the surface, mainly for swine and poultry in research environments, where a system will estimate the weight and growth trajectory of an animal and customize its nutrition accordingly,” she says. “Also, if we can use technology to detect a health event before a vet can, that can offer huge economic return to the producer.”
Another area is precision nutrition formulation at the feed mill through the prediction of pellet quality and durability by analyzing the nutrient composition of inputs. Real-time formulation adjustments could provide more consistent feed.
“We can predict a lot of things already, but we also need to know what to do with that information. We are still in exploratory days with how we generate value from all this and ultimately, how we can improve efficiency, increase productivity, and make life easier on the farm,” she says.
Ellis currently has two research projects in this field underway. One, in partnership with the Ontario Ministry of Agriculture, Food and Rural Affairs (OMAFRA) and Trouw Nutrition, is using machine learning algorithms to predict pellet quality at the feed mill.
The other, a collaboration with OMAFRA, Trouw and Wageningen University, brings together artificial intelligence and mechanistic modelling to develop a smart precision nutrition system that ties biological knowledge to predictability.
This article is provided by Livestock Research Innovation Corporation as part of LRIC’s ongoing efforts to report on Canadian livestock research developments and outcomes. It was originally printed in the February/March 2022 issue of Canadian Poultry.