Meat and Livestock Australia has partnered with 10 industry research and ag-tech stakeholders in a new $6.5m project to improve on-farm forecasting using Artificial Intelligence (AI).
The ‘Foragecaster’ project will develop a planning tool that utilises seasonal climate forecasts, on-farm management practices and modelled pasture and livestock growth to predict the future status of forage, grazing livestock and farm sustainability.
The tool will also provide users with the ability to develop a range of different management scenarios through its AI-supported predictions – allowing for better-informed decision making and future planning.
Project outputs
Led by AgriWebb, the project is currently one year into its three-year developmental phase and includes key inputs from Australian Feedbase Monitor provider Cibo Labs, leading sustainability models deliverer FlintPro, agrifood innovation deliverer Food Agility, and researchers from the University of Technology Sydney (UTS) and Queensland University of Technology (QUT).
According to project lead and AgriWebb’s VP of Research and Development, Dr Kenny Sabir, Foragecaster has three main areas of focus.
These include:
livestock growth
pasture growth and availability
sustainability metrics.
“Using data from 60 million animals tracked over the last eight years by more than 12,000 AgriWebb producer members, we’re able to generate machine-learning models that could help predict livestock growth,” Dr Sabir said.
“Then, by also taking into consideration weather events, climate changes and producer land management practices, the tool will be able to provide producers with a probabilistic forecast for pasture growth and availability, as well as the sustainability metrics for their natural capital.
“Both localised historical and forecasted weather information is provided to producers using the tool.
“Foragecaster predictive models will also use weather data alongside the grazing land management practices the producer inputs to determine the business’ current sustainability metrics and what they are likely to be six months into the future.
“This includes modelled predictions of the amount of carbon sequestered through vegetation and soil, the amount of emissions produced by the livestock, and biodiversity.”
Beta trial results
A year out of the project’s pilot phase, AgriWebb completed beta trials of the tool’s grazing planner capabilities with some of their member producers, testing its usability and on-farm production benefits.
Stacey Hogan, the Lead Product Manager at AgriWebb, said that throughout the trials, over 25% of the pasture predictions adjusted as producers actioned management strategies.
“As producers did things like log moved animals, the tool updated to reflect the most current status of on-farm production,” she said.
“This is one of the aspects we think makes the product so valuable and unique when compared to what’s already on the market.”
Foragecaster’s grazing planner is currently available in the AgiWebb Marketplace as an add-on to support rotational grazing land management.
Access the tool via agriwebb.com/grazing-management.
Moving forward, Dr Sabir said Foragecaster’s AI capabilities are to be further developed, incorporated and trialled on-farm over the next 2-3 years before becoming available for commercial use.
