Traditionally, foraging plans for grazing animals such as beef cattle, prime lamb, and wool sheep have been made using a combination of manual observations, grazing rotation calendars, and farmer instinct. While these can become refined over time, they struggle to account for specific farm characteristics or seasonal events. In recent years, satellite technology has opened the door to data-driven grazing decisions by providing information on pasture growth and vegetation. However, these static observations cannot be used to predict changes to the farm landscape caused by weather events, climate change, or farm management practices.
This 6-month sprint project aims to test the feasibility of a tailored pasture, livestock supply, and sustainability forecaster called ‘Foragecaster’, delivered via leading livestock management company AgriWebb’s existing platform. AgriWebb have partnered with Australian Feedbase Monitor provider CiboLabs, leading sustainability models provider FlintPro, and researchers from the University of Technology Sydney (UTS) and Queensland University of Technology (QUT).
The team will create AI models that forecast the growth of pasture quantity and quality. These models will be combined with detailed animal characteristics to build simulation tools that AgriWebb customers can use to improve their grazing management. Additional reports and forecasts of key sustainability indicators will assist producers to improve the natural capital of their property and future-proof their business.
Foragecaster Project Lead Dr Kenny Sabir firstname.lastname@example.org
Food Agility Project Lead Ash Rootsey email@example.com