Forecasting growth of forage, grazing livestock and farm sustainability using Artificial Intelligence.
Project complete

In Partnership With:

Cibo Labs
Meat & Livestock Australia
Queensland University of Technology (QUT)
University of New England
University of Technology Sydney
NSW Department of Primary Industries


The Challenge

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.

The Solution

This three-year $6.5 million partnership will create an AI-supported grazing planner that combines seasonal climate forecasts with modelled pasture and livestock growth, allowing producers to develop a range of different management scenarios.

Foragecaster will first generate forecasts for feed quantity and quality at paddock scale. These forecasts will be available for any farm in Australia by combining leading remote sensing technology, biophysical growth models, climate and weather inputs, pasture composition research and animal grazing monitoring. The forecasts will be delivered via AgriWebb’s existing platform.

This feed availability forecast will then be integrated with best-in-breed animal growth models and new machine learning approaches using AgriWebb’s extensive animal production history. The outcome is a comprehensive and accurate forecasting tool for livestock weight gain.

Additional reports and forecasts of key sustainability indicators will assist producers to improve the natural capital of their property and future-proof their business. This project follows a nine-month feasibility study which helped the research team understand the requirements and market demand for livestock planning tools.


  • Build an operational and accurate prediction tool for livestock producers to enable optimised farm management and business decision making.
  • Allow livestock producers to leverage predictive data to mitigate risks associated with changing climate conditions.
  • Assist in developing a culture where supply chain decisions are based on data capture and analysis.

For more information, please contact Food Agility Projects and Foragecaster Project Lead Dr Kenny Sabir

meet the team

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