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The future of forecasting is here. Introducing Foragecaster.

Forecast the growth of your forage, grazing livestock and farm sustainability using Artificial Intelligence and Machine Learning.

AgriWebb - how to use livestock management software during calving season

The challenge

AgriWebb has historically focused on record collection of actuals entered by the producers. The Foragecaster initiative will use these best of breed models as inputs as well as machine learning to create a livestock supply and sustainability forecaster. Foragecaster will use the 60 million animals tracked over the last 8 years from over 12,000 producers in AgriWebb to develop machine learning models to help predict livestock growth. The producer will get a probabilistic forecast for pasture growth and availability as well as the sustainability metrics for their natural capital. These will be exposed to the producer through new planning tools including a grazing planner and a scenario planner. 

Project outputs

  • Grazing Planner – the pasture and livestock forecasting models will feed into a new grazing planner to support decision-making by taking into account climate forecasting and growth models, allowing the producer to forward plan
  • Climate information – localised historical and forcast weather information will be provided to the farmers
  • Sustainability metrics – the Foragecaster predictive models will be used to determine sustainability metrics both current and also forecasting into the next 6 months. The sustainability metrics will include the amount of carbon sequestration through vegetation and soil and the amount of emissions from the livestock as well as other factors that contribute to natural capital such as biodiversity.

If I’m going to have a pasture cover that is above average then I’m on the right track…and if I’m going to have way more feed than I need I can start thinking about not selling animals I was going to sell or buying more animals in

– NSW Grazier

Partner contributions

A 6 month pilot started in January 2023 with the below partners for the researchers to understand the problem space and the existing data, hire the required postdocs and researchers, and to start prototyping different approaches.


AgriWebb is leading the Foragecaster project and will contribute its collection of realised data, its large customer base for anonymous and aggregated analysis via the product data warehouse and its strong software engineering and product development expertise.


Cibo labs will run remote sensing across all AgriWebb historical farms over the last 7 years, to determine historical feed availability and – together with AgriWebb’s livestock and paddock improvement information – determine historic pasture growth rate.


Flint Pro will improve their product to be able to provide forecasts of sustainability metrics. AgriWebb and Flint Pro already have a working prototype integration of current and 10 year historic vegetation sequestration. Soil carbon sequestration is an ongoing research topic and will be refined over the project timeline.


UTS will provide machine learning expertise for livestock growth models, help with climate forecasting source evaluation and modelling the quality of pasture from remote sensing data.


QUT will also provide machine learning expertise for pasture growth models, also bayesian network and statistical analysis for the planners. UNE Smart Farms will be involved in the project to help evaluate the models and give Ag Science expertise.

Food Agility

Food Agility is a major project sponsor who has helped broker the collaborative research partnership, supported the design and ongoing delivery of the Foragecaster project.

Got questions? We’re here to help!

With thanks to our project partners

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