Our agricultural systems modelling research aims facilitate practice change on farm, by providing innovative decision support systems and apps helping farmers make informed decisions that relate to their production practices, to improve their profitability and social and environmental sustainability.
Title: Diagnosis frameworks for multiple and complex soil constraints
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Funding Body: Soil CRC
This project will develop and validate a diagnostic framework that is capable of diagnosing soil constraints from the data that producers already have access to (e.g. crop yields, surface soil tests) in combination with information in the public domain (e.g. national soil grid, Landsat imagery).
Title: Customising ARMonline to make dry-land grain growers more productive, profitable and adaptive to drought creating environmental, economic and social resilience.
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Project Team: | Roy Anderson
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Funding Body: Future Drought Fund
This project combines an advanced but easy-to-use platform of crop and bioeconomic analysis () with game-based learning approaches to improve business, social and environment resilience to drought.
Read more >> Download the Increasing Drought Resilience of Broadacre Farming through Digital Supports Tools leaflet.
Title: Modelling based studies for sesame adoption in Australian farming system
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This project aims to provide this information in using a modelling-based framework in scaling localised research outputs and targeting the identified options and management practices to site specific environmental of production and of farming practices.
Title: Knowledge-guided machine learning optimisation of soil constraint management
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This project aims to find the best ways to manage multiple soil constraints such as sodicity, acidity, and salinity to help farmers make informed soil management decisions to maximise productivity and profitability.
Title: Trialling customisation of a water quality model in an ungauged catchment for DIN.
Leader: Cherie O’Sullivan
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Funding Body: Department of Environment and Science
This project will develop data and approaches to ensure water models are relevant, effective and efficient and capitalise on technological developments and consider non-traditional information sources.