Agricultural Data Science

The Agricultural Data Science research group employs data-centric approaches to tackle agricultural carbon and nitrogen challenges.

Leveraging machine learning, meta-analysis, and process-based modelling, we improve the reliability in predicting carbon and nitrogen processes and evaluate the effectiveness of strategies to mitigate nitrogen pollution and greenhouse gas emissions in agroecosystems. We establish sustainable nitrogen indices, aiming to substantiate Australia’s ‘clean and green’ food reputation and guiding consumer choices while incentivising sustainable farming practices. In collaboration with the HyperSens Lab, we utilise remote sensing and GIS to analyse spatio-temporal data, enabling optimal resource allocation and precise decision-making in agricultural production.