The following staff are available to supervise honours and masters research in the Agricultural Data Science research group.
A/Prof Shu Kee (Raymond) Lam
Raymond’s research focuses on understanding soil carbon and nitrogen dynamics, enhancing fertiliser nitrogen use efficiency, and mitigating greenhouse gas emissions. He integrates big data analytics, including meta-analysis and machine-learning modelling, with experimental findings from laboratory and field trials to improve soil management, agricultural practices, and environmental stewardship.
Project topics:
- Advancing agroecosystem modelling of nitrogen losses with machine learning
- Enhanced-efficiency fertilisers and their impacts on nitrogen losses in sugarcane and vegetable systems
- Meta-analysis of greenhouse gas mitigation strategies in agricultural systems
- Integrating top-down and bottom-up approaches for nitrogen assessment
- Carbon dynamics of peatland degradation and restoration
Dr Alexis Pang
Alexis' research mainly focuses on novel applications of technologies and platforms for sustainable agricultural intensification and food production. These include machine learning techniques and spatial modelling, remote-sensing, agricultural systems modelling and development/application of low-cost sensors/platforms. Alexis also researches into innovative technologically-enabled pedagogies for agricultural and environmental education.
Project topics:
- Application of data science techniques on soil databases unearth new insights into, and modelling approaches for soil C and N dynamics.
- Development of innovative techno-pedagogical designs for effective learning in the agricultural and environmental sciences.
Dr Baobao Pan
Baobao's research focuses on understanding agroecosystems' soil carbon and nitrogen dynamics by integrating big data analytics, machine learning techniques and process-based models. This research seeks to mitigate the environmental impact of climate change and improve sustainable management practices.
Project topics:
- Data-driven analysis incl. meta-analysis, 'big data' to optimize nitrogen management and reduce greenhouse gas in agriculture
- Hybrid modelling for accurate nitrogen process simulation in agricultural systems
- Simulate, predict, and evaluate the effectiveness of different management practices on carbon and nitrogen dynamics by process-based model (APSIM, DNDC) and machine learning
Dr Emma Liang
Dr Xia (Emma) Liang has developed a new research focus of evidenced-based environmental footprint and sustainability (green) index for Australian and global agricultural products. Agriculture is on the cusp of a ‘big data’ revolution and she has mastered the skills of global data synthesis and analyses and has built interdisciplinary expertise in life cycle assessment, soil science, environmental and food science, big data analytics and agricultural econometrics and GIS analysis.
Project topics:
- Evidenced-based environmental footprint and sustainability (green) index for Australian and global agricultural products.
- Cost-effective nitrogen uses for improved profitability and sustainability of rice production in Asia-Pacific countries, including Lao PDR, Myanmar, China and Australia.
- Identification and valuation of the social costs of nitrogen pollution, i.e. monetary value of the damage caused by nitrogen loss to the environment.
- Application of the “5 Ps” principles (Production, People, Planet, Policy and Partnerships) to shape guidelines for sustainable N management with multidimensional N metrics (i.e., N use efficiency, virtual N factor, N footprint, N neutrality, reactive N spatial intensity, N boundary, N price and N equity).