Research Projects & Applications
Interactive tools, R packages, and research initiatives in agricultural statistics
πΎ Interactive Applications
Yield Response Curves (YRC) Project
A comprehensive analysis platform for wheat and barley variety yield responses to disease pressure. This national collaboration with GRDC delivers quantitative information on crop resistance and yield relationships.
Key features: multi-environment trial (MET) analysis results, dynamic visualisation tools for disease comparison, export capabilities for graphs and tables, and regional pathogen prioritisation.
Partners: DPIRD WA, Department of Agriculture and Fisheries QLD, Agriculture Victoria, DPI NSW.
Crop Protection Analytics System (CPAS)
Jointly developed with CCDM and SAGI West, CPAS estimates the economic value of disease management in Australian broadacre cropping β quantifying production value lost to disease and quality downgrade, and the benefit of managing it, across GRDC agro-ecological zones, crops, and seasons. Originally an R Shiny app, now converted to a standalone website.
GRDC Disease and Pest Impact Overview
A visualisation tool for understanding disease and pest impact on crop yield and profits, supporting agricultural decision-making.
π± Apps & Platforms
Stats Journey
An interactive learning platform for statistics and data science education β intuitive visualisations, real-time calculations, and coverage from basic stats to machine learning. Built with React, TypeScript, and Tailwind CSS.
Journal Suggester
Paste a manuscript title and abstract to receive ranked journal suggestions using semantic similarity and domain heuristics.
Data to Decision (Open Day Demo)
Game-based web experience teaching data-driven decision making, built for Curtin Open Day 2026.
π¦ R Packages
V-Spline Package
An adaptive V-spline implementation with Bayesian estimates for trajectory reconstruction from noisy GPS data β adaptive smoothing splines, Bayesian parameter estimation, and real-time processing capabilities.
π¬ Research Initiatives
On-Farm Strip Trials Optimization (2020β2024, GRDC funding)
Research question: systematic vs. randomised design for on-farm experiments.
Key findings: systematic designs show superior efficiency in certain conditions; spatial correlation modelling improves trial precision; Bayesian approaches provide robust uncertainty quantification.
Publications: Optimal design for on-farm strip trials β systematic or randomised? Field Crops Research (2024); Bayesian inference of spatially correlated random parameters Field Crops Research (2022).
Bayesian Workflow for Agricultural Data (2018βpresent)
Developing comprehensive Bayesian workflows for complex agricultural datasets: spatially correlated random effects modelling, MCMC diagnostics, prior specification and sensitivity analysis, and posterior predictive checking. Applied to crop yield prediction, disease resistance assessment, and environmental impact analysis.
Economic Analysis of Crop Protection (2022β2024)
Quantifying the economic value of different crop protection strategies: fungicide resistance impact assessment, genetic improvement vs. chemical control comparison, and riskβbenefit analysis for agricultural decisions.
Publications: Economic analysis of crop protection strategies Pest Management Science (2024); Socio-economic impact of fungicide resistance Advances in Agronomy (2023).
π€ Collaboration Opportunities
Iβm always interested in new research collaborations and partnerships. Current areas of interest:
- π€ Machine learning in agriculture β integrating ML with traditional statistical methods
- β‘ High-performance computing β scaling statistical analyses for big data
- π§ Open source development β contributing to the statistical software ecosystem
- π Industry partnerships β applied statistical solutions for agricultural challenges
Get in touch: if youβre interested in collaborating on any of these areas, please reach out via email or LinkedIn.