James Cook University (JCU)

From March 2021 to November 2023, I served as an Agroecological Modeller and Senior Research Fellow at James Cook University, where I played a central role in developing innovative digital tools and modelling approaches to support sustainable irrigation and enhance natural-capital outcomes in Queensland’s sugarcane industry. I contributed to Phase 1 of the multi-partner Irrigation Rapid Assessment Tool (i-RAT) project in the Burdekin region, and initiate and led Phase 2 in the Mackay–Whitsunday region, overseeing technical design, stakeholder engagement, and delivery of a modern decision-support platform. This leadership included coordinating with growers, extension specialists, NRM groups, water boards, and government partners to co-design practical, data-driven solutions that addressed region-specific irrigation challenges. I also supervised a front-end developer responsible for migrating the original R Shiny prototype to a full React/JavaScript interface, ensuring the platform met industry expectations for usability, scalability, and long-term maintainability.

A major component of my work involved integrating crop physiology, systems modelling, and geospatial analytics into the i-RAT decision-support framework. I designed and implemented the first interactive prototype of i-RAT, hosted on AWS and built using R Shiny, which combined APSIM-Sugar simulations with real-world agronomic and environmental data to assess irrigation efficiency and sustainability outcomes. I later contributed to the architecture of the production version of i-RAT (i-rat.net), which integrates a React/JavaScript front end with an AWS-based back end. I also developed a comprehensive agronomic data cube, merging simulation outputs, meteorological records, and grower datasets to support long-term evaluation of irrigation-management strategies across contrasting soils and climates. These modelling and data-integration efforts supported broader Great Barrier Reef protection objectives by enabling scenario testing of reduced nutrient losses, improved water-use efficiency, and enhanced farm productivity.

My research also encompassed advanced data science, large-scale simulation, and cloud-based analytics. I processed and analysed large simulation, weather, and agronomic databases stored in SQL, CSV, and Parquet formats, using Python (pandas, numpy, scipy, matplotlib, seaborn) and R (ggplot2, tidyverse, data.table, doParallel, DBI, dbplyr). I contributed to the development of automated workflows for model execution, data cleaning, and exploratory analysis to ensure consistent, reproducible outputs for decision-support. These analyses provided valuable insights into irrigation performance, climate interactions, and environmental risk profiles across catchments. Through this blend of modelling, data engineering, and stakeholder engagement, I helped JCU strengthen its role in digital agriculture and delivered scalable, industry-ready tools that support evidence-based irrigation management in one of Australia’s most important agricultural regions.

Projects