Justin Sexton

Overview

Recently Justin has been contributing to the development of the Opticane.net web app. This has included working with integrating weather forecast data and on-line crop model as well as contributing to the development of the front end user experience.

Justin Sexton is currently a postdoctoral research fellow at James Cook University. He has recently completed his PhD at JCU in the area of Near Infra-Red Spectroscopy (NIRS). Justin has a background in statistical analysis. His research focus has primarily been within the Australian Sugarcane industry.

Aside from NIRS, Justin's research projects has included climate change analysis, agricultural systems simulation and crop model calibration. Justin has also contributed to research in mathematics and science teaching.

Current Projects

CSSIP (2020 - ): Climate Smart Sugarcane Irrigation Partnership

The CSSIP project aims to help farmers improve their sugarcane irrigation management. We are:

• Developing improved weather forecasts that account for variability across the Burdekin catchment;
• Integrating the weather forecasts within a crop model such as IrrigWeb; and
• Building software that empowers farmers to irrigate more efficiently.

The CSSIP project will develop weather forecasts with improved spatial resolution, accounting for the regional variability across the Burdekin catchment. Using historical weather data, we have identified five climate zones. The project will generate different weather forecasts for each zone. These will be based upon the Bureau of Meteorology forecasts but “downscaled” to account for systematic differences between zones. Improved spatial resolution in weather forecasts means that farmers can access more accurate information, allowing for better decision making and improved farm management. A current version of the web-app associated with this project is available here.

Higher Education Projects

PhD (2020): Statistical data mining algorithms for optimising analysis of spectroscopic data from on-line NIR mill systems

This Thesis looked at the idenitification of atypical (deteriorated) sugarcane samples using on-line Near Infrared (NIR) sensors. It was found that identifying atypical samples could help remove bias in estimates of Commercial Cane Sugar (CCS), a primary measure used in cane payment calculations. The methodology developed in this thesis could be used to track occurrences of atypical cane or improve quality estimates providing benefits at various stages along the industry value chain. The full thesis can be found here.

MSc (2014): Bayesian statistical calibration of variety parameters in a sugarcane crop model

The objectives of this thesis were to:

1. investigate the capability of the APSIM‐Sugar model to simulate yield differences between sugarcane varieties under different climatic conditions;
2. investigate the sensitivity of model outputs such as biomass and sucrose yields to key model input parameters; and
3. evaluate the use of two Bayesian approaches to calibrate variety parameters in the APSIM‐Sugar model.

This thesis developed and tested a methodological framework which include performing a global sensitivity analysis and a Bayesian approach to calibrate variety parameters in APSIM-Sugar. The methodological framework provided a validated strategy for improving and updating variety definitions. The full thesis can be found here.