Justin Sexton

Measuring and modelling CO2 effects on sugarcane

In order to fully capture the benefits of rising CO2 in adapting agriculture to climate change, we first need to understand how CO2 affects crop growth. Several recent studies reported unexpected increases in sugarcane (C4) yields under elevated CO2, but it is difficult to distinguish direct leaf-level effects of rising CO2 on photosynthesis from indirect water-related responses. A simulation model of CO2 effects, based purely on changes in stomatal conductance (indirect mechanism), showed transpiration was reduced by 30% (initially) to 10% (closed canopy) and yield increased by 3% even in a well-irrigated crop. The model incorporated the results of a field experiment, and a glasshouse experiment designed to disentangle the mechanisms of CO2 response: whole-plant transpiration and stomatal conductance were both 28% lower for plants growing with high-frequency demand-based watering at 720 vs 390?ppm CO2, but there was no increase in biomass, indicating that indirect mechanisms dominate CO2 responses in sugarcane. Novel glasshouse method separates direct and indirect effects of CO2 on crop growth.Novel modelling technique scales CO2 effects from glasshouse to field environments.Reduced transpiration under elevated CO2 accounts for sugarcane responses.Direct effects of elevated CO2 on sugarcane photosynthesis, if any, are small.Effect of CO2 on transpiration of field crops declines as canopy develops.

Harvest disruption projections for the Australian sugar industry

Purpose – This study aims to investigate the effects of climate change on harvestability for sugarcane-growing regions situated between mountain ranges and the narrow east Australian coastline. Design/methodology/approach – Daily rainfall simulations from 11 general circulation models (GCMs) were downscaled for seven Australian sugarcane regions (1961:2000). Unharvestable days were calculated from these 11 GCMs and compared to interpolated observed data. The historical downscaled GCM simulations were then compared to simulations under low (B1) and high (A2) emissions scenarios for the period of 2046-2065. The 25th, 50th and 75th percentiles of paired model differences were assessed using 95 per cent bootstrapped confidence intervals. Findings – A decrease in the number of unharvestable days for the Burdekin (winter/spring) and Bundaberg (winter) regions and an increase for the Herbert region (spring) were plausible under the A2 scenario. Spatial plots identified variability within regions. Northern and so...

Using student feedback to improve student attitudes and mathematical confidence in a first year interdisciplinary quantitative course: from the ashes of disaster!

Todays scientist is faced with complex problems that require interdisciplinary solutions. Consequently, tertiary science educators have had to develop and deliver interdisciplinary science courses to equip students with the skills required to solve the evolving real-world challenges of today and tomorrow. There are few reported studies of the lessons learned from designing and delivering first year compulsory interdisciplinary science subjects at regional universities. Even fewer studies assess the impact that teaching interventions within interdisciplinary courses have on students’ attitudes towards mathematics and technology, and mathematics anxiety. This paper discusses the feedback received from the first student cohort of a new compulsory, first year interdisciplinary science subject at a regional Australian university which resulted in curricular revisions. These revisions included a greater emphasis on the subject relevance and increased student support in tutorials. Assessment practices were also dramatically modified. The change in student attitudes and anxiety levels a priori and a posteriori to the interventions was measured quantitatively and qualitatively. Post-intervention, female and non-mathematics major students had grown in mathematical confidence and were less anxious. It is important that positive and negative research findings are reported, so science educators can learn from one another, and can better prepare their students for the challenges they will face in bringing interdisciplinary solutions to contemporary real-world problems.

A comparison of non-linear regression methods for improved on-line near infrared spectroscopic analysis of a sugarcane quality measure

On-line near infrared (NIR) spectroscopic analysis systems play an important role in assessing the quality of sugarcane in Australia. As quality measures are used to calculate the payment made to growers, it is imperative that NIR models are both accurate and robust. Machine learning and non-linear modelling approaches have been explored as methods for developing improved NIR models in a variety of industrial settings, yet there has been little research into their application to cane quality measures. The objective of this paper was to compare chemometric models of commercial cane sugar (CCS) based on four calibration techniques. CCS was estimated using partial least squares regression (PLS), support vector regression (SVR), artificial neural networks (ANNs) and gradient boosted trees (GBTs). Model performance was assessed on an independent validation data set using root mean square error of prediction (RMSEP) and r2 values. SVR (RMSEP = 0.37%; r2 = 0.92) and ANN (RMSEP = 0.36%; r2 = 0.93) performed similarly to PLS (RMSEP = 0.37%; r2 = 0.92) on the validation data set, while GBT exhibited a much lower skill (RMSEP = 0.51%; r2 = 0.85). Analysis of important wavelengths in each model showed that PLS regression, SVR and ANN techniques emphasized the importance of similar spectral regions. Future research should consider testing model robustness over seasons and/or regions. Comparisons of chemometric models should consider reporting variable importance as a way of understanding how models use spectral information.