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RESEARCH ARTICLE
Contents Vol 58(2)

A Bayesian modelling approach for long lead sugarcane yield forecasts for the Australian sugar industry

Y. L. Everingham A B E , N. G. Inman-Bamber A , P. J. Thorburn C and T. J. McNeill D
+ Author Affiliations
- Author Affiliations

A CSIRO Sustainable Ecosystems, Davies Laboratory, Townsville, Qld 4814, Australia.

B School of Mathematical and Physical Sciences, James Cook University, Townsville, Qld 4814, Australia.

C CSIRO Sustainable Ecosystems, Queensland Bioscience Precinct, 306 Carmody Rd, St Lucia, Qld 4067, Australia.

D Sugar InSite Pty Ltd, Kangaroo Pt, Brisbane, Qld 4169, Australia.

E Corresponding author. Email: yvette.everingham@jcu.edu.au

Australian Journal of Agricultural Research 58(2) 87-94 https://doi.org/10.1071/AR05443
Submitted: 19 December 2005  Accepted: 27 October 2006   Published: 22 February 2007

Abstract

For marketers, advance knowledge on sugarcane crop size permits more confidence in implementing forward selling, pricing, and logistics activities. In Australia, marketing plans tend to be initialised in December, approximately 7 months prior to commencement of the next harvest. Improved knowledge about crop size at such an early lead time allows marketers to develop and implement a more advanced marketing plan earlier in the season. Producing accurate crop size forecasts at such an early lead time is an on-going challenge for industry. Rather than trying to predict the exact size of the crop, a Bayesian discriminant analysis procedure was applied to determine the likelihood of a small, medium, or large crop across 4 major sugarcane-growing regions in Australia: Ingham, Ayr, Mackay, and Bundaberg. The Bayesian model considers simulated potential yields, climate forecasting indices, and the size of the crop from the previous year. Compared with the current industry approach, the discriminant procedure provided a substantial improvement for Ayr and a moderate improvement over current forecasting methods for the remaining regions, with the added advantage of providing probabilistic forecasts of crop categories.

Additional keywords: crop model, simulate, discriminant, prediction, climate, APSIM.


Acknowledgments

Funding for this research was provided by Queensland Sugar Limited and the Australian government through the Sugar Research and Development Corporation. Rainfall data were obtained from the Australian Bureau of Meteorology patched point dataset. Productivity data were provided by Queensland Sugar Limited. The first author was employed by CSIRO whilst this research was undertaken. The authors thank Carla Chen (JCU) for assisting with the preparation of this manuscript.


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