CSIRO Publishing blank image blank image blank image blank imageBooksblank image blank image blank image blank imageJournalsblank image blank image blank image blank imageAbout Usblank image blank image blank image blank imageShopping Cartblank image blank image blank image You are here: Journals > Crop & Pasture Science   
Crop & Pasture Science
Journal Banner
  Plant Sciences, Sustainable Farming Systems & Food Quality
blank image Search
blank image blank image
blank image
  Advanced Search

Journal Home
About the Journal
Editorial Board
Online Early
Current Issue
Just Accepted
All Issues
Special Issues
Research Fronts
Farrer Reviews
Sample Issue
For Authors
General Information
Notice to Authors
Submit Article
Open Access
For Referees
Referee Guidelines
Review an Article
Annual Referee Index
For Subscribers
Subscription Prices
Customer Service
Print Publication Dates

blue arrow e-Alerts
blank image
Subscribe to our Email Alert or RSS feeds for the latest journal papers.

red arrow Connect with us
blank image
facebook twitter youtube

red arrow Farrer Reviews
blank image

Invited Farrer Review Series. More...

red arrow PrometheusWiki
blank image
Protocols in ecological and environmental plant physiology


Article << Previous     |     Next >>   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, T. J. McNeill D

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
PDF (204 KB) $25
 Supplementary Material
 Export Citation


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.

Keywords: crop model, simulate, discriminant, prediction, climate, APSIM.

Subscriber Login

Legal & Privacy | Contact Us | Help


© CSIRO 1996-2015