Crop and Pasture Science Crop and Pasture Science Society
Plant sciences, sustainable farming systems and food quality

Improved wheat yield and production forecasting with a moisture stress index, AVHRR and MODIS data

A. G. T. Schut A B E , D. J. Stephens C , R. G. H. Stovold D , M. Adams D and R. L. Craig D

A Department of Spatial Sciences, Curtin University of Technology, GPO Box U1987, Perth, WA 6001, Australia.

B Cooperative Research Centre for Spatial Information, 723 Swanston Street, Parkville, Vic. 3052, Australia.

C Department of Agriculture and Food Western Australia (DAFWA), Locked Bag 4, Bentley Delivery Centre, WA 6983, Australia.

D Landgate, PO Box 2222, Midland, WA 6056, Australia.

E Corresponding author. Email:

Crop and Pasture Science 60(1) 60-70
Submitted: 29 May 2008  Accepted: 28 October 2008   Published: 5 January 2009


The objective of this study was to improve the current wheat yield and production forecasting system for Western Australia on a LGA basis. PLS regression models including temporal NDVI data from AVHRR and/or MODIS, CR, and/or SI, calculated with the STIN, were developed. Census and survey wheat yield data from the Australian Bureau of Statistics were combined with questionnaire data to construct a full time-series for the years 1991–2005. The accuracy of fortnightly in-season forecasts was evaluated with a leave-year-out procedure from Week 32 onwards. The best model had a mean relative prediction error per LGA (RE) of 10% for yield and 15% for production, compared with RE of 13% for yield and 18% for production for the model based on SI only. For yield there was a decrease in RMSE from below 0.5 t/ha to below 0.3 t/ha in all years. The best multivariate model also had the added feature of being more robust than the model based on SI only, especially in drought years. In-season forecasts were accurate (RE of 10–12% and 15–18% for yield and production, respectively) from Week 34 onwards. Models including AVHRR and MODIS NDVI had comparable errors, providing means for predictions based on MODIS. It is concluded that the multivariate model is a major improvement over the current DAFWA wheat yield forecasting system, providing for accurate in-season wheat yield and production forecasts from the end of August onwards.


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