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

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

Research output: Contribution to journalArticleAcademicpeer-review

29 Citations (Scopus)


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.
Original languageEnglish
Pages (from-to)60-70
JournalCrop and Pasture Science
Issue number1
Publication statusPublished - 2009


  • difference vegetation index
  • crop yield
  • time-series
  • satellite imagery
  • winter-wheat
  • ndvi data
  • model
  • prediction
  • nitrogen
  • calibration

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