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Lead time and skill of Australian wheat yield forecasts based on ENSO-analogue or GCM-derived seasonal climate forecasts – A comparative analysis

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Potgieter, A. B., Schepen, A., Brider, J. and Hammer, G. L. (2022) Lead time and skill of Australian wheat yield forecasts based on ENSO-analogue or GCM-derived seasonal climate forecasts – A comparative analysis. Agricultural and Forest Meteorology, 324 . p. 109116. ISSN 0168-1923

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Article Link(s): https://doi.org/10.1016/j.agrformet.2022.109116

Publisher URL: https://www.sciencedirect.com/science/article/pii/S0168192322003033

Abstract

Foresight of crop yield is fundamental to producers and industry to better manage climate risks and mitigate ebbs and troughs in crop production. Rain-fed grain production in Australia is highly volatile and producers and industry are progressively confronted with projected uncertainties due to climate variability and change, input costs and market prices. Thus, having advance knowledge of the likely impact of the coming season's climate on crop yield and production is critical for decisions across the supply chain. Here we explore and analyse the lead time and skill of a wheat yield forecasting system using a biophysical crop yield simulation model connected to either a statistical ENSO-analogue climate forecasting system or a dynamic general circulation model (GCM) derived climate forecasting system. The comparative skill was investigated for 16 wheat producing districts (shires) of the broad Australian winter cropping region, each containing 9–35 irregularly-spaced simulation points associated with climate stations. Both the ENSO-analogue and GCM-derived systems produced reliable wheat yield forecasts with the GCM-based approach having general improved skill, and particularly during the early months of the season (March to May) before sowing. The shift in the forecast yield distributions relative to the climatology-based yield distribution were dependent on location and time in the season, with the GCM-derived forecast shifts more widespread and earlier in the season. Overall, the GCM-based climate/crop forecasting system showed a significant improvement in lead time (greater than two months before the normal planting time of wheat), across the Australian wheat belt. This result demonstrates an avenue for improved efficacy in future commodity forecasting frameworks via likely enhanced relevance and utility to industry associated with the use of GCM-derived approaches.

Item Type:Article
Business groups:Crop and Food Science
Keywords:Seasonal climate forecast Wheat yield forecast Probabilistic forecast Forecast skill Forecast reliability Forecast lead time
Deposited On:22 Aug 2022 04:25
Last Modified:22 Aug 2022 04:25

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