Australian wheat plays an important role in global wheat supply, representing 11.5% of global exports, and it is particularly important as a source of high-protein wheat.
Using Gro’s Australian Wheat Yield Model model, Standard users can get an early read on Australia’s high-protein wheat yields at the sub-state level daily. These insights are critical when building the global wheat supply and demand balance sheets that procurement, sovereign food security, shipping, and asset management market participants use to manage supply risk, forecast price, and predict trade flows.
Customers Use the Tool to
Monitor crop production, the most significant part of the balance sheet, by tracking its most variable component, which is in-season yield
Monitor and predict wheat yields at the sub-state, state, and national level in Australia
Gauge crop availability and crop prices
Predict future trends for Australian crop yields, production, and agricultural input demand
Yield prediction can inform pest and disease forecast models by trying to quantify damage unrelated to climate
Why this matters
Australia produces 17-33 million tons of wheat annually; yields often vary considerably in drought years. As Australian white and amber wheat represents 11.5% of global exports and an even bigger portion of high-protein wheat exports, its wheat plays an important role in global wheat supply. Having insight into Australian wheat crop conditions is essential for building a global wheat supply and demand balance sheet and for understanding how trade flows will react.
As with all of Gro’s yield models, our Australian Wheat Yield Model updates daily at the sub-state level. Our machine-learning model takes into account the Australian Bureau of Statistics’ (ABS) data and uses environmental, climate, soil, and acreage-related variables to generate the best daily forecasts throughout the growing season. We’ve incorporated the Australian Wheat Yield Model into Gro’s automated balance sheets, which are used by procurement, sovereign food security, shipping, and asset management market participants to manage supply risk, forecast price, and predict trade flows.
With a mean absolute percentage error (MAPE) of 4.5% (on November 1), Gro’s Australia wheat yield model yield estimate was significantly more accurate than the USDA’s estimate, which had a MAPE of 7.0% (November WASDE report).
Gro’s Australian Wheat Yield Model’s accuracy aligns with Gro’s other wheat yield models, despite the limited history of ABS’ sub-state data, which reaches back only 10 years.
The inputs to our Australian Wheat Yield Model include:
- Normalized Differential Vegetation Index (NDVI)
- Gro Drought Index (GDI)
- Rainfall (NASA GPM)
- Evapotranspiration Anomaly (ETA)
- Land Surface Temperature (LST)
- Soil Moisture (SMOS)