Better soil information is a must for Australia to capture the full potential of emerging digital agriculture technologies. Australia’s broadacre agriculture, rangelands and forests are all limited by the supply of water and nitrogen for plant growth, and so forecasts of productivity require sound information about how much water is stored and how much nitrogen the soil can release.
Direct measurement of soil properties and crop roots is slow and expensive, and soil agencies in Australia have been constrained to sample the landscape much less intensively than in comparable countries. Measurements of the soil’s water-holding capacity are particularly sparse. To compensate for the relative lack of direct measurements, Australia’s soil science community has developed strong expertise in inferring soil information, culminating in the recent release of the Soil and Landscape Grid of Australia (SLGA).
Digiscape will be making use of the SLGA to support improved modelling of crops, to inform management decision making and to enable accurate forecasting of yields. We know, though, that for some purposes it will not be accurate enough (for in-season decision making at the paddock scale, for example).
New in-field and remotely-sensed data streams provide an opportunity for a new approach to predicting functional soil properties that will complement the spatial interpolations used to construct the SLGA. In Digiscape, we will combine these new data streams with robust process-based soil and crop simulation models to enable inverse prediction of functional soil properties. Model predictions should be better constrained using the new data streams and Bayesian approaches can be applied to understand the uncertainties.
If successful, this approach will generate new predictions of functional soil properties that will enhance the digital soil map of Australia, right across the broadacre agriculture regions. This data resource will greatly enhance the range of “modelling for management” applications from the Digiscape platform and associated digital technologies. It is a critical component in next-generation assisted decision making in agriculture and environmental management.
The science challenges / questions we’re addressing:
This project is led by Dr Enli Wang.