Improving Australia’s digital soil map

To improve yield forecasting and digitally-enabled crop management across Australia’s cropping regions through accurate and cost effective soil property characterisation

The challenges with Australia’s soil data

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 release of the Soil and Landscape Grid of Australia (SLGA).

Digiscape is using 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).

Improving Australia’s digital soil map

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 combine these new data with robust spatial and process-based inverse modelling approaches to enable prediction of functional soil properties. These predictions are used to better constrain simulation modelling with quantified uncertainties.

Our efforts so far have focused on prediction of the Plant Available Water Capacity (PAWC) of soils, that is the capacity of soils to store water for crops to use. Our process-based inverse modelling approach (using the agricultural systems modelling and simulations tool, APSIM) has demonstrated the potential to accurately predict PAWC. This is at paddock and sub-paddock scales across climatic regions with more than five years of crop yield data, together with management information. We’ve successfully predicted soil PAWC in case studies in Western Australia with actual wheat yields over 3-5 years.

Our inverse modelling also showed the potential to predict PAWC using leaf area index and green cover dynamics, implying possible use of remote sensing vegetation dynamics.

We have also completed our spatial modelling for predicting soil hydraulic properties using machine learning and incorporating the contributions of bioclimatic variables, topographical indices and remote sensing vegetation indices.

Next steps

In the next step, we will combine the results from spatial modelling and those from process-based inverse modelling, where data is available, to further refine soil profile characterisation and PAWC predictions at paddock and sub-paddock scales.

These newly generated data resources 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.

More information

Wang E, He D, Zhao Z, Smith CJ, Macdonald BCT (2020) Using a systems modeling approach to improve soil management and soil quality. Frontiers of Agricultural Science and Engineering 7, 289-295. doi:10.15302/J-FASE-2020337

Wang E, Smith CJ, Macdonald BCT, Hunt JR, Xing HT, Denmead OT, Zeglin S, Zhao ZG, Isaac P (2018) Making sense of cosmic-ray soil moisture measurements and eddy covariance data with regard to crop water use and field water balance. Agricultural Water Management 204, 271-280. doi:10.1016/j.agwat.2018.04.017

He D, Wang E (2019) The potential of using LAI time series to predict plant available water capacity (PAWC) of soils. In Pratley J (ed.), ‘Cells to Satellites: Proceedings of the 19th Australian Society of Agronomy Conference.’ Wagga Wagga, NSW, 25-29 August 2019.

A man
  • Enli is a Chief Research Scientist and project leader, based in Canberra. He leads CSIRO’s integrated agricultural modelling and decisions team, as well as Digiscape’s work on improving Australia’s digital soil map.