There have been several land resource assessments carried out in northern Australia over the last 50 years, however, a key recommendation common to all studies was that further soils data were needed before detailed agricultural suitability assessments could be conducted.
As part of the Flinders and Gilbert Agricultural Resource Assessment (FGARA), we developed a method of determining the land suitability of soils for those two catchments, which represents the largest land suitability assessment using digital soil mapping ever conducted in Australia.
That study established a valuable blueprint for future systematic resource evaluation over large areas at the regional scale.
Flinders and Gilbert
To further our understanding of the land suitability of soils in the Flinders and Gilbert catchments, three tasks were carried out:
- collection of new soils data by applying a statistically robust field sampling strategy
- use of existing and new soils information to produce maps of individual soil attribute layers of the Flinders and Gilbert catchments
- digital soil attribute layers were used to determine the area of land suitable for irrigated agricultural production in the two catchments.
A total of 76 irrigation land uses (crop and irrigation combinations), from 13 different land use categories, were evaluated in the Flinders and Gilbert catchments. For each of these land uses, the key limitations to production and associated soil attribute data were described.
The analysis for each catchment is summarised as part of the 'Opportunities for agriculture' sections of the following FGARA overview reports:
- Agricultural resource assessment for the Flinders catchment: Overview
- Agricultural resource assessment for the Gilbert catchment: Overview.
The full methods, results and analysis of that study are available in the report:
The soil-related datasets, including modelled spatial data on soil properties (e.g. soil depth, soil water availability), 76 modelled irrigation land uses (i.e. crop and irrigation combinations) and modelled confidence, are available from the CSIRO Data Access Portal: