Versatile agricultural land spatial datasets of the Fitzroy catchment WA, Darwin catchments and Mitchell catchment Qld generated by the Northern Australia Water Resource Assessment

This versatile agricultural land data is a collection of raster datasets used to provide a synopsis of the individual land suitability data of the 126 crops and their specific irrigation management systems and seasons generated by the Northern Australia Water Resource Assessment (NAWRA). Five datasets are presented for each of the NAWRA catchments. \nThe definitive versatile agricultural land dataset was determined by identifying where the largest number of 14 selected land management options were mapped as being suitable (i.e. suitability classes 1 to 3, refer to report cited with this metadata record). This analysis summarised the suitability of the selected land management options for each pixel, and highlights those pixels that are potentially more versatile for agricultural development because they are likely to suit a larger range of land use options and enterprises eg the score of zero represents the least versatile land, while the score of 14 represents the most versatile. The data values represent the number of land management options suitable for that pixel. The selected land management options for each catchment are different relative to general agronomic experience and development stakeholders of the catchment and were derived in consultation with the agricultural viability activity in NAWRA. These selections are presented in Table 3-1 of the published report; Land suitability of the Fitzroy, Darwin and Mitchell catchments. A technical report from the CSIRO Northern Australia Water Resource Assessment, part of the National Water Infrastructure Development Fund: Water Resource Assessments, CSIRO, Canberra, Australia Thomas et al 2018. Similarly. The selection of a different representative set of the 126 land use options for a particular study area would result in a different versatility map outcome for that catchment.\nIn addition to the selected set of 14 land management options for each study area, versatile agricultural land is also presented using the subsets of each of the irrigation types (and rainfed cropping). In this case, the 126 land management options were assigned to rainfed (20), furrow (42), spray (44) or trickle irrigation (20). The data values represent the number of land management options suitable for that pixel.\nAnalytical products like these help to identify land where particular types of irrigation-related infrastructure investment may be best targeted. This data provides improved land evaluation information to identify opportunities and promote detailed investigation for a range of sustainable development options. It is important to emphasize that this is a regional-scale assessment: further data collection and detailed analyses would be required to plan development at a scheme, enterprise or property scale. \nLineage: These versatile agricultural land raster datasets have been generated from a range of inputs and processing steps. Following is an overview. For more information refer to the CSIRO NAWRA published reports and in particular 'Land suitability of the Fitzroy, Darwin and Mitchell catchments. A technical report from the CSIRO Northern Australia Water Resource Assessment, part of the National Water Infrastructure Development Fund: Water Resource Assessments, CSIRO, Australia'. 1. Collated existing data (relating to: soils, climate, topography, natural resources, remotely sensed, of various formats: reports, spatial vector, spatial raster etc). 2. Selection of additional soil and land attribute site data locations by a conditioned Latin hypercube statistical sampling method applied across the covariate data space. 3. Fieldwork was carried out to collect new attribute data, soil samples for analysis and build an understanding of geomorphology and landscape processes. 4. Database analysis was performed to extract the data to specific selection criteria required for the attribute to be modelled. 5. The R statistical programming environment was used for the attribute computing. Models were built from selected input data and covariate data using predictive learning from a Random Forest approach implemented in the ranger R package. 6. Create Digital Soil Mapping (DSM) attributes raster datasets. DSM data is a geo-referenced dataset, generated from field observations and laboratory data, coupled with environmental covariate data through quantitative relationships. It applies pedometrics - the use of mathematical and statistical models that combine information from soil observations with information contained in correlated environmental variables, remote sensing images and some geophysical measurements. 7. Land management options were chosen and suitability rules created for DSM attributes. 8. Suitability rules were run to produce limitation subclass datasets using a modification on the FAO methods. 9. Final suitability data created for all land management options. 10. Companion predicted reliability data was produced. 11. QA. 12. Select the 14 land management options for each catchment in consultation with the agricultural viability activity. 13. Calculate the versatile agricultural land datasets