Versatile agricultural land spatial data of the Roper catchment NT generated by the Roper River Water Resource Assessment

This versatile agricultural land data is a collection of raster datasets (GeoTIFF format) used to provide a synopsis of the land suitability data of the 21 crop groups and their specific irrigation management systems and seasons in the Roper catchment of the Northern Territory. Five datasets are in this collection. \nThe definitive versatile agricultural land dataset (Ag_Versatility_14_Crops_R.tif) was determined by identifying where the largest number of the 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 management 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 were chosen to be relative to general potential agronomic experience and development aspirations of potential stakeholders in the catchment and were derived in consultation with the agricultural viability activity in ROWRA. These selections are presented in Table 3-26 of the published report; ' Soils and land suitability for the Roper catchment, Northern Territory’. A technical report from the CSIRO Roper River Water Resource Assessment to the Government of Australia. Similarly, the selection of a different representative set from the land management options would result in a different versatility map outcome.\nIn addition to the selected set of 14 land management options, versatile agricultural land is also presented using the subsets of each of the irrigation types (and rainfed cropping). In this case, the land management options were assigned to rainfed (8), furrow (17), spray (23) or trickle irrigation (10). The data values represent the number of land management options suitable for that pixel. Analytical 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 soil physical, chemical and nutrient analyses would be required to plan development at a scheme, enterprise or property scale. Several limitations that may have a bearing on land suitability were out of scope and not assessed as part of this activity (refer to the report), these limitations include biophysical and socio-cultural. For example these versatile agricultural land raster datasets do not include consideration of the licensing of water, flood risk, contiguous land, risk of irrigation induced secondary salinity, or land tenure and other legislative controls. Some of these may be addressed elsewhere in ROWRA eg flooding was investigated by the Earth observation remote sensing group in the surface water activity. \nThe Roper River Water Resource Assessment provides a comprehensive overview and integrated evaluation of the feasibility of aquaculture and agriculture development in the Roper catchment NT as well as the ecological, social and cultural (indigenous water values, rights and aspirations) impacts of development.\nLineage: These versatile agricultural land raster datasets has been generated from a range of inputs and processing steps. Following is an overview. For more information refer to the CSIRO ROWRA published report ' Soils and land suitability for the Roper catchment, Northern Territory’. A technical report from the CSIRO Roper River Water Resource Assessment to the Government of 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) attribute 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 from the 500 individual Random Forest attribute models created. 11. QA Quality assessment of these land suitability data was conducted by two methods. Method 1: Statistical (quantitative) assessment of the "reliability" of the spatial output data presented as a raster of the Confusion Index. Method 2: Collecting independent external validation site data combined with on-ground expert (qualitative) examination of outputs during validation field trips. A two-week validation field trip was conducted using a new validation site set which was produced by a random sampling design based on conditioned Latin Hypercube sampling. The modelled land suitability value was assessed against the actual on-ground value. These results are published in the report referenced above. 12. Select the 14 land management options for each catchment in consultation with the agricultural viability activity. 13. Calculate the versatile agricultural land datasets\n