Brief description
These land suitability raster data (in GeoTIFF format) indicates areas of potential suitability for 126 crops and their specific irrigation management systems and seasons in the Fitzroy catchment of Western Australia. This data provides improved land evaluation information to identify opportunities and promote detailed investigation for a range of sustainable development options and was created within the ‘Land suitability’ activity of the Northern Australia Water Resource Assessment (NAWRA). There are five land suitability classes coded 1-5. 1 – Highly suitable land with negligible limitations 2 – Suitable land with minor limitations 3 – Moderately suitable land with considerable limitations 4 – Currently unsuitable land with severe limitations 5 – Unsuitable land with extreme limitations. The land suitability evaluation methods used to produce this data are a modification of the Food and Agriculture Organisation (FAO) land evaluation approach. The land suitability analysis is described in full in the CSIRO NAWRA published report referenced in the "Citation" field of this metadata record. A companion dataset showing the reliability of this suitability data (showing areas of the catchment where there is greater or lesser confidence in the accuracy of the suitability data) is also supplied. The naming convention for these data is; ‘crop’ underscore ‘season’ underscore ‘irrigation type’ underscore ‘catchment code’ underscore ‘data type’ eg ‘SorgForage_dry_fur_F_Suit’ is Sorghum forage dry season furrow irrigated Fitzroy catchment suitability. The codes for season are; wet – wet season; dry – dry season; per – perennial; wet-dry – planted late wet season and grown through the dry season eg navy bean, soybean; wet-long – longer growing crops that grow through a wet season eg sugarcane. The codes for irrigation type are; spray – overhead spray irrigation; tric – trickle irrigation; mini-spray – mini spray irrigation; flood – flood irrigation; fur – furrow irrigation; rainfed – rainfed. The codes for data type are; suit – suitability data, CI – reliability data expressed as confusion index.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 (see section 1.1 and 2.1.2 of the cited report), these limitations include biophysical and socio-cultural. For example these land suitability 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 NAWRA eg flooding was investigated within the Earth observation remote sensing activity and the risk of irrigation induced secondary salinity was assessed as part of the groundwater investigations.
Lineage: These suitability 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 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. Across each of the study areas 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 using the reliability data of the attribute. The modelled land suitability value was assessed against the actual on-ground value. These results are published in the report cited in this metadata record.
Available: 2018-09-05
Subjects
Agricultural, Veterinary and Food Sciences |
African Mahogany |
Agricultural Spatial Analysis and Modelling |
Agriculture, Irrigation |
Agriculture, crops |
Agriculture, Land and Farm Management |
Almond |
Asian veg |
Asparagus |
Avocado |
Banana |
Capsicum chilli |
Cashew |
Cassava |
Chia |
Chickpea |
Citrus |
Coffee |
Cotton |
Cucurbit |
Fitzroy catchment (Western Australia) |
Indian Sandalwood |
Lab lab |
Land suitability |
Lentil |
Lychee |
Macadamia |
Maize |
Mango |
Millet |
Mungbean |
NAWRA |
Navy bean |
Papaya |
Peanut |
Poppy medicinal |
Primary Industry |
Quinoa |
Regional development |
Rhodes grass |
Rice |
Sesame |
Snake beans |
Sorghum |
Soybean |
Sugarcane |
Sunflower |
Sweet corn |
Sweet potato |
Teak |
Tomato |
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Identifiers
- DOI : 10.25919/5B8F3AB610C56
- Handle : 102.100.100/73051
- URL : data.csiro.au/collection/csiro:34102
