Land suitability for Capsicum and Chilli for the FGARA project

This land suitability for Capsicum and Chilli raster data (in GeoTIFF format) represents areas of potential suitability for this crop and its specific irrigation management systems in the Flinders and Gilbert catchments of North Queensland. \nThe data is coded 1-5: \n1 - Suitable with no limitations; \n2 - Suitable with minor limitations; \n3 - Suitable with moderate limitations; \n4 - Marginal; \n5 - Unsuitable. \nThe land suitability evaluation methods used to produce this data are a modification of methods of the Food and Agriculture Organisation of the UN (FAO).\nThis data is part of the Flinders and Gilbert Agricultural Resource Assessment (FGARA) project and is designed to support sustainable regional development in Australia being of importance to Australian Governments and agricultural industries. The project identifies new opportunities for irrigation development in these remote areas by providing improved soil and land evaluation data to identify opportunities and promote detailed investigation.\nA companion dataset exists, "Confidence of suitability data for the FGARA project". A link to this dataset can be found in the "related materials" section of this metadata record.\nLineage: These suitability raster data for Capsicum and Chilli and its individual irrigation management systems have been created from a range of inputs and processing steps. Below is an overview. For more information refer to the CSIRO FGARA published reports and in particular: Bartley R, Thomas MF, Clifford D, Phillip S, Brough D, Harms D, Willis R, Gregory L, Glover M, Moodie K, Sugars M, Eyre L, Smith DJ, Hicks W and Petheram C (2013) Land suitability: technical methods. A technical report to the Australian Government for the Flinders and Gilbert Agricultural Resource Assessment (FGARA) project, CSIRO. Broadly, the steps were to:\n1. Collate existing data (data related to: climate, topography, soils, natural resources, remotely sensed etc of various formats; reports, spatial vector, spatial raster etc).\n2. Select additional soil and attribute site data by Latin hypercube statistical sampling method applied across the covariate space.\n3. Carry out fieldwork to collect additional soil and attribute data and understand geomorphology and landscapes.\n4. Build models from selected input data and covariate data using predictive learning via rule ensembles in the RuleFit3 software.\n5. Create Digital Soil Mapping (DSM) key attributes output data. DSM is the creation and populat