Waukesha County developers and municipal officials identified site characteristics and infrastructure that are important in analyzing potential business park sites and planning for growth during a 2017 Waukesha County industrial park study. This interactive application was developed to provide consolidated access to such datasets for business/industrial site searches.

Parcel and Data Layer Details

   Parcel selection was determined by buffering 46

major transportation nodes within or adjacent to Waukesha County Business Park Suitability Analysis methodology document Searchable parcels are a minimum of 20 acres in area and contain at least one developable acre. Data layers are sorted by topic area:

o
Natural resource constraintso
Soils & topographyo
Municipal serviceso
Transportationo
Land use

Functionality:

   Queries of layers available with click of

checkbox, picklists and unique entries. Queries/filtering available by municipality, transportation node or by user defined area. Export query results to Excel to save/print.

The downloadable ZIP file contains model documentation and contact information for the model creator. For more information, or a copy of the project report which provides greater model detail, please contact Ryan Urie - traigo12@gmail.com.This model was created from February through April 2010 as a central component of the developer's master's project in Bioregional Planning and Community Design at the University of Idaho to provide a tool for identifying appropriate locations for various land uses based on a variety of user-defined social, economic, ecological, and other criteria. It was developed using the Land-Use Conflict Identification Strategy developed by Carr and Zwick (2007). The purpose of this model is to allow users to identify suitable locations within a user-defined extent for any land use based on any number of social, economic, ecological, or other criteria the user chooses. The model as it is currently composed was designed to identify highly suitable locations for new residential, commercial, and industrial development in Kootenai County, Idaho using criteria, evaluations, and weightings chosen by the model's developer. After criteria were chosen, one or more data layers were gathered for each criterion from public sources. These layers were processed to result in a 60m-resolution raster showing the suitability of each criterion across the county. These criteria were ultimately combined with a weighting sum to result in an overall development suitability raster. The model is intended to serve only as an example of how a GIS-based land-use suitability analysis can be conceptualized and implemented using ArcGIS ModelBuilder, and under no circumstances should the model's outputs be applied to real-world decisions or activities. The model was designed to be extremely flexible so that later users may determine their own land-use suitability, suitability criteria, evaluation rationale, and criteria weights. As this was the first project of its kind completed by the model developer, no guarantees are made as to the quality of the model or the absence of errorsThis model has a hierarchical structure in which some forty individual land-use suitability criteria are combined by weighted summation into several land-use goals which are again combined by weighted summation to yield a final land-use suitability layer. As such, any inconsistencies or errors anywhere in the model tend to reveal themselves in the final output and the model is in a sense self-testing. For example, each individual criterion is presented as a raster with values from 1-9 in a defined spatial extent. Inconsistencies at any point in the model will reveal themselves in the final output in the form of an extent different from that desired, missing values, or values outside the 1-9 range.This model was created using the ArcGIS ModelBuilder function of ArcGIS 9.3. It was based heavily on the recommendations found in the text "Smart land-use analysis: the LUCIS model." The goal of the model is to determine the suitability of a chosen land-use at each point across a chosen area using the raster data format. In this case, the suitability for Development was evaluated across the area of Kootenai County, Idaho, though this is primarily for illustrative purposes. The basic process captured by the model is as follows: 1. Choose a land use suitability goal. 2. Select the goals and criteria that define this goal and get spatial data for each. 3. Use the gathered data to evaluate the quality of each criterion across the landscape, resulting in a raster with values from 1-9. 4. Apply weights to each criterion to indicate its relative contribution to the suitability goal. 5. Combine the weighted criteria to calculate and display the suitability of this land use at each point across the landscape. An individual model was first built for each of some forty individual criteria. Once these functioned successfully, individual criteria were combined with a weighted summation to yield one of three land-use goals (in this case, Residential, Commercial, or Industrial). A final model was then constructed to combined these three goals into a final suitability output. In addition, two conditional elements were placed on this final output (one to give already-developed areas a very high suitability score for development [a "9"] and a second to give permanently conserved areas and other undevelopable lands a very low suitability score for development [a "1"]). Because this model was meant to serve primarily as an illustration of how to do land-use suitability analysis, the criteria, evaluation rationales, and weightings were chosen by the modeler for expediency; however, a land-use analysis meant to guide real-world actions and decisions would need to rely far more heavily on a variety of scientific and stakeholder input.

This land suitability for Avocado 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. The data is coded 1-5: 1 - Suitable with no limitations; 2 - Suitable with minor limitations; 3 - Suitable with moderate limitations; 4 - Marginal; 5 - Unsuitable. The land suitability evaluation methods used to produce this data are a modification of methods of the Food and Agriculture Organisation of the UN (FAO). This 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. A 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. Lineage: These suitability raster data for Avocado 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: 1. Collate existing data (data related to: climate, topography, soils, natural resources, remotely sensed etc of various formats; reports, spatial vector, spatial raster etc). 2. Select additional soil and attribute site data by Latin hypercube statistical sampling method applied across the covariate space. 3. Carry out fieldwork to collect additional soil and attribute data and understand geomorphology and landscapes. 4. Build models from selected input data and covariate data using predictive learning via rule ensembles in the RuleFit3 software. 5. Create Digital Soil Mapping (DSM) key attributes output data. DSM is the creation and population of a geo-referenced database, generated using field and laboratory observations, coupled with environmental 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. 6. Choose land management options and create suitability rules for DSM attributes. 7. Run suitability rules to produce limitation datasets using a modification on the FAO methods. 8. Create final suitability data for all land management options. Two companion datasets exist for this dataset. The first is linked to in the “related materials” section of this metadata record, entitled “Confidence of suitability data for the FGARA project”. The second (held by CSIRO Land and Water) includes expert opinion and knowledge about landscape processes or conditions that will influence agricultural development potential in these catchments, but were not captured sufficiently in the modelling process (and areas of expert opinion where the Mahanabolis method underestimates confidence). The two landscape features that require special attention are the basalt rock outcrops in the Upper Flinders catchment that were not well captured by the covariate data, and the secondary salinisation hazard in the central Flinders catchment. For more information refer to the report “Land suitability: technical methods. A technical report to the Australian Government for the Flinders and Gilbert Agricultural Resource Assessment (FGARA) project”.

This paper and accompanying maps focus on a land suitability assessment of the Olomouc region in the Czech Republic. All results were calculated in Urban Planner, a model designed by the authors of this paper. The method of calculation is based on a multi-criteria analysis (weighted overlay method), respects the principles of sustainable development, and allows for execution of several scenarios. The main result of this work is a set of maps. The first map sheet shows the land suitability for housing, recreation, public services, heavy industry, light industry, and transportation. The second map sheet consists of four maps showing different scenarios of land suitability for housing: one map showing the evaluation of existing proposals for housing from urban plans, and one map showing optimal areas for housing calculated by the Urban Planner model. The maps can be used as a significant source of information about the suitability of development in the Olomouc region in geographic or urban studies, both for experts and the general public. All thematic maps are on the scale of 1:125,000; supplementary maps are smaller.

This land suitability for Banana 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. The data is coded 1-5: 1 - Suitable with no limitations; 2 - Suitable with minor limitations; 3 - Suitable with moderate limitations; 4 - Marginal; 5 - Unsuitable. The land suitability evaluation methods used to produce this data are a modification of methods of the Food and Agriculture Organisation of the UN (FAO). This 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. A 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. Lineage: These suitability raster data for Banana 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: 1. Collate existing data (data related to: climate, topography, soils, natural resources, remotely sensed etc of various formats; reports, spatial vector, spatial raster etc). 2. Select additional soil and attribute site data by Latin hypercube statistical sampling method applied across the covariate space. 3. Carry out fieldwork to collect additional soil and attribute data and understand geomorphology and landscapes. 4. Build models from selected input data and covariate data using predictive learning via rule ensembles in the RuleFit3 software. 5. Create Digital Soil Mapping (DSM) key attributes output data. DSM is the creation and population of a geo-referenced database, generated using field and laboratory observations, coupled with environmental 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. 6. Choose land management options and create suitability rules for DSM attributes. 7. Run suitability rules to produce limitation datasets using a modification on the FAO methods. 8. Create final suitability data for all land management options. Two companion datasets exist for this dataset. The first is linked to in the “related materials” section of this metadata record, entitled “Confidence of suitability data for the FGARA project”. The second (held by CSIRO Land and Water) includes expert opinion and knowledge about landscape processes or conditions that will influence agricultural development potential in these catchments, but were not captured sufficiently in the modelling process (and areas of expert opinion where the Mahanabolis method underestimates confidence). The two landscape features that require special attention are the basalt rock outcrops in the Upper Flinders catchment that were not well captured by the covariate data, and the secondary salinisation hazard in the central Flinders catchment. For more information refer to the report “Land suitability: technical methods. A technical report to the Australian Government for the Flinders and Gilbert Agricultural Resource Assessment (FGARA) project”.

This land suitability for Grape 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. The data is coded 1-5: 1 - Suitable with no limitations; 2 - Suitable with minor limitations; 3 - Suitable with moderate limitations; 4 - Marginal; 5 - Unsuitable. The land suitability evaluation methods used to produce this data are a modification of methods of the Food and Agriculture Organisation of the UN (FAO). This 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. A 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. Lineage: These suitability raster data for Grape 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: 1. Collate existing data (data related to: climate, topography, soils, natural resources, remotely sensed etc of various formats; reports, spatial vector, spatial raster etc). 2. Select additional soil and attribute site data by Latin hypercube statistical sampling method applied across the covariate space. 3. Carry out fieldwork to collect additional soil and attribute data and understand geomorphology and landscapes. 4. Build models from selected input data and covariate data using predictive learning via rule ensembles in the RuleFit3 software. 5. Create Digital Soil Mapping (DSM) key attributes output data. DSM is the creation and population of a geo-referenced database, generated using field and laboratory observations, coupled with environmental 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. 6. Choose land management options and create suitability rules for DSM attributes. 7. Run suitability rules to produce limitation datasets using a modification on the FAO methods. 8. Create final suitability data for all land management options. Two companion datasets exist for this dataset. The first is linked to in the “related materials” section of this metadata record, entitled “Confidence of suitability data for the FGARA project”. The second (held by CSIRO Land and Water) includes expert opinion and knowledge about landscape processes or conditions that will influence agricultural development potential in these catchments, but were not captured sufficiently in the modelling process (and areas of expert opinion where the Mahanabolis method underestimates confidence). The two landscape features that require special attention are the basalt rock outcrops in the Upper Flinders catchment that were not well captured by the covariate data, and the secondary salinisation hazard in the central Flinders catchment. For more information refer to the report “Land suitability: technical methods. A technical report to the Australian Government for the Flinders and Gilbert Agricultural Resource Assessment (FGARA) project”.

Relative suitability results feature service containing the results for the Suitability analyses for the following submodels: Cultural Resources, Fisheries, Industry, National Security, and Natural Resources for the Petersburg Final Study Area.The data published in this service are subject to change following the public comment period. These data are published within the Alaska Aquaculture Opportunity Areas: NCCOS Draft Spatial Analysis WebMapper. For more information, please reference the NOAA Inport metadata record.

This land suitability for Coffee 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. The data is coded 1-5: 1 - Suitable with no limitations; 2 - Suitable with minor limitations; 3 - Suitable with moderate limitations; 4 - Marginal; 5 - Unsuitable. The land suitability evaluation methods used to produce this data are a modification of methods of the Food and Agriculture Organisation of the UN (FAO). This 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. A 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. Lineage: These suitability raster data for Coffee 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: 1. Collate existing data (data related to: climate, topography, soils, natural resources, remotely sensed etc of various formats; reports, spatial vector, spatial raster etc). 2. Select additional soil and attribute site data by Latin hypercube statistical sampling method applied across the covariate space. 3. Carry out fieldwork to collect additional soil and attribute data and understand geomorphology and landscapes. 4. Build models from selected input data and covariate data using predictive learning via rule ensembles in the RuleFit3 software. 5. Create Digital Soil Mapping (DSM) key attributes output data. DSM is the creation and population of a geo-referenced database, generated using field and laboratory observations, coupled with environmental 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. 6. Choose land management options and create suitability rules for DSM attributes. 7. Run suitability rules to produce limitation datasets using a modification on the FAO methods. 8. Create final suitability data for all land management options. Two companion datasets exist for this dataset. The first is linked to in the “related materials” section of this metadata record, entitled “Confidence of suitability data for the FGARA project”. The second (held by CSIRO Land and Water) includes expert opinion and knowledge about landscape processes or conditions that will influence agricultural development potential in these catchments, but were not captured sufficiently in the modelling process (and areas of expert opinion where the Mahanabolis method underestimates confidence). The two landscape features that require special attention are the basalt rock outcrops in the Upper Flinders catchment that were not well captured by the covariate data, and the secondary salinisation hazard in the central Flinders catchment. For more information refer to the report “Land suitability: technical methods. A technical report to the Australian Government for the Flinders and Gilbert Agricultural Resource Assessment (FGARA) project”.

This land suitability for Mungbean 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. The data is coded 1-5: 1 - Suitable with no limitations; 2 - Suitable with minor limitations; 3 - Suitable with moderate limitations; 4 - Marginal; 5 - Unsuitable. The land suitability evaluation methods used to produce this data are a modification of methods of the Food and Agriculture Organisation of the UN (FAO). This 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. A 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. Lineage: These suitability raster data for Mungbean 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: 1. Collate existing data (data related to: climate, topography, soils, natural resources, remotely sensed etc of various formats; reports, spatial vector, spatial raster etc). 2. Select additional soil and attribute site data by Latin hypercube statistical sampling method applied across the covariate space. 3. Carry out fieldwork to collect additional soil and attribute data and understand geomorphology and landscapes. 4. Build models from selected input data and covariate data using predictive learning via rule ensembles in the RuleFit3 software. 5. Create Digital Soil Mapping (DSM) key attributes output data. DSM is the creation and population of a geo-referenced database, generated using field and laboratory observations, coupled with environmental 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. 6. Choose land management options and create suitability rules for DSM attributes. 7. Run suitability rules to produce limitation datasets using a modification on the FAO methods. 8. Create final suitability data for all land management options. Two companion datasets exist for this dataset. The first is linked to in the “related materials” section of this metadata record, entitled “Confidence of suitability data for the FGARA project”. The second (held by CSIRO Land and Water) includes expert opinion and knowledge about landscape processes or conditions that will influence agricultural development potential in these catchments, but were not captured sufficiently in the modelling process (and areas of expert opinion where the Mahanabolis method underestimates confidence). The two landscape features that require special attention are the basalt rock outcrops in the Upper Flinders catchment that were not well captured by the covariate data, and the secondary salinisation hazard in the central Flinders catchment. For more information refer to the report “Land suitability: technical methods. A technical report to the Australian Government for the Flinders and Gilbert Agricultural Resource Assessment (FGARA) project”.

A plot proposed by a farmer is not always suitable as a whole for the application of industrial sludge. For this reason, the design office offers skill areas that are built within the plot. For a product, an area of aptitude may have three possible abilities: — 0 = unfit — 1 = constraint aptitude (soil, river, dwelling) — 2 = total aptitude, without constraint. This layer describes these areas of fitness.

This land suitability for Maize/corn 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. The data is coded 1-5: 1 - Suitable with no limitations; 2 - Suitable with minor limitations; 3 - Suitable with moderate limitations; 4 - Marginal; 5 - Unsuitable. The land suitability evaluation methods used to produce this data are a modification of methods of the Food and Agriculture Organisation of the UN (FAO). This 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. A 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. Lineage: These suitability raster data for Maize/corn 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: 1. Collate existing data (data related to: climate, topography, soils, natural resources, remotely sensed etc of various formats; reports, spatial vector, spatial raster etc). 2. Select additional soil and attribute site data by Latin hypercube statistical sampling method applied across the covariate space. 3. Carry out fieldwork to collect additional soil and attribute data and understand geomorphology and landscapes. 4. Build models from selected input data and covariate data using predictive learning via rule ensembles in the RuleFit3 software. 5. Create Digital Soil Mapping (DSM) key attributes output data. DSM is the creation and population of a geo-referenced database, generated using field and laboratory observations, coupled with environmental 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. 6. Choose land management options and create suitability rules for DSM attributes. 7. Run suitability rules to produce limitation datasets using a modification on the FAO methods. 8. Create final suitability data for all land management options. Two companion datasets exist for this dataset. The first is linked to in the “related materials” section of this metadata record, entitled “Confidence of suitability data for the FGARA project”. The second (held by CSIRO Land and Water) includes expert opinion and knowledge about landscape processes or conditions that will influence agricultural development potential in these catchments, but were not captured sufficiently in the modelling process (and areas of expert opinion where the Mahanabolis method underestimates confidence). The two landscape features that require special attention are the basalt rock outcrops in the Upper Flinders catchment that were not well captured by the covariate data, and the secondary salinisation hazard in the central Flinders catchment. For more information refer to the report “Land suitability: technical methods. A technical report to the Australian Government for the Flinders and Gilbert Agricultural Resource Assessment (FGARA) project”.

Woodland Creation forms part of the Scottish Rural Development Programme (SRDP) 2014 - 2020. The SRDP delivers Pillar 2 of the EU Common Agricultural Policy (CAP). Utilising some PS1,326m of European Agricultural Fund for Rural Development funding, plus Scottish Government match funding, it funds economic, environmental and social measures for the benefit of rural Scotland. The SRDP is co-funded by the European Commission and the Scottish Government and reflects the 6 EU Rural Development Priorities. The programme also reflects the Scottish Government National Policy Framework (NPF). The aim of the Forestry Grant Scheme woodland creation category is to support the creation of new woodlands that will provide a range of economic, environmental and social benefits which include: - delivery of the Scottish Government target to extend woodland cover by an additional 100,000 hectares over the period of 2012-2022 - climate change mitigation by tackling greenhouse gas emissions through carbon sequestration - restoration of lost habitats through developing forest habitat networks - underpinning a sustainable forest industry by providing a reliable timber supply - protecting the soil and water environment - providing community benefits through public access - enhancing urban areas and improving landscapes - supporting rural development through local businesses and farm diversification A fundamental consideration when creating new woodland is whether or not the tree species is appropriate to the site. You should carry out an appropriate site based assessment of soil and vegetation to match species choice with the particular site. Forestry Research 'Ecological Site Classification' (ESC) decision support system helps guide forest managers and planners to select ecologically suited species to sites. ESC considers: windiness; temperature; moisture; continentality; soil moisture and soil nutrients. This helps to determine suitability of the chosen species to the site and identifies it as: poor; marginal; suitable or very suitable. In order to be considered for SRDP grant support the overall suitability for your chosen species must be either 'very suitable' or 'suitable'. As an initial first step in determining suitability, the polygons in this dataset represent the climatic suitability of the chosen tree species to the site. Climatic suitability, based on ESC uses the following climatic site factors: - Accumulated temperature - Moisture deficit - Exposure (Detailed Aspect Method Scoring [DAMS]) - Continentality NOTE: This datasets does NOT take into account any soils information. Any application that is identified on the map as being either 'unsuitable' or 'marginal' may still be considered - but only if you clearly demonstrate that the site is 'suitable' for the chosen species of tree (for example where there is localised shelter in an otherwise exposed location). The woodland creation category has nine options and the associated aims are: - 'Conifer' To create conifer woodlands on land that is suitable for timber production and that is accessible for timber transport (including links to suitable public roads). This option is principally aimed at planting Sitka spruce. - 'Diverse Conifer' To create conifer woodlands on land that is suitable for timber production and that is accessible for timber transport (including links to suitable public roads). This option is aimed at planting conifer species other than Sitka spruce. - 'Broadleaves' To create broadleaved woodlands on land that is suitable for sawn and prime timber and that is accessible for timber transport (including links to suitable public roads). - 'Native Scots Pine' To create or expand native pinewood priority habitat (NVC) W18 - 'Native Upland Birch' The creation of native upland birch woodland of the National Vegetation Classification (NVC) W4: Downy Birch with Purple Moor Grass on shallow peaty soils. - 'Native Broadleaves' To create native broadleaved priority woodland habitats of the following National Vegetation Classification (NVC) types: W6 Alder with Stinging Nettle W7 Alder-Ash with Yellow Pimpernel W8 Ash, Field maple with Stinging Nettle W9 Ash, Rowan with Dogs Mercury W10 Oak (penduculate) with Bluebell Hyacinth W11 Oak (sessile), Downy Birch with Bluebell/wild Hyacinth W16 Oak, Birch W17 Oak (sessile), Downy Birch with Bilberry/Blaeberry - 'Native Low Density Broadleaves' To create specific native woodland or scrub habitats; including areas of ecotones for black grouse, treeline woodlands, juniper and other forms of scrub woodland and wood pasture systems. Normally associated with other woodland habitats in a transitional situation (eg. transition onto open hill: Black Grouse; Montane Scrub). - 'Small or Farm Woodland' To create small scale mixed broadleaved and conifer woodlands on farms and other rural land. - 'Native Broadleaves in Northern & Western Isles' To create native woodlands that contributes to the Orkney, Shetland or Western Isles woodland strategies. DATASET ATTRIBUTES: - Suitability - ie. 'Very Suitable', 'Suitable', 'Marginal', 'Unsuitable' or 'Inland Water'

This land suitability for Lychee 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. The data is coded 1-5: 1 - Suitable with no limitations; 2 - Suitable with minor limitations; 3 - Suitable with moderate limitations; 4 - Marginal; 5 - Unsuitable. The land suitability evaluation methods used to produce this data are a modification of methods of the Food and Agriculture Organisation of the UN (FAO). This 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. A 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. Lineage: These suitability raster data for Lychee 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: 1. Collate existing data (data related to: climate, topography, soils, natural resources, remotely sensed etc of various formats; reports, spatial vector, spatial raster etc). 2. Select additional soil and attribute site data by Latin hypercube statistical sampling method applied across the covariate space. 3. Carry out fieldwork to collect additional soil and attribute data and understand geomorphology and landscapes. 4. Build models from selected input data and covariate data using predictive learning via rule ensembles in the RuleFit3 software. 5. Create Digital Soil Mapping (DSM) key attributes output data. DSM is the creation and population of a geo-referenced database, generated using field and laboratory observations, coupled with environmental 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. 6. Choose land management options and create suitability rules for DSM attributes. 7. Run suitability rules to produce limitation datasets using a modification on the FAO methods. 8. Create final suitability data for all land management options. Two companion datasets exist for this dataset. The first is linked to in the “related materials” section of this metadata record, entitled “Confidence of suitability data for the FGARA project”. The second (held by CSIRO Land and Water) includes expert opinion and knowledge about landscape processes or conditions that will influence agricultural development potential in these catchments, but were not captured sufficiently in the modelling process (and areas of expert opinion where the Mahanabolis method underestimates confidence). The two landscape features that require special attention are the basalt rock outcrops in the Upper Flinders catchment that were not well captured by the covariate data, and the secondary salinisation hazard in the central Flinders catchment. For more information refer to the report “Land suitability: technical methods. A technical report to the Australian Government for the Flinders and Gilbert Agricultural Resource Assessment (FGARA) project”.

This model was created using the ArcGIS ModelBuilder function of ArcGIS 9.3. It was based heavily on the recommendations found in the text "Smart land-use analysis: the LUCIS model." The goal of the model is to determine the suitability of a chosen land-use at each point across a chosen area using the raster data format. In this case, the suitability for Development was evaluated across the area of Kootenai County, Idaho, though this is primarily for illustrative purposes. The basic process captured by the model is as follows: 1. Choose a land use suitability goal. 2. Select the goals and criteria that define this goal and get spatial data for each. 3. Use the gathered data to evaluate the quality of each criterion across the landscape, resulting in a raster with values from 1-9. 4. Apply weights to each criterion to indicate its relative contribution to the suitability goal. 5. Combine the weighted criteria to calculate and display the suitability of this land use at each point across the landscape. An individual model was first built for each of some forty individual criteria. Once these functioned successfully, individual criteria were combined with a weighted summation to yield one of three land-use goals (in this case, Residential, Commercial, or Industrial). A final model was then constructed to combined these three goals into a final suitability output. In addition, two conditional elements were placed on this final output (one to give already-developed areas a very high suitability score for development [a "9"] and a second to give permanently conserved areas and other undevelopable lands a very low suitability score for development [a "1"]). Because this model was meant to serve primarily as an illustration of how to do land-use suitability analysis, the criteria, evaluation rationales, and weightings were chosen by the modeler for expediency; however, a land-use analysis meant to guide real-world actions and decisions would need to rely far more heavily on a variety of scientific and stakeholder input.

This land suitability for Pineapple 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. The data is coded 1-5: 1 - Suitable with no limitations; 2 - Suitable with minor limitations; 3 - Suitable with moderate limitations; 4 - Marginal; 5 - Unsuitable. The land suitability evaluation methods used to produce this data are a modification of methods of the Food and Agriculture Organisation of the UN (FAO). This 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. A 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. Lineage: These suitability raster data for Pineapple 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: 1. Collate existing data (data related to: climate, topography, soils, natural resources, remotely sensed etc of various formats; reports, spatial vector, spatial raster etc). 2. Select additional soil and attribute site data by Latin hypercube statistical sampling method applied across the covariate space. 3. Carry out fieldwork to collect additional soil and attribute data and understand geomorphology and landscapes. 4. Build models from selected input data and covariate data using predictive learning via rule ensembles in the RuleFit3 software. 5. Create Digital Soil Mapping (DSM) key attributes output data. DSM is the creation and population of a geo-referenced database, generated using field and laboratory observations, coupled with environmental 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. 6. Choose land management options and create suitability rules for DSM attributes. 7. Run suitability rules to produce limitation datasets using a modification on the FAO methods. 8. Create final suitability data for all land management options. Two companion datasets exist for this dataset. The first is linked to in the “related materials” section of this metadata record, entitled “Confidence of suitability data for the FGARA project”. The second (held by CSIRO Land and Water) includes expert opinion and knowledge about landscape processes or conditions that will influence agricultural development potential in these catchments, but were not captured sufficiently in the modelling process (and areas of expert opinion where the Mahanabolis method underestimates confidence). The two landscape features that require special attention are the basalt rock outcrops in the Upper Flinders catchment that were not well captured by the covariate data, and the secondary salinisation hazard in the central Flinders catchment. For more information refer to the report “Land suitability: technical methods. A technical report to the Australian Government for the Flinders and Gilbert Agricultural Resource Assessment (FGARA) project”.

This land suitability for Teak 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. The data is coded 1-5: 1 - Suitable with no limitations; 2 - Suitable with minor limitations; 3 - Suitable with moderate limitations; 4 - Marginal; 5 - Unsuitable. The land suitability evaluation methods used to produce this data are a modification of methods of the Food and Agriculture Organisation of the UN (FAO). This 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. A 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. Lineage: These suitability raster data for Teak 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: 1. Collate existing data (data related to: climate, topography, soils, natural resources, remotely sensed etc of various formats; reports, spatial vector, spatial raster etc). 2. Select additional soil and attribute site data by Latin hypercube statistical sampling method applied across the covariate space. 3. Carry out fieldwork to collect additional soil and attribute data and understand geomorphology and landscapes. 4. Build models from selected input data and covariate data using predictive learning via rule ensembles in the RuleFit3 software. 5. Create Digital Soil Mapping (DSM) key attributes output data. DSM is the creation and population of a geo-referenced database, generated using field and laboratory observations, coupled with environmental 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. 6. Choose land management options and create suitability rules for DSM attributes. 7. Run suitability rules to produce limitation datasets using a modification on the FAO methods. 8. Create final suitability data for all land management options. Two companion datasets exist for this dataset. The first is linked to in the “related materials” section of this metadata record, entitled “Confidence of suitability data for the FGARA project”. The second (held by CSIRO Land and Water) includes expert opinion and knowledge about landscape processes or conditions that will influence agricultural development potential in these catchments, but were not captured sufficiently in the modelling process (and areas of expert opinion where the Mahanabolis method underestimates confidence). The two landscape features that require special attention are the basalt rock outcrops in the Upper Flinders catchment that were not well captured by the covariate data, and the secondary salinisation hazard in the central Flinders catchment. For more information refer to the report “Land suitability: technical methods. A technical report to the Australian Government for the Flinders and Gilbert Agricultural Resource Assessment (FGARA) project”.

This land suitability for African Mahogany 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. The data is coded 1-5: 1 - Suitable with no limitations; 2 - Suitable with minor limitations; 3 - Suitable with moderate limitations; 4 - Marginal; 5 - Unsuitable. The land suitability evaluation methods used to produce this data are a modification of methods of the Food and Agriculture Organisation of the UN (FAO). This 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. A 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. Lineage: These suitability raster data for African Mahogany 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: 1. Collate existing data (data related to: climate, topography, soils, natural resources, remotely sensed etc of various formats; reports, spatial vector, spatial raster etc). 2. Select additional soil and attribute site data by Latin hypercube statistical sampling method applied across the covariate space. 3. Carry out fieldwork to collect additional soil and attribute data and understand geomorphology and landscapes. 4. Build models from selected input data and covariate data using predictive learning via rule ensembles in the RuleFit3 software. 5. Create Digital Soil Mapping (DSM) key attributes output data. DSM is the creation and population of a geo-referenced database, generated using field and laboratory observations, coupled with environmental 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. 6. Choose land management options and create suitability rules for DSM attributes. 7. Run suitability rules to produce limitation datasets using a modification on the FAO methods. 8. Create final suitability data for all land management options. Two companion datasets exist for this dataset. The first is linked to in the “related materials” section of this metadata record, entitled “Confidence of suitability data for the FGARA project”. The second (held by CSIRO Land and Water) includes expert opinion and knowledge about landscape processes or conditions that will influence agricultural development potential in these catchments, but were not captured sufficiently in the modelling process (and areas of expert opinion where the Mahanabolis method underestimates confidence). The two landscape features that require special attention are the basalt rock outcrops in the Upper Flinders catchment that were not well captured by the covariate data, and the secondary salinisation hazard in the central Flinders catchment. For more information refer to the report “Land suitability: technical methods. A technical report to the Australian Government for the Flinders and Gilbert Agricultural Resource Assessment (FGARA) project”.

Woodland Creation forms part of the Scottish Rural Development Programme (SRDP) 2014 - 2020. The SRDP delivers Pillar 2 of the EU Common Agricultural Policy (CAP). Utilising some PS1,326m of European Agricultural Fund for Rural Development funding, plus Scottish Government match funding, it funds economic, environmental and social measures for the benefit of rural Scotland. The SRDP is co-funded by the European Commission and the Scottish Government and reflects the 6 EU Rural Development Priorities. The programme also reflects the Scottish Government National Policy Framework (NPF). The aim of the Forestry Grant Scheme woodland creation category is to support the creation of new woodlands that will provide a range of economic, environmental and social benefits which include: - delivery of the Scottish Government target to extend woodland cover by an additional 100,000 hectares over the period of 2012-2022 - climate change mitigation by tackling greenhouse gas emissions through carbon sequestration - restoration of lost habitats through developing forest habitat networks - underpinning a sustainable forest industry by providing a reliable timber supply - protecting the soil and water environment - providing community benefits through public access - enhancing urban areas and improving landscapes - supporting rural development through local businesses and farm diversification A fundamental consideration when creating new woodland is whether or not the tree species is appropriate to the site. You should carry out an appropriate site based assessment of soil and vegetation to match species choice with the particular site. Forestry Research 'Ecological Site Classification' (ESC) decision support system helps guide forest managers and planners to select ecologically suited species to sites. ESC considers: windiness; temperature; moisture; continentality; soil moisture and soil nutrients. This helps to determine suitability of the chosen species to the site and identifies it as: poor; marginal; suitable or very suitable. In order to be considered for SRDP grant support the overall suitability for your chosen species must be either 'very suitable' or 'suitable'. As an initial first step in determining suitability, the polygons in this dataset represent the climatic suitability of the chosen tree species to the site. Climatic suitability, based on ESC uses the following climatic site factors: - Accumulated temperature - Moisture deficit - Exposure (Detailed Aspect Method Scoring [DAMS]) - Continentality NOTE: This datasets does NOT take into account any soils information. Any application that is identified on the map as being either 'unsuitable' or 'marginal' may still be considered - but only if you clearly demonstrate that the site is 'suitable' for the chosen species of tree (for example where there is localised shelter in an otherwise exposed location). The woodland creation category has nine options and the associated aims are: - 'Conifer' To create conifer woodlands on land that is suitable for timber production and that is accessible for timber transport (including links to suitable public roads). This option is principally aimed at planting Sitka spruce. - 'Diverse Conifer' To create conifer woodlands on land that is suitable for timber production and that is accessible for timber transport (including links to suitable public roads). This option is aimed at planting conifer species other than Sitka spruce. - 'Broadleaves' To create broadleaved woodlands on land that is suitable for sawn and prime timber and that is accessible for timber transport (including links to suitable public roads). - 'Native Scots Pine' To create or expand native pinewood priority habitat (NVC) W18 - 'Native Upland Birch' The creation of native upland birch woodland of the National Vegetation Classification (NVC) W4: Downy Birch with Purple Moor Grass on shallow peaty soils. - 'Native Broadleaves' To create native broadleaved priority woodland habitats of the following National Vegetation Classification (NVC) types: W6 Alder with Stinging Nettle W7 Alder-Ash with Yellow Pimpernel W8 Ash, Field maple with Stinging Nettle W9 Ash, Rowan with Dogs Mercury W10 Oak (penduculate) with Bluebell Hyacinth W11 Oak (sessile), Downy Birch with Bluebell/wild Hyacinth W16 Oak, Birch W17 Oak (sessile), Downy Birch with Bilberry/Blaeberry - 'Native Low Density Broadleaves' To create specific native woodland or scrub habitats; including areas of ecotones for black grouse, treeline woodlands, juniper and other forms of scrub woodland and wood pasture systems. Normally associated with other woodland habitats in a transitional situation (eg. transition onto open hill: Black Grouse; Montane Scrub). - 'Small or Farm Woodland' To create small scale mixed broadleaved and conifer woodlands on farms and other rural land. - 'Native Broadleaves in Northern & Western Isles' To create native woodlands that contributes to the Orkney, Shetland or Western Isles woodland strategies. DATASET ATTRIBUTES: - Suitability - ie. 'Very Suitable', 'Suitable', 'Marginal', 'Unsuitable' or 'Inland Water'

This land suitability for Cotton 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. The data is coded 1-5: 1 - Suitable with no limitations; 2 - Suitable with minor limitations; 3 - Suitable with moderate limitations; 4 - Marginal; 5 - Unsuitable. The land suitability evaluation methods used to produce this data are a modification of methods of the Food and Agriculture Organisation of the UN (FAO). This 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. A 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. Lineage: These suitability raster data for Cotton 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: 1. Collate existing data (data related to: climate, topography, soils, natural resources, remotely sensed etc of various formats; reports, spatial vector, spatial raster etc). 2. Select additional soil and attribute site data by Latin hypercube statistical sampling method applied across the covariate space. 3. Carry out fieldwork to collect additional soil and attribute data and understand geomorphology and landscapes. 4. Build models from selected input data and covariate data using predictive learning via rule ensembles in the RuleFit3 software. 5. Create Digital Soil Mapping (DSM) key attributes output data. DSM is the creation and population of a geo-referenced database, generated using field and laboratory observations, coupled with environmental 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. 6. Choose land management options and create suitability rules for DSM attributes. 7. Run suitability rules to produce limitation datasets using a modification on the FAO methods. 8. Create final suitability data for all land management options. Two companion datasets exist for this dataset. The first is linked to in the “related materials” section of this metadata record, entitled “Confidence of suitability data for the FGARA project”. The second (held by CSIRO Land and Water) includes expert opinion and knowledge about landscape processes or conditions that will influence agricultural development potential in these catchments, but were not captured sufficiently in the modelling process (and areas of expert opinion where the Mahanabolis method underestimates confidence). The two landscape features that require special attention are the basalt rock outcrops in the Upper Flinders catchment that were not well captured by the covariate data, and the secondary salinisation hazard in the central Flinders catchment. For more information refer to the report “Land suitability: technical methods. A technical report to the Australian Government for the Flinders and Gilbert Agricultural Resource Assessment (FGARA) project”.

This land suitability for Tomato 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. The data is coded 1-5: 1 - Suitable with no limitations; 2 - Suitable with minor limitations; 3 - Suitable with moderate limitations; 4 - Marginal; 5 - Unsuitable. The land suitability evaluation methods used to produce this data are a modification of methods of the Food and Agriculture Organisation of the UN (FAO). This 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. A 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. Lineage: These suitability raster data for Tomato 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: 1. Collate existing data (data related to: climate, topography, soils, natural resources, remotely sensed etc of various formats; reports, spatial vector, spatial raster etc). 2. Select additional soil and attribute site data by Latin hypercube statistical sampling method applied across the covariate space. 3. Carry out fieldwork to collect additional soil and attribute data and understand geomorphology and landscapes. 4. Build models from selected input data and covariate data using predictive learning via rule ensembles in the RuleFit3 software. 5. Create Digital Soil Mapping (DSM) key attributes output data. DSM is the creation and population of a geo-referenced database, generated using field and laboratory observations, coupled with environmental 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. 6. Choose land management options and create suitability rules for DSM attributes. 7. Run suitability rules to produce limitation datasets using a modification on the FAO methods. 8. Create final suitability data for all land management options. Two companion datasets exist for this dataset. The first is linked to in the “related materials” section of this metadata record, entitled “Confidence of suitability data for the FGARA project”. The second (held by CSIRO Land and Water) includes expert opinion and knowledge about landscape processes or conditions that will influence agricultural development potential in these catchments, but were not captured sufficiently in the modelling process (and areas of expert opinion where the Mahanabolis method underestimates confidence). The two landscape features that require special attention are the basalt rock outcrops in the Upper Flinders catchment that were not well captured by the covariate data, and the secondary salinisation hazard in the central Flinders catchment. For more information refer to the report “Land suitability: technical methods. A technical report to the Australian Government for the Flinders and Gilbert Agricultural Resource Assessment (FGARA) project”.