Land suitability for Oats for the FGARA project

This land suitability for Oats 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 Oats 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 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.\n6. Choose land management options and create suitability rules for DSM attributes.\n7. Run suitability rules to produce limitation datasets using a modification on the FAO methods.\n8. Create final suitability data for all land management options.\nTwo 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”.

本燕麦土地适宜性栅格数据采用GeoTIFF格式存储，表征北昆士兰州弗林德斯与吉尔伯特流域内，燕麦及其特定灌溉管理系统的潜在适宜种植区域。
该数据采用1-5的分级编码：1级为无限制适宜；2级为存在轻微限制的适宜；3级为存在中等限制的适宜；4级为边际适宜；5级为不适宜。
本数据所采用的土地适宜性评价方法，是对联合国粮食及农业组织（FAO）现有方法的改进版本。
本数据隶属于弗林德斯与吉尔伯特农业资源评估（FGARA）项目，旨在支撑澳大利亚的可持续区域发展，对澳大利亚政府与农业产业均具有重要价值。该项目通过提供优化后的土壤与土地评价数据，助力识别灌溉开发新机遇并推动深入调研，从而发掘这些偏远地区的灌溉发展新空间。
本数据集配套存在一份名为"Confidence of suitability data for the FGARA project"的数据集，其链接可在本元数据记录的"相关资料"板块中获取。
数据溯源：本燕麦土地适宜性栅格数据及其各灌溉管理系统适配数据，由多类输入数据经一系列处理流程生成，以下为流程概述。如需获取更多细节，请参考澳大利亚联邦科学与工业研究组织（CSIRO）发布的FGARA项目报告，尤其是以下文献：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与Petheram C（2013）所著《土地适宜性：技术方法》，该报告为澳大利亚政府针对弗林德斯与吉尔伯特农业资源评估（FGARA）项目提交的技术报告，由CSIRO发布。整体流程如下：
1. 整合现有数据：涵盖多格式的气候、地形、土壤、自然资源、遥感等相关数据，以及各类报告、空间矢量、空间栅格等资料。
2. 采用拉丁超立方统计抽样法，在协变量空间中选取额外的土壤与属性样点数据。
3. 开展野外调查，采集补充的土壤与属性数据，并解析流域内的地貌与景观特征。
4. 依托RuleFit3软件中的规则集成预测学习方法，基于选定的输入数据与协变量数据构建模型。
5. 生成数字土壤制图（DSM）核心属性输出数据。数字土壤制图（DSM）指通过定量关系将野外与实验室观测数据结合环境数据，构建并填充地理参考数据库的过程，其依托土壤计量学（pedometrics）方法——即运用数学与统计模型，将土壤观测数据与相关环境变量、遥感影像及部分地球物理测量数据中蕴含的信息进行融合。
6. 选定土地管理方案，并为DSM属性制定适宜性评价规则。
7. 基于改进后的FAO方法运行适宜性评价规则，生成限制因子数据集。
8. 生成所有土地管理方案对应的最终适宜性数据。
本数据集另有两份配套数据集。第一份即前述元数据记录"相关资料"板块中链接的"Confidence of suitability data for the FGARA project"。第二份由CSIRO土地与水部门（CSIRO Land and Water）托管，其包含关于流域内影响农业开发潜力的景观过程与条件的专家意见与认知，但这些信息在建模过程中未得到充分体现（即马氏距离法低估置信度的专家判断区域）。其中有两类景观特征需重点关注：一是弗林德斯上游流域的玄武岩露头，协变量数据未能很好地覆盖该区域；二是弗林德斯中部流域的次生盐渍化风险。如需获取更多细节，请参考前述《土地适宜性：技术方法》报告，即澳大利亚政府为弗林德斯与吉尔伯特农业资源评估（FGARA）项目提交的技术报告。