Multi-source data integration and identification of uncertainties affecting production of a digital soil map
- Authors: Robinson, Nathan , Benke, Kurt , Hopley, J. , MacEwan, Richard , Clark, R. , Rees, David , Kitching, Matt , Imhof, Mark , Bardos, David
- Date: 2014
- Type: Text , Conference paper
- Relation: GlobalSoilMap: Basis of the Global Spatial Soil Information System - Proceedings of the 1st GlobalSoilMap Conference p. 353-358
- Full Text: false
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- Description: Production of a digital soil map for the state of Victoria in Australia is subject to various errors arising from the use of legacy data (as is often the case around the globe). Potential sources of uncertainty for inputs and methods undertaken in the creation of a Victorian DSM version 1.0 (VicDSMv1) are identified. These sources of uncertainty are recognised and issues discussed including their potential contribution to error propagation. Examples include possible errors associated with legacy soil maps, soil sites, laboratory analysis and predictive modelling by regression or spline approaches. Experiences in processing of legacy data in Victoria are described and some aspects of incorporating uncertainty in data discussed. As part of this initial DSM exercise these uncertainties and contextual information will be captured as associated metadata. A framework, as a five component process model for integrated assessment of uncertainty, is suggested based on uncertainty in the mapping process. © 2014 Taylor & Francis Group, London, UK.
A digital soil map of Victoria-VicDSMv1
- Authors: Hopley, J. , Rees, David , MaEwan, Richard , Clark, R. , Benke, Kurt , Imhof, Mark , Robinson, Nathan , Bardos, David
- Date: 2014
- Type: Text , Conference paper
- Relation: GlobalSoilMap: Basis of the Global Spatial Soil Information System - Proceedings of the 1st GlobalSoilMap Conference p. 185-189
- Full Text: false
- Reviewed:
- Description: This paper describes the production of the first version of a digital soil map for Victoria (VicDSMv1) which has combined existing soil point and polygon data. Four soil properties: pH, EC (electrical conductivity), clay percentage and Soil Organic Carbon (SOC) at the 6 depths specified by GlobalSoilMap.net have been mapped. The mapping has utilised data from 5,233 legacy sites collated from soil and land surveys conducted across Victoria since the 1930s and stored in the Victorian Soil Information System (VSIS). These sites were prepared by allocating property values to each of the 6 depths using equal area splines or depth weighted values. A land unit map for Victoria was derived from an overlay of map units from 32 surveys mapped at 100,000 scale or better. A dominant soil type at Suborder level in the Australian Soil Classification system (ASC) was assigned to each land unit. For each polygon a hierarchical grouping of sites from the VSIS was created using soil classification and location in relation to the polygon. A set of statistics for each soil property value at each set depth were calculated from the best available site cluster for each polygon. Metadata relating to property calculations have been collected. Creation of the VicDSMv1 has involved the preparation and entry of a large volume of legacy soil information into the VSIS. Consultation with current and retired soil surveyors during the process has enabled valuable expert knowledge to be captured into digital soil mapping. © 2014 Taylor & Francis Group, London, UK.
Online Farm Trials (OFT) – the past, present and future
- Authors: Robinson, Nathan , Dahlhaus, Peter , Feely, Paul , Light, Kate , MacLeod, Andrew
- Type: Text , Conference paper
- Relation: Proceedings of the 19th Australian Society of Agronomy Conference,25-29 August 2019, Wagga Wagga, NSW, Australia
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- Description: Online Farm Trials (OFT) (www.farmtrials.com.au) is a free web-based resource and trial discovery system that contains more than 7,100 trials from 76 different organisations from across Australia. Since its inception in 2013, OFT has developed via a collaborative approach with grower groups, research organisations, agricultural experts and grains industry organisations. This ensures the outcomes are highly relevant, practical and beneficial for growers. Users can view, analyse and export grains research data as well as compare trials based upon historical, geographic and crop-specific search filters. Current developments include seasonally relevant collections of trials to highlight priority topics and aid on-farm decision making. To meet the future needs of industry stakeholders, system developments are planned to include expanded trial research information access, foster innovation through publishing and promoting active trials and enhance trial data standards and quality. **Please note that there are multiple Federation University authors for this article, including the name of the first 5 and also including “Rob Milne, Julie Parker, Helen Thompson, Judi Walters and Ben Wills" is provided in this record**
Improving the FAIRness of Australia’s grains research sector data
- Authors: Willis, Ben , Parker, Julie , Robinson, Nathan , Wong, Megan
- Date: 2019
- Type: Text , Conference paper
- Relation: Proceedings of the 19th Australian Society of Agronomy Conference,25-29 August 2019, Wagga Wagga, NSW, Australia
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- Reviewed:
- Description: Across Australia’s arable landscapes, thousands of crop trials have been conducted to improve the profitability and sustainability of Australian grain production. Although there have been significant steps to make knowledge gained from trials available to users, there is the potential to further support the development of next generation data models and knowledge products by integrating trials from disparatei sources by adhering to FAIR principles of data management. That is, making data: findable, accessible, interoperable and reusable. This research explores whether Online Farm Trials increase the FAIRness of agricultural grains trial datasets through a comparison of the trial data capture and handling practices of organisations whose datasets are not discoverable through Online Farm Trials (OFT) (N = 50) with the FAIRness of the datasets discoverable through OFT. The findings demonstrate that OFT is helping to make the results of Australia’s grains trials more FAIR to the users of trial data, and suggests a number of improvements to the FAIRness of trial datasets, foremost through the use of machine-readable metadata.