Dataset

Soil organic carbon concentration in Eastern Australia

University of New England
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ctx_ver=Z39.88-2004&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Adc&rfr_id=info%3Asid%2FANDS&rft_id=http://www.une.edu.au/current-students/resources/academic-schools/school-of-environmental-and-rural-science/research/plant,-soil-and-environment-systems&rft.title=Soil organic carbon concentration in Eastern Australia&rft.publisher=University of New England&rft.description=This dataset contains measurements related to the depth distribution of organic carbon in soil in Eastern Australia. Soil organic carbon concentration (SOC) was measured to a soil depth of 1 m at 100 sites across NSW, Australia. Three machine learning algorithms were used to identify predictors important to the model parameters. Multiple regression models were then created based upon the machine learning results using bootstrapped stepwise regressions and the relative importance of the selected variables was assessed using proportional marginal variance decomposition. Predictor variables used in machine learning algorithms include climate, land-use, site and soil variables. This dataset is an output of the Importance of Deep Soil Carbon to Long Term Carbon Storage Project which is supported by funding from the Australian Government Department of Agriculture.&rft.creator=Anonymous&rft.date=2015&rft.coverage=147.017883,-32.161804&rft_rights= Researchers wishing to re-use this data are required to cite both the dataset and the scientific paper it is related to.&rft_subject=Carbon Sequestration Science&rft_subject=Environmental Sciences&rft_subject=Soil Sciences&rft_subject=Soil Biology&rft_subject=Ecological Impacts of Climate Change&rft_subject=Ecological Applications&rft_subject=Land Capability and Soil Degradation&rft_subject=Analysis of Algorithms and Complexity&rft_subject=Information and Computing Sciences&rft_subject=Computation Theory and Mathematics&rft.type=dataset&rft.language=English Go to Data Provider

Contact Information

School of Environmental and Rural Science
University of New England, Armidale, 2351, NSW

Full description

This dataset contains measurements related to the depth distribution of organic carbon in soil in Eastern Australia. Soil organic carbon concentration (SOC) was measured to a soil depth of 1 m at 100 sites across NSW, Australia.

Three machine learning algorithms were used to identify predictors important to the model parameters. Multiple regression models were then created based upon the machine learning results using bootstrapped stepwise regressions and the relative importance of the selected variables was assessed using proportional marginal variance decomposition.

Predictor variables used in machine learning algorithms include climate, land-use, site and soil variables.

This dataset is an output of the Importance of Deep Soil Carbon to Long Term Carbon Storage Project which is supported by funding from the Australian Government Department of Agriculture.

Created: 2014 to 2015

Data time period: 2014 to 2015

147.017883,-32.161804

147.017883,-32.161804