Data

University of Adelaide National Sodic field trial reference dataset for GRDC Machine Learning Project- UOA2002-007RTX.

Also known as: National Sodic Field Trial dataset
The University of Adelaide
Schilling , Rhiannon ; David, Rakesh ; Roy, Stuart
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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=info:doi10.25909/621c341c36f7b&rft.title=University of Adelaide National Sodic field trial reference dataset for GRDC Machine Learning Project- UOA2002-007RTX&rft.identifier=https://doi.org/10.25909/621c341c36f7b&rft.publisher=University of Adelaide&rft.description=The collection includes raw and processed data for machine learning compliance. Includes data from the National Sodic Field Trials - 16 wheat varieties grown at two sites with sodic subsoils - Mallala and Roseworthy (368 plots), spanning 2017-2019.  Raw data available as excel includes soil cores information,  grain yield, biomass, plant physiology, tolerance sensitive traits, genetic markers. In addition to the raw data the collection includes pre-processed versions of the dataset compliant with machine learning analytics.Application of Deep Learning convolution neural network in a case study to determine the effects of crop and soil factors on variable crop growth in sodic sites.&rft.creator=Schilling , Rhiannon &rft.creator=David, Rakesh &rft.creator=Roy, Stuart &rft.date=2022&rft.coverage=South Australia&rft_rights=CC-BY 4.0&rft_subject=sodic soils&rft_subject=wheat&rft_subject=machine learning&rft_subject=subsoil constraint&rft_subject=crop yield&rft_subject=CROP AND PASTURE PRODUCTION&rft_subject=AGRICULTURAL AND VETERINARY SCIENCES&rft_subject=SOIL SCIENCES&rft_subject=ENVIRONMENTAL SCIENCES&rft_subject=Agriculture, land and farm manager&rft.type=dataset&rft.language=English Access the data
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Licence & Rights:

Open Licence view details
CC-BY

CC-BY 4.0

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Contact Information

Rhiannon.Schilling@sa.gov.au Rhiannon Schilling
rakesh.david@adelaide.edu.au Rakesh David

Full description

The collection includes raw and processed data for machine learning compliance. Includes data from the National Sodic Field Trials - 16 wheat varieties grown at two sites with sodic subsoils - Mallala and Roseworthy (368 plots), spanning 2017-2019.  Raw data available as excel includes soil cores information,  grain yield, biomass, plant physiology, tolerance sensitive traits, genetic markers. In addition to the raw data the collection includes pre-processed versions of the dataset compliant with machine learning analytics.

Significance statement

Application of Deep Learning convolution neural network in a case study to determine the effects of crop and soil factors on variable crop growth in sodic sites.

Data time period: 01 01 2017 to 31 12 2019

This dataset is part of a larger collection

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Spatial Coverage And Location

text: South Australia

Subjects
Agricultural and Veterinary Sciences | Agriculture, land and farm manager | Crop and Pasture Production | Environmental Sciences | Soil Sciences | crop yield | machine learning | sodic soils | subsoil constraint | wheat |

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