Abstract
A critical shortage of ‘big’ agronomic data is placing an unnecessary constraint on the conduct of public agronomic research, imparting barriers to model development and testing. Here, we address this problem by providing a large non-relational database of agronomic trials, linked to intensive management and observational data, run under a unified experimental framework. The National Variety Trials (NVTs) represent a decade-long experimental trial network, conducted across thousands of Australian field sites using highly standardised randomised controlled designs. The NVTs contain over a million machine-measured phenotypic observations, aggregated from density-controlled populations containing hundreds of millions of plants and thousands of released plant varieties. These data are linked to hundreds of thousands of metadata observations including standardised soil tests, fertiliser and pesticide input data, crop rotation data, prior farm management practices, and in-field sensors. Finally, these data are linked to a suite of ground and remote sensing observations, arranged into interpolated daily- and ten-day aggregated time series, to capture the substantial diversity in vegetation and environmental patterns across the continent-spanning NVT network.
Highlights
Background & SummaryAgronomy has an enormous potential to benefit from the diverse and rapidly developing field of statistical methods falling under the ‘machine learning’ (ML) umbrella
A concerted, open, public effort to observe and predict crop behaviour[4], using any and all available tools such as machine learning and remote sensing data, can help these people meet the enormous challenges of climate change[5,6,7]
By running extensive missing-data imputation, digitisation, and quality controls, and linking these trials to extensive satellite and ground sensing observations, we have formatted these data for the rapid development of machine learning platforms and the testing of agronomic hypotheses on a scale previously inaccessible to other scientists
Summary
Background & SummaryAgronomy has an enormous potential to benefit from the diverse and rapidly developing field of statistical methods falling under the ‘machine learning’ (ML) umbrella. Key agronomic traits include flowering times, protein content, and over a hundred thousand variety-years of yield data (Fig. 2).
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
