Aussie grains corporation seeks help to maintain online farm trials database
The Australian Grains Research and Development Corporation (GRDC) has announced it is looking to invest in ongoing maintenance and support of its online farm trials (OFT) database and website.
The OFT platform was launched to provide open and free access to on-farm, or field-based, current and past crop research trial data and information, such as interactive maps. There are currently over 10,000 grain development and extension (D&E) trials that are searchable on the OFT website.
The GDRC said the OFT database to date has been maintained largely by the active support of OFT contributors, but now requires suitable service providers to deliver support for three specific outputs. These include ensuring that there is constant and ongoing availability of the OFT database and website, ongoing quality control of data entries, and support to current and prospective OFT data contributors and users.
"Delivery of these outputs will require professional and technical expertise across several disciplines," the GDRC said as part of its request for tender.
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According to the GDRC, the end goal is to ensure that by June 2023, Australian grains growers, advisers, and D&E providers are able to have access to "an online FAIR (findable, accessible, interoperable, and reusable) data platform that improves adoption of D&E outputs and D&E investment planning".
The delivery times for all three outputs are expected to take place between 2020 to 2023.
Applicants have until 19 May 2020 to submit their tender applications to deliver one or more of the required outputs.
Last year, the GDRC published seven separate request for tenders that had sought for help with machine learning projects aimed at aiding the Australian grains industry.
It had identified machine learning as a foundational technology that could potentially deliver value to Australian grain growers by assisting the GRDC "address a broad range of production constraints and opportunities".
At the time, it outlined how machine learning could potentially be used to help the grains industry improve its understanding and identify genetic material that could contribute to robust crop stress tolerance; and provide more accurate weather and/or climate forecasts to improve grower decision-making.
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