Graincast™: monitoring crop production across the Australian grainbelt
R. Lawes
A * , Z. Hochman B , E. Jakku C , R. Butler D , J. Chai E , Y. Chen F , F. Waldner B G , G. Mata A and R. Donohue H
+ Author Affiliations
- Author Affiliations
A CSIRO Agriculture and Food, 147 Underwood Avenue, Floreat, WA 6014, Australia.
B CSIRO Agriculture and Food, 306 Carmody Road, St Lucia, Qld 4067, Australia.
C CSIRO Land and Water, 306 Carmody Road, St Lucia, Qld 4067, Australia.
D CSIRO Data61, 315 Brunswick Street, Fortitude Valley, Qld 4006, Australia.
E CSIRO Data61, 26 Dick Perry Avenue, Kensington, WA 6151, Australia.
F CSIRO Data61, Goods Shed North, 34 Village Street, Docklands, Vic. 3008, Australia.
G European Commission Joint Research Centre, Food Security, 21027 Ispra, Varese, Italy.
H CSIRO Land and Water, GPO Box 1700, Canberra, ACT 2061, Australia.
Crop & Pasture Science - https://doi.org/10.1071/CP21386
Submitted: 4 June 2021 Accepted: 26 October 2021 Published online: 21 March 2022
© 2022 The Author(s) (or their employer(s)). Published by CSIRO Publishing. This is an open access article distributed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (CC BY-NC-ND)
Abstract
The Australian dryland grain-cropping landscape occupies 60 Mha. The broader agricultural sector (farmers and agronomic advisors, grain handlers, commodity forecasters, input suppliers, insurance providers) required information at many spatial and temporal scales. Temporal scales included hindcasts, nowcasts and forecasts, at spatial scales ranging from sub-field to the continent. International crop-monitoring systems could not service the need of local industry for digital information on crop production estimates. Therefore, we combined a broad suite of satellite-based crop-mapping, crop-modelling and data-delivery techniques to create an integrated analytics system (Graincast™) that covers the Australian cropping landscape. In parallel with technical developments, a set of user requirements was identified through a human-centred design process, resulting in an end-product that delivered a viable crop-monitoring service to industry. This integrated analytics solution can now produce crop information at scale and on demand and can deliver the output via an application programming interface. The technology was designed to underpin digital agriculture developments for Australia. End-users are now using crop-monitoring data for operational purposes, and we argue that a vertically integrated data supply chain is required to develop crop-monitoring technology further.
Keywords: application programming interface, big data analytics, crop modelling, integrated land management, land use mapping, participatory research, remote sensing, user centred design.
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