Canola Disease Modelling and Validation Datasets: Blackleg and Sclerotinia (BlacklegCM, SclerotiniaCM, UCI)
Document Type
Collection
Publication Title
Canola Disease Modelling and Validation Datasets: Blackleg and Sclerotinia (BlacklegCM, SclerotiniaCM, UCI)
Abstract
Datasets were collected from multiple sites in Western Australia under the project' Disease epidemiology, modelling and delivery of management decision support tools' (DAW2112-002RTX) from 2021 to 2026. The models were: BlacklegCM, SclerotinaCM, and Blackleg UCI (Blackleg Upper Canopy Infection) for the canola crop. DPIRD subcontracted to Marcoft Grains Pathology for service.
Publication Date
4-21-2026
Keywords
modelling, Brassica napus, blackleg (plant), Sclerotinia
Disciplines
Agriculture
Recommended Citation
Marcroft, S.
(2026), Canola Disease Modelling and Validation Datasets: Blackleg and Sclerotinia (BlacklegCM, SclerotiniaCM, UCI). Department of Primary Industries and Regional Development, Western Australia, Perth. Collection.
https://library.dpird.wa.gov.au/ba_grdc_ds/11
dc:access
Conditional
Funder
GRDC
Grant
DAW2112-002RTX
ROR of Contributing Organisation
https://ror.org/01awp2978
GRDC Project Code
DAW2112-002RTX
GRDC Project Title
Disease epidemiology, modelling and delivery of management decision support tools
ORCID of each Author
Spatial Coverage
Western Australia
Observations Start Date
2021
Observations End Date
2026
Comments
Datasets were collected from multiple sites in Western Australia under the project Disease epidemiology, modelling and delivery of management decision support tools (DAW2112-002RTX), from 2021 to 2026. Working models were: BlacklegCM, SclerotinaCM, and Blackleg UCI (Blackleg Upper Canopy Infection) for the canola crop. DPIRD subcontracted to Marcoft Grains Pathology for service. Datasets were used for model development and testing purposes. The key datasets were: disease and epidemiology datasets – infection severity scores, incidence %, disease progression, spore release timing, infection windows, plant growth stage, yield vs infection impact, canola variety susceptibility, environmental datasets – rainfall, temperature, relative humidity, leaf wetness duration, wind, agronomic management – sowing date, crop rotation, stubble management, fungicide application, DSM datasets – model input, model parameters and calibration datasets, and model validation datasets (training and testing datasets).