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The canola growth stage prediction model and sclerotinia stem rot risk index are useful tools for managing the disease.
Project Summary
Overview
The purpose of this research is to develop models and deploy forecasting tools for canola growth stage, sclerotinia stem rot risk and canola yield on a near real-time basis.
The major objectives of this project were to develop and deploy forecasting tools for canola growth stages, sclerotinia stem rot risk and canola yield. During the 2017 cropping season, canola trials were conducted in Manitoba in collaboration with industry and the University of Manitoba. The small-plot trials were conducted in collaboration with DL Seeds and Kelburn Farm in collaboration with Richardson International while the field scale trials were conducted in collaboration with Bayer Crop Science. In total there were 13 trials consisting of six small-plot trials and seven field-scale trials. A summer student was employed at the end of May 2017 to help with data collection.
In all four years 2014, 2015, 2016 and 2017 trials included small plot trials and field-scale trials. Each small plot trial had three varieties (representing short-, medium- and long-season cultivar groups) with four replications. The field-scale trials had one cultivar with four replicates within a large field. In all locations, canola growth stages were recorded using time-lapse cameras and also observed manually once a week. On-site, in-canopy and outside-canopy weather conditions were monitored during the entire growing season. In one location in Manitoba, sclerotia depots were deployed and sclerotia germination (Apothecia) was counted. Sclerotinia stem rot (SSR) was recorded 2-3 times after crop maturity before swathing and canola yield was obtained at maturity.
Canola growth stages extracted from the pictures taken by automated time lapse cameras and manual observations were used to compare specific growth stages (BBCH 9 to 89) with accumulated growing degree day (GDD), physiological days (P-days), and crop heat units (CHU). Accumulated heat units from the three thermal models required for 14 selected crop stages from emergence (BBCH 9) to ripe (BBCH 89) were compared. Among the three thermal models, accumulated P-days threshold were selected to predict the growth stages. The growth stages prediction thresholds for short, mid, and long season cultivars were also compared and we found differences among cultivar groups i.e., each cultivar group required different accumulated P-days for corresponding growth stages.
Outcomes
A sclerotinia risk model and sclerotinia score card was also successfully created and verified in 2017.
Using knowledge on the sclerotinia biology and disease cycle as well as SSR checklists previously developed, a SSR score card was developed. It was validated using three year field data from all the locations. The score card has both weather and agronomic variables as input variables (including cumulative rainfall, average Tmax: 2 weeks before BBCH 65, average daily wet hours, Cumulative rainfall, days of rainfall, average Tmax: 5 days before to 3 days after BBCH 65, number of years canola or host crop was present, disease incident in last years crop, plant density, and varietal resistance).
Both the sclerotinia risk model and canola growth stage model can be found on the webpage www.canoladst.ca. The website can be used to deploy site-specific advisory for growth stage prediction and sclerotinia stem rot risk in all three provinces (Manitoba, Saskatchewan and Alberta). Weather data from Weather Farm stations and other networks owned by Weather INnovations as well as Environment Canada stations is integrated with GIS system to provide field-specific advisory.
The system of ongoing communication with canola producers at the end of this project is a critical task. It is important to have two way communications with individual producers and crop advisors. The inbound communication, where producers provide input on their individual observations and experience must be extremely easy and efficient for producers to use. To this end, the approach is to piggyback other data collection systems instead of creating a new reporting task for producers. Outbound communication will be direct for anyone who provides in season data and/or requests, as well as generally in the usual extension channels.
