Disease management for hyper-yielding canola

2020

Research organisaton
Funding source

Trial details

Researcher(s) Nick Poole
Darcy Warren
Year(s) 2020
Contributor Field Applied Research (FAR) Australia
Trial location(s) Gnarwarre, VIC
Disease management for hyper-yielding canola locations
Aims

To determine optimum foliar fungicide management for hyper-yielding canola.

Individual objectives specific to the trial are:

Determine the value of contrasting major gene blackleg resistance groups in HYC environments:
a. BC (45Y93 CL) – largely ineffective major gene resistance (good minor gene resistance).
b. ABD (HyTTec Trifecta) – currently effective major gene resistance.

Determine the effect of fungicide management strategies on disease control (upper canopy
blackleg and sclerotinia), grain yield and profitability.

Key messages

 There was no effect of fungicide treatment on grain yield of either cultivar.
 Like other trials, 45Y93 CL was higher yielding than HyTTec Trifecta.
 There was no interaction between fungicide management and cultivar choice, effectively showing that there was no difference advantage of having a cultivar with effective major gene blackleg resistance compared with a cultivar with ineffective resistance.
 While sclerotinia, blackleg and Alternaria were present on site all disease where are low levels 45Y93 had a significantly higher incidence of sclerotinia infect in comparison to HyTTec Trifecta. However, disease pressure was not high enough to separate fungicide treatment effects.
 A disease management trial will be completed again in 2021 and will include a winter cultivar and early season (seed and 6-leaf) fungicide treatments.

Gnarwarre VIC 2020

Rotation position: 2019 Wheat
Soil type & management: Neutral-slightly alkaline Organosol (Peat soil) – high organic matter (0-30cm)
Mehlich P (ppm) 0-30 cm: 4.9
pH (CaCl2) 0-30 cm: 7.59
Organic Carbon (%) 0-30 cm: 24.9
 

Lead research organisation Field Applied Research (FAR) Australia
Host research organisation Field Applied Research (FAR) Australia
Trial funding source GRDC FAR2004-002SAX
Related program FAR Hyper Yielding Crops 2020-2022
Acknowledgments N/A
Other trial partners Not specified
Download the trial report to view additional trial information

Method

Crop type Oilseed: Canola
Treatment type(s)
  • Crop: Variety
  • Fungicide: Rate
  • Fungicide: Timing
  • Fungicide: Type
Trial type Experimental
Trial design Randomised,Replicated,Blocked

Gnarwarre 2020

Sow rate or Target density 60 seeds/m2
Sow date 8 May 2020
Harvest date 24 December 2020
Plot size Not specified
Plot replication Not specified
Fertiliser

Sowing Fertiliser: 100kg/ha MAP

Nutrition: 14 July - 46 N kg/ha (100 kg/ha urea)
                11 August - 30 kg/ha N (SOA 150 kg/ha)

Fungicide

Please refer to report for treatment details

Seed treatment Jockey + Poncho
Download the trial report to view additional method/treatment information
Trial source data and summary not available
Check the trial report PDF for trial results.
Observed trial site soil information
Trial site soil testing
Not specified
Soil conditions
Trial site Soil texture
Gnarwarre, VIC Not specified
Derived trial site soil information
Australian Soil Classification Source: ASRIS
Trial site Soil order
Gnarwarre, VIC Sodosol
National soil grid Source: CSIRO/TERN
NOTE: National Soil Grid data is aggregated information for background information on the wider area
Actual soil values can vary significantly in a small area and the trial soil tests are the most relevant data where available

Soil properties

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Climate

Derived climate information

No observed climate data available for this trial.
Derived climate data is determined from trial site location and national weather sources.

Gnarwarre VIC

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Some data on this site is sourced from the Bureau of Meteorology

SILO weather estimates sourced from https://www.longpaddock.qld.gov.au/silo/
Jeffrey, S.J., Carter, J.O., Moodie, K.B. and Beswick, A.R. (2001). Using spatial interpolation to construct a comprehensive archive of Australian climate data , Environmental Modelling and Software, Vol 16/4, pp 309-330. DOI: 10.1016/S1364-8152(01)00008-1.



Trial last modified: 29-03-2023 16:04pm AEST