Leigh Davis (SARDI)
Ashley Flint (SARDI)
Brian Purdie (SARDI)
Brenton Spriggs (SARDI)
Andrew Ware (SARDI)
|Contributor||SARDI Minnipa Agricultural Centre|
Minnipa Agricultural Centre, SA
To maximise canola productivity through creating soil specific management strategies that improve canola yields, profitability and establishment in field trials on lower and upper Eyre Peninsula (EP). In 2014, ten separate trials were conducted as part of this project at Minnipa Agricultural Centre, and Piednippie on upper EP. Four trials will be reported in this article but only from Minnipa Ag Centre trials, as the Piednippie trial site was too variable.
Early sowing (15 April) had the largest positive impact on canola yield when comparing a range of treatments trialled in 2014, similar to results observed in 2013. Sowing on 15 April improved yields up to 45%, depending on variety, compared to 13 May sowing date. Good seeding depth and the correct seed rate proved important in maximising canola yield at the sites trialled in 2014, but not to the same extent as time of sowing. Sowing at 4.5 kg/ha at a 2 cm depth gave a 13% yield improvement over sowing at 1.5 kg/ha at 1 cm depth. Achieving approximately 50 plants/m2 of triazine tolerant varieties and 40 plants/m2 of Clearfield tolerant varieties was needed to maximise canola yields in trials conducted at Minnipa Agricultural Centre in 2014. Using farmer retained open pollinated seed did not cause a yield penalty when compared to commercially purchased seed in trials conducted at Minnipa in 2014.
|Lead research organisation||N/A|
|Host research organisation||
SARDI Minnipa Agricultural Centre
|Trial funding source||SAGIT|
Thank you to the South Australian Grains Industry Trust (SAGIT) for providing the funding. Thank you to Minnipa Agricultural Centre for providing the land for the trials.
|Other trial partners||Not specified|
|Sow rate or Target density||The trial was planted at three rates (1.5 kg/ha, 3 kg/ha and 4.5 kg/ha).|
|Sow date||6 May 2014|
|Harvest date||Not specified|
|Plot size||1.5m x 10m|
This trial received a total of 71 kg/ha 19:13:0 S9% and 39 kg/ha Urea fertiliser, applied at seeding and 73kg/ha of Urea and 168 kg/ha SOA broadcast during the season (total of 110 kg/ha nitrogen).
The trial received knockdown of Roundup, plus 60 ml/ha Hammer. 650ml/ha Terbyne Extreme, 400 ml/ha Targa was applied to control weeds.
A bare earth insecticide of 1L/ha Chlorpyrifos at sowing. Multiple products were used during the season to control insects, which included aphids and diamond back moth.
||Grain yield (t/ha)||Emergence plants (plants/m2)|
|1||█ Canola:Hyola 450TT||█ Rate 1.5kg/ha||█ Depth 1cm||1.34||17|
|2||█ Canola:ATR Stingray||█ Rate 1.5kg/ha||█ Depth 1cm||1.53||38|
|3||█ Canola:Hyola 450TT||█ Rate 1.5kg/ha||█ Depth 2cm||1.31||32|
|4||█ Canola:ATR Stingray||█ Rate 1.5kg/ha||█ Depth 2cm||1.49||58|
|5||█ Canola:Hyola 450TT||█ Rate 1.5kg/ha||█ Depth 4cm||1.4||63|
|6||█ Canola:ATR Stingray||█ Rate 1.5kg/ha||█ Depth 4cm||1.44||71|
|7||█ Canola:Hyola 450TT||█ Rate 3kg/ha||█ Depth 1cm||1.39||32|
|8||█ Canola:ATR Stingray||█ Rate 3kg/ha||█ Depth 1cm||1.7||58|
|9||█ Canola:Hyola 450TT||█ Rate 3kg/ha||█ Depth 2cm||1.36||47|
|10||█ Canola:ATR Stingray||█ Rate 3kg/ha||█ Depth 2cm||1.72||65|
|11||█ Canola:Hyola 450TT||█ Rate 3kg/ha||█ Depth 4cm||1.48||40|
|12||█ Canola:ATR Stingray||█ Rate 3kg/ha||█ Depth 4cm||1.66||52|
|13||█ Canola:Hyola 450TT||█ Rate 4.5kg/ha||█ Depth 1cm||1.38||63|
|14||█ Canola:ATR Stingray||█ Rate 4.5kg/ha||█ Depth 1cm||1.62||71|
|15||█ Canola:Hyola 450TT||█ Rate 4.5kg/ha||█ Depth 2cm||1.56||58|
|16||█ Canola:ATR Stingray||█ Rate 4.5kg/ha||█ Depth 2cm||1.76||70|
|17||█ Canola:Hyola 450TT||█ Rate 4.5kg/ha||█ Depth 4cm||1.5||50|
|18||█ Canola:ATR Stingray||█ Rate 4.5kg/ha||█ Depth 4cm||1.75||76|
|Rainfall trial gsr (mm)||290mm|
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.