Windrow burning as a tool for snail control - Canola

2016

Research organisaton
Funding source

Trial details

Researcher(s) Svetlana Micic
Year(s) 2016
Contributor Department of Agriculture and Food WA
Trial location(s) Wellstead, WA
Related trials
Windrow burning as a tool for snail control - Canola locations
Aims

To determine the impact of stubble burning on snail populations in a canola paddock

Key messages

Windrow burning can reduce small pointed snail populations by over 90%. If there are weeds or fallen stubble that are not burnt then snails can still be present in the paddock.

Lead research organisation Department of Agriculture and Food WA
Host research organisation N/A
Trial funding source GRDC DAW00251
Related program N/A
Acknowledgments

The research undertaken as part of this project is made possible by the significant contributions of growers through both trial cooperation and the support of the GRDC’s Regional Cropping Solutions Network, as well as the farmer group Stirling to Coast Farmers, the authors would like to thank them for their continued support.


Other trial partners Not specified
Download the trial report to view additional trial information

Method

Crop type Oilseed: Canola
Treatment type(s)
  • Pest Management
Trial type Experimental
Trial design Unknown

Wellstead 2016

Sow date Not specified
Harvest date December 2015
Plot size Not specified
Plot replication Not specified
Soil amelioration

Canola was harvested in December 2015, standing stubble was swathed in January 2016.

In the canola paddock there was no fallen stubble or weeds on the inter-rows.  

Pre-burn assessments were done 11th March 2016, paddocks were burnt 19 March 2016 and post-burn counts were done 21st March 2016.

Along 2 windrows, 2 metres from the start of the windrow, at every second metre for 20 metres of each windrow, one metre square of windrow was removed and all snails on the ground were removed. The total number of live and dead snails was counted. The snails were considered live if they moved on moistened paper towelling within 24 hours.

Five days after windrow burning, the same windrows were sampled. This time sampling started at 22 metres from the end of the windrow to avoid areas already sampled. One metre square of burnt windrow was removed, every second metre for 20 met

Download the trial report to view additional method/treatment information

Download results

Trial results Table 1

@T1: Pre-burn @T2: Post-burn
# Treatment 1
Snail (number/square metre) Snail (number/square metre)
1 Windrow 245 0
1 Inter-row - 5
2
2

Snail number/square metre


Loading
Observed trial site soil information
Trial site soil testing
Not specified
Soil conditions
Trial site Soil texture
Wellstead, WA Not specified
Derived trial site soil information
Australian Soil Classification Source: ASRIS
Trial site Soil order
Wellstead, WA Sodosol
Soil Moisture Source: BOM/ANU
Average amount of water stored in the soil profile during the year, estimated by the OzWALD model-data fusion system.
Year Wellstead WA
2016 513.6mm
2015 485.3mm
2014 443.6mm
2013 474.5mm
2012 489.7mm
2011 477.2mm
2010 448.9mm
2009 488.8mm
2008 471.3mm
2007 428.7mm
2006 471.1mm
2005 501.0mm
2004 478.3mm
2003 503.9mm
2002 481.7mm
2001 450.8mm
2000 480.6mm
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

Loading

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.

Wellstead WA

Loading
Loading
Loading

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: 15-05-2019 12:00pm AEST