| Researcher(s) |
Laura Dorman (MIG) Debbie Gillam (MIG) |
|---|---|
| Year(s) | 2016 |
| Contributor | Mingenew-Irwin Group |
| Trial location(s) |
Irwin, WA
Mingenew, WA |
Sub-soil compaction, caused primarily by the unmanaged movement of heavy machinery, is having a negative effect on grain yield and quality across the Geraldton Port Zone (as well other port zones in WA). Compaction limits plant root growth, reduces plant available water capacity (PAWC) and in turn limits yield potential.
The profitability of a crop is dependent on the crop maximising the use of rainfall received and the PAWC of the soil is critical to this. This applies to all rainfall zones. Climate change however, has influenced the number of dry periods occurring during the growing season, so it’s more important than ever for crop roots to be able to explore the soil profile to the fullest possible extent.
Growers report that some soils on their property continue to underperform despite their fertiliser and system management strategy matching their soil type. The introduction of deeper deep-rippers to the region (550 – 700mm) has shown encouraging results, prompting growers to question current strategies and investigate the rippers as options for soil improvement.
In 2016 four growers within a 100km of each other currently employing deeper ripping strategies were asked to compare the performance of ‘deeper’ deep ripping (550-700mm) to standard deep ripping (250 – 350mm) and no ripping. A full soil, plant tissue and grain yield analysis was performed at each site to gather information on the base site parameters and breakdown of how each soil responded to the treatments.
There was an increasing response to deeper ripping associated with a decline in rainfall at the sites (about 100mm variance across sites). To achieve the best return on investment growers are recommended to identify the sub soil constraints in their paddock before choosing the right tillage machine to alleviate them.
| Lead research organisation |
Mingenew-Irwin Group |
|---|---|
| Host research organisation | N/A |
| Trial funding source | GRDC |
| Related program |
Deep Ripping, Deeper Deep Ripping & Water Use Efficiency |
| Acknowledgments |
|
| Other trial partners | Not specified |
| Crop type | Cereal (Grain): Wheat |
|---|---|
| Treatment type(s) |
|
| Trial type | Experimental |
| Trial design | Replicated |
| Sowing machinery | Not specified |
|---|---|
| Sow date | Not specified |
| Harvest date | Not specified |
| Plot size | Not specified |
| Plot replication | Not specified |
| Psuedoreplication | Not specified |
| Sowing machinery |
Four growers using deeper rippers in 2016 were selected for participation in this research based on their soil type and the tillage equipment being used by them. The soil types included: 2 white sand sites, a yellow sandplain and a gravel soil. Each soil was sampled and analysed to a depth of 800mm. The information from analysis was used for soil characterisation including soil moisture characterisation. Growers were required to replicate each tillage treatments and include strips in their paddock with no tillage for comparison. Deeper ripping is the new innovation being tested in this research and growers were required to be using an implement capable of ripping to between 550-700mm. Each grower also included replicated strips using a standard ripper (250-350mm). All ripping treatments were implemented prior to seeding. Soil moisture probes were installed at three of the sites in the nil treatment. Due to soil& |
|---|---|
| Sow date | Not specified |
| Harvest date | Not specified |
| Plot size | Not specified |
| Plot replication | Not specified |
| Psuedoreplication | Not specified |
| Rainfall avg ann (mm) | 400mm |
|---|---|
| Rainfall avg gsr (mm) | 350mm |
| Rainfall trial total (mm) | 450mm |
| Rainfall trial gsr (mm) | 400mm |
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.
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