| Researcher(s) |
Tabitha Armour (FAR NZ) Wes Arnott (FAR NZ) Nick Poole (FAR NZ) Rohan Wardle (SFS) |
|---|---|
| Year(s) | 2005 |
| Contributor | Southern Farming Systems |
| Trial location(s) |
Inverleigh, VIC
|
This trial had the objective of examining how different crop canopies influenced the need for disease control in winter wheat.
From this initial work it cannot be concluded that the later nitrogen timings implicit in a canopy management approach significantly influence the need for disease management.
| Lead research organisation | N/A |
|---|---|
| Host research organisation | N/A |
| Trial funding source | GRDC SFS00015 |
| Related program | N/A |
| Acknowledgments |
The following trial is part of a new GRDC funded project (SFS 00015) examining the integration of the principles of disease management and canopy management in cereal crops. |
| Other trial partners | Not specified |
| Crop type | Cereal (Grain): Wheat |
|---|---|
| Treatment type(s) |
|
| Trial type | Experimental |
| Trial design | Unknown |
| Sow date | 9 June 2005 |
|---|---|
| Harvest date | Not specified |
| Plot size | Not specified |
| Plot replication | Not specified |
| Sow date | 9 June 2005 |
|---|---|
| Harvest date | Not specified |
| Plot size | Not specified |
| Plot replication | Not specified |
| # | Variety |
Treatment 1
|
Protein (%) | Screenings (%) | Grain yield (t/ha) |
|---|---|---|---|---|---|
| 1 | █ Mackellar | █ Control (mean of 2 population densities) | 9.7 | 12.9 | 4.88 |
| 2 | █ Mackellar | █ Control (76 plants/m2) | 4.51 | ||
| 3 | █ Mackellar | █ Control (189 plants/m2) | 5.26 | ||
| 4 | █ Mackellar | █ 50 kg N/ha, 50% at sowing, 50% at GS31(mean of 2 population densities) | 10.5 | 12 | 5.41 |
| 5 | █ Mackellar | █ 50 kg N/ha, 50% at sowing, 50% at GS31 (76 plants/m2) | 4.97 | ||
| 6 | █ Mackellar | █ 50 kg N/ha, 50% at sowing, 50% at GS31 (189 plants/m2) | 5.85 | ||
| 7 | █ Mackellar | █ 50 kg N/ha, 50% at GS31, 50% at GS39 (mean of 2 population densities) | 11.5 | 8.6 | 5.24 |
| 8 | █ Mackellar | █ 50 kg N/ha, 50% at GS31, 50% at GS39 (76 plants/m2) | 5.17 | ||
| 9 | █ Mackellar | █ 50 kg N/ha, 50% at GS31, 50% at GS39 (189 plants/m2) | 5.31 | ||
| 10 | █ Mackellar | █ 50 kg N/ha, 100% at GS39 (mean of 2 population densities) | 10.3 | 10.2 | 5.16 |
| 11 | █ Mackellar | █ 50 kg N/ha, 100% at GS39 (76 plants/m2) | 4.95 | ||
| 12 | █ Mackellar | █ 50 kg N/ha, 100% at GS39 (189 plants/m2) | 5.36 | ||
| 13 | █ Mackellar | █ 50 kg N/ha, 100% at GS31(mean of 2 population densities) | 10.5 | 9.8 | 5.25 |
| 14 | █ Mackellar | █ 50 kg N/ha, 100% at GS31 (76 plants/m2) | 4.84 | ||
| 15 | █ Mackellar | █ 50 kg N/ha, 100% at GS31 (189 plants/m2) | 5.66 | ||
| 16 | █ Mackellar | █ 100 kg N/ha, 50% at sowing, 50% at GS31(mean of 2 population densities) | 9.5 | 12 | 4.96 |
| 17 | █ Mackellar | █ 100 kg N/ha, 50% at sowing, 50% at GS31 (76 plants/m2) | 4.92 | ||
| 18 | █ Mackellar | █ 100 kg N/ha, 50% at sowing, 50% at GS31 (189 plants/m2) | 5 | ||
| 19 | █ Mackellar | █ 100 kg N/ha, 50% at GS31, 50% at GS39 (mean of 2 population densities) | 9.8 | 11.7 | 5.29 |
| 20 | █ Mackellar | █ 100 kg N/ha, 50% at GS31, 50% at GS39 (76 plants/m2) | 4.99 | ||
| 21 | █ Mackellar | █ 100 kg N/ha, 50% at GS31, 50% at GS39 (189 plants/m2) | 5.58 | ||
| 22 | █ Mackellar | █ 100 kg N/ha, 100% at GS39 (mean of 2 population densities) | 10.1 | 11.6 | 5.19 |
| 23 | █ Mackellar | █ 100 kg N/ha, 100% at GS39 (76 plants/m2) | 4.81 | ||
| 24 | █ Mackellar | █ 100 kg N/ha, 100% at GS39 (189 plants/m2) | 5.57 | ||
| 25 | █ Mackellar | █ 100 kg N/ha, 100% at GS31(mean of 2 population densities) | 9.6 | 12.4 | 4.73 |
| 26 | █ Mackellar | █ 100 kg N/ha, 100% at GS31 (76 plants/m2) | 4.73 | ||
| 27 | █ Mackellar | █ 100 kg N/ha, 100% at GS31 (189 plants/m2) | 4.73 |
| # | Variety |
Protein (%) | Screenings (%) | Grain yield (t/ha) | Test weight (kg/hL) | ||
|---|---|---|---|---|---|---|---|
| 1 | █ Chara | █ Late N (50% N at GS31 & 50% N at GS39) | █ Untreated | 12.5 | 8.6 | 2.87 | 75.7 |
| 2 | █ Chara | █ Late N (50% N at GS31 & 50% N at GS39) | █ Impact in furrow 0.4L/ha at GS00 | 12.5 | 10.3 | 2.86 | 74.1 |
| 3 | █ Chara | █ Late N (50% N at GS31 & 50% N at GS39) | █ Impact in furrow 0.4L/ha at GS00, Opus 250 ml/ha at GS39 | 12.5 | 6.3 | 3.96 | 78.4 |
| 4 | █ Chara | █ Late N (50% N at GS31 & 50% N at GS39) | █ Opus 250ml/ha at GS39 | 12.4 | 6.7 | 3.54 | 78.1 |
| 5 | █ Mackellar | █ Late N (50% N at GS31 & 50% N at GS39) | █ Untreated | 11.5 | 13.4 | 4.53 | 75 |
| 6 | █ Mackellar | █ Late N (50% N at GS31 & 50% N at GS39) | █ Impact in furrow 0.4L/ha at GS00 | 11.5 | 12.8 | 4.84 | 75.1 |
| 7 | █ Mackellar | █ Late N (50% N at GS31 & 50% N at GS39) | █ Impact in furrow 0.4L/ha at GS00, Opus 250 ml/ha at GS39 | 11 | 10.2 | 5.36 | 77.5 |
| 8 | █ Mackellar | █ Late N (50% N at GS31 & 50% N at GS39) | █ Opus 250ml/ha at GS39 | 10.6 | 12.1 | 5.83 | 77.6 |
| 9 | █ Chara | █ Early N (50% N at sowing & 50% N at GS31) | █ Untreated | 12.2 | 8.7 | 3.24 | 76.9 |
| 10 | █ Chara | █ Early N (50% N at sowing & 50% N at GS31) | █ Impact in furrow 0.4L/ha at GS00 | 11.9 | 7.5 | 3.31 | 76.3 |
| 11 | █ Chara | █ Early N (50% N at sowing & 50% N at GS31) | █ Impact in furrow 0.4L/ha at GS00, Opus 250 ml/ha at GS39 | 12.4 | 5.9 | 4.08 | 78.2 |
| 12 | █ Chara | █ Early N (50% N at sowing & 50% N at GS31) | █ Opus 250ml/ha at GS39 | 12.6 | 6 | 3.92 | 78 |
| 13 | █ Mackellar | █ Early N (50% N at sowing & 50% N at GS31) | █ Untreated | 10.7 | 12.1 | 4.58 | 76.2 |
| 14 | █ Mackellar | █ Early N (50% N at sowing & 50% N at GS31) | █ Impact in furrow 0.4L/ha at GS00 | 11.3 | 13.3 | 4.4 | 74.3 |
| 15 | █ Mackellar | █ Early N (50% N at sowing & 50% N at GS31) | █ Impact in furrow 0.4L/ha at GS00, Opus 250 ml/ha at GS39 | 11 | 10.8 | 5.59 | 78.2 |
| 16 | █ Mackellar | █ Early N (50% N at sowing & 50% N at GS31) | █ Opus 250ml/ha at GS39 | 10.9 | 11.9 | 5.01 | 77.3 |
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.
