spaa project update 2015 - sam trengove & nicole dimos
TRANSCRIPT
SPAA Project Activities Sam Trengove & Nicole Dimos
Management Strategies for Improved Productivity and Reduced Nitrous Oxide Emissions
• Project led by FAR Australia with funding from Department of Ag through the Action on the Ground program, 1/7/2013 – 30/6/2017.
• The project will trial strategies for optimising nitrogen use efficiency and reducing nitrous oxide emissions in broadacre cropping systems through the use of nitrogen timing and rate, precision farming tools, nitrification inhibitors and legumes in crop rotations.
• Two sites: Hart, SA & Yarrawonga, Vic
• 1 N2O is equivalent to 298 CO2.
Hart methodology – trial design
2013 Blitz lentils and 44C79 canola established
Hart methodology – trial design
2013 Blitz lentils and 44C79 canola established
2014 sown to Mace wheat on May 13th
Nitrogen treatments
1) Nil nitrogen applied
2) 40 kg N/ha first node (GS31)
3) 80 kg N/ha GS31
4) 80 kg N/ha IBS
5) 80 kg N/ha Entec urea at GS31
6) Greenseeker® GS31 25 kg N/ha ex-lentil 51 kg N/ha to
ex-canola
*10 kg/ha 22:10 applied to all treatments at seeding.
• At Hart maximum of 0.4 kg N2O/ha (8-12 kg N/ha) released
during growing season. At Yarrawonga maximum of 1.9 kg
N2O/ha (38-57 kg N/ha)
• Emissions from N applied IBS > Nil, GS31
• Rotation?
• In 2014 best management strategy for reducing N2O
emissions was delaying N application GS31 which also
maximised productivity (grain yield) at Hart. However, at
Yarrawonga productivity was maximised by applying N IBS,
but this led to highest N2O emissions.
Results – N2O emissions
Treatment Hart (g N2O/ha) Yarrawonga (g N2O/ha)
Ex canola nil 94.4 211.5
Ex canola 80 kg N/ha IBS 360.4 1922.4
Ex canola 80 kg N/ha GS31 89.6 340.2
Ex legume nil 134.7 287.2
Ex legume 80 kg N/ha IBS 271.3 1686.4
Ex legume 80 kg N/ha GS31 106.1 389.9
In Crop Weed ID and Mapping
• Project led by SPAA with funding from SAGIT 1/7/2014 – 30/6/2017
• H-Sensor provided by Agri Con GmbH • Aims
– Build weed classifiers for use in Australian crops, including wheat, barley, canola, lentils, field peas, lupins and faba beans.
– Build weed classifiers for special case weeds. – Assess accuracy of weed classifiers in the field. – Assess the affect of varying stubble loads.
How the H-Sensor works…
Anwendungsszenario: Hirse in Mais
How the H-Sensor works…
Anwendungsszenario: Hirse in Mais
How the H-Sensor works…
Anwendungsszenario: Hirse in Mais
How the H-Sensor works…
Anwendungsszenario: Hirse in Mais
…. The result
Anwendungsszenario: Hirse in Mais
Wheat
Wheat
Total soil cover: 17.65 % Soil cover wheat: 16.69 % Soil cover weed: 0.96 %
Field Peas
Total soil cover: 24.14 % Soil cover pea: 22.87 % Soil cover weed: 1.27 %
Lentils
Total soil cover: 19.92 % Soil cover lentils: 19.07% Soil cover weed: 0.85%
Lupins
Total soil cover: 18.06 % Soil cover lupin: 15.77% Soil cover weed: 2.29%
Faba Beans
Total soil cover: 7.98 % Soil cover faba bean: 6.47% Soil cover weed: 1.51% Weed classification correct: 84%
Canola % ground cover
Grass % ground cover
Grass weed classification correct in canola: 68%
Take home mesages
• Nitrous oxide emissions were highest when N was applied at seeding.
• In season NDVI measurements detected crop response to N, but calculating the optimal N rate from this data (response index) requires more research.
• In crop weed ID developments show promise, though limitations need to be recognised.
• Work continues to fine tune classifiers and measure classification accuracy.
SPAA Project Activities
These projects are proudly funded by the following organisations
These projects are proudly delivered in collaboration with the following organisations