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