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Precision
Agriculture for pastures
Mark Trotter
Introduction
Mark Trotter – Senior Lecturer in Precision Ag at
UNE
Part of the UNE Precision Agriculture Research
Group (PARG)
PARG undertakes research across a variety of
sectors (grains, sugar, cotton, horticulture)
My interest is grazing systems (family background
in dairy and beef – Mid north coast NSW)
Key research interests – how can we improve
productivity and efficiency in pastures and
grazing systems?
The Precision Agriculture Research Group
A group of lecturers and researchers from across UNE
Agronomy
Animal science
Physics
Computer science
Adjunct staff
NSW DPI
Vic DEPI
Students
Honours
PhD
R,D & E partners?
Mud-map
Introduction
What are the challenges & opportunities?
What are we doing?
Sensing & management of soils
Measuring pasture biomass
Monitoring and managing the animal
SMARTfarm
What are the challenges?
Soils – nutrient management, fertiliser is one of the biggest inputs to a grazing system
Pasture plants – having access to information around the amount, growth rate and quality of pasture to enable optimal stocking rate
Animal monitoring – understanding where your animals are up to in terms of production, health
and welfare
Labour – using labour more efficiently, it’s the biggest cost to a grazing enterprise
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Mud-map
Introduction
What are the challenges &
opportunities?
What are we doing?
Sensing & management of soils
Measuring pasture biomass
Monitoring and managing the animal
SMARTfarm
How can we use fertiliser more efficiently?
Borrow from the cropping
industry?
Variable rate / site specific /
zonal fertiliser management?
What are the opportunities for
this in grazing systems?
How would we go about
implementing VR fertiliser in pastures?
Is there an opportunity in grazing systems?
Scoping study looking at two typical paddocks on the Northern Tablelands
Took soil samples on a 1 Ha grid
What is the scale of spatial variability in key soil nutrients in these paddocks?
Phosphorus (Colwel)
Mean = 30.5
Mean = 49.9
Sulfur (Hot KCl40)
Mean = 11.0
Mean = 8.3
Kirby
High
Low
Colwell P
(mg/kg)
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Why?
55%
70%
Variable rate fertiliser in pastures?
Trotter, M, Guppy, C, Haling, R, Edwards, C, Trotter, T, Lamb, D (2014) Spatial
variability in pH and key soil nutrients: is this an opportunity to increase fertiliser
and lime use efficiency in grazing systems? Crop and Pasture Science 65 817-827.
Value? How?
40 – 80 mS/m
80 – 110 mS/m
110 – 160 mS/m
40 – 80 mS/m
Value?
0 kg N/ha (as urea)
400 kg N/ha
200 kg N/ha
100 kg N/ha
50 kg N/ha
Results N response
for each
location
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50 units of N = <250kg/ha more growth 50 units of N = 1000kg/ha more growth (doubled production)
Where to from here?
What is the spatial variability in
response to fertiliser (more important than just the critical soil
test value?)
How do you go about developing
variable rate fertiliser systems in pastures (zone management?)
Value to industry?
Sheep System - $84 per hectare
per year
(MLA report B.GSM.0004)
Now happening - pH mapping!
What else is happening out there?
Optimum N – optimising N management for dairy
pastures in NZ
Driven by environmental legislation (fertiliser caps)
Integrating sensors,
pasture growth modelling and feed demand to advise the optimum N
rate
Mud-map
Introduction
What are the challenges &
opportunities?
What are we doing?
Sensing & management of soils
Measuring pasture biomass
Monitoring and managing the animal
SMARTfarm
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Pastures from Space
Pixel selection
Pixel selection
Landsat Imagery
Available through PASource
PFS limitations
Pixel masking issues
Pixel size
Temporal resolution
Active Optical Sensors
$5k (but getting cheaper)
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How does an AOS work?
NIR and red light emitted by LEDs
Reflectance measured by sensors
Red reflectance ~ chlorophyll
concentration
NIR reflectance ~ cellular structure
Normalised difference vegetation
index
NDVI = NIR-Red / NIR +Red
Sensor
Light
source
Red
NIR
0
500
1000
1500
2000
2500
3000
3500
4000
0 0.2 0.4 0.6 0.8 1
Fig. 1. Calibration curve for GDM versus SAVI (L = 0.5) generated by combining the
calibration and validation datasets.
1
R-square = 0.71
Log(GDM) = 2.53 + 6.63 * SAVI
SAVI
GD
M
Accuracy?
Trotter, M.G., Lamb, D.W., Donald, G.E., Schneider, D.A., 2010. Evaluating an
active optical sensor for quantifying and mapping green herbage mass and growth
in a perennial grass pasture. Crop and Pasture Science 61, 389-398.
• We have previously looked at the relationship of AOS to GDM in a Tall Fescue (Flecha) pasture
• NDVI calibration RSME 341 kg/ha (over the full season)
• SAVI calibration RMSE 288kg/ha
• Saturates above 3,500kg GDM
• Working range 750-3,000kg
How accurate can they be? Finally an affordable device…
$600
But
Missing a computational interface to
manage data
(for pastures)
B.GSM.0010 Real Time Pasture Biomass Estimation
B.GSM.0010 Real Time Pasture Biomass Estimation
1. Evaluate the potential for Active Optical Sensors
2. Develop a series calibrations for use by
producers (and self calibration process)
3. Develop a Mobile Device Application
(MDA) to support AOS
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Active optical sensors for pasture biomass estimation
Current
biomass Growth
rate
Paddock 963 23
Black leg 750 21
Plumb tree 894 25
Parkers 675 11
Parkers west 762 18
Gumtree 1 1256 31
Gumtree 2 1766 34
500
1000
1500
Export data
Advanced sensing systems…
Northern grazing systems
Mud-map
Introduction
What are the challenges &
opportunities?
What are we doing?
Sensing & management of soils
Measuring pasture biomass
Monitoring and managing the animal
SMARTfarm
Autonomous Livestock Monitoring (Livestock tracking)
RBT Ear Tag, Real-
time tracking and
whole of herd
GPS collar, store-on-board
and part of herd
Hmm, they
were here this
morning???
Where are my animals?
Meanwhile
2km down
the road
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Advanced applications…
Trotter, M (2013) PA Innovations in livestock, grazing systems and rangeland
management to improve landscape productivity and sustainability. Agricultural
Science 25, 27-31.
Day
Ave
rag
e m
ea
n d
aily s
pe
ed
(m
/s)
0.042
0.044
0.046
0.048
0.050
0.052
0.054
0.056
-7 -6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6 7
Lambing
GPS detection of lambing/calving
Dobos, R, Dickson, S, Bailey, DW, Trotter, M (2014) The use of GNSS technology to
identify lambing behaviour in pregnant grazing Merino ewes Animal Production Science 54,
1722-1727.
GPS oestrus detection
Fogarty, E, Manning, J, Trotter, M, Schneider, D, Thomson, P, Bush, R, Cronin, G (2014 in
press) GPS technology and its application for improved reproductive management in
extensive sheep systems. Animal Production Science
Oestrus
Control
GPS detection of dog predation events
Manning, J, Fogarty, E, Trotter, M, Schneider, D, Thomson, P, Bush, R, Cronin, G (2014) A
pilot study into the use of GNSS technology to quantify the behavioural responses of
sheep during simulated dog predation events. Animal Production Science 54, 1676-1681.
GPS detection of disease?
Sheep move more with an increase parasite load
Falzon, G, Schneider, D, Trotter, M, Lamb, DW (2013) Correlating movement patterns
of merino sheep to faecal egg counts using global positioning system tracking collars
and functional data analysis. Small Ruminant Research 111, 171-174.
GPS tracking for pasture management?
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GPS tracking for feed management? GPS tracking for feed management?
Fine scale behavioural monitoring…
Accelerometers
Magnetometers
Gyro meters
Barwick, Trotter, Dobos, Welch, Schnieder and Economou (2014) Understanding sheep behaviour from
a tri-axial accelerometer. 2014 Australian and New Zealand Spatially Enabled Livestock Management
Symposium, Hamilton NZ, Ed J Roberts, 18th November 2014, p. 9.
Leg band – 10 seconds of data
TIME
Ear tag – 10 seconds of data
TIME
Yield mapping the grazing industries?
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Zone % of area
of
paddock
% of
weight
gain
Low 82 44
Medium 13 33
High 5 23
Trotter, Economou, Barwick Dobos, and Lamb (2014) A “six inch front harvester” yield map:
developing measures of spatial variability in productivity that are meaningful to graziers. 2014
Australian and New Zealand Spatially Enabled Livestock Management Symposium, Hamilton
NZ, Ed J Roberts, 18th November 2014, p. 13
Autonomous livestock management (Virtual fencing)
But what about sheep?
Animal Management – Virtual Fencing
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Sheep…fast learners
A Normal behaviour was assumed to be behaviour of animal before receiving stimulus. B Not observed
Event Time Sheep ID Pause - EZ Stop - VF Reaction Return to normal behaviour A
1 12:21 2 Yes Stopped U-turn immediately 15sec
2 12:42 3 Yes Stopped U-turn immediately <5sec
3 12:58 6 B Stopped U-turn immediately Immediately
4 13:11 1 No Stopped U-turn immediately Immediately
5 13:48 4 No Stopped U-turn immediately Immediately
6 14:50 Whole mob Stopped Normal grazing behaviour
7 15:04 Whole mob Stopped Normal grazing behaviour
Next steps – can we use to rotationally graze?
Remote Piloted Aircraft
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Whoa there boffin, that’s all great but how do I get started????
Have you got a digital farm map?
How to set-up PASource: Google – “PASource” and “UNE”
Next step - Landsat Imagery
Whannel R, Tobias, S, Cosby A, Lamb D and Trotter (2014) Educating Australian high
school students in relation to the digital future of agriculture: A practice report.
Digital Rural Futures Conference, Toowoomba Qld
Capturing the imagination of the next generation of scientists and leaders…
Want more?
Study PA at UNE
Undergraduate…
Diploma in Agriculture (Precision Ag Major)
B. Rural Sci/Ag (Precision Ag honours)
Postgraduate
Graduate Certificate in Precision Ag
NEW!!! Masters in Science – Precision
Agriculture
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Stay in touch…
precision.agriculture