18/05/2015

1

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

18/05/2015

2

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)

18/05/2015

3

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

18/05/2015

4

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

18/05/2015

5

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)

18/05/2015

6

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

18/05/2015

7

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

18/05/2015

8

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?

18/05/2015

9

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?

18/05/2015

10

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

18/05/2015

11

18/05/2015

12

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

18/05/2015

13

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

18/05/2015

14

Stay in touch…

Facebook

precision.agriculture