a review of big data, smart and precision technologies in ... 2/session ii - estuary... · a review...
TRANSCRIPT
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A review of big data, smart and
precision technologies in grass based
dairying systems
Laurence Shalloo Leso L.1,2 , McDonagh A.1, Geoghegan A.1, Werner J.1,3 and O’Leary N.1
1Teagasc, Animal & Grassland Research and Innovation Centre, Moorepark, Fermoy, Co. Cork, Ireland 2University of Florence, Dept. of Agricultural, Food and Forestry Systems, 50145 Firenze, Italy
3Institute for Agricultural Engineering, University of Hohenheim, 70599 Stuttgart, Germany
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Overview
• Drivers of efficiency in a pasture based
systems
• State of the Art
• SWOT Analysis
• Strengths
• Weaknesses
• Opportunities
• Threats
• Future – Needs Driven Development
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Grass utilised per ha and net margin
per ha (National Farm Survey)
0
500
1000
1500
2000
2500
3000
3500
2000 4000 6000 8000 10000 12000 14000
€/H
a
kg DM/Ha
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Grassland systems
R2 = 0.9074
0
5
10
15
20
25
30
35
40
0 20 40 60 80 100Dietary grass proportion (%)
To
tal
co
sts
of
Pro
du
cti
on
(€ c
/l)
US
Confined US Grazing
DK
GER
FR
IRL
AU
NZ
UK NL
Source: Dillon et al. (2005)
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Six Week Calving Rate > 90%
50
55
60
65
70
75
80
85
90
2008 2010 2012 2014 2016
% c
ow
s c
alv
ing
in
6 w
eek
s
5 EGF 2018
Each 1% increase
in 6 week
€8.22/cow
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Overview
• Drivers of efficiency in a pasture based
systems
• State of the Art
• SWOT Analysis
• Strengths
• Weaknesses
• Opportunities
• Threats
• Future – Needs Driven Development
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7
State of the art • Precision dairy tech overview - what can be measured?
Health
• Mastitis
• Fertility
• Locomotion
• Metabolism
Production Performance
• Milk yield
• Milk quality
Behaviour
• Physical behaviour
• Feeding/rumination
• Spatial behaviour
• Social behaviour
Spectral analysis
• Biodiversity
• Growth
• Quality
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8
Health Mastitis
Locomotion
Lameness Fertility
Metabolism
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Overview
• Drivers of efficiency in a pasture based
systems
• State of the Art
• SWOT Analysis
• Strengths
• Weaknesses
• Opportunities
• Threats
• Future – Needs Driven Development
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Grazing behaviour and activity DAIRYMASTER
MooMonitor + Collar (Accelerometer)
ITIN+HOCH RumiWatch Noseband Sensor + Pedometer
•Noseband pressure
sensor
•Jaw movements
•Rumination, eating
and drinking
•Developed for
research
•Multi-axis
accelerometer-
based pedometer
•Walking, standing,
lying, amount of
strides
•Measures feeding, rumination,
activity levels - 3- axis
acceleration
•Measures acceleration
•Submits pre-processed data in
15-minute-summaries to base
station
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Grazing behaviour RumiWatch MooMonitor+
Mean difference = 4.4 min/h
rs = 0.96
CCC = 0.96
Mean difference = 1.0 min/h
rs = 0.94
CCC = 0.97
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Automated Body Condition Scoring
• BMF output in 0-5 point French scale
• Converted to 1-5 Irish scale (Decision Tree & Linear)
• A CCC of 0.7 with the average
score of two observers
• Compares well to the
agreement between those two
observers (0.69)
• INGENERA BodyMat
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Overview
• Drivers of efficiency in a pasture based
systems
• State of the Art
• SWOT Analysis
• Strengths
• Weaknesses
• Opportunities
• Threats
• Future – Needs Driven Development
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Sensor Development
Source: Rutten et al. (2013)
12
6 p
ub
lish
ed
stu
die
s d
escri
bin
g
13
9 s
ensor
syste
ms for
anim
al
he
alth
ma
na
ge
me
nt (t
ill 2
01
3)
NO
(o
r very
few
) stu
die
s
• Productivity did not change after investment in sensor systems
on dairy farms (Steeneveld et al., 2015)
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40000
45000
50000
55000
60000
65000
70000
75000
80000
1 2 3 4 5 6 7 8 9 10
Pro
fit
(€)
Year
AMS 20 Med spec
Return on investment for additional
investment associated with AMS
technology on Irish farms <0%
AMS v’s conventional milking systems
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Feed to Yield Milking systems
Feeding System
Return on investment for the additional
investment associated with Feed to Yield
technology on Irish farms <0%
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Overview
• Drivers of efficiency in a pasture based
systems
• State of the Art
• SWOT Analysis
• Strengths
• Weaknesses
• Opportunities
• Threats
• Future – Needs Driven Development
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18 18
Proliferation of Smart-Phone/Internet
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Data analytics development
Whole range of approaches newly develop
which have not extensively been applied to
agriculture
Scope to embed algorithms within
Databases
• Machine Learning
• Artificial Intelligence
Potential for a continuous learning process
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• Geo-referenced locations
• The user can input the influencing factors and calculate
the next area to be grazed
• The tool measures the biomass
• The data is transferred to the smartphone.
• Data can be uploaded to Pasturebase ‘Decision
Support Tool’
Grasshopper
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PastureBase Ireland- National Grassland Database
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Linear measures Body length
Back length
Chest width
Thurl width
Body depth
Chest depth
Flank depth
Length of rump
Height at wither
Height at rump
Rounding of ribs
Rump angle
Tail set
Width at hips
• Wiidth at pins
• Muscularity shoulder
• Back width
• Thickness of lion
• Thigh rounding
• Thigh width
• Thigh length
• Muzzle width
• Top line
• Front legs
• Fore pasterns
• Rear legs set
• Claw angle
• Thickness of bone
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23 23
LOCOMATION:
HEAD APPLITUDE
HEAD FREQUENCY
GAIT ANALYSIS
STRIDE LENGTH
SPEED
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Environmental
GHG emissions
Nutrient use efficiency
Water use Energy Use Biodiversity
Sustainability Assessment
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Overview
• Drivers of efficiency in a pasture based
systems
• State of the Art
• SWOT Analysis
• Strengths
• Weaknesses
• Opportunities
• Threats
• Future – Needs Driven Development
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Threats If the technology doesn’t work
Focus on technology rather than solution
• Technology provider working in isolation
Unwillingness to share Data – Business Model
• Perceived Monetary value
• GDPR – Data Ownership
• Loss of Control
Early adopters get their fingers burned
Lack of vision around the big picture
• Sum of the parts greater than the individual parts - ICBF
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Overview
• Drivers of efficiency in a pasture based
systems
• State of the Art
• SWOT Analysis
• Strengths
• Weaknesses
• Opportunities
• Threats
• Future – Needs Driven Development
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Edinburgh March 2018 28 28
Future – Needs Driven Approach • Move away from development because the
technology can be developed to development for an
industry need
• E.g. Grassland management
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Short term decision making
Teagasc Presentation Footer 29
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Medium term decision making - actions
Teagasc Presentation Footer 30
Solutions
Soil Fertility?
Drainage?
Variety?
?????????
Costing €250/Ha
Costing €30k annually this farm
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Long term
y = 1.0718x0.6143 R² = 0.3167
0
50
100
150
200
250
300
0 500 1000 1500 2000 2500 3000 3500
Gro
wth
Rat
e (
kg D
M/h
a)
Individual Paddock Cover kgDM/Ha
Growth 33kgDM/Ha
Growth 115kgDM/Ha
Growth 75kgDM/Ha
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Integrated solutions – future development
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Huge scope to increase sustainability in pasture based systems
Precision technologies will have a significant role
Need to provide integrated solutions
• Timely information
• Interpreted decisions
• Appropriate medium
Conclusion