Precision Agriculture in New Zealand
Professor Ian J. Yule New Zealand Centre for Precision Agriculture
Massey University, New Zealand
Presentation Contents
Introduction to New Zealand production. Small diverse, dynamic, export driven economy.
Development of PA using pasture sensing as an example.
Pasture dominates land area.
PA to Digital Agriculture: A disrupted journey. How do we transition?
34.8 million acres, of which: 19.2 in grassland 6.0 in tussock or Danthonia 4.0 in forest plantations 1.5 in crops, grains, nursery, vegetables and fruit 0.3 in horticulture 4.4 in other land or holdings Source: Statistics New Zealand (2016)
Farming Land in New Zealand (2015)
44% mainly sheep & beef farming 21% mainly dairy farming 15% horticulture & orchards 6% mixed livestock 5% crop farming 9% other
Source: Statistics New Zealand, 2012 Agricultural Census
Types of Farms in New Zealand (2012)
Wheat
117,832 acres in wheat - 129 bushels per acre1
Top farmers achieving - 248 bushels per acre2
Barley
158,553 acres in barley - 101 bushels per acre1
World Barley Record broken in 2015 with 205.2 bushels per acre with Blackman Agriculture’s 776 variety 3
1Statistics New Zealand 2FAR 2016 3www.NZFarmer.co.nz
New Zealand Cropping (2015)
Value of New Zealand Agricultural Exports. (NZ$ million)
2011 2015
Dairy 12,035.8 13,169.7
Meat 5,552.5 6,541.8
Forestry 4,480.2 4,595.3
Horticulture 2,158.1 2,635.8
Wine 1,098.9 1,421.5
Processed agriculture (excl wine) 1,086.1 1,108.6
Other animal products 763.9 852.7
Wool (raw) 716.9 805.0
Cereals 10.1 15.4
Source: Beef + Lamb New Zealand Economic Service, Statistics New Zealand
New Zealand Agricultural Exports by sector
Source: Beef + Lamb New Zealand Economic Service, Statistics New Zealand
% of NZ Agricultural Exports 2015
Wool (raw)
Meat
Dairy
Other animal products
Horticulture
Cereals
Processed agriculture
Wine
Forestry
Role of Precision Ag
• Pasture based
• Livestock, information about individual animals and herd. Dairy, beef and sheep.
• Horticulture, higher value, greater use of technology, sensing automation.
• Arable / Cropping
Beef and Sheep Precision Dairying
Cropping Metalp it; 0 5
47.3 ac.
Waitapia; 05
NIR Paddock; 05 (14.70 ha.)
100 0 100 200 300 400 Meters
Date: Nov 2 8, 2008
Field Name: NIR Paddock; 0 5
Farm Nam e: Waitapia
Client Name: Hug h Dalrym ple
Total Hectares: 14.70
Field Boundary Start Location:
Latitude: -40.224 15067
Long itud e: 1 75.2986 7435
Field Boundary
Yield Map 06
0.0 - 7 .3 (0.1 ha.)
7.3 - 10.7 (1.0 ha.)
10.7 - 12.5 (3 .8 ha.)
12.5 - 13.7 (6 .7 ha.)
13.7 - 18.3 (3 .1 ha.)
Metalp it; 0 5
47.3 ac.
Waitapia; 05
NIR Paddock; 05 (14.70 ha.)
100 0 100 200 300 400 Meters
Date: Nov 2 8, 2008
Field Name: NIR Paddock; 0 5
Farm Nam e: Waitapia
Client Name: Hug h Dalrym ple
Total Hectares: 14.70
Field Boundary Start Location:
Latitude: -40.224 15067
Long itud e: 1 75.2986 7435
Field Boundary
Yield Map 06
0.0 - 7 .3 (0.1 ha.)
7.3 - 10.7 (1.0 ha.)
10.7 - 12.5 (3 .8 ha.)
12.5 - 13.7 (6 .7 ha.)
13.7 - 18.3 (3 .1 ha.)Soil and Irrigation
(b)
Precision Agriculture, 2000
to 2014 at Massey.
Pasture Sensing 1999 to 2016
Pasture consumption at grazing. Pre and post grazing measurements using a rising plate meter. But how do we exploit the variability?
First mapping 1999
Mapping project with pre and post grazing maps.
• Maps were produced using a plate meter to measure pasture cover.
• On a 15m grid a large number of samples were taken.
• Maps showed differences in growth and cover before grazing and differences in the amount of grass removed during grazing.
BUT
Are we applying this technology to the correct questions?
We thought we knew what farmers wanted 17 years ago but that turned out to wrong.
What our dairy farmers wanted was a way to provide a reliable
feed wedge which described the pasture production on their farm.
Paddock by Paddock measurement
Started to develop a pasture
measurement device.
• Experimented for four years with a number of sensing methods and took it to a local company to commercialise (2004).
• It developed into the C-Dax Pasturemeter.
• Worked with the company for a further two years, first units sold in 2006.
– The Pasturemeter measures pasture height and mass can be calculated from calibrated equations held within the unit.
– Gave significant advantage over existing methods.
• In 2016, 25% of New Zealand dairy farmers have one.
Pasturemeter 2009
Pasturemeter 2013
Product philosophy and development. Better integration and ease of use through increased automation
Annual Paddock Performance
0
10
20
30
40
50
60
70
80
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31
Paddock Number
Pas
ture
Gro
wth
Rat
e kg
h
a-1 d
ay-1
Details of field performance allow better decisions to be made around changes such as winter cropping and re-grassing. Identifying poorly performing fields is important. Users achieve better outcomes through improved feed allocation of feed and utilization.
Sensor Based Pasture Yield Mapping Tests
The Cropspec sensor was chosen because it could fit on to the ground spreading truck and be safely used in this situation. The system senses out to the side of the truck in a 3m swath on each side.
The results from the sensor was compared to mapping completed by the C-Dax pasture meter. Pasture cuts were also taken and plate meter readings.
T1 - Medium
T2 - High
T3 - Low
T6 - Medium
T5 - High
T4 - Low CropSpec
Pasture meter
Pasture meter kgDM/ha
CropSpec Cs
Biomass Cut Locations
T3
T4
T2
T1
T6
T5
Pickwick: CropSpec and Pasture meter
Comparison of Methods
UAV’s and their application to pastoral farming.
A range of multirotor machines and sensors used. RGB cameras, infrared, Micasense Rededge, Tetracam. Trimble UX5 fixed wing. Standard Trimble offering.
Applications of UAV Sensors
Trimble UX5 System
• Flight at 115 m • Ground resolution 3.7 cm The visualisation has improved
Developing DTM. Optimum N project, identifying urine spots from dairy cows. Some promise but reliability was an issue in every grazing situation and level of cover, in all seasons, with and with out irrigation.
Optimum N
Micasense Spectral Bands
Micasense NDVI
Multirotor Ranges and Actual Flights
250m
1000m
Micasense NDVI
16 Years on.
UAV with Micasense, Red edge General spatial variation, still probably lacks the consistency and accuracy required.
Other Sensing Applications: Kiwifruit, orchards, farm mapping and forestry.
• Orchards:
• Physical inventory,
• Canopy, Cultivar.
• Multispectral or Orthophotographic
Forestry, Hyperspectral and Lidar?
Hyperspectral Imaging:
Fenix Airborne Sensor
Hyperspectral imaging in dairy farming
Hyperspectral Imaging
Summer 2016 survey
• Pasture Quality used to help plan:
• Fertiliser application.
• Irrigation.
• Calculating feed requirements and feed planning.
• Partial budget capital projects.
• Pasture renewal.
Adoption • Time. Farmers are time poor.
• Consistency. Be sure you know what they require.
• Accuracy requirement.
• Integration with software.
• Spatial pattern within a paddock not of prime concern
• Mixed approach may gain some traction.
• After 17 years we have one commercial sensor which 25% of dairy farmers own.
• The rest have failed to gain any significant market traction.
• Why?
• Variable Rate equipped contractors machines for fertiliser spreading. Paddock to Paddock variation in the main.
Hill Country Beef and Sheep
A new way to estimate productivity and better plan fertiliser application. Farmer benefits in terms of management. Computer controlled aircraft. Cost savings and increased productivity.
A new system
From Precision Agriculture to Digital Agriculture
• Data across physical scales.
• Data throughout the value chain.
• Data driven by the service sector.
Developing Digital Agriculture 8 physical scales we operate at:
Farm Catchment Region National Global Paddock
The traditional realm of PA. Applying the right product, at the
right rate, in the right place at
the right time.
Process control: e.g. Autosteer
Variable rate application.
Variable rate irrigation.
Farm optimisation.
Zone Plant
Least possible
impact off the
farm
Best possible product off the farm,
economic return
Global
Physical scales
At these scales objectives of individual producer and companies will dominate
At these scales the needs of the wider community will become more apparent.
But clearly these objectives are linked
Farm National Plant Zone Catchment Region Paddock
Objective: International trade, food
provenance and traceability. Product
branding to maximise value
Objective: International trade,
Biosecurity, Animal Welfare, National
branding.
PA and farm optimisation
Farm Zone Catchment Region National Global Plant Paddock
An Informed Value Chain
Best possible product off the farm,
maximized economic return
Least possible
impact off the
farm
Maximising product value,
minimised waste and
energy
Informed Natural resource
management.
Environmental quality,
benchmarking, nutrient
budgeting.
More appropriate regional
and government policy.
International trade,
Biosecurity, Animal Welfare,
International trade, food
provenance and traceability
Highly informed governance
PA and farm optimisation
Farm Zone Catchment Region National Global Plant Paddock
Value Chain Meat Data Economy
Best possible product off the farm,
economic return
Least possible
impact off the
farm
Maximising product value,
minimised waste and
energy
Natural resource management.
Environmental quality,
benchmarking, nutrient budgeting.
More appropriate regional and
government policy.
International trade,
Biosecurity, Animal Welfare, International trade, food provenance
and traceability. The consumer
High quality governance
Farm and feed quality
management, animal
nutrition, multi layer data
sets.
Environment and resource
management.
Nutrient Budgeting
Analysis down to the
individual level
Information on individual animals
slaughter, processing, packaging,
marketing, retail.
Biosecurity and quality assurance
for export and import.
Production optimisation
Feedback
Service Sector: Supply Chain
Farm Catchment Region National Global Paddock Zone Plant
Why do we have such a highly amped AgTech bubble?
Completely changed business model. Highly scalable. The service sector has the best opportunity to add value to their business and cut costs Tremendous value of data aggregation. Ability to strip out costs on the supply costs. Better yield and product prediction.
. . . . $ $
$
Management of Data and Digital Agriculture
• Watch out for disruption, it is not going to be plain sailing.
• Each physical scale has a community of users, not just a single user.
• How do we ensure that large parts of that community are not locked out?
• These are world wide concerns.
Digital Agriculture in New Zealand
• Digital Agriculture is important to us in terms of food production and environmental management.
• Digital Agriculture is important in achieving our ambitions in achieving high value export.
• Digital Agriculture could bring about significant disruption to the service sector.