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.