Precision Agriculture in Australian Viticulture

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Precision Agriculture in Australian Viticulture

• James Taylor

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Outline

• Australian Viticulture
• Precision Viticulture - why?
• Yield Monitoring
• Spatial Variability
• Examples of Variability in Vineyards
• The Future.

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Introduction

• Viticulture began in Australia with the first
  fleet
• Today there are gt4000 growers cropping 100,000
  ha of vineyards in Australia
• They produced almost 1.2 million tonnes of
  crushed grapes in 1999 (over double that produced
  in 1991)

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Export and Domestic sales of Australian Wine
1984-1998
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Australian Winegrape Production
State Tonnage
South Australia 448, 561
52.4 Victoria 208, 406 24.3 New South
Wales 174, 468 20.4
Ranked 11th in the World in production Major
varieties are Chardonnay, Shiraz and Cabernet
Sauvignon
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Why Precision Viticulture?

• Horticultural crops often drip irrigated
• good water management potential
• Decreased influence of water on growth, yield and
  quality leads to increase influence of other
  factors
• soil nutrients
• management decisions
• environmental variables

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Why Precision Viticulture?

• Horticultural industries - high profitability -
  intensively managed - very quality orientated
• Viticulture all these plus - dominated by large
  producers - growth industry - highly mechanized

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Why not so far?

• To date research has focused on broadacre
  cropping - wheat, cotton and sugarcane
• Horticultural and root crops lagged due to
  absence of effective yield monitors
• Diversification of PA into other agricultural
  industries

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Current Research

• Yield Monitoring and Sensors
• Dr Graham Moore - Uni. Of Melbourne
• Dr Francis Sevila - ENSAM Uni in Montpellier
• Dr Shrini Upadhyaya - UC Davis
• Industry - HarvestMaster, AB Electronics
• Agronomic interpretation
• NASA - remote sensing to predict quality

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Research Site

• Richmond Grove Vineyard, Cowra NSW
• Operated by Orlando-Wyndham
• 700 acres of vines including Chardonnay,
  Semillon, Pinot Noir, Cabernet Sauvignon and
  Cabernet Franc
• Vineyard self contained with own machinery and
  small laboratory

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Yield Monitoring
G 60K Pull type Grape Harvester
http//www.gregoire.com.au
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Types of Grape Yield Monitors

• Load cells - conventional horticultural crops,
  measures delta weigh of crop
• Ultra-sonic sensor - measures change in height of
  grapes and interpolates volume

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How Grape Yield Monitors WorkLoad Cells

• Off load ramp suspended in a cradle
• Load cells bear weight of the ramp
• Weigh bins
• Change in mass monitored
• Error source from vibrations

Load cells
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How Grape Yield Monitors WorkUltra-sonic sensor

• Sensor Mounted above the off load ramp
• Height above ramp measured by pulse
• Change in height leads to a change in pulse time
• Volume interpolated from ?height

Sensors
Grapes
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Yield Monitors - are they reliable?

• What sort of accuracy/precision do they give?
• Where are the error sources and how can they be
  improved?
• How does the monitor react under different
  situations?

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Yield Monitor Performance

• Ultra-sonic tested
• Accuracy was between -5.6 and 11.5
• Skewed towards over estimation of yield

-6
0
6
12
-3
3
9
Percentage error
Mean 3.1 Maximum 11.5 Minimum -5.6
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Raw yield at Richmond Grove Vineyard Cowra, 1999
400m
N
7
8
9
10
11
Yield (Mgha-1)
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Raw Yield Map of Block 25
100 m
7
10
Yield (Mgha-1)
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South 25 Raw Yield Description
Mean 8.8 Std Dev. 3.27 Median 8.7 25
Quantile 6.5 75 Quantile 10.9
Yield (Mgha-1)
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Yield Variogram for South 25
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• Range 12m
• Small compared to other crops
• Noisy data
• High spatial variability

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8
Gamma
7
6
5
10
15
20
25
Distance (m)
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South 25 Kriged Yield Map, Richmond Grove 1999
100 m
N
7
8
9
10
11
Yield (Mg/ha)
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Kriged Yield Map, 1999
400m
N
7
8
9
10
11
12
Yield (Mgha-1)
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Spatial Variability and Management Zones

• Soil ECa
• Yield indicators - bud, bunch counts
• An example only
• Look at one block (D3-4) of grapes
• relationship between yield, soil ECa, bud and
  bunch counts

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Apparent Soil Electrolytic Conductivity (ECa)

• Real time sensing using Veris 3100
• Soil moisture, salt and clay content

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Soil ECa Map of Richmond Grove, May 1999
400m
N
25
50
75
Soil ECa Quartiles
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Kriged Soil ECa map of D3-4, May 1999
100m
N
10
12.5
15
Soil ECa (mSm-1)
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Variogram for Soil ECa
Gamma
Distance (m)
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Bud Counts vs. Yield in Block D3-4
250

• Low yield associated with low bud counts
• High has no association with bud counts

200
Bud Count
150
100
4
6
8
10
12
14
Yield (Mg/ha)
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Bunch Counts vs. Yield in Block D3-4

• No relationship between yield and bunch

220
180
Bunch Count
140
100
4
6
8
10
12
14
Yield (Mg/ha)
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Variograms for Bud and Bunch Counts
900
700
700
Gamma bunch counts
Gamma bud counts
500
500
300
300
50
200
50
200
100
150
100
150
Distance (m)
Distance (m)
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Kriged Yield Map of D3-4, 1999
100m
N
7.5
9
10.5
12
Yield (Mgha-1)
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Irrigation plan for D3-4
100m
N
7.5
9
10.5
12
Yield (Mgha-1)
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Kriged Soil ECa map of D3-4, May 1999
100m
N
10
12.5
15
Soil ECa (mSm-1)
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Potential Management zones based on Soil ECa and
Yield map, 1999
Zone Yield ECa (MG/ha) (mS/m)
N
Red 7.2 10.6 Green 11.0
11.1 Blue 12.1 15.4
100m
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The Future...

• Immediate
• determine the potential for Precision Viticulture
  in Australia
• is there enough spatial variability
• can we manage this variability
• Long term
• application to table grapes other horticultural
  crops
• derivation of DSS (eg AUSVIT) and farmer friendly
  technology

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Our Research Goals

• Assessment of Yield Monitors
• Build an extensive vineyard database
• Yield maps
• Geo-referencing vines for crop counts
• Intensive site directed soil sampling
• Remote sensing
• Moisture - Veris calibration and intensive
  monitoring in crops

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The Quality Issue

• Value adding has big potential in viticulture
• Two issues
• Real time sensing of grape quality
• Prediction of wine quality in the vineyard

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Acknowledgements

• Orlando-Wyndham Pty Ltd
• Mark Sims - Assistant Manager at Richmond Grove
• Ron Campbell and HarvestMaster U.S. Pty Ltd

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Making Precision Viticulture Work

• Yield monitoring
• Quantifying spatial variation and interactions
  between yield determining factors
• Defining management zones and modelling
  interactions
• Tailoring agronomic practices to suit management
  zones

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Clusters Of Bud vs. Yield
Cluster Bud Yield (Count)
(MG/ha) Red 159 7.4 Green
160 11.4 Blue 224 12.8
250
200
Bud Count
150

• When Green cluster removed the linear fit is
  improved

100
4
6
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10
12
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Yield (Mg/ha)
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Spatial Location of Clusters

• Blue Management Zone- high yield, high bud
  count and high ECa
• Red management zone - low yield, Bud count and
  ECa
• Green Management Zone- High yield but low ECa
  and Bud count

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Spatial Variability in Vineyards

• Yield is spatially variable
• Other vineyard parameters
• soil properties
• physical
• chemical
• yield (quantity) indicators
• bud, bunch counts
• remote sensing
• quality indicators

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S25 - elevation yield and Eca and bud/bnch counts
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