Precision Viticulture Tools for Wine Grape Vineyard ...calasa.ucdavis.edu/files/259645.pdf ·...

Precision Viticulture Tools for Wine Grape Vineyard

Management in California

Brent Sams

California Plant and Soil Conference

American Society of Agronomy

Fresno, February 1 , 2017

Variable vineyards

Plant Available Water

Colony 13 – Petite Sirah

5x yield differences in the same row

Manage with Precision Viticulture:

• Management to optimize vineyard performance

– Responding to field variability

– Maximizing grape yield and quality

– Minimizing environmental footprint

Irrigation is biggest tool

• Other crops:

– Sensor-driven systems in nurseries

– Center pivot systems in grain crops

• Wine grapes: management zones

– Australia (McClymont et al. 2012; Proffitt and

Pearce 2004)

– Spain (Bellvert et al. 2012; Martínez-

Casasnovas et al. 2009)

– ….and California

Variable Rate Irrigation

Soil 1

Soil 2

Zon

al Ir

riga

tio

n

VF PUMP

Irrigation management zone 1

Irrigation management zone 2

Zon

al Ir

riga

tio

n

VF PUMP

Soil 3

Irrigation management zone 2

Irrigation management zone 1

Zon

al Ir

riga

tio

n

Soil 3

Soil 1

Soil 2

Mo

du

lar

Irri

gati

on

VF PUMP

Soil 3

Soil 1

Soil 2

Mo

du

lar

Irri

gati

on

VF PUMP

Soil 3

Soil 1

Soil 2

Mo

du

lar

Irri

gati

on

VF PUMP

Soil 3

Soil 1

Soil 2

Mo

du

lar

Irri

gati

on

VF PUMP

Wine Program A

Wine Program B

Mo

du

lar

Irri

gati

on

Research Objective

• Design and deploy a proof-of-concept VRDI prototype

• Increase yield in low vigor/low yielding areas while improving overall water use efficiency

Experiment location

Colony Ranch, Wilton CA

Field layout

Colony 2A Cabernet Sauvignon

• Wilton, California • 31.5 acres • 5 x 11 feet • 17-year old • Teleki 5C • Hand-pruned • Drip-irrigated

• San Joaquin silt loam (~ 75%) • San Joaquin-Galt complex (~ 25%) • 500 mm (20”) annual rainfall • Highly variable

2012 Yieldtons per acre

12

9

6

3

Block area: 31.5 acresVRI & CI: 10.0 acres

Field average: 9.17 tons/acre

Vari

ab

le r

ate

VR

DI

Co

nven

tio

na

l

CD

I

Field layout Landsat data

30 x 30 meter

2012 Yieldtons per acre

12

9

6

3

Block area: 31.5 acresVRI & CI: 10.0 acres

Field average: 9.17 tons/acre

140 Irrigation Zones

15 x 15 meter

VRDI General layout

Node-Solenoid, Check Valve, Tee

Flow meter

Flushing solenoid valve

Sub-main Valves

4 DL

4 DL

5 DL

5 DL

5 DL

4 DL

4 DL

4 DL

4 DL

5 DL

5 DL

5 DL

4 DL

5 DL

4 DL

4 DL

5 DL

5 DL

5 DL

4 DL

4 DL

4 DL

4 DL

5 DL

5 DL

5 DL

4 DL

5 DL

Variabl

e Flow

Pump

VRDI System design

solenoid valve

check valve

emitters

4”

Check valve

2” 2” Solenoid valve

Power loc adapter

Power loc tee

Tubing, 0.69”ID

emitters emitters

VRDI System design

Control board 480 VAC

115 VAC

Communications to South and North

12 VDC to 14 subnets

UPS box

Cell Antenna

Power distribution

box

Control box

Lightning arrestor

• METRIC (Mapping evapotranspiration at high resolution and internalized calibration)

• ET residual of surface energy balance

Rn + LE + G + H = 0

• Inputs – Landsat (visible & infrared) – CIMIS weather data

• Outputs – ETc – Kc (f/NDVI)

• Watering of each zone: ETc * Km (ETc = ETref * Kc)

Variable rate irrigation scheduling

Rn

G

LEH

• System can be used to:

1. Irrigate each zone according to vine size with-out altering natural variability uniform Km

2. Apply more water to low vigor areas in order to increase vine size and yield and decrease vineyard variability variable Km

We want #2!

But can default to #1 under severe water shortage

Variable rate irrigation scheduling

Irrigation management factor (Km)

May 4 weeks

June 4 weeks

July - Oct 16 weeks

0.0 – 1.0 0.5 – 0.8 0.6 – 1.0

Variable rate irrigation scheduling

Adjusting Km based on vine vigor

Landsat NDVIs NDVI = Normalized Difference Vegetation Index

% maximum

5/24/15 6/25/15 7/27/15 8/25/15

Variable rate irrigation scheduling

Normalized Yield

Mean = 8.9 t/a Mean = 7.4 t/a Mean = 8.7 t/a Mean = 4.6 t/a

Mean = 8.9 t/a Mean = 7.7 t/a Mean = 10.1 t/a Mean = 5.1 t/a

2012 2013 2014 2015

Var

iab

le R

ate

C

on

ven

tio

nal

VRI CI VRI CI

2012 8.9 8.9 0.0 5.93 5.93 0.0

2013 7.7 7.4 4.1 5.63 4.93 14.2

2014 10.1 8.7 16.1 7.43 7.08 4.9

2015 5.1 4.6 10.9 4.27 3.65 17.1

Year

Yield

(tons/acre)Gain

VRI/CI

(%)

WUE

(tons/acre-foot)Gain

VRI/CI

(%)

Yield and water use efficiency

Average 10% Average 12%

Yield and water use efficiency

0

10

20

30

40

50

60

70

80

90

Freq

uenc

y

Yield class (% max)

VRDI

CDI

2012

0

10

20

30

40

50

60

70

80

90

Freq

uenc

y

Yield class (% max)

VRDI

CDI

2013

0

10

20

30

40

50

60

70

80

90

Freq

uenc

y

Yield class (% max)

VRDI

CDI

2014

0

10

20

30

40

50

60

70

80

90

Freq

uenc

y

Yield class (% max)

VRDI

CDI

2015

Conclusions • Modular variable rate drip irrigation system

prototype was successfully implemented

• Vine growth was effectively and immediately manipulated with precision irrigation

• Yield was increased during 3 seasons by an average of 10% with up to 17% gain in water use efficiency

• Prototype expensive and complex but excellent template for 2nd generation system

Next steps: 2nd generation system

System tested in Israel on wine grapes and citrus during 2015

All valves and controls outside the vine rows!

Cheaper and simpler!

Next steps: 2nd generation system

Next steps:

• Streamline energy balance models for use in vineyards

• Automate calculation of appropriate management factors for VRDI through modeling

• Economics of irrigation zone (pixel) size

• Overlaying of variable rate fertilization

Acknowledgments • E&J Gallo Winery

– Viticulture Lab: Brent Sams, Maegan Salinas, Erin Troxell, George Zhuang, Cassandra Plank, Tian Tian, Cody Lichtfield, Nona Ebisuda

– Chemistry: Hui Chong, Bruce Pan, Natalia Loscos, Bianca Wiens

– Research Winery: David Santino

– GVI-CV: Alan Reynolds, John Owens VII, Christopher Bach, Evan Goldman

– GIS-CE: Andrew Morgan

– Nick Dokoozlian

• IBM (TJ Watson Lab & Data Center Services)

– TJ Watson Lab, NY: Levente Klein, Nigel Hinds, Hendrik Hamann

– Data Services, CA: Alan Claassen, David Lew

• Netafim Israel: Avi Schweitzer, Itamar Nadav, Yoram Engel, Gilad Narkis

• James Taylor, New Castle University, UK

• Ernie and Jeff Dosio, Pacific Agrilands

• Scott Britten & Associates, Bennett & Bennett

• Bernd Kleinlagel, ATV, Australia

Thank you