When and how much to irrigate:Vine-based tools

Futuristic vision of the multi-purpose sensor, 1966

Direct and Indirect measures of water stress in plants:

1) Plant water is under tension, especially when soil dries.

2) Assuming that the level of tension itself is the cause of water stress responses, the methods that measure this tension directly are:a) Pressure chamber/’bomb’b) Micro-tensiometerc) Psychrometer/Hygrometer

3) Some processes that can be used as indirect indicators of water stress are:a) Growth patterns of various parts

(e.g., shoots)b) Leaf or canopy temperaturec) (Many others)

The perfect water sensor? $

Sensors: Direct (SWP)

Pros: Established accuracy/repeatability; predictable, weather-based reference value (non-water stressed ’baseline’); mobile (test multiple trees/sites).

Cons: Not automated, typically used for daily (midday) snapshot.

1)Pressure chamber (many types)

M. Padgett-Johnson et al., (2003) JASHS 128:269-276.

Data for 17 Vitis Species, SJV, 1992-1993, Irrigated vs. Non-Irrigated vines.

Predawn vs. Leaf vs. Stem water potential, as compared to leaf photosynthesis over the season.

Photosynthesis: seasonal drop in both irrigated and non-irrigated vines, bigger drop in non-irrigated.

Water Potential: only midday SWP showed this pattern.

Photosynthesis

Water Potentials

Sensors: Direct (SWP)

Pros: Automated 24/7 data; robust.

Cons: Fixed location (vine); still working on accuracy/repeatability.

1)Pressure chamber (many types)2)Microtensiometer (FloraPulse)

1)Pressure chamber (many types)2)Microtensiometer (FloraPulse)3)Miniature SWP sensor (Saturas – not much experience)

Pros: Automated; presumably robust.

Cons: Fixed location (vine); accuracy/repeatability not yet clear.

Uncertain: Midday SWP reported, but based on correlation to 24h average (Good? Bad?).

Sensors: Direct (SWP)

Pros: Automated 24/7 data.

Cons: Fixed location (vine); difficult to know if data is correct without pressure chamber check; very temperature/handling sensitive; not robust – primarily a research tool.

Sensors: Direct (SWP)1)Pressure chamber (many types)2)Microtensiometer (FloraPulse)3)Miniature SWP sensor (Saturas – not much experience)4)Stem Psychrometer/Hygrometer (ICT)

Bradford and Hsiao, 1982, Encyclopedia of Plant Physiology. 12B:263-324.

Sensors: In-directMeasure one of the many, well-known physiological

plant responses to water stress

Reduce Growth

Close Stomata

Shed Leaves

Re-orient Leaves

Sensors: Indirect (growth/swell/shrink)Trunk or stem (or fruit, or whatever) dendrometers (Phytech)

Pros: Automated, 24/7 data; robust; inexpensive technology.

Cons: Indirect; limited (5) levels of ‘plant status’ (saturated, no, low, mild, or high stress); typically based on a minimum of 3 trees/vines.

Q: What should we expect from any vine water stress sensor?

A: Should be able to detect stress as it develops, in time for irrigation (or other management) decisions.

Greenspan (http://www.decagon.com/education/VS05192010/)

Water stress reduces apical growth in grapevine

Date, 2015MAR

SWP

Shoot Tip Rating

APR MAY JUNE JUL AUG SEP OCT

0

-20

-15

-10

-5

1

2

3

4

5

6

SWP

(bar

)Pattern of mean shoot tip rating and mean SWP on N or S

facing slopes, El Dorado Cabernet sav.

North Slope

South Slope

Rating

(BASELINE)

But SWP continued to fall.This was not the limit of stress.

Shoots stop growing here

-8SWP (bar)

-6 -4 -2 01

2

3

4

5

6Ti

p R

atin

g

-1-3-5-7-9

North Slope

South Slope

Close relation of shoot tip rating and SWP, so both tell the same story, SWP just continues the story beyond

where shoot tip rating stops.

Water stress reduces stomatal conductance, but conductance also changes with environmental conditions

Greenspan (http://www.decagon.com/education/VS05192010/)

Broad categories from “Luxury” to “Extremely Stressed”

Pinot Noir study – Irrigated vs. Non-irrigated:Non-Irrigated stomatal conductance (gs) dropped over time compared to Irrigated, but was 300 in JUNE, 450 in August (both ‘Luxury’). SWP dropped progressively.

Factor Partial R2 F value P > F

Wind (m s-1) 0.41 133.9 <0.0001

Temperature (oC) 0.14 131.2 <0.0001

SWP (MPa) 0.13 52.4 <0.0001

Radiation (W m-2) 0.12 66.2 <0.0001

VPD (KPa) 0.01 10.33 0.0009

LWP (MPa) N/A N/A ns at .015

Pinot Noir, UCDavisStepwise regression analysis: midday stomatal conductance

Question: What do you get for continuous, automated, tracking of direct (or indirect) measures of water stress?Answer: A more complete and detailed picture of plant stress.Question: Is it worth it?Answer: If you think so.

All of these commercial methods (and others not mentioned) produce continuous, automated information.

Conceptual model (Slatyer, 1967) showing how soil, root, leaf, and leaf water potential () are expected to change as soil dries.

Stem

wat

er p

oten

tial (

Mpa

)

Date (July, 2011)

14 15 16 17 18 19

In-situ psychrometer

Pressure chamber SWP

‘Real’ data (field cherry tree) showing a similar day down/up pattern but an interesting gradual overnight increase. Good agreement with pressure chamber

measured SWP. Note: not much drying over time.

FloraPulse microtensiometers:Installed sensors on non-irrigated 8 year old trellis demo vines at RMI (UC Davis)

in August, 2018.Two individual sensors/vine.

Stem

Wat

er P

oten

tial (

bar)

-10

-5

0

ETo

(mm

/h)

0

0.5

16

Date, August 2018

17 18 19 20 21

S2

S1PC

ETo

Example daily patterns for two microtensiometer sensors and bombSWP measurements, all on the same vine.

1) Good initial agreement between sensors at midday, then drifting apart.2) Overall day/night pattern consistent with ETo, nighttime recovery pattern different for

different sensors, but both sensors show highest recovery about 1h after dawn.3) Not good absolute agreement with the bomb, but maybe relative agreement.

S2 S

tem

Wat

er P

oten

tial (

bar)

PC Stem Water Potential (bar)

-6

-8

-10

-4

-2

0

-12

+2

+4

-6-8-10 -4 -2 0-12 +2 +4

Calibrating each individual sensor to the pressure chamber.

Typical variation (SD) for 14 sensors was about ± 1 bar.

(Early Morning)

(Midday)

-6

-8

-10

-4

-2

0

AUG SEPDate, 2018

OCT NOV DEC

Cal

ibra

ted

MT-

mea

sure

d SW

P (b

ar)

(RAIN)

Summary of the daily ‘predawn’ and midday SWP averaged across vines.1) Very little change in predawn SWP: vines clearly have access to water.

2) Midday values less stable, show ups and downs. Late season leaf senescence? Weather?

(Midday Baseline)

(Midday)

-6

-8

-10

-4

-2

0

AUG SEPDate, 2018

OCT NOV DEC

Cal

ibra

ted

MT-

mea

sure

d SW

P (b

ar)

(RAIN)

Whether effects: comparison of midday values to midday ‘baseline’ SWP(= fully irrigated condition) values.

1) Midday values above baseline for first week - probably a ‘settling’ artifact of the sensors.2) Otherwise, clear weather effects through September, after that, probably leaf senescence.

Vine-based measures for irrigation scheduling, do we

have a holy grail?

SWP: reliable ‘ground truth’ for plant stress, but you do need to be on the ground to measure it.Automated SWP gives 24/7 ‘ground truth’ on the web: still some calibration issues, but assuming that these can be solved –1) SWP is reliably related to economically important plant processes (shoot

growth, fruit composition/quality, yield, etc.) in other crops, should work in grapes.

2) SWP gives growers timely, actionable ‘Goldylocks’ information (too much, too little, or just right).

3) Requires a different mindset - adjust your irrigation schedule to suit the plant.

Thanks to the support and help from:

AVFCA walnut and Almond industriesColleagues, postdocs, students, technicians:Abe Stroock, Alan Lakso, Michael Santiago (Cornell)Vadim Volkov (UCD)Nate Kane (UCD)

(The Grail)