Transcript Improving Precision and Spatial Acuity in Point Quadrat

James M. Meyers & Justine E. Vanden Heuvel, Cornell University
The Basics of Canopy
Measurement
WAWGG – February 4, 2009
Agenda

Canopy Architecture and Sunlight

Measuring Canopy Architecture



Point Quadrat Analysis (PQA)
Enhanced Point Quadrat Analysis (EPQA)
Measuring Sunlight Distribution


Cluster Exposure Mapping
Leaf Exposure Mapping
Why We Measure Canopies
Canopy
measurements
provide
insight into
vine
performance
and fruit
quality.

Energy Production (photosynthesis)
 Exposed

Leaf Area
Fruit Quality
 Cluster
Exposure
 Functional Crop Load (yield vs. exposed
leaf area)
Agenda

Canopy Architecture and Sunlight

Measuring Canopy Architecture



Point Quadrat Analysis (PQA)
Enhanced Point Quadrat Analysis (EPQA)
Measuring Sunlight Distribution


Cluster Exposure Mapping
Leaf Exposure Mapping
Point Quadrat Analysis (PQA)

What is PQA?
 PQA
is a simple field method for measuring key
parameters of canopy architecture

Why perform PQA?
 PQA
metrics quantify canopy differences
 PQA metrics provide insight into vine performance
Point Quadrat Measurement Zone
(Photo J. Meyers)
Measurements are taken at consistent height (usually middle of
the fruiting zone), but can be somewhat dynamic due to variations
in vineyard floor and trellising.
Sampling Frequency
Canopy is sampled, at the designated height, at consistent
intervals along the row.
(Photo J. Meyers)
Sampling Vector
At each sampling location, data is collected from one
outer edge of the other outer edge.
(Photo J. Meyers)
Point Quadrat Data Collection
(Photo from Sunlight into
Wine, credited to B.W.)
A tape measure or meter stick is used to ensure
consistently spaced measurements.
Point Quadrat Data Collection
A rod is passed through the canopy. As the rod contacts
biomass, the contacts are identified and recorded.
(Photo from Sunlight into
Wine, credited to B.W.)
Sample Dataset
(Photo from Sunlight into
Wine, credited to R.S.)
In this example, ignoring shoots, the first insertion produces
the following dataset: “L L C”.
PQA: Standard Analysis Metrics
Sample Dataset
Vigor
Metric
Light Environment
20 cm
40 cm
60 cm
% Gaps
Leaf Layer Num % Interior Leaves % Interior Clusters
L
L
C
L
L
G
(PG)
(LLN)
(PIL) L L L L C L (PIC)
Formula
Result
33.33
3
55.55
100
PQA: Standard Analysis Metrics
Sample Dataset
20 cm
40 cm
60 cm
LLCLL
G
LLLLCL
Vigor
Metric
% Gaps
(PG)
Light Environment
Leaf Layer Num % Interior Leaves
(LLN)
(PIL)
% Interior Clusters
(PIC)
Formula
Result
33.33
3
55.55
100
PIC: Simplified Analysis Results
PIC is often used to
establish treatment
efficacy.
Control
Treatment
Panel 1
Panel 2
94.1
77.8
80
66.7
Average
% Difference
85.9
73.3
14.8
Enhanced Point Quadrat Analysis (EPQA)

What is EPQA?



Data collection method is the same as standard PQA
EPQA uses computer software to calculate canopy parameters
with more precision than standard PQA metrics
Why perform EPQA?



EPQA is more descriptive than standard PQA
EPQA adds metrics for canopy symmetry and trellising
consistency
EPQA provides the foundation for canopy exposure mapping
Agenda

Canopy Architecture and Sunlight

Measuring Canopy Architecture



Point Quadrat Analysis (PQA)
Enhanced Point Quadrat Analysis (EPQA)
Measuring Sunlight Distribution


Cluster Exposure Mapping
Leaf Exposure Mapping
Calibrated Exposure Mapping
(Photo from Sunlight into Wine, credited to B.W.)
(Photo from Decagon website)


Sunlight calibration curve is
unique to each canopy
Curve can be fitted with only
two known %PPF points




100% PPF always at layer 0
Measure %PPF at a second
known canopy layer (OLN/2)
Fit curve to the two points
Sample the fitted curve at
layer 1 to determine
calibration value (Ep1)
% Photon Flux
Calibrating a Canopy
100
90
80
70
60
50
40
30
20
10
(x=1, y=Ep1)
0 1 2 3 4 5 6
Canopy Layer
% Ambient Sunlight
90-100%
80-89.9%
70-79.9%
60-69.9%
40-49.9%
50-59.9%
Treatment
30-39.9%
Control
20-29.9%
10-19.9%
35%
30%
25%
20%
15%
10%
5%
0%
0-9.9%
The precise
exposure of
each cluster
in the PQA
dataset is
calculated.
% of clusters
Cluster Exposure Map
% Ambient PPF
90-100%
80-89.9%
70-79.9%
60-69.9%
40-49.9%
50-59.9%
Treatment
30-39.9%
Control
20-29.9%
10-19.9%
35%
30%
25%
20%
15%
10%
5%
0%
0-9.9%
The precise
exposure of
each leaf in
the PQA
dataset is
calculated.
% of leaves
Leaf Exposure Map
Treatment Effect: Cluster Exposure Map
Shoot Thinning (ST)
Hedging (H)
Combination (ST-H)
40%
Percentage of Clusters
Umbrella trained
hybrid canopy in
Finger Lakes
subjected to:
35%
30%
25%
Control
ST
H
ST-H
20%
15%
10%
5%
0%
10% 20% 30% 40% 50% 60% 70% 80% 90% 100%
Percentage of Full Sunlight
Treatment Effect: Leaf Pulling
Percentage of Clusters
Cluster
exposure map
comparing
control and
leaf-pulled
VSP canopies
on Long
Island.
60%
50%
40%
Control
30%
Leaf Pulled
20%
10%
0%
10%
20%
30%
40%
50%
60%
70%
80%
Percentage of Full Sunlight
(Data from J. Scheiner)
90%
100%
Natural Variability in Cluster Exposure
EPQA and CEM
data 18 panels
within a block of
Scott-Henry
trained Finger
Lakes Riesling.
Substantial
natural variation
was observed.
% Ambient
PPF
Map Your Own Canopies
An Excel spreadsheet
is available for
growers and
researchers who wish
to map exposure in
their own canopies.
Contact:
Jim Meyers
[email protected]
James M. Meyers & Justine E. Vanden Heuvel, Cornell University
The Basics of Canopy
Measurement
WAWGG – February 4, 2009