 

This is to certify that the
thesis entitled

EUTYPA DIEBACK: EFFECTS ON GROWTH AND YIELD
COMPONENTS, AND DIAGNOSIS IN ‘CONCORD’
GRAPEVINES

presented by

SUSAN CLARK BUTTERWORTH

has been accepted towards fulfillment
of the requirements for the

MS. degree in Plant Pathology

 

 

”MW/W

Major Progsor’s Signature

[0/21/03

Date

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6/01 c:/CIRC/DateDue.p6&p.15

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EUTYPA DIEBACK: EFFECTS ON GROWTH AND YIELD COMPONENTS,
AND DIAGNOSIS IN ‘CONCORD’ GRAPEVINES

By

Susan Clark Butterworth

A THESIS

Submitted to
Michigan State University
in partial fulﬁllment of the requirements
for the degree of

MASTER OF SCIENCE

Department of Plant Pathology

2003

EUTYPA I

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ABSTRACT

EUTYPA DIEBACK: EFFECTS ON GROWTH AND YIELD COMPONENTS,
AND DIAGNOSIS IN ‘CONCORD’ GRAPEVINES

By

Susan Clark Butterworth

The effects of Eutypa dieback, caused by Eutypa lata, on growth, yield, and juice
composition of ‘Concord’ grapevines were studied in one Michigan vineyard for three
years, and in another vineyard for one year. On severely symptomatic shoots, the most
signiﬁcantly reduced components were fruit weight (78-92%), berry number per cluster
(59-71%), leaf area (75-87%), node number (40-42%), and shoot length (60-74%) in
1999 and 2000. Brix (% soluble sugar), pH, and titratable acid content of the juice were
not affected. In the Lawton vineyard, symptoms of Eutypa dieback were observed on 18,
11, and 62 out of 1,799 vines in 1999, 2000, and 2001, respectively. In general, diseased
vines had fewer shoots, but the number of shoots per vine did not decline consistently.
The ITS region of an isolate (M12) from a grapevine trunk from Michigan and an isolate
of Eutypa lata from California (CA30) were sequenced. The Michigan isolate were later
identiﬁed as Eutypella vitis. Primer pairs designed from M12 detected M12, another
Michigan isolate, and CA30. Primer pairs speciﬁc to E. lata from France detected CA3O

but did not detect two additional California isolates or M12.

To Daijiro Okada, Francis M. Butterworth, and Barbara T. Butterworth

iii

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AKNOWLEDGEMENTS

I would especially like to thank my advisor, Dr. Annemiek Schilder, for her
guidance, encouragement, and for making this research possible, and my committee
members, Drs. Gordon Stanley Howell, Gerard Adams, and Joseph Vargas, who have
educated and advised me throughout. Thanks to Robin Mohney and Ralph Parker
without whose pleasant vineyards this work would have not been possible. Thanks go to
the Viticulture Consortium East and National Grape Cooperative for research funding.

I would also like to thank the numerous people who have helped me immensely
throughout my graduate degree days with scholarly and ﬁeldwork pursuits, questions and
answers, and emotional support: Tracee Beaumont, Murcel Catal, Jen Dickman, Jennifer
Dwyer, Gail Foster, Dr. Andrew Jarosz, Dr. Alan Jones, Dr. Sasha Kravchenko, Brian
Lehman, Wendy Mann, Kim Maxson-Stein, Gail McGhee, Carmen Medina-Mora,
Allison Miller, Subashini N agendran, Bill Nail, Dr. Rabiu Olatinwo, Stephanie Ollison,
Dr. Connie Page, John Rogers, Dr. Guido Schnabel, Roger Sysak, Dr. Phillip Wharton,
Acacia Williams, and Jeff Woodworth. A special thanks to Jerri Gillet for keeping me

focused with wisdom, and happy with smiles and toys.

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TABLE OF CONTENTS

LIST OF TABLES ................................................................................. viii
LIST OF FIGURES ................................................................................... x
CHAPTER 1. LITERATURE REVIEW .......................................................... 1
Grape production in Michigan ............................................................. l
Eutypa dieback ............................................................................... 7
Population genetics ........................................................................ 19
Disease development ...................................................................... 22
Losses due to the disease .................................................................. 28
Disease cycle ............................................................................... 31
Epidemiology ............................................................................... 36
Host effects on infection ................................................................... 43
Environmental effects on infection ...................................................... 46
Pathogen effects on infection .............................................................. 47
Cultural effects on infection .............................................................. 49
Disease control .............................................................................. 50
CHAPTER 2. SPATIAL ASPECTS OF EUTYPA DIEBACK IN A VINEYARD ...... 62
Introduction .................................................................................. 62
Materials and methods ..................................................................... 64
Results ........................................................................................ 75
Discussion ................................................................................... 89
CHAPTER 3. EFFECT ON THE NUMBER OF SHOOTS ................................ 93
Introduction .................................................................................. 93
Materials and methods ..................................................................... 94
Results ........................................................................................ 95
Discussion .................................................................................. 103

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ASPECTS...

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Protocols ______

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TABLE OF CONTENTS (continued)

CHAPTER 4. EFFECTS OF EUTYPA DIEBACK ON GROWTH AND YIELD

ASPECTS .......................................................................................... 106
Introduction ................................................................................ 106
Materials and methods ................................................................... 107
Results ...................................................................................... l 15
Discussion .................................................................................. 171

CHAPTER 5. MOLECULAR DETECTION AND DIAGNOSIS ......................... 180
Introduction ................................................................................ 180
Materials and methods ................................................................... 184
Results ...................................................................................... 189
Discussion .................................................................................. 204

APPENDD( A

Leaf area regression equations ................................................................... 211

APPENDIX B

Results of the statistical analysis using the MIX (mixed) procedure ........................ 213

APPENDIX C

Results of the statistical analysis using the CORR (correlation) procedure ............... 259

APPENDD( D

Protocols ............................................................................................ 269

APPENDIX E

Sequence Homology Reports ..................................................................... 273

APPENDIX F

Weekly weather data for regressions ............................................................ 284

vi

APPE\DL\' 6
Summary Stan's

BIBLIOGRAP

 

 

TABLE OF CONTENTS (continued)

APPENDIX G
Summary statistics and ANOVA of shoot count study ....................................... 286
BIBLIOGRAPHY ................................................................................. 291

vii

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1.1

1.2

2.1

2.2

3.1

5.1

LIST OF TABLES

Seasonal patterns of ascospore release. Comparative ascospore trapping reports
from Michigan, New York, and California, USA, and South Australia ............ 39

Grape cultivars grown in Michigan and their relative susceptibility to Eutypa
dieback (Carter, 1991; Gut, et al., 2001; Howell, et al., 1998; Mauro, et al., 1988;
Weigle, et al., 1997). R=resistant, l=slightly susceptible, 2=moderately
susceptible, 3=very susceptible, 4=unknown .......................................... 53

Regression relationships of minimum, maximum, and average weekly
temperature before bud break against the number of symptomatic vines in 1999,
2000, and 2001 .............................................................................. 85

Regression relationships of minimum, maximum, and average weekly
temperature after bud break against the number of symptomatic vines in 1999,
2000, and 2001 .............................................................................. 86

Analysis of variance (AN OVA) of the effects of vine health status and year on the
number of shoots per vine in a ‘Concord’ vineyard in Lawton, MI in 2000 and
2001 ........................................................................................ 102

Primer pairs used in molecular detection experiments. All primer pairs were
designed from the ITS region of the fungal material except the last pair that is a
standard primer pair used to detect the ITS region itself. Key: hp = base pair
............................................................................................... 187

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Detection of Eutypa lata and Eutypella vitis isolates from Michigan and
California with speciﬁc primer pairs. Isolates from Michigan are M12 and M13,
and isolates from California are CA30, CA3], and CA38. Key: -H- = isolate was
detected by primer pair in column labeled; clear band obtained with product size
equal to that indicated in table 5.. “+” = isolate was detected by primer pair in
column labeled; faint band obtained with product size equal to that indicated in
table 5.. “—“ = isolate was not detected by primer pair in column labeled. The
primer pairs were designed from sources as follows: pM12 from M12, pCA3O
from CA30, pFR-A and pFR-B from RAPD markers and from ITS region of
French isolate, respectively (Lecomte, et al., 2000). The PCR with all isolates and
the pITS primer pair yielded an ampliﬁcation product of approximately 650 bp
.............................................................................................. 196

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1.2

1.3

1.4

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1.7

1.8

1.9

2.1

LIST OF FIGURES

The hectarage of grapevines in Michigan, 1970 — 2000 (Kleweno, et al.,

2001) ........................................................................................... 3

Grape hectarage by cultivar (>100) in Michigan (Kleweno, et al.,
2001) .......................................................................................... 6

Typical foliar symptoms of Eutypa dieback on grapevines when shoots are

approximately 30 cm long in the spring. (Images in this thesis are presented in
color) .......................................................................................... 8

Typical foliar symptoms of Eutypa dieback after the leaves have fully expanded.
The ragged edges are a result of the leaves emerging in an upcupped manner.
Some chlorosis is often still visible ....................................................... 8

Perithecia in a stroma of Eutypa lata with ostioles in a row beneath the
surface ....................................................................................... 1 1

Asci and ascospores of Eutypa lata ...................................................... 11

A typical culture of the anamorph of Eutypa lata, Libertella
blepharis .................................................................................... 12

Conidia of a one month old culture of Libertella blepharis .......................... 12

The stroma of a canker of Eutypa lata with part of the surface scraped away to
reveal the perithecia ......................................................................... 13

Map of Michigan with the approximate location of vineyard A in east central, and
B in southeastern Van Buren county ..................................................... 66

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Symptomatic vines in a ‘Concord’ vineyard (A) near Lawton, Michigan — 1999.
Horizontal rows are numbered ............................................................ 68

Symptomatic vines in a ‘Concord’ vineyard (A) near Lawton, Michigan — 2000.
Horizontal rows are numbered ............................................................ 69

Symptomatic vines in a ‘Concord’ vineyard (A) near Lawton, Michigan — 2001.
Horizontal rows are numbered ............................................................ 70

Symptomatic vines in a ‘Concord’ vineyard (B) near Schoolcraft, Michigan —
2001 ............................................................................................ 72

Method for assigning block sized used for the quadrat analysis. The smallest
block is one vine ........................................................................... 74

Mean square versus block size graphs of all of the vantage points from which
quadrat variance was calculated in Vineyard A in 1999; northeast, northwest,
southeast and southwest. Block size 1 corresponds to one vine, block size 4 = 2 x
2 vines, block size 8 = 4 x 4 vines, etc. Blocks were oriented beginning with the
vines along the same row .................................................................. 78

Mean square versus block size graphs of all of the vantage points from which
quadrat variance was calculated in Vineyard A in 2000; northeast, northwest,
southeast and southwest. Block size 1 corresponds to one vine, block size 4 = 2 x
2 vines, block size 8 = 4 x 4 vines, etc. Blocks were oriented beginning with the
vines along the same row .................................................................. 79

Mean square versus block size graphs of all of the vantage points from which
quadrat variance was calculated in Vineyard A in 2001; northeast, northwest,
southeast and southwest. Block size 1 corresponds to one vine, block size 4 = 2 x
2 vines, block size 8 = 4 x 4 vines, etc. Blocks were oriented beginning with the
vines along the same row .................................................................. 80

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Mean square versus block size graphs of all of the vantage points from which
quadrat variance was calculated in Vineyard B in 2001; northeast, northwest,
southeast and southwest. Block size 1 corresponds to one vine, block size 4 = 2 x
2 vines, block size 8 = 4 x 4 vines, etc. Blocks were oriented beginning with the
vines along the same row .................................................................. 82

Daily minimum and maximum temperatures in the ﬁve-week period before bud
break in vineyard A ........................................................................ 87

Daily minimum and maximum temperatures in the four—week period after bud
break in vineyard A ........................................................................ 88

The number of shoots per vine on symptomatic vines (A) and apparently healthy
vines (B) in a ‘Concord’ vineyard in Lawton, MI in 2000 and
2001 .......................................................................................... 98

Total shoots per vine averaged over nine symptomatic and nine apparently
healthy vines in 2000 and 2001 ........................................................... 99

Number of symptomatic shoots (A) and percentage symptomatic shoots (B) on
nine symptomatic vines in a ‘Concord vineyard in Lawton, MI, in 2000 and
2001 ......................................................................................... 100

Number of shoots per vine lost or gained. The particular pair of apparently

healthy (AH) and symptomatic (D) vines is grouped next to each other. The vine
pairs are in order of the apparently healthy vine shoot number gain from lowest to
highest ...................................................................................... 101

Map of Michigan with the approximate location of vineyard A in east central, and
B in southeastern Van Buren county ................................................... 109

Mean number of clusters per shoot on ‘Concord’ grapes in Lawton, MI, in 1999-
2001. AH: shoots on apparently healthy vine; N S: shoots on non-symptomatic
half of diseased vine; M=mildly, I=intermediately, and S=severely symptomatic
shoots on diseased vine; D= symptomatic shoots on diseased vine (2001 only).
Columns with the same letter are not signiﬁcantly different (p=0.05) ............ 122

xii

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Mean number of clusters per shoot on ‘Concord’ grapes in Schoolcraft, MI, in
2001. AH: shoots on apparently healthy vine; NS= shoots on non-symptomatic
half of diseased vine; D= symptomatic shoots on diseased vine. Columns with
the same letter are not signiﬁcantly different (p=0.05) .............................. 123

Mean fruit weight per shoot on ‘Concord’ grapes in Lawton, MI, in 1999-2001.
AH: shoots on apparently healthy vine; N S: shoots on non-symptomatic half of
diseased vine; M=mildly, I=intermediately, and S=severely symptomatic shoots
on diseased vine; D= symptomatic shoots on diseased vine (2001 only). Columns
with the same letter are not signiﬁcantly different (p=0.05) ........................ 125

Mean fruit weight per shoot on ‘Concord’ grapes in Schoolcraft, MI, in 2001.
AH: shoots on apparently healthy vine; N S: shoots on non-symptomatic half of
diseased vine; D= symptomatic shoots on diseased vine. Columns with the same
letter are not signiﬁcantly different (p=0.05) .......................................... 126

Mean fruit weight per cluster on shoots of ‘Concord’ grapes in Lawton, MI, in
1999-2001. AH: shoots on apparently healthy vine; NS: shoots on non-
symptomatic half of diseased vine; M=mildly, I=intermediately, and S=severely
symptomatic shoots on diseased vine; D= symptomatic shoots on diseased vine
(2001 only). Columns with the same letter are not signiﬁcantly different

(p=0.05) .................................................................................... 128

Mean fruit weight per cluster on shoots of ‘Concord’ grapes in Schoolcraft, MI, in
2001. AH: shoots on apparently healthy vine; NS: shoots on non-symptomatic
half of diseased vine; D= symptomatic shoots on diseased vine. Columns with
the same letter are not signiﬁcantly different (p=0.05) .............................. 129

Mean number of berries per cluster on ‘Concord’ grape shoots in Lawton, MI, in
1999-2001. AH: shoots on apparently healthy vine; N S: shoots on non-
symptomatic half of diseased vine; M=mildly, I=intermediately, and S=severely
symptomatic shoots on diseased vine; D= symptomatic shoots on diseased vine
(2001 only). Columns with the same letter are not signiﬁcantly different

(p=0.05) .................................................................................... 131

Mean number of berries per cluster on ‘Concord’ grape shoots in Schoolcraft, MI,
in 2001. AH: shoots on apparently healthy vine; NS: shoots on non-
symptomatic half of diseased vine; D= symptomatic shoots on diseased vine.
Columns with the same letter are not signiﬁcantly different (p=0.05) ............ 132

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4.11

4.12

4.13

4.14

4.15

Mean weight per berry on ‘Concord’ grape shoots in Lawton, MI, in 1999-2001.
AH= shoots on apparently healthy vine; NS: shoots on non-symptomatic half of
diseased vine; M=mildly, I=intermediately, and S=severely symptomatic shoots
on diseased vine; D: symptomatic shoots on diseased vine (2001 only). Columns
with the same letter are not signiﬁcantly different (p=0.05) ........................ 134

Mean weight per berry on ‘Concord’ grape shoots in Schoolcraft, MI, in 2001.
AH: shoots on apparently healthy vine; NS: shoots on non-symptomatic half of
diseased vine; D: symptomatic shoots on diseased vine. Columns with the same
letter are not signiﬁcantly different (p=0.05) .......................................... 135

Mean sugar content per shoot on ‘Concord’ grapes in Lawton, MI, in 1999-2000.
AH: shoots on apparently healthy vine; N S: shoots on non—symptomatic half of
diseased vine; M=mildly, I=intermediately, and S=severe1y symptomatic shoots
on diseased vine. Columns with the same letter are not signiﬁcantly different

(p=0.05) .................................................................................... 136

Mean number of undeveloped berries per shoot on ‘Concord’ grape shoots in
Lawton, MI, in 1999-2001. AH: shoots on apparently healthy vine; N S: shoots
on non-symptomatic half of diseased vine; M=mildly, I=intermediately, and
S=severely symptomatic shoots on diseased vine; D= symptomatic shoots on
diseased vine (2001 only). Columns with the same letter are not signiﬁcantly
different (p=0.05) ......................................................................... 138

Mean number of undeveloped berries per shoot on ‘Concord’ grape shoots in

Schoolcraft, MI, in 2001. AH: shoots on apparently healthy vine; N S: shoots on
non-symptomatic half of diseased vine; D: symptomatic shoots on diseased vine.
Columns with the same letter are not signiﬁcantly different (p=0.05) ............ 139

Mean number of small berries per shoot on ‘Concord grape shoots in Lawton, MI,
in 1999-2001. AH: shoots on apparently healthy vine; NS: shoots on non-
symptomatic half of diseased vine; M=mildly, I=intermediately, and S=severely
symptomatic shoots on diseased vine; D: symptomatic shoots on diseased vine
(2001 only). Columns with the same letter are not signiﬁcantly different

(p=0.05) .................................................................................... 140

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4.16

4.17

4.18

4.19

4.20

4.21

4.22

Mean number of small berries per shoot on ‘Concord’ grape shoots in

Schoolcraft, MI, in 2001. AH: shoots on apparently healthy vine; NS= shoots on
non—symptomatic half of diseased vine; D: symptomatic shoots on diseased vine.
Columns with the same letter are not signiﬁcantly different (p=0.05).... . . . .. ....140

Mean percentage of green berries per shoot on ‘Concord’ grape shoots in Lawton,
MI, in 1999-2001. AH: shoots on apparently healthy vine; N S: shoots on non-
symptomatic half of diseased vine; M=mildly, I=intermediately, and S=severely
symptomatic shoots on diseased vine; D: symptomatic shoots on diseased vine
(2001 only). Columns with the same letter are not signiﬁcantly different

(p=0.05) .................................................................................... 141

Mean percentage of green berries per shoot on ‘Concord’ grape shoots in

Schoolcraft, MI, in 2001. AH: shoots on apparently healthy vine; N S: shoots on
non-symptomatic half of diseased vine; D: symptomatic shoots on diseased vine.
Columns with the same letter are not signiﬁcantly different (p=0.05) ............. 142

Mean leaf area per shoot on ‘Concord’ grapes in Lawton, MI, in 1999-2001. AH:
shoots on apparently healthy vine; NS: shoots on non-symptomatic half of
diseased vine; M=mildly, =intermediately, and S=severely symptomatic shoots
on diseased vine; D= symptomatic shoots on diseased vine (2001 only). Columns
with the same letter are not signiﬁcantly different (p=0.05) ........................ 147

Mean leaf area per shoot on ‘Concord’ grapes in Schoolcraft, MI, in 2001. AH:
shoots on apparently healthy vine; NS: shoots on non-symptomatic half of
diseased vine; D= symptomatic shoots on diseased vine. Columns with the same
letter are not signiﬁcantly different (p=0.05) ......................................... 148

Mean shoot length of ‘Concord’ grape shoots in Lawton, MI, in 1999-2001. AH:
shoots on apparently healthy vine; NS: shoots on non-symptomatic half of
diseased vine; M=mildly, I=intermediately, and S=severely symptomatic shoots
on diseased vine; D= symptomatic shoots on diseased vine (2001 only). Columns
with the same letter are not signiﬁcantly different (p=0.05) ....................... 150

Mean shoot length of ‘Concord’ grape shoots in Schoolcraft, MI, in 2001. AH:
shoots on apparently healthy vine; N S: shoots on non-symptomatic half of
diseased vine; D= symptomatic shoots on diseased vine. Columns with the same
letter are not signiﬁcantly different (p=0.05) ........................................ 151

XV

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4.24

4.26

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4.24

4.25

4.26

4.27

4.28

Mean number of nodes per shoot on ‘Concord’ grapes in Lawton, MI, in 1999-
2001. AH: shoots on apparently healthy vine; NS: shoots on non-symptomatic
half of diseased vine; M=mildly, I=intermediately, and S=severely symptomatic
shoots on diseased vine; D: symptomatic shoots on diseased vine (2001 only).
Columns with the same letter are not signiﬁcantly different (p=0.05) ............ 153

Mean number of nodes per shoot on ‘Concord’ grapes in Schoolcraft, MI, in
2001. AH: shoots on apparently healthy vine; NS: shoots on non-symptomatic
half of diseased vine; D= symptomatic shoots on diseased vine. Columns with
the same letter are not signiﬁcantly different (p=0.05) .............................. 154

Mean shoot diameter per shoot on ‘Concord’ grape shoots in Lawton, MI, in
1999-2001. Diameter measured at intemode above the second node of the shoot.
AH: shoots on apparently healthy vine; NS: shoots on non-symptomatic half of
diseased vine; M=mildly, I=intermediately, and S=severely symptomatic shoots
on diseased vine; D= symptomatic shoots on diseased vine (2001 only). Columns
with the same letter are not signiﬁcantly different (p=0.05) ........................ 156

Mean shoot diameter on ‘Concord’ grape shoots in Schoolcraft, MI, in 2001.
Diameter measured at intemode above the second node of the shoot. AH: shoots
on apparently healthy vine; N S: shoots on non-symptomatic half of diseased
vine; D= symptomatic shoots on diseased vine. Columns with the same letter are
not signiﬁcantly different (p=0.05) ..................................................... 157

Mean brix (% sugar) in juice from clusters on ‘Concord’ grape shoots in Lawton,
MI, in 1999-2001. AH: shoots on apparently healthy vine; NS: shoots on non-
symptomatic half of diseased vine; M=mildly, I=intermediately, and S=severely
symptomatic shoots on diseased vine; D= symptomatic shoots on diseased vine
(2001 only). Columns with the same letter are not signiﬁcantly different

(p=0.05) ................................................................................... 161

Mean pH in juice from clusters on ‘Concord’ grape shoots in Lawton, MI, in
1999-2001. AH: shoots on apparently healthy vine; N S: shoots on non-
symptomatic half of diseased vine; M=mildly, I=intermediately, and S=severely
symptomatic shoots on diseased vine; D= symptomatic shoots on diseased vine
(2001 only). Columns with the same letter are not signiﬁcantly different

(p=0.05) .................................................................................... 163

xvi

430

51

33

3163
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synu
sxrn]
(iUU

Sled
8T3“
113:
(hse;

1501;
prev
ug‘n
sequ
C010;

4.29

4.30

5.1

5.2

5.3

Mean titratable acids in juice from clusters on ‘Concord’ grape shoots in Lawton,
MI, in 1999-2001. AH: shoots on apparently healthy vine; N S: shoots on non-
symptomatic half of diseased vine; M=mildly, I=intermediately, and S=severely
symptomatic shoots on diseased vine; D: symptomatic shoots on diseased vine
(2001 only). Columns with the same letter are not signiﬁcantly different

(p=0.05) .................................................................................... 165

Mean brix (% sugar), pH, and titratable acids in juice from clusters on ‘Concord’
grape shoots in Schoolcraft, MI, in 2001. AH: shoots on apparently healthy vine;
NS: shoots on non-symptomatic half of diseased vine; D: symptomatic shoots on
diseased vine. Columns with the same letter are not signiﬁcantly different

(p=0.05) ..................................................................................... 167

Isolates of Eutypa lata from California and Michigan used in experiments
presented here after seven days on half-strength PDA with streptomycin (100
ug/ml). M12 and M13 were later identiﬁed as Eutypella vitis based on a
sequencing of the ITS region. Note differences in growth rate and

coloring .................................................................................... 190

Detection of the three California isolates by PCR with the pITSlF and pIT S4
primers. The DNA of the three California isolates was extracted with the kit
method, and one of the three (CA30) was extracted with phenol-chloroform. All
isolates and extractions methods show an ampliﬁcation product at 650 bp. To the
right of the KB ladder are CA30 (kit extraction) (lanes 1 and 2), CA30 (phenol-
chloroform) (lane 3), CA31 (kit extract) (lanes 4 and 5), CA38 (kit extract) (lanes
6 and 7), and positive control (Venturia ineaqualis, lane 8) and negative water
control (lane 9) ............................................................................ 192

The two isolates from Michigan and one California isolate, and a PCR with
pITSlF and pIT S4 primers. The DNA of all isolates was extracted by the kit
method. The ampliﬁcation products of all isolates are visible at 650 bp and
include CA30 (lane 1), M12 (lanes 2, 3 and 4), M13 (lanes 5, 6, 7 and 8), water

9) ........................................................................................... 193

xvii

55

i6

5.7

 

 

11111-11

Oneg
each -
ml
at are
FREE
of a Pl
150141:
10110“
and n4
EXtrac
P1131

 

5.4

5.5

5.6

5.7

Pestalotia sp., Phomopsis viticola, and Trichoderma sp., and a PCR with pITSlF
and pITS4 primers. The Michigan isolate M13 (lane 1), Pestalotia sp. (lane 2),
two isolates of Phomopsis viticola (lane 3 and 4), three isolates of Trichoderma
sp. (lane 5, 6, and 7), and water (lane 8) were combined in a PCR with the ITS
primers. An ampliﬁcation product of approximately 650 bp is visible for M13,
Pestalotia sp., one isolate of Phomopsis viticola, and all three Trichoderma sp.
isolates. No bands were visible for one Phomopsis viticola isolate (lane 4)
............................................................................................... 194

California isolate (CA30) and Michigan isolates and a PCR with the pMI-l and
pMI-2 primers. Primers designed from the Michigan isolate M12 was applied to a
PCR with CA30 (lane 1), two concentrations of stroma sample from Michigan
(lane 2 and 3), M12 (lane 4 and 5), M13 (lane 6 and 7), and water (lane 8). An
ampliﬁcation product with the size of approximately 400 bp shows up as bands
for samples CA30, M12 and M13; no hands were visible for the Michigan stroma
sample and water control ................................................................ 197

One California isolate (CA30) and the two Michigan isolates and a PCR with
each of the three species-speciﬁc primer pairs. Gel A shows the products of a
PCR with CA30, M12 (faint), and M13 (faint) and the primers pCA-l and pCA-2
at approximately 400 bp. Gel B shows the products of a PCR with CA30 and
primers pFR-Al and pFR-A2 at approximately 350 pb. Gel C shows the products
of a PCR with CA30 and primers pFR-Bl and pFR-B2 at less than 400 bp.
Isolates shown for each gel are in the same order (after the 1 Kb ladder) as
follows: CA30 (lane 1), M12 (lanes 2, 3, 4), M13 (lanes 5, 6, 7), CA31 (lane 8),
and negative water control (not shown). Comparison of phenol-chloroform DNA
extraction of fungal mycelium to extraction from stroma, and a PCR with the
pITSlF and pIT S4 primers. Lanes 1 and 2 show results from stroma extraction
and lanes 3 and 4 show results from mycelium extraction; ladder is shown in lane
5. All samples were phenol-chloroform extracted; bands at 650 bp are visible in
lanes 1, 3 and 4 ............................................................................. 198

Comparison of phenol-chloroform DNA extraction of fungal mycelium to
extraction from stroma, and a PCR with the pITSlF and pITS4 primers. Lanes 1
and 2 show results from stroma extraction and lanes 3 and 4 show results from
mycelium extraction; ladder is shown in lane 5. All samples were phenol-
chloroform extracted; bands at 650 bp are visible in lanes 1, 3 and 4..............200

xviii

5.9

Corr;
b11111:
10111
Zara I"
15 r111
produ
DNA
the 3:1
01 Sire
the DI
lime p

Sampl

 

COmI‘
11011111
bp 151
1116 re
141111 c
There

Sang

5.8

5.9

5.10

Phenol-chloroform and kit extractions of DNA from a California isolate (CA30)
and phenol-chloroform extraction of DNA from E. lata stroma, and a PCR with
pITS 1F and pIT S4 primers. Phenol-chloroform extracted DNA of CA30 isolate
(lanes 1, 2, 3 and 4) and samples of E. lata stroma (lanes 5, 6, 7 and 8), and kit
extracted DNA of CA30 isolate (lane 9); the positive control (Venturia inaequalis
ITS region) and water are shown in lanes 10 and 11, respectively. Bands of
ampliﬁcation product are visible at 650 bp for three concentrations of phenol-
chlorofonn extracted CA30 (lanes 1, 2, and 4), one concentration of kit extracted
CA30 (lane 9), and the positive control (lanelO) ...................................... 200

Comparison of DNA extraction methods of stroma tissue by kit method and by
boiling, and a PCR with the pITS 1F and pITS4 primers. The two gels represent a
total of six samples that are the result of extraction DNA from stroma of Eutypa
lata by kit method or by boiling small pieces of stroma in deiononized water for
15 minutes at 95°C. Gel A shows no ampliﬁcation product except for the 650 bp
product of M12 in lane 1; the remaining lanes (two lanes per isolate) represent the
DNA of three stroma samples that was extracted by the kit method. Gel B shows
the same control, M12 (lane 1), at 650 bp, and no other product from two samples
of stroma which DNA was kit extracted (lanes 2 — 5; two lanes per sample), and
the DNA of two stroma samples that was extracted by boiling (lanes 6 and 7; one
lane per

sample) ..................................................................................... 202

Comparison of DNA extraction methods of stroma tissue by kit method and by
boiling and a PCR with the pITSlF and pITS4 primers. The band in lane 1 at 650
bp is the result of a kit extraction of Pestalotia sp. The remaining nine lanes are
the result of the boiling extraction method; four different small pieces of stroma
that contain perithecia were heated in deionoized water for 15 minutes at 95°C.
There are no hands indicating ampliﬁcation product of any of the four boiled
samples (lanes 2 — 10) .................................................................... 203

xix

cum-:11 1
Grape prodl

C 11ml?

11131111 80.5 11‘
peniniula of
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«ISNA. 21:1}
timertzmt. S
the-Wing, r51;
graft unions 5
Pmlifnlt)‘ of
“Diet tempe
Michigan Cli:

Chence of laz.l

“Ming! the

 

 

[he ”tsp ERIC

19981.

 

CHAPTER 1. LITERATURE REVIEW
Grape production in Michigan
Climate

Although Michigan has a cool climate with severe winters, moderating effects of
Lake Michigan make the area suitable for grape production. Most grapes are grown
within 80.5 km of the lake, whether in southwest Michigan or in the northwest lower
peninsula of Michigan. The northwest region averages 145, and the southwest region
160 frost-free days per season (Anonymous, 1998b). Both are in regions 5 and 6 on the
USDA plant hardiness zone map; region 6 is found on the coast of Lake Michigan
(U SNA, 2001). The effect of Lake Michigan on the sustainable growth of grapes is
important. Snow cover in the winter protects the vines from alternate freezing and
thawing, retards bud break in spring to avoid frost damage, and can protect trunks and
graft unions from freezing winds (Howell, et al., 1998; Morton, 1985). The close
proximity of the lake can increase the growing season by up to 30 days and increase the
winter temperature lows by as much as 12°C (Morton, 1985). Cultivars suitable for
Michigan climate conditions start vegetative growth later in the spring, which reduces the
chance of late frost damage. As cool air prevails during late summer and autumn
ripening, the harvested berries retain their volatile composition which combines well with
the crisp acidic character of many cool-climate wines (Anonymous, 1998b; Howell, et al.,

1998).

Grape hem;

In .\1,
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and an 85"} :
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hybrid cross;
1998'- 5101113:
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10166‘} of to
gml’e juice (

Grape hectarage

In Michigan, 5,463 hectares of grapes were grown in 2000 (Figure 1.1) (Kleweno,
et al., 2001). This represents a 12.5% increase from the 4,856 hectares grown in 1991,
and an 85% increase from the 2,954 hectares grown in 1970. The grape hectarage
continues to increase (Anonymous, 1998a; Kleweno, et al., 2001). The vast majority of
Michigan grape production (96%) takes place in the southwestern part of the state
(Anonymous, 1998a; Kleweno, et al., 2001). Grapes grown in Michigan belong to the
species Vitis labruscana L.H. Bailey (synonym: Vitis labrusca L.) which includes
cultivars native to the Americas; Vitis vinifera L. which includes cultivars native to
Europe and the Middle East, and French-American or resistant hybrids resulting from
hybrid crosses of V. vinifera and American species (Ellis, et al., 1998; Howell, et al.,
1998; Morton, 1985; NRCS, 2002). With an total estimated 31,415 tonnes of grapes
worth $8.9 million produced in 2001, Michigan is fourth after California, Washington
and New York in grape production (Anonymous, 2002). The ‘Concord’ crop accounted
for 66% of total grape production in 2001 and is a popular cultivar for jams, jellies, and
grape juice (Anonymous, 2002). This cultivar has been compatible with the Michigan

climate for at least 75-100 years (Morton, 1985).

v
E LO
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Grape production
(hectares)

 

 

2,500

 

 

 

2,000 I I I I I I I I I I I I I I I I I I I I I r I T I I I I I l
1970 1973 1976 1979 1982 1985 1988 1991 1994 1997 2000

Year

Figure 1.1. The hectarage of grapevines in Michigan, 1970 — 2000 (Kleweno, et al.,
2001).

Mos
Van Buren 1
federally A;
the Penn Va
Traterse Bu

practicing n

l.-iﬂOn)m1‘1u

 

 

 

Most wine grape production occurs in Leelanau, Grand Traverse, Berrien, and
Van Buren counties (Anonymous, 1998a, 1998b). Michigan has four appellations, or
federally Approved Viticultural Areas (AVAs): Lake Michigan Shore in the southwest,
the Penn Valley in Allegan county, Old Mission Peninsula, and Leelanau Peninsula in the
Traverse Bay region (Anonymous, 1998a, 1998b). Michigan now has nearly 30 wineries
producing more than 200,000 cases of wine annually of red, white, dry and sweet styles

(Anonymous, 1998b; Kleweno, et al., 2001).

As markets for grape cultivars and products have changed, cultivar selections for
new plantings have also changed. Although the hectares planted to ‘Concord’ grapes has
increased over the past 30 years, the proportion dropped from 92% in 1970 to 68% in
2000 (Kleweno, et al., 2001). Since 1991, ‘Concord’ hectarage has decreased by 4.3%,
while ‘Niagara’ hectarage has increased by 96%. The hectarage of resistant hybrid
cultivars increased by 5% and of Vinifera cultivars by 288% between 1991 and 2000

(Kleweno, et al., 2001).

Grape cultivars

Southwest Michigan produces mainly Labmsca cultivars, and some Resistant
hybrids and Vinifera cultivars, while northwest Michigan produces exclusively Vinifera
and Resistant hybrid cultivars amounting to 4% of the total statewide grape crop.
Cultivars produced in Michigan in excess of 100 tonnes per year include Labrusca

cultivars, Concord and Niagara; resistant hybrid cultivars, Seyval, Vidal blanc, and

l'ignoles; ;
(Anonunn
Michigan ;
Semi. M.
include the
Franc. .4111
Saut'ignop

lirnrod. :1

Th
Bemen. :11
increase j;

411991.11

 

Vignoles; and Vinifera cultivars, Chardonnay and White Riesling (Figure 1.2)
(Anonymous, 1998a). In descending order of total hectarage, the cultivars planted in
Michigan are: Concord, Niagara, White Riesling, Chardonnay, Vidal blanc, Vignoles,
Seyval, Maréchal Foch, Pinot noir, and DeChanauc. Minor cultivars (under 20 hectares)
include the following in descending order: Pinot gris, Merlot, Chambourcin, Cabernet
Franc, Aurore, Cayuga white, Gewiirtztraminer, Chancellor, Fredonia, Cabernet
Sauvignon, Chardonel, Gamay noir, Delaware, Baco noir, Chelois, Pinot blanc, Catawba,

Himrod, and Bianca.

The majority of wine grape production occurs in Leelanau, Grand Traverse,
Berrien, and Van Buren counties (Anonymous, 1998a, 1998b). There has been a 57%
increase in the hectarage devoted to Vinifera and Resistant hybrid cultivars since 1991.
In 1991, 6% of the hectarage was planted with wine grape cultivars although many tones
of ‘juice’ grapes were fermented into wine, which increased in 2000 to 9%. Before 1970,

only 1% was planted to wine grape cultivars.

44'\

 

 

 

 

 

 

 

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(Hughes. ct

section. 016;
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years after 2:
and the pres:
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not removed

illunkvold.

 

When shOQts
Concealed as
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leaf edges, :1
““5154 proc
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Eutypa dieback
Symptoms

Eutypa dieback is characterized by the development of a cankerous lesion at or
below a pruning wound, visible beneath the bark, and can potentially girdle the vine
(Hughes, et al., 1998; Johnson, et al., 1983; Munkvold, et al., 1994). When cut in cross-
section, older wood shows a characteristic wedge of darkly stained vascular tissue
(Pearson, 1988). In commercial vineyards, symptoms are not detected until at least 2
years after infection, but in artiﬁcial inoculation studies, where symptom development
and the presence of cankers were more closely observed, cankers developed in 1-2 years
(Moller, et al., 1978; Munkvold, et al., 1995). Infected vines often show symptoms on
one cordon and healthy growth on the other (Johnson, et al., 1983). If infected wood is
not removed and the fungus grows down the main trunk, the entire vine eventually dies
(Munkvold, et al., 1994). The shoot and leaf symptoms are easily spotted in the spring
when shoots are approximately 28-30 cm long (Figure 1.3). Later, the symptoms can be
concealed as shoots of the same or on an adjacent vine ﬁlls the canopy area. Shoots
emerging from an infected cane or cordon show varying degrees of stunting, chlorosis of
leaf edges, and upward cupping of emerging leaves. As they grow, these shoots remain
stunted, producing sparse clusters with uneven berry maturity (Johnson, et al., 1983).
Often the most stunted shoots wither before ﬂowering (Butterworth, unpublished). As
cupped leaves expand, they often show ragged edges and persistent chlorosis (Figure 1.4)

(Johnson, et al., 1983; Moller, et al., 1978; Munkvold, et al., 1994; Pearson, 1988).

 

 

Figure 1.3.
aPPm-‘ttmatr

Fig“ 1.4.
expended.
Some Chlo

 

Figure 1.3. Typical foliar symptoms of Eutypa dieback on grapevines when shoots are
approximately 30 cm long in the spring. (Images in this thesis are presented in color.)

 

Figure 1.4. Typical foliar symptoms of Eutypa dieback after the leaves have fully
expanded. The ragged edges are a result of the leaves emerging in an upcupped manner.
Some chlorosis is often still visible.

 

Causal pathogen

Eutypa dieback (formerly called “dead arm” disease) is caused by the fungus
Eutypa lata Pers. Tul. & C. Tul. (syn. Eutypa armeniacae Hansf. & Carter) (Carter, 1957;
Glawe, Skotland, et al., 1982). Initially, Phomopsis viticola (Sacc.) Sacc. was identiﬁed
as the causal agent of a disease called “dead arm,” which actually was a complex of
Eutypa dieback and Phomopsis cane and leaf spot (Farr, et al., 1989; Moller, et al., 1978).
After the discovery of the anamorph (C ytosporina sp.) on apricot in Australia in the late
1950’s, the same fungus was isolated from grapevine, but researchers assumed it was a
saprophyte (Carter, 1991). Only in the late 20th century was E. lata shown to deﬁnitively
produce the symptoms in grapevine after a long period of apparent latency (Carter, 1991;

Moller, et al., 1978).

Eutypa lata is an ascomycete in the order Xylariales and belongs to the
Diatrypaceae family (Alexopoulos, et al., 1996; Carter, 1957; Hanlin, 1990). The order
Xylariales is characterized by dark, leathery perithecia embedded in a stroma that is part
host and part fungus. The Diatrypaceae family is characterized by sausage-shaped
ascospores (Alexopoulos, etal., 1996; Hanlin, 1990; In, et al., 1991). The ostiolate
perithecia of E. lata are up to 450 pm in diameter and form in a single layer in the stroma
(Figure 1.5). The perithecia contain paraphyses and periphyses, and are lined with
pseudoparenchyma cells (Carter, 1957). The asci of E. lata are cylindrical to clavate,
each containing eight ascospores per ascus, which are discharged in an adherent group
(Figure 1.6). The ascospores are 6.5-11 x 1.5-2 um, pale yellow-brown, and ameroid to

allantoid in shape (Figure 1.7) (Carter, 1991; Rappaz, 1984). The anamorph has been

 

identiﬁed a
1998; C1111?
however it i
1993). Uh
agar and is .
other side 11
COEI'lepi‘ttl
cultures for:
sunpcdirllg.
(Figure 1.9

and mes-stir;

Skotland, e1

 

 

identiﬁed as Libertella blepharis A. L. Smith (syn. Cytosporina sp. Sacc.) (Barnett, et al.,
1998; Carter, 1991). The genus Cytosporina is used as a synonym of L. blepharis,
however it is a recognized obligate anamorph of Dumortiera Westend (McKemy, et al.,
1993). Libertella blepharis mycelium on potato dextrose agar grows appressed to the
agar and is hyaline after one week, becoming whiter or sometimes pale yellow on the
other side (Figure 1.8). Some cultures are olivaceous to brown underneath.
Conidiophores are inside a small (<0.5 — 1 mm diameter) subconical pycnidium. Some
cultures form highly branched condiophores. Conidiogenous cells proliferate
sympodially or percurrently, are straight or slightly curved, and are 10-26 x 1.5-2.5 um
(Figure 1.9). Conidia are curved toward the apex and straight nearer the truncate base,
and measure 14-40 x l-l.3 um (Carter, 1957, 1991; Carter, and Talbot, 1974; Glawe,

Skotland, et al., 1982).

10

Figure 1. ;.
surface.

”Elm: 1.6.

 

 

Figure 1.5. Perithecia in a stroma of Eutypa lata with ostioles in a row beneath the
surface '

 

Figure 1.6. Asci and ascospores of Europa lata.

Figural ,

 

 

Figure 1.7. A typical culture of the anamorph of Eutypa lata, Libertella blepharr‘s.

 

Figure 1.8. Conidia of a one month old culture of Liberrella bIephan‘s.

   

Signs

Signs of Eutypa lata are most clearly visible aﬂer the grapevine has died. A
single layer of perithecia is produced in a stroma that appears as a burned or gray-black
area on the surface of the dead wood underneath the bark (Figure 1.9). The stroma
becomes exposed when the bark falls off. Ostioles of the perithecia give the stroma a
bubbly or rough surface. If a thin slice is cut across the stroma surface, the perithecia are
visible as dark holes (Carter, 1957, 1991; Moller, Braun, et al., 1977). White to green
paraphyses may be visible inside (Carter, 1957). Conidia that appear as orange or light
yellow tendrils that emerge from the pycnidia of the anamorph may be visible on
moistened dead wood, but also on younger infected trunks and canes (Moller, Braun, et

al., 1977).

 

Figure 1.9. The stroma of a canker of E urypa lata with part of the surface scraped away
to reveal the perithecia.

 

been 11
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Occurrence and distribution

Since its ﬁrst identiﬁcation on grapevine in 1957 in Australia, Eutypa dieback has
been reported in most grape-growing regions of the world, including South Africa,
Australia, and New Zealand, Europe, the Mediterranean region, and North America.
There has been no report of the disease in Asia, but that may not reﬂect its true

distribution (Carter, 1957, 1991).

In Michigan, Eutypa lata ascospores in perithecial stroma were ﬁrst observed on
trunks of already dead vines in 1977 (T rese, et al., 1980). Though the perithecial stage of
E. lata was found in 1977, diseased vines were likely present earlier. Since then, the
disease has been reported in many ‘Concord’ vineyards, some with as many as 33-50%
infected vines (M. Longstroth, personal communication). The approximate total
percentage of vineyard hectarage that is symptomatic remains low (1%). Field
observations indicate that Concord is the only cultivar so far to show Eutypa dieback
symptoms; symptomatic vines are at least 10— to 20 years old (M. Longstroth, personal
communication). Other unpublished reports estimate Eutypa dieback in 10% of mature

grapevines in Michigan (Gendloff, et al., 1983a).

Nomenclature and taxonomical classiﬁcation of the pathogen

The teleomorph was ﬁrst described in Australia in 1956 on apricot (Prunus
armeniaca) and named Eutypa armeniacae Hansf. & Carter in (Carter, 1957, 1991).

Eutypa armeniacae was found on grapevine in 1957, but was only conﬁrmed as the

14

causal age
1991:5101
discovered
(Carter, 19
Sphaen'a It
ameniam:
teleomorpk
nor found 1
been based
the teleom.
DeScenzo,
differences
[133565 On I
DO c:OlOl’ 01:
19831 Ger
Se‘lllcnces a
California 3:,

{mandmnei

 

h0g3 may in

i=5» lata {P}

CODClUded ll’

al., 1993).

 

causal agent of Eutypa dieback on grapevine nearly 20 years later in California (Carter,
1991; Moller, et al., 1978). The species of Eutypa was ﬁrst named for the host it was
discovered on, Prunus armeniaca, but later research showed it synonymous with E. lata
(Carter, 1991; Rappaz, 1984). Originally, E. lata was considered synonymous with
Sphaeria lata Pers.: Fr., but this species too is considered synonymous with E.
armeniacae (Carter, 1991; Rappaz, 1984). There are difﬁculties distinguishing the
teleomorph of the two species, especially in areas of low rainfall where the teleomorph is

not found (Glawe, Skotland, et al., 1982). Distinctions between the two species have

1.

been based on morphological differences of the anamorphs and genetic differences, but
the teleomorphic stages of both species are considered synonymous (Carter, 1991;
DeScenzo, et al., 1999; Glawe, and Rogers, 1982; Rappaz, 1984). Morphological
differences of the anamorphs of Eutypa lata and E. armeniacae include pale gray conidial
masses on the former and yellow conidial masses on the latter, and while the former had
no color on the reverse in culture and the latter was pale yellow (Glawe, and Rogers,
1982). Genetic sequence analysis with the ribosomal DNA internal transcribed spacer
sequences and ampliﬁed fragment length polymorphisms of isolates collected in
California suggested that the Eutypa species infecting grape was genetically distinct from
that found on native trees (Quercus lobata Nee. (valley oak) and Arbutus sp. L.
(mandrone). However, that both species were able to infect the native and cultivated
hosts may indicate non-grape sources of inoculum (DeScenzo, et al., 1999). They
concluded that the causal agent of Eutypa dieback is Eutypa armeniacae Hansf. & Carter
[=E. lata (Pers. : Fr.) Tul. & C. Tul. ﬁde Rappaz] (DeScenzo, et al., 1999; McKemy, et

al., 1993).

15

apricot
at the t
isolate
1991 ).
blepha
C110 5p

MCKer

 

it"

fly-

r

 

The anamorph was identiﬁed as Cytosporina sp. and conﬁrmed pathogenic on
apricot in 1938 in Australia by D. B. Adam, though the fungus was not described in detail
at the time (Carter, 1991). Interestingly, in England in 1901, the same fungus had been
isolated and identiﬁed — host unknown — as Libertella blepharis A.L. Smith (Carter,
1991). In 1957, E. W. Mason at the Commonwealth Mycological Institute suggested L.
blepharis as the E. lata anamorph, but was this ignored until it was determined that
Cytosporina is a recognized obligate anamorph of Dumortiera Westend. (Carter, 1991;

McKemy, et al., 1993).

16

Diagnosis

\‘ir
the spring
1994). Inf
ma'gins of
Spot becau
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needs to by
ephemeral
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Diagnosis and detection

Vines infected with Eutypa lata can be identiﬁed by observing shoot symptoms in
the spring when the shoots are from 15-60 cm long (Pearson, et al., 1981; Ramsdell,
1994). Infection by E. lata may be reconﬁrmed by isolation of the fungus from the
margins of cankers, not from the spring vegetative growth. Cankers are often difﬁcult to
spot because they appear beneath the bark and can be unobtrusive. Detecting E. lata
cankers on vines suspected of being infected can be destructive because the outer bark
needs to be carefully peeled off. Symptoms of Eutypa dieback have been found to be
ephemeral between seasons, and isolation of the fungus from cankers or stained wood
may be difﬁcult (Amborabe, et al., 2001; Moller, et al., 1978; Peros, et al., 1997; Peros, et
al., 1999; Tey-rulh, et al., 1991). The window of time that symptoms of Eutypa dieback
are easily visible can be less than a week due to rapid growth in the spring, therefore
symptoms of herbicide damage or virus infection may be confused with those of Eutypa
dieback. After isolations are made and there are no competing organisms in the culture,
the anamorph of E. lata may take up to a month to produce conidia and some isolates do
not produce them (Carter, 1991; Moller, et al., 1978; Peros, et al., 1997). There may also
be numerous other pathogens and saprophytes present that interfere with the isolation and
complicate the diagnosis (Carter, 1991). Many fungi have similar-looking white, ﬂuffy
or appressed mycelium. Eutypa dieback symptoms may not appear for 1-3 years after the
initial infection, which makes the disease difﬁcult to detect in the early stages of infection
(Carter, 1991; Moller, et al., 1978). Reliable, rapid methods that help accurately detect

infected vines are important to implement timely control measures.

17

Sc!

to detect 1
against ash
reliable ir.
antigenic;
Cn‘plm'a.‘
al., 1989).
reactions 1

minus cell

 

Hahn, Va 1‘
COmpared
Obtained u
mbody g
”a“ SPCci
Molecular
Empa [at
Thﬁ Stud}.
Mimoip'
new tr
all of ther
$wwﬁ

mfm’d ft

Serological techniques and molecular probes have been used with varying success
to detect Eutypa lata in grapevine tissues. Serological methods using antibodies made
against ascospores, mycelium, or various cell treatments of each have not been very
reliable in detecting E. lata in grapevines. Ascospores and mycelium were found to be 1
antigenically distinct. However, ascospores were serologically similar to those of
Cryptovalsa ampelina (N itschke) Fuckel, and thus not speciﬁc enough to E. lata (Farr, et
al., 1989). When cell walls and contents of mycelium were used, there were nonspeciﬁc
reactions to both and thus the results were variable (Francki, et al., 1970). Mycelium
minus cell walls of ﬁve related fungi (Eutypa lata, Eutypella sp., Valsaeutypella sp.
Hohn, Valsa sp. Fr., Leucostoma sp. (Nitschke) Hohn, and F usarium sp. Link: F.) were
compared serologically, and E. lata was found to be distinct. More reliable results were
obtained with gel diffusion methods (NRCS, 2002; Price, 1973). Using ﬂuorescent
antibody staining, whole cells and cells wall antiserum preparations of E. lata did not
react speciﬁcally to differentiate different genera of fungi tested (Gendloff, et al., 1983b).
Molecular probes using species-speciﬁc primers have been most successful at detecting
Eutypa lata mycelium in culture and in wood chips from cankers (Lecomte, et al., 2000).
The study tested three primer pairs designed from the sequences of random ampliﬁed
polymorphic DNA fragments and three primer pairs designed from the ribosomal DNA
internal transcribed spacer sequences of E. lata. Two pairs of primers were able to detect
all of the 60 E. lata isolates tested and none of the 100 non- E. lata isolates. This study
showed that molecular probes using species-speciﬁc primers is a rapid and accurate

method for detecting E. lata, because the researchers found that very little fungal material

18

is needed.

within eig

Host ran}.

 

 

 

 

eeUOU

Populam

Et

CO“.- , ,.
“"Pdlib

Eugpa la

ﬁr.
Juand

is needed, were able to detect the fungus in the host tissue, and the results were obtained

within eight hours (Lecomte, et al., 2000).

Host range of the pathogen

In addition to grapevine (Vitis spp.), Eutypa lata has been reported from over 50
different genera and 28 families of woody plant hosts. E. lata infects many woody crops
such as apricot (Prunus anneniaca L.), almond (Prunus dulcis (Mill.) Webb), apple
(Malus domestica Borkh.), black currant (Ribes nigrum L.), and walnut (Juglans regia
L.) (Carter, 1957, 1991). It has been found to a lesser extent on olive (Olea europaea L.)
(Rumbos 1993), sweet cherry (Prunus cerasus L.), pistachio (Pistacia vera L.), and
lemon (Citrus limon (L.) Burm. F.) (Carter, 1991). Woody plants grown for ornamental
value can also be hosts to E. lata, such as barberry (Beriberis darwinii Hook), Oleander
(Nerium Oleander L.), littleleafed linden (Tilia cordata L.), and cultivated rose (Rosa spp.
L.) (Carter, 1991; NRCS, 2002). In Europe, the fungus has been found to co—exist with
indigenous host species yet cause minimal damage. This has not been the case in

Australia where E. lata arrived via imported crops (Carter, 1991).

Population genetics

Eutypa lata has been identiﬁed and characterized by morphology, vegetative
compatibility, genetic relationships, and pathogenicity. Studying genetic relationships of
Eutypa lata isolates from single vines, vineyards or regions may assist to elucidate gene

flow and the source of infections (Carter, 1991; DeScenzo, et al., 1999; Glawe, and

19

Roi-”Em 15

 

Peres. 6‘ '

Mt
difﬁcult N
am not fro
was used I
Skotland. t
(11.22 vs.
produced d
and holobi.
Neither of
identined f
armeniaca
of the more
Were Coelc:
SUggests th.
h}?h0m}'ce-
1934, the te
in a long lj.

and V0150 l", I

 

 

Rogers, 1982; Glawe, Skotland, et al., 1982; McKemy, et al., 1993; Peros, et al., 1998;

Peros, et al., 1997).

Morphological differences between Eutypa lata and E. armeniacae have been
difﬁcult to decipher in the teleomorph; often the specimens used to study the differences
are not from grape or apricot. The morphology of the anamorph (Libertella blepharis)
was used to differentiate the teleomorph sources (Glawe, and Rogers, 1982; Glawe,
Skotland, et al., 1982). The conidia of E. lata were shorter than those of E. armeniacae
(14-22 vs. 15-35 pm), and E. lata produced a gray mass of conidia while E. armeniacae
produced a yellow mass of conidia. Both species were characterized by black pycnidia
and holoblastic and percurrent (or annellidic) conidiogenesis (Glawe, and Rogers, 1982).
Neither of the isolates described, however, were isolated from grapevine; E. lata was
identiﬁed from Acer platanoides L. (Norway maple) and E. armeniacae from Prunus
armeniaca L. (apricot) (Glawe, and Rogers, 1982; NRCS, 2002). In another comparison
of the morphology of 45 isolates of E. armeniacae isolated from Prunus armeniaca, most
were coelomycetous and a few were hyphomycetous (McKemy, et al., 1993). This
suggests that either within the species there is variation among the anamorphs or that the
hyphomycetous isolates were misidentiﬁed as E. armeniacae (McKemy, et al., 1993). In
1984, the teleomorphs of E. lata and E. armeniacae were determined to be synonymous
in a long list that included Eutypa ambigua, E. milliaria, Sphaeria lata, Diatrype lata,

and Valsa lata. (Rappaz, 1984).

Recent studies on vegetative compatibility and diversity in ampliﬁed fragment

length polymorphisms (AFLPs) of Eutypa armeniacae and E. lata isolates from a variety

20

‘1

of hosts in California, Michigan, New York, South Africa, Australia, and Italy suggest
the existence of two species (DeScenzo, et al., 1999). In dendrograms using AFLP data,
genetic similarities were found between a group (group B) of isolates from native hosts
(Quercus lobata (valley oak) and Arbutus sp.(mandrone) and one grape isolate, and
another large group (group A) of isolates from cultivated hosts (Vitis sp. (grape), Prunus
sp. (cherry) and Prunus armeniaca (apricot) (DeScenzo, et al., 1999; NRCS, 2002).
Group A members were more similar to each other than to members of group B.
Vegetative compatibility tests backed up the dendrogram ﬁndings: isolates with >0.15
genetic distance showed strong incompatibility reactions when paired, including barrage
zones of depressed or no mycelium. Isolates paired with <0.15 genetic distance showed
very weak reactions. All pairings with a madrone isolate in California, a group B
member, resulted in extensive mycelial lysis of the paired strain. The authors suggest that
the isolates from cultivated hosts are E. armeniacae, like the original pathogen found on
apricot, while isolates from native hosts are E. lata; however, both species are able to
infect cultivated as well as native hosts (DeScenzo, et al., 1999). Since this study is
inconclusive and recent literature uses E. lata to refer to the causal agent of Eutypa

dieback, the name E. lata will be used in this thesis.

Genetic diversity, vegetative compatibility and relative pathogenicity of isolates
of E. lata have been compared in two studies (Peros, et al., 1997; Peros, et al., 1999).
Isolates from different vines in a single vineyard and from a region of many vineyards in
France were characterized using random ampliﬁed DNA polymorphisms (RAPDs),
which showed evidence of random mating, as each had a unique genotype. All isolates

were vegetatively incompatible with each other and no correlation was found between the

21

genotipe
indicated
populati-
noneulté'I
I
isolates Ll
using nu.

We iron“

RKPD m;

 

 

difference.
ClllllVafs 2
beta cc“ ‘
tra‘teled 1

Disease t

Eric, I

genotype of an isolate and its pathogenicity (Peros, et al., 1997; Peros, et al., 1999). As
indicated by the work of DeScenzo et a1. (1999), gene ﬂow within the pathogen
population in grape must take into consideration that E. lata is likely mating randomly on
non—cultivated hosts as well (DeScenzo, et al., 1999). In a single vineyard in France, all
isolates taken from different vines were vegetatively incompatible and genetic analysis
using random ampliﬁed polymorphic DNA markers (RAPDs) suggested that isolates
were from an outside source of a random-mating population (Peros, et al., 1997). The
RAPD markers from isolates of another vineyard 390 km away, showed similar
differences between isolates of the population. Though the vineyards had different
cultivars and climate, the RAPD markers were similar enough to suggest gene ﬂow
between them. Vineyards are continuous in that region, and the ascospores may have

traveled in air currents or via non-grape hosts along the way (Peros, et al., 1998).

Disease development

Enzymatic eﬁects

Eutypa lata causes soft rot of infected wood (English, et al., 1978). In-vitro tests
showed that cellulase was present in cultures of E. lata grown with carbon sources of
carboxymethylcellulose or xylan. In the xylem, the fungus penetrates walls or pits with
cell-wall-degrading enzymes and grows longitudinally. It eventually kills the cambium
and invades and kills phloem and other cortical tissues, which results in a canker
(English, et al., 1978). When grapevine was inoculated with mycelium, cankers
averaging 228 mm in length developed 3 years after inoculation (Moller, et al., 1978).

The authors acknowledge that the observed soft rot was likely caused by secondary wood

22

 

 

rotting ft:
from inoc
effect on 5:
apricot [P
fungus in
tissues. D
aﬂ‘dl’rllCltii
Penmeeia
€153!- Hos

are rare in
Torin 62?}

ll
phl‘iSlOlOg
al., 19%;
concmm

eul-‘Pinol

rotting fungi, but only E. lata was used to inoculate test plants. Soft rot also resulted
from inoculation of E. lata on sterile birch blocks and showed that the histopathological
effect on grapevine may not be that different from its effect on birch (Betula sp. L.) or
apricot (Prunus armeniaca) (English, et al., 1978; NRCS, 2002). Non-recovery of the
fungus from stained areas in the wood was evidence for the death of the pathogen in these
tissues. Drier conditions within the vine may cause the fungus to die, rather than
antimicrobial compounds produced by the plant (English, et al., 1978). How the
perithecia are formed and under what conditions they form in the infected wood is not
clear. However, it appears that a certain amount of moisture is necessary since perithecia

are rare in arid climates (Carter, 1991; English, et al., 1978).

Toxin effects

The pathogen produces a toxin named eutypine that causes a variety of
physiological effects on the vine (Deswarte, Canut, et al., 1996; Deswarte, Eychenne, et
al., 1996; Mauro, et al., 1988; Tey-rulh, et al., 1991). The effects of eutypine are
concentration-dependent and can be reduced by the host by reduction of eutypine to
eutypinol (Colrat, Deswarte, et al., 1999). The existence of a toxin involved in the
Symptoms of Eutypa dieback of grapevine was proposed in 1981, because the pathogen’s
mycelium was detectable in the margins of cankers, but not in the symptomatic shoots,
leaves, and inﬂorescences that may show up long after cankers had developed (Moller, et
al., 1981). Approximately six years later, the toxin was discovered in ﬁltrates of E. lata
cultures (Mauro, et al., 1988). The fungus itself and the ﬁltrates were used in in vitro

bioassays on plantlets (propagated through tissue culture) and excised leaves to screen for

23

 

resistant ~
most sus.
Shunt. '
minutes.
proteins. .

plantlets. ’

 

aeted list.
inoculatity
decreasir:
from mod
resiStam C
the differe
lejn may

[Milli the

 

cOttelzited

memllbUl-

resistant somaclones of grape cultivars. The cultivars were ranked in order of least to

 

most susceptible as follows: Merlot, Semillon, Pinot noir, Cabernet Sauvignon, and
Mauzac. The crude ﬁltrate and fraction containing toxin were heat stable to 110°C for 20
minutes, which suggested that the toxin was not an enzyme since enzymes, like most
proteins, are denatured at 100°C. The polysaccharide fraction was non-toxic to the
plantlets, but the fraction containing the toxin was as toxic as the crude ﬁltrate, and it
acted faster. The ﬁltrate bioassays also produced the necrotic symptoms faster than
inoculations with E. lata mycelium. Dilution of the ﬁltrate was effective in delaying and
decreasing the response (Mauro, et al., 1988). The bioassays distinguished susceptible
from moderately susceptible cultivars (Mauro, et al., 1988). Differences between
resistant cultivars Merlot and Semillon were not apparent. In addition, the virulence of
the different E. lata isolates correlated well with the toxicity of the ﬁltrates (r=0.90). The
toxin may be detoxiﬁed in the vine or its transport impeded in resistant cultivars. Even
though the leaf bioassay was more rapid, the plantlet assay was more sensitive and

correlated well with the cultivar’s sensitivity to E. lata (Mauro, et al., 1988).

In 1991, the toxin produced by Eutypa lata was isolated in vivo from infected
vines’ inﬂorescences and sap, but not from healthy plant material. It was crystallized and
analyzed. The name eutypine was pr0posed for the compound: 4-hydroxy-3-(3-
methylbut—3-en—1-ynyl)benzylaldehyde, which has a molecular formula of C12 H1002.
Leaf assays of infected ‘Cabernet Sauvignon’ vines showed that the extent of necrosis
corresponded directly to the concentration of the eutypine. Protoplast assays showed that
the effect of the same concentration of eutypine on a susceptible cultivar (Cabernet

Sauvignon) was more severe than on a more resistant cultivar (Merlot). This study

24

  
 

SUEQCSIC.

CODCCDU;

Will [0 if:
tpKa=6.2
in cellulc;
mernbrar.
increased
Uptake. T

balance [if

 

he
I
”Spiration
betause 1h
SuspenSiOr
emmine.
OXidalivc:
direct inhi
uncouPlin
A 36,“)3
lC‘t’lllCeS ell

3.
.003). Th

suggested that susceptibility to Eutypa dieback may also be correlated with the toxin

concentration in the plant, not only the growth of the fungus (T ey-rulh, et al., 1991).

Five years later, cell suspensions of grapevine (Vitis Vinifera cv. Gamay) were
used to investigate the activity of (C-14 labeled) eutypine. Eutypine is a weak acid
(pKa=6.2) and has a lipOphilic character. This character gives it the ability to insert itself
in cellular lipids, which suggests that eutypine is a mobile proton carrier through cell
membranes. The uptake of eutypine by grapevine cells was rapid and the uptake
increased with higher concentrations. Agents that modify proteins did not affect its

uptake. The authors suggest that the eutypine toxin upsets respiration and the energy

balance of the grapevine cell (Deswarte, Eychenne, et al., 1996).

Isolated mitochondria from Vitis Vinifera cv. Gamay were used to compare the
respiration and membrane potential in contact with eutypine and methyl-eutypine,
beCause the latter is non-dissociable and shown to be non-toxic to grapevine cell

suspensions. The membrane potential was reduced and oxidation rate was stimulated by
ellliypine. Eutypine increased proton leaks and thus uncoupled the mitochondrial
oxidative phosphorylation. With concentrations over 200 1.1M, eutypine caused both
direct inhibition of electron transport and proton leaks, while at150 pM and under, the
unCOupling effect of the proton leaks was dominant (Deswarte, Eychenne, et al., 1996).
A 36~kDa enzyme was discovered in mung bean (Vigna radiata (L.) R. Wilczek) that
reduces eutypine to its non-toxic alcohol form, eutypinol (Guillen, et al., 1998; NRCS,
2002). The enzyme was named VR-ERE (Vigna radiata eutypine-reducin g enzyme).

Transformed grapevine cells that overexpress this gene were found to be resistant to

25

 

eutspine

At 500 u?

l
were con.
vine expr
al., 1998

Vigna rag

tern-perm:

 

Anon
Probable
biogenie '
Colrat, L3
(Vitis tint;
Ph‘ﬁotoxi.

ll" . .
illsgem;

eutypine. At 150 M eutypine, untransformed lines showed a 50% reduction in growth.
At 500 M eutypine, transgenic calli were not affected, whereas the untransformed lines
were completely inhibited. Researchers continue to examine the possibility of an entire
vine expressing the VR-ERE gene to confer resistance to eutypine and E. lata (Guillen, et
al., 1998). Another enzyme, although possibly the same, a 36-kDa enzyme isolated from
Vigna radiata, showed a high afﬁnity to eutypine and reduced it to eutypinol over a broad
temperature range (ZS-45°C) and pH values (6.2—7.5) (Colrat, Latche, et al., 1999).
Activity was determined by spectrophotometric methods. It was determined to be a
probable member of the aldo-keto reductase superfamily, which reduce a variety of
biogenic and xenobiogenic aldehydes to their corresponding alcohols, according to
Colrat, Latche, et al. 1999. A third enzyme of 54-56 kDa was isolated from grapevine
(Vitis vinifera)(Colrat, Deswarte, et al., 1999). Its pH range of detoxiﬁcation was 6.8-7.5.
Phytotoxicity experiments were conducted on grapevine cells cultured in vitro (Vitis
Vinifera cv. Gamay) with various eutypine and eutypinol concentrations. Excised leaves
from grapevine plantlets showed dramatic necrosis with 500 M eutypine, while the
531116 concentration of eutypinol did not cause leaf damage. Protoplast viability was
greatly affected by 200 M eutypine while 400 p.M of eutypinol did not reduce viability
of Protoplasts. The increase in the oxidation rate with eutypinol at even high
conCentrations was only 10% while eutypine caused a 140% increase. Furthermore,
eutypine affected the membrane potential and eutypinol did not. Either gene (one from
Vigha radiata the other from Vitis Vinifera) would be an attractive candidate for

tranSgenic host and resistant cultivar development (Colrat, Deswarte, et al., 1999).

26

gradient.
conduct:
only a pa;
Other res,
al., 1996.»
general, e
uncouplin

throngh m

 

activity of
in slmpto
al., 1997),
Wine
Orly One i,

CUEESPOIIC

Effects of eutypine and eutypinol were compared in three different systems —
Mimosa pudica L. (sensitive plant), Beta vulgaris L. (sugar beet), and Vitis Vinifera
(grapevine). The toxin was shown to act as a mobile proton carrier, disrupting the proton
gradient, hindering uptake of molecules of sugar and valine, and increasing the H+
conductance of the plasmalemma. The effect of eutypine on the plasmalemma may be
only a part of the whole plant’s physiological and biochemical reaction to the toxin.
Other research led to similar conclusions in studies on mitochondria (Deswarte, Canut, et
al., 1996) and thylakoid membranes of the chloroplasts (Deswarte, et al., 1994). In
general, eutypine will progressively reduce the energetic charge of the cells by
uncoupling and inhibiting photosynthesis and respiration. The eutypine is conducted
through the vascular system, traveling to the shoots in spring to effect symptoms. The
activity of eutypine is concentration—dependent, and this may explain in part the change
in Symmoms observed in a single vine over the years (Amborabe, et al., 2001; Peros, et
3L. l 997). HPLC analysis of culture ﬁltrates from three isolates of E. lata (two from
grapevine) showed that the presence of eutypine was variable (Molyneux, et al., 2002).
Only one isolate from grapevine produced eutypine while the other two produced the
Cortesponding alcohol, eutypinol. In addition, a novel metabolite was found (named

eulatinol), which suggested that there is more than one phytotoxic compound produced

by E. lata (Molyneux, et al., 2002).

27

Losses due to the disease

Yield loss

The effects of Eutypa dieback on yield in a vineyard can be signiﬁcant when the
proportion of infected vines is high. The yield of an infected vine declines annually,
sometimes unnoticeably, until the vine is killed (Johnson, 1987; Munkvold, et al., 1994).
Vineyards appear to have a higher incidence of Eutypa dieback as they age (>10 years
old) and on trunks with increasing diameter — a measure of the vine’s age (Johnson, 1987;
Munkvold, et al., 1994). In a lS-year old vineyard in Hungary, 15-20% of the vines were
killed by E. lata, and 68-85% of the surviving vines were infected (Rozsnyay, 1982). A
survey conducted on 11 vineyards ranging in age from 5-34 years found that the majority
of vines were symptomatic in older vineyards, but less than 50% of the spurs showed
sYmptoms. The proportion of vines with Eutypa dieback and the proportion of
SYIl'lptomatic spurs increased sigmoidally with age, and reached asymptotic levels (0.92
and 0.40, repetitively) by 17 and 21 years, respectively (Duthie, et al., 1991). Healthy
Vines or cultivars not susceptible to Eutypa dieback (e. g., cv. Barbera) normally show
incI‘eeased yields until 10 years of age, and then continue to produce consistent yields for
dec3ades. Yields in Chenin blanc vineyards with Eutypa dieback over 20 years old have

declined as much as 83% of what their normal healthy production would be (Munkvold,
8‘ al., 1994).

Eutypa dieback reduced yield components over time in Concord vineyards studied
In Washington State (Johnson, 1987). The mean yield decreased from13% in the ﬁrst
Year to 24% the second. Individual vines ranged from a 14% increase to a 61% decrease

28

\‘lllCS. h-
modern.
and this“,
were sig:
one loci:
lloderitt
diseased

of five C.
mOdcls .3:

01 Im'ssin

 

'Shitaz’ V
affected v

Smnted Ml

A k l,

“is. .
CQl‘UmlE

356851316 :

in mean yield per year. Infected vines with >60% symptomatic shoots were severely
diseased, and with <60% symptomatic shoots were moderately diseased. In most
vineyards, there were signiﬁcant differences between severely diseased and healthy
vines, between moderately diseased and severely diseased vines (but not between
moderately severe and the healthy vines) with respect to yield per vine, cluster number,
and cluster weight; however berry weight was generally not reduced signiﬁcantly. There
were signiﬁcant differences between healthy vines and moderately diseased vines in only
one location in 1983 (berry weight), and one location in 1984 (cluster number).
Moderately diseased vines showed 19-50% yield reduction (mean 41%) and severely
diseased vines showed 62-94% yield reduction (mean 75%) (Johnson, 1987). In a study
Of five California vineyards, yield loss was estimated to be 39-62% based on regression
models of disease severity (proportion of spurs on infected vines that were symptomatic
01' missing) versus cluster count, cluster weight, and vine yield (Munkvold, 1992). In a
‘Shiraz’ vineyard, yield losses ranged from 0.6 to 9 kg per vine from mildly and severely
affected vines, respectively (rated as mild with minimal leaf necrosis to severe with
Stullted shoots with few or no leaves). Compared with 150 clusters per healthy vines,
SYnlptomatic vines had an average of 50 clusters per vine. In ‘Shiraz’ vineyards with a

mean of 30% of the vines infected, yield losses were at least 860 kg/ha, and in ‘Cabernet

Sallvignon,’ losses were at least 740 leg/ha (Wicks, et al., 1999).

In Australia and California, loss in yield and cluster number per vine was
correlated with symptom severity. (Creaser, et al., 2000; Munkvold, et al., 1994). In

California, cluster weights per vine were signiﬁcantly different in one of the two years. In

the Same study in 1991, the number of diseased shoots did not have a signiﬁcant effect on

29

 

 

)ield a
1992.

yield 3
lines i
uninfe
vine s'
cordo'
grout
comp

“Elli

Vllli‘y

yield and cluster number per vine because their numbers were variable, but did so in

1992. In both years, the number of shoots without symptoms signiﬁcantly increased

yield and cluster number per vine (Munkvold, et al., 1994). Non-symptomatic cordons of
vines infected with Eutypa lata often show more vigorous growth than cordons of
uninfected vines. The vigorous growth of the non-symptomatic cordon on an infected
vine suggests that the vine compensates for the loss of growth on the symptomatic
cordons (Johnson, 1987). However, Eutypa dieback signiﬁcantly affected vegetative
growth as measured by pruning weight, but not as signiﬁcantly as it affected yield
components (Munkvold, et al., 1994). The pruning weights of infected vines were
Significantly lower than those of healthy vines in 2 of 3 vineyards in 1991 and both
vineyards in the study in 1992 (Munkvold, et al., 1994). The relationship of disease
severity (based on symptomatic, dead, and missing shoots) and yield (number of clusters)
and pruning weight was linear. If there had been compensation, yield and cluster number
per vine would not decrease as the disease severity increases until disease was so severe

that compensation by healthy shoots was insufﬁcient to make up for the lack of clusters

on the diseased portion of the vine (Munkvold, et al., 1994).

ECOnomic impact

Losses from Eutypa dieback are due directly to yield loss, and indirectly, to
r etl‘ajning, replacement, chemical application and sanitation costs, and the reduction of
Vineyard life span (Siebert, 2000). Symptoms of Eutypa dieback that occur without
regularity each year have led observers to believe that the impact of the disease is not

Important in South Australia; however, preventing the disease reduces the cost of losses

30

monei

uddhr

Amud
Bums
$0ur

onme

hiv-

A ‘1‘

ms t

more effectively than delaying preventative action until losses are incurred with greater

yield loss and vineyard management costs (Creaser, et al., 2000; Ramsdell, 1995).

Eutypa dieback has been found to occur on all susceptible cultivars in South
Australian and Californian grape growing areas (Creaser, et al., 2000; Siebert, 2000).
Estimated production loss per hectare, based on the percent vines infected, ranged from
570 to 1,500 kg per hectare (for 24-47% of the vines infected, respectively); depending
on the price per ton, losses of at least 1,000 Australian dollars/ha were estimated (Wicks,
et al., 1999). In California, at least three of the ﬁve cultivars that account for 70% of the
total gross value are susceptible to Eutypa dieback. In 1999, there were $1.7 billion

remrns total, and an estimated $260 million (16%) in losses incurred due to Eutypa

dieback (Siebert, 2000)-

Disease cycle
The infection process

Ascospores, the infectious propagules of E. lata, infect grapevines through
Pruning wound. The ascospores germinate in the xylem and the mycelium grows through
pits or, using cell wall-degrading enzymes, the cell walls. Hyphae are found in vessel
lulhens, ﬁber tracheids, and ray parenchyma cells; in advanced stages of infection they
are found to penetrate the secondary walls. They also grow into ligniﬁed tissues, causing
a soft rot of the wood. Gum plugs are evident in the discolored tracheids. The canker is
obServable when the mycelium reaches the cambium and bark (English, et al., 1978).
The canker girdles the area around the wound on the vine, eventually killing the vine
tissue above (Carter, 1957, 1991; English, et al., 1978; Munkvold, et al., 1994).

31

Ascospores inoculated onto apricot pruning wounds were found at a depth of 300
um to 4.5 mm within the xylem vessels after 21 hours. Most ascospores germinated
within the 21 hours, and germ tubes had grown twice the length of the ascospores in the
xylem vessels (Carter, 1960). Ascospores differentially applied to sapwood — the
functional outer region — and heartwood — the non-living inner region — on apricots
showed that infection occurred only in the sapwood; heartwood did appear stained after
infection but had been infected through the sapwood (Ramos, et al., 1975). Inoculation
experiments performed on apricot pruning wounds showed that inoculation of the xylem
produced the most infections; inoculation onto cambium tissue resulted in some infection
and little canker formation, and inoculation of phloem/inner bark resulted in no infection
(Ramos, et al., 1975). Infection by E. lata ascospores caused staining of all layers of
wood, which became brittle, an indication of soft rot (English, et al., 1978). Originally
the soft rot was attributed to a secondary organism in apricot, but it was found that E. lata
induced soft rot in the xylem of sterile branch segments of apricot and birch (Betula sp.)
inoculated with E. lata ascospores (English, et al., 1978). Other indications that E. lata
causes soft rot include observations of weight loss of inoculated wood, and cavities in the
secondary walls of ﬁber tracheids formed by enzymatic activity of xylanase and cellulase
detected in vitro (English, et al., 1978). The fungus either died or became inactive in all
or part of the infected or stained tissues, evidenced by non-recovery of the fungus from
these areas (English, et al., 1978). Because the pathogen invades the heartwood of the
host and other tissues only after it infects sapwood, its death may be due to unfavorable

conditions within the host, rather than any antimicrobial compounds produced by the host

(English, et al., 1978).

32

 

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Sporulation and overseasom'ng

Stromatic tissue produced by the fungus is made up of brown hyphae and
pseudoparenchyma (Carter, and Talbot, 1974). Stromata are found on trunks or cordons
and dead wood left on the vineyard ﬂoor (Moller, Braun, et al., 1977). Perithecia were
produced on limbs of apricot at least 6 years after inoculation in a study in Australia
(Carter, 1957). A study of natural infections showed that mature perithecia can develop
on apricot limbs after as few as three winters with a 625 mm mean annual rainfall from
the time the limb shows symptoms (Carter, 1960). Mature stromata generated productive
perithecia for at least 5 years after apricot branches were removed from the tree (Carter,
1957; Moller, et al., 1965). Perithecia of different ages are visible in the stroma,
suggesting that they are produced annually; however, perithecia may cease production of
viable ascospores after a certain number of years. The stroma increases in size each year
and it is unknown whether the perithecia are produced continuously throughout the

season (Moller, et al., 1965).

Role of conidia and mycelium in infection

The conidia of Libertella blepharis, the anamorph of Eutypa lata, are not
considered to be infectious on grapevine; however, the mycelium grown from ascospores
is infectious (Carter, 1957; Moller, et al., 1965; Moller, et al., 1978; Moller, et al., 1968;

Uycmoto, et al., 1976). Under laboratory conditions - 2% potato dextrose agar with 5

33

g/L yea
isolate ;

conidia

g/L yeast extract under 12 hours light/darkness at 20°C — conidia from a single E. lata
isolate germinated at a very low rate (0.0015%) after two to four days, and all germinated
conidia formed similar cultures to those produced by germinated ascospores (Ju, et al.,
1991). The function of the conidia of L. blepharis, as well as many other Diatrypaceae
family anamorphs, remains unclear (Ju, et al., 1991). Inoculation experiments in the ﬁeld
and greenhouse, have been performed with mycelium from single spore cultures of
ascospores or suspensions of ascospores, but there are no known inoculation experiments

that have used only conidia of L. blepharis (Carter, 1957; Ju, et al., 1991; Moller, et al.,

‘7

1978, 1979; Trese, et al., 1980). The conidia may function as spermatia, as evidenced by
their elongated shape, single nucleus, and low germination rate (Alexopoulos, etal.,
1996; Ju, et al., 1991). Often when Eutypa dieback is found, the asexual state is rarely
looked for. In arid areas where perithecia, the sexual state, are found less frequently, the
conidia may play an important role in spreading the fungus, even if only by occasional

rain splash or pruning methods (Ju, et al., 1991).

Artiﬁcial inoculation of apricot and grapevine wood with E. lata is commonly
done with mycelial agar plugs. It is possible that by pruning infected wood, chips from
that wood are carried to the next pruning site and the disease is spread with mycelium
(English, et al., 1978; Moller, et al., 1978; Peros, et al., 1994). Inoculation of unrooted
grapevine cuttings by inserting agar plugs of mycelium below buds has been used
successfully to assess pathogenicity of isolates of E. lata and differential susceptibility of
cultivars (Peros, et al., 1994). Because there are many wounds created by pruning a

grapevine, infection by mycelium through contaminated pruning equipment may be an

34

important way the disease is spread in arid areas, such as California, where there is little

evidence of the perithecia] stage (Carter, 1991; English, et al., 1978).

35

1

Epidemiology
Environmental conditions favoring ascospore release

Trapping studies of E. lata ascospores, the infective propagule, in Michigan, New
York, California, and southern Australia have found that ascospores are released from
mature perithecia only after rain events of as little as 2 mm when temperatures are above
0°C (Table 1.1) (Carter, 1991; Moller, et al., 1965; Pearson, 1980; Ramos, et al., 1975;
Trese, et al., 1980). Ascospore release is greatly reduced in the winter months in South
Australia, but the lowest winter temperatures were not reported (Carter, 1957; Moller, et
al., 1965). In the absence of rain, snowmelt in New York state has also been shown to
trigger ascospore release provided the temperatures are above freezing (Pearson, 1980).
Perithecia need to be thoroughly wetted to release ascospores, and according to trapping
and laboratory studies, maximum ascospore release occurs 0.5-3 hours after a rain event,
depending on the initial moisture content of the stroma. The later the rainfall occurred
during the day, the longer the stroma remained wet enough for ascospore release (Carter,
1991; Moller, et al., 1965; Pearson, 1980). Ascospore release continued for the duration
of stroma wetness, sometimes lasting for months (Moller, et al., 1965; Pearson, 1980). In
Michigan, relative humidity, solar radiation and wind speed did not play a signiﬁcant role
in determining ascospore release, but the maturity of the perithecia did (Trese, et al.,
1980). Ascospore release may be affected by differences in relative humidity but not
necessarily by temperature or light (Pearson, 1980). In the New York study, perithecia in
wood were held under constant temperature and light in the laboratory for a year; the

relative humidity ﬂuctuations were similar to those in the ﬁeld. The ascospore release

36

 

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ﬂuctuation patterns under these conditions followed those found in the ﬁeld; this suggests
that relative humidity may play a role in ascospore release (Pearson, 1980). A seasonal
cycle of ascospore release was observed in a Australian study. Ascospore counts from
perithecia on grapevine trunk pieces placed outside showed a pattern of lower release
concentration that coincided with winter, even though there were sufﬁcient rains to
moisten perithecia (Moller, et al., 1965). It is unknown if and how the seasonal nature of
ascospore release is related to effects of relative humidity, day-length, solar radiation, or
combinations thereof, but it is known that moisture plays an important role and that

perithecia are produced and rejuvenated over many years (Carter, 1991).

When perithecia were wetted, ascospores discharged readily through the ostioles
in groups of eight, held together by a mucilaginous material. The spore discharge
commenced after 5-10 minutes and continued for two or more hours (Carter, 1957; Trese,
et al., 1980). After landing on a surface, the octad was separated easily in moistened air

allowing the ascospores to spread out in a droplet of water (Carter, 1957).

Knowing the times of the year and under what ﬁeld conditions there is high
ascospore release may predict the likelihood of a pruning wound infection (Table 1.1).
Results of spore trapping from Michigan and New York indicate that dry perithecia and
freezing temperatures decrease the number of ascospores released (Pearson, 1980; Trese,
et al., 1980). Trapping studies in Michigan showed that ascospore release was greatest
throughout the spring and in the fall (T rese, et al., 1980). In New York, ascospore release
was greatest from late fall to late spring (Pearson, 1980). In Michigan and New York,

ascospore release declined in the summer, but any release corresponded with rain events

37

absence
“ELK due
counts t
latent llI
Bordtau
eKhauste
New Y0]
PeﬁOdlc

Peabon

 

 

(Pearson, 1980; Trese, et al., 1980). In Michigan, no ascospores were trapped from
December to February during the year of that study because no rain fell and temperatures
did not exceed 0°C (T rese, et al., 1980). Ascospore trapping studies in California and
Australia also indicate the importance of seasonal rainfall. In California, the greatest
ascospore release occurred in the spring and fall, and in Australia in the spring, summer,
and early fall (Carter, 1991; Ramos, 1975). Ascospore release declined in the late fall
through winter in California and in the winter in Australia (Carter, 1991; Ramos, 1975).
The lack of any ascospore release in the summer in California was due to the complete
absence of rainfall, and the severe decline of ascospore release in Australia in the winter
was due to sparse rainfall (Carter, 1957, 1991; Ramos, 1975). Any decline in ascospore
counts with favorable moisture and temperature conditions for release may be due to a
latent time during which perithecia rejuvenate (Moller, et al., 1965). A study in the
Bordeaux region in France indicated that it takes approximately 12 days for fully
exhausted perithecia to begin releasing ascospores again (Carter, 1991). In Michigan and
New York perithecia may be rejuvenating during the warm summer months when there is
periodic rainfall but a decline in ascospore release (Carter, 1991; Moller, et al., 1965;

Pearson, 1980; Trese, et al., 1980).

38

 

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Dissemination of the pathogen

In Australia and in California, stromata of Eutypa lata are found in corresponding
proportion to the relative amount of yearly rainfall. In Australia, in districts where
perithecia on apricot trees were rare, rainfall was <275 mm and disease incidence was
low (18%). Where perithecia were plentiful, rainfall was >325 mm and disease incidence
was higher (SO-85%) (Carter, 1957). In California, no stromata were found in infected
vineyards that received <250 mm rainfall per year”, however stromata with perithecia
were found in areas where rainfall exceeded 300 mm per year (Ramos, 1975).
Conversely, surveys of apricot in California have shown that perithecia are also found in
arid areas of California where sparse rainfall does not support its formation (Carter, 1957;
Moller, et al., 1966; Ramos, 1975). Previously, any occurrence of the perfect state in arid
areas had been attributed to irrigation and possible shading, but its dissemination to these
areas may also be due to spores blowing in on storms from less arid areas (Petzoldt, et al.,
1983; Ramos, 1975). In California, spore trapping studies using young apricot trees set in
different orchards that followed storm patterns from wet areas to arid areas 50-60 km
away found that ascospores produced from storms in wet areas were the likely source of
infection in dry areas (Petzoldt, et al., 1983). RAPD markers of isolates from two French
vineyards that were 390 km apart and had different cultural and climatic conditions were
analyzed to determine the genetic structure of their populations. The RAPD markers
showed that there was gene ﬂow between the two populations, which suggested that
ascospores traveled. The distance the ascospores traveled may be much shorter than 390

km; the authors suggested that ascospores traveled shorter distances to other vineyards

and host species present between the two vineyards may have been inoculum sources

(Peros, et al., 1998; Ramos, 1975).

The presence of perithecia of Eutypa lata in or near a vineyard affects the
incidence and distribution of Eutypa dieback. In vineyards in which the perfect state was
not found, disease incidence was the lowest and the distribution pattern of infected vines
was random (Munkvold, Duthie, et al., 1993). The random distribution was attributed to
infection by windblown ascospores from areas where the perfect state was present. In
vineyards with ascospore inoculum, the disease incidence was the highest and the
distribution pattern of infected vines was clustered. However, there was no evidence that
the disease spread from vine to vine without the perfect state in the vineyard. One
vineyard that had no inoculum showed an edge effect; however, its nonrandom pattern of
disease incidence was attributed to a nearby vineyard in which inoculum was found

(Munkvold, Duthie, et al., 1993).

Ascospore germination

Ascospores of E. lata germinate under a wide range of temperature and moisture
conditions, though there is an optimum combination of both factors. In a laboratory
experiment, ascospores germination was studied at 25°C over a range of relative
humidities (30-100%). Ascospores required at least 90% humidity to germinate and did
so within 1.2 days (Carter, 1957). Ascospore viability after drying was measured as
ability to form a germ tube on water agar plates after keeping the ascospores on glass
slides, dry and exposed to the air, for a certain amount of time. Depending on the season

41

the ascospores were released, drying time reduced germination rate. At least 90% of the
ascospores released in the early winter, spring and early summer germinated on agar
plates within 12 hours after drying for 14 hours. After 26 hours drying time, 50% of the
ascospores released in the early winter germinated within 26 hours; after 24 hours drying
time, 47% of the ascospores released in the spring germinated within 21 hours; and after
28 hours drying time, 85% of the ascospores released in the summer germinated within
16 hours (Carter, 1957). Alternate wetting and drying also reduced ascospore
germination. When ascospores in a water suspension on a depression slide was allowed

to dry out for 3 days 3 consecutive times, they ceased to germinate (T rese, et al., 1980).

The optimum temperature for ascospore germination is between 22 and 25°C.
Ascospores took up water and germinated at temperatures as low as 0°C, but did not take
up water or germinate at or above 35°C (Carter, 1957). When the maximum
temperatures were between 20 and 30 C, the majority of the ascospores germinated after
12—16 hours; when the maximum temperature was between 225° and 250°C, the
majority of the ascospores germinated in 11.5 hours. Germination of ascospores kept at
freezing and near-freezing temperatures varied with the duration of time below freezing
(T rese, et al., 1980). Fifty to 53% of the ascospores kept at < 0°C for 28 days
germinated, and 76-81% germinated when kept at <0°C for 1 day. When subjected to
multiple freezing and thawing cycles of three days at —10°C and 6 hours at 10°C,
ascospores were only slightly less likely to survive. After one and ﬁve cycles, there was
93% and 83% ascospore germination, respectively (Trese, et al., 1980). Freezing had an
effect on the mycelial growth after infection as well, but did not stop it. On plates held at

10°C, mycelial growth was slowed by about half when cultures were exposed to —10°C

42

or — 0°C for 1, 3, and 7 days, but not when control cultures were exposed to 0°C of any
duration (T rese, et al., 1980). After being exposed to temperatures between 05° and
20°C, the majority of ascospores germinated over 160 hours later. When they were

moved to room temperature, ascospores germinated within ﬁve hours (Carter, 1957).

Host effects on infection

Wound susceptibility

In order to avoid infection by E. lata, it is important to know when pruning
wounds are most susceptible. The age, moisture content, temperature, and 1i gniﬁcation
of a wound play important roles in how susceptible it is to infection (Chapuis, et al.,
1998; Munkvold, et al., 1995; Ramos, et al., 1975; Trese, et al., 1982). Pruning wounds
were most susceptible to infection within a week of pruning and susceptibility declined as
the time between pruning and inoculation increased (Chapuis, et al., 1998; Gendloff, et
al., 1983a; Petzoldt, et al., 1981). In a study in Michigan, wounds inoculated with 500
ascospores after one day were 37% infected in year one and 14.3% infected in year two.
Wounds inoculated after 14 days were 11.6% infected in year one and 12.2% in year two.
Wound susceptibility decreased signiﬁcantly with time one year, and though in the other
year the differences were not signiﬁcant, a similar trend was evident (Gendloff, et al.,

1983a).

Grapevine and apricot wounds have been found less susceptible to infection the

later in the season they are pruned (Chapuis, et al., 1998; Moller, et al., 1979; Munkvold,

43

et al., 1995; Petzoldt, et al., 1981; Ramos, et al., 1975). Wounds remained susceptible to
infection 6-7 weeks after pruning in December, 4-6 weeks after pruning in January, and
3-4 weeks after pruning in February (Chapuis, et al., 1998). Regardless of the amount of
ascospores inoculated onto pruning wounds, pruning in March and inoculating 1, 7, l4
and 21 days after pruning resulted in <10% infection rate (Petzoldt, et al., 1981).
Inoculated with 100 and 10 ascospores 1 day after pruning in February resulted in an
infection rate of 70% . Inoculated with 103 ascospores 1, 7 and 14 days after pruning in
December, wounds were the most susceptible, resulting in infection rates of 80%, 80%,
and 65%, respectively. In France and California, vines pruned in December developed
signiﬁcantly more infections compared to those pruned in January and February,
regardless of how many days after pruning the inoculations took place; this was true for

all grape cultivars used (Chapuis, et al., 1998; Munkvold, et al., 1995).

Temperature and moisture at the wound site affect wound susceptibility (Chapuis,
et al., 1998; Munkvold, et al., 1995). Moisture in the form of exudate from wounds made
in the spring may play a role in protecting wounds from infection, regardless of the
amount of ascospores present on the wound. Later pruning dates resulted in a higher
level of exudates from wounds that became the sites of more epiphytic organisms and
less infection by E. lata (Munkvold, et al., 1995). Apricot wounds were less susceptible
to infection at 20°C than at 3°C when inoculated with 100 ascospores per wound and
constant moisture was monitored by polyethylene wrap (Ramos, et al., 1975). Vines
accumulate suberin and lignin when pruned as measured by lignin-thioglycolic acid
accumulation (LGTA) levels and these levels are known to be temperature dependent

(Munkvold, et al., 1995). The LGTA levels correlated positively with degree-day

44

accumulation. When wounds had low levels of LGTA, the susceptibility of wounds
declined more slowly after pruning, if at all. This suggests that it is better to prune after a
certain degree-day accumulation rather than on a certain calendar date. Even though
1000 E. lata ascospores were used to inoculate wounds, the exudates and resulting

epiphytic organisms appeared to block infection (Munkvold, et al., 1995).

Wood age and wound size

Tree cankers were observed on older vines and it appeared that pruning wounds
on older wood are more susceptible to infections than on l-year-old wood (Carter, 1991).
In two studies, in California and Michigan, there was either no signiﬁcant difference in
infection rates of wounds on l-, 2- and 3-year old wood, or wounds on older wood
showed a lower infection rate (Munkvold, et al., 1995; Trese, et al., 1982). There was no
difference between infection rates of wounds that ranged from 6-11 mm in diameter on
‘Grenache’ vines (Petzoldt, et al., 1981). The number of pruning wounds, not wound
size, may affect the chance of infection, but whether the number of pruning wounds per

vine affects the chance of infection is unknown.

Vineyard age

Eutypa dieback symptoms are not often observed until vines are approximately 8
years old, due to a 1-3 year incubation time after infection (Carter, 1991; Duthie, et al.,

1991). The number of symptomatic vines and the number of symptomatic spurs per vine

45

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mt

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increased sigmoidally with time in a ‘French Columbard’ vineyard in California (Duthie,
et al., 1991). In this vineyard, most old vines showed Eutypa dieback symptoms, and
>50% of their spurs were symptomatic (Duthie, et al., 1991). Vineyards of trunks with
diameters over 45 mm had a signiﬁcantly increased incidence of Eutypa dieback with the
highest disease incidence (76%) observed in a vineyards with trunk diameters of >51
mm (Johnson, 1987). Higher incidence of infection of larger trunk diameters may be a
function of vine age rather than the trunk’s susceptibility to infection; the larger the trunk,

the older the vine, thus the more seasons of ascospore exposure it has had.
Environmental effects on infection
Temperature and moisture

High and low temperatures have been correlated with reduced infection by E. lata
in grapes (Munkvold, et al., 1995; Trese, et al., 1982). In California, higher temperatures,
in addition to later pruning, have shown increased growth of epiphytic microorganisms
on pruning wounds. The increased growth of epiphytes may also be due to the higher
nutrient content of exudate from wounds pruned later in the season. In the same
California study, degree—day accumulation was also signiﬁcantly correlated with lower
infection rates (Munkvold, et al., 1995). In another study in California, an increase in the
daily mean temperature for two weeks after inoculation was correlated with fewer
infections (Chapuis, et al., 1998). In Michigan, however, less infection resulted when
pruning was performed during 2-week periods of temperatures below 5°C (Trese, et al.,
1982). Humidity may also play an important role with temperature in how long the

ascospores remain viable in the xylem. In controlled laboratory studies, viability was

46

 

 

retained when ascospores were exposed to low temperatures (ranging from -20 to 0°C), or
alternate freezing (~10°C) and thawing (10°C), but when ascospores were subjected to
alternate wetting and drying at 24°C, there was an adverse affect on germination (T rese,
et al., 1980). These studies suggest that low temperature and humidity together may
prevent ascospore germination, and high temperature and humidity may encourage
growth of competing microorganisms. Pruning wounds in apricot healed faster and
became more resistant when they were covered with polyethylene ﬁlm to retain moisture
prior to inoculation (Ramos, et al., 1975). When trees were held at low temperatures
(3°C) and wounds were uncovered, there was 100% infection resulting from each
inoculation date (0, l4, and 28 days) after pruning. When trees were held at 20°C and the

wounds kept moist, infection rates were 95, 65, and 40% infection, respectively (Ramos,

et al., 1975).
Pathogen effects on infection
Infection eﬁiciency

The infection efﬁciency of ascospores appears to depend more on the
physiological status of the grapevines at the time of pruning than on the amount of
ascospores used. Studies on pruning wounds of one-year-old apricot trees show that
infection rates did not differ between 1, 10, and 1000 ascospores inoculated in January.
However, infection rates were signiﬁcantly lower in the single ascospore inoculation
treatment than the 10- and 1000- ascospore treatments when inoculated in February
(Ramos, et al., 1975). In a study of grapevines, pruning wounds were made in December,

47

 

February, and March, and inoculated at weekly intervals from the day after pruning with
either 100 or 1,000 ascospores per wound. Infection rates were not signiﬁcantly affected
by ascospore concentration for the December and March pruning dates, but were
signiﬁcantly higher with 1000 ascospores/wound when pruning was performed in

February (Petzoldt, et al., 1981).

Virulence

Isolates of Eutypa lata have been found to differ in aggressiveness or virulence on
apricot and grape, and pathotypes have been proposed (Carter, 1991; Peros, et al., 1994;
Peros, et al., 1999; Ramos, et al., 1975). Virulence of an isolate was not related to its
cultural traits or radial growth rate (Peros, et al., 1997). Pathogenicity and virulence were
not linked to any speciﬁc molecular pattern (using RAPDs) (Peros, et al., 1996). Tests on
apricot showed isolates from an area in California were signiﬁcantly more virulent than
an isolate from Australia, as measured by the length of canker and infection frequency
(Ramos, et al., 1975). Isolates of E. lata show variation in virulence as shown by a
correlation between virulence on plantlets and the concentration of toxin in the ﬁltrates;
each isolate may affect an individual vine of a speciﬁc cultivar differently (Mauro, et al.,

1988).

48

 

 

Cultural effects on infection

Pruning and training

Eutypa lata ascospores infect through pruning wounds that are an integral part of
viticulture. Fruit production in grapevines is regulated by careful consideration to the
pruning technique, and thus pruning of grapevines is unavoidable (Carter, 1991; Howell,
2001). Recently, grapevine pruning recommendations to avoid infection by E. lata
ascospores has centered on late-season pruning in Australia and France (Carter, 1991). In
the early 1960’s in Australia, it was recommended that apricot orchardists allow lateral
branches to grow a “safe distance” away for one year. In the second year the fruited
laterals are pruned such that the infected wounds can be pruned out without damage to
the trunk (Carter, 1991). This technique was presumably responsible for the prevention
of spread of Eutypa dieback in Australia, but there was no formal study conducted.
Another strategy for preventing infection may be ridding the vineyard of the cankerous
tissue that contains inoculum. Though there are no studies on grapevine, a study on
apricots in California compared the number of cankers observed after removal of dead
and infected branches in August or October. Both removal treatments were inoculated
with 100 ascospores per wound after the branches were removed in October. Two years
after the treatments were applied, the trees treated in October showed that 45% of the
wounding sites had developed cankers while the trees treated in August showed that 10%

of the wounding sites had developed cankers (Moller, Ramos, et al., 1977).

49

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Disease control
Host plant resistance

In Michigan, as in other grape-growing regions, cultivars are primarily chosen for
the qualities and ﬂavors of the juice and wine produced (Howell, et al., 1998). Also,
grape cultivars are chosen for desirable growth habits, and sufﬁcient cold hardiness and
productivity in the region in which they are to be grown (Howell, et al., 1998). Planting a
speciﬁc grape cultivar for its disease resistance may not be the primary concern for
growers, especially when a disease like Eutypa dieback is not seen on the fruit and does
not reduce the canopy for at least seven years after planting. Also, replacing an entire
vineyard of a cultivar that is producing marketable juice and/or wines is an expensive
undertaking, due to the cost of removal and replanting as well as a 4- to 5-year hiatus
from fruit production. Pruning out symptomatic cordons, roguing severely symptomatic
vines, and vine layerage are the most common disease management strategy

recommended by researchers and employed by growers.

Of the Michigan cultivars grown for wine production, many are susceptible to
infection by Eutypa lata (Table 1.2). Highly susceptible cultivars include Cabernet
Sauvignon, Concord, and Maréchal Foch. Susceptible cultivars are Chardonnay,
Gewiirztraminer, Pinot noir, Vignoles, and White Reisling. Moderately susceptible
cultivars are Cabernet franc, Catawba, Cayuga white, Chambourcin, Chancellor, Niagara,
and Seyval (Carter, 1991; Mauro, et al., 1988; Weigle, et al., 1997). ‘Merlot’ has been
rated resistant and susceptible in different locations (Carter, 1991; Gut, et al., 2001). The

susceptibility of a particular cultivar does not correspond with its breeding heritage or

50

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origin; Concord, a Vitis labrusca cultivar, is highly susceptible as is Cabernet Sauvignon,
a Vitis Vinifera, and the resistant hybrid, Maréchal Foch (Carter, 1991; Gut, et al., 2001).
Concord, a susceptible cultivar, has been grown in Michigan for a long time for
production of grape juice and jellies. Concord vineyards often are planted in response to
the prevailing prices for the product (Mark Longstroth, personal communication). As the
market for Michigan wines increases and more vineyards are planted to susceptible
grapes, management of vines to prevent infection by E. lata will become increasingly
important in order to protect the life of vines. A susceptible cultivar may simply show
symptoms sooner or more readily than resistant cultivars, and thus appear more
susceptible to infection by E. lata ascospores (Carter, 1991). In a study that compared a
susceptible cultivar (Cabernet Sauvignon) with a resistant cultivar (Merlot), the rates of
infection or duration of wound susceptibility were not signiﬁcantly different between the
cultivars, however the symptoms in the ﬁeld were different (Chapuis, et al., 1998).
Susceptibility to Eutypa dieback may be related more to a cultivar’s sensitivity to the
toxin produced by the fungus once it has colonized the wood rather than the pathogen’s
ability to infect the vine (Carter, 1991; Munkvold, et al., 1995). Merlot and Semillon are
the only cultivars that have shown ﬁeld resistance to Eutypa dieback, but in bioassays
that subjected cuttings from plantlets to E. lata ﬁltrate, both cultivars showed
susceptibility (Carter, 1991; Mauro, et al., 1988). In a particular vineyard in France,
‘Merlot’ had been observed with Eutypa dieback symptoms since 1988, even though it
was considered resistant (Tey-rulh, et al., 1991). Differences in susceptibility may allow
growers to select cultivars that are resistant or tolerant where possible. If susceptible

cultivars are chosen, more stringent disease control may be necessary.

51

In addition to traditional techniques of breeding and selection for disease
resistance, genetic transformation of grapevine has been used to confer resistance to viral
pathogens (grapevine fanleaf virus, grapevine leafroll associated closteroviruses),
bacterial pathogens (crown gall), and fungal pathogens (powdery mildew, downy
mildew, Eutypa dieback) (Colova-Tsolova, et al., 2001; Grzegorczyk, et al., 1998). In
parts of Europe where speciﬁc grape cultivars have high economic and cultural
importance, screening for grapevines’ resistance to the toxin produced by E. lata has
been a component of breeding and selection (Colova-Tsolova, et al., 2001; Teyrulh, et al.,
1991). The mechanism of resistance to Eutypa dieback is thought to be the grapevine’s
ability to detoxify or impede the travel of the toxin that causes symptoms (Colova-
Tsolova, et al., 2001; Colrat, Deswarte, et al., 1999; Mauro, et al., 1988). The delay in
symptoms development in transformed vines is attributed to phytoalexin production

(Colova-Tsolova, et al., 2001).

52

 

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53

Cultural control

Vineyard sanitation, such as roguing and either burying or burning the diseased
vines, is used to prevent the spread of inoculum from vines killed by Eutypa dieback. It
is recommended to cut out cankerous or discolored wood on the affected cane or cordon
of the trunk at least 10 cm below the canker and retrain the rest of the vine. Sometimes,
the entire vine may need to be removed, burned, and replaced. Layering from a nearby
vine is recommended, provided it too is not infected (Pearson, et al., 1981; Pearson,
1988; Weigle, et al., 1997). Regular scouting for symptoms when shoots are
approximately 30 cm long in the spring will help determine which vines are infected.
Symptomatic vines should be marked and revisited each year since symptoms do no
appear consistently each year. Vineyard management is a local solution to limiting the
inoculum source, but ascospores are able to travel long distances on winds from other
vineyards or from alternate hosts of E. lata from gardens and from woods (Carter 1991).
Vineyard management must be performed in tandem with chemical or biological control

methods to prevent the spread of the disease (Pearson, et al., 1981; Pearson, 1988).

Pruning practices are also important to consider when controlling Eutypa dieback
infection. Vines that are mechanically hedged in the northeastern United States have
many more pruning wounds than hand-pruned vineyards in California, which may
increase the risk of infection (Ramsdell, 1995). A study in California showed
unacceptably high infection rates even when vines were pruned to coincide with periods
of low inoculum levels (Table 1.1). This would entail summer pruning when the vines

are still bearing leaves in California and Michigan, or winter pruning in freezing

54

T

temperatures without rain or snow (Carter, 1991; Trese, et al., 1980). Pruning in early
spring, closer to bud break, has been effective in preventing infection by E. lata
ascospores in Australia, France, Switzerland, California and Michigan, but this also may
not be feasible when holdings are large (Carter, 1991; Petzoldt, et al., 1981; Trese, et al.,
1982). In large vineyards where pruning is performed by hand, Eutypa dieback
prevention and management is very labor-intensive. Another strategy for reducing the
effect of Eutypa dieback on vine longevity in the eastern United States has been to train
vines with a double trunk system (Pearson, et al., 1981; Ramsdell, 1994). If one trunk
becomes infected and the other does not, the infected trunk can be removed without
sacriﬁcing the entire vine. Regardless of the method used to prevent infection, removal
from the vineyard or burning of dead or infected wood pruned below visible staining are

good practices that prevent buildup of inoculum in a vineyard (Ramsdell, 1995).

Chemical control

Early attempts to control Eutypa lata infection of apricot pruning wounds on 1-
year old wood by chemical means were not successful (Carter, 1960). At least one half
of all the wounds treated with Bordeaux mixture (15: 10: 100) or copper oxinate (0.1%)
followed by inoculation with ascospores became infected, but an equal percent of
uninoculated wounds became infected. In another study, from 67% to 100% of
inoculated wounds treated with 0.2% Thiram, Ziram, or Ferbam became infected, while

60% of the untreated wounds became infected (Carter, 1960).

55

 

Benomyl has been shown since the late 1960’s to prevent infection by E. lata
ascospores of apricot and grapevine wounds when painted onto wounds or applied with
an air blast sprayer (Carter, 1991; Pearson, 1982; Ramsdell, 1995). The key to applying
benomyl is with high volume, rather than high concentration; benomyl is taken up into
the xylem but does not thoroughly saturate it unless at least 0.1 ml of water per cm2 is
applied. Benomyl was effective when applied at a rate of 1 1b a.i.lacre in an air blast

sprayer delivering 30 gal/acre (Ramsdell, 1995).

In poison agar tests, the EC50 (the concentration required to reduce radial growth

or germination by 50%) values of benomyl and ﬂusilazole were signiﬁcantly lower than
ﬁve other fungicides (vinclozolin, iprodione, triadimefon, fenarimol, and myclobutanil)
(Munkvold, and Marois, 1993a). Benomyl did not prevent ascospore germination even at
the highest concentration used (10 uglml), but germinated ascospores did not grow into
colonies (Munkvold, and Marois, 1993a). In upstate New York, wounds on ‘Concord,’

‘Catawba,’ and ‘Aurore’ grapevines were hand painted with benomyl (10 mg/ml) and

inoculated one day later with 2 x 104 E. lata ascospores. Eutypa lata was recovered the
autumn following treatment application from 92, 100, and 75% of the pruning wounds to
‘Concord,’ ‘Catawba,’ ‘Aurore,’ respectively (Pearson, 1982). When hand painting
treatments onto wounds, it is recommended to concentrate protection on wounds made in
wood older than one year, because even though 1-year old wood is as susceptible to
infection as the older wood, it would most likely be removed the next year (Pearson,
1982). Pneumatic sprayer-pruners that apply fungicide as the cut is made were not
signiﬁcantly better in preventing infection than hand-painting the fungicide on pruning
wounds. All the fungicides tested (benomyl, iprodione, tn'ademefon, vinclozolin,

56

myclobutanil, fenarimol, and flusilazole) resulted in signiﬁcant reductions in disease with
either application method. The pneumatic sprayer-pruner may save time for cuts <3 cm
in diameter, though reapplication of the fungicide by other means may be necessary after
making larger pruning cuts. Any cuts made without the pneumatic sprayer-pruner (e. g.
>3 cm) that require separate chemical application may be timed to coincide with times
that wounds are less susceptible to infection by E. lata (Munkvold, and Marois, 1993a).
When there are more than 50 wounds per vine, as are made in Michigan, hand painting
pruning wounds can be prohibitive (Ramsdell, 1995). Benomyl was signiﬁcantly
effective in preventing infection when applied to pruning wounds at 1.2 and 4.8 g/L and
wounds were inoculated with 500 E. lata ascospores on pruning day. Benomyl was
effective when applied at 4.8 g/L when wounds were inoculated 14 days after pruning
(Gendloff, et al., 1983a). Five-year studies with benomyl (1 lb a.i./acre) applied with an
air-blast sprayer showed that naturally occurring E. lata infection was reduced by 48.5
and 34% when vines were pruned in mid-January to mid-February (mid winter) and in
mid-February to mid-April, respectively (1ate-winter/early-spring), but not when pruned

in early December (Ramsdell, 1995).

In August of 2002, benomyl was withdrawn by the manufacturer. The EPA
proposes that growers will not be able to apply benomyl by the end of 2003, and
tolerances of benomyl on residues will be cancelled by the end of the growing season in
2008 at the latest (Thornton, 2002). Topsin M may serve as a replacement of benomyl

and received a registration for grapes in 2002 (Wise, et al., 2002).

57

.T .

Biological control

In order to compete effectively with Eutypa lata ascospores at the infection court,
a potential biological control agent (BCA) must not be pathogenic to the host, be able to
rapidly colonize the xylem vessels of the host at temperatures lower than 0°C, or produce
a substance inhibitory or toxic to E. lata ascospores or mycelium (Carter, 1971).
Numerous fungi and bacteria have been screened for their potential efﬁcacy at controlling
infection of E. lata ascospores. Those that have been the most promising include
Fusarium lateritium Nees: Fr., Bacillus subtilis, Cladosporium herbarum (Pers.:Fr.)
Link, Erwinia herbicola, and an unidentiﬁed actinomycete (Carter, 1971; Farr, et al.,
1989; Ferreira, et al., 1991; Munkvold, and Marois, 1993b; Schmidt, et al., 2001). The
efﬁcacy of benomyl-resistant isolates of F usarium lateritium have also been investigated
(Carter, et al., 1975; McMahan, et al., 2001). A BCA that takes time to become effective
may be used with a chemical it is resistant to in order to prevent germination of the
pathogen and give the BCA time to build up its population (Carter, and Price, 1974).
F usarium lateritium macroconida, from an isolate originally collected from apricot wood,
germinated faster and produced sporulating colonies faster than B. lata ascospores did on
agar media at room temperature and at or below 10°C (Carter, 1971). Fusarium
lateritium was present on 93% of the wounds of apricot that were sprayed with 104 spores
per ml and then inoculated a day later with 10 E. lata ascospores per milliliter. F usarium
lateritium was recovered from 96% and E. lata was recovered from 50% of wounds when
they were co-inoculated. Fusarium lateritium and E. lata were recovered from >50% of
pruning wounds when only E. lata ascospores were applied to wounds, which indicated a

naturally occurring colonization of F. lateritium (Carter, 1971).

58

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An isolate of the bacterium Bacillus subtilis showed inhibition of Eutypa lata
mycelial growth in vitro (Ferreira, et al., 1991). Eutypa lata ascospores did not germinate
when exposed to B. subtilis cells or 0.8 mg/ml of the cell free extract. Mycelial growth
was also inhibited by 88% by B. subtilis cells (Ferreira, et al., 1991). In a recent study,
188 bacterial isolates were screened in vitro for antagonism against E. lata mycelium on
four different agar media (Schmidt, et al., 2001). Of those that were antagonistic, 76
isolates were screened against E. lata on autoclaved grape wood. One Bacillus subtilis
strain, two Erwinia herbicola strains and one unidentiﬁed actinomycete were found to
inhibit growth of E. lata in grape wood by 70-100%. Both isolates of E. herbicola were
fungicidal, and the B. subtilis isolate inhibited E. lata growth into the wood for 28 days.
The ﬁltrate of E. herbicola isolates and the actinomycete also inhibited E. lata growth for
the same amount of time. While the B. subtilis isolate retarded growth, it only did so for
one week. The supernatant of E. herbicola and actinomycete isolates, and the cells of B.

subtilus were responsible for controlling E. lata growth (Schmidt, et al., 2001).

Biological control agents have been shown, under certain conditions, to be as
effective as fungicides in preventing infection by E. lata ascospores in the ﬁeld (Carter, et
al., 1975; Munkvold, and Marois, 1993b). Under natural infection conditions, pruning
wounds on apricot were treated within one hour of pruning with benomyl (125-ppm),
benomyl and F. lateritium macroconidia (lO’lml), and F. lateritium macroconidia alone.
The three treatments were equally effective at preventing infection by E. lata ascospores;
25% of the pruning wounds with no control became infected, and 0-9% of the pruning
sites with either of the three treatments became infected (Carter, et al., 1975). In another

study, F. lateritium and Cladosporium herbarum were as effective as benomyl in

59

the

pro

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reducing infection on ‘Thompson Seedless’ grapes (Munkvold, and Marois, 1993b). In
the same study, ‘Chenin blanc’ pruning wounds were treated with either one BCA (108
propagules/ml) or benomyl (1.25% a.i.) after pruning, and then inoculated with E. lata
ascospores (108/m1) 2- or 14 days afterwards. No BCA was as effective as benomyl
when E. lata was applied 2 days after treatment. However, when wounds were
inoculated 14 days after treatment, F usarium lateritium and Cladosporium herbarum
were as effective as benomyl. In another experiment of the same study, E. lata
ascospores were applied to wounds two days after application of benomyl (1.25% a.i.) or
F. lateritium. The BCA controlled infection better than no preventative application, but
not better than the benomyl (Munkvold, and Marois, 1993b). In a ﬁeld trial in Michigan,
Fusarium lateritium did not prevent infection by E. lata with 500 ascospores. F usarium
lateritium often became established in the wounds along with E. lata, but did not inhibit
its growth. The Californian isolate of F. lateritium used in the Michigan study may have
not been antagonistic to the Michigan isolate of E. lata or required higher temperatures
(Gendloff, et al., 1983a). Though ﬁeld trials have not shown that any of the BCAs are
consistently effective in controlling E. lata infection, they may be effective if adapted to
local conditions, applied generously to colonize the wound fully, and disease pressure is
lower than the artiﬁcially inoculated experiments (Carter, and Price, 1974; Munkvold,
and Marois, 1993b). Eutypa lata ascospores (5 x 103/ml) were applied to pruning
wounds of ‘Reisling’ grapevines 4 hours after one of the following treatments was
applied: benomyl (lOg/L), benomyl and B. subtilis (108 CPU/ml), cells alone, and B.
subtilis extract (2 mg/ml). Infection was low (23%), but only B. subtilis suppressed the

fungus completely in the ﬁrst year. In the second year, B. subtilis, benomyl and B.

 

sub
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of a

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subtilis, and B. subtilis extract signiﬁcantly suppressed the fungus compared to a water
control (Ferreira, et al., 1991). In the presence of 1,000 ug/ml benomyl, a benomyl-
resistant isolate of F. lateritium produced by UV mutagenesis controlled E. lata infection

of autoclaved grapevine segments (McMahan, et al., 2001).

Epilogue

Sustainable control of Eutypa dieback is important because symptoms do not
show for many years after infection, infection by ascospores coincides with pruning, and
economic losses over the life of vineyards can be drastic. Methods to control the spread
of Eutypa dieback will need to combine chemical, biological, and cultural control

strategies to be effective and avoid fungicide resistance development in the pathogen.

61

 

 

 

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CHAPTER 2. SPATIAL ASPECTS OF EUTYPA DIEBACK IN A VINEYARD
Introduction

Southwest Michigan provides excellent grape growing conditions due to the
buffering effect of Lake Michigan which reduces seasonal temperature ﬂuctuation,
especially in winter, that occur inland from the lake (Howell, et al., 1998; Kleweno, et al.,
2001; Morton, 1985). Recently, juice grape vineyards (‘Concord’ and ‘Niagara’) have
been joined by hectares of Vitis Vinifera and resistant hybrids for the wine grape industry
(Kleweno, et al., 2001). Eutypa dieback, a fungal disease affecting juice and wine
grapes, is caused by the release of ascospores by wind and rain (Carter, 1957, 1991).
Three or more years are required for infected vines to produce symptoms and ascospores
(Moller, et al., 1978). Many older ‘Concord’ vineyards in Michigan have at least a few
infected vines (M. Longstroth, personal communication). The history of destruction by
Eutypa dieback in other grape-growing regions and its latent characteristic warrant
research in understanding the spread of this disease (Carter, 1991; Munkvold, et al.,
1994).

In 1976, isolations from grapevine ( ‘Concord’ and ‘DeChaunac’) and test
inoculations onto apricot conﬁrmed Eutypa dieback in New York State. Stromata and
perithecia of the causal fungus were also observed in Ontario vineyards on cultivars
Concord, Delaware, and Chelois (Moller, et al., 1977; Uyemoto, et al., 1976). In 1977, E.
lata stromata and perithecia were conﬁrmed on dead grapevine trunks in Michigan
(T rese, et al., 1980). In surveys of south—central Washington State in 1983 and 1984,
‘Concord’ vineyards with vines with trunk diameters of >51mm had up to 76% incidence

of Eutypa dieback (Johnson, 1987). Disease incidence increased annually in every

62

T

vineyard surveyed in California over the three years of the study (1989—1991) regardless
of the presence or absence of perithecia in the vineyard (Munkvold, et al., 1993).

The three-year study in California showed that the presence of perithecia in a
vineyard infected with Eutypa lata correlates with a clustered or non-random distribution
(Munkvold, et al., 1993). Vines of eight vineyards were rated for Eutypa dieback and
spatial patterns were determined by ordinary runs, two-dimensional distance class, spatial
autocorrelation, and geostatistical analyses. The vineyards with known sources of
inoculum had more non-random patterns of disease, with one exception (Munkvold, et
al., 1993). The vineyards with no known inoculum source nearby showed random
patterns in the distribution of diseased vines, indicating that the ascospores were airborne
from various distant sources (Munkvold, et al., 1993). The incidence of disease in eight
vineyards in the study ranged from 3.4% - 81.5%.

’ The objective of this study was to record the incidence and to determine the
spatial pattern of Eutypa dieback symptoms in the vineyards over several seasons. In
addition, the study sought to determine whether there was a correlation between daily
temperature before and after estimated bud break, and the number of infected vines that

show symptoms in a given year.

63

 

Materials and methods
The vineyards

Two ‘Concord’ vineyards in southwest Michigan exhibiting Eutypa dieback
symptoms were identiﬁed for the study (Figure 2.1). The vineyards were chosen due to
grower observations of Eutypa dieback symptoms, and because the vineyards are located
in the southwestern part of the state, known for its long history of ‘Concord’ grapevine
production. Vineyard A, located 8 km south of Lawton MI (east central Van Buren
County) was studied in 1999, 2000, and 2001. The vines in this vineyard were planted at
a density of approximately 1,349 vines per hectare (2.4 m between vines, 3 m between
rows) at least 30 years before the study began. The vines were originally trained to
Hudson River Umbrella (HRU) and pruned manually. Since three years before the study
began, the vines have been hedged mechanically only. The HRU pruning method
employs a bilateral cordon at the top wire at 1.8 m high. The vines are planted on a 2.4 m
within row spacing and trained to 2 1.2 m bilateral cordons. Hand pruned vines are
pruned to retain an appropriate number of 8-node canes and 2-node renewal spurs based
on vine size (personal communication, G.S. Howell). Pruning and training with the HRU
method allows for mechanical harvesting, because the shoots are distributed horizontally
and vertically down from the trellis wire (Pool, 1980). The 1.2-hectare section of the
vineyard studied consisted of 25 rows; four rows with 56 vines and 21 rows with 74-76
vines. Vineyard B, located 11km east of Schoolcraft, MI (southeastern Van Buren
County), was studied in 2001. The nearly 1.4 hectare section of vineyard B consisted of
47 rows with 39 vines each, and was planted with the same vine density as vineyard A;

its vines were planted in 1973 and have been trained according to the HRU method.

 

After mechanical hedging, vineyard B is annually hand pruned. Vineyard B is regularly
managed primarily in the spring for Eutypa dieback; symptomatic shoots are pruned out,
and diseased vines are removed and burned. In vineyard B, vines are renewed (self-
layered) by choosing a healthy shoot or sucker with no signs of any disease and burying a
section of it so that there is enough length at the growing end to tie it to the trellis. The

shoot is left trained like this for two or three seasons before it is cut from the parent vine.

65

7’

 

 

A
east central
Van Buren Co.

 

 

 

B
southeastern
Van Buren Co.

 

 

 

Figure 2.1. Map of Michigan with the approximate location of vineyard A in east central,
and B in southeastern Van Buren county.

Disease scouting and incidence

When the shoots were about 25-30 cm long, during the first half of May, all vines
in the 25 rows at vineyard A (in 1999, 2000, and 2001) and 39 vines in the 47 rows at
vineyard B (in 2001) were scouted for symptoms of Eutypa dieback (1,799 and 1,833
vines per vineyards A and B, respectively). Stunted shoots with shortened intemodes,
and chlorotic, up-cupped leaves that appeared less expanded than healthy leaves indicated
Eutypa dieback (Figure 1.3). Furthermore, when the up-cupped leaves had expanded, a
ragged bottom edge that had become chlorotic was also visible (Figure 1.4). Often vines
were only symptomatic on one cordon, but vines in advanced decline showed symptoms
on both cordons. Only vines showing typical symptoms of Eutypa dieback - stunted
shoot growth, up-cupped leaves, chlorotic leaf edges — were considered infected.

In 1999 and 2000 in Vineyard A, all symptomatic vines were chosen for study; 18
and 11 vines respectively (Figures 2.2 and 2.3). In the same vineyard in 2001, 62 vines
were observed with typical disease symptoms (Figure 2.4). In vineyard B, in 2001, 11
vines were selected from 41 observed with symptoms (Figure 2.5). Each symptomatic
vine was tagged with a large yellow weatherproof tag in order to be able to track the
vine’s condition each year thereafter. The year that a speciﬁc symptomatic vine was ﬁrst

found infected was noted on the tag.

67

 

 

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70

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71

 

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72

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Spatial analysis

In each vineyard, all the vines were mapped and those exhibiting Eutypa dieback
symptoms were indicated. To investigate the type of spatial pattern among the
symptomatic vines in all years of the study, the quadrat variance technique was applied to
vineyard A and vineyard B. Quadrat variance provides information on the size of the
clusters of diseased vines by using continuous quadrats (rather than randomly scattered
quadrats); a quadrat of one block size — in this case one vine equals block size one — plus
its neighbor is combined to provide information about clusters, or blocks, of more than
one size (Figure 2.6). The variances of each block size are compared, and peaks in this
variance indicate approximate cluster sizes (Campbell, et al., 1990). The mean square
(Mr) is plotted against its block size (r) to obtain the graph. Because both vineyard areas
are rectangular, four areas were chosen to begin the quadrat patterning in order to include
as much of the vineyard in the analysis as possible. Quadrats were assigned from all four
corners of the vineyard; 16 rows of 64 vines were included from each corner in vineyard
A and 32 rows of 32 vines were included from each corner in vineyard B. All four areas
were analyzed in order to compare the results of the means square and the results graphed
by year (Figures 2.7, 2.8, 2.9 and 2.10). Block size 1 represents one vine; block size 2
represents a vine and its neighbor along the row; block size 4 represents two vines and

their neighbors across the row, etc. (Figure 2.6).

73

 

 

 

 

 

 

 

 

 

 

Figure 2.6. Method for assigning block sized used for the quadrat analysis. The smallest
block is one vine.

Correlation between spring temperatures and disease incidence

The effect of temperature on the incidence of infected vines was examined in
Vineyard A. The average daily temperature and rainfall were recorded in vineyard A.
Weekly weather data from ﬁve weeks before the time of bud break and four weeks after
the time of bud break was used to determine if there was a relationship between the
minimlnn, maximum or average temperature and the number of vines that showed Eutypa
dieback symptoms in a given year. Regressions were performed using Statgraphics

statistical software (Statgraphics Plus for Windows, 4.1, Statistical Graphics Corporation,

EnglewOod Cliffs, NJ. 07632 U.S.A.).

74

.7 :-

 

Re

 

 

f0"

H‘

 

 

Results

Disease scouting and incidence

For the three seasons our study was conducted, it is only possible to assume that

all the vines observed as symptomatic were infected prior to 1999. Since it takes at least

 

three years for infected vines to show symptoms, the vines observed in 1999 with
symptoms must have been infected by Eutypa lata during or prior to 1996 (Carter, 1991).
Furthermore, the symptomatic vines observed in 2001 could have been infected in 1999
or before. With that said, most of the symptomatic vines of one year did not exhibit

Eutypa dieback symptoms during the other years of the study.
In the three years of this study, of the 1,799 vines observed in vineyard A, a total

0f 88 different vines were symptomatic in the three years of the study. All symptomatic
Vines ranged in severity depending upon how many shoots were symptomatic at the time
Of Scouting. In the ﬁrst year, 18 symptomatic vines were observed, 11 in the second year,

and 62 in the third year (Figures 2.2, 2.3 and 2.4). The disease incidence for 1999, 2000

and 2001 was 1%, 0.6% and 4%, respectively.

All vines showing symptoms one year did not consistently show symptoms in
folk)Vving two years of the study. Only one vine showed symptoms all three years, and
another one vine showed symptoms in two consecutive years. Six vines showed
symptoms in the ﬁrst and third year of the study only. It is not possible to determine

Wheﬁller any of the vines found symptomatic in any year of the study were or were not

SylnPtomatic before or after the study.
In 1999 and 2000, the typical symptoms of Eutypa dieback were readily identiﬁed

i -
n v1Ileyard A: shortened shoots, up-cupped and ragged-edged leaves, some with

75

chlorotic margins. In 2001, in addition to the 62 symptomatic vines, there were 56 vines
that had symptoms that were suspected to be Eutypa dieback, but did not appear
completely so. All of the vines whose symptoms were not typically that of Eutypa
dieback showed shoots that were slightly stunted, but leaves were cupped downward, and
there was no evidence of raggedness or chlorosis on edges of leaves. In fact, many of the
shoots and leaves appeared normally green. The symptoms observed were more similar

to those caused by Peach Rosette Mosaic Virus (Ramsdell, 1994).

Vineyard B was ﬁrst observed in 2001, and 41 symptomatic vines were identiﬁed

(Figure 2.5). The disease incidence of 2001 for the vines scouted in vineyard B was 2%.

Spatial analysis - vineyard A

In 2001, vineyard A had 1,799 vines and 62 were visibly infected, showing
Symptoms of Eutypa dieback, or a 4% disease incidence (Figure 2.4). There appears to
be a “pattern” or clustering of infected vines between three vines in various places along
I‘(""’8 and between rows. The only visible edge effect is the noticeable lack of disease

nearer to rows of ‘Niagara’ grapevines, planted at the bottom of the map (the western

border of the vineyard) that begins at row 26.
The spatial analysis used quadrats of a total of 64 x 16 vines in vineyard A. Each

quad-Fat analysis began from one of the four corners in the vineyard. In 1999, the
combined graph of the mean square of each block size indicates that there is no variation
bemeen the different block sizes and thus a random pattern (Figure 2.7). This is also the
C333 for the combined graph of the mean square of each block size in 2000 (Figure 2.8).

W mean square values are expected when the disease incidence is very low and the

76

 

 

 

T

symptomatic vines appear to be scattered over such a large area. In 2001, the combined
graph of the mean square of each block size shows many peaks in the mean square versus
block size, though the highest mean square value is still low (<0.20) (Figure 2.9).
Though there are peaks of variation in the mean square vs. block size graph, the overall
low mean square shows that the pattern of infected vines in vineyard A is random. That
is, whether a vine is symptomatic or not is largely independent of any of the others being
symptomatic. There is also a slight downward trend in the graphs of vineyard A that
indicates the decreasing variation with increasing block size; as the number of vines in a
block increases, the variation of symptomatic vines decreases (Figure 2.9). The peaks are
often assessed subjectively in relation to successive analyses as there is no signiﬁcance

test (Campbell, et al., 1990). In this case, all analyses - one from each comer of the

vineyard — were compared.

77

 

 

 

 

 

 

 

 

 

 

 

1 999

0.15

g + INE comer

g 0.1 + NW corner

—I— SE corner

g + SW corner
0.05

1 2 4 8 16 32 64 128 256

block size

Figure 2.7. Mean square versus block size graphs of all of the vantage points from which
quadrat variance was calculated in Vineyard A in 1999; northeast, northwest, southeast
and southwest. Block size 1 corresponds to one vine, block size 4 = 2 x 2 vines, block
size 8 = 4 x 4 vines, etc. Blocks were oriented beginning with the vines along the same
row.

78

1:
d

o
to

 

 

 

 

 

 

 

 

 

 

 

2000

0.15
g + NE comer
§ 0.1 + NW comer
g —.— SE comer
g + SW comer

0.05

o I r l 7 T 'T‘ I I :
1 2 4 8 16 32 64 128 256

block size

Figure 2.8. Mean square versus block size graphs of all of the vantage points from which
quadrat variance was calculated in Vineyard A in 2000; northeast, northwest, southeast
and southwest. Block size 1 corresponds to one vine, block size 4 = 2 x 2 vines, block
size 8 = 4 x 4 vines, etc. Blocks were oriented beginning with the vines along the same
row.

79

 

 

0.2

 

 

 

 

 

 

 

 

 

 

 

 

 

2001

0.15
2 + NE comer
CO
3 + NW
8' 0.1 corner
5 —I- SE corner
2 + SW comer

0.05

O
1 2 4 8 16 32 64 128 256
block size

Figure 2.9. Mean square versus block size graphs of all of the vantage points from which
quadrat variance was calculated in Vineyard A in 2001; northeast, northwest, southeast
and southwest. Block size 1 corresponds to one vine, block size 4 = 2 x 2 vines, block
size 8 = 4 x 4 vines, etc. Blocks were oriented beginning with the vines along the same
row.

80

 

Spatial analysis — vineyard B

The area of vineyard B observed for the study comprised of 1,833 vines and had a
disease incidence of 2% in 2001. In vineyard B, diseased vines were clustered near
wooded areas that were the lowest in elevation in the southeast and southwest corners of
the vineyard. A lower disease incidence was noted in areas farther from the wooded
areas that are also higher in elevation (Figure 2.5).

The spatial analysis used quadrats of a total of 32 x 32 vines in vineyard B. Each
quadrat analysis began from one of the four corners in the vineyard. Quadrat variance
analysis of vineyard B shows in the graph below that in the vineyard areas with higher
disease incidence (southeast and southwest) there is a peak in mean square at block size
256 and block size 512 (Figure 2.10). In the areas of little disease (northwest and
northeast), there is only a small peak at the same block sizes.

Compared to the low mean square values of vineyard A (less than 0.15), the
higher mean square values of vineyard B (up to 2.5) show ﬂuctuations in mean square
values, especially in the southeast and southwest corners (Figure 2.10). The peaks
observed in mean square in the southeast and southwest comer graphs indicate clustering
at block size 256. The peaks in the southwest and southeast corner verify the cluster of

symptomatic vines in the southeast corner observed on the vineyard map (Figure 2.5).

81

 

SD
or

2001

(A)

 

to
or

 

 

 

+ NW comer

 

// + NE corner
LA

 

mean square
—l
.. a: m

 

 

 

 

 

—l— SE comer
i + SW comer
0.5 /
0
-0.5

 

16 32 64 128 256 512
blocksize

..A
N
,5 .
on

Figure 2.10. Mean square versus block size graphs of all of the vantage points from
which quadrat variance was calculated in Vineyard B in 2001; northeast, northwest,

southeast and southwest. Block size 1 corresponds to one vine, block size 4 = 2 x 2

vines, block size 8 = 4 x 4 vines, etc. Blocks were oriented beginning with the vines
along the same row.

82

q ~

Spring temperatures eﬂect on disease incidence

In 1999, 2000 and 2001, estimated bud break occurred on approximately May 27,
May 26 and May 21, respectively. The results of linear regression application show that
there are strong relationships between the number of symptomatic vines in the vineyard
and minimum temperatures up to ﬁve weeks before as well as four weeks after bud break,
but the regression relationships were not signiﬁcant. Tables 2.1 and 2.2 show the p-
values of the regression relationship, linear models, and the R2 (regression coefﬁcient)
value. There appears to be an especially strong relationship between the total number of
symptomatic vines per year and the minimum temperature four weeks before bud break
(R2 = 99.6%, p = 0.0402). Though the regression is not statistically signiﬁcant, the ﬁve
and three weeks before bud break correlation is strong (R2 = 91.64% (p = 0.32) at ﬁve
weeks and R2 = 60.69% (p = 0.43) at three weeks). Again, the regression relationships
were not signiﬁcant, but the correlation of maximum temperature and number of
symptomatic vines for ﬁve and four weeks before bud break is also strong (R2 = 76.70%,
p = 0.32; R2 = 96.93%, p = 0.11, respectively).

The regression relationship is equally strong between the number of symptomatic
vines per year and the temperatures from one to four weeks after bud break (Table 2.2).
Consistently, the minimum, maximum and average daily temperature of the ﬁrst, third
and fourth weeks after bud break showed strong relationships (all R2 values were above
67%); however, only average temperature four weeks after bud break was nearly
signiﬁcant at p<0.10.

The graphs of the minimum and maximum daily temperatures ﬁve weeks before

bud break of each year for vineyard A are shown in ﬁgure 2.11. Before bud break, clips

83

in temperature occur in all weeks in 2001 to approximately 0°C in week ﬁve; dips in
1999 and 2000 occur but not as low (approximately 5°C). The graphs of the temperatures
in vineyard A four weeks after bud break are shown in ﬁgure 2.12. In 1999, dips in
minimum temperature occurred in week one and three with declines in temperature, and
increases in minimum temperature occurred in week two and week four. In 1999, in
general, temperatures were high after bud break, which may have encouraged rapid shoot
growth and thus fewer symptoms. The decline in temperature in week three may account
for the fact that there were more symptomatic vines in 1999 than in 2000 where the

minimum temperatures did not dip in week three.

Table 2.1. Regression relationships of minimum, maximum, and average weekly
temperature before bud break against the number of symptomatic vines in 1999, 2000,

Model formula

 

y = mx
y = 222.393 - 86.9048x
y = 239.304 - 32.3317x
y = 50.3243 - 5.63656x
y = -78.0337 + 20.7865x

Model formula

 

y = -187.827 + 10.42l7x
y = 449.788 - 16.698x

y = 151.689 - 4.31104x
y = 166.347 - 4.88905x
y = -305.159 + 12.7161x

Model formula

 

and 2001.

MINIMUM TEMPERATURE:

week(s) before

bud break 2 value R2

5 0.3207 91 .64%
4 0.0402** 99.60%
3 0.4314 60.69%
2 0.7105 19.29%
1 0.7475 14.93%
MAXIMUM TEMPERATURE:

week(s) before

bud break 2 value R2

5 0.3207 76.70%
4 0.1 121 96.93%
3 0.8545 5.13%
2 0.8239 7.46%
1 0.2009 90.37%
AVERAGE TEMPERATURE:

week(s) before

bud break 2 value R2

5 0.3721 69.55%
4 0.544 43.1 1%
3 0.8443 5.86%
2 0.6767 23.65%
1 0.1743 92.69%

** Regression is signiﬁcant at p<0.05
* Regression is signiﬁcant at p<0. 10.

85

y = 230.708 - 22.8913x
y = 184.543 - 11.0756x
y = 11.1741 + 1.20752x
y = -79.2539 + 7.21443):
y = -150.688 + 11.9855x

Table 2.2. Regression relationships of minimum, maximum, and average weekly
temperature after bud break against the number of symptomatic vines in 1999, 2000, and

Magm—

 

y = 121.975 - 8.0458x

y = 64.3103 - 2.96397x
= 104.042 - 5.39858x

y = -254.639 + 19.3815x

WW

 

y = 130.074 - 4.74351x
y = 94.7448 - 2.8321x

y = 229.504 - 8.733x

y = -232. 149 + 9.97652x

Madm—

 

2001.

MINIMUM TEMPERATURE:

week(s) after

bud break value R2

1 0.3681 70.13%
2 0.595 35.30%
3 0.3879 67.25%
4 0.1665 93.31%
MAXIMUM TEMPERATURE:

week(s) after

bud break value R2

1 0.3236 76.31%
2 0.4892 51.70%
3 0.2808 81.78%
4 0.2063 89.86%
AVERAGE TEMPERATURE:

week(s) after

bud break value R2

1 0.3397 74.13%
2 0.5356 44.42%
3 0.3452 73.37%
4 0.0796* 98.45%

** Regression is signiﬁcant at p<0.05
* Regression is signiﬁcant at p<0. 10.

86

y = 127.189 - 5.97752x
y = 80.6811 - 2.94432x
y = 152.897 - 6.7232x

y = -26l.788 + 14.2452x

min. and max. temperature (°C)
0 or a a 8 1% 8 8’.

 

week 1 week 2 week 3 week 4 week 5

min. and max. temperature (°C)

 

week 1 week 2 week 3 week 4 week 5

min. and max. temperature (“C)

 

week2 week3 week4 weeks

Figure 2.11. Daily minimum and maximum temperatures in the ﬁve-week period before
bud break in vineyard A.

87

mln. and max. temperature (°C)
8 01 o u. 8 a: 8 8 8 8
x
d

 

week2 week3 week4

8888

.s
0|

min. and max. temperature (°C)
8

 

c'nour

8

week 2 week 3 week 4

8888

d-e
00!

min. and max. temperature (°C)

 

drool

week 2 week 3 week 4

5
0
Jr
.A

Figure 2.12. Daily minimum and maximum temperatures in the four-week period after
bud break in vineyard A.

88

Discussion

Vines infected with Eutypa lata and observed annually have appeared to either
recover or to decline over time (personal communication, R. Parker, M. Longstroth)
(Amborabe, et al., 2001; Peros, et al., 1997). The ephemeral nature of symptoms of
Eutypa dieback is illustrated in the three years that vineyard A was observed. Thus an
accurate number of infected vines in the study is difﬁcult to determine by foliar symptom
observation alone. It is difﬁcult to determine when the vines became infected as well,
because there are often no symptoms in the ﬁrst three years. What was observed in the
three years may only be “the tip of the iceberg.” Identifying infected vines by scouting
for cankers of Eutypa lata may be a secondary method of getting a more accurate count
of infected vines; however, cankers are not always easily seen and outer bark may need to
be stripped in order to identify them.

The results of the quadrat variance analysis indicated that the pattern of
distribution of symptomatic vines in vineyard A was random. Because there may be
more infected vines than those that showed symptoms in any given year, this may only
provide a partial picture. The random distribution of symptomatic vines in vineyard A
may indicate that the vines were infected by airborne inoculum from outside the vineyard
(Munkvold, et al., 1993; Waggoner, et al., 2000). Additionally, the foci of all initial
infections may not have spread to neighboring vines measurably in 1999 and 2000 or
enough to be considered clustered (Waggoner, et al., 2000). In the 2001 map of vineyard
A, there appears to be a clustering of several symptomatic vines in certain places. These
clusters may represent infection spreading from vine to vine, but in general, there are

fewer of these clusters than there are widely spaced symptomatic vines. With airborne

89

spores, nearby vines may be expected to have a greater chance of becoming infected, but
wind driven rain could disperse inoculum over a large area of the vineyard.

The results of the quadrat variance analysis indicated that the pattern of
distribution of symptomatic vines in vineyard B showed clustering at block size 256 (or
256 vines), but only in two of the four directions the analysis was performed. The two
directions from which analysis showed the clustering were the corners with the most
disease in the vineyard. Visually, the pattern of symptomatic vines in vineyard B
indicates an edge effect near the lower, wooded area. The higher disease incidence may
be due to inoculum that came from different hosts growing in the woods or to increased
susceptibility of the vines in lower areas to infection due to lower temperatures, and/or
higher humidity.

In this study, only vines showing typical foliar symptoms were counted.
However, if dead vines conﬁrmed to have been killed by Eutypa dieback were also
counted as well as vines showing atypical symptoms, perhaps the spatial analysis would
have shown different results. A reliable detection assay would be helpful in assessing the

true distribution of the disease in a vineyard.

Effects of spring temperature on disease incidence

Eutypine, the toxin produced by Eutypa lata, is conducted through the sap,
traveling from the canker to the shoots in spring to cause foliar symptoms, though the
toxin itself has not been detected in symptomatic tissues (T ey—rulh, et al., 1991). The
eutypine toxin upsets respiration and the energy balance of the grapevine cell. The effect

of the toxin may be the cause of vine decline over the years after infection, rather than the

90

 

T-

fungus growth itself (Deswarte, et al., 1996). The effect of the toxin is concentration
dependent; at lower concentrations, symptoms are less severe or take more time to
develop (Mauro, et al., 1988). This may explain in part the change in symptoms
observed in some vines over the years in vineyard A as well as at a research vineyard in
France of ‘Gramon’ grapevines (Amborabe, et al., 2001; Peros, et al., 1997).

The polyetic disease cycle of Eutypa dieback and the observation that the
symptoms do not show consistently each year suggest that the true number of infected
vines in vineyard A is higher than the number of symptomatic vines indicate. Symptom
expression may depend on environmental conditions during bud break and early shoot
growth. Favorable weather for growth may mask the true condition of the vine. Low
temperatures before or after bud break may make the shoot more susceptible to the
effects of the toxin, by slowing shoot growth and allowing more time for the tissues to be
exposed to the toxin. Temperature may also have a direct effect on toxin production by
the fungus; the higher the temperature the more toxin the active fungus can produce.

In this study of vineyard A, the minimum temperature seems to indicate best what
the relative number of symptomatic vines is. The maximum temperature and average
temperature were not as indicative of the number of symptomatic vines since regression
relationships were not as strong.

There are many strong relationships between some weeks’ minimum
temperatures, before and after bud break, and the number of symptomatic vines, though
the regressions were not signiﬁcant. It is possible that any dip in the temperature before
or after bud break is a good indication that the number of symptomatic vines will be

higher than if there was no reduction in temperature. The minimum temperature ﬁve,

91

 

four, and three weeks before bud break, however, seems to be the time most crucial for
the toxin to affect symptom expression and for many infected vines to show symptoms.
The strong regression relationships suggest that the minimum temperature three to
ﬁve weeks before bud break could be an indicator of how common symptoms will be that
year. This information would be useful to growers or researchers trying to get an idea of
the relative number of vines that will show infection in a given year. The preliminary
information here also suggests that the exposure time and the eutypine concentration in
the ﬁeld may be temperature-dependent. Little is known about eutypine production and
exposure under ﬁeld conditions. Research questions concerning the toxicity of eutypine
in a susceptible grapevine variety may use temperature as a factor in the study of
potential symptom development. The rate of bud break or shoot growth and the relative
susceptibility of the grapevine cultivar to the toxin may be additional factors responsible

for the effect of the toxin on symptom expression.

92

 

CHAPTER 3. EFFECT ON THE NUMBER OF SHOOTS
Introduction

Vines infected with Eutypa lata and observed annually have appeared to either
recover and to decline over time (Amborabe, et al., 2001; Peros, et al., 1997) (personal
communication, R. Parker, M. Longstroth). The effect of the disease on the number of
shoots, speciﬁcally, has not been examined in the literature. The effect of the disease on
the number of clusters per vine, berry weight, and the number of berries per cluster are
more often reported instead. A study of the effect of Eutypa dieback yield on vines of
‘Shiraz’ in various vineyards in Australia showed signiﬁcant reductions in total yield and
total clusters per vine due the disease (Wicks, et al., 1999). Whether this was due to a
reduction in the number of fruitful shoots or the number of clusters per shoot was not
reported. In a study in Washington state, the number of clusters and berries and total fruit
weight were taken, but the number of shoots was not noted (Johnson, 1987).

Yield reductions in infected vines in a California study were shown to be
signiﬁcantly correlated with the number of clusters per vine (Munkvold, et al., 1994).
The yield study counted the cluster number and the total yield per vine, but did not
directly report the number of shoots of vines in the study. The number of shoots per vine
was expressed as a proportion of shoots with symptoms or killed by Eutypa dieback.
Covariance tests showed that the number of shoots per vine was variable, but that the
disease reduced the number of productive cordons, which in turn reduces the number of
buds produced (Munkvold, et al., 1994).

Of interest here is the effect of Eutypa dieback on the number of shoots per vine

and the proportion of healthy shoots to symptomatic shoots on infected vines compared

93

 

with apparently healthy vines. The objective of this chapter is to examine the effect of
Eutypa dieback on the number of shoots on vines — a measure of vine vigor — over the

course of two seasons.

Materials and methods

Vine selection and evaluation

A ‘Concord’ vineyard near Lawton, Michigan (vineyard A) was the site of the
study. The vines were originally trained to Hudson River Umbrella (HRU) and pruned
manually. Since three years before the study, the vines have been hedged mechanically
only. In the early spring when the shoots were approximately 30-35'cm long,
symptomatic shoots were easily distinguished from non-symptomatic shoots, and all
shoots were counted. Stunted shoots with shortened intemodes, and chlorotic, up-cupped
leaves that appeared less expanded than healthy leaves indicated Eutypa dieback. On
May 23, 2000, nine vines exhibiting Eutypa dieback symptoms were chosen in vineyard
A. Additionally, an apparently healthy vine that was at least two vines from each
symptomatic vine on the same row was chosen for comparison. To be chosen, both
symptomatic and apparently healthy vines needed two cordons with shoots present, and
the symptomatic vine needed one cordon without symptomatic shoots. The number of
non-symptomatic and symptomatic shoots on each vine was counted using a hand-held

tally counter. In 2001 on May 9, the same vines’ shoots were counted.

94

Statistical analysis

The effects of year and of vine condition (symptomatic or apparently healthy) on
shoot number were tested separately and their interaction examined with a two-way
analysis of variance (ANOVA) (Statgraphics Plus for Windows, 4.1, Statistical Graphics

Corporation, Englewood Cliffs, NJ).

 

Results
Nine vines with symptomatic shoots and nine corresponding apparently healthy t“
vines were followed over two seasons (2000 and 2001) in which shoots were counted.
The number of shoots per vine in one year is not necessarily consistent with the number
the next year (Figure 3.1). In 2000, all symptomatic vines were symptomatic and all
apparently healthy vines were not. The mean number of total shoots on apparently
healthy vines was higher than on symptomatic vines in 2000: 114 and 84.3, respectively
(Figure 3.2). The mean number of symptomatic shoots on the symptomatic vines in 2000
was 12.3. The number of non-symptomatic shoots in 2000 ranged from 64 — 148 on
apparently healthy vines, and 1-133 on symptomatic vines. The total number of shoots
on symptomatic vines ranged from 7 - 147, of which 3 - 39 were symptomatic in 2000.
In 2001, only one of the nine symptomatic vines was symptomatic, and none of
the apparently healthy vines were symptomatic. The mean number of total shoots on
apparently healthy vines was higher than on symptomatic vines in 2001: 197 and 122.2,
respectively (Figure 3.2). The mean number of symptomatic shoots on the symptomatic
vines in 2001 was 0.7 (Figure 3.3). The number of non-symptomatic shoots in 2001

ranged from 94 -— 252 (apparently healthy vines) and 6 - 197 (symptomatic vines). The

95

total number of shoots on symptomatic vines ranged from 6 — 197, of which 0 — 6 were
symptomatic in 2001.

All nine of the apparently healthy vines and six of the symptomatic vines gained
shoots in the second year. The nine apparently healthy vines gained 30—167 shoots in
2001, compared to 2000, and the six symptomatic vines gained 51-101 shoots in 2001
compared to 2000. Three symptomatic vines lost 28-39 shoots per vine in 2001,
compared to 2000. The apparently healthy vines gained an average of 83 shoots in 2001
compared to 2000. The symptomatic vines that gained shoots gained an average of 74
shoots — 49.56 overall — in 2001 compared to 2000. The three symptomatic vines lost an
average of 35 shoots — 11.67 overall — in 2001 compared to 2000. None of the apparently
healthy vines lost shoots in 2001.

The change in mean shoot number in the apparently healthy and the symptomatic
vines shows that although the mean number increases on symptomatic vines from 84 to
122 shoots, the mean shoot number on apparently healthy vines increases more, from 114
to 197 shoots (Figure 3.2). Not all symptomatic vines lost shoots between 2000 and 2001
in a consistent manner (Figure 3.4). Only three of the nine symptomatic vines lost
shoots, one of which had symptomatic shoots both years.

The number of shoots on a vine one year does not appear to indicate the number

of shoots the next year, except that the infected vines gained fewer shoots than their
apparently healthy counterparts and that all apparently healthy vines consistently gained

shoots (Figure 3.1 and Figure 3.4).

96

 

Statistical analysis

The results of two-way analysis of variance show that year and healthy status had
a statistically signiﬁcantly effect on the number of shoots (p<0.05) (Table 3.3).
Undoubtedly, the number of shoots on symptomatic and apparently healthy vines
depends on the conditions the vines encounter during the year — winter severity,
temperature changes, cultivation practices, etc. Additionally, the number of shoots on
symptomatic and apparently healthy vines depend on the condition of the vine;
symptomatic vines have signiﬁcantly fewer shoots than apparently healthy vines.
However, there was no statistically signiﬁcant interaction between the year and the vine
type; the increase in the shoot number of symptomatic and apparently healthy vines was

statistically equal.

97

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Number of shoots
on apparently healhy vines

0|
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l

 

 

 

 

 

 

 

 

 

 

04 _

Figure 3.1. The number of shoots per vine on symptomatic vines (A) and apparently
healthy vines (B) in 3 ‘Concord’ vineyard in Lawton, MI in 2000 and 2001.

98

 

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1 75
1 50
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Figure 3.2. Total shoots per vine averaged over nine symptomatic and nine apparently
healthy vines in 2000 and 2001.

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Number of symptomatic
shoots per vrne

 

Percent of symptomatic
shoots per vine

 

Figure 3.3. Number of symptomatic shoots (A) and percentage symptomatic shoots (B)
on nine symptomatic vines in a ‘Concord vineyard in Lawton, MI, in 2000 and 2001.

100

 

200

 

 

 

AH vine
I D vine

 

 

 

 

 

 

 

 

 

 

number of shoots lost or gained

 

 

 

 

Figure 3.4. Number of shoots per vine lost or gained. The particular pair of apparently
healthy (AH) and symptomatic (D) vines is grouped next to each other. The vine pairs
are in order of the apparently healthy vine shoot number gain from lowest to highest.

101

 

Table 3.1. Analysis of variance (AN OVA) of the effects of vine health status and year on
the number of shoots per vine in a ‘Concord’ vineyard in Lawton, MI in 2000 and 2001.

Analysis of Variance for number of shoots - Type III Sums of Squares

 

 

 

 

Source (Type 111) Sum of Squar_;e_s Df Mea_rﬁ_qy_are F-Rﬂ'o P-Vaﬂg
MAIN EFFECTS

Vine health 24,5444 1 24,5444 11.92 0.0016
Year 32,8818 1 32,881.8 15.97 0.0004
INTERACTIONS

Vine health * year 4,578.78 1 4,578.78 2.22 0.1457
RESIDUAL 65.8896 32 2,059.05

TOTAL (CORRECTED) 127,895.0 35

102

Discussion

Eutypa dieback has been known to cause successively increasing symptoms that
eventually lead to the lack of production of shoots (Pearson, et al., 1988). Vines with
Eutypa dieback were expected to have fewer shoots the second year of this study and
have more symptomatic shoots. The results presented here show that this was not
entirely the case; not all vines identiﬁed as diseased in 2000 had fewer shoots the second
year and only one these vines had symptoms the second year. When ‘Concord’ vines
were minimally pruned, there were an estimated 300-450 shoots per vine (Lakso, et al.,
1997). If disease or winter injury has reduced the number of shoots this could be a high
estimate. The results here show that the nine apparently healthy vines averaged almost
half that, the symptomatic vines averaged about a third.

There may be a direct effect of the disease on the number of buds, which must
survive the winter and grow in the springtime. The effect of the disease may be more
evident in the number of buds producing shoots in spring rather than the number of
shoots showing symptoms. Results show that there are on average fewer shoots on
symptomatic vines than on apparently healthy vines and that the number of symptomatic
shoots does not necessarily increase over time. Vines that do not show symptoms but are
infected may also be losing shoots over the years. The symptomatic shoots of one year in
addition to the reduced number of total shoots will lessen the vine’s contribution to its
reserves required for the winter and following spring. The two-season duration of the
study only gives a snapshot.

It has been observed that infected vines show symptoms for a few years and then

they appear to “recover.” During the apparent recovery period, the infected vines lose

103

vigor, observed as reduced cluster size and less vegetative growth overall, then begin
their decline in the following years (personal communication, R. Parker). If the disease is
managed by pruning out cankerous and stained wood, and training new suckers from
below ground, the vine may continue to produce well. If not, eventually the entire vine is
killed or loses so much vigor that leaves do not develop (personal communication, R.
Parker). It appears that the vines under study here may have recovered the second year,
but they have not been cured. Differences in the number (and existence) of symptomatic
shoots between years may be due to a difference in the effects of the toxin in certain
years or temperature changes that affect the growth of the shoots. Shoots or buds may be
frozen off before they can break. Even though the symptoms are not visible, the fungus
can continue to invade the wood. In general, the effect of the disease on the vine —
invasion of the cambium that causes cankers and release of eutypine that leads to reduced
vegetation and fruit development, decreasing storage products for the preceding spring —
causes a gradual decline over the years.

The severity of the decline and when the vine actually dies may depend on many
factors, including the number of nodes that remain for shoot development and the
weather and soil conditions that assist the vine to survive and produce enough reserves in
spite of the disease effects. Vine decline due to Eutypa dieback is observed often by the
foliar symptoms alone that may not show consistently each year. Additionally, due to the
small window of observation of this study, there is a possibility that some of the
apparently healthy vines are infected and in some early stage of decline themselves.

The results of statistical analysis of this study show that the mean number of

Shoots on infected and apparently healthy vines is affected by the presence of infection

104

and by the year observed. The results show no interaction between year and vine
condition; the number of shoots of a symptomatic (or apparently healthy) vine is not
affected by the year it is observed. These results suggest that the disease affects the
number of shoots on a vine more than other factors that could affect all vines equally.
Perhaps the disease lowers the vigor of the vine in many ways other than shoot number.
Eutypa dieback symptoms reduce vegetative growth so that each year the infected vine is
not able to catch up energetically each year. Variation in the number of shoots per vine
and the number of symptomatic shoots per vine may be explained by the uncontrolled
number of nodes retained when vines are hedged rather than hand-pruned. Isolates in a
single vineyard has been shown to have considerable diversity and variable pathogenicity
(Peros, et al., 1997). The variability in symptom expression in the study vineyard may

due to the genetic variability of the inoculum.

105

CHAPTER 4. EFFECTS OF EUTYPA DIEBACK ON GROWTH AND YIELD

ASPECTS

Introduction

Yield loss assessments are important tool for decision making with regard to
disease control. As Eutypa infected vines lose vigor and begin to decline, the vine loses
its yield potential sooner than it takes for the vine to be killed. For this reason the
infected vine is not normally sacriﬁced when symptoms are ﬁrst observed. The
symptomatic shoots of an infected vine are most visible when shoots have grow to about
30 cm, but later in the season they are often overlooked when the non—symptomatic
shoots of the vine and shoots from neighboring vines expand to ﬁll the canopy. Eutypa
dieback symptoms affect yield according to the severity of symptoms on the vine
(Johnson, 1987; Munkvold, et al., 1994). The action and intensity of the toxin that
produces the symptoms, growing conditions of the fungus, environmental conditions, and
physiological conditions within the vines are some factors that may affect the intensity of
symptoms each season (Mauro, et al., 1988).

The effect of Eutypa dieback symptoms on yield components and the longevity of
vineyards has been studied (Johnson, 1987; Munkvold, et al., 1994). The effect of
Eutypa dieback on fruit set speciﬁcally has not been investigated, but uneven maturation
affects juice composition of ‘Concord’ grapes (Morris, et al., 1981). Non-symptomatic
Shoots from infected vines appear to compensate for the loss with increased growth and

yield, mostly via increased cluster size and number (Munkvold, et al., 1994). Individual

Symptomatic shoots and their contribution to yield have not been examined.

106

Additionally, estimating yield loss by measuring and comparing individual shoots of
differing symptom severity will allow for a more accurate yield loss estimate because of
the high variation between whole vines and between infected and apparently healthy
vines (W olpert, et al., 1980).

The objective of this study is to determine the effects of Eutypa dieback under
different severities on yield. Speciﬁcally, what makes up the yield is broken into three
parts that are essential to grapevine culture: yield, which addresses the economic return of
the crop; vegetative growth, that affects the future productivity of the vine; and juice
composition, which addresses the quality of the crop. Questions of compensation of non-

symptomatic shoots will be addressed as well as how the symptoms affect fruit set.

Materials and methods
Study vineyards
Two ‘Concord’ vineyards in southwest Michigan exhibiting Eutypa dieback

symptoms were identiﬁed for the study (Figure 4.1). Vineyard A, located 8 km south of
Lawton, MI (Van Buren County) was studied in 1999, 2000 and 2001. The vines in this
vineyard were planted at a density of 1,349 vines per hectare (2.4 m between vines, 3 m
between rows). The vines were originally trained by the Hudson River Umbrella (HRU)
method and pruned manually, but have been only mechanically pruned since 1995. The
HRU method employs a bilateral cordon at the top wire at 1.8 m high. The vines are
planted on a 2.4 m within row spacing and trained to two 1.2 m bilateral cordons. Hand
pruned vines are pruned to retain an appropriate number of 8-node canes and 2-node

renewal spurs based on the vine size (personal communication, G.S. Howell). Pruning

107

and training with the HRU method allows for mechanical harvesting, because the shoots
are distributed horizontally and vertically down from the trellis wire (Pool, 1980). The
1.2 hectare of the vineyard studied consisted of 25 rows: four rows with 56 vines and 21
rows with 75 vines. Vineyard B, located 11 km east of Schoolcraft, MI (southeastern Van
Buren County), was studied in 2001. The nearly 1.4 hectare section of vineyard B
consisted of 47 rows with 39 vines each with the same vine density as vineyard A,
planted in 1973. Vines are trained according to the HRU method, and are hand pruned
each year after mechanical hedging. Vineyard B is regularly managed for Eutypa
dieback; symptomatic shoots are pruned out and diseased vines are removed and burned.
In vineyard B, vines are renewed (self-layered) by choosing a healthy shoot or sucker
with no signs of any disease and burying a section if it so that there is enough length at
the growing end to tie to the trellis. The shoot is left trained like this for two to three

seasons before it is cut from the parent plant.

108

 

A
east central
Van Buren Co.

 

 

 

B .
southeastern '
Van Buren Co.

 

 

 

Figure 4.1. Map of Michigan with the approximate location of vineyard A in east central,
and B in southeastern Van Buren county.

Scouting and disease rating

When the shoots were about 25-30 cm long, during the ﬁrst half of May, all vines
in the 25 rows at vineyard A (in 1999, 2000, and 2001) and 39 vines in 47 rows at
vineyard B (in 2001) were scouted for foliar symptoms of Eutypa dieback (1,799 and
1,833 vines per vineyard A and B, respectively). Stunted shoots with shortened
intemodes and chlorotic, up-cupped leaves that appeared less expanded than healthy
leaves indicated Eutypa dieback (Figure 1.3). In 1999, 18 visibly symptomatic vines
were chosen in vineyard A. In 2000 and 2001, 11 symptomatic vines were chosen in
vineyard A. In 2001, 11 symptomatic vines were selected in vineyard B. In all years and
vineyards, an apparently healthy control vine was also selected for each symptomatic
vine in the same row 24 vines away. To be chosen for the study, the symptomatic vine
(“D”) needed to have two productive cordons with symptoms limited to one of the two,
and a nearby apparently healthy (or “AH”) vine, present in the same row, with two
productive cordons.

When shoots had grown to 30-35 cm long (in late May to early June) disease
severity was evaluated on all symptomatic shoots. Shoots were given a qualitative
overall rating as follows: mild (M): <25% of the leaves cupped, minimal stunting;
intermediate (1): 25-75% of the leaves cupped, some stunting; and severe (S): >75% of
the leaves cupped, moderate to severe stunting.

By the beginning of June, all of the symptomatic shoots on symptomatic vines
had been identiﬁed and tagged, and ﬁve non-symptomatic (NS) modal shoots (shoots that
represent the variety of shoot lengths available on that vine) were tagged on the opposite

cordon. On apparently healthy (AH) vines, in vineyard A, a total of ﬁve modal AH

110

shoots were selected from each entire vine in 1999 and 2000. In 2001, in both vineyards,
ﬁve modal shoots were chosen from each cordon for a total of ten AH shoots per vine.
Only in 2001, all growth and yield data on symptomatic shoots were combined in the

results, indicated as D shoots.

Yield measurements

In 1999, AH, NS and symptomatic shoots were cut from vines selected for study
and collected with their clusters still attached; all shoots were refrigerated within 12
hours of harvest. Clusters were removed from each shoot, counted, and weighed. Fully
developed berries in each cluster were weighed and counted, and undeveloped (smaller
than pea-size) berries were counted, but not included in the cluster weight. No green
berries were observed on clusters in 1999.

In 2000 and 2001, clusters were harvested from each tagged shoot approximately
5-7 days before the grapes were to be commercially harvested. The clusters were stored
in 4°C upon return to the laboratory. The clusters and berries were counted and weighed;
purple and green berry counts and weights were noted separately. Undeveloped berries
(smaller than pea size) were counted, but not included in the fruit weight. In 2001,

symptomatic shoots were not categorized by severity and thus are referred to as D shoots.

lll

Sugar content per shoot
In 1999 and 2000, the fruit weight of each speciﬁc shoot harvested from vineyard
A was matched with its mean brix (% sugar) and multiplied to obtain the total amount of

sugar (g) that each shoot produced.

Vegetative growth measurements

1922; In the ﬁrst year of the study, on September 14, 1999, all symptomatic
shoots were harvested with pruning shears below the ﬁrst node; if the intemode below
was less than a thumb’s width, the next node was considered the fust. From 11 to 34
symptomatic shoots were collected from each vine. Additionally, ﬁve non—symptomatic
shoots (NS) were harvested from the opposite cordon on the same vine. On September
16, 1999, ﬁve healthy shoots were collected from 18 apparently healthy vines (AH)
located two to three vines away from the symptomatic ones.

All shoots were refrigerated within 12 hours of harvest and fruit clusters were
removed for yield measurements. Shoots with leaves minus their clusters were stored at
4°C. Growth measurements were done on a subsample of 90 AH shoots, 95 NS shoots
and 217 symptomatic shoots. The length of the shoots and the diameter of the intemode
between the ﬁrst and second node were measured, and the number of nodes on each shoot
was counted. All leaves available on each shoot were pressed and dried and their area
measured by passing them through a leaf area meter (LI-CORR portable leaf area meter,
LI-3000, conveyor belt, LI-3050ASH, Lambda Instruments Corporation, Inc. Lincoln,

NE.).

112

20004and 2001: Leaf area was estimated by measuring leaf midrib lengths at
bloom in early June 2000 in vineyard A, and at véraison in early August 2000 and mid- to
late August in 2001 in both vineyards. The midrib length of each fully expanded leaf on
the tagged shoots of all vines was measured. In addition, 100 unblemished leaves of
various sizes were collected from each vineyard at the time midrib length was measured;
leaf area was measured using a leaf area meter (LI-CORR portable leaf area meter, L1-
3000, conveyor belt, LI-3050ASH, Lambda Instruments Corporation, Inc. Lincoln, NE.)
and leaf midrib was determined with a ruler. For each vineyard, each time leaf midrib
length was obtained and using the excel program, a regression equation of midrib against
leaf area was obtained and used to estimate the area of leaves on all tagged shoots. The
lengths of the tagged shoots were measured at the time midrib measurements were
obtained. After leaf fall in 2000 and 2001, the diameters of all tagged shoots were
measured at the intemode with digital calipers between the 2nd and 3rd node. If the ﬁrst
node was at least a thumb’s width from the shoot’s point of origin, it was counted as the
ﬁrst; otherwise the next node was designated the ﬁrst. Two measurements at 90° from
each other were averaged to obtain the shoot diameter. In 2001, symptomatic shoots

were not categorized by severity and thus are referred to as D shoots.

Juice composition analysis

In each year, clusters were frozen at —20°C after yield measurements for juice
composition analysis. Clusters from the same shoot were combined. Later, rachises were
removed if necessary from each bag of frozen berries, and the berries were thawed to

room temperature and crushed manually. The juice was strained through a layer of

113

cheesecloth and analyzed for brix (% soluble sugar) using a refractometer (ABBE Mark
II digital refractometer, model 10480, Reichert Scientiﬁc Instruments, Buffalo, NY,
USA) and pH using a pH meter (PethecT Log R benchtop pH meter, Model 370,
Thermo Orion, Beverly, MA). Tartaric acid content was determined by titration to pH
8.1-8.2 with 0.10 M NaOH; the amount of NaOH was multiplied by 0.15 to obtain the

tartaric acid amount (g/ 100 ml).

Statistical analysis

All analysis on shoot data was performed using the SAS program (SAS Institute
Inc., Cary, NC, USA). Data were analyzed by analysis of variance (PROC MIXED
procedure in SAS), using vegetative, fruit yield, and juice composition components as
dependent variables, disease severity as the ﬁxed factor, and vine as a random factor.
Potential correlation between shoots of different categories in the same vine were
accounted for by using the REPEATED statement with either Toeplitz or compound
symmetry covariance structures applied to vine as a subject. Multiple t-tests were used
for mean separation when the analysis of variance (ANOVA) was statistically signiﬁcant
for the category, or Tukey's procedure was used for mean separations. Normality of the
residuals of the data was assessed with the UNIVARIATE procedure. When they were
not normal log-transformation was used. Individual shoot data were analyzed for
possible correlation between all components; a correlation was considered when r>0.50

(PROC CORR procedure in SAS).

114

Results
Number of clusters

In vineyard A, the mean numbers of clusters, or cluster number, of the S shoots
were signiﬁcantly lower in 1999 and 2000 (Figure 4.2). Compared to the NS and AH
shoots, respectively, S shoots were 31% and 33% reduced in 1999, and 54% and 55%
reduced in 2000. Cluster number of M shoots was reduced by only 5.5% - 3% in 1999
and 2000. It is signiﬁcant to note that the S shoots had only an average of 0.97 clusters,
showing that some S shoots had no clusters. In 2001, symptomatic shoots were not
categorized by severity and thus are referred to as D shoots. In 2001, the cluster number
of the D shoots was reduced by 17% and 16% compared to the NS and AH shoots,
respectively.

In vineyard B, in 2001, cluster number was signiﬁcantly different between the D
shoots and both AH and NS shoots (Figure 4.3). Cluster number per D shoots was

reduced by 22% and 19% compared to the NS and AH shoots, respectively.

Fruit weight per shoot

In vineyard A, the fruit weight per shoot, or shoot weight, was about one third in
2001 of what it was in 1999 and 2000 (Figure 4.4). The NS shoots had signiﬁcantly
higher shoot weight than all other categories in 1999. In 1999, shoot weight of all
categories of shoots was signiﬁcantly different from each other. In 2000, S shoots had a
signiﬁcantly lower shoot weight than the AH, NS, and M shoots. The S shoots in 1999
were reduced by 81% and 78% compared to the NS and AH shoots, respectively, and the

AH shoots were 15% lower than the NS shoots. In 1999, the M shoots had a 42% and

115

32% lower shoot weight than the NS and AH shoots, respectively. In 2000, shoot weight
of S shoots was reduced by 93% and 92% compared to NS and AH shoots, respectively.
In 2000, the shoot weight of I shoots was also reduced by 74% and 70% compared to NS
and AH shoots, respectively. In 2001, symptomatic shoots were not categorized by
severity and thus are referred to as D shoots. Shoot weight of D shoots was reduced by
47% and 39% compared to NS and AH shoots, respectively.

In vineyard B, in 2001, the mean fruit weights per shoot and per cluster were
signiﬁcantly different between the D shoots and both AH and NS shoots (ﬁgure 4.5).
The mean weight per shoot of the D shoots was reduced by 59 and 22% compared to the

NS and AH shoots, respectively.

Fruit weight per cluster

In vineyard A, the mean fruit weight per cluster, cluster weight, of the S and I
shoots were signiﬁcantly lower than the M, NS and AH shoots in 1999 (Figure 4.6). The
cluster weight of the S shoots was reduced by 74% and 68% compared to the NS and AH
shoots, respectively. In 2000, cluster weight of S shoots was reduced by 83% and 80%
compared to the NS and AH shoots, respectively. Cluster weight of M shoots of 1999
and 2000 was reduced by approximately 29% compared to N S shoots, and 11%
compared to AH shoots in both 1999 and 2000. In 2001, cluster weight of D shoots was

reduced by 41% and 32% compared to NS and AH shoots, respectively.

116

In vineyard B, in 2001, cluster weight of the D shoots was signiﬁcantly lower
than the NS and AH shoots in 2001 (Figure 4.7). The cluster weight of D shoots was

reduced by 46% and 39% compared to the NS and AH shoots, respectively.

Number of berries per cluster

In vineyard A, the mean number of berries per cluster, berry number, on the I and
S shoots was signiﬁcantly lower than the NS and AH shoots (Figure 4.8). Compared to
the NS and AH shoots, berry number of S shoots was reduced by 66% and 59%,
respectively, in 1999, and 75 and 71%, respectively, in 2000. Even berry number on M
shoots was reduced by 25% and 24% in 1999 and 2000, respectively. In 2001, the D
shoots had a reduced berry number by 42% and 35% compared to NS and AH shoots,
respectively.

In vineyard B, in 2001, the berry number was signiﬁcantly different between the
D shoots and both AH and NS shoots (Figure 4.9). Compared to the NS and AH shoots,

berry number was reduced in D shoots by 42% and 38%, respectively.

Berry weight

In vineyard A, the differences between categories of berry weight were not as
signiﬁcant as they were for shoot and cluster weights (Figure 4.10). In 2001, the
categories AH, NS and D shoots were not signiﬁcantly different from each other. The
berry weight of the S categories of shoots was signiﬁcantly different from the AH and NS
categories in 1999 and 2000. There was an approximate reduction of berry weight of 22-

27% of S shoots compared to both NS and AH shoots. In 2001, though not signiﬁcantly

117

different, there was a 26% reduction in berry weight of the D shoots compared to the NS
shoots and a 4% reduction compared to the AH shoots.

In vineyard B, in 2001, though not signiﬁcantly different, the berry weight of D
shoots was reduced by 10% and 5% compared to the NS and AH shoots, respectively

(Figure 4.11). The berry weight for AH shoots was 4% higher than that of NS shoots.

Sugar content per shoot

In vineyard A, the amount of sugar produced per shoot was signiﬁcantly lower
between all shoot categories in 1999 (Figure 4.12). The amount of sugar produced by I
and S shoots was signiﬁcantly lower compared to the M, NS, and AH shoots in 2001. In
1999, the sugar produced was reduced in the S shoots by 83% and 76% compared to the
NS and AH shoots, respectively. In 2000, the sugar produced was reduced in the S
shoots by 93% and 92% compared to the NS and AH shoots, respectively. The sugar
produced was reduced in M shoots by 28% and18% compared to the AH shoots of 1999
and 2000, respectively. The sugar produced in AH shoots was 25% and 12% lower than

the NS shoots in 1999 and 2000, respectively.

Undeveloped berries

In vineyard A, in 1999 and 2000, the AH shoots had the most undeveloped berries
per shoot, but in 2000, the M shoots had the most undeveloped berries (Figure 4.13).
There were no signiﬁcant differences between the undeveloped berry number of any
categories of shoots in 1999, 2000, and 2001. In 1999, the I shoots had the least number

of undeveloped berries (1.09), and the AH shoots had more than double that (2.32). The

118

undeveloped berry number of S and NS shoots were approximately the same in 1999. In
2000, the undeveloped berry number of S shoots were about the same as the number on
AH shoots, but the M shoots had nearly twice as many per shoot. In 1999 and 2001, the
AH shoots had the most undeveloped berries. It is also important to note that the
differences between the highest and lowest average of undeveloped berries was not more
than about 1.5 berries per shoot.

In vineyard B, in 2001, there were no signiﬁcant differences between the
categories in the number of undeveloped berries per shoot; however, the AH and NS
shoots had 74% and 72% more undeveloped berries than D shoots, respectively (Figure

4.14).

Small berries

In vineyard A, only in 2001, small berries that were purple and did not contain
seeds were found on clusters on many of the shoots (Figure 4.15). The berries were
about the size of a large garden pea. There were signiﬁcant differences between the
number of these small berries on D and NS shoots and between D and AH shoots. Small
berries made up 19% of the total number of berries on AH shoots; 18% on NS shoots,
and 8% on D shoots.

In vineyard B, in 2001, there were no signiﬁcant differences between the
categories in the number of small berries per shoot (Figure 4.16). The NS shoots had the
fewest number of small purple berries compared to the D and AH shoots (59% less than

the D shoots, 57% less than AH shoots).

119

Green berries

In vineyard A, there were no signiﬁcant differences between the mean number of
green berries in any of the shoot categories in 2000 and 2001 (Figures 4.17, 4.18). The
number of green berries on S shoots was highest in 2000 compared to all other shoot
categories. In 2001, there were fewer green berries per shoot (<1%) and with less weight
(<0.50%), compared to 2000.

In vineyard B in 2001, there were no signiﬁcant differences between the
categories in the number of green berries per shoot (Figure 4.19, 4.20). The NS shoots

had 56% less and the AH shoots had 37% less green berries per shoot than D shoots.

120

Figure 4.2. Mean number of clusters per shoot on ‘Concord’ grapes in Lawton, MI, in

1999-2001. AH: shoots on apparently healthy vine; NS: shoots on non-symptomatic

half of diseased vine; M=mildly, I=intermediately, and S=severely symptomatic shoots
on diseased vine; D: symptomatic shoots on diseased vine (2001 only). Columns with
the same letter are not signiﬁcantly different (p=0.05).

121

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Figure 4.3. Mean number of clusters per shoot on ‘Concord’ grapes in Schoolcraft, MI,
in 2001. AH: shoots on apparently healthy vine; N S: shoots on non-symptomatic half
of diseased vine; D= symptomatic shoots on diseased vine. Columns with the same letter
are not signiﬁcantly different (p=0.05).

123

Figure 4.4. Mean fruit weight per shoot on ‘Concord’ grapes in Lawton, MI, in 1999-
2001. AH: shoots on apparently healthy vine; N S: shoots on non-symptomatic half of
diseased vine; M=mildly, I=intermediately, and S=severely symptomatic shoots on
diseased vine; D= symptomatic shoots on diseased vine (2001 only). Columns with the
same letter are not signiﬁcantly different (p=0.05).

124

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Figure 4.5. Mean fruit weight per shoot on ‘Concord’ grapes in Schoolcraft, MI, in 2001.
AH: shoots on apparently healthy vine; NS: shoots on non-symptomatic half of diseased
vine; D= symptomatic shoots on diseased vine. Columns with the same letter are not
signiﬁcantly different (p=0.05).

126

Figure 4.6. Mean fruit weight per cluster on shoots of ‘Concord’ grapes in Lawton, MI,
in 1999-2001. AH: shoots on apparently healthy vine; N S: shoots on non-symptomatic
half of diseased vine; M=mildly, I=intermediately, and S=severely symptomatic shoots
on diseased vine; D= symptomatic shoots on diseased vine (2001 only). Columns with
the same letter are not signiﬁcantly different (p=0.05).

127

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Figure 4.7. Mean fruit weight per cluster on shoots of ‘Concord’ grapes in Schoolcraft, ,.
MI, in 2001. AH: shoots on apparently healthy vine; NS: shoots on non-symptomatic
half of diseased vine; D= symptomatic shoots on diseased vine. Columns with the same
letter are not signiﬁcantly different (p=0.05).

129

Figure 4.8. Mean number of berries per cluster on ‘Concord’ grape shoots in Lawton,
MI, in 1999-2001. AH: shoots on apparently healthy vine; NS: shoots on non-
symptomatic half of diseased vine; M=mildly, I=intermediately, and S=severely
symptomatic shoots on diseased vine; D: symptomatic shoots on diseased vine (2001
only). Columns with the same letter are not signiﬁcantly different (p=0.05).

130

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Figure 4.9. Mean number of berries per cluster on ‘Concord’ grape shoots in Schoolcraft,
MI, in 2001. AH: shoots on apparently healthy vine; N S: shoots on non-symptomatic
half of diseased vine; D: symptomatic shoots on diseased vine. Columns with the same
letter are not signiﬁcantly different (p=0.05).

132

Figure 4.10. Mean weight per berry on ‘Concord’ grape shoots in Lawton, MI, in 1999-
2001. AH: shoots on apparently healthy vine; NS: shoots on non-symptomatic half of
diseased vine; M=mildly, I=intermediately, and S=severely symptomatic shoots on
diseased vine; D= symptomatic shoots on diseased vine (2001 only). Columns with the
same letter are not signiﬁcantly different (p=0.05).

133

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Figure 4.11. Mean weight per berry on ‘Concord’ grape shoots in Schoolcraft, MI, in
2001. AH: shoots on apparently healthy vine; N S: shoots on non-symptomatic half of
diseased vine; D= symptomatic shoots on diseased vine. Columns with the same letter
are not signiﬁcantly different (p=0.05).

135

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Figure 4.12. Mean sugar content per shoot on ‘Concord’ grapes in Lawton, MI, in 1999—
2000. AH: shoots on apparently healthy vine; NS= shoots on non-symptomatic half of
diseased vine; M=mildly, I=intermediately, and S=severely symptomatic shoots on
diseased vine. Columns with the same letter are not signiﬁcantly different (p=0.05).

136

Figure 4.13. Mean number of undeveloped berries per shoot on ‘Concord’ grape shoots
in Lawton, MI, in 1999-2001. AH: shoots on apparently healthy vine; NS: shoots on
non-symptomatic half of diseased vine; M=mildly, I=intermediately, and S=severely
symptomatic shoots on diseased vine; D= symptomatic shoots on diseased vine (2001
only). Columns with the same letter are not signiﬁcantly different (p=0.05).

137

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Figure 4.14. Mean number of undeveloped berries per shoot on ‘Concord’ grape shoots
in Schoolcraft, MI, in 2001. AH: shoots on apparently healthy vine; NS: shoots on non-
symptomatic half of diseased vine; D= symptomatic shoots on diseased vine. Columns
with the same letter are not signiﬁcantly different (p=0.05).

139

 

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Figure 4.15. Mean number of small berries per shoot on ‘Concord grape shoots in
Lawton, MI, in 1999-2001. AH: shoots on apparently healthy vine; NS: shoots on non-
symptomatic half of diseased vine; M=mildly, I=intermediately, and S:severely
symptomatic shoots on diseased vine; D: symptomatic shoots on diseased vine (2001
only). Columns with the same letter are not signiﬁcantly different (p=0.05).

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Figure 4.16. Mean number of small berries per shoot on ‘Concord’ grape shoots in
Schoolcraft, MI, in 2001. AH: shoots on apparently healthy vine; NS: shoots on non-
symptomatic half of diseased vine; D: symptomatic shoots on diseased vine. Columns
with the same letter are not signiﬁcantly different (p=0.05).

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Figure 4.17. Mean percentage of green berries per shoot on ‘Concord’ grape shoots in
Lawton, MI, in 1999-2001. AH: shoots on apparently healthy vine; NS: shoots on non-
symptomatic half of diseased vine; M=mildly, I=intermediately, and S=severely
symptomatic shoots on diseased vine; D: symptomatic shoots on diseased vine (2001
only). Columns with the same letter are not signiﬁcantly different (p=0.05).

141

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Figure 4.18. Mean percentage of green berries per shoot on ‘Concord’ grape shoots in

Schoolcraft, MI, in 2001. AH: shoots on apparently healthy vine; NS: shoots on non-
symptomatic half of diseased vine; D: symptomatic shoots on diseased vine. Columns
with the same letter are not signiﬁcantly different (p=0.05).

142

Leaf area

In vineyard A, the mean leaf area of the S shoots was signiﬁcantly lower in 1999
and 2000 compared to the AH and NS shoots (Figure 4.19). Compared to the NS shoots,
leaf area of S shoots was reduced by 83% in 1999 and 76% in 2000. Compared to AH
shoots, leaf area of S shoots was reduced by 87% in 1999 and 75% in 2000. Leaf area of
M shoots was reduced compared to the NS and AH shoots by 26% and 42%,
respectively, in 1999. The M shoots were reduced compared to the NS and AH shoots by
37% and 33%, respectively, in 2000. In 2001, leaf area of D shoots was reduced by 73%
and 74% compared to the NS and AH shoots, respectively. In 2001, leaf area of AH
shoots was 2% higher than that of NS shoots. In all years there was no signiﬁcant
difference between the AH and NS shoot categories.

In vineyard B, in 2001, leaf area per shoot was signiﬁcantly different between D
and NS and D and AH shoots; there was a reduction of 51% and 53%, respectively
(Figure 4.20). Leaf area of the NS shoots were reduced by 4% compared to the AH

shoots.

Shoot length

In vineyard A, the mean shoot length of the S shoots was signiﬁcantly lower in
1999 and 2000 compared to the AH and NS shoots (Figure 4.21). In 1999, the S shoots
were reduced by 71% and 74% compared to the NS and AH shoots, respectively. Shoot
length of M shoots was signiﬁcantly higher than AH and NS in 1999, but only 5% more
than the AH shoots. In 2000, compared to the NS shoots, shoot length of the S shoots

were reduced by 71 %, and compared to the AH shoots, reduced by 60%. Shoot length of

143

M shoots was also reduced by 51% compared to the NS shoots, but only 6% compared to
the AH shoots. Shoot length on D shoots in 2001 was reduced by 61% and 65%
compared to the N S and AH shoots, respectively.

In vineyard B, in 2001, shoot length of D shoots was signiﬁcantly reduced by
53% and 59% compared to the AH and NS shoots, respectively (Figure 4.22). The shoot

length of the NS shoots was reduced by 12% compared to the AH shoots.

Node number

In vineyard A, the mean number of nodes, or node number, on S shoots was
signiﬁcantly lower than on AH and NS shoots in 1999 (Figure 4.23). Compared to both
NS and AH shoots, node number of S shoots was reduced by 42% in 1999. Node number
on M shoots was reduced by 17% and 18% compared to the NS and AH shoots,
respectively. In 2000, node number of S and I shoots was signiﬁcantly lower than of M,
NS and AH shoots. Compared to the NS and AH shoots, node number of S shoots was
reduced by 45% and 40%, respectively. Node number on M shoots was reduced by 13%
compared to the NS shoots and only 4% compared to AH shoots. In 2001, node number
of D shoots was 38% and 37% reduced compared to NS and AH shoots, respectively.

In vineyard B, in 2001, node number was signiﬁcantly different between the D
and the NS and D and AH shoots; 44% and 37%, respectively (Figure 4.24). Node

number of the NS shoots were reduced by 11% compared to the AH shoots.

144

Shoot diameter

In vineyard A, the mean shoot diameter of the S shoots was signiﬁcantly lower
than that of AH and NS shoots in 1999 and 2000 (Figure 4.25). In 1999, shoot diameter
of S shoots was reduced by 35% and 27%, compared to the NS and AH shoots,
respectively. In 2000, shoot diameter of S shoots was reduced by 49% compared to both
AH and NS shoots. Shoot diameter of M shoots was not signiﬁcantly lower than that of
AH or NS shoots but was numerically reduced by 16%. In 2001, the D shoots showed a
28% reduction in shoot diameter compared to AH and NS shoots.

In vineyard B, in 2001, shoot diameter was signiﬁcantly different between the D
and NS and D and AH shoots; 23% and 32%, respectively (Figure 4.26). Shoot diameter

of the NS shoots was 6% less than the AH shoots.

145

Figure 4.19. Mean leaf area per shoot on ‘Concord’ grapes in Lawton, MI, in 1999-2001.
AH: shoots on apparently healthy vine; NS: shoots on non-symptomatic half of diseased
vine; M=mildly, I=intermediately, and S=severely symptomatic shoots on diseased vine;
D: symptomatic shoots on diseased vine (2001 only). Columns with the same letter are
not signiﬁcantly different (p=0.05).

146

 

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147

leaf area
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Figure 4.20. Mean leaf area per shoot on ‘Concord’ grapes in Schoolcraft, MI, in 2001.
AH: shoots on apparently healthy vine; NS: shoots on non—symptomatic half of diseased
vine; D: symptomatic shoots on diseased vine. Columns with the same letter are not

signiﬁcantly different (p=0.05).

148

Figure 4.21. Mean shoot length of ‘Concord’ grape shoots in Lawton, MI, in 1999-2001.
AH: shoots on apparently healthy vine; NS: shoots on non-symptomatic half of diseased
vine; M=mildly, I=intermediately, and S=severely symptomatic shoots on diseased vine;
D: symptomatic shoots on diseased vine (2001 only). Columns with the same letter are
not signiﬁcantly different (p=0.05).

149

 

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150

shoot length (cm)

 

AH NS D

Figure 4.22. Mean shoot length of ‘Concord’ grape shoots in Schoolcraft, MI, in 2001.
AH: shoots on apparently healthy vine; NS: shoots on non-symptomatic half of diseased
vine; D: symptomatic shoots on diseased vine. Columns with the same letter are not
signiﬁcantly different (p=0.05).

151

Figure 4.23. Mean number of nodes per shoot on ‘Concord’ grapes in Lawton, MI, in
1999-2001. AH: shoots on apparently healthy vine; NS: shoots on non-symptomatic
half of diseased vine; M=mildly, I=intermediately, and S=severely symptomatic shoots
on diseased vine; D: symptomatic shoots on diseased vine (2001 only). Columns with
the same letter are not signiﬁcantly different (p=0.05).

152

 

 

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Figure 4.24. Mean number of nodes per shoot on ‘Concord’ grapes in Schoolcraft, MI, in
2001. AH: shoots on apparently healthy vine; N S: shoots on non-symptomatic half of
diseased vine; D: symptomatic shoots on diseased vine. Columns with the same letter
are not signiﬁcantly different (p=0.05).

154

Figure 4.25. Mean shoot diameter per shoot on ‘Concord’ grape shoots in Lawton, MI, in
1999-2001. Diameter measured at intemode above the second node of the shoot. AH:
shoots on apparently healthy vine; NS: shoots on non-symptomatic half of diseased vine;
M=mildly, I=intermediately, and S=severely symptomatic shoots on diseased vine; D:
symptomatic shoots on diseased vine (2001 only). Columns with the same letter are not
signiﬁcantly different (p=0.05).

155

 

 

 

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156

 

shoot diameter (mm)
0 -l 10 (D h 01 O: \l 00

AH NS D

Figure 4.26. Mean shoot diameter on ‘Concord’ grape shoots in Schoolcraft, MI, in
2001. Diameter measured at intemode above the second node of the shoot. AH: shoots
on apparently healthy vine; NS: shoots on non-symptomatic half of diseased vine; D:
symptomatic shoots on diseased vine. Columns with the same letter are not signiﬁcantly

different (p=0.05).

157

Juice composition
Vineyard A

The mean brix (percent sugar), pH, and titratable acid per shoot between
categories were not signiﬁcantly different in all years (Figures 4.27, 4.28, 4.29).
Comparisons between categories of all years suggest that the disease does not
signiﬁcantly affect juice composition. Brix of S shoots was numerically reduced by 8%
in 1999 and by 30% in 2000 compared to NS shoots. Compared to the AH shoots, brix of
S shoots was reduced by 25% in 2000. The pH of each category differed in each year,
but in none of the years were the differences signiﬁcant. In 1999, S shoots had the
highest pH, but in 2000 S shoots had the lowest mean pH. The pH of M shoots was
highest in 2000, but second lowest to the NS shoots in 1999. While the pH of S shoots
was highest in 1999, the titratable acids of S shoots in 1999 was between the lowest (AH)
and highest (M). In 2001, there was a 11% reduction of titratable acids of all of the D
shoots compared to the AH shoots, while there was only a 1% reduction of the pH of all

the D shoots.

Vineyard B

In 2001, in all components of juice composition, the D shoots had the highest
mean brix, pH and titratable acids per shoot (Figure 4.30). The brix of D shoots were
signiﬁcantly higher than the NS shoots. The pH of D shoots were signiﬁcantly higher
than the AH and NS shoots. There were no signiﬁcant difference between categories in
titratable acids. The NS shoots had the least brix and pH per shoot, and the AH shoots

had the least titratable acids per shoot. The NS shoots brix was reduced the largest

158

compared to the D shoots (10%), and the AH shoots titratable acids was reduced the
largest compared to the D shoots (12%). The reduction of pH among the categories is

<1%.

159

Figure 4.27. Mean brix (% sugar) in juice from clusters on ‘Concord’ grape shoots in
Lawton, MI, in 1999-2001. AH: shoots on apparently healthy vine; N S: shoots on non-
symptomatic half of diseased vine; M=mildly, I=intermediately, and S=severely
symptomatic shoots on diseased vine; D: symptomatic shoots on diseased vine (2001
only). Columns with the same letter are not signiﬁcantly different (p=0.05).

160

 

 

 

 

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161

Figure 4.28. Mean pH in juice from clusters on ‘Concord’ grape shoots in Lawton, MI,
in 1999-2001. AH: shoots on apparently healthy vine; N S: shoots on non-symptomatic
half of diseased vine; M=mildly, I=intermediately, and S=severely symptomatic shoots

on diseased vine; D: symptomatic shoots on diseased vine (2001 only). Columns with

the same letter are not signiﬁcantly different (p=0.05).

162

 

 

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163

Figure 4.29. Mean titratable acids in juice from clusters on ‘Concord’ grape shoots in
Lawton, MI, in 1999-2001. AH: shoots on apparently healthy vine; NS: shoots on non-
symptomatic half of diseased vine; M=mildly, I=intermediately, and S=severely
symptomatic shoots on diseased vine; D: symptomatic shoots on diseased vine (2001
only). Columns with the same letter are not signiﬁcantly different (p=0.05).

164

Figure 4.29

 

 

 

 

 

 

 

0.2 ~

165

Figure 4.30. Mean brix (% sugar), pH, and titratable acids in juice from clusters on
‘Concord’ grape shoots in Schoolcraft, MI, in 2001. AH: shoots on apparently healthy
vine; N S: shoots on non-symptomatic half of diseased vine; D: symptomatic shoots on
diseased vine. Columns with the same letter are not signiﬁcantly different (p=0.05).

166

Figure 4.30

 

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167

Correlations
_Y_ie_lg

Within AH vines in 1999, the sugar content per shoot was correlated with the
number of berries per shoot (:079) and leaf area (r=0.63), and in 2000 with the number
of berries per shoot (r=0.88), number of berries per cluster (r=0.7l), and clusters per
shoot (r=0.59) (Appendix C). Within D vines, sugar content per shoot was correlated

with the weight per shoot (r=0.98), weight per cluster (1:091), weight per berry (r=0.6l),

 

number of clusters (1:0.61), number of berries per cluster (tr-0.80) and per shoot
(1:091), and shoot diameter (1:064) in 1999. In 2000, sugar content per shoot within D
vines was correlated with weight per shoot (r=0.95), weight per cluster (r=0.85), weight
per berry (r=0.59), number of clusters (r=0.57), and the number of berries per cluster
(1:080) and per shoot (r=0.91).

Within AH vines, the fruit weight per shoot was correlated with cluster weight
(r=0.85), number of berries per cluster (r=0.79), number of berries per shoot (r=0.90),
amount of sugar (r=0.95), and leaf area (r=0.55) in 1999. In 2000 within AH vines, fruit
weight per shoot was correlated with cluster weight (1:081), amount of sugar (r=0.94),
number of clusters (r=0.68), and berries per cluster (r=0.75) and per shoot (r=0.98).
Within the AH vines in 2000, fruit weight per shoot was not correlated with leaf area.
Within D vines in 1999, fruit weight per shoot was correlated with cluster weight
(r=0.90), berry weight (r=0.50), cluster number (r=0.67), number of berries per cluster
(1:084) and per shoot (r=0.97), amount of sugar (r=0.98), and shoot diameter (r=0.65).
In 2000 within D vines, with the exception of shoot diameter, fruit weight per shoot was

correlated with all in components as in 1999: cluster weight (1:088), berry weight

168

(r=0.56), cluster number (r=0.68), number of berries per cluster (1:085) and per shoot
(r=0.98), and amount of sugar (r=0.95). Leaf area per shoot was not correlated with fruit
weight per shoot in 1999 or 2000 within D vines (1:039 and r:0.49, respectively), but
was correlated with fruit weight of clusters in both years (1:053 and r:0.52,
respectively).

Within the AH vines, the number of undeveloped berries per shoot was correlated
with the percent of the green berry weight (1:099) and titratable acids (r=0.55), and
inversely correlated to pH (1:074) in 2000. The number of undeveloped berries per
shoot was not correlated with any other component in 1999. Within the D vines, the
number of undeveloped berries per shoot was not correlated with any other component in
1999 and 2000.

Within AH vines in 2000, the percent number of green berries per shoot was
correlated with brix inversely (r=-0.79), pH inversely (r:-0.62), titratable acids (r=0.68),
but within D vines, only correlated with brix inversely (r:-0.73). Within AH and D vines
in 2000, the percent weight of green berries per shoot was correlated with pH inversely
(r=-0.78) and titratable acids (1:0.61), but not with brix per shoot. There were no green

berries on clusters in 1999.

Vegetative growth
Within AH vines in 1999, the leaf area per shoot was correlated with shoot length

(1:076), number of nodes (1:054), shoot diameter (r=0.57), weight per shoot (r=0.55),
weight per cluster (r=0.55), the number of berries per cluster (r=0.52), sugar content per

shoot (1:063), and berries per shoot (r=0.50). Within D vines in 1999, the leaf area per

169

shoot was correlated with shoot length (r=0.73), number of nodes (1:063), weight per
cluster (1:053), and weight per berry (1:071). Within AH vines in 2000, the leaf area
per shoot was correlated with shoot length (1:088) and number of nodes per shoot
(1:083). Within the D vines in 2000, the leaf area per shoot was correlated with shoot
length (1:091), number of nodes (r=0.85), weight per cluster (1:052), and weight per
berry (r=0.50).

Within AH vines in 1999, the mean shoot length was correlated with leaf area
(1:076), number of nodes (r=0.73), and shoot diameter (r=0.62). Within D vines in
1999, the shoot length was correlated with leaf area (r=0.73), number of nodes (r=0.90),
and shoot diameter (r=0.60). Within AH vines in 2000, shoot length was correlated with
leaf area (r=0.88) and number of nodes (1:0.83). Within D vines in 2000, shoot length

was correlated with leaf area (1:091) and number of nodes (r=0.87).

Juice composition

Juice composition elements of different years within symptomatic and AH vines
were not regularly correlated with many of the other components. In 2000, within AH
vines, brix was inversely correlated with titratable acids per shoot (r=—0.58), and percent
of green berries per shoot (1:079), and positively correlated with weight per berry
(r=0.67). In 1999, there were no correlations. In 2000 within AH vines, the pH per shoot
was inversely correlated with number of undeveloped berries (r:-0.74), green berries (r=-
0.62), and weight of green berries per shoot (1:078). The mean titratable acids per

shoot of AH vines in 2000 was inversely correlated with weight per berry (1:064).

170

Juice composition elements were not consistently correlated with each other in
2000 and not at all in 1999. In 2000, within AH vines, mean brix, pH and titratable acids
per shoot were correlated with each other; brix and pH (r=0.50), brix and titratable acids
(r=-0.58), and pH and titratable acids (r:-0.84). In 2000, within D vines, the titratable
acids were correlated with pH (#084) and brix (m-0.58). In 2000 within D and AH
vines, titratable acids and pH, and titratable acids and brix were always inversely
correlated. In 2000, the correlation between brix and pH was not as strong (r: or > 0.50)
as the correlation between titratable acids and pH (r:-0.84). The mean titratable acids,

pH, and brix per shoot on symptomatic vines in 1999 were not correlated with each other.

Discussion

There are few studies done on the effects of Eutypa dieback symptoms on
‘Concord’ yield, vegetative growth and juice composition, and there are no known
studies of the effect on individual shoots. Loss due to Eutypa dieback is often correlated
with disease severity (Creaser, et al., 2000; Johnson, 1987; Munkvold, et al., 1994).
Results presented here show that the symptoms of Eutypa dieback in general signiﬁcantly
affected the yield and growth on shoots, but did not signiﬁcantly affect juice
composition. In at least two of the three years, there were signiﬁcant differences in fruit
weight per shoot, number of berries per cluster, leaf area, shoot length, and sugar
produced per shoot between all of the symptomatic shoot categories, and the non-
symptomatic (NS) and apparently healthy (AH) shoots. In all years, many components

of yield and growth on NS shoots were signiﬁcantly higher than AH shoots.

171

Vegetative growth, measured using pruning weights, was signiﬁcantly reduced by
Eutypa dieback, but not as much as yield (Munkvold, et al., 1994). In studies in Australia
and California, total yield and the number of clusters per vine were signiﬁcantly reduced
on ‘Shiraz,’ ‘French Colombard,’ and ‘Chenin blanc’ vines according to disease severity
(Creaser, et al., 2000; Munkvold, et al., 1994). In Washington, total yield, cluster number
per vine, cluster weight, and 100-berry weight were signiﬁcantly reduced on ‘Concord’
vines according to disease severity (Johnson, 1987). Results here show that similar and
additional components of yield were signiﬁcantly reduced by the disease. The number of
berries per cluster, cluster weight, and shoot weight were affected signiﬁcantly, but the
number of clusters per shoot and berry weight was not. Also, measuring leaf area, shoot
length, shoot diameter, and the number of nodes may give additional information about
the affect of the disease on vegetative growth.

The reduction in shoot length and leaf area due to the disease symptoms may
work in tandem to reduce yield by preventing the already reduced photosynthetic tissue
from getting sun exposure. Reduced vegetative growth on infected vines may over time
reduce the spring reserves, which may be a factor in vine decline. Lower shoot lengths
means in 1999 may have been due to loss of shoot ends during the drying period. There
was a correlation between leaf area and shoot length in all years; the AH vines with
longer shoots produced more leaf area than the symptomatic vines’ shorter shoots.
Similar M shoot length and leaf area means compared to NS and AH shoots suggests that
the effects of mild foliar symptoms are negligible. The high percent reductions (up to

75% between S and AH shoots) show that leaf area and shoot length are affected by the

symptoms.

172

T'

The reduced number of nodes decreases the number of clusters on a shoot, but
differences were not signiﬁcant between shoots categories. The effect of the disease may
decrease the length of the intemodes more than reduce node number, leaving the nodes
on each shoot to develop. A reduction of the number of clusters a shoot produces may be
more affected by the disease than the number of nodes. In fact, cluster numbers of S
shoots were reduced compared to AH shoots by 55%.

Shoot diameter reduction may increase the chance of winter damage, reducing the
number of canes that bear shoot the following spring. Our results showed shoot diameter
reductions were not always signiﬁcant between shoot categories. Diameters of S shoots
were signiﬁcantly reduced, possibly because of the severe reduction in shoot length.
When shoot diameter is reduced the chance of winter damage increases, and this reduces
the number of canes that bear shoots the following spring. The variable correlations
between vegetative growth components suggest that the disease does not affect each
component equally each year or within each vine condition.

The number of clusters per shoot depends on the number of nodes per shoot. In
this study, cluster numbers were not signiﬁcantly reduced by the disease except on S
shoots. When shoots were severely stunted, the reduced shoot length may have
contributed to the reduction in the number of cluster, but when the stunting was less
severe, the number of clusters was reduced, but not signiﬁcantly.

Eutypa dieback affected the 100—berry weight and number of clusters per vine
signiﬁcantly, but variably affected the weight of clusters (Johnson, 1987; Munkvold, et
al., 1994). In both studies, no count of berries was reported. In results reported here, the

number of berries was most affected by the disease symptoms. In both vineyards, there

173

were signiﬁcantly fewer berries on the symptomatic shoots compared to the AH and the
NS shoots. In vineyard A, M shoots had approximately the same as the AH and NS
shoots. The signiﬁcant additive effects of the number of berries rather than their
individual weight make the total fruit weight per shoot signiﬁcantly different between
disease severity shoot categories.

The disease affected the number of berries per cluster more than the number of
clusters and nodes, which suggests intemodes are shortened rather than eliminated.
Signiﬁcant reduction in berry number suggests that Eutypa dieback reduces fruit set, but
there is no data from this study of the potential number of berries per cluster. Berries
may have not set fruit or have fallen off before harvest. The remaining berries on all
shoot categories ripen similarly, perhaps at a time in the growth that the toxin is not
active. The remaining berries on symptomatic shoots will also receive less photosynthate
due to the reduced leaf area and shoot length.

All of the components of vegetative growth and number of benies add to the
effect the disease symptoms have on the grapevine when measuring shoot weight. The
signiﬁcant differences between shoot categories of the number of berries per cluster and
the number of clusters per shoot constitutes the fruit weight per shoot. Reductions in
shoot weight were up to 92% and 78% comparing S shoots and AH shoots in 2000 and
1999, respectively. The short-term effects of shoot length and leaf area reduction affect
the photosynthates available to the berries, and the long-term reduction affects ability of
the vine to produce reserves for the following spring, which contributes to additional

yield reduction. The shoot weight of M shoots in 2000 were not different from the NS or

174

AH shoots, which indicates the effect of the disease is not as pronounced as on the I and
S shoots.

Shoot weight was correlated with cluster weight and berry number within AH and
symptomatic vines for both years. This result is consistent with cluster weight being
made up of many berries, not necessarily heavier berries. As expected, the shoot weight
was always correlated with sugar per shoot in all years and within AH and D vines.
Shoot weight was only correlated with leaf area within AH vines for 1999 and within D
vines for 2000.

Cluster weight was reduced by the disease by up to 80% on S shoots compared to
AH shoots. Because the disease did not signiﬁcantly affect the berry weight, the reduced
number of berries that make up the cluster contributed to the reduction of cluster weights
in symptomatic shoots.

The berry weight and cluster number make up the shoot weight, but the berry
weights were not as different among categories as cluster weight and shoot weight. The
signiﬁcant additive effects of berry number rather than their individual weight make the
total fruit weight per shoot signiﬁcantly different between disease severity shoot
categories. Berry weight may not be affected as much by the symptoms because the
toxin may be inactive at the time the berries develop their weight.

The temperature, soil moisture, and relative cluster exposure in which berries
mature is important to evenness of ripening and the levels of brix, pH and titratable acids
in the juice (Cawthon, et al., 1983; Jackson, et al., 1993). In results presented here,
differences were not signiﬁcant between shoot categories for all juice composition

components, which suggests that environmental conditions rather than Eutypa symptoms

175

affect ripening evenness and juice composition. Factors such as low temperatures,
excessive soil moisture, and canopy shading during certain stages of berry development
could have affected all vines’ juice components equally (Jackson, et al., 1993). The
number of undeveloped and green berries did not appear affected by the disease as well.
The differences in shoot categories were not signiﬁcant and the categories showed
inconsistencies between years. A higher number of nodes per vine retained at pruning
has been shown to increase the number of green berries at harvest, which may have
contributed to the large number in 2000 in vineyard A (Morris, et al., 1981). Other
factors that increase the number of unevenly developed berries may be due to vineyard
conditions that vary over the years such as temperature, soil moisture and cluster
exposure rather than the disease symptoms.

The brix may be greater if the canopy has been reduced by Eutypa dieback,
allowing more exposure of existing clusters and leaves to sunlight, though it is expected
that the yield would be reduced on these shoots. Also, uneven ripening of berries
contributes to the lowered brix in grape juice composition (Lider, et al., 1975; Morris, et
al., 1981). This may be the casein this study in 2000, when the high numbers of
undeveloped and green berries correlated with the lower brix.

The somewhat negligible differences that the results of juice composition found
between shoot categories suggest that the disease would not affect the total brix of a large
volume of harvest. If the disease incidence is high, the brix per shoot may remain the
same, but the total harvest (and total sugar production) would be reduced by the disease.

In this study, the highest incidence of disease was 4% in 2001 in vineyard A, however,

176

incidences of up to 76% and 81% have been reported (Johnson, 1987; Munkvold, et al.,
1993).

The amount of sugar produced per shoot, a function of the total weight and the
brix (% sugar), was reduced signiﬁcantly by Eutypa dieback. Because the brix per shoot
was not signiﬁcantly different between shoot categories, the signiﬁcance between shoot
categories of sugar produced per shoot mimics that of shoot weight. In 1999, the
differences between all shoot categories were signiﬁcant, the non-symptomatic shoots
seemingly producing enough to make up for the loss on symptomatic shoots.

Total sugar content per shoot is not consistently correlated to leaf area within AH
shoots in each year, and not at all correlated within symptomatic shoots. However, sugar
content is correlated within symptomatic shoot categories with various yield components.
This suggests that the vegetative growth is not as related to sugar production per shoot as
berry numbers and the collective weight of the berries is.

In Washington state, Eutypa dieback reduced yield by 13% in the ﬁrst year to
24% in the second year in a study of ‘Concord’ (Johnson, 1987). However, the
researchers used different vines each year. In results presented here, yield per shoot may
not decline steadily on infected vines. Individual vines appear to recover or be less
affected by the toxin, depending on many factors.

The vigorous growth of the non-symptomatic cordon on an infected vine suggests
that the vine compensates for the loss of growth on the symptomatic cordons (Johnson,
1987). Higher yield and more vegetative growth of non-symptomatic shoots in infected
vines has been noted as a way the vine may initially recover yield loss from symptoms of

Eutypa dieback (Munkvold, et al., 1994). In both vineyards in Michigan, certain yield

177

and growth components of non-symptomatic shoots (N S) were higher than the apparently
healthy shoots (AH). Infected vines that appear to have recovered and have no
symptomatic shoots have been observed to decline rapidly afterwards (R. Parker,
personal communication).

“Compensation” by non-symptomatic shoots resembles the effect of pruning as
the normal ﬂow of water and nutrients to the vine tissue above the infection site is
prevented by the growth of the fungus, and wood rot the fungus causes. However,
Eutypa dieback signiﬁcantly affected vegetative growth as measured by pruning weight,
but not as signiﬁcantly as it affected yield components (Munkvold, et al., 1994).

In vineyard B, the NS shoots had higher yield components similar to those in
vineyard A. However, the AH shoots in vineyard B had higher mean leaf area, shoot
length, and shoot diameter than the NS shoots. This suggests no compensatory growth in
infected vines’ non-symptomatic cordons, possibly because symptomatic shoots are often
pruned out in spring. Corrective pruning — pruning diseased portions of the vines — may
help the vine recover if this increases healthy vegetative growth and reduces the crop
load, but is not a long-term solution.

Correlations between reduced yield and disease severity have been reported in
studies in California and Washington (Johnson, 1987; Munkvold, et al., 1994). Less
severely diseased vines had reduced yields, though not always signiﬁcant. Likewise, in
at least one year in this study, mildly symptomatic (M) shoots had signiﬁcantly reduced
shoot weight, berry number, and leaf area.

The data presented here is based on measurements of speciﬁc shoots within vines

and not the whole vine, due to high variability between vines in a vineyard. Since shoots

178

become canes that produce more (or fewer) shoots in the following years after
observation, further studies of the effect of Eutypa dieback on ‘Concord’ grapevines
would be useful to include whole vines studies. Possible research questions include how
corrective pruning of infected vines affects short- and long-term yield loss, and if
mechanical hedging with or without hand pruning follow-up affects the yield and decline

of infected vines.

179

CHAPTER 5. MOLECULAR DETECTION AND DIAGNOSIS
Introduction

Eutypa dieback symptoms develop slowly on grapevines and this makes early
detection difﬁcult (Carter, 1991; Moller, et al., 1978). Infected vines can be identiﬁed by
shoot symptoms in the spring when shoots are about 30 cm long. Symptoms of Eutypa
dieback have been found to be ephemeral. The window of time that symptoms of Eutypa
dieback are easily visible can be less than a week due to rapid growth in the spring, and
symptoms of herbicide damage or virus infection may be confused with those of Eutypa
dieback as well. Infection by E. lata may be reconﬁrmed by isolating the fungus from
the margins of cankers; however, cankers are often difﬁcult to spot because they occur
beneath the bark and can be unobtrusive. Detecting cankers on vines can be destructive
because the outer bark needs to be carefully peeled off. In addition, isolation of the
fungus from cankers or stained wood may be difﬁcult and give false negative results
(Moller, et al., 1978; Peros, et al., 1997; Tey-rulh, et al., 1991). After the isolations are
made and if there are no competing organisms in the culture, the anamorph of E. lata,
Libertella blepharis, may take up to a month to produce conidia and some isolates do not
produce them (Carter, 1991; Moller, et al., 1978; Peros, et al., 1997). There may also be
numerous other pathogens and saprophytes present producing similar-looking white,
ﬂuffy or appressed mycelium that interfere with the isolation and complicate the
diagnosis (Carter, 1991). Reliable, rapid methods that accurately detect the mycelium or
ascospores of the fungus in wood to identify infected vines are important for timely

implementation of control measures.

180

Serological methods

Polyclonal antisera produced using the mycelium and ascospores of E. lata were
tested for speciﬁcity using the double-diffusion precipitation technique in agarose and on
slides, respectively. Ascospores and mycelium of E. lata appeared to be antigenically
distinct (Francki, et al., 1970). Ascospores reacted strongly to homologous antiserum,
but mycelial cell walls did not react speciﬁcally with any of the antisera. Because the
contents of the cell (e. g., proteins) may have been what the antisera were reacting to,
further studies to increase speciﬁcity of the antiserum to the mycelia were recommended.
Using puriﬁed hyphal cell walls would eliminate the cell contents from the preparation of
the antiserum. Though the ascospores of both E. lata and Cryptovalsa ampelina (Nits.)
Fuckel were serologically similar, the mycelium of E. lata was distinguishable from that
of C. ampelina with gel-diffusion (Francki, et al., 1970).

When mycelial cell walls were removed from E. lata and various species of
Eutypella, Valsaeutypella, Valsa, Leucostoma, and F usarium, all cultures of E. lata
reacted positively with antiserum produced against wall-less mycelium of E. lata in gel
diffusion assays, while cultures of the other fungi did not show a precipitate (Price,
1973). All cultures were subjected to gel diffusion and ﬂuorescence antibody staining
techniques. The latter showed variable results and was generally less reliable than the gel
diffusion technique (Price, 1973).

In another study, whole cell and cell wall polyclonal antisera were prepared from
E. lata mycelium (Gendloff, et al., 1983). Though neither reacted speciﬁcally, when the
cell wall antiserum conjugate was cross-adsorbed with Phomopsis viticola, the speciﬁcity

to E. lata increased. Cross—adsorption with Epicoccum nigrum, with or without P.

181

2’

viticola, did not increase speciﬁcity to E. lata. An indirect method of staining wood
samples containing E. lata hyphae was found to be effective in distinguishing the various
fungi that could infect grapevines. The indirect method involved treatment of samples
with anti-Eutypa rabbit serum followed by Rhodarrrine isotlriocynanate (RITC)-labeled
goat anti-rabbit gamma globulin; the direct method used just Rl'TC-labeled gamma
globulin. The direct method was less effective in distinguishing E. lata from the other
fungi present because the hyphae ﬂuoresced less brightly. Though the indirect method
was not foolproof, it allowed for differentiation between E. lata and other fungi more
often than the direct method. The technique was promising, even though a completely

speciﬁc antiserum to E. lata was not produced (Gendloff, et al., 1983).

Molecular methods

Molecular probes utilizing the polymerase chain reaction (PCR) and species-
speciﬁc primers to detect pathogens have been successful for many fungal pathogens,
especially those that are difﬁcult to isolate or that cause symptomless infection (Brown,
et al., 1993; Jasalavich, et al., 2000; Lee, et al., 2001; Rollo, et al., 1990).

In a French study, six primer pairs — three designed from ribosomal DNA internal
transcribed spacer sequences (ITS) and three designed from randomly ampliﬁed
polymorphic DNA fragments (RAPD) — were tested using PCR on isolates of Eutypa lata
and on other microorganisms (fungi and bacteria) associated with grapevine (Lecomte, et
al., 2000). The six primer pairs detected all E. lata isolates tested; two of the six detected
an additional 45 more E. lata isolates. None of the other microorganisms were detected

by the six primer pairs. A positive check of their DNA was conﬁrmed by detection with

182

universal fungal and bacterial primers. Eutypa lata was detected in crude mycelium
preparations of cultures contaminated with saprophytic organisms of the isolates, as well
as from wood chips, which were boiled in order to extract E. lata DNA. Fifteen
mycelium samples that were not visually identiﬁed as E. lata were subjected to PCR with
the two primer pairs that detected the most isolates. Twelve of the 15 isolates were
identiﬁed as E. lata using PCR, though not visually identiﬁable before. Re-culturing the
12 isolates conﬁrmed the PCR result of E. lata identiﬁcation. The importance of a rapid
DNA test was illustrated in this study; the primers can detect small amounts of sample
from a culture even when mixed, and the extraction method from wood chips from the
canker margins by boiling allows detection of fungal DNA (Lecomte, et al., 2000).

The swift identiﬁcation of infection by Eutypa lata is important for controlling the
spread of this disease. Being able to rapidly detect E. lata from wood chips will also
make it easier to obtain genetic information to study the epidemiology and spread of the
pathogen. The objective of this study was to develop an accurate, reliable method for
detecting E. lata infection in grapevines in Michigan using published primers as well as

newly developed primers.

183

 

Materials and methods

Isolates

All isolates were grown and stored on half-strength potato dextrose agar (PDA) at
room temperature (Difco bactopotato dextrose agar and bactoagar, Becton Dickinson,
Microbiology Systems, Sparks, MD) supplemented with 100 ug/L of streptomycin
(streptomycin sulfate, cat # 6501, Sigma, Inc., St. Louis, MO). Three isolates originating
from ascospores on grapevines were obtained from the laboratory of Doug Gubler, UC
Davis, California. Isolates from Michigan were obtained from ascospores in stromata in
a large canker area on a ‘Concord’ grapevine trunk from a vineyard in Lawton, Michigan.
Stroma pieces up to 2 cm in diameter were placed on ﬁlter paper in petri dishes and
moistened. Perithecial contents swelled with the moisture and ascospores were removed
from the perithecia with sterile tweezers. To obtain single spore cultures, ascospores
were placed in a sterilized tube of deionized water and spread out on water agar using
sterilized glass rods. Single ascospores were separated using sterile needles or scalpels
and placed onto Mt strength PDA. Isolates from Michigan were designated M12 and M13
(both from the same trunk), and the isolates from California were designated as CA30,
CA31 and CA38. Isolates were allowed to grow to at least halfway to the edge of the
dish prior to DNA extraction.

Isolates used for testing for the speciﬁcity of primers included one isolate of
Pestalotia, three isolates of Trichoderma sp., one isolate of Penicillium, two isolates of
Phomopsis viticola, and a mixture of Brachysporium and Phomopsis obtained from the
surface of a grapevine trunk The pure cultures of Phomopsis viticola were isolated from

a ‘Concord’ berry, Pestalotia was collected as a contaminant of an E. lata culture from

184

grapevine wood surface, and the Trichoderma isolates were isolated from strawberry
fruits. The Penicillium isolate, and the Brachysporium and Phomopsis combination were

collected from contaminated cultures.

DNA extraction and sample preparation

Fungal DNA was extracted from the mycelium, or from wood chips that contain
stroma and perithecia and stomatal tissue using three different methods (Appendix D).
Primarily, DNA was extracted from fungal mycelium and stromatal tissue on wood chips
using a DNA extraction kit (Qiagen, DNeasy tissue kit, cat # 69504, Qiagen, Inc.,
Valencia, CA) according to instructions in the kit. Other methods included extraction by
phenol-chloroform, and boiling the wood chips with stromata as described previously in
Lecomte, et al., 2000. Puriﬁcation of DNA for purposes of sequencing was performed
using the Wizard PCR preps (Wizard Puriﬁcation Systems, cat # A7107, Promega,
Madison, WI). DNA was quantiﬁed with a spectrophotometer (GeneQuant, 80-2105-79,

Amersham Pharmacia Biotech, Inc., Piscataway, NJ).

Primers

Five pairs of primers were tested for detection of E. lata (Table 5-1). All primers
used in the experiment were produced by the Macromolecular Structure Sequencing and
Synthesis Facility (Department of Biochemistry, Michigan State University, East
Lansing, MI). The universal fungal primers, lTSlF and ITS4, that amplify the ribosomal
DNA (rDNA) internal transcriber spacer (ITS) region of the fungal genome, were used to

test the DNA of the isolates for similar amplicon sizes and to obtain a product that could

185

be sequenced to obtain primers. The product that resulted from ampliﬁcation with the
ITSlF and IT S4 primers was puriﬁed using the Wizard prep kit, and was sequenced by
the ABI PRISM 3100 Genetic Analyzer (Genomics Technology Support Facility,
Michigan State University, East Lansing, MI). The two primer pairs, pFR-A and pRF-B,
were designed from RAPD fragments and from the ITS region of E. lata isolates,
respectively (Lecomte, et al., 2000). The two primer pairs, pMI and pCA, were designed
from the ITS sequence of two different isolates — M12 and CA30, respectively — using the
“primerselect” function of the computer program DNAStar (DNAStar Inc., Madison,
WI). The primer pair selected for use produced the longest expected ampliﬁcation

product and both primers annealed at a temperature difference of <20°C.

186

 

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187

Detecting E. lata using the polymerase chain reaction (PCR)

The DNA thatwas extracted from all isolates was subjected to detection using the
PCR with all five primer pairs separately. Equal amounts of master mix were added to
each reaction tube (master mix includes 100 mM of dNTPs, SOmM MglCz, and 10x Tris
buffer; taq polymerase, eat # 18038-018, Invitrogen, Carlsbad, CA). Approximately 0.5
M of each primer was consistently used in each reaction. Extracted DNA concentration
was measured with a spectrophotometer. Template amounts appropriate to the desired
DNA concentration were added to the master mix for a total reaction amount of 50 ul per
tube. The reaction mixture was placed into thermocycler wells and initially denatured for
two minutes at 94°C (Gene Amp PCR system 2400 thermocycler, PerkinElmer,
Wellesley, MA). The reaction mixture was subjected to 37 cycles of the PCR; each cycle
consisted of 30-60 seconds at 94°C for denaturing, 30—60 seconds of annealing at a
speciﬁc temperature depending on the primer pairs used (Table 5.1), and an extension
time of one minute at 72°C. The PCR product was then loaded into wells of a 1%
agarose gel and run with 0.5 M TBE buffer (tris borate EDTA) at approximately 90-100
volts. The reference DNA was 1 Kb ladder (1 KB Plus Ladder, Invitrogen, Carlsbad,
CA). After electrophoresis, gels were stained with ethidium bromide and visualized
under UV light.

Isolates of Eutypa lata and other non-E. lata species were tested using the PCR
with the primers, ITS-1F and IT S-4, that amplify the internal transcriber spacer (ITS)
regions of fungi. All isolates, three E. lata from California and two Eutypella vitis from
Michigan, were tested using the species-speciﬁc primers pairs (pCA, pMI, pFR-A and

pFR-B) (Table 5.1). ITS primers were also used to compare various DNA extraction

188

methods of E. lata fungal mycelium and of sections of stroma, including DNA extraction

by the kit method, phenol-chloroform, and by boiling.

Results
Isolates

The isolates from Michigan were collected from a canker that was black and
appeared burned. The stromata were characteristic of Eutypa lata; the surface under a
dissecting microscope appeared roughened and the perithecia] locules were visible when
scratched (Figure 1.9). Longitudinal cross sections of the stroma showed perithecia lined
up in a single layer close to the surface (Figure 1.5). The ascospores were ameroid to
allantoid in shape, pale yellow-brown, and measured 7-11 um x 1.5-2 um. Mycelium of
the isolates grew appressed to the agar in a unidirectional slight swirl from the ascospore
(Figure 5.1). After the mycelium grew over the petri dish, it became ﬂufﬁer, though
remained closely appressed to the agar. Some isolates developed a dark green,
olivaceous color on the surface and reverse side, but most isolates remained white to light
gray (Figure 5.1). The conidia were borne in wet, light to dark yellow masses emerging
from a pycnidium. The conidia were curved toward the apex and straight nearer the
truncate base (Figure 1.8). The descriptions of the mycelium and conidia correspond to
the description of Libertella blepharis (syn. Cytosporina sp.) (Carter, 1991; Glawe, et
al., 1982). All isolates eventually produced conidia after at least three weeks under

alternating 12-hour ﬂuorescent light and dark periods at room temperature.

189

 

Figure 5.1. Isolates of E utypa lata from California and Michigan used in experiments
presented here aﬁer seven days on half-strength PDA with streptomycin (100 ug/ml).
MI2 and MI3 were later identiﬁed as Eutypella vitis based on a sequencing of the ITS
region. Note differences in growth rate and coloring.

190

 

Detection of all isolates with universal fungal primers

Each isolate, two from Michigan and three from California, showed an
ampliﬁcation product size of 650 bp using the ITS primers (Figure 5.2 and 5.3).
Ampliﬁcation products of 650 bp are shown for all California isolates, CA30, CA31, and
CA38 (Figure 5.2). The DNA of isolate CA30 was extracted with phenol-chloroform and
DNA of all other isolates was extracted using the kit method. Ampliﬁcation products of
650 bp are shown for CA30, M12 and M13 (Figure 5 .3). Some isolates of three different
genera of fungi (Pestalotia sp., Trichoderma sp., Phomopsis viticola) showed an
ampliﬁcation product size of 650 bp using the ITS primers (Figure 5.4). Clear bands are
shown for Pestalotia sp., all three isolates of Trichoderma sp., and faint bands of one
isolate of Phomopsis viticola and of M13. One isolate of P. viticola did not show an

ampliﬁcation product.

191

Laneskb123456789

 

Figure 5.2. Detection of the three California isolates by PCR with the pITSlF and pITS4
primers. The DNA of the three California isolates was extracted with the kit method, and
one of the three (CA30) was extracted with phenol-chloroform. All isolates and
extractions methods show an ampliﬁcation product at 650 bp. To the right of the KB
ladder are CA30 (kit extraction) (lanes 1 and 2), CA30 (phenol-chloroform) (lane 3),
CA31 (kit extract) (lanes 4 and 5), CA38 (kit extract) (lanes 6 and 7), and positive control
(Venturia ineaqualis, lane 8) and negative water control (lane 9).

192

Laneskb123456789

 

Figure 5.3. The two isolates from Michigan and one California isolate, and a PCR with
pITS 1F and pITS4 primers. The DNA of all isolates was extracted by the kit method.
The ampliﬁcation products of all isolates are visible at 650 bp and include CA30 (lane 1),
M12 (lanes 2, 3 and 4), M13 (lanes 5, 6, 7 and 8), water (lane 9).

193

LaneskblZ3 45678

 

Figure 5.4. Pestalotia sp., Phomopsis viticola, and Trichoderma sp., and a PCR with
pITS 1F and pITS4 primers. The Michigan isolate M13 (lane 1), Pestalotia sp. (lane 2),
two isolates of Phomopsis viticola (lane 3 and 4), three isolates of Trichoderma sp. (lane
5, 6, and 7), and water (lane 8) were combined in a PCR with the ITS primers. An
ampliﬁcation product of approximately 650 bp is visible for M13, Pestalotia sp., one
isolate of Phomopsis viticola, and all three Trichoderma sp. isolates. No bands were
visible for one Phomopsis viticola isolate (lane 4).

194

Species-speciﬁc primer tests on all isolates

None of the primers detected all isolates (Table 5.2). In each case, the primer pair
detected the isolate it was designed from. Both Michigan isolates were detected by the
primer pair designed from the Michigan isolate, M12. The primer pair designed from the
California isolate, CA30, only detected that isolate, and did not detect the two other
California isolates, CA31 and CA38. All primer pairs, regardless of their source,
detected the California isolate, CA30. Ampliﬁcation products of approximately 400 bp
were obtained with a PCR reaction and pMI primers (pMI—l and pMI-2), shown for
isolates CA30, M12, and M13 (Figure 5.5). A PCR reaction with each of the three pairs
of primers was performed with isolates from California (CA30) and Michigan (MIZ and
M13) (Figure 5.6). Gel A is the result of ampliﬁcation with the pCA primer pair,
designed from isolate CA30; only the CA30 isolate showed an ampliﬁcation product at
approximately 400 bp. Gel B is the result of ampliﬁcation with the pFR-A primer pair;
only the CA30 isolates showed an ampliﬁcation product at approximately 350 bp. Gel C
is the result of ampliﬁcation with the pFR-B primer pair; only the CA30 isolate showed

an ampliﬁcation product at a little less than 400 bp (Figure 5.6).

195

Table 5.2. Detection of Eutypa lata and Eutypella vitis isolates from Michigan and
California with speciﬁc primer pairs. Isolates from Michigan are M12 and M13, and
isolates from California are CA30, CA31, and CA38. Key: -H- = isolate was detected by
primer pair in column labeled; clear band obtained with product size equal to that
indicated in table 5.1. “+” = isolate was detected by primer pair in column labeled; faint
band obtained with product size equal to that indicated in table 5.1. “—“ = isolate was not
detected by primer pair in column labeled. The primer pairs were designed from sources
as follows: pM12 from M12, pCA3O from CA30, pFR-A and pFR—B from RAPD markers
and from ITS region of French isolate, respectively (Lecomte, et al., 2000). The PCR
with all isolates and the pITS primer pair yielded an ampliﬁcation product of
approximately 650 bp.

 

Primers -) pM12 pCA30 pFR-A pFR-B pITS

Isolates 9 M12 + - - - ++
MI 3 ++ - - - ++
CA 30 4+ -H- ++ -H- ++
CA 31 — - - - ++
CA 38 - - - - .H.

 

196

Laneskb12345678

 

Figure 5.5. California isolate (CA30) and Michigan isolates and a PCR with the pMI-l
and pMI-2 primers. Primers designed from the Michigan isolate M12 was applied to a
PCR with CA30 (lane 1), two concentrations of stroma sample from Michigan (lane 2
and 3), M12 (lane 4 and 5), M13 (lane 6 and 7), and water (lane 8). An ampliﬁcation
product with the size of approximately 400 bp shows up as bands for samples CA30, M12
and M13; no bands were visible for the Michigan stroma sample and water control.

197

A B C

kb12345678kb1234567812345678xwat

 

Figure 5.6. One California isolate (CA30) and the two Michigan isolates and a PCR with
each of the three species-speciﬁc primer pairs. Gel A shows the products of a PCR with
CA30, M12 (faint), and M13 (faint) and the primers pCA-l and pCA-2 at approximately
400 bp. Gel B shows the products of a PCR with CA30 and primers pFR-Al and pFR-
A2 at approximately 350 pb. Gel C shows the products of a PCR with CA30 and primers
pFR-Bl and pFR-B2 at less than 400 bp. Isolates shown for each gel are in the same
order (after the 1 Kb ladder) as follows: CA30 (lane 1), M12 (lanes 2, 3, 4), M13 (lanes 5,
6, 7), CA31 (lane 8), and negative water control (not shown).

198

Extractions with phenol-chloroform from mycelium and stroma

The three California isolates and samples from stroma collected in Michigan were
subjected to a PCR with ITS primers in order to compare DNA extraction from fungal
mycelium and from stroma by phenol-chloroform and by the kit method (Figures 5.7 and
5.8). Ampliﬁcation products were obtained at 650 pb for phenol-chloroform extractions
of Eutypa lata stroma and mycelium extractions. Both DNA extracted with phenol-
chloroform as well as the kit gave an ampliﬁcation product of 650 bp as a result of a PCR
with the mycelium of the California isolate, CA30 (Figure 5.8). None of the DNA of
stroma samples extracted here by phenol-chloroform produced an ampliﬁcation product.
Extraction with phenol-chloroform was not consistent, especially with stroma tissues, and

thus extraction by the kit method was preferred.

199

Lanesl 234kb

 

Figure 5.7. Comparison of phenol-chloroform DNA extraction of fungal mycelium to
extraction from stroma, and a PCR with the pIT S 1F and pITS4 primers. Lanes 1 and 2
show results from stroma extraction and lanes 3 and 4 show results from mycelium
extraction; ladder is shown in lane 5. All samples were phenol—chloroform extracted;
bands at 650 bp are visible in lanes 1, 3 and 4.

Lanes kb123 45678910

 

Figure 5.8. Phenol—chloroform and kit extractions of DNA from a California isolate
(CA30) and phenol-chloroform extraction of DNA from E. lata stroma, and a PCR with
pITSlF and pITS4 primers. Phenol-chloroform extracted DNA of CA30 isolate (lanes 1,
2, 3 and 4) and samples of E. lata stroma (lanes 5, 6, 7 and 8), and kit extracted DNA of
CA30 isolate (lane 9); the positive control (Venturia inaequalis ITS region) and water are
shown in lanes 10 and 11, respectively. Bands of ampliﬁcation product are visible at 650
bp for three concentrations of phenol-chloroform extracted CA30 (lanes 1, 2, and 4), one
concentration of kit extracted CA30 (lane 9), and the positive control GanelO).

200

Extraction of DNA from stroma by kit method and boiling

The fungal ITS primers (pITSlF and pITS4) were used in a PCR to compare
DNA extraction methods from Eutypa lata stroma; by kit method and by boiling wood
chips in deionized water for 15 minutes at 95°C (Figure 5.9 and Figure 5.10). Five
stroma samples were tested; three samples in gel A and two in gel B with the positive
control M12 in each (Figure 5.9). The M12 isolate showed the expected ampliﬁcation
product size of 650 bp, but none of the DNA of the ﬁve stroma samples showed
ampliﬁcation products, regardless of the extraction method. Another attempt at
extraction with the kit method and by boiling shows that only the control, its DNA
extracted by the kit method, Pestalotia sp., produced an ampliﬁcation product with the

ITS primers (Figure 5.10). The four stroma samples that were boiled were not detected

(Figure 5.10).

201

Lanes kb 1 kb 1

 

Figure 5.9. Comparison of DNA extraction methods of stroma tissue by kit method and
by boiling, and a PCR with the pITSlF and pITS4 primers. The two gels represent a total
of six samples that are the result of extraction DNA from stroma of Eutypa lata by kit
method or by boiling small pieces of stroma in deiononized water for 15 minutes at 95°C.
Gel A shows no ampliﬁcation product except for the 650 bp product of M12 in lane 1; the
remaining lanes (two lanes per isolate) represent the DNA of three stroma samples that
was extracted by the kit method. Gel B shows the same control, M12 (lane 1), at 650 bp.
and no other product from two samples of stroma which DNA was kit extracted (lanes 2
— 5; two lanes per sample), and the DNA of two stroma samples that was extracted by
boiling (lanes 6 and 7; one lane per sample).

202

Lanes kb12345678910

 

Figure 5.10. Comparison of DNA extraction methods of stroma tissue by kit method and
by boiling and a PCR with the pITS 1F and pITS4 primers. The band in lane 1 at 650 bp
is the result of a kit extraction of Pestalotia sp. The remaining nine lanes are the result of
the boiling extraction method; four different small pieces of stroma that contain
perithecia were heated in deionoized water for 15 minutes at 95°C. There are no bands
indicating ampliﬁcation product of any of the four boiled samples (lanes 2 — 10).

203

Genome database search of ITS regions of CA30 and M12

The ITS region of the CA30 isolate had homology to many submissions of Eutypa
lata (and Eutypa armeniacae) though not as similar to Eutypella vitis (see Appendix E).
The sequence of the ITS region of the M12 isolate matched the submitted sequence of
Eutypella vitis, and it showed homology to Eutypa lata. The sequence submitted from

Eutypella vitis was from an isolate collected in the USA on Vitis labrusca.

Discussion

Results comparing methods of DNA extraction from mycelium suggest that the
kit method was the most reliable, however, the fungal material from stroma is not easily
extracted by either the kit, phenol-chloroform, or boiling. There may be a number of
complications that cause this difﬁculty. The fact that the kit method and phenol-
chloroforrn were successful for extracting DNA from fungal mycelium suggests there
may have been no fungal material remaining in the wood chips, the DNA extracted was
from the grape host or using a plastic pestle to smash the mycelium was inadequate to
smash the wood tissue.

Results presented here suggest that the isolates tested are different enough from
each other to need more than one species-speciﬁc primer to detect them. Namely, one of
three California isolates was detected by the primers designed from itself, and from
primers designed from the Michigan and French isolates, while both Michigan isolates
were detected only by the primer pair designed from the Michigan isolate. Genetic

research comparing sequences of ITS regions and AFPLs suggest there may be two

204

species of Eutypa (E. lata and E. armeniaca) involved in the cause of Eutypa dieback in
North America (DeScenzo, et al., 1999).

The variable responses of the primers to the California isolates may be due to
large genetic differences. Isolates from different vines in a single vineyard and from a
region of many vineyards in France using random ampliﬁed polymorphism DNA
(RAPD) and vegetative compatibility tests showed evidence of random mating, as each
isolate had a unique genotype and were vegetatively incompatible (Peros, et al., 1997;
Peros, et al., 1999). Additionally, infections in a single vineyard were caused by
ascospores originating from a random-mating population outside that vineyard (Peros, et
al., 1997). There is evidently plenty of gene ﬂow between pathogen populations on
cultivated grapevine and non—cultivated hosts (DeScenzo, et al., 1999; Peros, et al., 1998;
Peros, et al., 1997). The results presented here represent only three isolates from one
state, two from a single trunk, and thus more samples would need to be taken for there to
be any conclusion of relatedness between populations.

The teleomorphs of Eutypa lata and E. armeniacae were determined to be
synonymous in a long list that included Eutypa ambigua, E. milliaria, Sphaeria lata,
Diatrype lata, Valsa lata (Rappaz, 1984). Recent studies on vegetative compatibility and
diversity in ampliﬁed fragment length polymorphisms (AFLPs) of Eutypa armeniacae
and E. lata isolates from a variety of hosts in California, Michigan, New York, South
Africa, Australia, and Italy suggest the existence of two species (DeScenzo, et al., 1999).
From results of vegetative compatibility tests, the Eutypa samples isolated from
cultivated hosts are actually E. armeniacae, like the original pathogen found on apricot,

while isolates from native hosts are E. lata; however, both species are able to infect

205

 

cultivated as well as native hosts (DeScenzo, et al., 1999). The results in Table 5.2 show
that the Californian isolates CA31 and CA38 were not detectable by the primer pair
designed from the California isolate CA30. Thus, CA31 and CA38 may be a different
species (E. lata or E. armeniaca) from CA30.

It is possible that the fungus has been evolving and diverging genetically.
Additionally, ITS region sequence similarity between a Michigan isolate of E. lata and an
isolate of Eutypella vitis submitted to the genetic database, GeneBank, suggests the
presence of two different genera that infect or are saprophytic on grapevine. Eutypella
vitis may not be pathogenic and appear as a saprophyte around the time the sexual state of
E. lata develops. According to Hanlin (1990), the perithecia of a species of Eutypella, E.
citricola, have longer perithecia] necks that converge in a circle at the surface of the
stroma in a sort of bouquet arrangement. The perithecia with relatively short ostiole
necks of Eutypa lata are arranged in a single row just under the surface of the large,
extended and irregularly shaped stroma (Hanlin, 1990). While Eutypa lata is found on
decorticated wood and sometimes bark, Eutypella spp are found on dead branches
(Hanlin, 1990). These differences may allow for misidentiﬁcation when a circular group
of Eutypella vitis perithecia are growing in between the spread out stroma and shorter
ostiole necks of the Eutypa lata perithecia. When the ascospores are examined, there are
no noticeable differences except that those of E. lata are allantoid to suballantoid while
Eutypella spp. spores are allantoid (Hanlin, 1990; Munk, 1957).

Further sampling and correctly identifying the morphological traits of each isolate
collected would assist in getting conﬁrmation of the genus as well as the relatedness of

isolates of Eutypa lata from regions around the world. It is possible that the isolates from

206

the Great Lakes region (NY, MI, PA, Ontario, etc.) could be a different species, or have
diverged enough to not be detectable with primers designed from European or
Californian isolates. California has a longer history of grapevine cultivation and more of
their varieties are from European descent or stock. Isolates of E. lata from the
northwestern USA could be more similar to Californian isolates (by transport on wind
currents) than to European isolates.

Ideally a primer pair that detects a variety of isolates of Eutypa lata is desired. If
Eutypella vitis is in fact present, the primer pair must not detect that, giving false
positives for E. lata detection. More isolates from the same region — New York,
Pennsylvania, Ohio, Indiana, Illinois, Ontario, etc. - must be used to test this primer pair
out. The primer pair must detect all isolates of Eutypa lata no matter how genetically
diverse, and not detect closely related species such as Eutypella vitis that occur on
grapevine. Although successful on many European isolates, the species-speciﬁc primers
from France have not consistently detected E. lata isolates from North America. Of the
three isolates from California, CA30, CA31 and CA38, the two latter isolates were not
detected by any of the primer pairs, but the former was detected by all primer pairs. This
suggests that CA31 and CA38 are less related to CA30 than CA30 is to the Michigan
isolates of Eutypella vitis. Many more isolates need to be tested with existing primer
pairs, and further design of primer pairs that detect all E. lata isolates is needed.

In this study, a species-speciﬁc primer pair was not obtained that detected all E.
lata isolates in culture and wood. Designing and testing different primer pairs, further
tests on more isolates that originate from many areas and hosts, and possibly examining

the results of RFLP analysis may collectively lead to a molecular methodology to detect

207

the pathogen in vines suspected of being infected with E. lata. An unanticipated ﬁnding
of the experiments was that an additional genus was found on grapevines, identiﬁed as
Eutypella vitis, though its pathogenicity on grapevine is unknown. This may be found
only in northeastern or Midwest USA and only on Vitis labrusca. The detection of one
California isolate by primers designed from Eutypella vitis suggests that there may be
more genetic similarities between the genera. Further research is needed to determine the

role of this fungus in disease development.

208

APPENDICES

209

APPENDIX A

Leaf area regression equations

210

Vineyard A:

1999

Power model:

y = 1.2834x ”658 R2 = 0.93

2000
Power model:
y = 1.0565169724 R2 = 0.93

2001
Power model:
y = 0.902x2-0189 R2 = 0.81

Vineyard B:

2001

Power model

y = 0.9849x""968 R2 = 0.73

211

APPENDD( B

Results of the statistical analysis using the MIX (mixed) procedure

212

 

1999 A — Fruit weight per shoot

Categ It Mean“ StdErrror
AH 90 150.1 d 6.8
I 74 60.0 b 8.3
M 65 102.3 c 10.6
NS 95 177.3 e 9.6
S 78 33.7 a 5.8
* means followed by the same letter are not signiﬁcantly different (P=0.05)
Type 3 Tests of Fixed Effects
Num Den
Effect DF DF F Value Pr > F
cat 4 67 79.69 <.0001

Least Squares Means
Effect cat Estimate StdErr DF t Value Pr > M
cat AH 150.14 6.7816 67 22.14 <.0001
cat I 59.5828 8.2500 67 7.22 <.0001
cat M 102.35 10.5657 67 9.69 <.0001
cat NS 177.31 9.6042 67 18.46 <.0001
cat 8 33.6667 5.8187 67 5.79 <.0001

Differences of Least Squares Means
Effect cat _cat Estimate StdErr DF t Value Pr > It]
cat I 90.5620 10.2375 67 8.85 <.0001
cat M 47.7957 12.2628 67 3.90 0.0002
cat NS -27. 1633 12.7951 67 -2. 12 0.0375
cat S 116.48 8.9358 67 13.04 <.0001
cat M 42.7662 12.7500 67 -3.35 0.0013
cat NS -1 17.73 12.2630 67 -9.60 <.0001
cat S 25.9161 10.0956 67 2.57 0.0125
cat NS -74.9590 13.3337 67 -5.62 <.0001

S

S

33"""EEEE

cat 68.6824 12.0620 67 5.69 <.0001
cat 143.64 11.2293 67 12.79 <.0001

2
to

213

1999 A -— Cluster weight

Categ 11 Mean* StdErrror
AH 90 57.69 c 2.48
I 74 29.56 b 3.16
M 65 50.40 c 5.46
NS 95 70.57 d 3.12
S 78 18.41 a 2.01
* means followed by the same letter are not signiﬁcantly different (P=0.05)
Type 3 Tests of Fixed Effects
Num Den
Effect DF DF F Value Pr > F

cat

67 74.23 <.0001

Least Squares Means

Effect cat Estimate
57.6919
29.5563
50.4025
70.5696
18.4146

cat AH
cat I
cat M
cat NS
cat S

StdErr DF tValue Pr>|t|
2.4788 67 23.27 <.0001
3.1632 67 9.34 <.0001
5.4601 67 9.23 <.0001
3.1230 67 22.60 <.0001
2.0099 67 9.16 <.0001

Differences of Least Squares Means
Effect cat cat Estimate StdErr DF t Value Pr > Itl

cat AH I
eat AH M
cat AH NS
cat AH S
cat I

cat I

cat I S

cat M NS
cat M S
cat NS 8

1 1.1416
-20. 1671 5.2616 67 -3.83 0.0003
31.9879 5.7976 67 5.52 <.0001
52.1549 3.6216 67 14.40 <.0001

28.1356 3.5287 67 7.97 <.0001
7.2894 5.8699 67 1.24 0.2186
- 12.8777 3.7661 67 -3.42 0.001 1
39.2772 3.0737 67 12.78 <.0001
M -20.8462 5.3810 67 -3.87 0.0002
NS -41.0133 4.3333 67 -9.46 <.0001

3.7232 67 2.99 0.0039

214

1999 A - Berry weight

Categ n Mean* StdErrror
AH 90 2.65 bcd 0.07
I 74 2.16 a 0.12
M 65 2.56 be 0.12
NS 95 2.70 bed 0.07
S 78 2.08 a 0.13
* means followed by the same letter are not signiﬁcantly different (P=0.05)
Type 3 Tests of Fixed Effects
Num Den
Effect DF DF F Value Pr > F
cat 4 67 9.97 <.0001
Least Squares Means

Effect cat Estimate StdErr DF t Value Pr > M
cat AH 2.6541 0.06545 67 40.55 <.0001
cat I 2.1561 0.1176 67 18.34 <.0001
cat M 2.5624 0.1 184 67 21.65 <.0001
cat NS 2.7001 0.06908 67 39.09 <.0001
cat S 2.0776 0.1278 67 16.25 <.0001

Differences of Least Squares Means
Effect cat _cat Estimate StdErr DF t Value Pr > ltl
cat 1 0.4980 0.1051 67 4.74 <.0001
cat M 0.09176 0.1146 67 0.80 0.4262
cat NS -0.04599 0.06619 67 -0.69 0.4896
cat 8 0.5765 0.1203 67 4.79 <.0001
cat M 04062 0.1259 67 -3.23 0.0019
cat NS -0.5440 0.1132 67 -4.81 <.0001
eat
eat
eat
eat

S 0.07851 0.1231 67 0.64 0.5259
NS -0.1377 0.1079 67 -l .28 0.2062
S 0.4847 0.1459 67 3.32 0.0014
8 0.6225 0.1 144 67 5 .44 <.0001

53:”“EEEE

215

1999 A — Number of clusters per shoot

Categ n Mean“ StdErrror
AH 90 2.61 c 0.074
I 74 2.01 ab 0.19
M 65 2.14 b 0.13
NS 95 2.54 c 0.10
S 78 1.75 a 0.12
* means followed by the same letter are not signiﬁcantly different (P=0.05)
Type 3 Tests of Fixed Effects
Num Den
Effect DE DE F Value Pr > F
cat 4 67 13.48 <.0001

Least Squares Means
Effect cat Estimate StdErr DF t Value Pr > |t|
cat AH 2.6097 0.07425 67 35. 15 <.0001
cat 1 2.0077 0.1902 67 10.56 <.0001
cat M 2.1429 0.1304 67 16.43 <.0001
cat NS 2.5394 0.1000 67 25.39 <.0001
cat S 1.7539 0.1 185 67 14.80 <.0001

Differences of Least Squares Means
Standard
Effect eat eat Estimate StdErr DF t Value Pr > M

cat AH I 0.602] 0.1944 67 3.10 0.0029
cat AH M 0.4668 0.1448 67 3.22 0.0020
cat AH NS 0.07034 0. 1 124 67 0.63 0.5337
cat AH S 0.8558 0.1290 67 6.64 <.0001
cat I M -O. 1353 0.1928 67 -0.70 0.4854
cat I NS -0.5317 0.191 1 67 -2.78 0.0070
cat I S 0.2538 0.1677 67 1.51 0.1349

cat M NS -0.3965 0. 1418 67 -2.80 0.0067
cat M S 0.3891 0.1630 67 2.39 0.0198
cat NS S 0.7855 0.1338 67 5.87 <.0001

216

1999 A — Number of berries per cluster

Categ 11 Mean“ StdEmor
AH 90 21.59 c 0.68
I 74 13.05 b 1.40
M 65 19.65 c 1.48
NS 95 26.27 (1 0.81
S 78 8.86 a 0.78
* means followed by the same letter are not signiﬁcantly different (P=0.05)
Type 3 Tests of Fixed Effects
Num Den
Effect DE DE F Value Pr > F
cat 4 67 69.16 <.0001

Least Squares Means
Effect cat Estimate StdErr DF t Value Pr > M
cat AH 21.5854 0.6762 67 31.92 <.0001
cat I 13.0471 1.4004 67 9.32 <.0001
cat M 19.6508 1.4803 67 13.28 <.0001
cat NS 26.2666 0.8087 67 32.48 <.0001
cat S 8.8598 0.7810 67 11.34 <.0001

Differences of Least Squares Means
Effect cat _cat Estimate StdErr DF t Value Pr > III
cat I 8.5384 1.5551 67 5.49 <.0001
cat M 1.9347 1.6274 67 1.19 0.2387
cat NS 4.6812 1.0542 67 -4.44 <.0001
cat S 12.7256 1.0331 67 12.32 <.0001
cat M -6.6037 1.9925 67 -3.31 0.0015
cat NS -13.2195 1.5524 67 -8.52 <.0001
cat
eat
eat
cat

8 4.1872 1.4936 67 2.80 0.0066
NS -6.6158 1.6623 67 -3.98 0.0002
S
S

33"""EEEE

10.7909 1.6547 67 6.52 <.0001
17.4068 1.1175 67 15.58 <.0001

Z
a:

217

1999 A - Total soluble sugar content per shoot

Categ 11 Mean“ StdErrror
AH 90 16.9967 d 0.8705
I 58 7.3133 b 0.9458
M 63 12.1894 c 1.0731
NS 90 22.8086 e 1.2285
S 65 3.9700 a 0.8255
* means followed by the same letter are not signiﬁcantly different (P=0.05)
Type 3 Tests of Fixed Effects
Num Den
Effect DF DF F Value Pr > F
cat 4 44 57.35 <.0001

Least Squares Means
Effect cat Estimate StdErr DF tValue Pr> Itl

cat AH 16.9967 0.8705 44 19.53 <.0001
cat 1 7.3133 0.9458 44 7.73 <.0001
cat M 12.1894 1.0731 44 l 1.36 <.0001
cat NS 22.8086 1.2285 44 18.57 <.0001
cat S 3.9700 0.8255 44 4.81 <.0001

Differences of Least Squares Means
Effect cat cat Estimate StdErr DF t Value Pr > ltl

cat AH 1 9.6835 1.2895 44 7.51 <.0001
cat AH M 4.8073 1.3887 44 3.46 0.0012
cat AH NS -5.81 19 1.5200 44 -3.82 0.0004
cat AH S 13.0267 1.1996 44 10.86 <.0001
cat I M -4.8762 1.4358 44 -3.40 0.0015
cat 1 NS -15.4953 1 .5571 44 -9.95 <.0001
cat I S 3.3433 1.2553 44 2.66 0.0108

cat M NS -10.6192 1.6426 44 -6.46 <.0001
cat M S 8.2194 1.3539 44 6.07 <.0001
cat NS S 18.8386 1.4800 44 12.73 <.0001

218

1999 A — Number of undeveloped berries per shoot

Categ 11 Mean” StdErrror
AH 90 2.322 a 0.09945
I 47 1.085 a 0.1845
M 48 1.270 a 0.2252
NS 49 1.612 a 0.2355
S 31 1.387 a 0.2376
*means followed by the same letter are not signiﬁcantly different (P=0.05)
Type 3 Tests of Fixed Effects
Num Den
Effect DF DF F Value Pr > F
cat 4 33 3.31 0.0220

Least Squares Means
Effect cat Estimate StdErr DF tValue Pr> Itl

cat AH 0.8760 0.1237 33 7.08 <.0001
cat 1 0.04313 0.3514 33 0.12 0.9031
cat M 0.2034 0.3274 33 0.62 0.5386
cat NS 0.4923 0.2410 33 2.04 0.0491
cat S 0.3080 0.2254 33 1.37 0.1810

Differences of Least Squares Means

Effect cat _cat Estimate StdErr DF tValue Pr> Itl Adjustment Adj
cat I 0.8329 0.3538 33 2.35 0.0247 Tukey-Kramer 0.1538
cat M 0.6726 0.3307 33 2.03 0.0501 Tukey-Kramer 0.2729
cat NS 0.3837 0.2459 33 1.56 0.1282 Tukey-Kramer 0.5323
cat 8 0.5680 0.2283 33 2.49 0.0181 Tukey-Kramer 0.1181
cat M -0.1603 0.4617 33 0.35 0.7306 Tukey-Kramer 0.9967
cat NS -0.4492 0.4077 33 -1.10 0.2786 Tukey-Kramer 0.8044
cat 8 -0.2649 0.3916 33 -0.68 0.5035 Tukey-Kramer 0.9602
cat NS -0.2889 0.3845 33 -0.75 0.4578 Tukey-Kramer 0.9425
cat 8 -0.1046 0.3711 33 -0.28 0.7799 Tukey-Kramer 0.9985
cat S 0.1843 0.3057 33 0.60 0.5508 Tukey-Kramer 0.9737

5“""EEEE

219

1999 A — Leafarea per shoot

Categ It Mean“ StdErrror
AH 18 525.93 a 59.37
I 14 314.93 a 144.99
M 15 304.22 a 109.26
NS 16 412.20 a 128.84
S 14 69.45 b 63.82
* means followed by the same letter are not signiﬁcantly different (P=0.05)
Type 3 Tests of Fixed Effects
Num Den
Effect DE DE F Value Pr > F
cat 4 3.03 7.12 0.0684

Least Squares Means
Effect cat Estimate StdErr DF t Value Pr> |t|

cat AH 525.93 59.3747 8.61 8.86 <.0001
cat 1 314.93 144.99 1.1 2.17 0.2566
cat M 304.22 109.26 2.1 2.78 0.1030
cat NS 412.20 128.84 1.6 3.20 0.1132
cat S 69.4530 63.8230 2.15 1.09 0.3834

Differences of Least Squares Means
Effect eat eat Estimate StdErr DF tValue Pr > M

cat AH 1 211.01 150.13 1.5 1.41 0.3316
cat AH M 221.72 117.21 3.43 1.89 0.1433
cat AH NS 113.73 135.03 2. 0.84 0.4791
cat AH S 456.48 87.8413 5.07 5.20 0.0033
cat I M 10709410363 7.56 0.10 0.9204
cat I NS -97.2779 118.39 16.1 -0.82 0.4233
cat I S 245.47 151.84 1.56 1.62 0.2808
cat M NS -107.99 95.8585 23.8 -1.13 0.2712
cat M S 234.76 119.78 3.73 1.96 0.1266
cat NS S 342.75 136.71 2.42 2.51 0.1074

220

1999 A - Shoot length

Categ 11 Mean* StdErrror
AH 90 43.177 b 0.07320
I 62 19.101 3 0.1803
M 87 45.621 c 0.1703
NS 95 39.105 b 0.07688
S 64 11.336 a 0.1306
*means followed by the same letter are not signiﬁcantly different (P=0.05)
Type 3 Tests of Fixed Effects
Num Den
Effect DF DF F Value Pr > F
cat 4 45 25.09 <.0001

Least Squares Means
Estimates here are based on the log of the data for shoot length; stderr are from log/SAS run

Effect cat Estimate StdErr DF t Value Pr > M
cat AH 3.691 1 0.07320 45 50.42 <.0001
cat 1 2.6286 0.1803 45 14.58 <.0001
cat M 3.1022 0.1703 45 18.21 <.0001
cat NS 3.5321 0.07688 45 45.94 <.0001
cat S 2.3233 0.1306 45 17.79 <.0001

Differences of Least Squares Means

Effect cat _cat Estimate StdErr DF t Value Pr > It]
cat AH I 1 .0625 0. 1946 45 5.46 <.0001
cat AH M 0.5889 0.1854 45 3.18 0.0027
cat ‘ AH NS 0.1590 0.1062 45 1.50 0.1412
cat AH S 1.3678 0.1497 45 9.14 <.0001
cat I M -0.4736 0.1517 45 -3.12 0.0031
cat I NS -0.9035 0. 1779 45 -5 .08 <.0001
cat I S 0.3053 0.1854 45 1.65 0.1066
cat M NS -0.4299 0.1685 45 -2.55 0.0142
cat M S 0.7789 0.1795 45 4.34 <.0001
cat NS S 1.2088 0.1410 45 8.57 <.0001

221

1999 A - Node number per shoot

Categ 11 Mean" StdEmor
AH 90 7.47 c 0.28
I 62 5.33 ab 0.68
M 87 6.12 b 0.58
NS 95 7.40 c 0.39
S 64 4.30 a 0.50
* means followed by the same letter are not signiﬁcantly different (P=0.05)
Type 3 Tests of Fixed Effects
Num Den
Effect DF DF F Value Pr > F
cat 4 62 9.83 <.0001

Least Squares Means
Effect cat Estimate StdErr DF t Value Pr > M
cat AH 7.4667 0.2816 62 26.51 <.0001
cat 1 5.3251 0.6828 62 7.80 <.0001
cat M 6.1204 0.5847 62 10.47 <.0001
cat NS 7.4008 0.3838 62 19.28 <.0001
cat S 4.3036 0.5024 62 8.57 <.0001

Differences of Least Squares Means
Effect cat cat Estimate StdErr DF tValue Pr> M

cat AH I 2.1415 0.7386 62 2.90 0.0052
cat AH M 1 .3463 0.6490 62 2.07 0.0422
cat AH NS 0.06584 0.4760 62 0.14 0.8904
cat AH 8 3.1631 0.5759 62 5.49 <.0001
cat I M -0.7953 0.7250 62 -1.10 0.2769
cat I NS -2.0757 0.5808 62 -3.57 0.0007
cat I S 1.0216 0.6644 62 1.54 0.1292

cat M NS -1.2804 0.5963 62 -2. 15 0.0357
cat M S 1.8168 0.6230 62 2.92 0.0049
cat NS S 3.0973 0.5619 62 5.51 <.0001

222

1999 A - Shoot diameter

Categ 11 Mean“ StdErrror
AH 90 4.0889 b 0.1025
I 62 3.4163 a 0.1643
M 87 3.8372 b 0.1766
NS 95 4.5830 c 0.1290
8 63 2.9466 a 0.2140
* means followed by the same letter are not signiﬁcantly different (P=0.05)
Type 3 Tests of Fixed Effects
Num Den
Effect DF DF F Value Pr > F

cat

4 45 20.31 <.0001

Least Squares Means

Effect cat Estimate StdErr DF t Value Pr > It]
cat AH 4.0889 0.1025 45 39.90 <.0001
cat I 3.4163 0.1643 45 20.79 <.0001
cat M 3.8372 0.1766 45 21.73 <.0001
cat NS 4.5 830 0.1290 45 35.52 <.0001
cat S 2.9466 0.2140 45 13.77 <.0001

Differences of Least Squares Means

Effect cat _cat
cat AH I
eat AH M
cat AH NS
cat AH S
cat I M
cat I NS
cat I 8

cat M NS
cat M S
cat NS 8

Estimate StdErr DF t Value Pr > M
0.6726 0.1937 45 3.47 0.001 1
0.2517 0.2042 45 1.23 0.2241
-0.4942 0.1648 45 -3.00 0.0044
1.1423 0.2373 45 4.81 <.0001
04209 0.1815 45 -2.32 0.0250
-1. 1668 0.1725 45 -6.76 <.0001
0.4697 0.2269 45 2.07 0.0442
-0.7458 0.1721 45 -4.33 <.0001
0.8906 0.2225 45 4.00 0.0002
1.6365 0.2106 45 7.77 <.0001

223

1999 A — Brix

Categ 11 Mean" StdErrror
AH 90 11.2661 a 0.2570
1 62 12. 1019 a 0.9427
M 64 11.8210 a 0.4935
NS 91 12.5152 a 0.2409

S 65 11.5197 a 0.6419
* means followed by the same letter are not signiﬁcantly different (P=0.05)

Type 3 Tests of Fixed Effects
Num Den
Effect DF DF F Value Pr > F
cat 4 46 6.58 0.0003

Least Squares Means
Effect cat Estimate StdErr DF tValue Pr > It]

cat AH 1 1.2661 0.2570 46 43.84 <.0001

cat 1 12.1019 0.9427 46 12.84 <.0001

cat M 1 1.8210 0.4935 46 23.95 <.0001

cat NS 12.5152 0.2409 46 51.94 <.0001

cat S 11.5197 0.6419 46 17.95 <.0001

Differences of Least Squares Means

Effect cat _cat Estimate StdErr DF t Value Pr > It]
cat AH I -0.8358 0.8663 46 -0.96 0.3396
cat AH M -0.5549 0.4355 46 -1.27 0.2090
cat AH NS -1.2491 0.2506 46 -4.98 <.0001
cat AH 8 —0.2536 0.5804 46 -0.44 0.6642
cat I M 0.2810 0.7463 46 0.38 0.7083
cat I NS -0.4132 0.8989 46 -0.46 0.6479
cat I S 0.5822 0.7191 46 0.81 0.4223
cat M NS -0.6942 0.4590 46 -1.51 0.1373
cat M S 0.3013 0.5287 46 0.57 0.5715
cat N S S 0.9955 0.6052 46 1.64 0.1068

224

1999 A — pH

Categ 11 Mean” StdErrror
AH 90 3.1966 a 0.03255
I 58 3.2769 a 0.05895
M 55 3.1592 a 0.03204
NS 87 3.1419 a 0.02855

S 58 3.3172 a 0.06556
* means followed by the same letter are not signiﬁcantly different (P=0.05)

Type 3 Tests of Fixed Effects
Num Den
Effect DF DF F Value Pr > F
cat 4 44 2.57 0.0511

Least Squares Means
Effect cat Estimate StdErr DF tValue Pr> ItI

cat AH 3.1966 0.03255 44 98.21 <.0001
cat I 3.2769 0.05895 44 55.59 <.0001
cat M 3.1592 0.03204 44 98.59 <.0001
cat NS 3.1419 0.02855 44 1 10.03 <.0001
cat S 3.3172 0.06556 44 50.59 <.0001

Differences of Least Squares Means
Effect cat _cat Estimate StdErr DF tValue Pr> Itl Adjustment Adj P
cat AH I -0.08027 0.07064 44 -1.14 0.2620 Tukey-Kramer 0.7865
cat AH M 0.03743 0.04383 44 0.85 0.3977 Tukey-Kramer 0.9119
cat AH NS 0.05467 0.04340 44 1.26 0.2144 Tukey-Kramer 0.7166
cat AH S 01206 0.07572 44 -1.59 0.1183 Tukey-Kramer 0.5097
cat I M 0.1177 0.06676 44 1.76 0.0848 Tukey-Kramer 0.4075
cat 1 NS 0.1349 0.06749 44 2.00 0.0518 Tukey-Kramer 0.2833
Category I 8 -0.04035 0.09486 44 -0.43 0.6727 Tukey—Kramer 0.9929
cat M NS 0.01724 0.04032 44 0.43 0.6710 Tukey-Kramer 0.9928
cat M S 01581 0.07198 44 -2.20 0.0334 Tukey-Kramer 0.2003
cat NS S 01753 0.07294 44 —2.40 0.0205 Tukey-Kramer 0.1336

225

1999 A - Titratable acids

Categ 11 Mean" StdErrror

AH 90 0.57 a 0.01

I 55 0.62 ab 0.02

M 54 0.65 ab 0.02

NS 86 0.62 ab 0.02

S 56 0.62 ab 0.03

* means followed by the same letter are not signiﬁcantly different (P=0.05)

Type 3 Tests of Fixed Effects

Num Den .

Effect DF DF F Value Pr > F
cat 4 60 9.91 <.0001

Least Squares Means
Effect cat Estimate StdErr DF t Value Pr > M
cat AH 0.5719 0.009451 60 60.51 <.0001
cat I 0.6169 0.02840 60 21.72 <.0001
cat M 0.6490 0.01521 60 42.66 <.0001
cat NS 0.6164 0.01621 60 38.03 <.0001
cat S 0.6188 0.02946 60 21.00 <.0001

Differences of Least Squares Means
Effect cat cat Estimate StdErr DF t Value Pr > M

cat AH I -0.04500 0.02993 60 -1.50 0.1379
cat AH M -0.07706 0.01791 60 -4.30 <.0001
cat AH NS -0.04447 0.01876 60 -2.37 0.0210
cat AH S -0.04682 0.03094 60 -1.51 0.1355
cat I M -0.03206 0.02090 60 -1.53 0.1303
cat 1 NS 0.000535 0.0315 1 60 0.02 0.9865
cat I 8 -0.00181 0.03962 60 -0.05 0.9636
cat M NS 0.03260 0.01568 60 2.08 0.0420
cat M S 0.03025 0.03378 60 0.90 0.3742
cat NS S -0.00235 0.02605 60 -0.09 0.9284

226

2000 A - Fruit weight per shoot

Categ 11 Mean* StdErrror
AH 49 122.16 b 9.5705

I 45 37.1048 a 11.5691
M 68 109.77 b 20.9546
NS 41 144.83 b 16.4643
S 6 10.1398 a 25.3422

* means followed by the same letter are not signiﬁcantly different (P=0.05)

Type 3 Tests of Fixed Effects
Num Den
Effect DF DF F Value Pr > F
cat 4 20 14.90 <.0001

Least Squares Means
Effect cat Estimate StdErr DF t Value Pr > M

cat AH 122.16 9.5705 20 12.76 <.0001
cat I 37.1048 11.5691 20 3.21 0.0044
cat M 109.77 20.9546 20 5.24 <.0001
cat NS 144.83 16.4643 20 8.80 <.0001
cat 8 10.1398 25.3422 20 0.40 0.6933

Differences of Least Squares Means
Effect eat eat Estimate StdErr DF t Value Pr > M

cat I 85.0570 13.4193 20 6.34 <.0001
cat M 12.3890 22.0235 20 0.56 0.5800
cat NS -22.6727 17.8372 20 -1.27 0.2183
cat S 112.02 26.2384 20 4.27 0.0004
cat M -72.6680 23.2435 20 -3. 13 0.0053
cat NS -107.73 19.6024 20 -5.50 <.0001

(0

26.9651 26.5391 20 1.02 0.3217
NS -35.0617 29.4237 20 -1. 19 0.2474
99.6331 32.7614 20 3.04 0.0064
134.69 29.9461 20 4.50 0.0002

eat
eat
eat
cat

5”"“EEEE

m

227

2000 A — Cluster weight

Categ 11 Mean" StdErrror
AH 49 55.7645 b 3.8615
1 45 21.9541 a 3.5673
M 68 49.4001 b 6.7239
NS 41 67.6156 b 5.9076
S 6 11.2647 a 9.1570
* means followed by the same letter are not signiﬁcantly different (P=0.05)
Type 3 Tests of Fixed Effects
Num Den
Effect DF DF FValue Pr> F
cat 4 20 19.42 <.0001
Least Squares Means
Effect cat Estimate StdErr DF tValue Pr > Itl
cat AH 55.7645 3.8615 20 14.44 <.0001
cat I 21.9541 3.5673 20 6.15 <.0001
cat M 49.4001 6.7239 20 7.35 <.0001
cat NS 67.6156 5.9076 20 11.45 <.0001
cat 8 11.2647 9.1570 20 1.23 0.2329
Differences of Least Squares Means
Effect cat _cat Estimate StdErr DF tValue Pr > M
cat AH I 33.8104 5.3206 20 6.35 <.0001
cat AH M 6.3644 8.0182 20 0.79 0.4367
cat AH NS -11.8510 7.3383 20 -1.61 0.1220
cat AH S 44.4998 9.9485 20 4.47 0.0002
cat I M -27.4460 7.7932 20 -3.52 0.0021
cat 1 NS 45.6615 6.9497 20 -6.57 <.0001
cat I S 10.6894 9.8373 20 1.09 0.2901
cat M NS -18.2155 9.3731 20 -1.94 0.0662
cat M S 38.1354 11.3801 20 3.35 0.0032
cat NS S 56.3509 10.9061 20 5.17 <.0001

228

2000 A - Berry weight

Categ 11 Mean* StdErrror
AH 49 2.9640 c 0.08295
I 45 2.5086 ab 0.09956
M 68 2.8076 bc 0.09055
NS 41 3.0041 c 0.08761
S 6 2.1961 a 0.1896
* means followed by the same letter are not signiﬁcantly different (P=0.05)
Type 3 Tests of Fixed Effects
Num Den
Effect DF DF F Value Pr > F
cat 4 20 8.07 0.0005

Least Squares Means
Effect cat Estimate StdErr DF t Value Pr > Itl

cat AH 2.9640 0.08295 20 35 .73 <.0001
cat 1 2.5086 0.09956 20 25.20 <.0001
cat M 2.8076 0.09055 20 31.01 <.0001
cat NS 3.0041 0.08761 20 34.29 <.0001
cat 8 2.1961 0.1896 20 11.58 <.0001

Differences of Least Squares Means
Effect cat cat Estimate StdErr DF t Value Pr > Itl

cat AH I 0.4554 0.1403 20 3.25 0.0041
cat AH M 0.1564 0.1341 20 1.17 0.2570
cat AH NS -0.04003 0.1319 20 -0.30 0.7647
cat AH S 0.7679 0.2138 20 3.59 0.0018
cat I M -0.2990 0.1606 20 -1.86 0.0774
cat 1 NS -0.4954 0.1372 20 -3.61 0.0017
cat I S 0.3125 0.2315 20 1.35 0.1922

cat M NS -0.1965 0.1322 20 -1.49 0.1530
cat M S 0.61 14 0.2237 20 2.73 0.0128
cat NS 8 0.8079 0.2031 20 3.98 0.0007

229

2000 A - Number of clusters per shoot

Categ n Mean* StdEmor
AH 49 2.1408 c 0.1198
I 45 1.5544 b 0.1411
M 68 2.0628 c 0.1855
NS 41 2.1306 c 0.1883
S 6 0.9705 a 0.2695

* means followed by the same letter are not signiﬁcantly different (P=0.05)

Type 3 Tests of Fixed Effects
Num Den
Effect DF DF F Value Pr > F
cat 4 20 7.89 0.0005

Least Squares Means
Effect cat Estimate StdErr DF tValue Pr>|t|

cat AH 2.1408 0.1198 20 17.87 <.0001
cat 1 1.5544 0.1411 20 11.01 <.0001
cat M 2.0628 0.1855 20 11.12 <.0001
cat NS 2.1306 0.1883 20 11.31 <.0001
cat S 0.9705 0.2695 20 3.60 0.0018

Differences of Least Squares Means
Effect cat cat Estimate StdErr DF t Value Pr > Itl

cat AH 1 0.5863 0.1421 20 4.13 0.0005
cat AH M 0.07803 0.1863 20 0.42 0.6798
cat AH NS 0.01023 0.1895 20 0.05 0.9575
cat AH S 1.1703 0.2700 20 4.33 0.0003
cat I M -0.5083 0.1885 20 -2.70 0.0139
cat 1 NS -0.5761 0.2047 20 -2.81 0.0107
cat I S 0.5840 0.2710 20 2.16 0.0435

cat M NS -0.06779 0.2191 20 -0.31 0.7602
cat M S l .0923 0.3037 20 3.60 0.0018
cat NS S 1.1601 0.3068 20 3.78 0.0012

230

2000 A - Number of berries per cluster

Categ 11 Mean“ StdErrror
AH 49 18.5847 b 1.2970
1 45 8.3149 a 1.0254
M 68 17.1296 b 1.8420
NS 41 21.1428 b 1.4649
S 6 5.3333 a 2.8082
* means followed by the same letter are not signiﬁcantly different (P=0.05)
Type 3 Tests of Fixed Effects
Num Den
Effect DF DF F Value Pr > F
cat 4 20 17.99 <.0001

Least Squares Means
Estimate StdErr
18.5847 1.2970 20
8.3149 1.0254 20
17.1296 1.8420 20
21 . 1428 1.4649 20
5.3333 2.8082 20

DF t Value Pr > Itl
14.33 <.0001
8.11 <.0001
9.30 <.0001
14.43 <.0001
1.90 0.0721

Effect cat
cat AH
cat I
eat M
cat NS
cat 8

Differences of Least Squares Means
Effect cat _cat Estimate StdErr DF
cat I 10.2698 1 .6534 20
cat M 1.4551 2.0804 20
cat NS -2.5581 1.8029 20
cat S 13.2514 3.0932 20
cat M 88147 2.1082 20
cat NS - 12.8279 1.7881 20
cat S 2.9816 2.9895 20
cat NS -4.0132 2.1897 20
cat S 1 1.7963 3.3584 20
cat S 15.8095 3.1673 20

t Value Pr > Itl
6.21 <.0001
0.70 0.4923
-1.42 0.1713
4.28 0.0004
-4. 18 0.0005
-7. 17 <.0001
1.00 0.3305
-1.83 0.0818
3.51 0.0022
4.99 <.0001

533””Eééé

231

2000 A - Total soluble sugar content per shoot

Categ n Mean“ StdErrror
AH 44 13.2060 b 1.5742
1 36 3.9711 a 1.0275
M 56 10.8161 b 2.5745
NS 33 15.0095 b 2.2080
S 5 1.0199 a 2.7571
* means followed by the same letter are not signiﬁcantly different (P=0.05)
Type 3 Tests of Fixed Effects
Num Den
Effect DF DF F Value Pr > F
cat 4 19 11.62 <.0001

Least Squares Means
Effect cat Estimate StdErr DF t Value Pr > Itl

cat AH 13.2060 1.5742 19 8.39 <.0001
cat I 3.971 1 1.0275 19 3.86 0.0010
cat M 10.8161 2.5745 19 4.20 0.0005
cat NS 15.0095 2.2080 19 6.80 <.0001
cat S 1.0199 2.7571 19 0.37 0.7155

Differences of Least Squares Means
Effect eat eat Estimate StdErr DF t Value Pr > Itl

cat AH 1 9.2348 1.8799 19 4.91 <.0001
cat AH M 2.3898 3.0998 19 0.77 0.4502
cat AH NS -1.8036 2.7931 19 —0.65 0.5262
cat AH 8 12.1861 3.1749 19 3.84 0.0011
cat I M -6.8450 2.7720 19 -2.47 0.0232
cat I NS -1 1.0384 2.4353 19 -4.53 0.0002
cat I S 2.9513 2.9423 19 1.00 0.3284

cat M NS -4. 1934 3.4811 19 -1.20 0.2431
cat M 8 9.7963 3.7722 19 2.60 0.0177
cat NS S 13.9897 3.5322 19 3.96 0.0008

232

2000 A — Number of undeveloped berries per shoot

Categ 11 Mean” StdErrror
AH 49 0.88 a 0.23
I 45 0.99 a 0.38
M 68 1.34 a 0.30
NS 41 1.10 a 0.29
S 6 0.67 a 0.66
* means followed by the same letter are not signiﬁcantly different (P=0.05)
Type 3 Tests of Fixed Effects
Num Den
Effect DF DF F Value Pr > F
cat 4 29 0.52 0.7221

Least Squares Means
Effect cat Estimate StdErr DF t Value Pr > Itl
cat AH 0.8750 0.2326 29 3.76 0.0008
cat I 0.9952 0.3825 29 2.60 0.0145
cat M 1.3392 0.3046 29 4.40 0.0001
cat NS 1.1015 0.2938 29 3.75 0.0008
cat S 0.6667 0.6579 29 1.01 0.3193

Differences of Least Squares Means
Effect eat eat Estimate StdErr DF tValue Pr> Itl

cat AH I -0. 1202 0.4477 29 -0.27 0.7902
cat AH M -0.4642 0.3832 29 -1.21 0.2356
cat AH NS -0.2265 0.3748 29 -0.60 0.5504
cat AH S 0.2083 0.6978 29 0.30 0.7674
cat I M -0.3440 0.3836 29 -0.90 0.3773
cat I NS -0. 1063 0.4263 29 -0.25 0.8049
cat I S 0.3285 0.7610 29 0.43 0.6691

cat M NS 0.2377 0.3835 29 0.62 0.5401
cat M S 0.6725 0.7250 29 0.93 0.3612
cat NS S 0.4348 0.7205 29 0.60 0.5509

233

2000 A — Percent green berries per shoot

Categ It Mean* StdErrror
AH 48 54.28 a 7.33
I 45 34.48 a 12.58
M 68 37.05 a 9.82
NS 41 45.36 a 15.57
S 6 74.82 a 23.54
* means followed by the same letter are not signiﬁcantly different (P=0.05)
Type 3 Tests of Fixed Effects
Num Den
Effect DF DF F Value Pr > F
cat 4 20 1.54 0.2280

Least Squares Means
Effect cat Estimate StdErr DF t Value Pr > Itl
cat AH 54.2846 7.3337 16 7 .40 <.0001
cat 1 34.4829 12.5838 16 2.74 0.0145
cat M 37.0463 9.8194 16 3.77 0.0017
cat NS 45.3613 15.5708 16 2.91 0.0102
cat S 74.8220 23.5392 16 3.18 0.0058

Differences of Least Squares Means
Effect cat _cat Estimate StdErr DF t Value Pr > Itl

cat AH I 19.8017 12.6491 16 1.57 0.1370
cat AH M 17.2383 11.0589 16 1.56 0.1386

cat AH NS 8.9233 18.4198 16 0.48 0.6346
cat AH S -20.5374 23.8786 16 -0.86 0.4025
cat I M -2.5634 12.5153 16 -0.20 0.8403
cat I NS -10.8784 18.2846 16 -0.59 0.5602
cat I S 40.3391 29.4777 16 -1.37 0.1901
cat M NS -8.3150 15.7779 16 -0.53 0.6054
cat M S —37.7757 23.6450 16 -1.60 0.1297
cat NS S -29.4607 27.4926 16 -1.07 0.2998

234

2000 A - Leaf area per shoot

Categ n Mean* StdErrror
AH 67 968.5518 c 0.1341
I 83 286.5175 a 0.2886

M 95 649.9418 b 0.1922
NS 55 1035.041 c 0.2085

S 40 245.3375 a 0.4600
* means followed by the same letter are not signiﬁcantly different (P=0.05)
Type 3 Tests of Fixed Effects
Num Den
Effect DF DF F Value Pr > F
cat 4 35 15.65 <.0001

Data transformed: log (used for mean separation) Non-tr below.
Least Squares Means

Effect cat Estimate StdErr DF tValue Pr> Itl

cat AH 6.6470 0.1341 35 49.58 <.0001
cat 1 5.2042 0.2886 35 18.03 <.0001
cat M 6.0564 0.1922 35 31.50 <.0001
cat NS 6.7885 0.2085 35 32.55 <.0001
cat S 4.4870 0.4600 35 9.75 <.0001

Differences of Least Squares Means

Effect cat cat Estimate StdErr DF tValue Pr>|t|

cat AH I 1.4429 0.2962 35 4.87 <.0001
cat AH M 0.5907 0.2075 35 2.85 0.0074
cat AH NS -0.1414 0.2216 35 -0.64 0.5274
cat AH S 2.1600 0.4622 35 4.67 <.0001
cat I M -0.8522 0.1879 35 4.53 <.0001
cat 1 NS -1.5843 0.2101 35 -7.54 <.0001
cat I S 0.7172 0.2688 35 2.67 0.0115

cat M NS -0.7321 0.1732 35 -4.23 0.0002
cat M S 1.5694 0.3477 35 4.51 <.0001
cat NS S 2.3015 0.3530 35 6.52 <.0001

Data not transformed here:
Least Squares Means
Effect cat Estimate StdErr DF tValue Pr

> Itl

cat AH 970.03 141.01 45 6.88 <.0001
cat I -33459 84593 45 -0.40 0.6943
cat M 786.04 336.12 45 2.34 0.0239
cat NS 1191.96 231.35 45 5.15 <.0001
cat S 391.24 252.61 45 1.55 0.1284

Differences of Least Squares Means
Effect cat cat Estimate StdErr DF tValue

Pr > M
.6856
0.4885

0.3151

6866
.6834

0.0736

cat AH I 34429 84490 45 0.41 0

cat AH M 183.99 263.42 45 0.70

cat AH NS -221.93 218.47 45 -1.02

cat AH S 578.79 258.53 45 2.24 0.0302
cat I M -34245 84327 45 -0.41 0.

cat I NS -3465 1 84429 45 -0.4 1 0

cat I S -33850 84490 45 -0.40 0.6906
cat M NS ~405.92 . 221.60 45 -1.83

cat M S 394.80 249.98 45 1.58 0.1213
cat NS S 800.72 188.37 45 4.25 0

235

.0001

2000 A — Shoot length

Categ 11 Mean* StdErrror
AH 67 67.72 be 4.06
I 83 37.81 a 13.21
M 95 63.81 b 16.14
NS 55 103.62 c 29.07
S 40 29.85 a 15.93
* means followed by the same letter are not signiﬁcantly different (P=0.05)
Type 3 Tests of Fixed Effects
Num Den
Effect DF DF F Value Pr > F
cat 4 45 27.12 <.0001

Least Squares Means
Effect cat Estimate StdErr DF t Value Pr > Itl
cat AH 67.7245 4.0635 45 16.67 <.0001
cat I 37.8102 13.2145 45 2.86 0.0064
cat M 63.8149 16.1413 45 3.95 0.0003
cat NS 103 .62 29.0710 45 3.56 0.0009
cat S 29.8546 15.9315 45 1.87 0.0674

Differences of Least Squares Means
Effect cat _cat Estimate StdErr DF t Value Pr > Itl
cat I 29.9143 13.2984 45 2.25 0.0294
cat M 3.9097 16.1039 45 0.24 0.8093
cat NS -35.8985 28.7938 45 -1.25 0.2189
cat S 37.8699 16.0133 45 2.36 0.0224
cat M -26.0046 5.8607 45 -4.44 <.0001
cat NS -65.8128 17.3328 45 -3.80 0.0004
eat
eat
cat
cat

S 7.9556 6.8312 45 1.16 0.2503
NS -39.8081 14.6436 45 -2.72 0.0093
S
S

33"”EEEE

33.9602 6.3521 45 5.35 <.0001
73.7684 15.7420 45 4.69 <.0001

2
m

236

2000 A - Node number per shoot

Categ 11 Mean“ StdErrror
AH 67 2.2716 b 0.1038
I 83 1.9748 a 0.1 180
M 95 2.3106 b 0.1 123
NS 55 2.4125 b 0.1089

S 40 1.8528 a 0.1350
* means followed by the same letter are not signiﬁcantly different (P=0.05)

Type 3 Tests of Fixed Effects
Num Den
Effect DE DE P Value Pr > F
cat 4 35 5.64 0.0013

Least Squares Means
Effect cat Estimate StdErr DF t Value Pr > Itl

cat AH 2.2716 0.1038 35 21.88 <.0001
cat 1 1.9748 0.1 180 35 16.74 <.0001
cat M 2.3106 0.1 123 35 20.57 <.0001
cat NS 2.4125 0.1089 35 22.16 <.0001
cat S 1.8528 0.1350 35 13.72 <.0001

Differences of Least Squares Means
Effect cat cat Estimate StdErr DF t Value Pr > Itl

cat AH I 0.2967 0. 1244 35 2.38 0.0226
cat AH M -0.03907 0.1 190 35 -0.33 0.7447
cat AH NS -0.1410 0.1156 35 -l .22 0.2306
cat AH S 0.4188 0.1406 35 2.98 0.0052
cat I M -0.3358 0.1281 35 -2.62 0.0129
cat I NS -0.4377 0.1293 35 -3.38 0.0018
cat I S 0.1221 0.1500 35 0.81 0.4212

cat M NS -0.1019 0.1240 35 -0.82 0.4168
cat M S 0.4579 0.1462 35 3.13 0.0035
cat N S S 0.5598 0.1459 35 3.84 0.0005

237

2000 A — Shoot diameter

Categ 11 Mean“ StdErrror
AH 55 4.50 bed 0.24
I 24 4.20 bcd 0.62
M 39 3.76 b 0.25
NS 44 4.52 cd 0.22
S 9 2.31 a 0.64
* means followed by the same letter are not signiﬁcantly different (P=0.05)
Type 3 Tests of Fixed Effects
Num Den
Effect DE DE F Value Pr > F
cat 4 32 4.13 0.0082

Least Squares Means
Effect cat Estimate StdErr DF t Value Pr > Itl
cat AH 4.5026 0.2380 32 18.92 <.0001
cat 1 4.2007 0.6225 32 6.75 <.0001
cat M 3.7603 0.2516 32 14.95 <.0001
cat NS 4.5225 0.2232 32 20.26 <.0001
cat S 2.3068 0.6400 32 3.60 0.0010

Differences of Least Squares Means
Effect eat eat Estimate StdErr DF t Value Pr > Itl

cat AH 1 0.3020 0.6915 32 0.44 0.6653
cat AH M 0.7423 0.3410 32 2.18 0.0370
cat AH NS -0.01993 0.2382 32 -0.08 0.9338
cat AH S 2.1958 0.6254 32 3.51 0.0014
cat I M 0.4404 0.6969 32 0.63 0.5319

cat 1 NS -0.3219 0.6612 32 -0.49 0.6297
cat I S 1.8939 0.8024 32 2.36 0.0245

cat M NS -0.7623 0.3495 32 -2. 18 0.0366
cat M S 1 .45 35 0.6777 32 2.14 0.0397
cat NS S 2.2158 0.6710 32 3.30 0.0024

238

2000 A— Brix

Categ 11 Mean” StdErrror
AH 46 11.2583 a 0.7881
1 39 1 1.4039 a 0.6352
M 65 1 1.7508 a 0.7862
NS 35 12.0161 a 0.5735
S 5 8.4316 a 1.1752
* means followed by the same letter are not signiﬁcantly different (P=0.05)
Type 3 Tests of Fixed Effects
Num Den

Effect cat ca

cat
cat
cat
cat
cat
eat
eat
cat
cat
cat

Effect

cat
C31
cat
cat
C31

ﬂywﬁéé

mmzmaz

m ~'
gig

(I)

1

Effect DF DF FValue Pr>F
cat 4 20 2.53 0.0728

Least Squares Means

cat Estimate StdErr DF t Value Pr > Itl
AH 11.2583 0.7881 20 14.28 <.0001

11.4039 0.6352 20 17.95 <.0001
11.7508 0.7862 20 14.95 <.0001

NS 12.0161 0.5735 20 20.95 <.0001

8.4316 1.1752 20 7.17 <.0001

Differences of Least Squares Means
Estimate StdErr DF tValue Pr> Itl Adjustment Adj P
-0.1455 0.7093 20 021 0.8395 Tukey-Kramer 0.9996
-0.4924 0.5168 20 -0.95 0.3520 Tukey-Kramer 0.8726
-0.7577 0.7899 20 -0.96 0.3489 Tukey-Kramer 0.8699
2.8268 1.2974 20 2.18 0.0415 Tukey-Kramer 0.2278
-0.3469 0.6707 20 -0.52 0.6107 Tukey-Kramer 0.9846
-0.6122 0.6537 20 -0.94 0.3602 Tukey-Kramer 0.8792
2.9723 1.1311 20 2.63 0.0161 Tukey-Kramer 0.1028
-0.2653 0.7865 20 -0.34 0.7394 Tukey-Kramer 0.9970
3.3192 1.2925 20 2.57 0.0183 Tukey-Kramer 0.1150
3.5845 1.1858 20 3.02 0.0067 Tukey-Kramer 0.0470

239

2000A—pH

Categ It Mean“ StdErrror
AH 45 3.20 a 0.031
I 30 3.18 a 0.029

M 65 3.27 a 0.066
NS 33 3.18 a 0.047
S 2 3.09 a 0.096
* means followed by the same letter are not signiﬁcantly different (P=0.05)

Type 3 Tests of Fixed Effects
Num Den
Effect DF DF F Value Pr > F
cat 4 29 0.83 0.5180

Least Squares Means
Effect cat Estimate StdErr DF t Value Pr > Itl
cat AH 3.2049 0.03077 29 104.16 <.0001
cat 1 3.1795 0.02921 29 108.83 <.0001
cat M 3.2720 0.06596 29 49.61 <.0001
cat NS 3.1845 0.04718 29 67.50 <.0001
cat S 3.0850 0.09606 29 32.12 <.0001

Differences of Least Squares Means
Effect cat _cat Estimate StdErr DF t Value Pr > Itl
cat 1 0.02543 0.03926 29 0.65 0.5223
cat M -0.06710 0.05184 29 -1.29 0.2057
cat NS 0.02034 0.03774 29 0.54 0.5940
cat S 0.1199 0.1009 29 1.19 0.2442
cat M -0.09254 0.06650 29 -1.39 0.1747
cat NS -0.00509 0.05079 29 -0. 10 0.9209
cat
eat
eat
eat

S 0.09446 0.1004 29 0.94 0.3546
NS 0.08745 0.06260 29 1.40 0.1731
8 0.1870 0.1165 29 1.60 0.1194
S 0.09955 0.1070 29 0.93 0.3599

5337””EEEE

240

2000 A - Titratable acids

Categ 11 Mean“ StdErrror
AH 45 1.0662 a 0.1201
I 30 1.0420 a 0.1327
M 65 1.0208 a 0.1243
NS 33 1.0511 a 0.1683

S 2 1.2433 a 0.2969
* means followed by the same letter are not signiﬁcantly different (P=0.05)

Type 3 Tests of Fixed Effects
Num Den
Effect DF DF F Value Pr > F
cat 4 20 0.22 0.9218

Least Squares Means
Effect cat Estimate StdErr DF tValue Pr> Itl

cat AH 1.0662 0.1201 20 8.88 <.0001
cat 1 1.0420 0.1327 20 7.85 <.0001
cat M 1.0208 0.1243 20 8.21 <.0001
cat NS 1.051 1 0.1683 20 6.25 <.0001
cat S 1.2433 0.2969 20 4.19 0.0005

Differences of Least Squares Means
Effect cat cat Estimate StdErr DF tValue Pr> Itl Adjustment Adj P

cat AH 1 0.02427 0.08869 20 0.27 0.7872 Tukey-Kramer 0.9987
cat AH M 0.04547 0.07564 20 0.60 0.5545 Tukey-Kramer 0.9733
cat AH NS 0.01515 0.1365 20 0.11 0.9127 Tukey-Kramer 1.0000
cat AH 8 -0.1770 0.2812 20 -0.63 0.5362 Tukey-Kramer 0.9685
cat I M 0.02121 0.07423 20 0.29 0.7780 Tukey-Kramer 0.9984
cat I NS -0.00911 0.1485 20 -0.06 0.9517 Tukey-Kramer 1.0000
cat I S -0.2013 0.2845 20 -0.71 0.4874 Tukey-Kramer 0.9524
cat M NS -0.03032 0.1410 20 -0.21 0.8320 Tukey-Kramer 0.9995
cat M S -0.2225 0.2813 20 -0.79 0.4382 Tukey-Kramer 0.9302
cat NS S -0.1922 0.2852 20 —0.67 0.5081 Tukey-Kramer 0.9598

241

2001 A - Fruit weight per shoot

Categ 11 Mean* StdErrror
AH 98 45.68 b 0.08532
D 89 27.96 a 0.1890
NS 51 52.68 b 0.1415
* means followed by the same letter are not signiﬁcantly different (P=0.05)
Estimates are LOG of data.
Type 3 Tests of Fixed Effects
Num Den
Effect DF DF F Value Pr > F
cat 2 20 14.39 0.0001

Least Squares Means
Effect cat Estimate StdErr DF tValue Pr > Itl

cat AH 3.5541 0.08532 20 41.66 <.0001
cat D 2.7537 0.1890 20 14.57 <.0001
cat NS 3.7162 0.1415 20 26.27 <.0001
Differences of Least Squares Means
Effect cat _cat Estimate StdErr DF tValue Pr > Itl
cat AH D 0.8003 0.2073 20 3.86 0.0010
cat AH NS —0.1622 0.1652 20 -0.98 0.3380
cat D NS -0.9625 0.1803 20 -5.34 <.0001

2001 A —- Cluster weight

Categ 11 Mean” StdErrror
AH 98 23.12 ab 1.57
D 89 15.64 a 2.22
NS 51 26.38 ab 4.83
* means followed by the same letter are not signiﬁcantly different (P=0.05)
Type 3 Tests of Fixed Effects
Num Den
Effect DF DF F Value Pr > F
cat 2 30 4.41 0.0210

Least Squares Means
Effect cat Estimate StdErr DF t Value Pr > Itl
cat AH 23.1 173 1.5679 30 14.74 <.0001
cat D 15.6356 2.2150 30 7.06 <.0001
cat NS 26.3847 4.8271 30 5.47 <.0001

Differences of Least Squares Means
Effect cat _cat Estimate StdErr DF t Value Pr > Itl
cat AH D 7.4818 2.8366 30 2.64 0.0131
cat AH NS -3.2674 5.7341 30 -0.57 0.5730
cat D NS -10.7492 5.6410 30 -1.91 0.0663

242

2001 A - Berry weight

Categ 11 Mean* StdErrror
AH 98 2.49 a 0.32
D 89 2.59 a 0.34
NS 51 3.50 a 1.01
* means followed by the same letter are not signiﬁcantly different (P=0.05)
Type 3 Tests of Fixed Effects
Num Den
Effect DF DF F Value Pr > F
cat 2 30 0.44 0.6475
Least Squares Means

Effect cat Estimate StdErr DF t Value Pr > Itl
cat AH 2.4947 0.321 1 30 7.77 <.0001
cat D 2.5927 0.3412 30 7.60 <.0001
cat NS 3.5021 1.0143 30 3.45 0.0017

Differences of Least Squares Means
Effect cat _cat Estimate StdErr DF t Value Pr > Itl
cat AH D —0.09799 0.4664 30 -0.21 0.8350
cat AH NS -1 .0074 1.0791 30 -0.93 0.3580
cat D NS -0.9094 1.0252 30 -0.89 0.3821

2001 A - Number of clusters per shoot

Categ n Mean“ StdErrror

AH 98 1.97 b 0.097

D 89 1.66 a 0.086

NS 51 2.00 b 0.1 1

* means followed by the same letter are not signiﬁcantly different (P=0.05)

Type 3 Tests of Fixed Effects
Num Den
Effect DF DF F Value Pr > F
cat 2 30 3.79 0.0341

Least Squares Means
Effect cat Estimate StdErr DF tValue Pr > Itl
cat AH 1.9736 0.09674 30 20.40 <.0001
cat D 1.6577 0.08607 30 19.26 <.0001
cat NS 1.9998 0.1132 30 17.67 <.0001
Differences of Least Squares Means
Effect cat _cat Estimate StdErr DF tValue Pr > M
cat AH D 0.3159 0.1345 30 2.35 0.0256
cat AH NS -0.02628 0.1425 30 -0.18 0.8549
cat D NS -0.3422 0.1437 30 -2.38 0.0238

243

2001 A — Number of berries per cluster

Categ It Mean“ StdErrror
AH 98 9.30 b 0.4984
D 89 6.00 a 0.7263

NS 51 10.28 b 1.4078
* means followed by the same letter are not signiﬁcantly different (P=0.05)

Type 3 Tests of Fixed Effects
Num Den
Effect DF DF F Value Pr > F
cat 2 30 7.09 0.0030

Least Squares Means
Effect cat Estimate StdErr DF tValue Pr> |t|
cat AH 9.2994 0.4984 30 18.66 <.0001
cat D 6.0078 0.7263 30 8.27 <.0001
cat NS 10.2801 1.4078 30 7.30 <.0001

Differences of Least Squares Means
Effect cat _cat Estimate StdErr DF t Value Pr > M
cat AH D 3.2916 0.9229 30 3.57 0.0012
cat AH NS -0.9806 1.6339 30 -0.60 0.5529
cat D NS -4.2723 1.7380 30 -2.46 0.0200

2001 A — Number of undeveloped bem'es per shoot

Categ 11 Mean“ StdErrror
AH 98 2.44 a 0.35
D 89 2.25 a 0.38
NS 51 1.55 a 0.33
* means followed by the same letter are not signiﬁcantly different (P=0.05)
Type 3 Tests of Fixed Effects
Num Den
Effect DF DF F Value Pr > F
cat 2 30 1.85 0.1751
Least Squares Means

Effect cat Estimate StdErr DF t Value Pr > Itl
cat AH 2.4360 0.3471 23 7.02 <.0001
cat D 2.2477 0.3777 23 5.95 <.0001
cat NS 1.5490 0.3332 23 4.65 0.0001

Differences of Least Squares Means
Effect cat _cat Estimate StdErr DF t Value Pr > Itl
cat AH D 0.1884 0.5648 23 0.33 0.7418
cat AH NS 0.8871 0.5005 23 1.77 0.0896
cat D NS 0.6987 0.5180 23 1.35 0.1905

244

2001 A — Percent green berries per shoot

Categ 11 Mean“ StdErrror
AH 98 0.85 a 0.28
D 89 0.34 a 0.27

NS 51 0.44 a 0.36
* means followed by the same letter are not signiﬁcantly different (P=0.05)

Type 3 Tests of Fixed Effects
Num Den
Effect DE DE F Value Pr > F
cat 2 30 0.90 0.4187

Least Squares Means
Effect cat Estimate StdErr DF t Value Pr > Itl
cat AH 0.8502 0.2832 30 3.00 0.0054
cat D 0.3440 0.2697 30 1.28 0.21 19
cat NS 0.4437 0.3557 30 1.25 0.2219

Differences of Least Squares Means
Effect cat _cat Estimate StdErr DF t Value Pr > It]
cat AH D 0.5062 0.4201 30 1.20 0.2377
cat AH NS 0.4065 0.41 15 30 0.99 0.331 1
cat D NS -0.09968 0.4670 30 -0.21 0.8324

2001 A —- Number of small berries per shoot

Categ 11 Mean“
AH 98 19.20 b
D 89 17.20 b

NS 51 8.00 a
* means followed by the same letter are not signiﬁcantly different (P=0.05)
The NPARlWAY Procedure (AH vs D) Wilcoxon Scores (Rank Sums) for Variable smallOl
Classiﬁed by Variable cat
Sum of Expected Std Dev Mean
cat N Scores Under H0 Under H0 Score
AH 98 12958.0 11711.00 463.908990 132.224490
D 89 9018.0 10635.50 456.082985 101.325843
NS 51 6465.0 6094.50 386.777876 126.764706
Average scores were used for ties.
Kruskal-Wallis Test
Chi-Square 12.8455
DF 2
Pr > Chi-Square 0.0016
* pairwise comparisons with Wilcoxon test:;
data com_ah_d; set smallOl; if cat NE 'NS' ; run; proc print data=com_ah_d; run; proc nparl way
data=com_ah_d Wilcoxon; class cat; var sma1101; run;

The NPARlWAY Procedure (AH vs NS) Wilcoxon Scores (Rank Sums) for Variable smallOl
Classiﬁed by Variable cat
Sum of Expected Std Dev Mean
cat N Scores Under H0 Under H0 Score
AH 98 10338.0 9212.0 323.818763 105.489796
D 89 7240.0 8366.0 323.818763 81.348315
Average scores were used for ties.

245

 

 

Wilcoxon Two-Sample Test

Statistic 7240.0000
Normal Approximation
Z -3.4757

One-Sided Pr < Z 0.0003

Two-Sided Pr > IZI 0.0005

t Approximation

One-Sided Pr < Z 0.0003

Two-Sided Pr > IZI 0.0006

Z includes a continuity correction of 0.5.

data com_ah_ns; set sma1101; if cat NE 'D' ; run; proc print data=com_ah_ns; run;
proc nparlway data=com_ah_ns wilcoxon; class cat; var smallOl; run;

The NPARlWAY Procedure (D vs NS) Wilcoxon Scores (Rank Sums) for Variable smallOl
Classiﬁed by Variable cat
Sum of Expected Std Dev Mean
cat N Scores Under H0 Under H0 Score
AH 98 7471.0 7350.0 232.769096 76.234694
NS 51 3704.0 3825.0 232.769096 72.627451
Average scores were used for ties.
Wilcoxon Two-Sample Test

 

Statistic 3704.0000
Normal Approximation
Z 05 177

One-Sided Pr < Z 0.3023

Two-Sided Pr > IZI 0.6047

t Approximation

One-Sided Pr < Z 0.3027

Two-Sided Pr > IZI 0.6055

Z includes a continuity correction of 0.5.

data com_d_ns; set smallOl; if cat NE 'AH' ; run; proc print data=com_d_ns; run; proc nparlway
data=com_d_ns wilcoxon; class cat; var smallOl; run;

The NPARlWAY Procedure Wilcoxon Scores (Rank Sums) for Variable smallOl

Classiﬁed by Variable cat
Sum of Expected Std Dev Mean

cat N Scores Under H0 Under H0 Score

D 89 5783.0 6274.50 194.160821 64.977528

NS 51 4087.0 3595.50 194.160821 80.137255

Average scores were used for ties.

Wilcoxon Two-Sample Test

Statistic 4087.0000
Normal Approximation
Z 2.5288

One-Sided Pr > Z 0.0057
Two-Sided Pr > IZI 0.0114
t Approximation
One-Sided Pr > Z 0.0063
Two-Sided Pr > IZI 0.0126

Z includes a continuity correction of 0.5.

246

 

2001 A - Leaf area per shoot

Categ 11 Mean” StdErrror
AH 107 683.25 b 34.0454
D 52 179.66 a 19.0075
NS 178 667.26 b 50.8413
* means followed by the same letter are not signiﬁcantly different (P=0.05)
Type 3 Tests of Fixed Effects
Num Den
Effect DF DF P Value Pr > F
cat 2 20 87.83 <.0001

Least Squares Means
Effect cat Estimate StdErr DF tValue Pr > M

cat AH 683.25 34.0454 20 20.07 <.0001
cat D 179.66 19.0075 20 9.45 <.0001
cat NS 667.26 50.8413 20 13.12 <.0001

Differences of Least Squares Means
Effect cat _cat Estimate StdErr DF t Value Pr > Itl
cat AH D 503.60 38.9920 20 12.92 <.0001
cat AH NS 15.9950 48.6776 20 0.33 0.7459
cat D NS -487.60 54.2782 20 -8.98 <.0001

2001 A - Shoot length

Categ 11 Mean” StdErrror
AH 109 70.89 b 5.06
D 183 24.48 a 2.72
NS 54 63.15 b 5.41
* means followed by the same letter are not signiﬁcantly different (P=0.05)
Type 3 Tests of Fixed Effects
Num Den
Effect DF DF F Value Pr > F
cat 2 30 27.31 <.0001

Least Squares Means
Effect cat Estimate StdErr DF t Value Pr > Itl
cat AH 70.8919 5.0634 30 14.00 <.0001
cat D 24 .4755 2.7241 30 8.98 <.0001
cat NS 63.1546 5.4143 30 1 1.66 <.0001

Differences of Least Squares Means
Effect cat _cat Estimate StdErr DF t Value Pr > Itl
cat AH D 46.4164 6.8310 30 6.79 <.0001
cat AH NS 7.7373 7.3496 30 1.05 0.3009
cat D NS -38.6791 6.9609 30 -5.56 <.0001

247

2001 A - Node number per shoot

Categ 11 Mean" StdErrror
AH 109 11.45 b 0.53
D 183 7.25 a 0.44
NS 54 11.77 b 0.91
* means followed by the same letter are not signiﬁcantly different (P=0.05)
Type 3 Tests of Fixed Effects
Num Den
Effect DF DF F Value Pr > F
cat 2 30 25.25 <.0001

Least Squares Means
Effect cat Estimate StdErr DF t Value Pr > M
cat AH 11.4519 0.5333 30 21.47 <.0001
cat D 7.2475 0.4351 30 16.66 <.0001
cat NS 11.7748 0.9117 30 12.92 <.0001

Differences of Least Squares Means
Effect cat _cat Estimate StdErr DF t Value Pr > Itl
cat AH D 4.2044 0.6253 30 6.72 <.0001
cat AH NS -0.3229 0.8795 30 -0.37 0.7161
cat D NS -4.5273 0.9290 30 -4.87 <.0001

2001 A — Shoot diameter

Categ It Mean“ StdErrror
AH 96 4.52 b 0.13
D 93 3.25 a 0.20
NS 48 4.53 b 0.17
* means followed by the same letter are not signiﬁcantly different (P=0.05)
Type 3 Tests of Fixed Effects
Num Den
Effect DF DF F Value Pr > F
cat 2 30 14.89 <.0001

Least Squares Means
Effect cat Estimate StdErr DF t Value Pr > Itl
cat AH 4.5240 0.1313 30 34.46 <.0001
cat D 3.2482 0.2012 30 16.14 <.0001
cat NS 4.5289 0.1698 30 26.68 <.0001

Differences of Least Squares Means
Effect cat _cat Estimate StdErr DF t Value Pr > Itl
cat AH D 1.2758 0.2436 30 5.24 <.0001
cat AH NS -0.00486 0.1955 30 -0.02 0.9804
cat D NS -1.2806 0.2669 30 -4.80 <.0001

248

2001 A — Brix

Categ 11 Mean“ StdErrror
AH 96 15.87 a 0.19
D 86 15.47 a 0.24
NS 48 15.96 a 0.33
*means followed by the same letter are not signiﬁcantly different (P=0.05)
Type 3 Tests of Fixed Effects
Num Den
Effect DF DF F Value Pr > F
cat 2 30 0.91 0.4141

Least Squares Means
Effect cat Estimate StdErr DF t Value Pr > M
cat AH 15 .8667 0.1913 30 82.95 <.0001
cat D 15.4703 0.2441 30 63.39 <.0001
cat NS 15.9612 0.3308 30 48.25 <.0001

Differences of Least Squares Means
Effect cat _cat Estimate StdErr DF t Value Pr > Itl
cat AH D 0.3964 0.3101 30 1.28 0.2109
cat AH NS -0.09451 0.3821 30 -0.25 0.8063
cat D NS -0.4909 0.4579 30 -1.07 0.2922

2001 A - pH

Categ 11 Mean“ StdEm'or
AH 96 3.28 a 0.026

D 86 3.26 a 0.026

NS 42 3.26 a 0.023
* means followed by the same letter are not signiﬁcantly different (P=0.05)

Type 3 Tests of Fixed Effects
Num Den
Effect DF DF F Value Pr > F
cat 2 30 0.29 0.7500

Least Squares Means
Effect cat Estimate StdErr DF t Value Pr > Itl
cat AH 3.2846 0.02562 30 128.21 <.0001
cat D 3.2649 0.02592 30 125.95 <.0001
cat NS 3.2635 0.02950 30 1 10.64 <.0001

Differences of Least Squares Means
Effect cat _cat Estimate StdErr DF t Value Pr > Itl
cat AH D 0.01969 0.03485 30 0.56 0.5764
cat AH NS 0.021 10 0.03120 30 0.68 0.5041
cat D NS 0.001410 0.03785 30 0.04 0.9705

249

2001 A - Titratable acids

Categ 11 Mean* StdErrror
AH 55 5.91 b 0.25
D 42 5.24 a 0.15
NS 25 5.43 ab 0.21
* means followed by the same letter are not signiﬁcantly different (P=0.05)
Type 3 Tests of Fixed Effects
Num Den
Effect DF DF F Value Pr > F
cat 2 27 4.70 0.0177

Least Squares Means
Effect cat Estimate StdErr DF tValue Pr > Itl
cat AH 5.9052 0.2509 27 23.54 <.0001
cat D 5.2372 0.1463 27 35.80 <.0001
cat NS 5.4257 0.2121 27 25.58 <.0001
Differences of Least Squares Means
Effect cat _cat Estimate StdErr DF tValue Pr > It]
cat AH D 0.6681 0.2233 27 2.99 0.0059
cat AH NS 0.4795 0.3052 27 1.57 0.1278
cat D NS 01885 0.2220 27 -0.85 0.4032

2001 B - Fruit weight per shoot

Categ 11 Mean“ StdErrror
AH 85 83.04 b 11.79
D 62 44.15 a 7.17
NS 43 107.06 b 23.84
* means followed by the same letter are not signiﬁcantly different (P=0.05)
Type 3 Tests of Fixed Effects
Num Den
Effect DF DF F Value Pr > F
cat 2 30 6.87 0.0035

Least Squares Means
Effect cat Estimate StdErr DF t Value Pr > Itl
cat AH 6.6536 0.1692 30 39.33 <.0001
cat D 4.4675 0.2873 30 15.55 <.0001
cat NS 6.2326 0.3142 30 19.84 <.0001

Differences of Least Squares Means
Effect cat _cat Estimate StdErr DF t Value Pr > Itl
cat AH D 2.1861 0.3444 30 6.35 <.0001
cat AH NS 0.4209 0.2707 30 1.55 0.1305
cat D NS -1.7651 0.4464 30 -3.95 0.0004

250

2001 B - Cluster weight

Categ It Mean* StdErrror
AH 85 36.53 b 4.73
D 62 22.33 a 2.64
NS 43 41.55 b 7.96
* means followed by the same letter are not signiﬁcantly different (P=0.05)
Type 3 Tests of Fixed Effects
Num Den
Effect DF DF F Value Pr > F
cat 2 30 5.43 0.0097

Least Squares Means
Effect cat Estimate StdErr DF t Value Pr > Itl
cat AH 36.5252 4.7278 30 7.73 <.0001
cat D 22.3175 2.6365 30 8.46 <.0001
cat NS 41.5549 7.9643 30 5.22 <.0001

Differences of Least Squares Means
Effect cat _cat Estimate StdErr DF t Value Pr > Itl
cat AH D 14.2077 5.1669 30 2.75 0.0100
cat AH NS -5.0297 8.6220 30 -0.58 0.5640
cat D NS -19.2374 8.1188 30 -2.37 0.0244

2001 B — Berry weight

Categ 11 Mean“ StdErrror
AH 85 2.87 a 0.15
D 62 2.70 a 0.2
NS 43 3.01 a 0.10
* means followed by the same letter are not signiﬁcantly different (P=0.05)
Type 3 Tests of Fixed Effects
Num Den
Effect DF DF F Value Pr > F
cat 2 30 0.92 0.4098
Least Squares Means

Effect cat Estimate StdErr DF t Value Pr > M
cat AH 2.8698 0.1466 30 19.58 <.0001
cat D 2.7039 0.1980 30 13.65 <.0001
cat NS 3.0051 0.1039 30 28.93 <.0001

Differences of Least Squares Means
Effect cat _cat Estimate StdErr DF t Value Pr > Itl
cat AH D 0.1659 0.1739 30 0.95 0.3477
cat AH NS —0.1353 0.1796 30 -0.75 0.4571
cat D NS -0.3012 0.2236 30 -l.35 0.1881

251

 

_ns -‘ ....

2001 B - Number of clusters per shoot

Categ 11 Mean* StdErrror
AH 85 2.27 b 0.12
D 62 1.83 a 0.13
NS 43 2.36 b 0.14
* means followed by the same letter are not signiﬁcantly different (P=0.05)
Type 3 Tests of Fixed Effects
Num Den
Effect DF DF F Value Pr > F
cat 2 30 3.74 0.0354

Least Squares Means
Effect cat Estimate StdErr DF t Value Pr > Itl
cat AH 2.2686 0.1170 30 19.38 <.0001
cat D 1.8289 0.1291 30 14.17 <.0001
cat NS 2.3629 0.1441 30 16.40 <.0001

Differences of Least Squares Means
Effect cat _cat Estimate StdErr DF t Value Pr > Itl
cat AH D 0.4397 0.1997 30 2.20 0.0356
cat AH NS -0.09430 0.21 19 30 -0.45 0.6594
cat D NS -0.5340 0.2151 30 -2.48 0.0189

2001 B — Number of berries per cluster

Categ 11 Mean* StdErrror
AH 85 12.44 b 1.34
D 62 7.70 a 0.73
NS 43 13.37 b 2.38
* means followed by the same letter are not signiﬁcantly different (P=0.05)
Type 3 Tests of Fixed Effects
Num Den
Effect DF DF F Value Pr > F
cat 2 30 7.67 0.0020

Least Squares Means
Effect cat Estimate StdErr DF t Value Pr > Itl
cat AH 12.4372 1.3444 30 9.25 <.0001
cat D 7.6985 0.7295 30 10.55 <.0001
cat NS 13.3722 2.3754 30 5.63 <.0001

Differences of Least Squares Means
Effect cat _cat Estimate StdErr DF t Value Pr > Itl
cat AH D 4.7387 1 .4701 30 3.22 0.0030
cat AH NS 0.9351 2.7827 30 -0.34 0.7392
cat D NS -5.6738 2.4194 30 -2.35 0.0258

252

 

2001 B - Number of undeveloped bem'es per shoot

Categ 11 Mean* StdErrror

AH 85 1.29 a 0.54

D 62 0.33 a 0.29

NS 43 1.19 a 0.42

* means followed by the same letter are not signiﬁcantly different (P=0.05)

Type 3 Tests of Fixed Effects
Num Den
Effect DF DF F Value Pr > F
cat 2 30 1.99 0.1549

Least Squares Means
Effect cat Estimate StdErr DF t Value Pr > Itl
cat AH 1.2925 0.5353 30 2.41 0.0221
cat D 0.3299 0.2878 30 1.15 0.2607
cat NS 1.1878 0.4164 30 2.85 0.0078

Differences of Least Squares Means
Effect cat _cat Estimate StdErr DF t Value Pr > Itl
cat AH D 0.9626 0.5593 30 1 .72 0.0955
cat AH NS 0.1047 0.4866 30 0.22 0.8312
cat D NS -0.8579 0.4755 30 -1.80 0.0812

2001 B - Percent number of green per shoot

Categ 11 Mean” StdErrror
AH 85 1.90 a 0.72
D 62 3.02 a 1.86

NS 43 1.32 a 0.59
* means followed by the same letter are not signiﬁcantly different (P=0.05)

Type 3 Tests of Fixed Effects
Num Den
Effect DF DF F Value Pr > F
cat 2 30 0.52 0.6011

Least Squares Means
Effect cat Estimate StdErr DF t Value Pr > M
cat AH 1.8972 0.7249 30 2.62 0.0137
cat D 3.0217 1.8579 30 1.63 0.1 143
cat NS 1.3231 0.5933 30 2.23 0.0334

Differences of Least Squares Means
Effect cat _cat Estimate StdErr DF t Value Pr > Itl
cat AH D -1.1245 2.0752 30 -0.54 0.5919
cat AH NS 0.5741 0.9367 30 0.61 0.5445
cat D NS 1.6986 1.9503 30 0.87 0.3907

253

2001 B - Number of small berries per shoot

Categ 11 Mean" StdEmor
AH 85 0.21 a 0.079
D 62 0.22 a 0.067

NS 43 0.09 a 0.079
* means followed by the same letter are not signiﬁcantly different (P=0.05)

Type 3 Tests of Fixed Effects
Num Den
Effect DF DF F Value Pr > F
cat 2 30 0.76 0.4759

Least Squares Means
Effect cat Estimate StdErr DF t Value Pr > Itl
cat AH 0.2128 0.07894 30 2.70 0.01 14
cat D 0.2155 0.06743 30 3.20 0.0033
cat NS 0.09625 0.07912 30 1.22 0.2332

Differences of Least Squares Means
Effect cat _cat Estimate StdErr DF t Value Pr > Itl
cat AH D -0.00279 0.1 123 30 -0.02 0.9804
cat AH NS 0.1 165 0.1 170 30 1.00 0.3275
cat D NS 0.1 193 0.1059 30 1.13 0.2687

2001 B — Leaf area per shoot

Categ 11 Mean“ StdErrror
AH 101 150.61 b 7.27
D 91 69.62 a 8.43
NS 55 143.51 b 8.73
* means followed by the same letter are not signiﬁcantly different (P=0.05)
Type 3 Tests of Fixed Effects
Num Den
Effect DF DF F Value Pr > F
cat 2 30 51.60 <.0001

Least Squares Means
Effect cat Estimate StdErr DF t Value Pr > Itl
cat AH 150.61 7.2655 30 20.73 <.0001
cat D 69.6245 8.4281 30 8.26 <.0001
cat NS 143.51 8.7347 30 16.43 <.0001

Differences of Least Squares Means
Effect cat _cat Estimate StdErr DF t Value Pr > Itl
cat AH D 80.9817 8.2598 30 9.80 <.0001
cat AH NS 7.1007 7.7495 30 0.92 0.3668
cat D NS -73.8810 9.1516 30 -8.07 <.0001

254

2001 B - Shoot length

Categ It Mean“ StdErrror
AH 101 186.83 b 14.75
D 91 76.69 a 12.67
NS 55 164.41 b 16.20
* means followed by the same letter are not signiﬁcantly different (P=0.05)
Type 3 Tests of Fixed Effects
Num Den
Effect DF DF F Value Pr > F
cat 2 30 17.76 <.0001

Least Squares Means
Effect cat Estimate StdErr DF 1 Value Pr > Itl
cat AH 186.83 14.7469 30 12.67 <.0001
cat D 76.6902 12.6734 30 6.05 <.0001
cat NS 164.41 16.1982 30 10.15 <.0001

Differences of Least Squares Means

Effect cat _cat Estimate StdErr DF t Value Pr > Itl
cat AH D 110.14 18.5647 30 5.93 <.0001
cat AH NS 22.4221 15.4921 30 1.45 0.1582
cat D NS -87.7181 19.8067 30 -4.43 0.0001

2001 B — Node number per shoot

Categ It Mean" StdErrror
AH 101 5.68 b 0.89
D 91 3.56 a 0.68
NS 55 6.36 b 0.80
* means followed by the same letter are not signiﬁcantly different (P=0.05)
Type 3 Tests of Fixed Effects
Num Den
Effect DF DF F Value Pr > F
cat 2 30 5.98 0.0065

Least Squares Means
Effect cat Estimate StdErr DF t Value Pr > Itl
cat AH 5.6838 0.8948 30 6.35 <.0001
cat D 3.5641 0.6764 30 5.27 <.0001
cat NS 6.3603 0.8018 30 7.93 <.0001

Differences of Least Squares Means

Effect cat _cat Estimate StdErr DF t Value Pr > Itl
cat AH D 2.1 196 0.8703 30 2.44 0.0210
cat AH NS -0.6766 0.9567 30 -0.71 0.4849
cat D NS -2.7962 0.8691 30 -3.22 0.0031

255

2001 B - Shoot diameter

Categ 11 Mean* StdErrror
AH 96 6.65 b 0.17
D 66 4.47 a 0.29
NS 51 6.23 b 0.31
* means followed by the same letter are not signiﬁcantly different (P=0.05)
Type 3 Tests of Fixed Effects
Num Den
Effect DF DF F Value Pr > F
cat 2 30 21.78 <.0001

Least Squares Means
Effect cat Estimate StdErr DF t Value Pr > Itl
cat AH 6.6536 0.1692 30 39.33 <.0001
cat D 4.4675 0.2873 30 15.55 <.0001
cat NS 6.2326 0.3142 30 19.84 <.0001
Differences of Least Squares Means

Effect cat _cat Estimate StdErr DF t Value Pr > M
cat AH D 2.1861 0.3444 30 6.35 <.0001
cat AH NS 0.4209 0.2707 30 1 .55 0.1305
cat D NS -1.7651 0.4464 30 -3 .95 0.0004

2001 B - Brix
Categ It Mean" StdErrror
AH 78 16.37 ab 0.49
D 59 17.33 b 0.52
NS 41 15.62 a 0.49
* means followed by the same letter are not signiﬁcantly different (P=0.05)
Type 3 Tests of Fixed Effects
Num Den
Effect DF DF F Value Pr > F
cat 2 30 2.31 0.1168

Least Squares Means
Effect cat Estimate StdErr DF t Value Pr > Itl
cat AH 16.3712 0.4879 30 33.56 <.0001
cat D 17.3339 0.5204 30 33.31 <.0001
cat NS 15.6262 0.4885 30 31.99 <.0001

Differences of Least Squares Means

Effect cat _cat Estimate StdErr DF t Value Pr > Itl
cat AH D -0.9628 0.8298 30 -1 .16 0.2551
cat AH NS 0.7449 0.7086 30 1 .05 0.3015

cat D NS 1.7077 0.7987 30 2.14 0.0408

256

2001 B — pH

Categ 11 Mean* StdErrror
AH 78 3.24 b 0.029
D 59 3.27 a 0.054
NS 41 3.23 b 0.043
* means followed by the same letter are not signiﬁcantly different (P=0.05)
Type 3 Tests of Fixed Effects
Num Den
Effect DF DF F Value Pr > F
cat 2 30 0.18 0.8383

Least Squares Means
Effect cat Estimate StdErr DF t Value Pr > Itl
cat AH 3.2407 0.02896 30 1 1 1.91 <.0001
cat D 3.2733 0.05410 30 60.50 <.0001
cat NS 3.2333 0.04349 30 74.35 <.0001

Differences of Least Squares Means
Effect cat _cat Estimate StdErr DF t Value Pr > Itl

cat AH D 0.03261 0.06194 30 -0.53 0.6025
cat AH NS 0.007399 0.05087 30 0.15 0.8853

cat D NS 0.04000 0.07018 30 0.57 0.5729

2001 B - titratable acids

Categ It Mean“ StdErrror
AH 61 6.30 a 0.15
D 25 7.14 a 0.42
NS 30 6.79 a 0.30
* means followed by the same letter are not signiﬁcantly different (P=0.05)
Type 3 Tests of Fixed Effects
Num Den
Effect DF DF F Value Pr > F
cat 2 24 2.63 0.0928

Least Squares Means
Effect cat Estimate StdErr DF t Value Pr > Itl
cat AH 6.3016 0.1556 24 40.50 <.0001
cat D 7.1444 0.4212 24 16.96 <.0001
cat NS 6.7900 0.3028 24 22.42 <.0001

Differences of Least Squares Means
Effect cat _cat Estimate StdErr DF t Value Pr > Itl
cat AH D 08428 0.4354 24 -1.94 0.0648
cat AH NS 04883 0.3156 24 -l.55 0.1349
cat D NS 0.3544 0.4914 24 0.72 0.4777

257

APPENDD( C

Results of the statistical analysis using the CORR (correlation) procedure

258

 

KEY TO COMPONENT ABBREVATIONS

wtprsht = fruit weight per shoot

wtprcst = fruit weight per cluster
wtprbry = fruit weight per berry
clstnum = cluster number per shoot
bryclst = berry number per cluster

sugar = sugar content per shoot

brysht = berries per shoot
gmum=number of green berries per shoot
gmwt=weight of green berries per shoot
undrv = undeveloped berries per shoot
lfarea = leaf area per shoot

length = shoot length

node = number of nodes per shoot

diam = shoot diameter

brix = brix (% soluble sugar) per shoot
ph = pH per shoot

ta = titratable acids per shoot

259

 

 

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APPENDIX D

Protocols

268

A.
1.

DNA extraction protocols
DNgsy rodent tail extraction method. revised

(Qiagen, DNeasy tissue kit, cat # 69504, Qiagen, Inc., Valencia, CA)

1.

Approximately 1 cm2 of mycelium with as little agar attached as possible was
collected from the isolate culture using a sterile scalpel. In a micro-centrifuge
tube 180 pl of ATL buffer was added and sample was smashed with a pestle in
the tube until macerated well and buffer has foamed; at least 3 minutes.

Proteinase was added to each sample (20 pl of 20 mg/ml) and incubated for
three hours or overnight at 55°C.

Mixture was vortexed and a mixture of 200 u] AL buffer and 200-210 111 100%
ethanol was added, and vortexed and mixed vigorously.

Entire contents was pipetted into DNeasy column that has been placed inside a
collection tube. Tubes were centrifuged for 1 minute at 8,000 g and ﬂow-
through is discarded.

Column was placed in a new collection tube and 500 .11 AW 1 buffer was added.
This was centrifuged at 8,000 g for one minute. Flow-through was discarded.

Column was placed into a third collection tube and 500 pl AW 2 buffer was
added. This was centrifuged at 10,000 g for three minutes to dry membrane.
Flow-through was discarded.

Mini-column was placed into a new micro-centrifuge tube and 200 pl preheated
AE buffer was added directly onto the membrane in the column. This was
incubated at room temperature, then centrifuged at 8,000 g for one minute to
elute the DNA material.

Step 7 was repeated in order to elute more material; a different tube was used.
DNA was stored at -20°C.

2. Boiling method
(After Lecomte, et al., 2000)

1.

Wood chips containing perithecia of Eutypa lata were surface-sterilized by
ﬂaming brieﬂy with a ﬁne mist spray of ethanol.

The chips were cut with a sterile scalpel and pieces of approximately l-l.5 cm2
were placed in micro-centrifuge tubes with 50 u] deionoized water.

The samples were boiled on a heating block for 15 minutes at 95°C.
Immediately after boiling, the tubes were placed in -20°C.

269

3.

Phenol-chloroform extraction:

Extraction buffer:

2 % cetyltrimethylarnmonium
1.4 M NaCl

1% polyethylene glycol 8000
20 mM EDTA, pH 8

1% 2-mercaptoethanol

100 mM Tris-HCL, pH 9.5

Fungal material (minus agar whenever possible) was ground up in a sterile mortar
and pestle with 800 pl CTAB extraction buffer until a uniformly milky slurry was
obtained.

Extracts were vortexed brieﬂy and incubated at 65°C for at least 1 h.

Following the addition of 600 pl phenol: chloroform: isoarnyl alcohol (25:24: 1)
and a brief vortexing, extracts were centrifuged at 10,000 g for 5 minutes.

The top (aqueous) phase was transferred to a new tube and precipitated with an
equal volume of cold isopropanol and centrifuged at 10,000 g for 10 minutes.

The pellet of DNA at bottom was washed with cold 70% ethanol, air dried for 12
hours, and resuspended in at least 20 pl 1x TRIS EDTA buffer, pH 8, warmed to
65°C.

Samples were stored at -20°C.

DNA puriﬁcation of ampliﬁcation product protocol

1. Wizard PCR prep DNA puriﬁcation system (Wizard Puriﬁcation Systems, cat #
A7107, Promega, Madison, W1)

1.

2.

Sample was transferred to clean micro—centrifuge tube.

Direct puriﬁcation buffer (100 pl) was aliquotted into micro-centrifuge tubes into
which 30 - 300 pl of the PCR reaction mix was added.

Contents were vortexed brieﬂy to mix.

Resin was added (1 ml), shaken well, and vortexed brieﬂy three times over a one-
minute period.

The resin/DNA mixture was pipetted into a syringe barrel, and the plunger was

gently depressed to push slurry into a mini-column provided. The resin caught
the DNA and the rest passed through the ﬁlter.

270

6.

10.

The mini—column was detached from the syringe and the plunger was removed.
The mini-column was reattached to the syringe and 2 ml of 80% isopropanol was
added to the column. Plunger was inserted and the isopropanol was pushed
through the column.

The mini~column was transferred to a micro-centrifuge tube and centrifuged for 2
minutes at 10,000 g to remove isopropanol from the resin/DNA.

Mini-column was transferred to a new micro—centrifuge tube and 50 pl of TE
buffer or deionized water was added.

After one minute, mini-column was centrifuged for 20 seconds at 10,000 g to
elute the DNA fragment.

The mini-column was discarded. The micro-centrifuge tube that contains the
puriﬁed DNA was stored at this point.

271

APPENDIX E

Sequence Homology Reports

272

Sequence Homology Reports from the National Center for Biotechnology Information
(website address: http://www.ncbi.nlm.nih.gov/BLAST/)
l. ITS sequence of isolate CA30 and search results:

GAGGGATCATTACAGAG'ITACCT AACT CCAAACCCATGTGAAC’I'I‘ ACCT ATGT
TGCCTCGGCGGGGAAGCCTACCCGGTACCI‘ACCCTGTAGCTACCCGGGAGCG
AGCT ACCCT GTAGCT CGCT GCAGGCCT ACCCGCCGGTGGACAC'IT AAACT CIT
G'I'I'I'I'ITI‘AGTGA'I‘TATCTGAGTG'I‘TI‘ATAC'I'I‘AATAAG’ITAAAACITTCAACA
ACGGATCTCTFGGTTCTGGCATCGATGAAGAACGCAGCGAAATGCGATAAGT
AATGTGAA'ITGCAGAATTCAGTGAATCATCGAATCTITGAACGCACATTGCG
CCCA'I'I‘AGTATTCTAGTGGGCATGCCTGTTCGAGCGTCAT'ITCGACC'ITCAAG
CCCTAGCTGC'I'I‘GGTGTTGGGAGCCTATCTCCGGATAGCTCCTGAAAAGCA'IT
GGCGGAGTCGCGGTGACCCCAAGCGTAGTAA'ITC'ITCI‘CGCITI‘AGGTGTGTC
ACGGCTGACGTC'I'TGCCGTTAAACCCCCAA'ITI'I'I'I‘AAATGGT'I‘GACCTCGGA
TCAGGTAGGAATACCCGCT GAACTT AA

BLASTN 2.2.1 [Apr-13-2001]

Reference: Altschul, Stephen R, Thomas L. Madden, Alejandro A. Schaffer,
Jinghui Zhang, Zheng Zhang, Webb Miller, and David J. Lipman (1997),
"Gapped BLAST and PSI-BLAST: a new generation of protein database search
programs", Nucleic Acids Res. 25:3389-3402.

RID: 101 1390026-366-22898

Query: (555 letters)

Database: All GenBank+EMBL+DDBJ+PDB sequences (but no EST, STS, 688,
or phase 0, l or 2 HTGS sequences)
1,072,659 sequences; 4,597,704,364 total letters

(E value = the expectation value; the number of different alignments that are expected to
be found by chance whose score (bits) are better than or equal to the sequence searched
for.)

~ Score E
Sequences producing signiﬁcant alignments: (bits) Value
gi|16215425|emb|AJ302446.1|EAR302446 Eutypa armeniacae 188 rRNA 983 0.0
gi|16215429|emb|AJ302450.1|ELA302450 Eutypa lata 18S rRNA gene (... 973 0.0
gi|16215424|emb|AJ 302445.1IEAR302445 Eutypa armeniacae 18S rRNA 959 0.0

gi|9545987|gb|AF099911.1|AF099911 Eutypa lata internal transcrib... 880 0.0
gi|16215440|emb|AJ 302459.1IELA302459 Eutypa lata 18S rRNA gene (... 872 0.0
gi|1621543l|embIAJ302451.1IELA302451 Eutypa lata var. aceri 188 833 0.0
gi|16215435|emb|AJ 302455.1IEPE302455 Eutypa petrakii var. petrak... 827 0.0
gi|16215436|emb|AJ 302456.1IEPE302456 Eutypa petrakii var. petrak... 811 0.0

gi|16215428|emb|AJ 302449.1IELA302449 Eutypa laevata 18S rRNA gen... 809 0.0
gi|16215432|emb|A1302452.l|ELA302452 Eutypa lata 188 rRNA gene (... 765 0.0
gi|16215401|emb|AJ 302428.1IDFL302428 Diatrype ﬂavovirens 188 rR... 755 0.0

273

gi|16215399|emb|AJ302426.1|DFL302426 Diatrype ﬂavovirens rss rR...
gi|16215326|emb|AJ302417.1|CEU302417 Cryptosphaeria eunomia var....
gi|8809731|gb|AF163032. 1 IAF163032 Xylaria cubensis CBS 116.85 in...
gi|162154l8|emb|AJ302440.1|DFA302440 Diatrypella favacea l8S rRN...
gi|16215421|emblA1302443.1|DPU302443 Diatrypella pulvinata 188 r...
gi|16215437|emb|AJ302457.1|EFL302457 Eutypa ﬂavovirens 188 rRNA...
gi|16215403|emb|AJ302430. 1 |DFL302430 Diatrype ﬂavovirens 188 rR...
gi|16215402|emb|AJ302429.1|DFL302429 Diatrype ﬂavovirens 188 IR...
gi|16215400|emb|AJ302427.l|DFL302427 Diatrype ﬂavovirens 188 rR...

gi|16215404|emb|A1302431.1IDMA302431 Diatrype macowaniana 188 rR...

gi|16215441|embIAJ302460.1|ECA302460 Eutypella caricae 188 rRNA
gi|16215419|emblA1302441.1|DFR302441 Diatrypella frostii 188 rRN...
gi|13249047|gb|AF192322.l|AF192322 Diatrypella frostii 28S ribos...
gi|16215335|emb|AJ302421.1|CEU302421 Cryptosphaeria eunomia var....
gi|16215420|emb|AJ302442.1|DPR302442 Diatrypella prominens 188 r...

gi|16215417|embIAJ302439.1IDST302439 Diatrype stigma 18S rRNA ge...
gi|16215413|emb|A1302436.1|DUN302436 Diatrype undulata 188 rRNA

gi|16215333|emb|AJ302420. l |CSU302420 Cryptosphaeria subcutanea 1...
gi|16215423|emb|AJ302444.1IDQU302444 Diatrypella quercina 188 rR...
gil16215408|emb|AJ302433.1|DSP302433 Diatrype spilomea 18s rRNA
gi|16215329|emb|AJ302418.1|CLI302418 Cryptosphaeria ligniota 188...

gi|16215426|emb|AJ302447.llECO302447 Eutypa consobrina 18S rRNA
gi|16215410|emb|A1302434.1IDST302434 Diatrype stigma 188 rRNA ge...
gil16215405|emb|AJ302432.1|DP0302432 Diatrype polycocca 188 rRNA...

gi|16215332|emb|AJ302419.1|CPU302419 Cryptosphaeria pullmanensis...
gi|13249048|gb|AF192323.llAF192323 Diatrype stigma 28$ ribosomal...
gi|16215427|emb|AJ 302448.1IECR302448 Eutypa crustata 188 rRNA ge...
gi|16215394|emb|AJ302422.1|DBU302422 Diatrype bullata 188 rRNA g...
gi|16215439|emb|A1302458.l|EAS302458 Eutypa astroidea 18$ rRNA g...
giI16215416|emb|AJ302438.1|DST302438 Diatrype stigma 188 rRNA ge...
gi|16215414|emb|A1302437.1|DDI302437 Diatrype disciformis 18S rR...
gi|16215397|emb|AJ 302425.1IDD1302425 Diatrype disciforrnis 188 IR...
gi|16215396|emb|AJ302424.1|DDI302424 Diatrype disciforrnis 18$ rR...
gi|16215395|emb|AJ302423.1|DDI302423 Diatrype disciforrnis 18S rR...
gi|16215411|emb|AJ302435.1|DST302435 Diatrype stigma 188 rRNA ge...
gi|12038859|emb|AJ390410.1|HAN390410 Diatrype disciformis 18$ rR...
gi|16215445|emb|AJ 302464.1IEPR302464 Eutypella prunastri 18S rRN...

gi|16215434|emb|AJ302454.1|EMA302454 Eutypa maura 18$ rRNA gene

gi|16215433|emb|AJ302453.1[ELE302453 Eutypa leptoplaca 188 rRNA
gill6215450|emb|AJ302468.l|ECE302468 Eutypella cerviculata 183 r...
gi|16215448|emb|AJ 302466.1IEV1302466 Eutypella vitis 18S rRNA ge...
gi|16215442|emb|A1302461.1IECE302461 Eutypella cerviculata 188 r...
gi|16215444|emb|AJ302463.1|ELE302463 Eutypella leprosa 183 rRNA
gi|18033499|gb|AF340014.1|AF340014 Monosporascus cannonballus in...
gi|18000996|gb|AF280629.llAF280629 Nodulisporium sp. JP807 18S r...
gi|10732541|gb|AF153740.1|AF15374O Xylaria sp. M81033 internal t...

274

755 0.0

749 0.0

741 0.0

739 0.0

726 0.0

722 0.0

722 0.0

722 0.0
620 e-l75
607 e-17l
599 e—l68
579 e—l62
571 e-160
545 e-152
480 e-133
476 e-131
464 e-128
464 e-128
462 e-127
456 e-125
456 e-125
454 e-125
454 e-125
450 e-124
450 e-124
448 e-123
448 e-123
448 e-123
444 e-122
444 e-122
440 e-121
440 e-121
440 e-121
440 e-121
436 e-120
436 e-l20
434 e-119
428 e-117
426 e-ll7
379 e-102
379 e-102
373 e-100
371 e-100
365 3e-98
359 2e-96
359 2e-96

gi|10732536|gb|AF153735.l|AF153735 Xylaria sp. M3395 internal tr...
gi|4519363|dbj|AB017661.l|AB017661 Rosellinia quercina DNA for 1...
gil 1 803 3498] gbIAF 34001 3. 1 IAF34001 3 Monosporascus ibericus intern...

gill8157728|gbIAF432179.lIAF432179 Arthroxylaria elegans strain

gi|10732533|gb|AF153732.1|AF153732 Xylaria sp. M3366 internal tr...
gi|10732532|gb|AF153731.1|AF15373l Xylaria sp. M3339 internal tr...
gi|8809737|gb|AF163038.1|AF163038 Xylaria longipes CBS 148.73 in...
gi|17863468|gb|AF373064. 1 |AF373064 Eutypella scoparia MUT 485 18...
gi|16215447|emb|AJ302465.1|ESC302465 Eutypella scoparia 188 rRNA...
gi|6003453|gbIAF176958.1|AF 176958 Daldinia concentrica 16S small...
gi|6003452|gb|AF 176957.1IAF 176957 Daldinia concentrica 168 small...
gil6003451lgbIAF 176956.1IAF 176956 Daldinia concentrica 168 small...
gi|6003450|gb|AF 176955. 1 IAF 176955 Daldinia concentrica 16S small...
gi|6003449|gb|AF 176954.1|AF 176954 Daldinia concentrica 168 small...

gill 1967307IgbIAF201708.llAF201708 Daldinia concentrica 18$ ribo...

gill 1066006|gb|AFl94027.1|AF194027 Xylaria hypoxylon 5.83 riboso...
gi|10732546|gb|AF153745.1|AF 153745 Xylaria sp. M81092 internal t...
gi|10732545|gb|AF153744.l|AF153744 Xylaria sp. M31083 internal t...
gi|10732534|gb|AF153733.1|AF153733 Xylaria sp. M3370 internal tr...
gi|10732527|gb|AF153726.llAF153726 Xylaria sp. M3259 internal tr...
gi|10732526|gb|AF153725.1|AF153725 Xylaria sp. M3358 internal tr...
gi|10732525|gbIAF153724.1|AF153724 Xylaria sp. M31066 internal t...
gi|8809739|gb|AF163040.1|AF163040 Xylaria mali CBS 385.35 intern...
gi|8809730|gb|AF 163031.1IAF 163031 Xylaria cornmdamae CBS 724.69...
gi|8809728lgbIAF 163029.1IAF 163029 Xylaria arbuscula CBS 452.63 i...
gi|8809727|gb|AF163028.1|AF163028 Xylaria arbuscula CBS 454.63 i...
gi|12038883|emblAJ390434.1|HAN390434 Kretzschmaria clavus 188 rR...
gi|12038874|emb|AJ390425.1|HAN 390425 Creosphaeria sassafras 188
gi|12038869lemb|AJ 390420.1lHAN 390420 Whalleya nricroplaca 188 rRN...
gi|12038868|emb|A13904191|HAN390419 Whalleya microplaca 18S rRN...
gi|6103018|emb|A1012300.1IPD1012300 Phialemonium dimorphosporum
gi|6103017|emb|A1012299.1|PD1012299 Phialemonium dimorphosporum
gi|6103016|emb|A1012298.1|PD1012298 Phialemonium dimorphosporum
gi|6003470|gb|AF176975.1|AF176975 Daldinia petriniae 16S small 3...
gi|6003469|gb|AF176974.1|AF176974 Daldinia petriniae 16S small 3...
gi|6003468|gb|AF 176973.1|AF 176973 Daldinia petriniae 168 small s...
gi|6003467|gb|AFl76972.l|AF176972 Daldinia petriniae 163 small s...
gi|6003466|gb|AFl76971.1|AF176971 Daldinia petriniae 168 small s...
gi|6003465|gb|AFl76970.1|AF176970 Daldinia petriniae 168 small s...
gi|6003464|gb|AF 176969.1IAF 176969 Daldinia loculata 168 small su...
gi|6003463|gb|AF176968.llAF176968 Daldinia loculata 168 small su...
gi|6003462|gb|AF176967.lIAF176967 Daldinia loculata 168 small su...
gi|6003461|gb|AF 176966.1[AF176966 Daldinia loculata 16S small su...

275

359 2e-96
359 2e-96
357 7e-96
351 4e-94
351 4e-94
351 4e-94
351 4e-94
347 6e-93
347 6e-93
343 le-9l
343 1e-91
343 le-9l
343 le-9l
343 1e-91
343 le-9l

343 1e-91
343 1e-91
343 1e-91
343 1e-91
343 1e-91
343 1e-91
343 1e-91
343 1e-91
343 1e-91
343 le-9l
343 1e-91
343 1e-91
343 le-91
343 1e—91
343 le-9l
343 le-91
343 le-91
343 1e-91
341 4e-91
341 4e-91
341 4e-91
341 4e-91
341 443-91
341 4e-91
341 4e-91
341 4e-91
341 4e-91
341 4e-91

ALIGNMENTS

>gi|16215425|embIAJ302446.1|EAR302446 Eutypa armeniacae 188 rRNA gene
(partial), 5.88 rRNA gene, 288 rRNA gene (partial), internal transcribed spacer 1 (ITSl)
and internal transcribed spacer 2 (I'I‘S2), isolate E37C Length = 525

Score = 983 bits (496), Expect = 0.0
Identities = 516/525 (98%)
Strand = Plus / Plus

Query: 3 ggatcattaca gagttacctaactccaaacccatgtgaacttacctatgttgcctcggc 62

Sbjct: 1 gggatcattacagagttacctaactccaaacccatgtgaacttacctatgttgcctcggc 60

uery: 63g gaa gcctaccc ggtacctaccctgtagctacccgggagcgagctaccctgtagctcg 122

IIIIIIIIIIgIIgIIIgIIIIIIIIlIgIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIII ||
Sbjct: 61 ggggaagcctacccggtacctaccctgtagctacccgggagcgagctaccctgtagcccg 120

uery: 123 ctgcaggcctacccgccggtggacacttaaactcttgnnnnnnnagtgattatctgagtg 182

Sbjct: 121 ctgcaggcctacccgccggtggacacttaaactcttgtttttttagtgattatctgagtg 180

uery: 183 tttatacttaataagttaaaactttcaacaacggatctcttggttctggcatcgatgaag 242

Sbjct: 181 tttatacttaataagttaaaactttcaacaacggatctcttggttctggcatcgatgaag 240

uery: 243 aac ggca cg aaatg c gataagtaatgtgaattgcagaattcagtgaatcatcgaatcttt 302

Sbjct: 241 aacgcagcgaaatgcgataagtaatgtgaattgcagaattcagtgaatcatcgaatcttt 300

uezry 303 ggaac cacattg c gcccattagtattctagtgggcatgcctgttcgagcgtcatttcgac 362

IlIIIIIIIIIIIIIIIIII|I||I|I|II|IIIIgIIIIIIIIIIIIIIIIIIIIIIIII
Sbjct: 301 gaacgcacattgcgcccattagtattctagtgggcatgcctgttcgagcgtcatttcgac 360

1'y:363 cttcaag ccctag ctg ctt gggtgtt ggagcctatctccggatagctcctgaaaagcatt 422

Sbjct: 361 cttcaagccctagctgcttggtgttgggagcctatctccggatagctcctcaaaagcatt 420

Que uezry 423g ggc ggagtc cggtgaccccaagcgtagtaattcttctcgctttaggtgtgtcacggctg 482

IIIIIIIIIIIgIIIgIIIIgIIIIIIIIIIIIIIIIIII||II||III|||I|||||||III
Sbjct: 421 ggcggagtcgcggtgaccccaagcgtagtaattcttctcgctttaggtgtgtcacggctg 480

uezry 483 acgtcttg ccgttaaacccccaattttttaaatggttgacctcgg 527

Sbjct: 481 acgtcttgccgttaaacccccaattttttaaatggttgacctcgg 525

276

>gi|16215429|emb|AJ302450.1|ELA302450 Eutypa lata 188 rRNA gene (partial), 5.88
rRNA gene, 288 rRNA gene (partial), internal transcribed spacer 1 (IT S1) and internal
transcribed spacer 2 (ITS2), isolate E41T Length = 524

Score = 973 bits (491), Expect = 0.0
Identities = 516/525 (98%), Gaps = 1/525 (0%)
Strand = Plus / Plus

Query:3 ggatcattacagagttacctaactccaaacccatgtgaacttacctatgttgcctcggc 62

Sbjct: 1 gggatcattacagagttacctaactccaaacccatgtgaacttacctatgttgcctcggc 60

Que ue:ry 63g gaa gcctacccggtacctaccctgtagctacccgggagcgagctaccctgtagctcg 122

IIIIIIIIIIgIIIIIgIIII|IIIIIllIIIIIIIIIIIIIIIIIIIIIIIIIIIIII ll
Sbjct: 61 ggggaagcctacccggtacctaccctgtagctacccgggagcgagctaccctgtagcccg 120

ue:ry 123 ctg cag gcctaccc gccggtggacacttaaactcttgnnnnnnnagtgattatctgagtg 182

Sbjct:121 ctgcaggcctacccgccggtggacacttaaactcttgtttttt-agtgattatctgagtg 179

Query: 183 tttatacttaataagttaaaactttcaacaacggatctcttggttctggcatcgatgaag 242

Sbjct: 180 tttatacttaataagttaaaactttcaacaacggatctcttggttctggcatcgatgaag 239

uery: 243 aac gca ggc aaatg cg ataagt gtaatgtgaattgcagaattcagtgaatcatcgaatcttt 302

Sbjct: 240 aacgcagcgaaatgcgataagtaatgtgaattgcagaattcagtgaatcatcgaatcttt 299

ue:ry 303 gaacgcacattgc gcccattagtattctagtgggcatgcctgttcgagcgtcatttcgac 362

IIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIII
Sbjct: 300 gaacgcacattgcgcccattagtattctagtgggcatgcctgttcgagcgtcatttcgac 359

ue:ry 363 cttcaa ggcccta ctg cttggt gtggtt ggagcctatctccggatagctcctgaaaagcatt 422

Sbjct: 360 cttcaagccctagctgcttggtgttgggagcctatctccggatagctcctcaaaagcatt 419

Que ezry 423g ggc ggagtc c gggt accccaagcgtagtaattcttctcgctttaggtgtgtcacggctg 482

IIIIIIIIIIIgIIIgIIIIgIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIII
Sbjct: 420 ggcggagtcgcggtgaccccaagcgtagtaattcttctcgctttaggtgtgtcacggctg 479

Que ue:ry 483 acgtcttg ccgttaaacccccaattttttaaatggttgacctcgg 527

||||I||II|||IIlIIIIIIIIIIIIIIIIIIIIIIIIIIIIII
Sbjct: 480 acgtcttgccgttaaacccccaattttttaaatggttgacctcgg 524

277

2. ITS sequence of isolate N112 and search results:

TGATATGC'IT AAG'ITCAGCGGGXT ATI‘CCT ACCTGTATCCGAAGTCAAACCI‘ A
TAAAAA'ITAGGGGT'I'I'AGTGGCCAGATGCCAGCGTI'AACACT ACT AAAGCGA
GAAGAAT'I‘ACTACGCTCAGGGTCTCCGCGACTCCGCCAATATATI'I‘GAGGAG
'I'TATCCCGCAACTGCAGGGTAAGCTCCCAACGCCAAGCAAATAGGGCI'I‘GAT
GGTCGAAATGACGCTCGAACAGGCATGCCCACTAGAATACI‘AATGGGCGCAA
TGTGCGTTCAAAGA’ITCGATGA'ITCACT GAA'I'I'CI‘ GCAA'I'I‘CACA'ITACIT AT
CGCATT'I'CGCTGCGTTC'ITCATCGATGCCAGAACCAAGAGATCCG'ITG'I'I‘GAA
AG'I'I‘TTAA'ITTA'ITAAG'ITAAAACACTCAGAAGTTTCATAGAAAAACAGAG'IT
TAATGGTCCGTCGACGAGTA'I'I‘CC'ITACAGGGTAGCTACAGGGTAGCTACCG
GGTAG'I'I'I‘CCCCGCCGAGGCAACATAGGTAAGTTCACATGGG'ITTGGAG'ITG
'ITGTAAAAACT ACT C'ITT AATGATCCXXCT CCGCT GG'ITCACCAACGGAGACC
TI‘G'I'I‘ACGACTI'I'I‘ AGTTCCT CI‘ AGTA'ITCCT ACCT GTATCCGAAGTCAAACCT
ATAAAAA'ITAGGGG'ITI‘AGTGGCCAGATGCCAGCG'ITAACACT ACT AAAGCG

BLASTN 2.2.1 [Apr-13-2001]

Reference: Altschul, Stephen E, Thomas L. Madden, Alejandro A. Schaffer, J inghui
Zhang, Zheng Zhang, Webb Miller, and David J. Lipman (1997), "Gapped BLAST and
PSI-BLAST: a new generation of protein database search programs", Nucleic Acids Res.
25:3389-3402.

RID: 1011901972-21486-9879 Query: (680 letters)

Database: All GenBank+EMBL+DDBJ+PDB sequences (but no EST, STS, GSS,or

phase 0, 1 or 2 HTGS sequences) 1,075,967 sequences; 4,615,672,615 total letters
Score E

Sequences producing signiﬁcant alignments: (bits) Value

gi|16215448|emb|AJ 302466.1[EVI302466 Eutypella vitis 18S rRNA ge... 1001 0.0

gi|16215444|emb|AJ302463.1|ELE302463 Eutypella leprosa 188 rRNA 662 0.0

gi|18033499|gb|AF340014.1|AF340014 Monosporascus cannonballus in... 406 e-110
gi|16215410|emb|AJ302434.1|DST302434 Diatrype stigma 183 rRNA ge... 396 e-107
gi|16215435|emb|AJ302455.1|EPE302455 Eutypa petrakii var. petrak... 385 e-104
gi|17863468|gb|AF373064.1|AF373064 Eutypella scoparia MUT 485 18... 383 e-103
gi|16215447|embIAJ302465.1|ESC302465 Eutypella scoparia 18$ rRNA... 383 6-103
gi|9545987|gb|AF099911.1|AF09991l Eutypa lata internal transcrib... 379 e-102
gi|16215432|emb|AJ302452.l|ELA302452 Eutypa lata 183 rRNA gene (.. 379 e-102
gi|16215429|emb|AJ 302450.1[ELA302450 Eutypa lata 18S rRNA gene (... 379 e-102
gi|16215425|emb|AJ302446.IIEAR302446 Eutypa armeniacae 183 rRNA 379 e—102
gi|16215424|emb|AJ302445.l|BAR302445 Eutypa armeniacae l8S rRNA 379 e-102
gi|16215421|emb|AJ302443llDPU302443 Diatrypella pulvinata 18S r... 379 e-102
gi|16215418|emb|A1302440.lIDFA302440 Diatrypella favacea 18S rRN... 379 e-102
gi|16215335|emb|AJ 302421.1ICEU302421 Cryptosphaeria eunomia var.... 377 e-102
gi|16215326|emb|AJ302417.1|CEU302417 Cryptosphaeria eunomia var.... 377 e-102
gi|16215442lembIAJ302461.IIECE302461 Eutypella cerviculata 188 r... 375 e-lOl
gi|16215404|emb|AJ302431.llDMA302431 Diatrype macowaniana 188 rR... 375 e-101

278

 

gi|16215441|emb|AJ302460.1|ECA302460 Eutypella caricae 18S rRNA

gi|16215437|embIAJ302457.1IEFL302457 Eutypa ﬂavovirens 188 rRNA...

gi|16215403 IembIAJ 302430.1IDFL302430 Diatrype ﬂavovirens 18S rR...
gi|16215402|emb|A1302429.1|DFL302429 Diatrype ﬂavovirens 18S rR...
gi|16215401|emb|A1302428.1[DFL302428 Diatrype ﬂavovirens 18S rR...
gi|16215399|emb|AJ302426.lIDFL302426 Diatrype ﬂavovirens 183 rR...

gi|16215394|embIAJ302422.1|DBU302422 Diatrype bullata 18S rRNA g...

gi|16215329|emb|AJ302418.1|CLI302418 Cryptosphaeria ligniota 183...
gi|13249047lgb|AF 192322.1IAF192322 Diatrypella frostii 28S ribos...

gi|16215440|emb|AJ 302459.1IELA302459 Eutypa lata 18S rRNA gene (...

gi|16215436|emb|AJ302456. l |EPE302456 Eutypa petrakii var. petrak...
gi|16215431|emb|AJ 302451.1IELA302451 Eutypa lata var. aceri 18S
gi|16215419|emb|AJ302441.1|DFR302441 Diatrypella frostii 18S rRN...

gi|16215408|emb|AJ 302433.1IDSP302433 Diatrype spilomea 18S rRNA

gi|16215400|emb|AJ302427.1|DFL302427 Diatrype ﬂavovirens 18S rR...
gi|16215333|emb|A1302420.lICSU302420 Cryptosphaeria subcutanea 1...
gi|16215449|emb|AJ302467.1|EAL302467 Eutypella alsophila 18S rRN...
gi|16215450|emb|AJ302468.1|ECE302468 Eutypella cerviculata 183 r...

gi|16215433|emb|AJ302453.1|ELE302453 Eutypa leptoplaca 18S rRNA
gi|1621541l|emb|AJ302435.1IDST302435 Diatrype stigma 188 rRNA ge...

gi|16215423|emb|AJ302444.1|DQU302444 Diatrypella quercina 18S rR...
gi|16215420|emb|AJ302442.1|DPR302442 Diatrypella prominens 18S r...
gi|16215414|emb|AJ302437.1|DD1302437 Diatrype discifornris 183 rR...
gi|16215397|emb|AJ 302425.1IDDI302425 Diatrype disciformis 18S rR...
gi|16215396|emb|AJ302424.llDDI302424 Diatrype disciformis 18S rR...
gi|16215395|emb|AJ 302423.1IDD1302423 Diatrype disciformis 18S rR...
giI 162 1 5332|embIAJ 302419. 1|CPU302419 Cryptosphaeria pullmanensis...
gi|12038859|emb|AJ390410.1|HAN390410 Diatrype disciformis 18S rR...

gi|16215426|emb|AJ302447.1[EC0302447 Eutypa consobrina 18S rRNA
gi|162154l7|emb|AJ 302439.1IDST302439 Diatrype stigma 188 rRNA ge...
gi|16215413|embIAJ302436.lIDUN302436 Diatrype undulata 18$ rRNA

gi|8809731|gb|AF163032.l|AFl63032 Xylaria cubensis CBS 116.85 in...

gi|16215443|emb|AJ 302462.1[EK0302462 Eutypella kochiana 188 rRNA...
gill6215405 IembIAJ 302432.1IDPO302432 Diatrype polycocca 18$ rRNA...

gi|18157728|gb|AF432179.1|AF432179 Arthroxylaria elegans strain
giIlO732533IgbIAF 153732.1IAF 153732 Xylaria Sp. M8366 internal tr...
gi|10732532|gbIAF15373l.IIAF 153731 Xylaria sp. M8339 internal tr...

gi|16215428|emb|AJ302449.1|ELA302449 Eutypa laevata 18S rRNA gen...

gi|l3249048|gbIAF 192323.1|AF 192323 Diatrype stigma 283 ribosomal...
gi|16215445lemb|A1302464.l|EPR302464 Eutypella prunastri 188 rRN...

gi|16215434|emb|A1302454.1|EMA302454 Eutypa maura 18S rRNA gene ..
gi|16215427|embIAJ302448.1|ECR302448 Eutypa crustata 188 rRNA ge...

gi|18033498|gb|AF340013. 1|AF34001 3 Monosporascus ibericus intern...
gi|6003453|gb|AF 176958.1|AF176958 Daldinia concentrica 168 small...
gi|6003452|gbIAF176957.1|AF176957 Daldinia concentrica 168 small...
gi|6003451|gbIAF176956.1|AF 176956 Daldinia concentrica 168 small...

279

375
375
375
375
375
375
373
373

e-lOl
e-lOl
e-101
e-lOl
e-lOl
e—101
e-lOO
e-100
371 e-100
371 e-100
371 e-100
371 e-100
371 e-100
371 e-100
369 2e-99
369 2e-99
367 9e-99
365 3e-98
365 3e-98
365 3e-98
363 1e-97
363 1e-97
363 le-97
363 le-97
363 le-97
363 1e-97
363 1e-97
363 1e-97
361 5e-97
355 3e-95
355 3e-95
351 5e-94
351 5e-94
351 5e-94
349 2e-93
349 2e-93
349 2e-93
349 2e-93
347 8e-93
347 8e-93
347 8e-93
347 8e-93
345 3e-92
345 3e-92
345 3e-92
345 3e-92

gil6003450lgblAF 176955.1lAF176955 Daldinia concentrica 168 small...
gil6003449lgblAF 176954. lIAF 176954 Daldinia concentrica 168 small...
gill 1967318|gblAF2017l9.llAF201719 Biscogniauxia bartholomaei 18...
gill 1967307|gb|AF201708.llAF201708 Daldinia concentrica 18s ribo...
gi|12038874|emb|AJ390425.1|HAN390425 Creosphaeria sassafras 188

345
345
345
345
345

gi|12038853lemblA1390404.1|HAN390404 Hypoxylon fuscum 18S rRNA g. .345

gi|16215416|emb|A1302438.1lDST302438 Diatrype stigma 188 rRNA ge...
gil10732546|gblAF153745.1lAF153745 Xylaria sp. M31092 internal t...
gi|10732545|gb|AF153744.1|AF153744 Xylaria sp. M31083 internal t...
gi|10732541|gb|AF153740.1IAF153740 Xylaria sp. M31033 internal t...
gi|10732536|gb|AF153735.1IAF153735 Xylaria sp. M3395 internal tr...
gi|10732534lgb|AF153733.1lAF153733 Xylaria sp. M3370 internal tr...
gil10732526lgbIAF 153725 llAF 153725 Xylaria Sp. M8358 internal tr...
gr|10732525lgblAF 153724 1lAF153724 Xylaria sp. M81066 internal t...
|8809730|gblAF 163031 llAFl6303l Xylaria comu-damae CBS 724. 69...
gi|8809728lgblAF163029. llAFl63029 Xylaria arbuscula CBS 452. 63 1..
gi|8809727lgb|AF163028.1IAF163028 Xylaria arbuscula CBS 454.63 i...
gi|45 19363|dbj|AB017661.1IAB017661 Rosellinia quercina DNA for l...
gil18000996lgb|AF280629.1|AF280629 Nodulisporium sp. JP807 188 r...

gil16215439lemb|AJ302458.llEAS302458 Eutypa astroidea 188 rRNA g...

gill 1967316lgblAF201717.1lAF201717 Hypoxylon multiforme 288 ribo...

gill 19673 14lgblAF201715.1lAF2017 15 Hypoxylon fuscum 18S ribosoma...

gi|16215451lemb|AJ 302469.1lEQU302469 Eutypella quatemata 188 rR...
gil12038873lemblAJ390424.1|HAN390424 Creosphaeria sassafras 188..

343
341
341
341
341
341
341
341
341
341
341
341
339

3e-92
3e-92
3e-92
3e-92
3e-92
3e-92
1e-91
5e-9l
5e-91
5e-91
5e-9l
5e-91
5e-91
5e-9 l
5e-91
5e-91
5e-9l
5e-91
2e-90

339 2e-90

337
337
337
337

gi|12038854|emblAJ390405. llHAN390405 Hypoxylon fuscum 188 rRNA g. 337

g1l12038848lemb|AJ 390399 llHAN390399 Hypoxylon cohaerens var. mic...

gr|6003477|gblAF 176982 llAF 176982 Daldinia grandis 168 small sub...

gil6103018lemb|AJOl2300.1lPD1012300 Phialemonium dimorphosporum
gi|6103017|emb|A1012299.llPD1012299 Phialemonium dimorphosporum
gil6103016|emblA1012298.llPD1012298 Phialemonium dimorphosporum

gill 1967310|gblAF20171 1.1|AF20171 l Xylaria hypoxylon 188 ribosom...
gill 1967306|gblAF201707.1|AP201707 Nemania serpens variety macro...
gi|8809737lgb|AF163038.lIAF163038 Xylaria longipes CBS 148.73 in...

gi|12038882lembIAJ390433.1|HAN390433 Nemania serpens var. macros...

gil18000995|gblAF280628.1lAF280628 Nodulisporium sp. JP3665 188
gil4519362ldbj|AB017660.1lAB017660 Rosellinia arcuata DNA for 18...

280

337
335
335
335
335
333
333
333
333
331
331

8e-90
8e-90
8e-90
8e-90
8e-90
8e-90
3e-89
3e-89
3e-89
3e-89
1e-88
le-88
1e-88
1e-88
5e—88
5e-88

 

ALIGNMENTS
>gil16215448lemblAJ302466.1lEVI302466 Eutypella vitis 188 rRNA gene (partial),
5.88 rRNA gene, 288 rRNA gene (partial), internal transcribed Spacer 1 (H81) and
internal transcribed spacer 2 (IT82), isolate EL57A Length = 513 Score = 1007 bits
(508), Expect = 0.0 Identities = 511/512 (99%) Strand = Plus / Plus

Query:1ggatcattaaagagtagtttttacaacaactccaaacccatgtgaacttacctatgttgc 60

Sbjct: 2 ggatcattaaagagtagtttttacaacaactccaaacccatgtgaacttacctatgttgc 61

ry:61 ctcg ggc ggggaaactacccggta ctaccctgtagctaccctgtaaggaatactcgtcga 120

ryIIIIIIIIIIIIIIIIgIIgIIIIIIIIglIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIII
Sbjct: 62 ctcggcggggaaactacccggtagctaccctgtagctaccctgtaaggaatactcgtcga121

ry:121 cg gaccattaaactctgtttttctatgaaacttctgagtgttttaacttaataaattaaa 180

Sbjct: 122 cggaccattaaactctgtttttctatgaaacttctgagtgttttaacttaataaattaaa 181

ue:ry 181actttcaacaacggatctcttggttctggcatcgatgaagaacgcagcgaaatgcgataa 240

Sbjct:182 actttcaacaacggatctcttggttctggcategatgaagaacgcagcgaaatgcgataa 241

Query: 241 gtaatgtgaattgcagaattcagtgaatcatcgaatctttgaacgcacattgcgcccatt 300

Sbjct: 242 gtaatgtgaattgcagaattcagtgaatcatcgaatctttgaacgcacattgcgcccatt 301

Que ue:ry 301 agtattctagtgg gcat gcctgttcgagcgtcatttcgaccatcaagccctatttgcttg 360

lII|||I|I||||I||||IIIIII|IIIIIIIIIIIIgI||I|II|I|I|IIIII|||III
Sbjct: 302 agtattctagtgggcatgcctgttcgagcgtcatttcgaccatcaagccctatttgcttg 361

Que uery. 361 gggcgtt ga gcttaccct gcagttgcgggataactcctcaaatatattggcggagtcgcg 420

IIIIIIIIIIIIIIIIIII|IIIIgIIgIIIIIgIIIIII|II||||||I|III||II|||II
Sbjct: 362 gcgttgggagcttaccctgcagttgcgggataactcctcaaatatattggcggagtcgcg 421

ry:421gga accctg a gcgtagtaattcttctc gctttagtagtgttaacgctggcatctggccac 480

ry|III||I| IIgIIIIIgIIIIIIIIIIIIgIIIIIII|I|||||IIIIIIIIIIIIIIIIII
Sbjct: 422 gagaccctaagcgtagtaattcttctcgctttagtagtgttaacgctggcatctggccac 481

ry:481 taaacccctaatttttata gggttt acctcgg 512

Sbjct: 482 taaacccctaatttttataggtttgacctcgg 513

281

>gi|9545987|gb|AF099911.1|AF099911 Eutypa lata internal transcribed spacer 1, partial
sequence; 5.88 ribosomal RNA gene, complete sequence; and internal transcribed spacer
2, partial sequence Length = 574 Score = 379 bits (191), Expect = e-102

Identities = 212/219 (96%) Strand = Plus / Plus

uery: 153 tctgagtgttttaacttaataaattaaaactttcaacaacggatctcttggttctggcat 212

Sbjct: 184 tctgagtgtttatacttaataagttaaaactttcaacaacggatctcttggttctggcat 243

uery: 213 cgatgaagaacgcagcgaaatgcgataagtaatgtgaattgcagaattcagtgaatcatc 272

Sbjct: 244 cgatgaagaacgcagcgaaatgcgataagtaatgtgaattgcagaattcagtgaatcatc 303

Que ue:ry 273 ggaatcttt aacg cacattg c gcccattagtattctagtgggcatgcctgttcgagcgtc 332

I|I|I|I||IIgIIIIIIIII|I|IIIgIIIIIIIIIIIIIIIIIIIIIIIgIIIIIIIIIII
Sbjct: 304 gaatctttgaacgcacattgcgcccattagtattctagtgggcatgcctgttcgagcgtc 363

Query: 333 atttcgaccatcaagccctatttgcttggcgttgggagc 371

IIIIIIIII IIIIIIIIII IIIIIII IIIIIIIII
Sbjct: 364 atttcgaccttcaagccctagctgcttggtgttgggagc 402

Score = 99.6 bits (50), Expect = 2e-18
Identities = 136/162 (83%), Gaps = 2/162 (1%)
Strand = Plus / Plus

Query. 388g gataactcctcaaatatattggcggagtcgcggagaccctgagcgtagtaattcttctc 447

IIIII IIIIIIIII IIIIIIIIIIIIIIII I III IIIIIIIIIIIIIIII I
Sbjct: 411 ggatagctcctcaaaagcattggcggagtcgcggtggccccaagcgtagtaattcttcgc 470

Que ery: 448 ggctttagtagtgttaac ctggcatctggccactaaacccctaa-tttttataggtttga 506

"III" IIIII IIII IIIIIII ||IIII||II|IIIII|I|I||
Sbjct: 471 gctttaggtgtgtcacggctgacgtcttgccgttaaacccccaattttttaaatggttga 530

uery: 507 cctcggatcaggtaggaatacccgctgaacttaagcatatca 548

Sbjct: 531 cctcggataaggtaggaata-ccgctgaacttaagcatatca 571

Score = 54.0 bits (27), Expect = le-04
Identities = 34/35 (97%), Gaps = 1/35 (2%)
Strand = Plus / Plus

uery: 28 aactccaaacccatgtgaacttacctatgttgcct 62

Sbjct: 33 aactccaaacccatgtgaactta-ctatgttgcct 66

282

APPENDIX F

Weekly weather data for regressions

283

Weekly weather data for regressions

 

 

week(s)
BEFORE
budbreak year 8 vine (#) min temp (°C) max temp (°C) avg temp (°C)
5 2000 1 1 -0.04 18.48 8.83
5 1999 18 -1.66 21.22 9.72
5 2001 62 -3.99 23.10 7.71
4 2000 11 2.45 25.98 13.54
4 1999 18 2.33 26.14 ' 15.72
4 2001 62 1.85 23.24 12.51
3 2000 11 7.21 29.68 19.87
3 1999 18 6.25 26.79 9.54
3 2001 62 5.93 27.98 18.19
2 2000 1 1 2.43 26.75 13.06
2 1999 18 6.03 29.59 16.52
2 2001 62 2.18 27.12 15.99
1 2000 11 5.51 24.42 14.16
1 1999 18 4.62 26.19 13.51
1 2001 62 5.51 28.54 17.64
week(s)
AFTER
budbreak year 8 vine (#) min temp (°C) max temp (°C) avg temp (°C)
1 2000 11 11.63 21.86 16.74
1 1999 18 14.14 25.65 19.89
1 2001 62 8.40 15.57 1 1.98
2 2000 1 l 9.67 21.79 15.73
2 1999 18 17.68 30.19 23.93
2 2001 62 7.04 16.25 11.64
3 2000 1 l 18.27 25.74 22.01
3 1999 18 12.63 22.66 17.64
3 2001 62 10.06 20.02 15.04
4 2000 11 14.10 23.80 18.95
4 1999 18 13.73 26.09 19.91
4 2001 62 16.28 29.04 22.66

284

APPENDD( G

Summary Statistics and ANOVA of shoot count study

285

(All text and plots from two-way AN OVA performed with Statgraphics program)

Analysis of Variance for number of shoots - Type III Sums of Squares

 

 

 

Source Sum of Squares Df Mean Square F-Ratio P-Value
MAIN EFFECTS
szine 24544.4 24544.4 1 1.92 0.0016
Bzyear 32881.8 1 32881.8 15.97 0.0004
INTERACTIONS
AB 4578.78 1 4578.78 2.22 0.1457
RESIDUAL 65889.6 32 2059.05
TOTAL (CORRECTED)

1278950 35

 

All F-ratios are based on the residual mean square error.

Table of Least Squares Means for number of shoots with 95.0 Percent Conﬁdence

 

 

Intervals

Lower Upper
Level Count Mean StndErr Limit Limit
GRAND MEAN 36 129.389
vine
ah 18 155.5 10.6954 133.714 177.28
(1 18 103.278 10.6954 81.4919 125.064
year
2000 18 99.1667 10.6954 77.3808 120.953
2001 18 159.611 10.6954 137.825 181.397
vine by year
ah 2000 9 114.0 15.1256 83.1901 144.81
ah 2001 9 197.0 15.1256 166.19 227.81
(I 2000 9 84.3333 15. 1256 53.5235 115.143
(1 2001 9 122.222 15.1256 91.4124 53.032

286

Multiple Range Tests for number of shoots by vine

 

Method: 95.0 percent LSD

 

 

vine Count LS Mean Homogeneous Groups
(1 18 103.278 X
ah 18 155.5 X
Contrast Difference +/- Limits

 

ah - (1 *52.2222 30.8099

 

*denotes a statistically Si gniﬁcant difference.

 

 

Interaction Plot
200 year
:3 — 2000
a 160
941-1
0 140
8
120
E
a 100
80
all (I
vine

Residual Plot for number of shoots

 

 

 

 

120 1
80 . ..
. I d
76‘ 40 r 1
=3 l :
.‘i. 0 .
(I) I | 1
2 -40 I g 1
-30 e .5
' 1'.
-120 ' -

ah d

vine

287

 

~1q1d1d‘1-GIGG1III‘ lddd- ﬂddd-

Scatterplot by Level Code

 

_PE—thb—thh-Db-D—hpbn-DDP-b

 

 

mmmmmwo

3027, m0 898::

vine

Means and 95.0 Percent LSD Intervals

 

 

—d-d—ddd—ddd-1d—ddqq

PPrLP-b—pbb—n-DPFB—

 

 

mmnﬂmm
11 1

8005 (Lo c365:

vine

Residual Plot for number of shoots

 

IIqCIdI-Idl

T-
f
1
f
r
v

v
v
v
V
v
v
v
v
V
v
v
v
v
ﬁt
v
v
v

v

v

v

'
.bhbbbbb .b-D

 

ddd- I‘d-‘1‘-

J
A

A A A A A A A A A I

-A A A A A l

...-DID-tb

 

 

 

mwwomm

12668

-120

100 150
predicted number of shoots

50

288

Data

 

Vine type year # shoots per vine Vine type year # of shoots per vine
ah 2000 148 d 2000 98
ab 2001 222 d 2001 157
ah 2000 147 d 2000 79
ah 2001 240 d 2001 165
ah 2000 150 d 2000 102
ah 2001 199 d 2001 74
ah 2000 96 d 2000 7
ah 2001 143 d 2001 104
ah 2000 64 d 2000 45
ah 2001 94 d 2001 6
ah 2000 85 d 2000 104
ah 2001 252 d 2001 156
ah 2000 124 d 2000 81
ah 2001 211 d 2001 132
ah 2000 108 d 2000 147
ah 2001 193 d 2001 109
ah 2000 104 d 2000 96
ah 2001 219 d 2001 197

289

 

BIBLIOGRAPHY

290

 

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