clubroot in canola and cabbage in relation to soil ... · clubroot in canola and cabbage in...
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Clubroot in canola and cabbage in relation to soil temperature, plant growth and host resistance
By
Thomas Vinzenz Gludovacz
A Thesis presented to
The University of Guelph
In partial fulfillment of requirements for the degree of
Master of Science in
Plant Agriculture
Guelph, Ontario, Canada
© Thomas Vinzenz Gludovacz, May, 2013
ABSTRACT
CLUBROOT IN CANOLA AND CABBAGE IN RELATION TO SOIL
TEMPERATURE, PLANT GROWTH AND HOST RESISTANCE
Thomas Vinzenz Gludovacz Advisors: University of Guelph, 2013 Dr. Mary Ruth McDonald
Dr. Bruce D. Gossen
The effects of diurnal temperature fluctuation and the utility of degree days for
modeling clubroot on canola (Brassica napus L.) caused by Plasmodiophora brassicae
Woronin were assessed using microscopy and qPCR, and in field trials. Temperature
fluctuation had little effect on pathogen development. The optimal temperature for root
hair infection was 25° C. Air and soil degree days and rainfall were used as metrics for
estimating clubroot development, with only limited success. Several cultivars of cabbage
(Brassica oleracea L. var. capitata) with unknown clubroot resistance mechanism(s)
were assessed using staining and microscopy, and qPCR. In field trials, ‘Bronco’ was
susceptible to clubroot (100 DSI), ‘Kilaherb’ was resistant (0 DSI), and ‘B-2819’ was
intermediate (53 DSI). Plasmodiophora brassicae was present in cortical tissue of all
cultivars. A delayed disease phenotype in ‘B-2819’ may indicate a quantitative resistance
genotype that could be exploited in research on resistance genes and breeding.
iii
ACKNOWLEDGEMENTS
Completing my Masters of Science degree has been the most challenging
undertaking of the first 24 years of my life. It has been an honour to spend a few years in
the Department of Agriculture at the University of Guelph. I would like to thank my
advisors Dr. Mary Ruth McDonald and Dr. Bruce Gossen for their guidance,
encouragement to succeed, and for challenging me to be a better scientist. I would like to
thank my committee member Dr. Sean Westerveld for providing me with very valuable
criticism and suggestions for my research.
I owe a great deal of gratitude to Dr. Kalpana Sharma, Dr. Abhinandan Deora, Dr.
Monica Parker, Hema Kasinathan, Nael Thaher and the rest of my lab for their
mentorship, guidance, and assistance in my research, I learned so much about agriculture,
plant pathology, and research from them.
I am particularly grateful for the training in molecular biology given by Dr.
Rachid Lahlali and for mentoring me throughout my program. Assistance provided by
Dr. Michael Tesfaendrias and Dr. Cezarina Kora in helping to setup and troubleshoot the
ThetaProbe for my field studies was greatly appreciated. I wish to acknowledge the help
provided by Laura, Shawn, Michael, Mitchigo and Dennis from the Muck Station, as well
as Ken Bassendowski, and Linda McGregor from the Saskatoon Research Centre
(AAFC) for their technical assistance and field research training. I am indebted to Ken
for his help in operating the thermal gradient plates.
Thanks to the Animal Health Laboratory at the University of Guelph for preparing
the cortical tissue slides for Chapter 4, and to Chris Granger for helping to setup the Real-
Time PCR machine in the Crop Science building. Special thanks to Kalpana Adhikari and
iv
Dr. Sean Westerveld again for loaning the field data that was incorporated into the
clubroot prediction models in Chapter 3. My thanks are extended to the staff of the Crop
Science building and the Department of Plant Agriculture for working behind the scenes
and doing all the paper work to make everyone’s research and programs of study run
more smoothly.
Thank you to Christophe Liseron-Monfils and Jerlene Nessia for their
encouragement and advice in my research and studies. I would like to express my very
great appreciation to Stephanie Khurana for her support during this program and for the
countless hours of editing of this thesis and other course work. Editorial advice given by
Alixandra Bamford has been a great help in improving my writing. Finally, I would like
to thank my mother, father, and friends for their support throughout my program.
v
TABLE OF CONTENTS
ABSTRACT ........................................................................................................................ ii
ACKNOWLEDGEMENTS ............................................................................................... iii
TABLE OF CONTENTS .................................................................................................... v
LIST OF TABLES ........................................................................................................... viii
LIST OF FIGURES ........................................................................................................... ix
CHAPTER ONE LITERATURE REVIEW ....................................................................... 1
1.1 Agricultural significance of canola and other Brassica spp. .................................... 1
1.1.1 Canola (B. napus and B. rapa) ........................................................................... 1
1.1.2 Head cabbage (B. oleracea var. capitata) and other Brassica vegetables ......... 3
1.1.3 Diseases of Brassica spp. ................................................................................... 5
1.2 Clubroot of Brassica ................................................................................................. 8
1.2.1 Significance ........................................................................................................ 8
1.2.2 Plasmodiophora brassicae ................................................................................. 9
1.2.3 Characterization and distribution of P. brassicae populations ........................ 18
1.3 Factors affecting clubroot severity ......................................................................... 20
1.3.1 Temperature ..................................................................................................... 21
1.3.2 Soil pH ............................................................................................................. 24
1.3.3 Soil moisture .................................................................................................... 25
1.3.4 Spore load ........................................................................................................ 26
1.3.5 Light intensity .................................................................................................. 27
1.4 Clubroot management ............................................................................................. 27
1.4.1 Cultural controls ............................................................................................... 28
1.4.2 Biocontrols ....................................................................................................... 34
1.4.3 Fungicide management .................................................................................... 36
1.4.4 Host resistance ................................................................................................. 40
1.5 Techniques for quantifying clubroot development ................................................. 45
1.5.1 Microscopy ...................................................................................................... 45
1.5.2 Molecular techniques ....................................................................................... 47
1.5.3 Clubroot symptoms .......................................................................................... 48
vi
1.6 Summary and objectives ......................................................................................... 49
CHAPTER TWO EFFECT OF CONSTANT AND FLUCTUATING TEMPERATURES ON THE INCIDENCE AND SEVERITY OF CLUBROOT ........................................... 52
2.1 Introduction ............................................................................................................. 52
2.2 Materials and methods ............................................................................................ 54
2.2.1 Constant and fluctuating temperatures trials .................................................... 54
2.2.2 Range of temperature fluctuation ..................................................................... 58
2.2.3 Statistical analysis ............................................................................................ 58
2.3 Results ..................................................................................................................... 60
2.3.1 Root hair infection ........................................................................................... 60
2.3.2 Molecular quantification of in planta colonization of root hairs ..................... 63
2.4 Discussion ............................................................................................................... 68
CHAPTER THREE DEGREE DAY MODELING OF CLUBROOT INCIDENCE AND SEVERITY ON CANOLA ............................................................................................... 76
3.1 Introduction ............................................................................................................. 76
3.2 Materials and methods ............................................................................................ 79
3.2.1 Seeding date trial .............................................................................................. 79
3.2.2 Degree day calculation ..................................................................................... 82
3.2.3 Statistical analysis ............................................................................................ 83
3.3 Results ..................................................................................................................... 85
3.3.1 Weather ............................................................................................................ 85
3.3.2 Clubroot incidence and severity ....................................................................... 89
3.3.3 Disease model calibration ................................................................................ 97
3.3.4 Disease model validation ............................................................................... 100
3.4 Discussion ............................................................................................................. 102
CHAPTER FOUR IN PLANTA QUANTIFICATION AND MICROSCOPY OF ROOT HAIR AND CORTICAL INFECTION IN CABBAGE CULTIVARS INFECTED WITH CLUBROOT ................................................................................................................... 114
4.1 Introduction ........................................................................................................... 114
4.2 Materials and methods .......................................................................................... 116
4.2.1 Plant materials ................................................................................................ 116
4.2.2 Field trial ........................................................................................................ 116
vii
4.2.3 Controlled environment trials ........................................................................ 118
4.2.4 Statistical analysis .......................................................................................... 121
4.3 Results ................................................................................................................... 123
4.3.1 Assessment of clubroot response in the field ................................................. 123
4.3.2 Root hair infection ......................................................................................... 126
4.3.3 Cortical infection ........................................................................................... 129
4.3.4 Clubroot incidence and severity ..................................................................... 132
4.3.5 Molecular quantification of in planta colonization of roots .......................... 134
4.4 Discussion ............................................................................................................. 136
CHAPTER FIVE GENERAL DISCUSSION ................................................................ 144
REFERENCES ............................................................................................................... 155
APPENDIX 1: ANOVA TABLES FOR CHAPTER TWO ........................................... 173
APPENDIX 2: ANOVA TABLES FOR CHAPTER THREE ....................................... 178
APPENDIX 3: SUPPLEMENATRY TABLES FOR CHAPTER THREE ................... 180
APPENDIX 4: ANOVA TABLES FOR CHAPTER FOUR ......................................... 190
APPENDIX 5: RAW DATA FOR CHAPTER TWO .................................................... 203
APPENDIX 6: RAW DATA FOR CHAPTER THREE ................................................ 213
APPENDIX 7: RAW DATA FOR CHAPTER FOUR................................................... 219
viii
LIST OF TABLES
Table 2.1 Correlation matrix of the relationship (r above, P below) among temperature means, gDNA concentration of P. brassicae, incidence of primary plasmodia, mature zoosporangia, dehisced zoosporangia and total root hair infection. ................................. 67
Table 3.1 Mean monthly air temperature and rainfall during the growing period of canola for clubroot assessment at the Muck Crops Research Station, Holland Marsh, ON, 2011 and 2012. ........................................................................................................................... 86
Table 3.2 Linear correlations (r) between clubroot incidence and severity over time and accumulated rainfall, air and soil degree days, and mean soil moisture for 10 seeding dates of canola ‘InVigor 5030 LL’ grown at the Holland Marsh, ON, 2011 and 2012. ... 96
Table 3.3 Stepwise regression of the effect of accumulated rainfall and degree days (°D) for air and soil temperature over selected time intervals on clubroot incidence (CI) and severity (DSI) over time on Chinese flowering cabbage and canola at the Holland Marsh, ON. .................................................................................................................................... 98
Table 3.4 Stepwise regression of the effect of accumulated rainfall and degree days for air and soil temperature over selected time intervals on final clubroot incidence (CI) and severity (DSI) on Chinese flowering cabbage and canola at the Holland Marsh, ON. .... 98
Figure 4.1 Clubroot incidence and severity on green and red cabbage cultivars grown in naturally infested soil at the Muck Crop Research Station, Holland Marsh, ON in 2011 and 2012. ......................................................................................................................... 124
Table 4.2 Correlation matrix of the relationship (r above, P below) among arcsine transformations of clubroot incidence and severity (disease severity index) and log transformation of marketable yield. ................................................................................ 124
Figure 4.1 Yield of green and red cabbage cultivars grown in naturally infested soil at two sites (high vs. low inoculum density) at the Muck Crop Research Station, Holland Marsh, ON, 2011 and 2012. ............................................................................................ 125
Table 4.2 Incidence of primary infection (%) of root hairs on canola at 4 and 12 DAI with pathotype 3. ............................................................................................................. 128
Table 4.3 Percent area of cortical infection and incidence of cortical cells containing selected developmental stages on cabbage at 28 days after inoculation (DAI) with pathotype 3. ..................................................................................................................... 131
Table 4.4 The amount of P. brassicae genomic DNA detected in cabbage roots at 4, 12, and 28 days after inoculation (DAI) with pathotypes 3 and 6. ....................................... 134
Table 4.5 The amount of P. brassicae genomic DNA detected in cabbage roots at 4, 12, and 28 days after inoculation (DAI) with pathotype 3. .................................................. 135
Table 4.6 Correlation matrix of the relationship (r above, P below) between the concentration of P. brassicae gDNA determined by qPCR and incidence of P. brassicae developmental stages in inoculated canola roots at 4, 12, and 28 DAI. ......................... 136
ix
LIST OF FIGURES
Figure 2.1 Cells of the 176 well thermal-gradient plate (a), and cells of the 96 well thermal-gradient plate (b). ................................................................................................ 55
Figure 2.2 Incidence and regression of total root hair primary infection (%) with mean temperatures (constant and fluctuating treatments combined), based on counts of root hair infections on canola at 10 days after inoculation. ............................................................. 62
Figure 2.3 Incidence and regression of root hairs with mature zoosporangia (%) based on counts of root hair infection of canola at 10 days after inoculation. ................................. 62
Figure 2.4 Incidence and regression of root hairs with dehisced zoosporangia (%) based on counts of root hair infection of canola at 10 days after inoculation. ............................ 63
Figure 2.5 Effect of mean temperature on the concentration of P. brassicae genomic DNA detected in canola roots grown at 10 days after inoculation (DAI). ....................... 65
Figure 2.6 Concentration of P. brassicae genomic DNA detected in canola roots grown at fluctuating mean temperatures at 14 days after inoculation, first repetition. ................ 65
Figure 2.7 Concentration of P. brassicae genomic DNA detected in canola roots grown at fluctuating mean temperatures at 14 days after inoculation, second repetition. ........... 66
Figure 3.1 Clubroot severity rating scale. (a) 0 = no symptoms; (b) 1 = root clubbing < 1/3; (c) 2 = 1/3 < root clubbing <2/3; (d) 3 = root clubbing > 2/3. ................................... 81
Figure 3.2 Weather data for the Muck Crop Research Station, Holland Marsh, ON, 2011. The bar graph denotes daily precipitation (mm), solid line denotes mean air temperature (°C), and dotted line denotes mean soil temperature (°C, 5 cm below the surface). ........ 87
Figure 3.3 Weather data for the Muck Crop Research Station, Holland Marsh, ON, 2012............................................................................................................................................ 88
Figure 3.4 Clubroot incidence on canola planted at 2-wk intervals in muck soil naturally infested with Plasmodiophora brassicae at the Holland Marsh, ON, 2011. .................... 90
Figure 3.5 Clubroot severity (DSI) on canola planted at 2-wk intervals in muck soil naturally infested with Plasmodiophora brassicae at the Holland Marsh, ON, 2011. ..... 91
Figure 3.6 Clubroot incidence (CI) on canola planted biweekly in muck soil naturally infested with Plasmodiophora brassicae at the Holland Marsh, ON, 2012. .................... 93
Figure 3.7 Clubroot severity (DSI) on canola planted biweekly in muck soil naturally infested with Plasmodiophora brassicae at the Holland Marsh, ON, 2012. .................... 94
Figure 3.8 Relation between accumulated degree days for air temperature and validation set of clubroot incidence over time on canola and Chinese flowering cabbage at the Holland Marsh, ON. .......................................................................................................... 99
Figure 3.9 Relation between accumulated degree days for soil temperature in the two weeks before sampling date and validation set of final clubroot incidence of canola and Chinese flowering cabbage at the Holland Marsh, ON. ................................................... 99
x
Figure 3.10 Scatter plot of deviations by observed clubroot incidence on canola and Chinese flowering cabbage over time in the validation data subset of the canola and Chinese flowering cabbage clubroot prediction model. ................................................. 100
Figure 3.11 Scatter plot of deviations by observed clubroot severity over time of the validation data subset for the canola and Chinese flowering cabbage clubroot prediction model............................................................................................................................... 101
Figure 3.12 Scatter plot of deviations by observed final clubroot incidence over time of the validation data subset for the canola and Chinese flowering cabbage clubroot prediction model. ............................................................................................................ 102
Figure 4.2 Cabbage in the field (a) before harvest, and (b) trimmed heads representing marketable yield. ............................................................................................................. 125
Figure 4.3 Stages of root hair infection: (a) primary plasmodium, (b) mature zoosporangia, and (c) close-up of mature secondary zoospores in an epidermal cell. ... 128
Figure 4.4 Cross-sections of cabbage roots stained with methylene blue. (a) Non-inoculated control, (b) ‘Bronco’, (c) ‘B-2819’, and (d) ‘Kilaherb’. ............................... 130
Figure 4.5 Box plot of clubroot incidence on cabbage cultivars inoculated with pathotypes 3 and 6 of P. brassicae under controlled conditions. ................................... 133
Figure 4.6 Box plot of clubroot severity on cabbage cultivars inoculated with pathotypes 3 and 6 of P. brassicae under controlled conditions. ...................................................... 133
1
CHAPTER ONE
LITERATURE REVIEW
1.1 Agricultural significance of canola and other Brassica spp.
1.1.1 Canola (B. napus and B. rapa)
Canola is one of Canada’s most important cash crops, contributing one-
third to one-half of the revenue of 52,000 Canadian farmers (Canola Council of
Canada, 2009a). The seed is processed into oil and meal. The oil is used in the
production of food products, such as salad oils, margarine, and shortening. The
meal is used as feed for cattle and poultry (Canola Council of Canada, 2009b).
The crop contributes about $15.4 billion in economic activity to the nation’s
economy annually; $1.1 billion in Ontario and Quebec, and $14.3 billion in
western Canada. The canola industry is attributed with providing 228,000
Canadian jobs and $8.2 billion in employee wages (Canola Council of Canada,
2011a). Canada exported 7.1 million MT of canola worldwide in 2011 (Statistics
Canada, 2012). A record high area of 8.7 million hectares seeded with canola was
reported by Prairie producers in 2012 (Statistics Canada, 2012).
Canola is a trademarked protected term, and the seed must conform to a
regulated quality definition requiring it to contain less than 2 % erucic acid and
less than 30 µmoles glucosinolates (Canola Council of Canada, 2009b). Erucic
acid was associated with heart disease in animal studies, while glucosinolates can
decompose into compounds that are toxic to humans and livestock (Food
2
Standards Australia New Zealand, 2003). Reduction of these compounds, and of
saturated fat, began in the 1970's when canola was developed from B. napus L.
Rapeseed (B. napus) has historically been cultivated in Asia and Europe as
a source of oil for cooking and lighting. Rapeseed oil is an excellent industrial
lubricant and was critical to the operation of naval ships in World War II. The
Argentine type of rapeseed (B. napus) was introduced to Canada in 1943 from
Argentina by American seed companies. Polish rapeseed (B. rapa) was introduced
to Canada by a Polish-Canadian farmer, who received the seed from Poland in
1936. Originally described by Linnaeus as separate species, B. rapa and B.
campestris L. were united under the name B. rapa by Metzger in 1833 (Toxopeus
et al., 1984). The Saskatoon Research Centre of Agriculture and Agri-Food
Canada (AAFC) and the Saskatchewan Wheat Pool (SWP) have also developed
canola-quality cultivars of B. juncea L. (brown mustard) using conventional
breeding techniques. This type of canola is well-adapted to the hotter and more
arid regions of the Canadian Prairies. It is only produced under contract (Canola
Council of Canada, 2003).
In Canada, canola is principally grown in Alberta, Manitoba and
Saskatchewan. There is modest production in Ontario, Quebec and British
Columbia. Average yields by area for 1990 to 2000 were similar across the Prairie
Provinces: Manitoba produced approximately 1.2 – 1.3 MT/ha, Saskatchewan 1.2
MT/ha, and Alberta 1.2 – 1.3 MT/ha (Canola Council of Canada, 2003). In
Manitoba, crops of B. napus are usually seeded from May 5 to May 31 at a rate of
3
5.6 – 7.8 kg/ha, and B. rapa canola is seeded May 5 to June 15 at a rate of 4.5 –
6.7 kg/ha (Manitoba Agriculture, Food and Rural Initiatives, 2011). Earlier
seeding dates generally produce higher yields. Seeding B. napus between the first
and third weeks of May in northwestern Alberta was found to minimize the risk of
yield losses (Christensen et al., 1985). Brassica napus is swathed in the last week
of August through to mid to late September in Alberta and Saskatchewan
(Kirkland and Johnson, 2000; Kondra, 1977). Swathing is generally earlier for
B. rapa, and takes place in early to late August in Alberta.
1.1.2 Head cabbage (B. oleracea var. capitata) and other Brassica
vegetables
Canada produces thousands of tonnes of Brassica vegetables annually,
including head cabbage (B. oleracea L. var. capitata L.), a staple food vegetable
introduced to North America by German and North European immigrants. The
morphology of cabbage consists of unbranched stems, sessile basal leaves
attached directly to the stem, and upper leaves that overlap to form a compact
spherical or ellipsoidal head over the stem apex. Leaves are waxy and range in
colour from blue-green to red (Dixon, 2007). In 2010, the estimated total
Canadian production of cabbage was 152,000 metric tonnes (this includes not
only regular cabbage, but napa cabbage, discussed later). Canada’s production of
other Brassica vegetables that year totaled 48,000 tonnes of rutabaga
(B. napobrassica Mill.) and turnip (B. rapa L.), 39,000 tonnes of broccoli
(B. oleracea L. var. italica Plenck), 32,000 tonnes of cauliflower (B. oleracea L.
4
var. botrytis L.), and 13,000 tonnes of radish (Raphanus sativus L.) (Statistics
Canada, 2011).
Cabbage is produced in Ontario by growing transplants in the greenhouse,
usually in plug trays filled with soil-less mix. After growing in trays for 4 to 6
weeks, the seedlings are transplanted into the field (LeBoeuf, 2012). Cabbage is
harvested when heads are compact and firm, and cover leaves are bright green.
Harvest includes pruning of damaged outer leaves (Allen, 1996). The timing of
these processes depends on whether the cabbage is intended for the fresh market
or for storage. Fresh market cabbage is generally transplanted during April and
May, and harvested in July and August. Storage cabbage is generally transplanted
during August, and harvested in October and November (Agriculture and Agri-
Food Canada, 2005; Uyenaka, 1990). Ontario produced 47,038 metric tonnes of
marketable cabbage (includes napa cabbage and regular cabbage) in 2008, with a
farm value of $14.8 million. In that same year, Ontario produced 11,884 tonnes of
cauliflower, 17,373 tonnes of rutabaga and turnip, and an undisclosed amount of
Brussels sprouts (B. oleracea L. var. gemmifera Zenk.) (Mailvaganam, 2010).
Asian immigration has influenced the cultivation of several Brassica
vegetables of Asian origin in southern Ontario, including pak choy and napa
cabbage. Pak choy (B. rapa L. subsp. Chinensis (L.) Hanlet) is a non-heading cole
crop of Chinese origin. Pak choy in Cantonese means white vegetable (Lee,
1982). Morphological characteristics of the subspecies include petioles that are
winged, wide, thick and spiraling. Leaves are dull green, smooth, and oval shaped
5
with a narrowing at the base. The communis variety of pak choy (B. rapa subsp.
Chinensis var. communis Tsen and Lee) has thin white petioles. Shanghai pak
choy (B. rapa subsp. Chinensis var. utilis Tsen and Lee) has leaves that are olive
green with green petioles (Chaput, 1998; Lovatt, 2010).
Napa cabbage (B. rapa L. subsp. Pekinensis (Lour) Hanlet), sometimes
referred to as Chinese cabbage or celery cabbage, was developed from crosses
between pak choy and turnip. Napa cabbage develops into a head, and varieties
can be distinguished by the degree of leaf wrapping over the top (Dixon, 2007;
Lee, 1982). In southern Ontario, napa cabbage is seeded into trays in early May
and grown in greenhouses for later transplanting, or is directly seeded into the
field after late April. It generally takes 60 to 95 days to mature. Harvesting is
done by hand when heads are formed, and runs from mid July until November
(Shattuck and Shelp, 1986). Korea is a major consumer of napa cabbage. It is
used in a traditional and staple dish called kimchi, in which the cabbage leaves are
smeared with a garlic and tomato paste and then fermented (Dixon, 2007).
1.1.3 Diseases of Brassica spp.
Issues with sanitation, crop rotation, fertilizer utilization and demand for
greater aesthetic appearance of food have contributed to changes in agricultural
production that make crop production more conducive to pathogen proliferation.
Consequently, annual crop losses as a result of plant diseases in North America
have risen slowly but steadily from 10 % in the 1940s to 12 % in the 1990s, even
6
though the performance and availability of fungicides have improved in the past
50 years (Rimmer et al., 2003).
Several pathogens of canola contribute substantially to Canada’s share in
these losses, including powdery mildew (Erysiphe cruciferarum Opiz ex L.
Junell), Alternaria black spot (Alternaria spp.), blackleg (Leptosphaeria maculans
(Sowerby) P. Karst.), root rot (Fusarium oxysporum Snyd. & Han.; Rhizoctonia
solani Kuhn), gray stem / white leaf spot (Pseudocercosporella capsellae (Ellis &
Everh.) Deighton), and sclerotinia stem rot (Sclerotinia sclerotiorum (Lib.) de
Bary) (Dixon, 1981, pg. 112-142; Rimmer et al., 2003).
Alternaria black spot can severely damage flowering and seed production,
reducing canola yield by 20 – 40 % in years when epidemics are severe. Every 1
% increase in the incidence of infection of canola stems or pods results in 1 %
loss in yield (Rimmer et al., 2003). A 2011 survey of canola diseases in
Saskatchewan reported Alternaria black spot occurred in 31 % of fields (75 of 241
fields), with a mean severity of less than 1 % (Dokken-Bouchard et al., 2012).
Blackleg currently poses a major threat to canola crops in western Canada.
A 2011 survey of canola diseases in Saskatchewan reported blackleg occurred in
24 % of fields (58 of 241 fields). A low severity of damage to lower stems was
attributed to cultivar resistance (Dokken-Bouchard et al., 2012). Blackleg has
both strongly aggressive and weakly aggressive strains are found in Canada. A
50% yield loss was attributed to blackleg in the Prairie provinces in the 1980s.
Resistant cultivars, crop rotation, and control of sources of inoculum can reduce
7
blackleg severity, but it remains a problematic disease in Canada, Europe and
Australia (Rimmer et al., 2003).
Losses caused by sclerotinia stem rot are highly variable across years and
among fields. It is uncommon for yield losses to exceed 20 % because final
disease severity at crop maturity is generally lower if infection begins during later
growth stages of the crop. Disease development can be halted by rapid rises in
temperature or sudden declines in soil moisture (Rimmer et al., 2003). A 2011
survey of canola diseases in Saskatchewan reported sclerotinia stem rot occurred
in 81 % of fields (195 of 241 fields). The provincial mean incidence of disease
was 9.4 % and the mean disease rating was 2.4 (5 being the most severe rating)
(Dokken-Bouchard et al., 2012).
Downy mildew (Peronospora parasitica (Pers. ex Fr.) Fr.) and white rust
/staghead (Albugo candida (Pers.) Kunze) are caused by oomycete pathogens
(Dixon, 1981, pg. 112-142; Rimmer et al., 2003). Downy mildew cannot survive
summer temperatures on canola and accordingly has little impact on seed quantity
and quality. White rust have been effectively managed in canola through
deployment of resistance and are no longer a major cause of crop damage
(Rimmer et al., 2003).
Diseases caused by viruses and bacteria are of less economic importance
in Canada than those caused by fungi, but they do occur. The diseases caused by
viruses are Broccoli Necrotic Yellows Virus, Cauliflower Mosaic Virus, Radish
Mosaic Virus, Turnip Crinkle Virus, Turnip Rosette Virus, and Turnip Yellow
8
Mosaic Virus (Dixon, 1981, 142-149). Diseases with bacterial causal agents
include blackrot (Xanthomonas campestris pv. campestris Dowson), bacterial leaf
spot (Pseudomonas syringae pv. maculicola (McCullorh) Young et al.) and
bacterial soft rot (Erwinia carotovora (Jones) Bergey et al.).
The protozoan Plasmodiophora brassicae Woronin causes clubroot
disease (Dixon, 1981; Schaad and Dianese, 1981; Wukasch and Dhanvantari,
1980). Clubroot has been a serious disease of Brassica vegetable crops in Canada
for many years, but it had not been reported on canola grown in Alberta until the
2000’s. This more recent occurrence of the disease poses a threat to the
production of canola throughout the prairies (Tewari et al., 2005).
1.2 Clubroot of Brassica
1.2.1 Significance
Clubroot is the most economically important disease of Brassica crops
worldwide. Mikhail S. Woronin identified P. brassicae as the cause of the disease
in 1878. The common English names of the disease include clubroot, finger-and-
toe, and hernia (Dixon, 2009a; Karling, 1968). The disease is also known as
herma or kapoustnaja kila in Russian, kropfkrankheit des kohles in German, gros
pied in French, kallbrok in Danish, and klumprotsjuka in Swedish (Karling,
1968). Clubroot has a cosmopolitan distribution internationally, and is responsible
for up to 50 – 100 % yield loss of turnip, swedes, and cabbage. Infestation of a
field by P. brassicae resting spores quickly becomes widespread, and this
9
contamination eliminates the possibility of clubroot-free Brassica crops (Karling,
1968).
Prior to 2003 in Canada, P. brassicae had been reported on Brassica
vegetables in British Columbia, Ontario, Quebec and the Maritime provinces, and
there were only anecdotal reports of clubroot in isolated home gardens on turnip,
broccoli, cabbage and cauliflower in the Prairies. The source of initial inoculum is
unknown, but it could have been introduced through infected turnip brought to
Canada by European colonists (Howard et al., 2010). In 2003, P. brassicae was
detected on canola near Edmonton, Alberta (Tewari et al., 2005). Since that first
report, the incidence of clubroot on canola in Alberta has risen. Clubroot was
found in 41 of 112 surveyed canola (B. napus) fields near Edmonton in 2005, and
was classified as ECD -/15/12 or pathotype 3 (Strelkov et al., 2007). A survey in
2011 found 103 of 447 canola fields were infested with clubroot. More new
identifications occurred that year than any year since 2003, the beginning of the
targeted surveys (Strelkov et al., 2012). The population of P. brassicae is usually
more concentrated near the entrance of commercial fields, which indicates that the
pathogen is likely being spread between fields by farming equipment (Cao et al.,
2009).
1.2.2 Plasmodiophora brassicae
Plasmodiophora brassicae Woronin is a eukaryote belonging to the
phylum Cercozoa, subphylum Endomyxa within the kingdom Protozoa (Cavalier-
Smith, 1998, 2002). It is often referred to as a protist, which indicates that it
10
belongs to the kingdom Protista. However, as part of a re-evaluation of kingdoms,
kingdom Protista was determined to be too heterogeneous to be taxonomically
meaningful and was subsequently dismantled and its genera distributed among the
other kingdoms. Protozoa was raised to the rank of kingdom, having more stable
usage and historical naming precedence over Protista (Cavalier-Smith, 1998).
Currently, the term protist is considered vague since it simply refers to the
unicellular body plan and degree of organization of some eukaryotes (Cavalier-
Smith, 1981). Therefore, the term protozoan is a more accurate and precise
description of the classification of P. brassicae. Protozoans are phagotrophic, can
be unicellular, plasmodial or colonial, with uninucleate or multinucleate
plasmodia or syncytia, and tubular or vesicular mitrochondial cristae (Cavalier-
Smith, 1998, 2002). A distinctive behavior of species within the subphylum
Endomyxa is plasmodial endoparasitism within other eukaryotes (Cavalier-Smith,
2002). Plasmodiophora brassicae is a physiologically obligate parasite,
completing its life cycle as a parasite within host roots, and is resistant to an
axenic culturing on non-living media (Brian, 1967).
The life cycle of P. brassicae occurs in three phases: in the soil, within
root hairs, and within the root cortex. In the soil phase, the pathogen's long-term
dormancy strategy for winter and other stressful conditions is a resting spore,
which has a half-life of about 3.6 years. In Sweden, it can take 18 years for the
resting spore inoculum of a heavily infested field to decline to undetectable levels
(Wallenhammar, 1996).
11
Resting spores germinate and release a single primary zoospore that
infects a root hair by penetration. This is the primary infection phase. Within the
root hair, the pathogen develops into a uninucleate primary plasmodium. Nuclei
undergo mitotic division within the plasmodium and then the cytoplasm divides to
form zoosporangia, each containing between 1 and 6 nuclei. Within each
zoosporangium, nuclei undergo mitosis and cleavage of the cytoplasm, which
results in the formation of 4 to 16 uninucleate secondary zoospores. The
zoospores dehisce and move back into the rhizosphere, leaving a hollow
zoosporangium (Kageyama and Asano, 2009). In the final phase of the life cycle,
the secondary zoospores infect the root cortex. The secondary zoospores develop
into secondary amoeboid plasmodia (Ingram and Tommerup, 1972). Plasmodia
migrate between cortical cells through disrupted cell walls, possibly by means of
enzyme-mediated cell wall degradation (Mithen and Magrath, 1992). Nuclei
undergo mitotic division within each plasmodium. Cleavage of the cytoplasm of
the mature secondary plasmodium leads to the formation of resting spores
(Ingram and Tommerup, 1972).
Plasmodiophora brassicae causes root hair and cortical infections in
plants of many species in the Brassicaceae family. Species in each genera of the
family are susceptible. Most research studies investigating plant-microbe
interactions have used the genera Brassica, Raphanus, and Arabidopsis as model
systems (Dixon, 2009a). The pathogen can also cause root hair infections in
species of the Poaceae, Rosaceae and Papaveraceae families (Dixon, 2006).
12
Symptoms of infection by P. brassicae appear in various tissues. Cortical
infection of roots results in swelling, disruption of vascular tissue, discolouration,
and characteristic clubbing symptoms on roots. Clubs result from hyperplasia and
hypertrophy of cortical cells caused by young plasmodia clustering and maturing
into groups of intensely infected cells, where they then differentiate into resting
spores (Ingram and Tommerup, 1972; Sharma et al., 2011b). These distinctive
and characteristic features lend the disease its common name, clubroot. The
compromised physiological condition of the roots results in above-ground
symptoms as well. Chlorosis, necrosis and abscission occur in leaves, especially
in seedlings. Determinate growth of flowers is hastened, resulting in
underdeveloped morphology of fruit carpels. In canola, the quantity of seed and
quality of pressed oil are reduced. Plant growth is stunted and the mechanical
strength of the stem is diminished (Dixon, 2009a). Clubroot symptoms on the
foliage can resemble physiological disorders such as drought and nutrient
deficiency (Howard et al., 2010).
Some Brassicaceae species have distinctive hypertrophy phenotypes. In
wild cabbage (Brassica oleraceae L.), full clubbing is found. The main root of tall
tumble mustard (Sisymbrium altissimum L.) usually forms clubs, while lateral
roots are unaffected. In contrast, the lateral roots of hedge mustard (Sisymbrium
officinale L.) and wormseed mustard (Erysimum cheiranthoides L.) become
clubbed, while the main root is unaffected. Only the lower portion of the roots of
garden cress (Lepidium sativum L.) becomes clubbed, while the upper portion is
13
unaffected. Tumor-like nodules and darken rotting spots develop on roots of
radish (Karling, 1968).
Several compounds involved in the signaling pathways within the host are
induced during club development. The morphological changes in cortical tissue of
roots infected with P. brassicae are linked with the disequilibrium of plant growth
regulators auxins and cytokinins. Developing plasmodia act as a sink for IAA,
which accumulates in the periphery of clubs (Ludwig-Müller et al., 2009). There
is a correlation between clubroot severity and the indole glucosinolate (GSL)
content of Brassica cultivars and A. thaliana mutants. One possible explanation
for the relationship is that benzyl-GSL is a precursor to phenylacetic acid (PAA),
which has the auxin plant hormone activity. The higher GSL content may be more
conducive P. brassicae to cause hypertrophy of cells it inhabits (Ludwig-Müller
et al., 2009). Induction of the arginase-encoding gene ARGAH2, a negative
regulator of auxin-induced root development, also limited development of clubs
caused by P. brassicae. Arginase induction was reported to be a response to
auxin/cytokinin-triggered cell proliferation, and not to pathogen recognition
(Gravot et al., 2012).
Cytokinins are plant growth hormones that promote cell division. Turnip
(B. rapa L. var. rapa) plants infected with clubroot accumulate higher levels of
cytokinin than healthy plants This up-regulation of cytokinins during clubbing
leads to cellular multiplication and elongation, and symptoms of hyperplasia
(Dekhuijzen and Overeem, 1971). Cytokinin receptor genes were up-regulated
14
during clubroot development in Arabidopsis, increasing the sensitivity of host
tissues to cytokinin signals, and cytokinin degrading oxidases/dehydrogenases
were down-regulated. Over-expression of cytokinin oxidase/dehydrogenase was
also found to be associated with clubroot resistance (Siemens et al., 2006).
Clubroot development increases the proliferation of vascular cambium and
phloem parenchyma cells in the hypocotyl. In a study on club formation in
A. thaliana at 16 days after infection, xylogenesis related genes were down-
regulated and xylem differentiation was inhibited. Phloem forming genes were
also up-regulated (Malinowski et al., 2012). Using an A. thaliana cambial stem
cell maintenance mutant line and cell cycle inhibitors that reduced club
development, P. brassicae development was not halted. This result led the authors
to conclude that the production of club symptoms affected the abundance of
resting spores formed but was not required for completion of the life cycle of
P. brassicae (Malinowski et al., 2012).
Infection by P. brassicae affects the amount of carbohydrates synthesized
and stored by host plants. Plants of A. thaliana infected with P. brassicae
accumulated less starch in leaves and exhibited increased expression of sucrose
synthase and starch synthase compared to controls. The clubbed roots inhabited
by P. brassicae become a carbon sink, causing the host to up-regulate production
of glucose (Ludwig-Müller et al., 2009). The expression of invertase, an enzyme
that hydrolyzes sucrose to fructose and glucose, is important for club development
in infected plants (Siemens et al., 2011).
15
Pathogens interact with their plant hosts in a number of ways. A pathogen
and plant host are said to have a compatible interaction, resulting in disease, when
the plant is susceptible and the pathogen is virulent. Infection progresses because
the plant does not recognize the presence of infection quickly enough to respond
effectively. An incompatible interaction, resulting in no disease symptoms, occurs
between a resistant plant and an avirulent pathogen (Katagiri et al., 2002; Lindsay
et al., 1993). Effector-triggered immunity results when nucleotide-binding-site
leucine-rich repeat (NBS-LRR) proteins encoded by a host recognizes virulence
effectors deployed by a pathogen (Jones and Dangl, 2006). Several processes that
may be activated during the induction of effector-triggered immunity include
production of phytoalexins and reactive oxygen species (ROS), systemic acquired
resistance (SAR), hypersensitive response (HR), and pathogenesis-related (PR)
protein expression.
Phytoalexins are compounds that have broad-spectrum antimicrobial
activity and are generally up-regulated and accumulated in response to avirulent
pathogens (Glazebrook and Ausubel, 1994). Plants in the family Brassicaceae
accumulate and up-regulate expression of the phytoalexin camalexin in clubs
during P. brassicae infection. In development of clubroot, it is not known if the
pathogen or host is the source of hormone abundance (Ludwig-Müller et al.,
2009).
A hypersensitive response occurs in some incompatible interactions
between plants in the family Brassicaceae and P. brassicae. Various studies have
16
revealed a few mechanisms that may potentially be involved in this response. For
example, a hypersensitive response in cortical sections of a resistant turnip
cultivar at 11 days after inoculation with P. brassicae (Dekhuijzen, 1979). An
analysis of the canola (B. napus) proteome during infection by P. brassicae
revealed a decrease in a cytokinin-regulating enzyme, adenosine kinase, a
decrease in a lignin biosynthesis enzyme, caffeoyl-CoA O-methytransferase, and
a decrease in enzymes associated with detoxifying reactive oxidative species
(ROS). These changes to the host metabolism may contribute to susceptibility to
pathogen proliferation (Cao et al., 2008). In resistant cultivars of canola at 18 and
35 days after inoculation (DAI), ROS were found to accumulate in the
endodermis, pericycle and vascular cambium, while in a susceptible cultivar, ROS
was found to have accumulated at 18 DAI and disappeared at 35 DAI. In the same
study, colonization of susceptible canola roots by P. brassicae was found to
reduce lignin in cell walls of stele and pith cells (Deora et al., 2013). Gene
expression of some plant lipid-transfer proteins (LTP), which are involved in
transferring lipids among membranes in vitro, was down-regulated during
clubroot infection at 10 and 23 DAI, while some other LTP were up-regulated at
23 DAI. Studies of genetically altered A. thaliana supported an inverse
relationship between symptom severity and LTP expression. Clubroot severity
was lower in transgenic lines of A. thaliana with over-expression of LTP genes,
and higher in LTP knock-out plants compared to the wild type (Jülke and
Ludwig-Müller, 2008). Application of 5 mM salicylic acid to roots of broccoli up-
17
regulated expression of SAR related PR-1 and PR-2 genes and reduced club
formation by 25 to 65 % at 6 weeks after inoculation (Lovelock et al., 2012).
Since P. brassicae is an obligate pathogen, study of the gene expression
during infection has been difficult and has progressed slowly. In a study of
expressed sequence tags of a compatible P. brassicae–canola interaction, 24 new
P. brassicae genes were identified. Among 10 of the genes tested, seven were up-
regulated and three were down-regulated at 7 DAI, as compared to expression in
resting spores. Similarly, five canola genes were up-regulated and two were
down-regulated in canola at 7 DAI compared to a nontreated control. The
researchers were able to construct a cDNA library of expressed genes during a
P. brassicae–canola interaction that can be utilized in further studies on
P. brassicae pathogenicity and canola resistance (Feng et al., 2012b). In a study
of P. brassicae gene expression during infection of A. thaliana, PbBrip9 and
PbCC249 were reported to be expressed during resting spore development, but
homology between these two genes and other genes was not found (Siemens et
al., 2009). In a subsequent study of P. brassicae gene expression during infection
of B. rapa, Pb-YPT (homologous to a membrane transport protein gene), Pb-
Brip9, and Pb-PSA (homologous to a puromycin-sensitive aminopeptidase gene)
were expressed during clubroot development, as late as 41 DAI, which was the
last time point tested (Wu et al., 2012).
18
1.2.3 Characterization and distribution of P. brassicae populations
Races within a pathogen species are not distinguished from one another by
their morphology, but rather by their ability to infect particular cultivars
(Stakman, 1914). Races are monophyletic populations that can be categorized
based on their geographical or ecological origins (Sturhan, 1985). The designation
of pathotype is an artificial classification system used to differentiate phenotypes
based on patterns of virulence on differential host sets in situations where gene-
for-gene relationships are not known or are not applicable (Flor, 1971). Genetic
background is not a trait used in the formulation of pathotypes (Sturhan, 1985).
One system for classifying P. brassicae was developed based on the
response of four Brassica cultivars: two cabbage cultivars (‘Jersey Queen’ and
‘Badger Shipper’), and two rutabaga cultivars (‘Laurentain’ and
‘Wilhelmsburger’) (Williams, 1966). Pathotypes of P. brassicae were identified
based on the combination of susceptible or resistant reactions with these four
cultivars. Buczacki et al. (1975) developed a classification system for
P. brassicae called the European Clubroot Differential (ECD) set. The system
tested populations of P. brassicae against five cultivars of each of three species:
B. rapa L. senu lato, B napus L., and B. oleracea L. The differential response to
the interaction, resulting in either a resistant or susceptible reaction, is scored for
each set of five test plants represented using a system of binary nomenclature and
presented as three sums (e.g., 20 + 21 + 22 + 24 / 20 / 22 + 23 = 23/1/12).
19
More than one pathotype of P. brassicae may be present in a field, each
representing a separate a subpopulation with a shared gene pool, also known as a
deme (Buczacki et al., 1975). Heterogeneity of pathotypes has been demonstrated
in clubs from the same field (Jones et al., 1982). The authors also demonstrated
that hosts were a source of selective pressure on the pathotypes within
P. brassicae populations, based on a study of serial inoculation and recovery of
new clubs between compatible and incompatible hosts.
Several pathotypes of P. brassicae have been identified in Canada. The
pathotypes of P. brassicae on canola (B. napus) in Alberta have been identified as
ECD -/15/12 (pathotype 3) and ECD 16/15/0 (pathotype 5) (Strelkov et al., 2006;
Strelkov et al., 2007). The populations of P. brassicae in British Columbia,
Ontario, and Quebec were identified as pathotype 6 (Williams, 1966). In one
study, the Ontario population was identified as ECD 16/0/14 (pathotype 6) and
the British Columbia population was determined to be ECD 16/2/12 (pathotype 6)
(Strelkov et al., 2006). More recently, pathotype 6 was reported in British
Columbia, pathotypes 3, 5, and 8 in Ontario, pathotypes 2 and 3 in St. Albert, AB,
pathotypes 3 and 8 in Edmonton, AB, and pathotypes 3 and 6 in Leduc, AB,
based on inoculations with single resting spores (Xue et al., 2008).
Pathotypes of P. brassicae have also been assessed at locations around the
world. In the USA, pathotype 6 has been isolated from Hawaii and Ohio,
pathotype 7 from California, and pathotypes 6 and 7 from Wisconsin (Rowe,
1980; Williams, 1966). Pathotypes 3, 6, and 7 have been reported in Australia
20
(Williams, 1966). Across 41 populations tested in Australia, 23 different reactions
to the ECD set were found, ECD 16/3/12 (equivalent to pathotype 7) and 16/3/31
(equivalent to pathotype 3 and/or 6) occurred more frequently (Donald et al.,
2006). A survey of clubroot in Korea identified 13 pathotypes (pathotypes 6, 10
and 12 were not found) of P. brassicae distributed in fields across the country.
Pathotype 8, the most common pathotype, was especially prevalent in regions
where Chinese cabbage was produced (Cho et al., 2003). Pathotype 2 was
reported to be the most common in Finland (Linnasalmi and Toiviainen, 1991).
Pathotype 4 was the most common in Japan (Tanaka et al., 1998). These reports
of different pathotypes being predominate in different regions of the world
indicate that some pathotypes are more wide spread than others, or that selective
pressure from the frequent cultivation of particular cultivars in the region has
favored some pathotypes over others.
1.3 Factors affecting clubroot severity
The virulence and abundance of a pathogen, as well as the susceptibility of
the host, influence the severity of disease. Environmental factors affect the
development and yield of crops, and when a plant is infected with a pathogen,
they also affect the severity of disease. Since P. brassicae is a soil-borne pathogen
that affects plant roots, soil temperature, pH, moisture and mineral composition
all have an effect on clubroot severity.
21
1.3.1 Temperature
Temperature is an abiotic factor that is generally regarded as one of the
most influential environmental factors affecting the development of pathogens
and the diseases they cause (Colhoun, 1973). In a greenhouse study, symptoms of
clubroot developed on cabbage grown between soil temperatures of 15 and 30 °C,
but not at temperatures equal to or less than 12 °C or equal to 35 °C (Monteith,
1924). In a greenhouse study, the incidence of clubroot on turnip rape was highest
(90 %) at 22 °C and lowest (20 %) at 12 °C, which were the highest and lowest
temperature tested (Rastas et al., 2012). Another early study indicated that the
temperature for maximum resting spore germination was 25 ºC, with 18 ºC and 35
ºC being the lower and upper limit temperatures for germination (Wellman,
1930). Buczacki et al. (1978) reported that 100 % infection could occur with a
minimum mean temperature of 19.5 ºC during the second week after sowing.
Early studies such as that of Monteith (1924) suffered from an inability to
maintain temperatures long enough for disease to develop. More recently, studies
were conducted to confirm the findings of early experiments, using growth
chambers that could maintain temperatures more precisely and for long periods of
time. In a controlled temperature study, clubroot severity in cabbage, Chinese
cabbage, radish, and mustard were highest at soil temperatures of 21–22 °C, based
on quadratic regression (Thuma et al., 1983). In another controlled temperature
study, no clubroot developed at 28 DAI in Shanghai pak choy grown at 10 and 15
ºC (Sharma et al., 2011a). In a study examining the effect of temperature shifts
during vegetative growth, the relationship between clubroot severity and
22
temperature treatments was found to be similar in Shanghai pak choy and canola
in early and late developmental stages of the plants. Exposure to temperatures
between 20–26 ºC resulted in the highest amount of clubbing in Shanghai pak
choy and canola, while exposure to 17 ºC or lower completely suppressed
clubbing. Temperatures of 30 ºC or above were also associated with a reduction in
clubbing. Applying suppressive temperatures (<17° C) affected the disease
similarly across the stage of plant development: in the first 3 weeks after seeding
it inhibited root hair infection and symptom development; in later weeks (4–6
weeks) it inhibited the incidence and severity of clubbing (Gossen et al., 2012b).
Symptom development and severity caused by P. brassicae are affected
by air and soil temperatures at all stages of disease progression. Root hair
infection by P. brassicae occurred most quickly at 25 ºC and most slowly at 10
ºC. In Shanghai pak choy grown at 25 ºC, root hair infection occurred at 2 DAI
and taproot swelling was observed at 10 DAI. Both higher and lower temperatures
correlated with slower infection. Root hair infection appeared at 4 DAI in plants
grown at 15, 20 and 30 ºC, and at 6 DAI at 10 ºC. The optimal temperature for
cumulative root hair infection has been interpolated to be 26 ºC (Sharma et al.,
2011a). In a companion study, cortical infection in Shanghai pak choy occurred
earliest and to the greatest extent at 25 °C. The lower limit for cortical infection
and symptom development was between 10 and 15 °C (Sharma et al., 2011b).
One previous study examined the potential to predict clubroot severity in
the field using environmental parameters. Accumulated degree days based on soil
23
temperature were found to be highly correlated with clubroot severity on radish.
There is also a significant correlation between air temperature and severity, but
the relationship is not as strong as that between soil temperature and severity
(Thuma et al., 1983). Low soil temperatures (less than 12 ºC) in the 10 days prior
to harvest were associated with low incidence and severity of clubroot at harvest
of Shanghai pak choy and Chinese flowering cabbage (McDonald and
Westerveld, 2008). Selecting a seeding date for short-season Brassica crops such
that the soil temperatures during initial infection or symptom development are not
conducive to clubroot development represents an effective strategy for clubroot
management (Gossen et al., 2012a). Manipulation of seeding date for control of
clubroot is covered in more detail later in this chapter (Section 1.4.1).
Temperature interacts with other environmental factors to affect clubroot
severity. In an controlled environment study, some clubbing occurred at a soil
temperature of 9 °C on cabbage grown in sandy loam soil with high organic
content and maintained at 90 % soil moisture, but clubbing did not occur in
cabbage grown in a clay loam soil maintained at 75 % soil moisture (Monteith,
1924). Severe clubbing occurred at a soil temperature of 20 °C and soil moisture
at 75% of water holding capacity, and decreased with increasing temperature
(Monteith, 1924). In alkaline soil with a pH of 7.8, the highest clubroot incidence
occurred with a mean air temperature of 23 ºC or higher (Colhoun, 1952). In a
study on the interactions between temperature and pH, clubroot severity was > 20
DSI in canola plants maintained at 10 or 15 °C in combination with pH treatments
of 6.0, 6.5, 7.0, 7.5 and 8.0 (Kasinathan, 2012). An extensive review of the
24
existing literature led one author to conclude that a soil temperature of 24 ºC with
a pH 6.0–6.7, makes the most favorable conditions for resting spore germination,
while temperatures of 45 ºC or greater are lethal to the pathogen (Dixon, 2009b).
1.3.2 Soil pH
Soil pH can influence the severity and incidence of clubroot on crops. In a
survey of fields in Finland, clubroot was more severe and occur more frequently
in soils with pH below 6.5 compared to soils above pH 6.5 (Rastas et al., 2012).
Liming of soils has been used for managing clubroot for centuries, but its efficacy
is inconsistent (Karling, 1968). In controlled environmental studies, infection
success and subsequent clubroot symptom development were generally higher at
pH 5.4 to 7.1 than at pH 7.3 to 8.0 (Colhoun, 1953; Myers and Campbell, 1985).
Resting spore germination was reported to occur slowly in limed soils with pH of
8.0 compared to acidic soils of pH 5.8 (Macfarlane, 1952).
The effects of alkaline pH and calcium in the suppression of clubroot
symptoms are independent and synergistic. Each is separately associated with
resistance to infection. Clubroot-resistant roots cultured in Murashige and Skoog
agar medium and exposed to resting spores increased the alkalinity of their culture
medium from pH 5.2 to 5.6 in 3 days, but the pH levels did not change for
cultures of susceptible roots (Takahashi et al., 2006). Calcium's role in resistance
is to mediate induction of phenylalanine ammonia-lyase (PAL) activity in
response to contact by P. brassicae. PAL, in turn, is necessary for expression of
clubroot resistance in turnip (Takahashi et al., 2002). Calcium was also found to
25
be vital for induction of cell death by P. brassicae within clubroot-resistant roots
(Takahashi et al., 2006). The two variables interact in a few ways. The alkalinity
of soil can prime the suppressive effect calcium has on clubbing. Hydrogen
cations may compete with or act against calcium in plant tissues (Dixon, 2009b).
Alkaline pH increases calcium absorption in roots. Calcium treatments at pH 6.2
suppress clubroot symptoms at low inoculum levels of P. brassicae, comparable
to calcium treatments at pH 7.2 at higher inoculum levels (Webster and Dixon,
1991a).
In a study on the interaction between temperature and pH, the highest
clubroot severity developed in canola at 25 ºC and at pH 6.0. Severity declined
but still developed (40 DSI) at the optimum temperature at pH 8 (Kasinathan,
2012; Kasinathan et al., 2010).
1.3.3 Soil moisture
Soil moisture is an abiotic factor that affects the development of clubroot.
Soil moisture influences the motility of zoospores in soil, affecting their ability to
migrate to roots (Colhoun, 1973). Clubroot severity is proportional to soil
moisture (Monteith, 1924). Soil at a water holding capacity of 70 % was the most
favorable for P. brassicae resting spore germination and infection of roots
(Colhoun, 1952). Poorly drained and low lying soils foster high levels of
proliferation of P. brassicae (Dixon, 2009b). Clubroot can develop in soil with
soil moisture of 60 % to 100 % of water holding capacity, but does not develop in
soil with 45 % or less of water holding capacity. In a greenhouse study, clubroot
26
incidence was higher (100 %) on turnip rape irrigated daily to maximum holding
capacity compared to plants watered only when they showed symptoms of wilting
(60 %). Additionally, daily irrigation resulted in lower yield (500 kg ha-1)
compared to the wilting-only treatment (1,500 kg ha-1) (Rastas et al., 2012).
Rainfall at 2 to 3 weeks after seeding was positively correlated with clubroot
severity, while total rainfall was positively correlated with incidence and severity
in vegetable crops on muck soils (Gossen et al., 2012a; Thuma et al., 1983). Dry
soils with moisture saturation of 30 % or lower were reported to delay resting
spore germination (Macfarlane, 1952).
1.3.4 Spore load
The number of root hair infections increases with the concentration of
P. brassicae resting spores in the soil for crops such as canola, cabbage, kale
(B. oleracea L. var. acephala D.C.), cauliflower, Brussels sprouts, turnip, swede
(B. napus L. var. napobrassica (L.) Rchb.), radish, and garden cress (Lepidium
sativum L.) (Hwang et al., 2011b; Macfarlane, 1952). For symptom development
to occur and yield to be affected on most hosts, resting spore levels need to be
greater than 1000 spores g-1 of dry soil (Donald and Porter, 2009; Faggian and
Strelkov, 2009). However clubroot developed on napa cabbage cv. Shin-Azuma
when resting spore concentrations were 10 spores g-1 soil (Murakami et al., 2002).
Increasing inoculum concentration from 1 x 105 to 1 x 108 resting spores
cm-3 increased clubroot severity in canola (Hwang et al., 2011c). The rate of
seedling emergence, plant height and seed yield per pot of a susceptible canola
27
cultivar decreased exponentially with increasing inoculum density (Hwang et al.,
2011b). Susceptible volunteer canola growing in the same field as resistant canola
developed clubroot symptoms and so increased the resting spore density in soil.
Also, the higher the proportion of susceptible canola in the population, the larger
the increase in soil resting spore density (Hwang et al., 2012a). Similarly,
increasing inoculum concentrations from 1 × 103 to 1 × 107 resting spores g-1 of
dry soil increased clubroot severity and decreased foliar weight in napa cabbage
(Hildebrand and McRae, 1998).
1.3.5 Light intensity
Even though P. brassicae is a soil-borne pathogen, the light intensity
received by the host affects the severity of clubbing. Increasing the light energy
(Wh/m2) accumulated by the host may increase photosynthate availability (Dixon,
2009b). Increasing light energy may play a role in increasing the accumulation of
glucobrassicin in the host, a precursor of indoleacetonitrile, which is associated
with clubbing (Buczacki et al., 1978). Light has also been suggested as an
inhibitory factor on resting spore germination (Dixon, 2009b).
1.4 Clubroot management
Growers can manage clubroot in their fields by modifying specific
practices related to crop cultivation. Common cultural practices include crop
rotation with non-hosts, sanitation of farming equipment, and reduced soil tillage.
Biological control (biocontrol) with microorganisms can be applied to plants or
soil to attack and reduce pathogen inoculum. Pesticides can be used to eradicate
28
or reduce pathogen populations. They may provide a chemical or mechanical
barrier against infection, such as surfactants. Pesticides can be applied as granular
solids, gaseous fumigants, and liquid drenches or sprays. Clubroot-resistant crops
are an effective and inexpensive (once the cultivars have been developed) method
for clubroot management and have become more frequently utilized in agriculture
by way of a combination of traditional breeding, genetic marker assisted
selection, and genetic engineering.
1.4.1 Cultural controls
An 18-year-long crop rotation trial on a heavily infested field in Jokioinen,
Finland, compared a continuous cropping of turnip rape, a 3-year rotation of
spring wheat–barley–oat, perennial grass, and open fallow. The inoculum of
P. brassicae declined in the cereal rotation, fallow, and grass treatments to almost
zero after 7 years, based on bioassay of susceptible turnip planted yearly in soil
taken from the fields. The inoculum of the turnip rape rotation declined in the first
4 years of the trial, then fluctuated higher and lower each year afterwards (Rastas
et al., 2012). Longer rotations away from Brassica crops can reduce clubroot
incidence on subsequent Brassica cultivations. In a survey of Brassica cultivation
in Finland, there was a negative relationship between years since the last Brassica
crop and the incidence and severity of clubroot. The risk of clubroot was 6 times
more likely following a 1- to 2-year break after a Brassica crop compared to a
break of 7 years or more (Rastas et al., 2012).
29
Decoy crops, sometimes referred to as bait crops, are plants that trigger the
germination of resting spore inoculum. This reduces inoculum populations while
preventing production of new resting structures, and allows the subsequent crop to
be under reduced disease pressure (Schroth and Hildebrand, 1964). Growing
decoy plants prior to Brassica cultivation could reduce the population of resting
spores in soil. Crop rotation with non-susceptible decoy plants such as oat (Avena
sativa L.) or spinach (Spinacia oleracea L.) promotes resting spore germination
and primary root infection. The pathogen subsequently cannot form new resting
spores and the concentration of initial inoculum is reduced (Macfarlane, 1952).
In a study under controlled conditions, planting leafy daikon (Raphanus
sativus L. var. longipinnatus) prior to Chinese cabbage reduced the number of
resting spores by 94% (from 7.4 × 105 to 4 × 104), although it had no effect on
clubroot severity (Murakami et al., 2000). When the study was expanded to
include more non-host species, decoy plants of oat, spinach and leafy daikon
artificially infested with 1 × 106 spores l reduced resting spore density by 29–62%
compared to a fallow control. After decoy plants were planted in soil artificially
inoculated with 1 × 104 spores g-l and their roots were left to decompose, clubroot
severity on Chinese cabbage was reduced to 51–70 DSI compared to the fallow
control (95 DSI). When soil inoculation was increased to 1 × 106 spores g-l prior
to planting, a decoy plant did not reduce severity on the subsequent Chinese
cabbage crop. This indicates that the efficacy of decoy crops is limited to lower
inoculum densities (Murakami et al., 2001). In a study where decoy crops of leek
(Allium porrum), winter rye (Secale cereale), perennial ryegrass (Lolium
30
perenne), and red clover (Trifolium pratense) were continuously grown in plots
for 4 years, none of the decoy crops affected clubroot severity on Chinese
cabbage compared to a nontreated control when grown in soil collected from
those plots (Friberg et al., 2006). In a more recent study in fields naturally
infested with means of 3.1 × 106 and 9.4 × 106 resting spores g-l, decoy crops did
not effectively reduce clubroot severity on subsequent canola cultivations and
reduction in resting spores was inconsistent (Ahmed et al., 2011).
Soil solarization, which heats the upper layer of soil by capturing solar
energy passing through a transparent insulating layer (e.g., plastic sheeting), can
reduce populations of resting spores of P. brassicae and many fungal pathogens,
but is best suited to warm climates like Australia (Donald and Porter, 2009).
Solarization with a polyethylene sheet elevated the maximum temperatures of soil
at 5 cm below the surface by 14 °C. Napa cabbage grown in solarized soil had
lower clubroot severity and higher yield compared to an nontreated control
(Porter and Merriman, 1985).
Manipulation of seeding date and the timing of exposure of hosts to
P. brassicae can affect the incidence and severity of clubroot. Based on the
clubroot response of successive plantings of Shanghai pak choy and Chinese
flowering cabbage, McDonald and Westerveld (2008) concluded that seeding in
early May or late August and September can reduce clubroot severity on
Brassicas in Ontario. Early or late planting of Shanghai pak choy in May, August
or September reduced clubroot incidence to 0–15 %, compared to 64–87 % in
31
June and July plantings when soil temperatures were warm (Gossen et al., 2012a).
Increasing the time from seed germination to first exposure of seedlings to
P. brassicae has been shown to decrease clubroot severity and increases seed
yield of canola (Hwang et al., 2011c).
Calcium cyanamide provides control of clubroot in Brassica vegetables,
acting as both a fertilizer and a pesticide. The compound reacts with the moisture
in soil to decompose into hydrogen cyanamide and hydrated lime. Hydrogen
cyanamide has fungicidal and herbicidal properties and can further decompose to
urea, ammonia, nitrate and dicyandiamide over a time period of 3 weeks.
Hydrated lime will decompose into calcium ions by hydrolysis and increase soil
pH (Donald and Porter, 2009; Klasse, 1996). Calcium cyanamide also promotes
the proliferation of beneficial and pathogen-suppressive rhizobacteria (Dixon,
2012). No spores survived incubation in 400 mg/L calcium cyanamide for more
than 15 days, or 1600 mg/L calcium cyanamide for more than 5 days (Naiki and
Dixon, 1987). Calcium cyanamide incorporated as a granular into a mixture of
two volumes of soil and one of peat at a concentration of 2000 mg a.i./kg
eliminated all clubroot symptoms and improved plant height on napa cabbage
compared to a nontreated control (Naiki and Dixon, 1987). In Canada, the
application of the calcium cyanamide formulation Perlka (50% calcium oxide,
19.8% nitrogen, 1.5% magnesium oxide) to muck soil prior to seeding reduced
the incidence of clubroot on four Asian Brassica vegetables: Shanghai pak choy,
flowering Chinese cabbage (B. rapa L. ssp. Chinensis (Rupr.) Olson var. utilis
Tsen and Lee), Chinese broccoli (B. alboglabra Bailey), and big leaf mustard
32
(B. juncea L. Coss var. folisa Bailey). Broadcast application of Perlka at 500 or
1000 kg ha-1 at 7 or 14 days prior to seeding reduced clubroot incidence on Asian
Brassica vegetables (average 16 %) compared to the nontreated control (43 %)
and lime treatments (33 %) (McDonald et al., 2004). The compound is most
effective when applied in a small particle formulation (98% w/w <300 µm), and
in large quantities (1000 kg/ha) (Donald et al., 2004). In a more recent field study,
calcium cyanamide applied at rates of 0.5 or 1.0 t ha-1 to soil infected with
P. brassicae had no effect on canola yield, emergence, mortality, height or
clubroot severity compared to a nontreated control (Hwang et al., 2011a).
Host nutrition can have an effect on clubroot development. Calcium can
reduce resting spore viability and germination. Calcium chloride reduced resting
spore viability of P. brassicae at low inoculum levels (Myers and Campbell,
1985). Calcium treatments at pH 6.2 result in suppression of low inoculum levels,
comparable to calcium treatments at pH 7.2 and higher inoculum levels (Webster
and Dixon, 1991a).
Boron plays a role in plant metabolism, cellular differentiation and lignin
synthesis. Boron, alone; in forms such as boric acid, sodium tetraborate or
combination with other compounds, can decrease clubroot symptoms by as much
as 50 % and improve crop yields by as much as 40 % on napa cabbage (Dixon,
2006; Webster and Dixon, 1991b). However, the specific role that boron plays in
the interaction between P. brassicae and host root tissue is still unknown. There
was an interaction between pH, resting spore concentration, and rate of boron
33
required to suppress clubroot in a sand culture study. For plants grown in sand at
pH 7.2 and inoculated with 5 × 107 resting spores mL-1, application of boron at
10–20 ppm reduced both root hair infection and development of primary
zoosporangia. In plants grown at the same pH but with a lower spore load of 5 ×
105 resting spores mL-1, boron applied at 10 ppm eliminated infection and
clubroot symptoms. When the sand culture was adjusted to pH 6.2 and boron was
applied at 30 ppm, the incidence of root hair infection and clubroot severity
decreased to 20 % and 13 DSI respectively, compared to 90 % incidence and 85
DSI in the treatment with 1 ppm boron. Concentrations of 50 ppm and above
resulted in severe phytotoxic effects (Webster and Dixon, 1991b). In field trials of
canola grown in muck soil, boron at 4 kg/ha reduced severity by 64% compared
to a nontreated control, without causing phytotoxic symptoms (Deora et al.,
2011). In controlled environment studies, the incidence of root hair infection
declined with increasing concentrations of boron applied. However, rates higher
than 2 kg/ha resulted in phytotoxicity on canola seedlings (Deora et al., 2011).
Magnesium has pH-dependent inhibitory effects on clubroot development.
Concentrations of magnesium as high as 25.0 mM decreased root hair infection
and clubbing of the susceptible broccoli cultivar ‘Topper’ grown in quartz sand
(Myers and Campbell, 1985).
Both the source and combination of nutrients may affect the suppression
of clubroot. Calcium nitrate alone or combined with boron reduced clubroot
34
severity and increase yield of napa cabbage compared to ammonium nitrate and
calcium ammonium nitrate alone (Dixon and Page, 1983).
The cost of the large amounts of nutrients such as calcium that would be
required to achieve gains in yield from reduction in clubroot exceed the potential
economic returns, and makes most soil amendments uneconomical on canola.
However, for high-value Brassica vegetables grown in situations with low
inoculum pressure, amendment with calcium and boron might be more
economical (Donald and Porter, 2009).
1.4.2 Biocontrols
Biological control agents are living organisms that are introduced to the
host ecosystem to suppress the growth or otherwise decrease the negative effects
of a pathogen on a host (Eilenberg et al., 2001) through mechanisms that include
hyperparasitism, competition, production of suppressive enzymes, antibiotics, and
chemicals, and triggering of induced host resistance (Pal and Gardener, 2006).
There are no biocontrol agents currently registered for the control of clubroot on
any crops in Canada, but several biocontrol agents have been shown to reduce
clubroot severity. Inoculation of A. thaliana with the endophyte fungus
Acremonium alternatum Link prior to inoculation with P. brassicae reduced club
size and the number of resting spores that developed, possibly due to the release
of inhibitory toxins (Jäschke et al., 2010). A biocontrol formulation of effective
microorganisms, marketed as EM-1, containing 70 species of lactic acid bacteria,
phototrophic bacteria, actinomycetes, fungi, and yeasts, reduced root hair
35
infection by P. brassicae in four Brassicaceae species, possibly affecting the
pathogen through competition (Kurowski et al., 2009). A strain of Streptomyces
griseoruber that was identified in a study of actinobacteria isolated from the
rhizosphere of Chinese cabbage grown in China, reduce clubroot severity on
Chinese cabbage grown in the greenhouse (26 DSI) and field (27 DSI) when
inoculated into soil before seeding compared to a nontreated control (97 DSI and
79 DSI, respectively) (Wang et al., 2012).
Five biocontrol agents registered in Canada for control of other soil-borne
diseases, Bacillus subtilis (Serenade ASO, AgraQuest Inc., Davis, CA, USA),
Gliocladium catenulatum (Prestop, Verdera Oy, Espoo, Finland), Streptomyces
griseoviridis (Mycostop, Verdera Oy, Espoo, Finland), Trichoderma harzianum
(Root Shield, BioWorks Inc., Victor, NY USA) and Streptomyces lydicus De
Boer et al. 1956 strain WYEC 108 (Actinovate SP, 0.371%; Natural Industries,
Inc. Houston, TX), were evaluated for their control of clubroot (Peng et al.,
2011). Each treatment suppressed clubbing on a susceptible canola cultivar by up
to 73 % compared to an inoculated control when applied as a drench or seed
treatment, but efficacy was inconsistent among trials. In a field study where
Prestop and Seranade were applied as an in-furrow spray, clubroot severity was
reduced on Chinese cabbage compared to the nontreated control and efficacy was
comparable to fungicide treatments of fluazinam and cyazofamid. The authors
noted that biocontrol efficacy was reduced at high inoculum density.
36
The efficacy of biocontrols is also diminished under high inoculum
densities in the field. For example, the fungal endophyte Heteroconium
chaetospira (Grove) M.B. Ellis suppressed clubbing when applied to soil
containing resting spores at densities between 104 to 105 spores g-1 and pH 5.5 to
7.2, but was not effective at higher inoculum concentrations (Narisawa et al.,
2005). Soil type may also influence the efficacy of biocontrols. Prestop and
Serenade reduced clubroot incidence and severity on Shanghai pak choy grown in
muck and mineral soil and sand, but Serenade was not effective in soil-less mix
(Kasinathan, 2012). Prestop application reduced clubroot severity and incidence
on canola grown in muck soil, while Mycostop and Serenade reduced clubroot
incidence. However, the reductions were relatively small and probably would not
be economical for growers (Kasinathan, 2012).
1.4.3 Fungicide management
Only a few synthetic fungicides have efficacy against P. brassicae, and
there are no fungicides registered for control of clubroot on canola in Canada
(Hartman, 2004). On vegetables, Quintozene (pentachloronitrobenzene)
(AMVAC Chemical Corp., Newport Beach, CA) is registered and recommended
as a transplant treatment applied at a rate of 1–3 kg per 400 L water, 250–250 mL
per plant, for control of clubroot in Ontario (OMAFRA, 2008). Quintozene
reduced clubroot severity on cabbage when applied as a powder or liquid
suspension at 34 or 68 kg/ha, but the liquid formulation reduced yield due to
phytotoxicity (Wimalajeewa, 1975). Application of Quintozene (34 or 68 kg/ha)
to silty clay or loam soil reduced canola mortality caused by clubroot and
37
increased plant height and seed yield compared to a nontreated control, but the
increase was not economical (Hwang et al., 2011a).
Fluazinam (Allegro 500 F, ISK Biosciences, Concord, OH) is a protectant
fungicide that is also registered and recommended in Ontario for management of
clubroot on Brassica vegetables. It is applied as a broadcast before bed formation
at 2.9 L in 500 L water/ ha, or immediately after transplanting at 50 mL/100 L and
100 mL per plant (OMAFRA, 2010). Fluazinam disrupts oxidative
phosphorylation, so early application blocks primary and secondary infection by
P. brassicae and reduces clubroot severity (Donald and Porter, 2009; Kurowski et
al., 2009). Fluazinam applied to the susceptible cabbage cultivar Bronco
improved the health of roots and prevented their decay compared to a nontreated
control, but did not reduce the incidence or severity of clubroot or increase yield
(Saude et al., 2012). Fluazinam applied to susceptible Shanghai pak choy grown
in “conetainers” of soil-less mix reduced clubroot severity to zero, but was not
effective in field trials (Adhikari, 2010).
Flusulfamide (Nebijin, Mitsui Chemicals Agro, Inc., Tokyo, Japan) and
cyazofamid (Ranman, ISK Biosciences Corporation, Concord, OH) suppress
germination of P. brassicae resting spores. Treating soil with flusulfamide dust at
0.9 μg a.i. g−1 dry soil nearly eliminated root hair infection and suppressed
clubbing compared to the nontreated control. Flusulfamide had no suppressive
effect on cortical infection by P. brassicae (Tanaka et al., 1999). In a more recent
study in a highly infested commercial field in Brazil, flusulfamide applied at 20 L
38
ha-1 before planting of cauliflower, cabbage, and Chinese cabbage reduced
clubroot severity on all three crops compared to lower rates, application of the
fungicides chlorothalonil (40 kg ha-1) or quintozene (40 kg ha-1), liming (5 ton ha-
1), or the nontreated control (Kowata-Dresch and May-De Mio, 2012).
Cyazofamid may also suppress primary zoospore motility in soil.
Cyazofamid applied at 1 mg kg-1 of dry soil reduced root hair infection by 100%
(Mitani et al., 2003). Cyazofamid may act on P. brassicae through disruption of
mitochondria cytochrome bc1 complex, which is the mode of action in the
oomycete pathogen Pythium spinosum Sawada (Mitani et al., 2003). Cyazofamid
suppressed clubroot development on Shanghai pak choy grown in soil-less mix in
a controlled environment, but had no effect on clubroot compared to an
nontreated control when grown in the field in muck soil (Adhikari, 2010). In
contrast, a soil-drench of cyazofamid reduced clubroot severity on Shanghai pak
choy and Chinese flowering cabbage planted in May, June and July compared to a
nontreated control. When plantings were done in the cooler months of August and
September, clubroot severity was low on the nontreated control and cyazofamid
did not reduce clubroot severity further (Gossen et al., 2012a).
Fumigation with metham sodium is a popular method to reduce soil
populations of P. brassicae resting spores in many countries due to its low
implementation cost (Donald and Porter, 2009). Vapam HL (metham sodium) is
registered in Canada for control of clubroot on Brassicas. Methyl bromide applied
at 47.8 g/m2 eliminated clubroot disease on cabbage seedlings grown in a nursery
39
and on transplants in the field, but was too costly and hazardous for repeated
control of clubroot in Sri Lanka (Wimalajeewa, 1975) and its use has been
reduced or eliminated around the world because of its role in release of
greenhouse gases. Chloropicrin and dazomet had some efficacy against clubroot,
but were inconsistent and affected by the sealing method (polyethylene sheeting
working better than soil compaction or water drench) (White and Buczacki,
1977).
Several other fungicides provide effective control of clubroot, but are no
longer available as commercial treatments because of issues relating to toxicity or
persistence in the environment. A root dip of mercurous chloride reduced clubroot
severity compared to a water control or chlorinated hydrocarbons, but stunted
plant growth and reduced yield in cabbage. The chlorinated hydrocarbons dieldrin
and endrin reduced or completely eliminated root hair infection in cabbage
(Channon et al., 1965).
Surfactants, which are chemicals that reduce the surface tension between
liquids and solids and so increase their miscibility, have also been evaluated for
the management of clubroot. Surfactants may lyse zoospores directly, or interfere
with zoospore mobility and their ability to penetrate root hairs. Liquid
preparations of the nonionic surfactant AquaGro 2000-L were more effective and
less phytotoxic than the granular formulation or other surfactants (Agral and
Citowett Plus) (Hildebrand and McRae, 1998). A combination of direct soil
application and a pre-planting soak of transplants with sodium dioctyl
40
sulphosuccinate or alkyl phenvl ethylene oxide increased the fresh top weights of
plants. Alkyl phenvl ethylene oxide was not toxic to seedlings and consistently
improved yield compared to nontreated controls (Humpherson-Jones, 1993).
None of these materials are registered for control of clubroot in Canada.
A combination of several techniques can increase the overall efficacy of
clubroot management. In a field study of broccoli growing in a heavily infested
field in Brazil, soil solarization for 60 days plus lime at 4.5 ton ha-1, soil
solarization plus flusulfamide applied at 20 g ha-1, or liming, solarization and
flusulfamide, all reduced clubroot severity compared to a nontreated control. The
combination of liming, solarization and flusulfamide was more effective at
reducing clubroot than any of the individual treatments (Kowata-Dresch and May-
De Mio, 2012).
1.4.4 Host resistance
Plant breeders have been actively searching for a source of broad-spectrum
resistance to P. brassicae pathotypes. Genes for resistance to P. brassicae, called
clubroot resistance (CR) genes, have been identified in B. rapa, B. oleracea and
B. napus (Piao et al., 2009). The naming system for clubroot resistance genes is
not standardized: Crr and CR series in B. rapa; CR2, Pb, and Pb-Bo series in B.
oleracea; and Pb-Bn and PbBn series in B. napus (Piao et al., 2009). This lack of
uniformity is a concern because it can make comparison between species
confusing.
41
Clubroot resistance genes have been transferred into B. rapa, B. oleracea
and B. napus from European fodder turnip. The resistance conferred by CR genes
is generally pathotype—specific (Piao et al., 2009). Mapping studies have
revealed the presence of over 55 clubroot resistance loci. Further research in
clubroot genetics is aimed at developing markers for marker-assisted selection
breeding, and determining the mechanisms of resistance (Piao et al., 2009). In a
recent study of mapping quantitative trait loci (QTL) of clubroot genes from
crosses between clubroot resistance European turnip and susceptible Chinese
cabbage, two major (Pb-Br3 and Pb-Br8) and one minor QTL were linked with
resistance to pathotype 4. These markers will be utilized in future marker-assisted
selection in breeding new clubroot-resistant crops (Cho et al., 2012). In a study to
identify the CR gene in a Chinese cabbage cultivar that confer resistance to
pathotype 3, the CRb gene was identified based on a correlation between
resistance phenotypes in a F2 population and genotypes of known CR loci (Kato
et al., 2012). These authors also reported two simple sequence repeats that could
be used in marker-assisted selection of the resistance gene in Chinese cabbage.
CR genes can reduce or delay secondary infection by P. brassicae, and
halt development of secondary plasmodia (Diederichsen et al., 2009; Piao et al.,
2009). In a recent gene mapping and transcription study, the gene CRa was
responsible for clubroot resistance in B. rapa, and was identified to encode a Toll
Interleukin 1 receptor region–nucleotide binding site-leucine rich repeat (TIR-
NBS-LRR) protein (Ueno et al., 2012). In another study, the clubroot resistance
locus Crr1 was comprised of two genes, Crr1a and Crr1b. Crr1a was identified to
42
encode for a TIR-NB-LRR protein that is expressed in the stele and cortical cells
of the roots and hypocotyl of a clubroot-resistant cultivar of European fodder
turnip, but not in root hairs (Hatakeyama et al., 2013). NBS-LRR proteins are
common products of resistance genes, and are capable of recognizing pathogen
effectors, molecules that enable pathogens to colonize hosts and inhibit immune
responses, resulting in a effector-triggered immunity (Jones and Dangl, 2006).
Pioneer Hi-Bred was the first company in Canada to release a clubroot
resistant cultivar of canola ‘45H29’, with high resistance to pathotype 3, the most
prevalent pathotype in Alberta, and some resistance to pathotype 2, 5, 6, and 8
(Pioneer Hi-Bred, 2011). An additional five canola hybrids with clubroot
resistance have been commercialized in Canada: ‘Proven 9558C’ (Viterra,
Regina, SK), ‘D3152’ (DuPont Canada, Mississauga, ON), ‘73-67 RR’ and ‘73-
77’ RR (Monsanto, Winnipeg, MB), and ‘1960’ (Canterra Seeds, Winnipeg, MB)
(Strelkov et al., 2011). Pioneer canola line ‘45H29’ has been used as a model
resistant crop in studies of cultivar resistance (Hwang et al., 2011b; Peng et al.,
2011). Root hair infection developed more slowly in ‘45H29’ compared to
susceptible canola cultivars (Deora et al., 2012a; Hwang et al., 2011b). Also,
‘45H29’ was resistant to cortical infection by pathotypes 3 and 6, but cultivar
‘45H21’ was resistant to pathotype 6 only (Deora et al., 2012a). In a follow up
study, the clubroot-resistant cvs. ‘73-67 RR’, ‘73-77 RR’, ‘Proven 9558’ and
‘45H29’ were all resistant to pathotypes 2, 3, 5 and 6. The authors concluded that
since the response of the resistant cultivars tested in the study was uniform at root
hair and cortical infection levels, it is possible or even likely that the resistance in
43
each cultivar is conditioned by a gene(s) from a single source that confers broad
resistance, because most of known sources of resistance to P. brassicae are
pathotype specific.
Brassica lines from the Rapid Cycling Brassica Collection (RBCB,
Madison, WI) have been examined for their reaction to selected pathotypes of
P. brassicae and evaluated for their usefulness as model crops. RCBC lines of B.
carinata and B. juncea could be used as clubroot-susceptible model crops because
of their consistently high clubroot incidence and severity when grown in soil
naturally infested with pathotype 6 (P6). Similarly, RBCB lines of Raphanus
sativus and B. napus had a high level of resistance to P6, and might be suitable for
us as model resistant crops (Adhikari, 2010). Also, the RBCB line of B. napus
was resistant to P2, P3, P5 and P6, the B. oleraceae line was resistant to P2, P3,
and P5, the B. carinata and B. rapa lines were resistant to P2 and P5, and B.
juncea had intermediate resistance to P2 and P3. Also, several mutant lines of
A. thaliana had a differential resistance response to pathotypes P2, P3, P5 and P6
(Sharma et al. (2013).
Syngenta Seeds (Boise, ID) and Bejo Seeds (Geneva, NY) have
commercialized clubroot-resistant cultivars of green cabbage (‘Kilaton’, ‘Tekila’,
‘Kilaxy’, and ‘Kilaherb’) and napa cabbage (‘Yuki’, ‘Bilko’, ‘Deneko’, ‘China
Gold’, and ‘Emiko’) for cultivation in Canada. The napa cabbage cultivar ‘Yuki’,
has been shown to be highly resistant to clubroot, with 99% lower DSI compared
to a susceptible cultivar (Adhikari, 2010; Peng et al., 2011). Napa cabbage cvs.
44
‘Deneko’ and ‘Bilko’ were also highly resistant to clubroot on organic and
mineral soils in Ontario where pathotype 6 is predominate (Adhikari, 2010; Saude
et al., 2012).
Clubroot-resistant cultivars of several Brassica vegetables have been
screened for and identified internationally. Researchers screened 50 commercial
cultivars of Chinese cabbage for resistance to clubroot in three P. brassicae
infested agricultural regions of Korea. The authors did not identify the pathotypes
of each region, but a differential response in clubroot susceptibility was found
among cultivar and region. The Chinese cabbage cvs ‘Chuwol’ and
‘Gohyangssam’ were 100 % resistant in all three regions, and so were
recommended for organic production (Kim et al., 2012). In a controlled
environment study, 50 cultivars of cauliflower were screened for resistance to a
highly virulent isolate of ECD 16/31/31 from the Czech Republic, which
corresponds to either pathotype 4 or 10 in the Williams (1966) system. Two
cultivars, ‘Brilant’ (51 DSI) and ‘Agora’ (52 DSI), were found to be the most
resistant to clubroot, but severity was still moderate on each one (Kopecký et al.,
2012).
A paper examining the reaction of broccoli, Brussels sprouts, Shanghai
pak choy, green cabbage, and napa cabbage to the predominant pathotypes of
P. brassicae in Canada, including data on cabbage from this thesis, has recently
been submitted for publication. The study will report that clubroot-resistant
45
cultivars of each of these crops had higher yield compared to standard cultivars
under moderate to high disease pressure (K. Sharma, personal communication).
1.5 Techniques for quantifying clubroot development
1.5.1 Microscopy
Resting spores of P. brassicae can be visualized using a variety of
microscopy techniques. Resting spores stained with a fluorochrome dye can be
quantified using a fluorescent microscope. Calcofluor White M2R binds to chitin
in resting spore walls. Ethidium bromide stains damaged resting spore cells
(Faggian and Strelkov, 2009). An assay based on light microscopy of resting
spores stained with Evans blue was found to be effective for testing the viability
of resting spores treated with the fungicide flusulfamide. Evans Blue penetrates
damaged and dead resting spores and stains their cytoplasm, but healthy spores do
not take up the stain. Dead resting spores stain an opaque blue, while viable
resting spores are light in colour and translucent (Tanaka et al., 1999).
Sand-solution culturing of seedlings inoculated with P. brassicae has the
advantage of precisely controlling pH and nutrient conditions in studies, but has
the disadvantage of being contaminated by algal growth. A sand-solution
culturing technique was developed by Donald et al. (2004) as an effective method
for studying the effect of nutritional treatments on root hair and cortical infection.
The technique involves sowing single seeds in coarse sand in 5-mL pipette tips,
and placing the tips in groups of three in a 50 mL Falcon tube containing a
nutrient solution. Plants can be harvested from the solution and sand can be
46
removed from roots without damaging root hairs. This allows for cleaner
visualizations of roots in microscopic studies and for contaminate-free molecular
studies.
Root fixation and staining has been used with success to analyze the effect
of treatments on primary infection by P. brassicae (Sharma et al., 2011a;
Voorrips, 1992). In these studies, seedlings were inoculated with a known
quantity of resting spores and then roots are harvested from sand-liquid cultures at
specific times after inoculation and stored in a fixative solution (1:1; 95% acetic
acid: 95% ethanol) to halt further development of the pathogen and host. Roots
were washed with water and stained with a 125 ppm aniline-blue solution in 50 %
(v/v) acetic acid for 1 minute, and then rinsed with water. The developmental
stages of the pathogen were observed with a compound light microscope and the
amount of root hair infection assessed (Donald and Porter, 2004; Sharma et al.,
2011a).
To study cortical infection, Sharma et al. (2011b) modified the
methodology by Kobelt et al. (2000). After fixing roots in acetic acid and ethanol,
6-um-thick cross sections were cut from roots, stained with methylene blue, and
mounted on glass slides. The pathogen could then be identified and categorized
into young plasmodia (small lightly staining spheres), mature plasmodia (large
irregularly shaped and darkly staining) and resting spores (masses of darkly
staining spheres). Images (digital micrographs) of the sections were collected and
processed using image analysis software (Assess, The American
47
Phytopathological Society, St. Paul, MN) to calculate the percent area of cortical
tissue occupied by the pathogen and the number of infected host cells per field of
view.
To study the development of secondary infection in planta, Deora et al.
(2013) utilized transmission electron microscopy (TEM) and scanning electron
microscopy (SEM). Characterization of host cell contents, pathogen colonization,
developmental stage, and symptoms of hypersensitive response were performed
using TEM. Characterization of xylem enlargement and lignification were
assessed using SEM. Transmission electron microscopy has the advantage of
higher resolution images, but is only possible with extremely thin samples.
Scanning electron microscopy has the advantage of characterizing the depth and
features of surfaces (Radboud University Nijmegen, 2010).
1.5.2 Molecular techniques
In planta quantification of P. brassicae can be established by measuring
arachidonic acid content and measuring DNA using quantitative polymerase chain
reaction (qPCR) (Sundelin et al., 2010). PCR primers targeting an 18S rDNA
repeat fragment (PbITS6: CAACGAGTCAGCTTGAATGC) and internal
transcribed spacer (ITS) regions (Pb4-1: TACCATACCCAGGGCGATT)
amplified DNA of P. brassicae but not of the host plant or other soil
microorganisms like the related pathogen Spongopora subterranea (Wallr.)
Lagerheim. The 18S rDNA is a segment of DNA that codes for ribosomal RNA
(rDNA). The high conservation of flanking ends of the segment make them
48
excellent targets for the development of candidate primers for molecular studies
(Meyer et al., 2010). The ITS regions are non-functional segments of rDNA that
are generally variable among genera and conserved among species. For species in
the kingdom Fungi, or formally designated as Fungi (Phytomyxea), the variability
of the ITS is often high enough to be used to separate races within a species
(Faggian et al., 1999).
A TaqMan probe-based qPCR performed in a StepOnePlus Real Time PCR
System (Applied Biosystems) has been used to quantify resting spore populations
in soil (Hwang et al., 2011b). The presence of P. brassicae can be detected in root
tissue samples using PCR as early as 3 days after inoculation, which is
advantageous compared to field or controlled environment trials, where symptoms
take at least 24 days of growth to develop (Cao et al., 2007). Quantitative PCR
offers scientists and farmers a rapid and high-throughput methodology for
assessing P. brassicae populations in the soil, and quantifying clubroot severity in
crops.
1.5.3 Clubroot symptoms
A number of methods for quantifying the severity of clubroot have been
proposed and utilized in previous studies. Buczacki et al. (1975) proposed a
grading based on the extent of root swelling: 0, no swelling; 1, very little swelling
of lateral roots; 2, moderate swelling of whole root; and 3, severe swelling of
whole root. Wallenhammar (1996) categorized plants into only two classes,
healthy plants or plants with visible root swelling, and essentially reported
49
clubroot incidence. A 0 to 3 rating scale used to calculate a disease severity index
was first developed by Crête et al. (1963). Roots are categorized based on the
proportion of root infected, where 0 = no symptoms; 1 = 1–29% clubbing; 2 =
30–59% clubbing; and 3 = 60–100%. A pathological index (IP) is then calculated
using the following formula:
IP = (category)(number of roots in each category)(total number of roots examined)(3) × 100
Strelkov et al. (2006) utilized slightly different demarcation for the categories,
where: 0 = no clubbing; 1 = small clubs only; 2 = moderate sized clubs; and 3 =
severe clubbing. A disease severity index (DSI) was calculated for each
experimental uni : t and expressed as a percentage using the formula
DSI = ∑[(rating class)(no. plants in the rating class)](no. plants in treatment)(3) × 100
The formula to calculate IP from Crête et al. (1963) is mathematically equivalent
to the DSI formula used by Strelkov et al. (2006). The only difference is the
boundaries between the rating categories.
1.6 Summary and objectives
Clubroot is an economically important disease of canola and Brassica
vegetables worldwide. Cultural management of clubroot using application of soil
nutrients can be effective but is costly. Also, if used at inappropriate rates, there is
a risk of crop damage and reduced yield. The relationship between soil
temperatures and clubroot development is well established, but there are still
some knowledge gaps on temperature thresholds of development and the effect of
50
temperature fluctuation around the mean. Manipulation of seeding date to
minimize exposure of crops to warm soil temperatures at critical periods in
disease development has been shown to reduce clubroot severity. Disease
prediction models have been developed for radish and Asian vegetables, but a
model has yet to be constructed for clubroot on canola. There are positive results
from trials of biocontrols for management of clubroot under controlled conditions,
but their efficacy is inconsistent when their implementation is scaled up in the
field. Many fungicides have efficacy against clubroot, but their registration in
Canada is extremely limited and the possibility of future registration is uncertain.
New cultivars of clubroot-resistant cabbage and other Brassica have been
commercialized, but their mechanism(s) of resistance are not well understood, and
the source(s) of resistance is proprietary knowledge. Differences between resistant
and susceptible canola cultivars in development of P. brassicae have been
examined, but comparisons of resistant and susceptible cultivars in other species
could uncover novel mechanisms for clubroot resistance.
There is a need for an economical, effective, and environmentally and
ecologically sustainable strategy for mitigating the effect of clubroot on canola
production in Canada. The current research project was focused on developing
information on several components of clubroot management. The objectives of
this research were:
1. To determine if diurnal fluctuations around mean temperatures result
in the same level of pathogen development as constant temperatures,
51
and if the amplitude of diurnal fluctuation affects the level of pathogen
growth.
2. To develop, calibrate and validate a model to predict clubroot severity
on canola based on temperature and rainfall.
3. To determine what phase of pathogen development that is affected by
resistance and where that resistance to clubroot is expressed within the
roots of resistant and moderately susceptible cabbage lines.
The following hypotheses were tested:
1. The incidence of root hair infection and concentration of P. brassicae
gDNA in planta do not differ between fluctuating and constant
temperature regimes.
2. Soil degree days provide the most useful variable for use in prediction
of clubroot incidence and severity.
3. Host resistance in cabbage affects the extent of pathogen development
by P. brassicae.
52
CHAPTER TWO
EFFECT OF CONSTANT AND FLUCTUATING TEMPERATURES ON
THE INCIDENCE AND SEVERITY OF CLUBROOT
2.1 Introduction
Clubroot has emerged as a potentially limiting factor in the cultivation of
canola on the Canadian prairies. Previous studies have established that soil
temperature is an influential factor affecting the development of P. brassicae
within host roots (Gossen et al., 2012b; Sharma et al., 2011a).
The development of clubroot symptoms is strongly affected by the
temperature during plant growth. Clubroot on cabbage was most severe at 20 –25
°C, and less severe near 15 °C or 30 °C, and symptom free at temperatures of 35
°C or above and 12 °C and below (Monteith, 1924). The optimal temperature for
the development of clubroot on cabbage was 23 °C, even when the cabbage was
grown in alkaline soil (pH 7.8), which was expected to reduce severity (Colhoun,
1953). This is consistent with a recent study that demonstrated that clubroot
development in canola and Shanghai pak choy was optimal between 20 °C and 26
°C, and that temperature affected both infection and subsequent symptom
development (Gossen et al., 2012b). Companion studies reported that temperature
had a consistent effect on the development of P. brassicae across all stages of
disease progression. Root hair infection occurred most quickly and root hair
infection was highest at 25 °C. At 10 °C, no clubroot symptoms were observed,
but root hair infection was present at low incidence (Sharma et al., 2011a).
53
Similarly, cortical infection in Shanghai pak choy occurred most quickly and to
the greatest extent at 25 °C, and the lower limit for cortical infection and clubbing
was between 10 and 15 °C (Sharma et al., 2011b).
Most of the research on the impact of temperature on clubroot
development has been conducted at constant mean temperatures (Adhikari, 2010),
which raises the question of how diurnal fluctuations in temperature might
influence clubroot development. Temperatures following seeding of canola,
which generally occurs in early May on the Canadian Prairies, can fluctuate 15 °C
between the daily maximum and daily minimum (Canola Council of Canada,
2003; Environment Canada, 2012). Studies that incorporate temperature
fluctuations representative of field conditions are needed to determine how
closely studies at constant temperatures reflect real world conditions. Day/night
temperature regimes with fluctuation of between 5 and 15 °C around a mean have
been studied in the interaction of Pythium spp. on field pea (Pisum sativum L. var.
arvense (L.) Poir.) and Rhizoctonia solani Kühn on chickpea (Cicer arietinum L.)
and lentil (Lens culinaris Medikus). Temperature fluctuation had no effect on
pathogen development on those hosts (Chang et al., 2004; Chang et al., 2008;
Hwang et al., 2000), but it is not known if a similar lack of response occurs in a
temperature-sensitive pathogen such as P. brassicae. Such a study could provide
an insight into the applicability of previous controlled environment experiments to
field situations, where temperature fluctuation can be very substantial.
54
The objectives of this study were to determine if diurnal fluctuations
around mean temperatures result in the same level of pathogen development as
constant temperatures, and if the amplitude of diurnal fluctuation affects the level
of pathogen development. The hypothesis was that the incidence of root hair
infection and concentration of P. brassicae gDNA in planta differed between
fluctuating and constant temperature regimes.
2.2 Materials and methods
2.2.1 Constant and fluctuating temperatures trials
Experiments comparing constant and fluctuating temperatures were
conducted as a two-way factorial study arranged in a randomized complete block
design. The study was conducted on a 176-well thermal-gradient plate (T176;
AAFC; Figure 2.1). The T176 is an arrangement of independently controlled,
temperature-regulated aluminum cells that can accommodate standard 10-cm-dia
Petri dishes. Each cylindrical cell is 3 cm deep, 11 cm in diameter, and is
controlled using a computer interface. The temperature gradient plate was
designed and custom-built by AAFC, and so represents a unique resource for
these experiments.
The temperature combinations (ºC day/night) were: 10/10, 12.5/12.5,
15/15, 17.5/17.5, 20/20, 22.5/22.5, 25/25, 27.5/27.5, 30/30, 32.5/32.5, 35/35,
15/5, 17.5/7.5, 20/10, 22.5/12.5, 25/15, 27.5/17.5, 30/20, 32.5/22.5, and 35/25.
The plants were grown with a 12-hr-day photoperiod. There were four
replications per treatment and 10 seedlings per repetition. The trial was repeated
once.
55
Figure 2.1 Cells of the 176 well thermal-gradient plate (a), and cells of the 96 well thermal-gradient plate (b).
a b
Fifteen seeds of canola (B. napus) cv. 46A76 (Pioneer Hi-Bred, Caledon,
ON) or ACS –N39 (AAFC breeding line) were planted in 10-cm-dia. glass Petri
dishes filled with autoclaved coarse sand and allowed to germinate at 25 °C. At
10 days after seedling, the seedlings were thinned to a maximum of 10 seedlings
per dish and inoculated with resting spores of P. brassicae pathotype 3 extracted
from clubbed roots of canola.
Inoculum was prepared following the methods of Sharma et al. (2011a).
Briefly, roots were washed and soaked in deionized water for 2 hr prior to
extraction. Roots were cut into small pieces, and 3 g of clubbed root was
homogenized in 100 mL deionized water for 2 min in a blender at high speed. The
56
mixture was filtered through 16 layers of cheese cloth. Resting spore
concentration was estimated using a haemocytometer and diluted to 1 × 106
spores/mL. The growth medium was inoculated with 1 mL of the resting spore
suspension per dish.
The sand growth medium was watered daily with water acidified to pH 6.3
with commercial white vinegar, beginning 2 days prior to seed sowing. Seedlings
were harvested and assessed 10 days after inoculation.
For each experimental unit, a subsample of 100 mg of seedling taproot
(representing the roots of about five seedlings) was assessed using qPCR.
Differences in the number of seedlings used were due to heterogeneity in the size
of seedlings. Roots were cut into 1-cm segments and stored at -20 ºC until time of
assessment. DNA was extracted using a DNeasy Plant Mini Kit amplified with
primers Pb4-1 (TACCATACCCAGGGCG ATT) and PbITS6
(CAACGAGTCAGCTTGAATGC). Quantitative PCR amplification was carried
out in triplicate in a total volume of 20 µL using a StepOne real-time thermal
cycler (ABI, Streets Ville, ON) equipped with the StepOne v2.1 software
following the program specifications: 10 min at 95° C (an initial denaturation),
followed by 60 cycles of 15s at 95° C and 1 min at 60° C. Each reaction mixture
of 20 µL contained 2 µL of genomic DNA template, 0.1 µL of each primer (50
nM), 10 μL of 2 × SYBR Green master mix (ABI), and 7.8 µL of sterile deionized
water. A template control of water was included in every qPCR assay. A series of
serial dilutions of P. brassicae DNA of known concentrations ranging from 1
57
ng/µL to 1 x 10-4 ng/µL was included on each plate. Fluorescence was checked
after each cycle. After amplification, a melting-curve analysis and electrophoresis
(2 % gel) were performed to ensure that only the target PCR product had been
amplified.
The remaining seedlings were washed with water and stored in a fixative
solution (50% acetic acid and 50 % ethyl alcohol) in Eppendorf tubes for at least
24 hr until required for assessment. Roots were stained with aniline blue (125
ppm) applied for 1 min, and then washed with water for 1 min (Voorrips, 1992).
The percent of root hair infection was estimated by assessing 100 root hairs on
each of two plants, from the region 1 cm below the hypocotyls, under a light
microscope at 250 × (objective 20 × and eye piece 12.5 ×) magnification. The
stages of development were differentiated as follows: a primary plasmodium
presented as a translucent unicellular body within the root hair. Mature
zoosporangia presented as fully differentiated opaque beads, in a row or in wide
bundles. Empty or partially empty zoosporangia were classified as ‘dehisced
zoosporangia’, and presented as a translucent network of empty circular structures
(Sharma et al., 2011a). Root hair infection was assessed on selected treatments
(12.5/12.5, 15/15, 20/20, 25/25, 30/30, 17.5/7.5, 20/10, 25/15, 30/20, and 35/25
ºC), which represent a range of constant and fluctuating temperature treatments
from lowest to highest.
58
2.2.2 Range of temperature fluctuation
Studies to assess the effect of the range of temperature fluctuation were
conducted on a 96-well thermal-gradient plate (T96, AAFC; Figure 2.1). As with
the T176, the T96 was designed and built by AAFC and represents a unique
resource. The T96 is an arrangement of independently controlled cells that can
accommodate 10-cm-dia. glass Petri dishes. Each cylindrical cell is 12 cm deep,
10 cm in dia. The T96 was selected for this experiment because the wells are
deeper than the T176, and so can accommodate the height of older seedlings.
However, access to the equipment was limited because of high demand, so only
eight treatments could be accommodated.
Seedlings of canola ACS-N39 were produced, inoculated and collected as
described above, except that they were thinned to a maximum of 20 per dish and
harvested at 14 days after inoculation.
The temperature combinations (ºC day/night) were: 17.5/17.5, 17.5/12.5,
22.5/7.5, 20/15, 25/10, 22.5/17.5, 27.5/12.5, and 20/20 ºC. There were four
replications per treatment, arranged in a randomized complete block design. The
plants were grown with a 12-hr-day photoperiod. For each experimental unit,
subsamples of three to five seedlings were assessed using qPCR as described
above. This trial was repeated once.
2.2.3 Statistical analysis
All of the statistical analyses were performed with SAS software (version
9.2 SAS Institute, Cary, NC) with a type I error set at P = 0.05. Data were tested
59
for normality using the Shapiro-Wilk test of residuals and for outliers using
Lund's test. The incidence of root hair infection and incidence of primary
plasmodia were normally distributed, but the distributions for mature sporangia
(W = 0.839, P < 0.0001; positive skewness statistic, g1 = 1.55) and dehisced
sporangia (W = 0.509, P < 0.0001; positive skewness statistic, g1 = 2.84) were not
normal. No outliers were identified for the developmental stages data set. Also,
the distribution was not normal for P. brassicae gDNA concentration in the
constant and diurnal fluctuation around the mean trial (W = 0.67, P < 0.0001;
positive skewness statistic, g1 = 1.69). Eight outliers were identified and removed.
The distribution was not normal for P. brassicae gDNA concentration in the trial
on range of diurnal fluctuation around the mean trial (W = 0.61, P < 0.0001;
positive skewness statistic, g1 = 1.79). The skewness and non-normality in both
trials was due to the high frequency of zero values for treatments at low
temperatures, so no transformation correction to the data was possible.
A mixed model analysis of variance (ANOVA) was conducted using
PROC MIXED, where the fixed effects were mean temperature and temperature
range, and the random effects were block and repetition of the experiment. Mean
comparisons of P. brassicae gDNA concentration was performed using Tukey's
test. Orthogonal partitions of variance (linear, quadratic, cubic), regression
analysis, and multiple comparisons of regression coefficients were performed
using PROC GLM. There was no quadratic component in the orthogonal partition
of dehisced sporangia based on the complete data set. However, the incidence at
12.5 °C and 15 °C were all zeros, so the analysis was repeated with the
60
observations at 12.5 °C excluded. Estimation and comparison of multiple
regression coefficients for best-fit polynomial regressions of root hair infection
data were performed using PROC GLM.
Pearson correlations were used to assess the strength of the relationship
among temperature means, gDNA concentration, incidence of primary plasmodia,
mature zoosporangia, dehisced zoosporangia, and total root hair infection using
PROC CORR.
2.3 Results
2.3.1 Root hair infection
Infection of canola root hairs by P. brassicae occurred in each of the
temperature regime treatments. There was no trial repetition effect for any
developmental stage of root hair infection. Also, there was no interaction between
temperature and fluctuation range around the mean for the incidence of any of the
stages of root hair infection (Tables A1.1–A1.4). The incidence of total root hair
infection (P < 0.0001) and primary plasmodia (P < 0.0001) were slightly higher in
treatments where temperatures fluctuated around the mean (55 % and 46 %,
respectively) than at a constant temperature (49 % and 38 %, respectively), but
fluctuation did not affect the incidence of mature or dehisced zoosporangia (Table
A1.5–A1.8).
The relationship between the total incidence of root hair infection and
temperature was quadratic (P < 0.0001) (Table A1.1). The least square regression
equation for incidence of root hair infection of mean temperatures was YRHI = -
61
13.08 + 6.30x - 0.14x2, R2 = 0.52, where ‘x’ represents mean temperature. The
optimal mean temperature for root hair infection was 23 °C, based on quadratic
regression (Figure 2.2).
Primary plasmodia developed at each of the temperatures tested. The
relationship between the incidence of primary plasmodia and temperature was
linear (Ypp = 49.54 - 0.40x) (P = 0.008) (Table A1.2). The quadratic response was
not significant. The highest incidence of primary plasmodia at 10 DAI occurred in
the mean temperatures of 12.5 °C (40–46 %), and 15 °C (41–51 %), as well as at
25/15 °C (45 %), 30/20 °C (42 %), and 35/25 °C (43 %).
Mature zoosporangia developed at all of the temperatures tested. The
relationship between the incidence of mature zoosporangia and temperature was
quadratic (P <0.0001) (Table A1.3). The least square regression equation for the
incidence of mature zoosporangia of mean temperatures was Ymz = -58.20 + 6.22x
-0.13x2, R2=0.63, where ‘x’ is the mean temperature. The optimal mean
temperature for mature zoosporangia growth was 24 °C, based on the quadratic
regression, although the data points at 25 °C were well above the curve (Figure
2.3).
Figure 2.2 Incidence and regression of total root hair primary infection (%) with mean temperatures (constant and fluctuating treatments combined), based on counts of root hair infections on canola at 10 days after inoculation. Circles indicate treatment means for constant temperature; triangles indicate fluctuating temperatures. Data combined across two repetitions of the study.
0
10
20
30
40
50
60
70
12.5 15.0 17.5 20.0 22.5 25.0 27.5 30.0
Roo
t hai
r inf
ectio
n (%
)
Temperature (°C)
Figure 2.3 Incidence and regression of root hairs with mature zoosporangia (%) based on counts of root hair infection of canola at 10 days after inoculation. Circles indicate constant mean treatments; triangles indicate fluctuating means. Data combined across two repetitions of the study.
0
5
10
15
20
25
30
12.5 15.0 17.5 20.0 22.5 25.0 27.5 30.0
Mat
ure
zoos
pora
ngia
(%)
Temperature (°C)
62
Dehisced zoosporangia were found in treatments of mean temperature of
20 °C and above, but were not present in mean temperatures of 15 °C and below
(Figure 2.4, Table 2.1). There was a significant quadratic relationship between
incidence of dehisced sporangia and temperature (P = 0.004) (Table A1.4). The
least square regression equation for incidence of dehisced zoosporangia of mean
temperatures was Ydz = -61.81 + 5.14x – 0.10 x2, R2 = 0.88, where ‘x’ is the mean
temperature (Figure 2.4). The optimal mean temperature for development of
mature zoosporangia was 25° C, based on the quadratic regression.
Figure 2.4 Incidence and regression of root hairs with dehisced zoosporangia (%) based on counts of root hair infection of canola at 10 days after inoculation. Circles indicate constant mean treatments; triangles indicate fluctuating means. Data combined across two repetitions of the study.
00.5
11.5
22.5
33.5
44.5
20.0 22.5 25.0 27.5 30.0
Deh
isce
d zo
ospo
rang
ia (%
)
Temperature (°C)
2.3.2 Molecular quantification of in planta colonization of root hairs
Genomic DNA of P. brassicae in canola roots was present in all of the
temperature treatments. There was no effect of trial repetition on P. brassicae
gDNA, so the data were pooled for analysis. The linear, quadratic, and cubic 63
64
contrasts for the effect of temperature on the concentration of P. brassicae gDNA
were all significant. However, the cubic relationship best represented the observed
data (Figure 2.5). The least square regression equation for gDNA of mean
temperatures was gDNA = 3.46 - 0.42x + 0.02x2 - 0.0002x3, R2 = 0.74, where ‘x’
is the mean temperature (Figure 2.4). The optimal mean temperature for gDNA
was 27 °C; the minimal mean temperature was 21 °C, based on the cubic
regression, although the data point at 27 °C is below the curve. The concentration
of P. brassicae gDNA found in canola roots was highest at 10, 12.5, and 25 °C.
The lowest concentration of P. brassicae gDNA occurred at mean temperatures of
15, 17.5, 20, 22.5, 27.5, 30, 32.5 and 35 °C (Figure 2.5).
In the study of the amount of the temperature fluctuation around the mean,
genomic DNA of P. brassicae in canola roots was present in all of the
temperature treatments. There was an effect of repetition (P = 0.006) in the study.
Levels of gDNA in repetition 2 were substantially higher (~1000 x) than in
repetition 1. Temperature mean and range of temperature did not affect levels of
pathogen gDNA in this study in either trial repetition (Figures 2.6 and 2.7) (Table
A1.11).
Figure 2.5 Effect of mean temperature on the concentration of P. brassicae genomic DNA detected in canola roots grown at 10 days after inoculation. Dotted line indicates regression line. Means with the same letter are not significantly different at P = 0.05 based on Tukey’s multiple means comparison test. Data combined across two repetitions of the study.
a
ab
bc
bc cbc
ab
c c c c0
0.10.20.30.40.50.60.70.80.9
gDN
A (n
g/g
root
)
Temperature (°C)
65
0.0000.0050.0100.0150.0200.0250.0300.0350.040
gDN
A (n
g/g
root
)
Day / Night Temperature (°C)
Mean 15 | 17.5 | 20 Range 5 15 0 5 15 0 5 15
Figure 2.6 Concentration of P. brassicae genomic DNA detected in canola roots grown at fluctuating mean temperatures at 14 days after inoculation, first repetition.
66
Figure 2.7 Concentration of P. brassicae genomic DNA detected in canola roots grown at fluctuating mean temperatures at 14 days after inoculation, second repetition.
0
5
10
15
20
25
30
35
gDN
A (n
g/g
root
)
Day / Night Temperatures (°C)
Mean 15 | 17.5 | 20 Range 5 15 0 5 15 0 5 15
There was no clear pattern of relationship among the response variables.
There was a negative correlation between mean temperature and P. brassicae
gDNA concentration in planta, and mean temperature was correlated with mature
zoosporangia, and dehisced zoosporangia, and total root hair infection (Table 2.1).
There was also a negative correlation between the incidence of primary plasmodia
and the incidences of mature zoosporangia and dehisced zoosporangia. There was
a positive correlation between the incidence of mature zoosporangia and the
incidence of dehisced zoosporangia, as well as between incidence of total root
hair infection and the incidences of primary plasmodia, mature zoosporangia, and
dehisced zoosporangia.
67
Table 2.1 Correlation matrix of the relationship (r above, P below) among temperature means, gDNA concentration of P. brassicae, incidence of primary plasmodia, mature zoosporangia, dehisced zoosporangia and total root hair infection.
gDNA1 Primary plasmodia
Mature zoosporangia
Dehisced zoosporangia
Total root hair
Infection Mean Temperature
-0.42 -0.21 0.47 0.44 0.26 0.0001 ns <0.0001 0.0001 0.02
gDNA1 -0.15 0.09 0.05 -0.09 ns ns ns ns
Primary Plasmodia
-0.47 -0.41 0.51 <0.0001 0.0002 <0.0001
Mature zoosporangia
0.75 0.52 <.0001 <.0001
Dehisced zoosporangia
0.41 0.0002
1 gDNA – P. brassicae gDNA concentration in planta 2 ns not significant. 3 Data combined across two repetitions of the study.
68
2.4 Discussion
This study utilized a precise and accurate apparatus, the thermal gradient
plate, to provide consistent and reliable soil temperature regimes that were used to
investigate the effects of temperature on the development of P. brassicae in
canola. The sensitivity of this pathogen to the effects of temperature under both
controlled environmental conditions and in the field have been demonstrated in a
series of recent studies (Gossen et al., 2012a; Sharma et al., 2011a, 2011b). As a
result, development of clubroot on canola provides an excellent model system to
examine the impact of temperature fluctuation on infection and symptom
development. Fluctuations of this type have been examined in some detail for
insects (Brakefield and Kesbeke, 1997; Howe, 1967), but the impact on plant
pathogens has only rarely been examined (Chang et al., 2008; Hwang et al.,
2000). Many of the early studies of the role of temperature on the development of
clubroot were limited by methods that often resulted in variable temperatures for
the investigation, such as studies of the impact of temperature conducted in a
greenhouse (Monteith, 1924). Analysis of clubroot development on Shanghai pak
choy and Chinese flowering cabbage indicated that air temperatures were an
acceptable alternative to soil temperatures for estimating disease progression
(McDonald and Westerveld, 2008). As a result, subsequent studies included air
temperature as the treatment parameter because these data are more readily
available than soil temperature data (Gossen et al., 2012b; Sharma et al., 2011a).
The precise temperature control that was possible in this study was expected to
69
produce more accurate results than had previously been possible in temperature
studies on clubroot.
In the current study, the incidence of root hair infection was highest at
mean soil temperatures of 23 °C, based on quadratic response over the range of
12.5 to 30 °C. However, examination of individual data points showed a
maximum at 25°C, which is consistent with the results of several recent studies
(Sharma et al., 2011a).
The main objective of this study was to determine if diurnal fluctuations
around a mean temperature result in the same level of pathogen development as a
constant temperature. The hypothesis was that the incidence of root hair infection
and concentration of P. brassicae gDNA in planta did not differ between
fluctuating and constant temperature regimes. The hypothesis was rejected for
root hair infection, but accepted for gDNA. The observation that these two
measures did not produce a similar pattern of response was highly unexpected.
Temperature fluctuation resulted in a higher incidence of total root hair
infection and primary plasmodia than the constant mean temperature regimes,
with the exception of mean 25 °C. This could be due to the fluctuation ranging
near the optimal temperature of 25 °C. Across each of the developmental stages,
primary infection develops more quickly at temperatures above 20 °C, and very
slowly at temperatures below 15 °C.
The incidence of primary plasmodia was lowest in the 25/25 °C
temperature regime. Similarly, a numerically lower incidence of primary
70
plasmodia had previously been reported at 25 °C in Shanghai pak choy (Sharma
et al., 2011a). This suggests that at optimal temperatures, P. brassicae progresses
through the primary plasmodia stage more quickly than at temperatures outside of
the optimal range, so the incidence of mature and dehisced zoosporangia was
higher and the incidence of primary plasmodia was lower than at sub-optimum
temperatures.
The incidence of mature zoosporangia and dehisced zoosporangia were
affected by mean temperature but not temperature fluctuation, with the highest
incidence occurring at 25 °C. Sharma et al. (2011a) also reported that the highest
incidence of both mature and dehisced zoosporangia occurred at 25 °C. They
observed dehisced sporangia at 15 °C, but no dehisced sporangia were observed
in the current study. The quadratic partition of dehisced sporangia was significant
for mean temperatures from 20 to 30 °C, but was not significant when
observations at mean temperatures 12.5 and 15 °C were included. This is likely
due to the zero incidence of the life stage at 12.5 and 15 °C. Total root hair
infection, mature zoosporangia, and dehisced zoosporangia showed similar
quadratic responses to temperature.
The optimal temperature for root hair infection was estimated to be 23 °C,
although the actual data points at 25 °C were higher than the regression line. This
is slightly lower that the estimate of 26 °C as the optimal temperature based on
quadratic regression in a recent study (Sharma et al., 2011a). This difference was
likely associated with a small difference in methodology; the previous study
71
assessed cumulative RHI from 0 to 10 DAI, while this study was limited to
assessment at 10 DAI. Similarly, the highest clubroot incidence in canola
occurred at 26 °C and the highest severity occurred between 22 and 24 °C in
another recent study (Gossen et al., 2012b). This result is also similar to those in
earlier reports of optimal temperatures for clubroot severity, e.g., 20–25 °C on
cabbage (Monteith, 1924), and 21–22 °C on radish (Thuma et al., 1983), where
22 °C was the highest soil temperature recorded. There were similar quadratic
regression response coefficients between mature zoosporangia and total root hair
infection. However, the total root hair infection response to temperature is likely
more representative of the response to temperature than the response of any
individual life stage of P. brassicae.
The concentration of P. brassicae gDNA in planta was not affected by the
range of fluctuation around the mean. This result is in direct contrast with
previous observations in the current study that temperature fluctuation around the
mean affected the incidence of root hair infection. One possible explanation for
the lack correlation between P. brassicae gDNA and the incidence of its life
stages is that the genomic copy number of the pathogen is not directly linked to its
observable differentiation in root hairs. The highest amount of P. brassicae
gDNA concentration in canola roots occurred at 10, 12.5, and 25 °C, and the
lowest at temperatures above 15 °C, with the exception of 25 °C. This result was
unexpected based on the results of the quadratic regression of total root hair
infection. However the incidence of primary plasmodia was also found to be
highest at the mean temperature of 12.5 °C, as well as the 30/25 °C temperature
72
regime. This indicates that initial root hair infection by primary zoospores, which
results in the occurrence of primary plasmodia, can persist at temperatures that are
normally prohibitive to development of secondary zoospores.
The absence of association between the observed levels of root hair
infection and gDNA assessments is likely due, at least in part, to loss of pathogen
gDNA from the root when zoospores dehisce. At 10 DAI at 10–15 °C, primary
plasmodia are the predominate developmental stage, mature zoospores are not yet
formed, and secondary infection has not yet begun (Sharma et al., 2011a).
Quantitative PCR of roots at those temperatures likely quantifies the extent of
primary infection only. In a previous study, root hair infection in susceptible
canola was highest at 6–8 days after sowing and declined afterward (Hwang et al.,
2011b), which indicates that the rate of release of secondary zoospores into the
soil exceeded the rate of new infection of root hairs. Quantitative PCR of roots at
this stage would quantify both primary infection, which is past its peak, and
secondary infection, which is accelerating. As the primary infection progresses,
secondary zoospores continuously mature, dehisce and are released into the
rhizosphere. The proportion of secondary zoospores that successfully infect roots
is not known, but it is likely that a significant portion of P. brassicae gDNA that
was initially present in root hairs is lost into the rhizosphere as unsuccessful
secondary zoospores and so is not captured with DNA extraction and qPCR. At
10 DAI, a larger proportion of infection events are in this stage of development at
25 °C than at 15 °C. The complex movement of the pathogen out of the host and
back into the rhizosphere prior to secondary infection may account for the
73
absence of a quadratic relationship between temperature and concentration of
P. brassicae gDNA in planta, and for the negative correlation found between
mean temperature with pathogen gDNA in planta, and the incidence of mature
and dehisced zoosporangia during primary infection.
There was a positive correlation between the incidence of mature and
dehisced zoosporangia and a negative correlation between the incidence of
primary plasmodia and mature or dehisced zoosporangia. This pattern of
relationship was expected because these are sequential developmental stages. As
the pathogen develops from plasmodia to zoosporangia, the number of
zoosporangia will increase at the expense of the number of plasmodia. In contrast,
as the pathogen develops towards mature zoosporangia, the probability of the
zoospores dehiscing also increases and more empty zoosporangia will be present.
Also, a study was conducted to determine if the amplitude of diurnal
fluctuation affected pathogen growth and development. There was no effect of
temperature range in P. brassicae gDNA at 14 DAI. However, there was a
substantial difference between the repetitions; the gDNA concentration in
repetition two was roughly 1000 times greater than in repetition one. This large
difference in scale may be due, at least in part, to running the repetitions on
different real-time PCR machines. However, the calibration for each machine was
up-to-date and the assessments were conducted by the same operator. The quality
or maturity of resting spores used between experiments may have varied, which
could affect their ability to infect roots. This could have been verified by doing a
74
viability test with an Evans blue staining assay on the resting spores used as
inoculum (Tanaka et al., 1999). In this study, no differences were found between
the temperature ranges of 0, 5 and 15 °C at mean temperatures of 15, 17.5 and 20
°C. Based on the high variability within treatments and between repetitions of the
study, qPCR is not an informative method for assessing growth of P. brassicae at
time points late in primary infection or the early stages of secondary infection. An
additional repetition could have been useful in determining if the variability
observed between the two repetitions is a systemic problem with the design of the
experiment, however the temperature gradient plate used in this trial was shared
with many other researchers, and timely access for another repetition was not
possible. Quantitative PCR may be more useful in examining temperature effects
on primary infection at an earlier time point of 4 and 6 DAI (Hwang et al.,
2012a).
Differences between constant and fluctuating temperature regimes for the
incidence of primary plasmodia were small, and there were no differences in the
incidence of mature and dehisced sporangia. This shows that diurnal fluctuations
of 10 °C or less had little or no effect on primary infection. This indicates that
researchers can have confidence in the applicability of the results of previous
studies on the effect of temperature that utilized constant temperatures instead of
diurnal fluctuations as a model for field situations. The effect of diurnal
temperatures fluctuations greater than 10 °C is not known, but such large
fluctuations over a short time period would be unusual in an actual field situation.
This also shows that mean temperatures can be used in the field or in models
75
forecasting clubroot development in the field. Maximum and minimum
temperatures, to calculate a mean, may be sufficient.
The current study supports the results of previous studies on temperature
effects on root hair infection. The optimal temperature for root hair infection was
25 °C, and infection was reduced above 30 °C and below 15 °C. The current study
also provides an indication that qPCR may not be well suited for assessment of
the late stages of primary infection. This is because release of zoospores into the
rhizosphere from root hairs and cortical infection occur concurrently. Both of
these events potentially confound quantification of the pathogen. Instead, root hair
staining and microscopy may provide a more accurate and consistent method for
studying primary infection by P. brassicae. Quantitative PCR is a valuable
technology for studying P. brassicae development in planta, but due to the
complex life cycle of the pathogen, these results indicate that its utilization would
be most applicable to experiments at early points of infection (4 DAI and earlier),
or in later stages of secondary infection (28 DAI and later), as presented in
Chapter 4.
76
CHAPTER THREE
DEGREE DAY MODELING OF CLUBROOT INCIDENCE AND
SEVERITY ON CANOLA
3.1 Introduction
There are a number of environmental factors that contribute to clubroot
development in the field. Severe clubroot is often associated with acidic soils,
while clubroot incidence and severity tend to be lower in alkaline soils. In
situations where alkaline pH would be a limiting factor for clubroot severity,
other factors influence the incidence and severity of clubroot, in particular spore
load, temperature and soil moisture (Colhoun, 1953).
Temperature affects all stages of clubroot development. The upper limit
for resting spore germination and consequently for clubroot infection is 35 ºC,
which is above the suitable mean temperature for many Brassica crops (Wellman,
1930). In a study under controlled environment conditions to quantify the effect
of temperature on development of P. brassicae, cortical infection on Shanghai
pak choy was initiated most quickly and developed to the greatest extent at 25 °C.
The lower limit for cortical infection and symptom development was between 10
and 15 °C (Sharma et al., 2011b). Similarly, soil temperatures of 12 °C and below
suppress clubroot on cabbage (Monteith, 1924).
Degree days have been used to predict an organism’s development based
on accumulated temperature over time. This method for estimating growth and/or
development is based on the observation that the development of many organisms
is closely linked to the temperature of its environment, with a threshold minimum
77
and maximum temperature for growth. A degree-day model involves a base
(minimum) temperature, and so might better reflect temperature effects, as
compared to daily mean temperatures (Wilson and Barnett, 1983). Soil
temperature, calculated as cumulative day degrees, was found to be the most
important variable for predicting the severity of clubroot on Raphanus sativus L.
grown on muck soils (Thuma et al., 1983). Soil degree days calculated for the 6th
week of growth and the cumulative rainfall for the first 2 weeks were the best
predictors of clubroot severity. In a more recent study, also on muck soils,
clubroot incidence and severity of Shanghai pak choy and Chinese flowering
cabbage were most closely correlated with air temperature during the last 10 days
before harvest (McDonald and Westerveld, 2008). In a study to evaluate the effect
of seeding date on clubroot development in Brassica vegetables grown in muck
soil, the highest clubroot incidence and severity coincided with the warmest
temperatures, which were experienced by crops seeded during July. Similarly, the
lowest clubroot incidence and severity coincided with the lowest temperatures,
which were experienced by crops seeded in May and September. Clubroot
severity was also strongly correlated with season-long rainfall for Shanghai pak
choy (r = 0.74) and Chinese flowering cabbage (r = 0.83) (Gossen et al., 2012a).
Neither of these recent studies evaluated the utility of calculating accumulated
degree days in relation to clubroot development.
The mean maximum daily air temperature during July is 24.5 °C in Owen
Sound, Ontario (Grey County, a major canola production area) and 22.2 °C in
Edmonton, Alberta, which is the origin of the clubroot outbreak on canola in
78
western Canada (Environment Canada, 2012). In Ontario, the recommendation is
to seed canola as soon as soil temperatures rise above 3 °C (early to mid-May).
Yield is likely to be limited by seeding after June 1st in areas where swede midge
(Contarinia nasturtii (Keiffer)) is present (OMAFRA, 2009). In western Canada,
early seeding on May 6th (±5 days) resulted in the highest oil content and the
highest crop yield 70 % of the time, compared to seeding on May 18 (normal
seeding) or 27th (late seeding). Late seeding resulted in yields 12 % lower than
early seeding and 0.87 % lower oil content (Canola Council of Canada, 2011b).
No work has been done on forecasting clubroot incidence and severity on canola.
Additional data and experimentation are needed for the development of
environmentally linked predictive modelling of clubroot severity in the field.
Predictive modelling of clubroot development could be combined with existing
clubroot management strategies to reduce the incidence of clubroot and protect
crops from yield loss. For example, clubroot forecasting could be useful in
deciding if a fungicide drench at seeding is necessary for a Brassica vegetable
crop or for timing fungicide applications on canola seedlings as temperatures
become conducive for infection, in a similar manner to the management of
Sclerotinia stem rot on carrot (Parker, 2012). Presently there are no fungicides
registered in Canada for management of clubroot on canola, and it probably
would not be practical or economical to drench a fungicide on canola for clubroot
control.
The objective of this experiment was to develop, calibrate and validate a
model based on temperature and rainfall to predict clubroot severity on canola. It
79
was hypothesized that soil accumulated degree days would provide the most
useful prediction of final clubroot incidence and severity, and clubroot incidence
and severity over time. This study will aid in characterizing the development of
clubroot on canola in relation to temperature and rainfall, and so contribute to our
understanding of the epidemiology of clubroot and provide information that can
be used for forecasting clubroot severity in the field.
3.2 Materials and methods
3.2.1 Seeding date trial
The trial was conducted in organic soil (pH ≈ 6.6, organic matter ≈ 70–80
%) at the Muck Crops Research Station, Holland Marsh, Ontario (Tesfaendrias et
al., 2010) in 2011 and 2012. The soil is naturally infested with P. brassicae
pathotype 6. Canola ‘InVigor 5030 LL’ (Bayer CropScience, ON, Canada), a
cultivar moderately susceptible to P6 (Deora et al., 2012a), was seeded at about 2-
wk intervals each year: 25 May, 10 June, 22 June and 06 July in 2011; 01 May, 15
May, 29 May, 13 June, 28 June and 10 July in 2012. Each seeding date was
considered a treatment. An Earthway push seeder with a 1002-9 mustard disc was
used for seeding, which planted a mean of 18 seeds per m of row. The study was
laid out in a randomized complete block design with four replicates. Each plot
consisted of seven 5-m-long rows with 20 cm between rows. In 2011, assessments
were started when roots started to display infection symptoms, 50 plants from
each replicate were uprooted and assessed for clubroot incidence and severity at
each sampling date, starting 3 weeks after seeding for the 25 May seeding date
80
treatment, 4 weeks after seeding for the 10 June seeding, and 5 weeks after
seeding for the 22 June and 06 July seeding dates. In 2012, initiation of
assessments was standardized to 4 weeks after seeding, 50 plants from each
replicate were uprooted and assessed for clubroot incidence and severity starting 4
weeks after seeding for each treatment. Sampling ceased after eight assessments
or when there were no more plants. Roots were assessed for clubroot incidence
and severity using a 0 to 3 scale, where: 0 = no symptoms; 1 = root clubbing <
1/3; 2 = 1/3 < root clubbing <2/3; 3 = root clubbing > 2/3 (Figure 3.1). A disease
severity index (DSI) was calculated using the following equation (Crête et al.,
1963; Strelkov et al., 2006):
DSI = ∑[(𝑐𝑙𝑎𝑠𝑠 𝑛𝑜. )(𝑛𝑜. 𝑜𝑓 𝑝𝑙𝑎𝑛𝑡𝑠 𝑖𝑛 𝑒𝑎𝑐ℎ 𝑐𝑙𝑎𝑠𝑠)](𝑡𝑜𝑡𝑎𝑙 𝑛𝑜. 𝑝𝑙𝑎𝑛𝑡𝑠 𝑝𝑒𝑟 𝑠𝑎𝑚𝑝𝑙𝑒)(𝑛𝑜. 𝑐𝑙𝑎𝑠𝑠𝑒𝑠 − 1) x 100
81
a b
Figure 3.1 Clubroot severity rating scale. (a) 0 = no symptoms; (b) 1 = root clubbing < 1/3; (c) 2 = 1/3 < root clubbing <2/3; (d) 3 = root clubbing > 2/3.
c d
Weather parameters were measured with a CR21X weather station (Campbell
Scientific, Edmonton, AB, Canada) located in the Muck Crop Research Station.
Air temperature at 1.2 m above ground and soil temperature at 5 cm below the
soil surface were measured hourly with a HMP35C probe, and rainfall data was
collected hourly using a TE35C tipping bucket rain gauge. Daily minimum,
maximum and mean (average of minimum and maximum) temperatures, as well
as rainfall, were calculated based on 24-hour averages for the period from seeding
to final sampling date of each treatment each year.
In 2012, an ML2x ThetaProbe (Delta-T Devices Ltd., Cambridge,
England) soil moisture sensor was installed in the middle of the experimental plot
82
and volumetric soil moisture data was collected hourly from the first seeding date
until the final sampling date of the trial. The ThetaProbe uses a 100MHz
sinusoidal standing wave signal to measure the dielectric constant of soil, which is
primarily a function of soil water content (Delta-T Devices Ltd., 1999). A linear
correlation has been found between the square-root of the dialectic constant and
the percent volumetric moisture in many soil types (Topp et al., 1980; Whalley,
1993; White et al., 1994). Soil moisture (m3 m-3) is calculated with the following
formula:
θ𝑣 = [1.07 + 6.4𝑉 − 6.4𝑉 + 4.7𝑉 ] − 𝑎𝑎
where V is the ThetaProbe output in volts, a0 = 1.26, and a1 = 6.53. Constants a0
and a1 were calculated specifically for muck soil in the Holland Marsh, ON (Kora,
2004).
3.2.2 Degree day calculation
Degree days (°D) were calculated for air and soil (5-cm depth)
temperatures using the following equation:
°D = [(Tmax + Tmin) / 2] - Tbase
Where Tmax is the daily maximum temperature, Tmin is the daily minimum
temperature and Tbase is equal to 14 °C. Tbase was chosen based on the P. brassicae
83
developmental limits reported by Sharma et al. (2011b) and Monteith (1924), as a
compromise between 12 °C where clubroot does not develop and 15 °C where
clubroot can still develop.
3.2.3 Statistical analysis
All of the statistical analyses were performed with SAS software (version
9.2 SAS Institute, Cary, NC). The data were tested for normality using the
Shapiro-Wilk test of residuals and for outliers using Lund's test. All of the data
sets were normal and no outliers were identified. A mixed model analysis of
variance was conducted using PROC MIXED, where seeding date and sampling
week were the fixed effects and the year and block were the random effects. Mean
comparisons of clubroot incidence and severity were performed using Tukey's
test. Clubroot levels in one block (replicate) of the 2012 trial were consistently
and significantly lower than in the other three blocks. This may have been due to
differences in drainage or uneven distribution of inoculum, so the data from this
block was excluded from subsequent analysis.
Multiple comparisons of regression responses for of clubroot incidence
and severity were performed at each seeding date, and least-square regression
equations were obtained using PROC GLM.
Pearson correlations were calculated to determine the relationship between
clubroot incidence and severity with accumulated air degree days, soil degree
days and season total rainfall (0- and 1- week delays, first 2 and 3 weeks after
seeding, and last 2 and 3 weeks before sampling date) using PROC CORR. A 1-
week delay was assessed to account for a potential lag in biological response to
84
changing environmental variables. Correlations of clubroot level with
accumulated air and soil degree days, rainfall, and soil moisture were assessed for
each sampling date. For correlations with final disease levels, several additional
parameters were added: accumulated air and soil degree days, the season total
rainfall, and soil moisture during the first 2 and 3 weeks after seeding, and the last
2 and 3 weeks before sampling.
Stepwise regressions were performed using PROC REG for final clubroot
incidence and severity, and for incidence and severity for each sampling date.
Data on Chinese flowering cabbage (B. rapa subsp. Chinensis (Rupr.) var. utilis
Tsen and Lee) from 1999–2002 (McDonald and Westerveld, 2008), were included
in the analysis, and data from 2008 and 2009 (Adhikari, 2010) were also included
in some models. This added 57 additional data points for the calibration and
validation of the stepwise regression analyses. Data on Shanghai pak choy was
initially also included in these analyses, but was subsequently excluded because
the pattern of response appeared to be different than that for canola and Chinese
flowering cabbage. The data on canola and Chinese flowering cabbage were
pooled and randomly partitioned into two subsets using the random function in
Excel. One subset was used to produce the stepwise regression models. The
significance level to enter the model was set at P = 0.15, and the significance
level to stay in model was P = 0.10. The second subset was used to validate the
efficacy of the models produced based on the first subset.
Root and shoot weights were collected from some samplings for
contribution to another project and paper on the relationship between root and
85
shoot weight of canola infected with the club root in comparison to other Brassica
species.
3.3 Results
3.3.1 Weather
The mean monthly air temperature in 2011 was 1 to 2 °C higher than the
long-term average, and 0 to 3 °C higher in 2012 (Table 3.1). The temperature
trend was similar between 2011 and 2012; the highest mean occurred in July. In
2011 and 2012, there were substantial fluctuations in rainfall compared to the
long-term average. In 2011, rainfall was above average in May and August and
below average in June and July. In 2012, rainfall was above average in July and
September and below average in May and June (Table 3.1).
In 2011, there were several instances where between-day fluctuations in
mean air temperature of greater than 10 °C occurred during the first 3 weeks of
the trial, and another 10 °C fluctuation within a 10-day span in mid July (Figure
3.2). Soil temperatures had smaller fluctuations throughout the season,
consistently less than 5 °C in a week. The field site did not experience more than
2 weeks between rainfall events during the growing season.
In 2012, there were several instances where between-day fluctuations in
mean air temperature of greater than 10 °C occurred in June (Figure 3.3). Soil
temperature had similar large temperature fluctuations, approximately 10 °C in 2
weeks. Heavy rainfall immediately after the first seeding date flooded one section
of the plot area, and the plant stand was low in that area. After that first rainfall of
86
the growing season, the field experienced a 3-week-long period without rain, and
volumetric soil moisture dropped from 75 % to 45 % before the next rainfall. Soil
moisture did not increase to 60 % until mid-July. The lowest volumetric soil
moisture experienced in this trial was 43 % on 29 May, and the highest
experienced was 78 % on 05 May. Soil moisture generally followed the trends in
rainfall, increasing after precipitation, and slowly decreasing over time until the
next rainfall. The ThetaProbe failed to log soil moisture data from 28 June to 03
July. The probe was subsequently reset and data logging was continued.
Table 3.1 Mean monthly air temperature and rainfall during the growing period of canola for clubroot assessment at the Muck Crops Research Station, Holland Marsh, ON, 2011 and 2012.
Month Temperature (°C) Rainfall (mm)
LTA1 Actual LTA Actual 2011 May 12.8 14.1 73 92 June 17.7 18.4 76 68 July 20.3 22.8 84 56 August 19.2 20.2 80 113 2012 May 12.3 15.9 77 49 June 18.2 20.1 74 55 July 20.7 22.2 81 140 August 19.5 20.1 67 69 September 15.8 14.8 74 94
1Long-term average (10-year mean) (Source: Muck Vegetable Cultivar and Research Report 2011)
0
5
10
15
20
25
30
35
40
45
0
5
10
15
20
25
30
35
25-May-11 8-Jun-11 22-Jun-11 6-Jul-11 20-Jul-11 3-Aug-11 17-Aug-11
Rai
nfal
l (m
m)
Tem
pera
ture
(ºC
)
Rain Fall Mean Air Temperature Mean Soil Temperature 5 cm
Figure 3.2 Weather data for the Muck Crop Research Station, Holland Marsh, ON, 2011. The bar graph denotes daily precipitation (mm), solid line denotes mean air temperature (°C), and dotted line denotes mean soil temperature (°C, 5 cm below the surface).
87
0
10
20
30
40
50
60
70
80
90
0
5
10
15
20
25
30
Rai
nfal
l (m
m) a
nd s
oil m
oist
ure
(%)
Tem
pera
ture
(ºC
)
Rain Fall Mean Air Temperature Mean Soil Temperature 5 cm Soil Moisture
Figure 3.3 Weather data for the Muck Crop Research Station, Holland Marsh, ON, 2012. The bar graph denotes daily precipitation (mm), solid line denotes mean air temperature (°C), dotted line denotes mean soil temperature (°C, 5 cm below the surface), and dashed line denotes mean volumetric soil moisture (%, 5 cm below the surface).
88
89
3.3.2 Clubroot incidence and severity
In 2011, clubroot levels increased over time. Sampling date after seeding
had an effect (P < 0.0001) on clubroot incidence and severity, as expected seeding
date had an effect on clubroot incidence in 2011 (P = 0.04) (Table A2.1). The
incidence of clubroot in 2011 was highest for the 22 June seeding (30 %), lowest
for the 06 July seeding (17 %), and the 25 May (25 %) and 10 June (22 %)
seeding dates were intermediate (Figure 3.4). This result is confounded by an
abrupt decline in the 06 July seeding treatment, likely due to the death of all the
plants by 5 weeks after seeding, caused by secondary unidentified bacterial or
fungal infection The maximum clubroot incidence was 42 % for the 25 May
seeding, 39 % for 10 June seeding, 48 % for 22 June seeding, and 17 % for 06
July seeding. The mean DSI across all seeding dates was 10. The maximum DSI
was 19 for the 25 May seeding date treatment, 17 for 10 June, 22 for 22 June, and
7 for the 06 July (Figure 3.5).
Figure 3.4 Clubroot incidence on canola planted at 2-wk intervals in muck soil naturally infested with Plasmodiophora brassicae at the Holland Marsh, ON, 2011. Asterisks (*) in the same sampling week indicate significant difference at P = 0.05 based on Tukey’s multiple mean comparison test.
*
*0
10
20
30
40
50
60
3 4 5 6 7 8 9 10 11
Clu
broo
t inc
iden
ce (%
)
Weeks after seeding
25-May-11
10-Jun-11
22-Jun-11
In 2012, there was a seeding date by sample date interaction for clubroot
incidence (P < 0.0001) and clubroot severity (P = 0.02) (Table A2.4). The
quadratic partitions were significant for clubroot incidence (P = 0.0001) and
clubroot severity (P = 0.03) (Figures 3.6 and 3.7). All of the 2012 seeding dates
except the 27 June seeding had negative quadratic coefficients for their predicted
clubroot incidence and severity regression equations. The quadratic coefficient
was positive for clubroot incidence for the 27 June seeding, with a substantially
lower R2 value than the other seeding dates.
90
Figure 3.5 Clubroot severity (DSI) on canola planted at 2-wk intervals in muck soil naturally infested with Plasmodiophora brassicae at the Holland Marsh, ON, 2011. Asterisks (*) in the same sampling week indicate significant difference at P = 0.05 based on Tukey’s multiple mean comparison test.
*
*0
5
10
15
20
25
3 4 5 6 7 8 9 10 11
Dis
ease
Sev
erity
Inde
x
Weeks after seeding
25-May-11
10-Jun-11
22-Jun-11
Clubroot levels were substantially higher in 2012 than in 2011. For
example, the highest incidence observed in 2011 was 29 %, compared with 72 %
in 2012. The 30 May, 13 June and 27 June seeding dates had higher clubroot
incidence and severity than the 2 May and 16 May seeding dates at most
assessment times. The maximum clubroot incidence was 60, 47, 79, 72, 71 and 60
% for 02 May, 16 May, 30 May, 3 June, 27 June, and 11 July 2012 seeding,
respectively. The maximum clubroot severity was 21, 19, 48, 43, 50 and 45 % for
91
92
02 May, 16 May, 30 May, 3 June, 27 June, and 11 July 2012 seeding,
respectively.
Air and soil temperature and rainfall were all correlated with clubroot
incidence and severity. There were strong correlations between clubroot incidence
and severity over time with no delay (r = 0.58 and 0.65, respectively) (range = 83
– 536 °D) and a 1-week delay (r = 0.67 and 0.70, respectively) accumulated air
degree days (range = 35 – 520 °D). The correlations were also strong with no
delay (r = 0.61 and 0.70, respectively) (range = 45 – 528 °D) and 1-week delayed
(r = 0.67 and 0.73, respectively) accumulated soil degree days (range = 11 – 507
°D) in 2011 and 2012 (Table 3.2). Similarly, clubroot incidence and severity over
time were weakly correlated with accumulated rainfall, with no delay (range = 28
– 272 mm) (r = 0.44 and 0.58, respectively) and a 1-week delay (range = 11 – 260
mm) (r = 0.26 and 0.39, respectively) across 2011 and 2012.
Figure 3.6 Clubroot incidence (CI) on canola planted biweekly in muck soil naturally infested with Plasmodiophora brassicae at the Holland Marsh, ON, 2012. Values for each sampling date followed by the same letter do not differ at P = 0.05 based on Tukey’s multiple mean comparison test.
c
c
b
ab
ab
b
bc
b
ab b
b
ab
bc
bc
ab
a
ab
a
bc
a a ab
ab
aa
b
ab ab
a
a
bb
ab
ab
ab
ab
0
10
20
30
40
50
60
70
80
90
4 5 6 7 8 9 10 11 12
Clu
broo
t inc
iden
ce (%
)
Weeks after seeding
2-May-12
16-May-12
30-May-12
13-Jun-12
27-Jun-12
11-Jul-12
CI = -191.61 + 64.04x - 4.41x2
R2=0.75
CI = -39.06 + 16.38x - 0.81x2
R2=0.77
CI = -141.87 + 47.92x - 2.68x2
R2=0.82
CI = -85.95 + 40.41x - 2.59x2
R2=0.66
CI = 71.62 -5.77x + 0.46x2
R2=0.05
CI = -40.86 + 22.65x - 1.35x2
R2=0.89
93
Figure 3.7 Clubroot severity (DSI) on canola planted biweekly in muck soil naturally infested with Plasmodiophora brassicae at the Holland Marsh, ON, 2012. Values for each sampling week followed by the same letter do not differ at P = 0.05 based on Tukey’s multiple mean comparison test.
cd
abab
bbc
bcd
bb
ab b
bccd
ab
ab
a
ab
abc
aa
ab
ab
a
bc
ab
aab
a
ab
b
ab
a
ab
0
5
10
15
20
25
30
35
40
45
50
4 5 6 7 8 9 10 11 12
Dis
ease
Sev
erity
Inde
x
Weeks after seeding
2-May-12
16-May-12
30-May-12
13-Jun-12
27-Jun-12
11-Jul-12
DSI = -64.17 + 21.43x -1.47x R2=0.76
DSI = = -16.08 + 6.46x -0.31x2
R2=0.76
DSI = -68.52 + 21.29x -1.08x2
R2=0.70
DSI = -65.83 + 27.47x -1.72x2
R2=0.79
DSI = 0.28 + 4.41x -0.02x2
R2=0.79
DSI = -51.52 + 19.88x -1.10x2
R2=0.85
94
95
Accumulated rainfall in the final 3 weeks before sampling date (range = 11 – 140
mm) was correlated with final clubroot incidence (r = 0.70), but not with severity. Also,
accumulated rainfall in the first 2 and 3 weeks after seeding and the last 2 weeks of
accumulated rainfall were not correlated with final clubroot levels. Final clubroot
incidence and severity correlated with air temperature degree days in the first 2 weeks
(range = 12 – 123 °D) (r = 0.76 and 0.85, respectively) and first 3 weeks (range = 35 –
198 °D) (r = 0.77 and 0.88, respectively) and soil temperature degree days in the first 2
weeks (range = 3 – 108 °D) (r = 0.77 and 0.86, respectively) and 3 weeks (range = 13 –
162 °D) (r = 0.77 and 0.89, respectively) after seeding. Mean air and soil temperature
during the last 2 and 3 weeks before each sampling date were not correlated with clubroot
incidence or severity. The strongest correlation was between clubroot incidence and soil
moisture during the last 3 weeks before sampling date (CI = 46 – 67 %) (r = 0.95), but
this was based on only six data values (Table 3.2).
Accumulated air and soil degree days were positively correlated with each other
and with the last 2 and 3 weeks of rainfall, air and soil degree days (Table A3.8,
Appendix 3). Also, soil moisture in the first 2 and 3 weeks after seeding was positively
correlated with soil moisture in the last 2 and 3 weeks before sampling. Accumulated
rainfall and air and soil degree days in the first 2 and 3 weeks after seeding were not
correlated with accumulated rainfall and air and soil degree days in the last 2 and 3 weeks
before sampling. Also, as expected, clubroot incidence, severity, air and soil degree days
(0 and 1-week delay), and soil moisture in the last 2 and 3 weeks before sampling were
all positively correlated with time.
96
Table 3.2 Linear correlations (r) between clubroot incidence and severity over time and accumulated rainfall, air and soil degree days, and mean soil moisture for 10 seeding dates of canola ‘InVigor 5030 LL’ grown at the Holland Marsh, ON, 2011 and 2012.
Time interval and variables Sample
Size Incidence Severity (DSI) r P r P Degree days Air (no delay) 67 0.58 <0.0001 0.65 <0.0001 Soil, 5-cm (no delay) 67 0.61 <0.0001 0.70 <0.0001 Air (1-wk delay) 67 0.67 <0.0001 0.70 <0.0001 Soil, 5-cm (1-wk delay) 67 0.67 <0.0001 0.73 <0.0001 Rainfall (mm) Season total (no delay) 67 0.44 0.0002 0.58 <0.0001 Season total (1-wk delay) 67 0.26 0.04 0.39 0.0010 First 2 weeks after seeding 10 0.19 NS 0.22 NS First 3 weeks after seeding 10 0.23 NS 0.39 NS Last 2 weeks before sampling 10 0.62 NS 0.60 NS Last 3 weeks before sampling 10 0.70 0.02 0.51 NS Air temperature First 2 weeks after seeding 10 0.76 0.01 0.85 0.002 First 3 weeks after seeding 10 0.77 0.01 0.88 0.0007 Last 2 weeks before sampling 10 0.25 NS 0.05 NS Last 3 weeks before sampling 10 0.32 NS 0.19 NS Soil temp., 5-cm depth First 2 weeks after seeding 10 0.77 0.009 0.86 0.001 First 3 weeks after seeding 10 0.77 0.009 0.89 0.0006 Last 2 weeks before sampling 10 0.44 NS 0.27 NS Last 3 weeks before sampling 10 0.48 NS 0.38 NS Soil moisture (volumetric %) First 2 weeks after seeding 6 -0.84 NS -0.54 NS First 3 weeks after seeding 6 -0.79 NS -0.40 NS Last 2 weeks before sampling 6 0.50 NS 0.73 NS Last 3 weeks before sampling 6 0.95 0.004 0.67 NS
97
3.3.3 Disease model calibration
In the initial analysis using stepwise regression, data on clubroot development in
Chinese flowering cabbage and Shanghai pak choy from McDonald and Westerveld
(2008) and Adhikari (2010) were combined with data from canola developed in the
current study. However, no parameters for a model of clubroot incidence or severity
were significant in this combined analysis. In subsequent analyses, the data on Shanghai
pak choy were excluded because this crop is even more susceptible than canola and
Chinese flowering cabbage, so many data points were 100 % for both incidence and
severity. In contrast, the reaction of Chinese flowering cabbage was more similar to
canola. Significant stepwise regression results were obtained from the model that
included only data from studies of Chinese flowering cabbage and canola.
A subset of 1999–2002, 2008 and 2009 data of Chinese flowering cabbage and
2011 and 2012 data of canola was used to estimate prediction parameters for clubroot
incidence and severity at individual time points throughout the growing season and at the
final sampling date. In this analysis, when clubroot severity during the growing season
was assessed, accumulated degree days for air temperature (range = 70 – 520 °D)
accounted for 61 % of the variation in clubroot incidence, and the combination of
accumulated soil degree days with a 1-wk delay (range = 0 – 482 °D) plus season total
rainfall with a 1-wk delay (range = 9 – 391 mm) accounted for 56 % of the variation
(Table 3.3 and Figure 3.8). Final clubroot incidence in the field was predicted by the
combination of the last 2 weeks of accumulated soil degree days (range = 0 – 116 °D)
plus the first 2 weeks of accumulated rainfall (range = 0 – 114 mm). These two
parameters accounted for 45 % of the variation of final clubroot incidence (Table 3.4 and
98
Figure 3.9). No combination of parameters was consistently associated with final
clubroot severity.
Table 3.3 Stepwise regression of the effect of accumulated rainfall and degree days (°D) for air and soil temperature over selected time intervals on clubroot incidence (CI) and severity (DSI) over time on Chinese flowering cabbage and canola at the Holland Marsh, ON.
Step Parameter Partial R2 Model R2 F Value Pr > FCI 1 Air °D 0.61 0.61 92.02 <0.0001
DSI 1 Soil °D (1-wk delay) 0.54 0.54 75.45 <0.0001
2 Season total rainfall (1-wk delay)
0.02 0.56 2.93 0.09
CI = - 0.35 + 0.130 × (Air °D) DSI = 4.73 + 0.088 × (Soil °D, 1-wk delay) - 0.025 × (Season Total Rainfall, 1-wk delay)
Table 3.4 Stepwise regression of the effect of accumulated rainfall and degree days for air and soil temperature over selected time intervals on final clubroot incidence (CI) and severity (DSI) on Chinese flowering cabbage and canola at the Holland Marsh, ON.
Step Parameter Partial R2 Model R2 F Value Pr > FCI 1 Soil (last 2 wks) 0.30 0.30 3.42 0.09
2 Rain (first 2 wks) 0.16 0.45 6.75 0.02
DSI 1 Not significant
CI = 30.9 - 0.356 × (Rain, first 2 wk) + 0.40 × (Soil, last 2 wk)
Figure 3.8 Relation between accumulated degree days for air temperature and validation set of clubroot incidence over time on canola and Chinese flowering cabbage at the Holland Marsh, ON.
0102030405060708090
100
0 100 200 300 400 500
Inci
denc
e,
over
time
(%)
Air degree days (°D)
Observed mean
Figure 3.9 Relation between accumulated degree days for soil temperature in the two weeks before sampling date and validation set of final clubroot incidence of canola and Chinese flowering cabbage at the Holland Marsh, ON.
0102030405060708090
100
0 20 40 60 80 100
Inci
denc
e, fi
nal (
%)
Soil degree days (last 2 weeks) (°D)
Observed mean
99
3.3.4 Disease model validation
The highest absolute deviation between observed and predicted clubroot incidence
over time was 43 % and the majority of deviations fell within the range of ±20 % (Table
A3.1). The mean bias of the model was 2.8 %. The plot of observed deviations illustrates
a systemic bias, where low clubroot incidence was predicted to be higher than the actual
value, and when clubroot incidence was approximately 60 % and higher the model
predicted it to be lower than the actual values (Figure 3.10).
100
Figure 3.10 Figure 3.10 Scatter plot of deviations by observed clubroot incidence on canola and Chinese flowering cabbage over time in the validation data subset of the canola and Chinese flowering cabbage clubroot prediction model.
-50
-40
-30
-20
-10
0
10
20
30
40
0 20 40 60 8
Observed clubroot incidence (%)Dev
iatio
n (p
redi
cted
–ob
serv
ed)
0
The pattern of systemic bias for prediction of clubroot incidence was also
observed for severity (DSI). When clubroot severity was 20 DSI and higher, the predicted
value was consistently lower than the actual value (Figure 3.11). The highest absolute
deviation between observed and predicted clubroot severity over time was 41 DSI and the
majority of deviations fell within the range of ±10 (Table A3.2). The mean bias of the
model was -8.3.
101
Figure 3.11 Scatter plot of deviations by observed clubroot severity over time of the validation data subset for the canola and Chinese flowering cabbage clubroot prediction model.
-50
-40
-30
-20
-10
0
10
20
0 10 20 30 40 5
Observed clubroot severity (DSI)Dev
iatio
n (p
redi
cted
–ob
serv
ed)
0
The systemic bias for prediction of clubroot incidence at harvest (final) was
unique compared to the other two predictions; the predicted value was consistently higher
than the actual value through the whole range of observations (Figure 3.12). The mean
bias of the model was 14.1 %. The highest absolute deviation between observed and
predicted final clubroot incidence was 50 % and the majority of deviations fell within the
range of ±30 % (Table A3.3). Alternative Tbase of 12 °C and 17 °C was also tested (data
not presented). A Tbase of 17 °C resulted in regressions of lower R2 (R2 = 0.39 – 0.58) and
slightly higher mean biases (1.4 – 59), while Tbase of 12 °C resulted in similar R2 (R2 =
0.51 – 0.61) and higher mean biases (-5 – -19).
102
Figure 3.12 Scatter plot of deviations by observed final clubroot incidence over time of the validation data subset for the canola and Chinese flowering cabbage clubroot prediction model.
-40-30-20-10
0102030405060
0 10 20 30 40 50 60 7
Observed final clubroot incidence (%)Dev
iatio
n (p
redi
cted
–ob
serv
ed)
0
3.4 Discussion
This study confirmed that temperature and rainfall have an important effect on the
development of clubroot, and identified environmental parameters that can be used to
estimate clubroot levels throughout a growing season and at crop maturity. As expected,
there was a positive correlation between time and accumulation of air (r = 0.81 – 0.83)
and soil degree days (r = 0.31 – 0.35). This underlying relationship complicates the use
of these parameters to estimate clubroot levels throughout the growing season. However
the parameters identified for estimating final clubroot incidence (accumulated rainfall in
the first 2 weeks and soil degree days in the last 2 weeks) were not correlated with time,
indicating that they were not confounded by autocorrelation and are reliable predictors.
Clubroot levels were higher in 2012 than 2011. The mean temperature in each
month of 2012 was equal to or higher than in 2011, which may account for the higher
disease levels. In 2011, seeding date had an effect on clubroot incidence but not severity.
103
For example, the mean clubroot incidence across samplings dates of the 22 June seeding
(30 %) was substantially higher than that of the 06 July seeding (17 %). However, when
sampled at 5 weeks after seeding, the mean incidence of clubroot in the 22 June seeding
(2 %) was not statistically different than the 06 July seeding (17 %). The lack of
differences among the other treatments in 2011 was likely due to the narrow range of soil
temperature fluctuations (range = 42 – 100 °D) and minimal temperature differences
among the seeding date treatments during the early weeks after seeding. Of all the
predictive regression equations, the quadratic coefficient for clubroot incidence was
positive only for the 27 June seeding, and the R2 value was substantially lower compared
to the other seeding dates. This was likely a result of a high estimate of clubroot
incidence at 4 weeks after seeding, which was lower for the sampling 5 to 7 weeks after
seeding. However by 8 weeks after seeding, clubroot incidence had returned to the same
level as at 4 weeks after seeding. This is a result of variation associated with destructive
sampling and small sample size, not by the plant curing itself and then being re-infected.
In 2012, there was an interaction between seeding date and sampling date for
clubroot incidence and severity. Incidence and severity over time were consistently lower
in the 02 May and 16 May seedings compared to the later seedings. This trend is
consistent with two previous reports that seeding Chinese flowering cabbage and
Shanghai pak choy in mid-May resulted in lower clubroot incidence and severity
compared to seeding in summer (Gossen et al., 2012a; McDonald and Westerveld, 2008).
The authors concluded that seeding clubroot susceptible Brassica in early May would be
an effective practice for minimizing clubroot severity.
104
The lower threshold for the degree day calculation, Tbase, at which development is
expected to stop, was 14 °C. This value was chosen based on reports by Sharma et al.
(2011b) and Monteith (1924), as a compromise between 12 °C, where clubroot did not
develop and 15 °C, where clubroot developed very slowly. This Tbase is only 1.8 °C
different than the Tbase of 12.2 °C chosen by Thuma et al. (1983) based on a regression of
disease ratings in 1980. A Tbase of 17 °C was recommended as a practical cut-off based on
the results from a controlled environment study, that clubroot development was slowed or
halted on Shanghai pak choy at air or soil temperatures below 17 °C (Gossen et al.,
2012a; Gossen et al., 2012b). Based on the amount of variation in clubroot incidence and
severity explained by degree days (R2 = 0.54 – 0.66), a Tbase of 14 °C is an adequate
estimate of the minimum threshold of clubroot development.
Accumulated season total rainfall with a 1-wk delay, degree days for soil
temperature with a 1-wk delay, and air temperature with no delay were correlated with
clubroot incidence and severity on canola in 2011 and 2012. There was a stronger
correlation between CI and 1-week delay (r = 0.67) on accumulated soil degree days than
0-delay (r = 0.61). The trend was different with the accumulated rainfall parameters; CI
was more strongly correlated with 0-week delay (r = 0.44) than 1-week delay (r = 0.26).
The 1-week delay on some parameters may still add to their predictive power.
Stepwise regression was used to predict clubroot levels at the last sampling date
for canola and Chinese flowering cabbage. This estimate would be useful for predicting
potential crop loss, but the robustness and reliability of the model is questionable because
the sample size was small (n = 16). Therefore, the capacity to identify the elements of a
predictive model was limited. The stepwise regression models for estimating clubroot
105
incidence and severity throughout the season were developed using a much larger data set
(n = 62). This larger dataset increased the ability of the stepwise regression to test the
predictive capacity of environmental parameters. This type of model would be useful for
predicting clubroot levels at any time point throughout the growing season, which would
be useful for deciding whether the loss of yield due to clubroot as exceeded an economic
return.
Stepwise regression to examine the relationship between weather parameters and
clubroot incidence and severity produced models with different parameters when based
on samples assessed during crop development and at final sampling. The best predictive
parameters during crop growth were accumulated air degree days and soil degree days (1-
week delay), but the best predictive parameter at plant maturity was accumulated soil
degree days over the last 2 weeks before sampling date.
The simple regression approach did not provide a useful description of clubroot
severity at the final sampling date. Also, there was a large systemic bias when the models
were assessed against the validation data set. The patterns of deviations versus observed
values, positive at clubroot severity of 20 DSI and below, and negative at severities of up
to 50 DSI, indicate that other factors may influence clubroot at various stages of
development. An example of temperature having different effects at different stages of
P. brassicae development was demonstrated in Chapter 2. It was demonstrated at 10
DAI, total root hair infection and the individual developmental stages of mature and
dehisced zoosporangia exhibited a quadratic response to temperature, with a maximum at
23 °C. In contrast, primary plasmodia still occurred at a high frequency at mean
106
temperature 12.5 °C. Since the deviation between observed and predicted clubroot
incidence and severity was different at low and high disease, segmentation into multiple
different models for different disease ranges (three segments for example: 0 – 33 DSI, 34
– 66 DSI, 67 – 100 DSI), may reduce the systemic bias in this study. Alternatively, the
influential parameters that were not measured in this study need to be identified and
included in future regressions, for example sunlight irradiance.
The hypothesis that accumulated soil degree days would be the most useful
estimator of clubroot incidence and severity was rejected. We expected that accumulated
soil degree days with a 1-week delay would provide a more accurate and consistent
representation of the influence of temperature on clubroot development in the field
compared to accumulated air degree days. This was because the pathogen is limited
exclusively to host plant roots and symptom development requires growth of the host,
which was expected to result in some delay in response. However, accumulated air
degree days were a better predictor of clubroot incidence over time than soil temperature,
with or without a delay. This may mean there is little or no lag in symptom development
in response to temperature changes (less than 1 week). Perhaps with more years of data
and further model calibration, accumulated soil degree days may outperform air degree
days as a predictor of clubroot. Also, the results may be confounded by the strong
positive correlations of accumulated air (r = 0.81–0.83) and soil degree days (r = 0.31–
0.35) with time. Clubroot symptoms develop over time and so severity increases over
time, and degree days accumulate over time. As a result, both factors will naturally
increase over time, resulting in a correlation. Clubroot incidence and severity on
Shanghai pak choy and Chinese flowering cabbage was also reported to correlate more
107
strongly with air temperature compared to soil temperature (McDonald and Westerveld,
2008). The coefficient of the soil degree day variable calculated for the DSI over time
model (0.088) is very similar to the constants calculated by Thuma et al. (1983) in their
stepwise regression modeling of clubroot index on radish (0.063 and 0.073).
The regression parameters for estimating clubroot incidence at the final sampling
date in the current study is similar to the regression parameters calculated by Gossen et
al. (2012a). They found that rainfall at 11–20 days before sampling date, in combination
with soil temperature 10 days before sampling date were good predictors of final clubroot
incidence and severity on Chinese flowering cabbage and Shanghai pak choy. However,
stepwise regression in the current study did not identify a combination of factors that
accounted for the variance observed in clubroot severity at final sampling date across
canola and flowering Chinese cabbage.
Rainfall and soil moisture during the 2 weeks after seeding had a negative
association with clubroot incidence at sampling date in regression analysis, despite
having a positive correlation. A previous report suggested that clubroot infection on
cabbage did not occur in organic soil below 60 % gravimetric soil moisture (Hamilton
and Crête, 1978), which implies a positive association between soil moisture and final
clubroot severity. Also, plants in several seeding date treatments in this trial experienced
long periods where the volumetric soil moisture dropped below 60 %, but clubroot
symptoms still developed. Aside from differences in host species and host susceptibility
to clubroot, these apparent contradictions can be resolved by the possibility that too much
rainfall may leech zoospores deeper into the soil or remove them by surface run-off.
Heavy rain could remove zoospores from the rhizosphere, which would decrease the
108
incidence of infection. The Holland Marsh, where this study was conducted, is
surrounded by a drainage canal. It should be noted that at the end of some clubroot trials,
the collected clubs are routinely incorporated into the soil at the Muck Crop Research
Station to maintain soil inoculum levels for subsequent clubroot trials. This would offset
any inoculum loss from the previous growing season. Monteith (1924) has commented on
how a soil type that drains well could enable crops to escape infection by clubroot.
Additionally, this study has demonstrated that clubroot can occur in organic soil at
volumetric soil moisture as low as 43 % (the lowest value recorded). Thuma et al. (1983)
also reported finding a negative interaction between rainfall for the 6-week growth period
and accumulated soil degree days for that period, on clubroot development on radish in
one year of the study, but the authors did not offer any explanation for this result.
Soil moisture during the 2 weeks before sampling accounted for the largest
amount of variability in clubroot incidence in 2012. Similarly, the correlation between
soil moisture during the 3 weeks before sampling and clubroot incidence was the
strongest of all environmental variables tested. Soil moisture is important for zoosporic
organisms like P. brassicae because free water enables the zoospores to swim to the root
hairs and roots of hosts prior to infection. It may also be important for the kinetics of
penetration of zoospores into root hairs and cortical tissue (Dixon, 2009b). Additionally,
the water availability in soil may also be an important factor in enabling the pathogen to
cause hypertrophy (cell expansion) of cortical cells during the later stages of symptom
development. Flooding stress has been reported to cause hypertrophy in the hypocotyl
region in sunflower (Helianthus annuus L.), which was linked to an up-regulation of
endogenous auxin and ethylene in that tissue (Wample and Reid, 1979).
109
Direct measurement of volumetric soil moisture using a ThetaProbe was expected
to be a better parameter than accumulated rainfall for predicting symptom development
of clubroot. The ThetaProbe has potential for further use as a measure of soil moisture’s
effect on clubroot. Unfortunately, only one year of soil moisture data was collected, and
so the data could not be included in the stepwise regression models calculated across
years.
Each of the seeding date treatments in this trial was, by necessity, initiated in the
spring, so temperature increased, and degree days and rainfall accumulated over time
irrespective of treatment. Similarly, clubroot is a monocyclic disease that develops as the
plant grows, so incidence and severity also increased over the growing season. As a
result, significant correlations among these parameters and variables were expected.
Accumulated air degree days with no delay were the best parameter for estimating
clubroot incidence at any specific time point in the growing season, accounting for 61 %
of the variation. The best combination of parameters for estimating clubroot severity at a
specific time point was a combination of accumulated soil degree days (1-week delay)
and season total rainfall (1-week delay), which accounted for 45 % of the variation.
There is a high proportion of unexplained variation in these estimates, especially
for clubroot severity. At least a portion of this unexplained variation may be associated
with the slightly lower susceptibility to clubroot in Chinese flowering cabbage (Adhikari,
2010) than in ‘InVigor 5030’ (Deora et al., 2012b). The amount of data on canola that
could be derived from two seasons was very limited, so 57 additional data points from six
years of studies on Chinese flowering cabbage grown on the same research area were
integrated into the calibration and validation of the clubroot development models. This
110
almost doubled the sample size of the data set for analysis. Chinese flowering cabbage
was included based on its similar response to clubroot (moderate susceptibility) as canola
‘InVigor 5030 LL’. Clubroot incidence ranged from 0 to 76 % (mean = 16 %) in Chinese
flowering cabbage and 1 to 79 % (mean = 26 %) in canola, and severity ranged from 0 to
45 DSI (mean = 10 DSI) in Chinese flowering cabbage and 0 to 50 DSI (mean = 10 DSI)
in canola. Despite those similarities, data incorporated from Adhikari (2010) introduced
20 data points of zero clubroot incidence and severity under conditions where air and soil
degree days had accumulated to levels associated with 39 % incidence and 16 DSI on
canola. This data weakens the potential for identifying relationships between
accumulated degree days and clubroot development. For the same reason, the data set for
Shanghai pak choy could not be integrated into the model because pak choy is much
more susceptible to clubroot than either canola or flowering cabbage. This resulted in
many data points with close to 100% incidence and severity, which confounded
identification of significant parameters. Parallel repetitions of the trial in another region
with different weather than southern Ontario, for example in western Canada, might have
provided data that could have strengthened the potential relationship between
accumulated degree days and clubroot.
The objective of this study was to develop, calibrate and validate a degree day
model to predict clubroot severity on canola. The clubroot model validation demonstrated
that the models provided an adequate prediction (R2 > 0.50) of clubroot development on
moderately susceptible canola and Chinese flowering cabbage grown in muck soil during
the growing season. However, the model for prediction of final clubroot incidence was
weak (R2 < 0.50) and no model for clubroot severity at harvest (final sampling date)
111
predicted s significant proportion of the variance. The stepwise regression analyses
indicate that temperature was the primary abiotic variable influencing clubroot
development in the field. Calibration with additional years of field trials with canola
‘InVigor 5030LL’ is required to eliminate the systemic biases. Although canola is
normally grown on mineral soil which is the soil type that predominates in the canola
growing regions of the Prairies, this predictive model was calibrated on crops grown in
muck soil, and their applicability to the mineral soils on the Prairies will require
additional studies on those soils. The rationale for conducting this study at the Muck
Station in Ontario rather than a site on the Prairies was that this research site was
naturally infested with clubroot, and no comparable research site was available in western
Canada at that time. Since the study was initiated, a clubroot nursery at Edmonton has
been developed by Alberta Agriculture and Food, but space and access are limited
because of the strict sanitation requirements that are employed to limit the spread of
pathotype 3 into regions vulnerable to crop loss by clubroot. At present, there are no field
research sites available for clubroot trials outside of Alberta and Quebec.
Degree days are generally employed as a tool for deciding when to seed a crop,
determine if a crop is developing on schedule, and when to implement steps for pest
control in anticipation of an outbreak (Wilson and Barnett, 1983). The utility in these
clubroot forecasting models is the potential for integration into broader disease
management strategies. At this time, there are no registered fungicides for control of
clubroot on canola in Canada. In the future, if new economical fungicides are developed
and approved for use, these models are a starting point for calculating if environmental
conditions will be conducive for clubroot, and for identifying thresholds to determine if
112
fungicide applications would be warranted. Degree day models can be used to estimate
the potential losses in yield in a crop due to disease severity (Wilson and Barnett, 1983).
Predictive forecasting of disease empowers farmers and decision makers to choose
whether they want to risk the cost of inputs to a potentially failed crop, expedite swathing
and harvest date to capture current yield before the crop declines further, or disk the crop
under rather than spending more time and money to grow a failed crop.
Since clubroot development was found to be a function of accumulated degree
days over time, anyone interested in predicting clubroot incidence and severity will need
to do the calculations separately for each seeding date. Early seeding dates will generally
accumulate fewer degree days and at a slower rate than later seeding dates. This means
that early seeding provides an opportunity for the crop to escape early infection by
P. brassicae. When seedlings can avoid early infection, clubroot severity at later growth
stages is reduced and the reduction in plant height and yield associated with disease is
minimized (Hwang et al., 2012b). Air and soil temperatures were strongly and positively
correlated, and this study demonstrates they are both effective for estimating the effect of
temperature on clubroot development. In-field measurement of air and soil temperatures
is advised over regional estimates of temperature. Regional temperatures may not
accurately capture the climate experienced in fields, especially the maximum and
minimum temperatures, which could lead to over or under estimation of accumulated
degree days. However, the results from Chapter 2 indicate that mean temperature has a
larger influence on P. brassicae development than the maximum and minimum
temperatures. This means daily mean temperatures could be used as an alternative to
daily maximum and minimum for calculation of degree days. If in-field measurements
113
are not available, regional measurements may still be a satisfactory estimate of
temperature for a less precise degree day calculation.
114
CHAPTER FOUR
IN PLANTA QUANTIFICATION AND MICROSCOPY OF ROOT HAIR AND
CORTICAL INFECTION IN CABBAGE CULTIVARS INFECTED WITH
CLUBROOT
4.1 Introduction
Cultivars with resistance to clubroot have recently been commercialized in
canola, cabbage and several other Brassica crops. The sources and mechanism(s) of
resistance in these cultivars is proprietary and not well understood (Deora et al., 2012a;
Deora et al., 2013), which has implications for the management and durability of
resistance. Characterization of complete and partial resistance to clubroot could provide
information on how breakdown of resistance to clubroot occurs, and could lead to better
recommendations for management of clubroot in Brassica vegetable and canola
production.
Cabbage cultivars ‘Kilaherb’ ‘Kilaxy’, ‘Tekila’, and ‘Kilaton’, which are
marketed as clubroot resistant by seed companies, were highly resistant to pathotype 6 of
P. brassicae in Ontario. The authors concluded that the use of resistant cultivars was an
effective technique for management of clubroot on cabbage in Ontario (Saude et al.,
2012). In a study that assessed the reaction of four commercial clubroot-resistant
cultivars of canola against four pathotypes, cortical infection was limited to rare
occurrence of plasmodia by 24 DAI, compared to complete progression to resting spores
in a susceptible cultivar. The uniform resistance phenotype across cultivar-pathotype
combinations indicates that these lines all carry one or more broad-spectrum resistance
115
gene(s) that originate from a single source, because most sources of resistance are
pathotype specific (Deora et al., 2013).
The objective of this study was to identify the phase of pathogen development
that is affected by resistance and where that resistance to clubroot is expressed within the
roots of resistant and moderately susceptible cabbage lines. It was hypothesized that host
resistance in cabbage affects the extent of pathogen development by P. brassicae, and
that the resistant response pattern in cabbage would be similar to that recently
characterized in canola by Deora et al. (2013).
The current study examined three cabbage cultivars that differ in resistance
reactions to pathotype 3 at four time points, using several techniques for characterizing
clubroot severity. Pathogen development in root hairs was assessed at 4 and 8 days after
inoculation (DAI), and the extent of cortical tissue colonization by the pathogen was
assessed at 28 DAI. The amount of pathogen genomic DNA at each time point was also
quantified using qPCR. The incidence and severity of clubroot on each cultivar was
assessed at maturity in field trials and at 6 weeks after inoculation in growth room
studies. This project, which extends recent research conducted on host reaction in canola
(Deora et al., 2013) onto cabbage, will be used to determine if the mechanism of
resistance to clubroot currently available in commercial cabbage cultivars is similar to the
clubroot resistance in canola.
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4.2 Materials and methods
4.2.1 Plant materials
Seven cultivars of cabbage that were believed to differ in reaction to clubroot
were selected and their clubroot reaction was verified under field conditions. The
cultivars chosen were ‘Kilaherb’, ‘Kilaton’, ‘Kilaxy’, ‘Tekila’ (Syngenta Seeds), ‘B-
2819’, ‘Bronco’, and ‘Klimaro’ (Bejo Seeds). Cultivars ‘Kilaherb’ (resistant; ‘R’), ‘B-
2819’ (moderately susceptible; ‘MS’), and ‘Bronco’ (susceptible; ‘S’) were chosen for
further controlled environment and molecular studies, based on resistance reactions in the
field.
4.2.2 Field trial
The trial was conducted on organic soil at the Muck Crops Research Station,
Holland Marsh, Ontario, in 2011 and 2012. The site was naturally infested with pathotype
6 of P. brassicae. In 2011, the trial was arranged in a randomized complete block design
with four replicates per treatment at a site with a high resting spore density (range 4). In
2012, a second site where the density of resting spores in the soil was lower (range 6) was
also examined, in addition to a trial on range 4. One experimental unit consisted of 30
plants.
An assessment of the resting spore density in ranges 4 and 6 was conducted at the
end of the trial. A 25 g sample of soil was added to 100 ml of sodium hexametaphosphate
dissolved in deionized water and mixed. The solution was stored for 24 hours in a
refrigerator (5 °C). The solution was filtered through 16 layers of cheesecloth and
centrifuged at 1000 g for 30 minutes. The supernatant was poured off and kept to confirm
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the absence of resting spores. The remaining grey-brown pellet was re-suspended water,
and resting spores were counted using a haemocytometer. The mean density of resting
spores on range 4 was 1.95 × 107 spores g-1 dry soil and on range 6 was 1.25 × 106spores
g-1.
In 2011, cabbage cultivars ‘Kilaherb’, ‘Kilaton’, ‘Kilaxy’, ‘B-2819’, and
‘Klimaro’ were seeded into 128-cell plug trays on 09 May, and grown in a greenhouse.
‘Bronco’ was originally seeded at the same time as the other cultivars, but germination
was poor, likely due to the age of the seed. New seed was planted on 27 May, resulting in
a delayed transplanting of the cultivar. Two treatments of ‘Bronco’ were included as a
susceptible control to aid in analyses because a non-uniform distribution of inoculum had
been observed in previous trials on range 4. On 10 June, ‘Kilaherb’, ‘Kilaton’, ‘Kilaxy’,
‘Tekila’, ‘B-2819’, and ‘Klimaro’ were hand-transplanted into two 7.5-m-long rows, 86-
cm apart, with 45-cm in-row spacing. On 23 June, the two treatments of ‘Bronco’ were
similarly hand-transplanted. Each cultivar was harvested at maturity, when the majority
of heads was judged to be compact and had reached a marketable size. Cultivar ‘Tekila’
was harvested on 18 August, ‘Kilaherb’ on 19 August, ‘Kilaxy’, ‘Kilaton’, ‘Klimaro’,
and ‘B-2819’ on 7 September, and ‘Bronco’ on 13 September.
In 2012, the cabbage cultivars ‘Kilaherb’, ‘Bronco’, ‘B-2819’, and ‘Klimaro’
were seeded into 128-cell plug trays on 8 May and grown in a greenhouse. On 8 June, the
seedlings were hand-transplanted into each plot as described previously. Cultivars
‘Klimaro’ and ‘Bronco’ were harvested on 23 August, and ‘Kilaherb’ and ‘B-2819’ on 28
August. ‘Klimaro’ was harvested relatively early due to the decline in plant health
because of clubroot severity. ‘B-2819’ was harvested when heads were fully compact,
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although full marketable size had not been reached, in order to be comparable to
‘Klimaro’. A subset of 20 plants was cut, trimmed and weighed for marketable yield and
root and shoot weights were assessed on each of 10 untrimmed plants (Appendix 7).
Roots were assessed for clubroot incidence and severity using the standard 0 to 3 scale, as
described previously.
4.2.3 Controlled environment trials
The growth chamber trials were conducted in trays for the assessments at 4 and 12
DAI, and tall thin plastic pots (“conetainers”) (Stuewe Sons Inc. Corvallis, OR) filled
with autoclaved noncalcareous coarse sand for the assessments at 28 and 42 DAI. The
cultivars were classified as susceptible ‘Bronco’, resistant ‘Kilaherb’, and moderately
susceptible ‘B-2819’ were selected for the study based on their clubroot reaction in the
field trial.
In the first repetition of root hair and cortical infection trials, pathotypes 3 and 6
were tested, as well as a nontreated control. In the second repetition of trials, only
pathotype 3 was tested, as the responses to pathotypes 3 and 6 were very similar.
Resting spores of pathotypes 3 and 6 were extracted from frozen clubbed roots of
Shanghai pak choy as described previously. Resting spore concentration was estimated
using a haemocytometer and diluted to 1 × 106 spores mL-1. The growth medium was
inoculated with 5 mL of the P. brassicae resting spore suspension at 10 days after
seeding. The plants were watered daily with water acidified with commercial white
vinegar to pH 6.3, beginning 2 days prior to seed sowing. Plants were harvested and
assessed at 4, 12, 28, and 42 DAI.
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The plants were grown in a growth room at 20 °C night and 25 °C day
temperature, a 16-hour photoperiod (8-hour dark), with light irradiance of 200–250 μmol
m-2 s-1, and a relative humidity of 65 %. HOBO Temperature and Relative Humidity
loggers were used to record temperature and relative humidity. Plants were fertilized
twice each week by watering with a solution containing 1 g L-1 N-P-K 20-20-20 and 1 g
L-1 magnesium sulphate. For assessment of root hair infection, seedlings were germinated
in trays filled with washed and autoclaved coarse sand. Plants were harvested at 4 and 12
DAI. There were 45 plants per experimental unit and four replications.
The roots of three plants per replicate were stored in a fixative (70 % ethyl
alcohol) in Eppendorf tubes for at least 24 hr and then prepared for microscopic
assessment. Roots were stained with aniline blue (125 ppm) for 1 min, and then washed
with water for 1 minute (Voorrips, 1992). The percent of root hair infection was
estimated by assessing 100 root hairs on each of two plants, from the region 1 cm below
the hypocotyl under a light microscope at 250 × (objective 20 × and eye piece 12.5 ×)
magnifications. Root hairs were categorized as either: not infected, or containing primary
plasmodia, mature zoosporangia, or partially or fully dehisced zoosporangia (Sharma et
al., 2011a). The stages of development were differentiated as follows: a primary
plasmodium presented as a translucent unicellular cell that occupied a portion of the root
hair. Mature zoosporangia presented as fully differentiated opaque beads, in a row or in
wide bundles. Empty or partially empty zoosporangia were classified as dehisced
zoosporangia, and presented as a cyan translucent network of empty circular structures.
For each experimental unit, 100 mg subsamples of seedling root tissue (each
consisting of the entire taproot of 5 to 10 seedlings) were assessed using quantitative
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PCR. Differences in the number of seedlings used were due to heterogeneity in the size of
seedlings. Roots were cut into 1-cm-long segments and stored at -20 ºC until the time of
assessment. DNA of P. brassicae within the root sample was extracted using a DNeasy
Plant Mini Kit amplified with primers Pb4-1 (TACCATACCCAGGGCG ATT) and
PbITS6 (CAACGAGTCAGCTTGAATGC). Quantitative PCR amplification was carried
out in triplicate in a total volume of 20 µL using a StepOne real-time thermal cycler
(ABI, Streets Ville, ON) equipped with the StepOne v2.1 software, following the
program specifications: 10 min at 95° C (an initial denaturation), followed by 60 cycles
of 15s at 95° C, and 1 min at 60° C. Each reaction mixture of 20 µL contained 2 µL of
genomic DNA template, 0.1 µL of each primer (50 nM), 10 μL of 2 × SYBR Green
master mix (ABI), and 7.8 µL of sterile deionized water. A template control of water was
included in every qPCR assay. A series of serial dilutions of P. brassicae DNA of known
concentrations ranging from 1 to 1 x 10-4 ng µL-1 was included on each plate.
Fluorescence was checked after each cycle. After amplification, a melting-curve analysis
and electrophoresis (2 % gel) were performed to ensure that only the target PCR product
had been amplified.
To examine the extent of cortical infection, plants were grown in tall thin plastic
pots (conetainers) (Stuewe Sons Inc. Corvallis, OR) filled with washed and autoclaved
coarse sand and harvested at 24 and 42 DAI. There were three plants per replicate, but
only one plant per replicate was chosen for assessment on each date. The study was laid
out in a randomized complete block design with four replications per cultivar. One plant
per replication was harvested at 28 DAI, and analyzed by sectioning, staining and
microscopy according to the methodology of Sharma et al. (2011b). Briefly, a 0.5-cm-
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thick cross-section of the tap root was cut from 1 cm below the hypocotyl. Root hairs
were removed using a feather scalpel so that they would not interfere with the sectioning
and preservation processes (Feather Safety Razor Co. Ltd., Osaka, Japan), and the root
were stored in a fixative (70 % ethyl alcohol) until they were assessed. Samples were
treated twice with a neutral buffered solution for 45 min each time and then dehydrated
with an increasing ethanol series (70, 95 and 100 %). and embedded in paraffin. Cross-
sections (4 μm in thickness) were obtained using a microtome (Leica 2255, Germany).
Four sections per root were placed on a glass slide and stained in 0.5% methylene blue
for 5 minutes. The sectioning and embedding were conducted by technicians at the
Animal Health Laboratory of the University of Guelph. Five pictures per section were
taken using a compound light microscope at 125 × (objective 10 × and eye piece 12.5 ×)
magnification and the proportion of infected area was calculated using image analysis
software (Assess version 2.0, American Phytopathological Society Press, Minneapolis,
MN). The number of infected cells and the life stage of P. brassicae in one field of view
were also assessed. The two remaining plants were assessed using qPCR, which was
carried out as described above.
Pathotypes 3 and 6 were assessed in both repetitions of the study. Clubroot
incidence and severity were assessed on 10 plants per experimental unit, harvested 42
DAI as described above.
4.2.4 Statistical analysis
All of the statistical analyses were performed with SAS software (version 9.2
SAS Institute, Cary, NC) with a type I error set at P = 0.05. Data were tested for outliers
using Lund’s test and no outliers were found. Data were also tested for normality using
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Shapiro-Wilk test of residuals, which indicated that the field data and growth room data
for disease severity index, clubroot incidence, marketable yield and in planta
concentration of P. brassicae gDNA were not normal. A closer approximation to a
normal distribution was achieved for the field data of disease severity index and clubroot
incidence using an arcsine transformation prior to analyses, and a logarithm
transformation improved the normality of yield and in planta concentration of
P. brassicae gDNA data. However arcsine transformation did not improve normality of
growth room data of disease severity index or clubroot incidence.
The studies were arranged in a randomized complete block design. Mixed model
analysis of variance (ANOVA) was conducted for the field trials and the growth room
studies using PROC MIXED, in which cultivar, pathogen pathotype, and site-year were
fixed effects, and block and experiment repetition were random effects. Mean
comparisons were performed using Tukey’s test and single degree of freedom contrasts.
In the analyses of variance of data combined across repetition, pathotype 6 was excluded
to balance the statistical tests.
To analyze the final clubroot levels in the growth room trial, a nonparametric
ANOVA was performed using the PROC NPAR1WAY, and the Kruskal–Wallis test was
used to test whether samples of clubroot incidence and severity come from cultivar
populations with equal medians.
Pearson correlations were performed using PROC CORR to compare the
concentration of P. brassicae gDNA obtained from qPCR with the developmental stages
in the root hairs and the root cortex.
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4.3 Results
4.3.1 Assessment of clubroot response in the field
Differences were found in clubroot level (incidence and severity) and marketable
yield per head, among cultivars of cabbage grown at the Muck Crop Research Station in
2011 and 2012 (Figure 4.1). In 2011, cultivars ‘Kilaxy’, ‘Kilaton’, ‘Tekila’ and
‘Kilaherb’ had no clubroot symptoms and thus were confirmed as highly resistant to
pathotype 6, which is the dominant pathotype at this site. Cultivars ‘Bronco’ and
‘Klimaro’ were susceptible to clubroot and ‘B-2819’ was moderately susceptible. This
result supported the results of previous assessments on the reaction of these cultivars, so
the subsequent trial in 2012 was reduced to include ‘Bronco’, ‘Klimaro’, ‘B-2819’, and
‘Kilaherb’ only.
There was a large effect of site-year for clubroot incidence and severity, and no
interaction between cultivar and site-year (Table A4.1, A4.2). Single-degree–of-freedom
contrasts demonstrated that there was no difference between the results from the high
inoculum site in 2011 and 2012, but both were different from the low inoculum site.
However, the pattern of response was similar across all three sites. Clubroot incidence
and severity were highest in ‘Klimaro’ and ‘Bronco’ (near 100% incidence), intermediate
in ‘B-2819’, and no clubroot symptoms were observed on ‘Kilaherb’ (Table 4.1).
There was a cultivar × site-year interaction and a large effect of site-year on yield
(P < 0.0001) (Table A4.3). On average, the high inoculum sites produced 44 % higher
clubroot incidence, 59 % higher severity, and 1.8 kg/head lower yield (Table A4.4). Yield
was highest in ‘Kilaherb’ in all three site-years (3.6–5.1 kg) and ‘Bronco’ in the low
inoculum site in 2012 (5.4 kg) (Figure 4.1). Yield was intermediate in ‘Klimaro’ grown
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in the low inoculum plot in 2012 (2.6 kg). The lowest yield was observed in ‘Bronco’ and
‘Klimaro’ grown in the high inoculum site-years, and ‘B-2819’ in all three site-years (1.3
– 2.0 kg).
There was a positive correlation between clubroot incidence and severity, and
negative correlations between yield and clubroot level (incidence and severity) (Table
4.2).
Table 4.1 Clubroot incidence and severity on green and red cabbage cultivars grown in naturally infested soil at the Muck Crop Research Station, Holland Marsh, ON in 2011 and 2012. High inoculum site Low inoculum site Cultivar Clubroot
incidence (%) Disease Severity
Index Clubroot
incidence (%) Disease Severity
Index Klimaro 100 a1 100 a 72 a 27 a Bronco 100 a 100 a 71 a 24 a B-2819 98 b 53 b 11 a 4 a Kilaherb 0 c 0 c 0 b 0 b 1 Columns with the same letter do not differ at P = 0.05, based on Tukey’s multiple means comparison test. 2 ‘Bronco’ and ‘Kilaherb’ are green cabbage, ‘Klimaro’ and ‘B-2819’ are red cabbage. Table 4.2 Correlation matrix of the relationship (r above, P below) among arcsine transformations of clubroot incidence and severity (disease severity index) and log transformation of marketable yield.
Disease severity index Yield (kg /head)
Incidence 0.91 -0.71
< 0.0001 < 0.0001
Disease severity index -- -0.76 -- < 0.0001
ab
cd cdd
ab
dcd d
a a
cdbc
0
1
2
3
4
5
6
Kilaherb Bronco B-2819 Klimaro
Yiel
d (k
g/he
ad)
High 2011 High 2012 Low 2012
Figure 4.1 Yield of green and red cabbage cultivars grown in naturally infested soil at two sites (high vs. low inoculum density) at the Muck Crop Research Station, Holland Marsh, ON, 2011 and 2012. Bars with the same letter do not differ at P = 0.05, based on Tukey’s multiple means comparison test.
125
Figure 4.2 Cabbage in the field (a) before harvest, and (b) trimmed heads representing marketable yield.
a b
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4.3.2 Root hair infection
In the first repetition of the study, root hair infection (RHI) was observed in each
of the cultivars at 4 and 12 DAI for both pathotypes 3 and 6 (Figure 4.3). There was an
interaction between cultivar and pathotype (P = 0.005) for incidence of RHI at 4 DAI, but
none of the cultivar × pathotype combinations were significantly different based on
Tukey’s multiple mean comparison test. Also, there was no cultivar × pathotype
interaction at 12 DAI.
In the first repetition of the study, there was a cultivar effect on the incidence of
total root hair infection (P = 0.03) at 12 DAI with pathotypes 3 and 6, but there was no
pathotype x cultivar interaction. The highest incidence of root hair infection was
observed in ‘B-2819’ (82 %), the lowest was observed in ‘Kilaherb’ (73 %), and
‘Bronco’ was intermediate (75 %). The incidence of primary plasmodia (P = 0.015) at 12
DAI had a similar pattern; with the highest incidence in ‘B-2819’ (79 %), the lowest in
‘Kilaherb’ (69 %), and ‘Bronco’ was intermediate (72 %). There were no cultivar or
pathotype effects or interaction for the incidence of mature zoosporangia. However, there
was a pathotype effect on the incidence of dehisced zoosporangia (P = 0.04), with
pathotype 3 (0.2 %) greater than pathotype 6 (0.0%).
In the second repetition of the study, which only assessed pathotype 3, there were
no differences among cultivars in the incidence of total root hair infection or the
individual developmental stages (primary plasmodia, mature zoosporangia, and dehisced
zoosporangia) at 4 or 12 DAI. No dehisced zoosporangia were observed at 4 DAI.
When the two repetitions of the trial were combined (pathotype 3 only), there was
no cultivar by repetition interactions for the incidence of total root hair infection or the
127
individual developmental stages (primary plasmodia, mature zoosporangia, and dehisced
zoosporangia) at 4 or 12 DAI, so the data were pooled. At 4 DAI, there was a small
repetition effect on the total incidence of root hair infection (P = 0.048) and primary
plasmodia (P = 0.049). The incidence of total root hair infection and primary plasmodia
was an average 12 % higher in repetition 1 compared to repetition 2. There was a cultivar
effect on the total incidence of root hair infection (P = 0.001) and primary plasmodia (P =
0.01) (Table 4.2). Incidence was higher in ‘Bronco’ and ‘B-2819’ compared to
‘Kilaherb’. There were no differences among cultivars in the incidence of mature
zoosporangia or dehisced zoosporangia.
At 12 DAI, there was a small repetition effect on the incidence of primary
plasmodia (P = 0.03) and mature zoosporangia (P = 0.03). The incidence of primary
plasmodia was an average 12 % higher in repetition 1 compared to repetition 2, and the
incidence of mature zoosporangia was an average 10 % higher in repetition 2 compared
to repetition 1. There was a cultivar effect on the incidence of primary plasmodia (P =
0.02), and mature zoosporangia (P = 0.03) (Table 4.2). Incidence was higher in ‘Bronco’
and ‘B-2819’ compared to ‘Kilaherb’. There were no differences among cultivars in the
incidence of total root hair infection or the individual developmental stages of mature
zoosporangia or dehisced zoosporangia.
Table 4.2 Incidence of primary infection (%) of root hairs on canola at 4 and 12 DAI with pathotype 3. Cultivar & Sampling date
Root hair infection
(%) 1
Primary plasmodia
(%)
Mature zoosporangia
(%)
Dehisced zoosporangia
(%) 4 DAI
Bronco 61 a 61 a 1 ns 0 ns
B-2819 60 a 59 a 0 0
Kilaherb 52 b 51 b 0 0
12 DAI
Bronco 77 ns 68 a 7 ns 0 ns
B-2819 75 70 a 9 1
Kilaherb 72 61 b 10 1 1 Values in the same column and date followed by the same letter are not significantly different at P = 0.05, based on Tukey’s multiple means comparison test. 2 Data combined across two repetitions of the study; 2 plants per replication x 4 replications x 2 trial repetitions.
a b c
Figure 4.3 Stages of root hair infection: (a) primary plasmodium, (b) mature zoosporangia, and (c) close-up of mature secondary zoospores in an epidermal cell.
128
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4.3.3 Cortical infection
In the first repetition of the study, cortical infection was observed in each of the
cultivars at 28 DAI with pathotypes 3 and 6 (Figure 4.4). There were substantial
differences (P < 0.0001) among the cultivars for each of the variables examined: area of
infection in cortical sections, total number of cells infected, the number of cells infected
with young plasmodia, with mature plasmodia, and with resting spores.
‘Bronco’ had the highest percent area of infection (21 %), the highest number of
infected cells that contained young plasmodia (67), mature plasmodia (41), and resting
spores (29), and the highest number of infected cells overall (137). ‘B-2819’ and
‘Kilaherb’ had intermediate levels of area of infection and cells with young plasmodia
and resting spores. However, ‘B-2819’ had an intermediate level of cells with mature
plasmodia and total infected cells (26 and 82, respectively), and ‘Kilaherb’ had the fewest
(6 and 52, respectively). Similarly, inoculation with pathotype 3 resulted in slightly
higher numbers of cells with young plasmodia (P = 0.006) and total number of infected
cells (P = 0.007), (99 and 60, respectively) than pathotype 6 (81 and 49, respectively).
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Figure 4.4 Cross-sections of cabbage roots stained with methylene blue. (a) Starch granules in non-inoculated control, (b) mature plasmodia in ‘Bronco’, (c) young plasmodia in ‘B-2819’, and (d) young plasmodia in ‘Kilaherb’. Black triangles point to pathogen growth and unfilled triangles with black outlines point to starch granules.
In the second repetition of the study, cortical infection was observed in each of
the cultivars at 28 DAI (pathotype 3 only). There were substantial differences (P ≤
0.0006) among the cultivars for each of the variables examined, with a similar pattern of
response to that observed in the first repetition. ‘Bronco’ had the highest area of infection
(25 %), most total infected cells (102), most cells infected with mature plasmodia (21),
131
and most cells infected with resting spores (30). ‘B-2819’ also had a high number of total
infected cells (102), cells infested with young plasmodia (78) and mature plasmodia (19),
but had a low area of infection (7 %) and number of infected cells with resting spores (4).
‘Kilaherb’ also had a low area of infection (7%), and the fewest cells infected (52).
When the two repetitions of the trial were combined, there was no repetition
effect or interactions with cultivar. There were differences among the cultivars for
percent area of infection (P < 0.0001), total cells infected (P < 0.0001), number of cells
with young plasmodia (P = 0.01), number of cells with mature plasmodia (P < 0.0001),
and number of cells with resting spores (P < 0.0001). ‘Bronco’ had the highest area of
infection (23 %), total cells infected (128), most cells with young plasmodia (64), mature
plasmodia (34), and resting spores (31) (Table 4.3). ‘B-2819’ and ‘Kilaherb’ had a
similar area of infection (8 %; 7 %, respectively), and number of cells with resting spores
(4; 0, respectively).
Table 4.3 Percent area of cortical colonization and incidence of cortical cells containing selected developmental stages on cabbage at 28 days after inoculation (DAI) with pathotype 3.
Cultivar Area
infected (%) Total cells infected
Number of cells with
young plasmodia
Number of cells with mature
plasmodia
Number of cells with
resting spores Bronco 23 a1 128 a 64 ab 34 a 31 a
B-2819 8 b 96 b 67 a 25 a 4 b
Kilaherb 7 b 58 c 52 b 7 b 0 b 1 Values in the same column followed by the same letter are not significantly different at P = 0.05, based on Tukey’s multiple means comparison test. 2 Data combined across two repetitions of the study, with 16 pictures in total of cross-sections of root below the hypocotyl for four plants (four pictures x four replications),
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4.3.4 Clubroot incidence and severity
The mean scores, the mean rank of each cultivar, of clubroot incidence and severity
were significantly different among the three cultivars (Figures 4.5 and 4.6; Table A4.53).
‘Bronco’ had the highest clubroot incidence and severity mean ranks (CI = 59, DSI = 61).
‘B-2819’ had intermediate mean ranks (CI = 38, DSI = 37). The mean scores of clubroot
incidence and severity on ‘Kilaherb’ were both 13, although no clubroot was found on
any plants in the field or in controlled conditions. The mean scores of clubroot incidence
and severity did not differ between pathotypes 3 and 6.
0102030405060708090
100
Bronco B-2819 Kilaherb
Clu
broo
t inc
iden
ce (%
)
Cultivar
Figure 4.5 Box plot of clubroot incidence on cabbage cultivars inoculated with pathotypes 3 and 6 of P. brassicae under controlled conditions. Data combined across two repetitions (10 plants per replication x 4 replications x 2 repetitions).
Figure 4.6 Box plot of clubroot severity on cabbage cultivars inoculated with pathotypes 3 and 6 of P. brassicae under controlled conditions. Data combined across two repetitions (10 plants per replication x 4 replications x 2 repetitions).
0102030405060708090
100
Bronco B-2819 Kilaherb
Dis
ease
Sev
erity
Inde
x
Cultivar
133
134
4.3.5 Molecular quantification of in planta colonization of roots
In the first repetition of the study, the concentration of genomic DNA (gDNA) of
pathotype 3 was higher than for pathotype 6 at 4 DAI (P = 0.003) and 12 DAI (P =
0.0008), but there was no difference at 28 DAI (Table 4.4). No differences were found
among cultivars at 4, 12 or 28 DAI with pathotype 3 or 6.
Table 4.4 The amount of P. brassicae genomic DNA detected in cabbage roots at 4, 12, and 28 days after inoculation (DAI) with pathotypes 3 and 6.
Pathotype
Log (P. brassicae gDNA ng/ g of root)
4 DAI 1 12 DAI 28 DAI
3 1.14 a 1.76 a 4.57 ns
6 0.43 b 0.81 b 4.46 1 Values in the same column followed by the same letter are not significantly different at P = 0.05, based on Tukey’s multiple means comparison test.
When pathotype 3 was evaluated in a repetition of the experiment, there were no
differences in the gDNA among cultivars at 4 DAI and 12 DAI. At 28 DAI, there was a
cultivar effect on P. brassicae gDNA (P < 0.0001). ‘Bronco’ had the highest logarithmic
concentration of pathogen gDNA (5.40 ng/ g of root), and ‘Kilaherb’ and ‘B-2819’ were
both lower (Table 4.5).
When the repetitions were combined, cultivar had no effect on logarithmic
concentration of pathogen gDNA at 4 or 12 DAI. At 28 DAI, there was an repetition
effect and a cultivar effect (P < 0.0001). Repetition 1 was 3 × 105 greater than repetition
2. ‘Bronco’ had the highest logarithmic concentration of pathogen gDNA (6.06 ng/ g of
root), and ‘Kilaherb’ and ‘B-2819’ were both lower.
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As expected, the concentration of P. brassicae gDNA was positively correlated
with the incidence of developmental stages of the pathogen as assessed by staining and
microscopy at 4, 12 and 28 DAI. The amount of gDNA was highly correlated with the
incidence of primary plasmodia at 4 DAI (r = 0.9) and 12 DAI (r = 0.65), and with total
root hair infection at 4 DAI (r = 0.99) and 12 DAI (r = 1.00), but only weakly correlated
with the incidence of dehisced zoosporangia at 4 DAI (r = 0.36, P = 0.045). At 28 DAI,
gDNA was also strongly correlated with cells containing mature plasmodia (r = 0.73),
resting spores (r = 0.91), total cells infected (r = 0.77) and with the area of infection (r =
0.85) (Table 4.6).
Table 4.5 The amount of P. brassicae genomic DNA detected in cabbage roots at 4, 12, and 28 days after inoculation (DAI) with pathotype 3.
Cultivar
Log (P. brassicae gDNA ng/ g of root)
4 DAI 1 12 DAI 28 DAI
Combined Exp 1 Exp 2
Bronco 1.29 ns 1.68 ns 6.06 a 7.01 ns 5.40 a
B-2819 1.67 2.01 3.74 b 7.37 0.00 b
Kilaherb 1.22 1.92 3.90 b 7.40 0.00 b 1 Values in the same column followed by the same letter are not significantly different at P = 0.05, based on Tukey’s multiple means comparison test. ns, not significant.
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Table 4.6 Correlation matrix of the relationship (r above, P below) between the concentration of P. brassicae gDNA determined by qPCR and incidence of P. brassicae developmental stages in inoculated canola roots at 4, 12, and 28 DAI.
Genomic DNA1
Root hair infection
(%) Plasmodia Zoosporangia Dehisced
zoosporangia
4 DAI 0.99 0.99 0.28 0.36
< 0.0001 < 0.0001 ns 0.045
12 DAI 0.99 0.65 0.10 0.05
< 0.0001 < 0.0001 ns ns
Total cells infected
Cells with young
plasmodia
Cells with mature
plasmodia Cells with
resting spores
Area Infected
(%)
28 DAI 0.77 0.19 0.73 0.91 0.85
< 0.0001 ns < 0.0001 < 0.0001 < 0.00011 Log transformed P. brassicae gDNA concentration in planta. 2 Data from two repetitions of the experiment.
4.4 Discussion
The reaction of the cabbage cultivars to P. brassicae in field and growth room
studies was consistent with previous reports. ‘Kilaherb’, ‘Kilaton’, ‘Tekila’ and ‘Kilaxy’
were completely resistant to clubroot (pathotype 6) in the field. Also, ‘Kilaherb’ was also
completely resistant (0 % incidence) in a growth room study. Incidence and severity of
‘Bronco’ was at or near 100 % in all of the trials. The results are consistent with those
from a recent trial in Ontario , where ‘Kilaherb’, ‘Kilaton’, ‘Tekila’ and ‘Kilaxy’ were
resistant to pathotype 6 and ‘Bronco’ was very susceptible (Saude et al., 2012). Incidence
and severity on ‘B-2819’ were lower in growth room studies than at the high inoculum
field site, but higher than at the low inoculum site. ‘Bronco’, ‘B-2819’ and ‘Kilaherb’
reacted as susceptible, moderately susceptible and resistant to clubroot, respectively.
These three cultivars were selected for further study.
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The yield of ‘Kilaherb’ at the high inoculum site in 2011 (3.6 kg per head) was
similar to its yield at the Holland Marsh in a previous study (Saude et al., 2012) in 2009
(3.7 kg per head) and much higher than in 2010 (2.0 kg). The yield of ‘Bronco’ in 2011
was comparable to the results in 2010, when clubroot incidence and severity were very
high (100 DSI), and lower than in 2009, when clubroot levels were not as high (42 DSI).
In 2012, yield was higher for ‘Bronco’, ‘Kilaherb’ and ‘Klimaro’ grown in the low
inoculum site compared to the high inoculum site that same year. The marketable yield of
‘Kilaherb’ was higher at the low inoculum site than at the high inoculum site, despite
having no clubbing symptoms in either site. This result is consistent with a previous
report of reduction in plant height and rate of development in canola inoculated with
P. brassicae in all reaction types (Deora et al., 2012a). Also, another report indicated that
yield, height, and emergence of a resistant canola cultivar declined as inoculum
concentration increased (Hwang et al., 2011b). This may indicate that there is a
physiological cost to clubroot resistance. Also, the physiological cost associated with the
resistance reaction occurs in more than one species of Brassica crops.
In the root hair study, infection of root hairs took place in each cultivar and there
were no differences among cultivars in the incidence of individual developmental stages.
This result mirrors the report by Kroll et al. (1983), who also found no differences in the
incidence of root hair infection between resistant, partially resistant, and susceptible
radish cultivars. This contrasts with the results of a recent study, where P. brassicae
developed most quickly in susceptible canola cultivars, slightly more slowly in resistant
cultivars, and slowest in moderately susceptible cultivars (Deora et al., 2012a).
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Even though there were no measurable differences in the incidence of root hair
infection among cultivars that differed in disease reaction type, root hair infection may
have an impact on the expression of clubroot resistance. A study of root hair infection in
a non-host (perennial ryegrass, Lolium perenne L.) indicates that root hair infection can
prime host resistance to cortical infection, perhaps by way of recognition of pathogen-
associated molecular patterns (PAMPs). These authors also found that the host for
primary infection also has an effect on the pathogenicity of secondary zoospores
produced during that infection stage; susceptible hosts produce secondary zoospores that
are more pathogenic than those from non-hosts (Feng et al., 2012c).
The results of the qPCR analysis were consistent with the trends at 4 and 12 DAI
using root hair staining and microscopy. There were no differences among cultivars for
the amount of P. brassicae gDNA found at 4 and 12 DAI, which supports the observation
based on microscopy that there were no differences in root hair infection among the
cultivars. There were strong correlations between the incidence of total root hair infection
or primary plasmodia and the concentration of P. brassicae gDNA at 4 and 12 DAI. This
indicates that qPCR and microscopy were measuring the same phenomena of root hair
infection. This finding is consistent with the results of Hwang et al. (2012a), who
reported a strong linear relationships between gDNA and root hair infection in several
canola cultivars that differed in susceptibility to clubroot. However, this contradicts the
result from Chapter 2, where there was no correlation between gDNA and root hair
infection.
The area of infection was highest in the susceptible cultivar ‘Bronco’, as expected
based on the high severity of clubbing that subsequently developed in that cultivar.
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However, the resistant cultivar ‘Kilaherb’, had levels of infection similar in some
measures to those in the moderately susceptible ‘B-2819’, even though no clubs
developed. This result was not expected. It indicates that the resistance to clubroot in
‘Kilaherb’ is expressed in the root cortex after young plasmodia form but prior to the
formation of mature plasmodia, and may interfere with the development of resting spores.
This is not the first report of cortical infection being observed in clubroot resistant
Brassica cultivar. Kroll et al. (1983) reported that root colonization by secondary
plasmodia could occur in susceptible, partially resistant, and resistant radish plants
without clubbing symptoms developing. Non-symptomatic plants occurred infrequently
in susceptible cultivars, but resistant cultivars were always non-symptomatic. By 25 DAI,
uni- and bi-nucleate secondary plasmodia occurred most frequently in the resistant
cultivar, at intermediate frequency in the partially resistant cultivar, and the numbers
were lowest in the susceptible cultivar. By 30 and 36 DAI, there were high numbers of
mature plasmodia and resting spores in the susceptible cultivar, fewer in the partially
resistant cultivar, but neither developmental stage was found in the resistant cultivar.
From those results, it appears that host resistance was expressed as an absence of
clubbing: the host tolerated the presence of the pathogen without producing symptoms.
Secondary plasmodia in the cortical cells of resistant canola cultivar ‘45H29’ have been
reported previously, but the study was limited to 14 days after sowing (Hwang et al.,
2011b). Deora et al. (2012a) reported an absence of P. brassicae at 28 DAI in cortical
cross-sections of incompatible interactions with ‘45H29’. It is possible that the pathogen
had been suppressed or eliminated by 28 DAI in this cultivar. Donald et al. (2008)
reported the occurrence of an amoeboid form of P. brassicae in the root cortex of
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clubroot-resistant cauliflower (B. oleracea cv. R10146) and an absence of cell wall
degradation compared to a susceptible cultivar. Cell wall reinforcement (lignin deposition
and oxidative cross-linking of polymers) that restricts P. brassicae movement between
cells, possibly related to oxidative bursts associated with hypersensitive response, may be
one of the mechanisms preventing clubbing symptoms in ‘Kilaherb’ (Lamb and Dixon,
1997). The genes for this resistance may have a quantitative effect, since similar levels of
cortical infection were observed in the moderately susceptible ‘B-2819’ that did develop
symptoms. Further studies would be necessary to test this hypothesis.
When the frequency of the selected developmental stages were compared among
the cultivars, ‘B-2819’ had similar numbers of cells infected with young and mature
plasmodia as ‘Bronco’, but did not differ from ‘Kilaherb’ in the incidence of resting
spores at 28 DAI. These results indicate that the intermediate level of resistance to
clubroot in ‘B-2819’ is expressed after secondary infection but prior to the formation of
resting spores. This resistance may be expressed as a reduction in the rate of development
of the pathogen at the mature plasmodia stage. Partial resistance to clubroot in
Arabidopsis thaliana was associated with tolerance to higher accumulations of trehalose,
a disaccharide that is up-regulated when cortical cells are infected with P. brassicae. In
healthy plants, it is associated with drought and desiccation tolerance (Gravot et al.,
2011). Further study of ‘B-2819’ should include metabolite and starch quantification to
determine whether trehalose is also associated with the partial resistance to clubroot in
this cultivar.
The results of the second repetition of the qPCR assessment of cultivars at 28 DAI
are consistent with the results from the cortical sectioning and staining analysis. The
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pattern in both measurements were similar; the highest amount of pathogen gDNA and
area of cortical infection was found in ‘Bronco’, and the amount of pathogen gDNA and
area of cortical infection was lower and approximately the same in ‘B-2819’ and
‘Kilaherb’. No P. brassicae gDNA was reported for ‘B-2819’ and ‘Kilaherb’, however
trace amounts were still found in some treatment replications on the order of 10-5 ng/ g of
root. Analysis of the first repetition and the combined results of the qPCR assessments
did not identify differences among cultivars. Even though cross-sections were taken from
the same regions in all experimental units, variation in the axial distribution of cortical
infection by P. brassicae may account for the large difference in gDNA between the
repetitions. Plasmodiophora brassicae can migrate radially into the stele region (Deora et
al., 2013), so perhaps it can also migrate axially up and down the hypocotyls, resulting in
different quantities of the pathogen at the same position in the stem across individual
plants.
There were strong correlations between the amount of pathogen gDNA and the
extent of cortical infection at 28 DAI. The strongest correlations were between gDNA
and the number of cells occupied with resting spores or the percent area of cortical
infection as calculated using image analysis. These relationships make logical sense,
given that assessment of cross-sections of roots and qPCR are both quantitative estimates
of P. brassicae proliferation. A similar positive linear relationship was reported between
resting spore concentration and the percent area of cortical infection in Shanghai pak
choy (Sharma et al., 2011b).
The objective of this experiment was to determine the phase of pathogen
development that is affected by resistance to clubroot and where that resistance s
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expressed within the roots of resistant and moderately susceptible cabbage lines. This
study has shown that the timing of expression of resistance to clubroot differs among
cultivars of cabbage. The hypothesis that host resistance in cabbage would prevent any
occupation of cortical tissue by P. brassicae is rejected. Resistance to clubroot in
‘Kilaherb’ occurs during cortical colonization and development of plasmodia, and
inhibits pathogen development before plasmodia can mature. The intermediate resistance
in ‘B-2819’ appears to restrict the growth of secondary plasmodia prior to development
into resting spores. Each cultivar also develops different symptoms (some or none) in
response to the presence of the pathogen in cortical cells.
In this portion of the study, qPCR appeared to be a viable, high throughput
alternative to staining and microscopy of root hairs and cortical cross-sections.
Depending on the resources available and the objectives of the work, researchers can
decide which approach (microscopic or molecular quantification) would be most suitable.
Use of microscopy in the current project allowed for identification of the developmental
stage that was affected by host resistance.
Moderately susceptible cultivars like ‘B-2819’ could be incorporated into studies
of quantitative resistance and factors affecting expression of resistance to clubroot.
Effectors associated with quantitative resistance have been studied in segregating
populations of cultivars with partial disease resistance in other host-pathogen systems.
For example, the potato (Phytophthora infestans) cultivar ‘Sarpo Mira’ was utilized to
identify the avirulence effectors associated with specific qualitative (race specific)
resistance genes or with quantitative (race nonspecific) resistance genes.
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Cabbage cultivars may also have potential as model crops for mapping of clubroot
resistance genes and studying the mechanisms of resistance. Hybrid lines can be used for
genetic mapping of resistance genes. For example, the hybrid oilseed rape cultivar
‘Mendel’ has been used as a parent in crosses with a susceptible cultivar to map the
dominant clubroot resistance genes in segregating populations of F2-selfing progeny and
backcrosses. Molecular markers linked to resistance were used to identify which
resistance genes were carried or lost from the progenitors of ‘Mendel’ (Diederichsen et
al., 2006).
This study also supports the conclusion from a previous study (Saude et al., 2012)
that farmers would benefit from using clubroot-resistance cabbage cultivars in field
infested with either pathotypes 3 or 6. This study has also shown that there is an apparent
physiological cost in clubroot resistant cabbage to resisting P. brassicae. This ‘cost’
results in a yield reduction when a resistant cultivar is grown in a field with high
inoculum levels. However this small reduction in yield is much less than the reduction in
yield caused by high clubroot severity on susceptible cultivars.
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CHAPTER FIVE
GENERAL DISCUSSION
Plasmodiophora brassicae and the clubroot disease it causes on Brassica have
developed into a difficult challenge for farmers, crop managers, and researchers in the
past decade. A driving force behind initiation of this research was the recent
identification and spread of clubroot on canola near Edmonton in 2003 (Tewari et al.,
2005).
A portion of this thesis program was designed to investigate some gaps in
research on temperature in relation to clubroot. Adhikari (2010) suggested evaluating the
effect of temperature fluctuation on symptom development using a precise method such
as a thermal gradient plant. Additionally, the magnitude of temperature fluctuation
around the mean was also considered. This project is the first to evaluate temperature
effects on P. brassicae development using a thermal gradient plate. There were small
differences in root hair infection and the incidence of primary plasmodia when constant
temperatures were used, as compared to temperatures that varied by 10 °C around the
mean, as evaluated using microscopy. The current study also confirmed that there was a
quadratic relationship between temperature and root hair infection, with an optimum of
25 °C. The author believes that this represents the most accurate and precise estimate of
optimal soil temperature for clubroot development that is currently available.
Wider temperature fluctuations of up to 15 °C at 14 DAI did not differ in extent
of clubroot infection, compared to constant mean temperatures, based on evaluation using
qPCR. However, temperature fluctuations may still have an impact at specific time points
during primary or secondary infection. To confirm this, an additional study on the
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thermal gradient plate could be conducted on seedlings at 4 DAI, although the minimum
amount of seedling tissue required to get an amount of extractable P. brassicae gDNA
suitable for qPCR assessment may be as high as 20 plants. There was a linear correlation
between P. brassicae gDNA and root hair infection at 4 and 8 DAI in chapter 4, but no
correlation was found between P. brassicae gDNA and root hair infection in chapter two
where assessments were conducted at 10 DAI. This discrepancy may be associated with
the difference in assessment dates, 10 DAI may be too late for capturing treatment
differences on primary infection based on qPCR. Quantitative PCR may be unsuitable for
study of all time points of primary infection, as it may be confounded by the progression
of the pathogen into the secondary infection stage of its life cycle. Secondary infection
has been shown to occur quickly under ideal conditions. For example, secondary
infections were observed 3 days after inoculation with resting spores (Feng et al., 2012a).
The current project confirmed the importance of temperature and rainfall (as a
metric of soil moisture) on the development of clubroot on canola, and identified
parameters that can be used to estimate clubroot levels throughout a growing season and
at final harvest. The ThetaProbe for measure of volumetric soil moisture showed
potential for further use in studies of the effect of soil moisture on clubroot.
Unfortunately, only one year of data was collected with this method. While it was
possible to integrate air and soil temperatures and rainfall from previous years of trials
with Chinese flowering cabbage, no data was collected on soil moisture during those
trials. To improve the degree day models calibrated in this project and remove some of
the systemic biases, additional trials with canola ‘InVigor 5030LL’ should be conducted.
Two additional years of data with the ThetaProbe may be sufficient for the parameter to
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be incorporated into clubroot modeling, but data from even more years would be helpful.
Future trials should also be conducted in parallel on mineral soil to incorporate the
variation and interactions that soil type can have on clubroot development. The first week
of May is a good starting date for the first seeding of these trials, as temperatures will be
low enough throughout the growth period to decrease the impact clubroot will have on
the crop. The seeding time is also comparable to canola production practices in the
Prairies. For future trials, even earlier seeding should be considered if soils are thawed
and tillable. In retrospect, rather than incorporating data from Chinese flowering cabbage
field trials, a parallel repetition of the trial in another region with different weather than
southern Ontario, for example in western Canada, might have provided data that could
have strengthened the potential relationship between accumulated degree days and
clubroot.
As expected, clubroot incidence and severity increased over time, providing a
consistent correlation. This underlying correlation relationship may mean that the power
of the environmental parameters modeled to predict clubroot development could be
overestimated if not taken in to consideration in the analysis.
Another component of this project was to follow up to the work by Deora et al.
(2012a) and Saude et al. (2012) on host resistance in relation to clubroot development.
Using root hair and cortical section staining and microscopy, Deora et al. (2012a)
compared newly commercialized canola cultivar (sources and mechanisms of clubroot
resistance unknown) to susceptible cultivars to identify differences in primary and
secondary infection of P. brassicae. They found that resistance to clubroot was expressed
at the secondary infection stage. This study used the same methodology as Deora et al.
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(2012a) to examine differences in development between clubroot resistant and
susceptible cabbage cultivars identified by Saude et al. (2012). Resistance to clubroot in
cabbage cultivar ‘Kilaherb’ was demonstrated to occur in the secondary infection stage in
a similar timing after inoculation as found in canola.
The resistant cabbage cultivar ‘Kilaherb’ and moderately susceptible cultivar ‘B-
2819’ exhibited similar amounts of area of infection in sections of cortical tissue at 28
DAI. This result was unexpected because there were no symptoms of clubroot on
‘Kilaherb’, but 43 – 59 % clubroot incidence (with P6 and P3, respectively) and 17 – 25
DSI for ‘B-2819’ at 42 DAI. The question this result elicits is what mechanism halts
clubroot symptom development between 28 and 42 DAI in ‘Kilaherb’ but not ‘B-2819’?
During the interaction between P. brassicae and its host, the pathogen affects the
secondary metabolism of the host; therefore it is reasonable to believe that the difference
in final clubroot levels between these two cabbage cultivars is linked to differences in
host biochemistry (Ludwig-Müller et al., 2009). Further studies are necessary to identify
the differences in host physiology that contribute to cultivar resistance during infection
with P. brassicae.
Two cultivars with partial resistance to clubroot, canola cv. ‘InVigor 5030 LL’
and cabbage cv. ‘B-2819’, were evaluated in the current study. Clubroot incidence in
‘InVigor 5030 LL’ differed depending on seeding date and weather variables, with higher
incidence in June and July seedings and at warmer temperatures. Similarly, ‘B-2819’
responded with higher clubroot incidence when grown in high inoculum soils compared
to low inoculum soils, which contrasts the incidences of ‘Bronco’ and ‘Kilaherb’ which
were consistently completely susceptible and resistant, respectively. The variability in
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response of the partially resistant cultivars may indicate that symptom development is
more responsive to changes in inoculum pressure or environmental variables in these
lines than in resistant cultivars. This means that these partially resistant cultivars could
reduce clubroot symptoms to acceptable levels similar to those of completely resistant
cultivars when growing conditions are not optimal for clubroot development, but resting
spores are still present in the field. Their use would also be advantageous in reducing the
use of cultivars with resistance that is at risk of being broken down through applying high
selection pressure on P. brassicae.
Based on previous evidence of up-regulation of auxins and cytokinins during
clubroot development (Dekhuijzen and Overeem, 1971; Gravot et al., 2012), plant
hormones are good candidates for further study in cabbage cultivars ‘Kilaherb’ and ‘B-
2819’. In an experiment using microarrays to analyze the transcriptome of clubroot
development in Arabidopsis, there was an association between up-regulation of cytokinin
receptor genes, down-regulation of cytokinin degradation, and clubroot symptom
development. Auxin related nitrilases, enzymes and transport proteins were also up-
regulated (Siemens et al., 2006).
When subsequent studies on the interactions associated with clubroot
development are undertaken, one potential hypothesis that should be considered is that
lower auxin and/or cytokinin levels may be present in the resistant cultivar ‘Kilaherb’
compared to the moderately susceptible cultivar ‘B-2819’ following 14, 21, 28, 35, 42
DAI with P. brassicae resting spores. The null hypothesis is that there is no difference in
auxin and cytokinin levels between ‘Kilaherb’ and ‘B-2819’ inoculation by P. brassicae.
This hypothesis is based on the report that over-expression of cytokinin
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oxidase/dehydrogenase or arginase-encoding gene ARGAH2, a negative regulator of
auxin-induced root development, is associated with limited development of clubs caused
by P. brassicae (Gravot et al., 2012; Siemens et al., 2006)
One approach that could be used to examine differences in auxin and cytokinin
between cabbage cultivars is to extract, purify and quantify several individual hormones
at various time points after inoculation of the host with P. brassicae. This could be
accomplished by adapting the methodology for hormone extraction from Zhang et al.
(2010). The extraction can then be analyzed by enzyme-linked immunosorbent assay with
monoclonal antigens and antibodies against hormones physiologically important to cell
division, isopentenyladenosine, trans-zeatin riboside (cytokinins) and indole-3-acetic acid
(auxin) (Matsumoto-Kitano et al., 2008). The absolute quantities of cytokinin and auxin
can be reported on a dry weight basis.
If differences in the quantities of cytokinins are identified, a follow-up experiment
can measure whether there are differences in cytokinin oxidase/dehydrogenase activity,
which is responsible for cytokinin degradation. The hypothesis for this portion of the
study would be that cytokinin oxidase/dehydrogenase activity is higher in the resistant
cultivar ‘Kilaherb’ compared to the moderately susceptible cultivar ‘B-2819’. The null
hypothesis is that there is no difference in cytokinin oxidase/dehydrogenase activity
between ‘Kilaherb’ and ‘B-2819’ inoculated with P. brassicae.
This hypothesis could also be assessed using an adaption of the methodology for
enzyme extraction described above (Zhang et al. (2010). In this method, samples of roots
harvested at 14, 21, 28, 35, 42 DAI were ground using a mortar and pestle in liquid
nitrogen and TRIS-HCl buffer with phenylmethylsulphonyl fluoride and Triton X-100.
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The material was then centrifuged to remove debris. The extract was then loaded into a
Sephadex G-25 column and the protein fraction was obtained. The activity of the protein
fraction was assessed by Bradford protein-dye binding, where the fraction was first
incubated with McIlvaine buffer electron acceptor 2,3-dimethoxy-5-methyl-1,4-
benzoquinone and substrate iPR for 2-10 hours at 37 °C, and a spectrophotometer was
used to measure the absorbance. Using a standard curve created from samples of bovine
serum albumin, the concentration of cytokinin oxidase/dehydrogenase was extrapolated
(Bradford, 1976).
The parallel use of root hair staining and microscopy with qPCR in this project
allowed for some inference into the role of the developmental stages of primary infection
and on the utility of qPCR to accurately measure the growth of P. brassicae. Microscopy
is still the gold standard for study of root hair infection throughout this process. Future
use of qPCR on P. brassicae pathosystems might focus on study of the secondary
infection stage of clubroot, at least 21 DAI under optimal temperature conditions, when
primary infection is expected to be complete and differences in secondary infection are
large enough to be measured.
In Chapter 4, the correlation among gDNA of P. brassicae with root hair infection
and percent area of cortical infection and the number of infected cells (mature plasmodia
and resting spores) on cabbage was very high. However in Chapter 2, there was no
correlation between gDNA and root hair infection in canola. The specific reason for this
discrepancy is not known. Compared to the assessments at 4 and 8 DAI in the cabbage
trial, the temperature trial was assessed at 10 DAI. It is possible that the relationship was
confounded by a significant change in P. brassicae in planta gDNA associated with the
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transition from the primary infection stage to the secondary infection stage. In the
transition between primary and secondary infection, a portion of P. brassicae gDNA that
was initially present in root hairs may not be captured with DNA extraction and qPCR.
The proportion of secondary zoospores that successfully re-infect roots is not known.
The cortical sectioning and staining process is an effective method for study of
cortical infection. However, it can be hampered by uneven cross sections that tear the
root tissue, making analysis difficult. Additional individual image processing with
software like Adobe Photoshop or GNU Image Manipulation Program (GIMP) can be
used to improve visualization of the pathogen by Assess software, and identification and
count of infection of cells needs to be done manually. These two processes have the
potential for introducing experimenter bias. As a result, large differences could arise
between experiments by different researchers. Also, the process is laborious and time
consuming. Despite this, cortical sectioning with staining and microscopy has the
advantage of tracking the developmental stages of the pathogen in the secondary
infection phase of the life stage. Implementation of quantitative PCR where information
of the life stage is not necessary has the advantage of being high throughput and allows
for larger sample sizes.
Future research focused on clubroot management should examine the effect of
soil moisture and its interaction with temperature and host resistance on clubroot
development. This can be accomplished through further use of the ThetaProbe to measure
volumetric soil moisture, which was tested but not fully utilized in this project.
A great deal remains unknown about the evolution and ecology of P. brassicae. It
is still not fully known why the pathogen evolved separate primary and secondary
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infection phases. It is possible that root hair infection was the initial mechanism of
parasitism that is now a vestigial behavior and may be circumvented via cortical
infection. Studies have already been conducted to examine differences in virulence
between primary and secondary zoospores and their interactions with hosts. So far, it has
been demonstrated that secondary zoospores can cause both root hair and cortical
infection simultaneously (Feng et al., 2012a; Feng et al., 2012c). A follow-up study to
this is underway (K. Sharma, personal communication). The preliminary results indicate
that secondary zoospores can cause root hair and cortical infection on canola, even on
resistant cultivars. However, the phenotype of clubbing on the resistant cultivars was
unusually small and bead like. The assumption that fusion of P. brassicae nuclei into
diploids and meiosis back to haploids occurs with some regularity is unconfirmed
(Kageyama and Asano, 2009). A study is underway to examine this theory (A. Deora,
personal communication). Plasmogamy or karyogamy between secondary zoospores was
not observed in these assessments. Research on the reproductive biology of P. brassicae
should focus on further observation and possible in vitro induction of secondary zoospore
fusion. The cytokinetics of P. brassicae with hosts should be investigated with a time
series of root hair and cortical infection. The processes of penetration and encystment
into cortical tissue by secondary zoospores are also of interest with respect to clubroot
resistance. Additionally, the karyology of the secondary infection life stage of
P. brassicae should be examined to characterize if and when nuclei fuse and dikaryons
form.
Another factor that is not well understood in the ecology and genetics of
P. brassicae is its population dynamics. Several differential sets of hosts are currently in
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use worldwide for differentiating populations of the pathogen. These sets have a lot of
utility in the pathology of clubroot and for the breeding of resistant crops, but are
incomplete systems for study of the population genetics of P. brassicae. How the new
demes of pathotype 3 in the Prairies arose, became more virulent, and what their genetic
relationships are to the pathotype 6 populations in Ontario are still unknown. Since the
pathogen is resistant to axenic culturing, genetic studies on the organism are difficult.
Genomic characteristics such as gene expression, recombination rates and mutation rates
would all be useful information to further the understanding of the biology of
P. brassicae.
In summary, this project has evaluated the effect of diurnal fluctuating
temperature on root hair infection and secondary development of P. brassicae. It is the
first study on clubroot to utilize the highly precise and accurate thermal gradient plate to
control soil temperatures. It found only a minor difference between constant and
fluctuating mean temperatures on the incidence of primary plasmodia, specifically at the
cooler temperature mean of 15 °C; the concentration of P. brassicae in planta is not
affected by the range of fluctuation around the mean temperatures of 17.5, 20 and 25 °C.
Lastly the optimal temperature of development was found to be 25 °C which is consistent
with previous estimates (Sharma et al., 2011a). This project also initiated the first
modeling of clubroot development on canola with respect to environmental variables.
The temperature parameters modeled to estimate disease development in this study were
similar to the parameters of previous models of disease development on radish and Asian
vegetables. These studies on temperature effects on clubroot inform researchers that
implementation of constant mean temperatures between 17.5 and 25 °C are a valid
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methodology for study of temperature effects on clubroot, and that diurnal temperatures
are not necessary for approximating field conditions. These studies also confirm the
important role of temperature accumulation on development of clubroot. This project was
the first study to show the potential of a quantitative clubroot resistance trait in a cabbage
cultivar and is a starting point to investigate the biochemical mechanisms of this
resistance. It confirms that clubroot resistance is expressed via suppression of cortical
infection in cabbage, and likely many other hosts. The trends in pathogen development
found between resistant and susceptible cabbage cultivars contrast with the findings of
previous research in canola, in that cortical infection was observable and P. brassicae
gDNA was detectable in a cultivar showing no overt clubbing symptoms. This may
indicate that more than one source of clubroot resistance has been used in the breeding
for clubroot resistance in various Brassica crops. Microscopy and qPCR showed a strong
relationship between the amount of pathogen visually observed and the amount of
pathogen gDNA detected molecularly. It also showed that the relationship can be
observed in the primary (4 and 8 DAI) and secondary (28 DAI) infection stages and they
were more strongly correlated with specific developmental stages of the pathogen
(primary plasmodia and resting spores). This has implications for the design of future
experiments. Studies investigating differences in the development of P. brassicae using
molecular approaches should consider timing evaluations and comparisons when the
primary plasmodia stage or resting spore stage is dominant. This project found that
microscopic and molecular techniques for quantifying pathogen growth can be used
together synergistically for more detailed and informative analyses of P. brassicae
development and host reaction to clubroot infection.
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REFERENCES
Adhikari, K. K. 2010. Effect of temperature, biofungicides and fungicides on clubroot of selected brassica crops. M.Sc. thesis, University of Guelph, Guelph, Ontario.
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APPENDIX 1: ANOVA TABLES FOR CHAPTER TWO Table A1.1 Root hair infection: Total incidence; Temp 12.5-30°C Source df Mean Square F value Pr>F Block(Repetition) 6 341.85 3.37 0.0037Repetition 1 1.29 0.01 0.9104Temperature Range (R) 1 1667.03 16.42 <.0001Temperature (T) 4 1674.08 16.49 <.0001 T linear (1) 1459.15 14.37 0.0002 T quadratic (1) 3238.04 31.89 <.0001 T residual (2) 1002.34 9.87 <.0001R × T 4 208.74 2.06 0.0891 R × T linear (1) 412.52 4.06 0.0455 R × T quadratic (1) 104.48 1.03 0.3119 R × T residual (2) 158.98 1.57 0.2121Error 161 101.53 RHI Total = -13.08 + 6.30x - 0.14x2, R2=0.52, where ‘x’ is equal to mean temperature
Table A1.2 Root hair infection: Primary plasmodia; Temp 12.5-30°C Source df Mean Square F value Pr>F Block(Repetition) 6 5.24 5.24 0.0001Repetition 1 0.01 0.00 0.9948Temperature Range (R) 1 2624.64 22.60 <.0001Temperature (T) 4 416.94 3.59 0.0078 T linear (1) 1154.82 9.95 0.0019 T quadratic (1) 4.30 0.04 0.8476 T residual (2) 254.58 2.19 0.1150R × T 4 30.79 0.27 0.9000 R × T linear (1) 2.07 0.02 0.8941 R × T quadratic (1) 0.95 0.01 0.9279 R × T residual (2) 60.07 0.52 0.5971Error 161 116.11 RHI pp = 49.54 - 0.40x, where ‘x’ is equal to mean temperature
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Table A1.3 Root hair infection: Mature zoosporangia; Temp 12.5-30°C Source df Mean Square F value Pr>F Block(Repetition) 6 63.34 1.48 0.1878Repetition 1 0.85 0.02 0.8883Temperature Range (R) 1 59.18 1.38 0.2412Temperature (T) 4 2343.73 54.79 <.0001 T linear (1) 3657.27 85.50 <.0001 T quadratic (1) 2864.33 66.96 <.0001 T residual (2) 1426.46 33.35 <.0001R × T 4 213.46 2.09 0.0901 R × T linear (1) 362.64 8.48 0.0041 R × T quadratic (1) 141.73 3.31 0.0706 R × T residual (2) 174.73 4.08 0.0186Error 161 42.77 RHI mz = -57.55 + 6.21 - 0.13x2; R2=0.63, where ‘x’ is equal to mean temperature
Table A1.4 Root hair infection: Dehisced zoosporangia; Temp 20-30°C Source df Mean Square F value Pr>F Block(Repetition) 6 4.32 1.21 0.3039Repetition 1 0.08 0.02 0.8795Temperature Range (R) 1 11.59 2.06 0.1534Temperature (T) 4 81.79 13.67 <.0001 T linear (1) 137.03 38.41 <.0001 T quadratic (1) 29.79 8.35 0.0044 T residual (2) 65.56 18.38 <.0001R × T 4 2.07 0.58 0.6767 R × T linear (1) 7.32 2.05 0.1541 R × T quadratic (1) 0.50 0.14 0.7090 R × T residual (2) 0.24 0.07 0.9350Error 161 3.57 RHI dz = -61.81 + 5.14x – 0.10 x2, R2=0.88, where ‘x’ is equal to mean temperature
Table A1.5 Root hair infection: Total incidence Random effects Estimate Standard error Z value Pr>Z Block(Repetition) 10.99 9.37 1.17 0.1203Residual 107.62 12.35 8.71 <.0001Fixed effects Numerator df Denominator df F value Pr>F Temperature Range (R) 1 152 15.55 <.0001Temperature (T) 4 152 15.54 <.0001R × T 4 152 1.94 0.1071Repetition 1 6 0.00 0.9650Repetition × R × T 9 152 0.00 1.0000
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Table A1.6 Root hair infection: Primary plasmodia Random effects Estimate Standard error Z value Pr>Z Block(Repetition) 21.89 15.88 1.38 0.0839 Residual 122.99 14.11 8.72 <.0001 Fixed effects Numerator df Denominator df F value Pr>F Temperature Range (R) 1 152 21.45 <.0001 Temperature (T) 4 152 3.37 0.0112 R × T 4 152 0.25 0.9091 Repetition 1 6 0.00 0.9984 Repetition × R × T 9 152 0.00 1.0000
Table A1.7 Root hair infection: Mature zoosporangia Random effects Estimate Standard error Z value Pr>Z Block(Repetition) 0.96 1.61 0.60 0.2754Residual 45.15 5.16 8.75 <.0001Fixed effects Numerator df Denominator df F value Pr>F Temperature Range (R) 1 152 1.38 0.2421Temperature (T) 4 152 51.93 <.0001R × T 4 152 4.73 0.0008Repetition 1 6 0.00 0.9693Repetition × R × T 9 152 0.00 1.0000 Table A1.8 Root hair infection: Dehisced zoosporangia Random effects Estimate Standard error Z value Pr>Z Block(Repetition) 0.03 0.09 0.30 0.3834Residual 3.55 0.39 9.01 <.0001Fixed effects Numerator df Denominator df F value Pr>F Temperature Range (R) 1 152 2.05 0.1544Temperature (T) 4 152 19.76 <.0001R × T 4 152 0.60 0.6647Repetition 1 6 0.00 0.9564Repetition × R × T 9 152 0.00 1.0000
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Table A1.9 qPCR: Fluctuating and Constant Temperatures, Exp 1 and 2 (Mean Temp 10-35°C) Source df Mean Square F value Pr>F Block(Repetition) 6 0.02 0.25 0.9596Repetition 1 0.04 0.39 0.5346Temperature Range (R) 1 0.19 2.00 0.1603Temperature (T) 10 0.71 7.31 <.0001 T linear (1) 3.72 38.40 <.0001 T quadratic (1) 0.57 5.92 0.0170 T cubic (1) 0.94 9.66 0.0025 T residual (7) 0.25 2.56 0.0191R × T 8 0.03 0.28 0.9704 R × T linear (1) 0.00 0.00 0.9898 R × T quadratic (1) 0.00 0.03 0.8553 R × T cubic (1) 0.02 0.16 0.6919 R × T residual (5) 0.04 0.41 0.8389Error 89 0.10 gDNA = 3.46 - 0.42x + 0.02x2 - 0.0002x3, R2=0.74, where ‘x’ is equal to mean temperature
Table A1.10 qPCR: Fluctuating and Constant Temperatures, Exp 1 and 2 (Mean Temp 10-35°C) Random effects Estimate Standard error Z value Pr>Z Block(Repetition) 0.00 . . . Residual 0.09 0.001 6.93 <.0001Fixed effects Numerator df Denominator df F value Pr>F Temperature Range (R) 1 74 1.68 0.1990Temperature (T) 10 74 7.47 <.0001R × T 8 74 0.27 0.9751Repetition 1 74 0.17 0.6791R × T × Repetition 19 74 0.58 0.6791
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Table A1.11 qPCR: Ranges of Fluctuating Temperatures, Exp 1 and 2 (Mean 15-20 °C, Range 0-15 °C) Random effects Estimate Standard error Z value Pr>Z Block(Repetition) 9.31 12.79 0.73 0.2334Residual 98.80 21.56 4.58 <.0001Fixed effects Numerator df Denominator df F value Pr>F Temperature Range (R) 2 42 1.36 0.2683Temperature (T) 2 2 0.38 0.6850R × T 3 2 1.79 0.1630Repetition 1 6 17.71 0.0056R × T × Repetition 7 42 1.29 0.2802
Table A1.12 qPCR: Ranges of Fluctuating Temperatures, Exp 1 (Mean 15-20 °C, Range 0-15 °C) Random effects Estimate Standard error Z value Pr>Z Block 0.00 0.00 0.22 0.4139Residual 0.00 0.00 3.24 0.0006Fixed effects Numerator df Denominator df F value Pr>F Temperature Range (R) 2 21 0.76 0.4816Temperature (T) 2 21 1.98 0.1636R × T 3 21 1.51 0.2420
Table A1.13 qPCR: Ranges of Fluctuating Temperatures, Exp 2 (Mean 15-20 °C, Range 0-15 °C) Random effects Estimate Standard error Z value Pr>Z Block 18.62 36.18 0.51 0.3034Residual 197.59 60.97 3.24 0.0006Fixed effects Numerator df Denominator df F value Pr>F Temperature Range (R) 2 21 1.36 0.2787Temperature (T) 2 21 0.38 0.6873R × T 3 21 1.80 0.1789
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APPENDIX 2: ANOVA TABLES FOR CHAPTER THREE Table A2.1 Clubroot Incidence: 2011 Source df Mean Square F value Pr>F Block 3 1897.06 8.98 <.0001Seeding Date (S) 3 602.35 2.85 0.0443Time (T) 8 1875.03 8.88 <.0001 T linear (1) 14192.96 67.21 <.0001 T quadratic (1) 142.25 0.67 0.4149 T residual (6) 110.84 0.52 0.7873S × T 10 177.98 0.84 0.5899 S × T linear (2) 215.61 1.02 0.3661 S × T quadratic (2) 285.38 1.35 0.2663 S × T residual (6) 129.64 0.61 0.7183Error 63 211.17 CI1 = 24.5 CI2 = 21.6 CI3 = 29.8 CI4 = 16.6 Table A2.2 Disease severity index: 2011 Source df Mean Square F value Pr>F Block 3 397.77 6.72 0.0005Seeding Date (S) 3 134.27 2.27 0.0893Time (T) 8 370.26 6.25 <.0001 T linear (1) 2824.58 47.69 <.0001 T quadratic (1) 35.52 0.60 0.4416 T residual (6) 17.00 0.29 0.9410S × T 10 43.75 0.74 0.6854 S × T linear (2) 69.00 1.17 0.3185 S × T quadratic (2) 68.51 1.16 0.3211 S × T residual (6) 68.59 0.46 0.8371Error 63 59.22 DSI Mean = 10.4
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Table A2.3 Clubroot incidence: 2012 Source df Mean Square F value Pr>F Block 2 1479.35 11.24 <.0001Seeding Date (S) 5 2939.26 22.34 <.0001Time (T) 8 2221.31 16.88 <.0001 T linear (1) 11588.34 88.07 <.0001 T quadratic (1) 4116.46 31.28 <.0001 T residual (6) 296.96 2.26 0.0459S × T 31 476.40 3.62 <.0001 S × T linear (5) 530.62 4.03 0.0026 S × T quadratic (5) 758.94 5.77 0.0001 S × T residual (21) 396.22 3.01 0.0002Error 81 131.58 CI1 = -191.61 + 64.04x - 4.41x2, R2=0.75, where ‘x’ is equal to weeks after seeding CI2 = -39.06 + 16.38x - 0.81x2, R2=0.77, where ‘x’ is equal to weeks after seeding CI3 = -141.87 + 47.92x - 2.68x2, R2=0.82, where ‘x’ is equal to weeks after seeding CI4 = -85.95 + 40.41x - 2.59x2, R2=0.66, where ‘x’ is equal to weeks after seeding CI5 = 71.62 -5.77x + 0.46x2, R2=0.05, where ‘x’ is equal to weeks after seeding CI6 = -40.86 + 22.65x - 1.35x2, R2=0.89, where ‘x’ is equal to weeks after seeding Table A2.4 Disease Severity Index: 2012 Source df Mean Square F value Pr>F Block 2 757.06 10.29 0.0001Seeding Date (S) 5 2013.38 27.37 <.0001Time (T) 8 1189.00 16.16 <.0001 T linear (1) 8505.74 115.63 <.0001 T quadratic (1) 704.82 9.58 0.0027 T residual (6) 121.10 1.65 0.1451S × T 31 132.12 1.80 0.0192 S × T linear (5) 191.28 2.60 0.0312 S × T quadratic (5) 151.20 2.06 0.0795 S × T residual (21) 113.49 1.54 0.0862Error 81 73.56 DSI1 = -64.17 + 21.43x -1.47x2, R2=0.76, where ‘x’ is equal to weeks after seeding DSI2 = = -16.08 + 6.46x -0.31x2, R2=0.76,where ‘x’ is equal to weeks after seeding DSI3 = -68.52 + 21.29x -1.08x2, R2=0.70,where ‘x’ is equal to weeks after seeding DSI4 = -65.83 + 27.47x -1.72x2, R2=0.79, where ‘x’ is equal to weeks after seeding DSI5 = 0.28 + 4.41x -0.02x2, R2=0.79, where ‘x’ is equal to weeks after seeding DSI6 = -51.52 + 19.88x -1.102x, R2=0.85,where ‘x’ is equal to weeks after seeding
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APPENDIX 3: SUPPLEMENATRY TABLES FOR CHAPTER THREE Table A3.1 Comparison of canola and Chinese flowering cabbage clubroot incidence over time in the field in 2008, 2009, 2011, and 2012 using regression equation: Y = - 0.34804 + 0.12955 × (Air °D) Clubroot Incidence (%)
Seeding Harvest Observed Predicted Deviation (Predicted –
Observed) 1999/06/01 4 45.4 22.5 -22.9 1999/08/09 6 1.9 22.5 20.6 2001/06/01 6 38.3 25.9 -12.4 2002/05/21 5 25.0 17.8 -7.2 2002/05/24 6 50.0 27.2 -22.8 2002/07/24 5 24.2 29.8 5.6 2008/05/13 5 12.4 23.4 11.0 2008/06/11 1 0.0 14.0 14.0 2008/06/11 2 4.3 18.8 14.6 2008/06/11 3 18.1 24.3 6.2 2008/06/11 4 36.0 31.7 -4.4 2008/07/09 1 0.0 14.2 14.2 2008/07/09 2 6.6 19.9 13.3 2008/07/09 4 61.0 27.4 -33.6 2008/07/09 5 63.8 31.2 -32.6 2008/08/06 1 0.0 7.2 7.2 2008/08/06 2 0.0 10.4 10.4 2008/08/06 3 0.0 15.3 15.3 2008/08/06 4 0.9 17.3 16.4 2008/09/03 1 0.0 5.9 5.9 2009/06/11 3 59.9 16.4 -43.5 2009/07/08 3 47.8 15.0 -32.8 2009/07/08 4 57.8 20.2 -37.5 2009/08/05 3 0.0 19.2 19.2 2009/08/05 4 1.0 22.5 21.5 2009/08/05 5 17.3 23.5 6.2 2009/09/02 1 0.0 7.2 7.2 2009/09/02 2 0.0 7.4 7.4 2009/09/02 4 0.0 7.4 7.4 2011/05/25 1 1.0 11.1 10.1 2011/05/25 3 15.5 20.8 5.3 2011/05/25 4 22.0 27.3 5.3 2011/05/25 7 35.8 52.0 16.1 2011/06/10 1 2.9 18.2 15.3 2011/06/10 4 12.0 42.8 30.8 2011/06/10 5 24.6 50.6 26.0 2011/06/10 6 36.5 56.8 20.3
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2011/06/10 7 39.0 63.7 24.7 2011/06/10 8 39.2 69.7 30.6 2011/06/22 1 1.5 37.2 35.6 2011/06/22 2 29.0 44.9 15.9 2011/07/06 1 16.6 40.7 24.1 2012/05/02 3 31.9 18.3 -13.6 2012/05/02 4 47.0 24.5 -22.5 2012/05/02 5 59.5 32.9 -26.5 2012/05/02 6 22.3 39.9 17.6 2012/05/16 1 8.5 16.7 8.2 2012/05/16 5 28.7 44.5 15.8 2012/05/16 8 46.0 66.8 20.8 2012/05/30 2 23.3 38.0 14.7 2012/05/30 3 49.6 43.3 -6.2 2012/05/30 5 58.9 52.9 -6.0 2012/05/30 6 75.6 57.7 -17.8 2012/05/30 7 54.3 63.3 9.0 2012/06/13 3 66.0 44.8 -21.2 2012/06/13 5 60.0 59.1 -0.9 2012/06/27 1 71.3 31.9 -39.4 2012/06/27 2 34.7 37.7 3.0 2012/06/27 3 46.7 46.2 -0.5 2012/06/27 9 59.3 69.8 10.4 2012/07/11 3 44.7 38.6 -6.1 2012/07/11 6 52.7 52.7 0.1 Sum 173.1 Mean bias1 2.8 1 Mean bias = sum / 62 Table A3.2 Comparison of canola and Chinese flowering cabbage clubroot severity over time in the field in 2008, 2009, 2011, and 2012 using regression equation: Y = 4.72524 + 0.08780 × (Soil °D (1 Week Delay)) - 0.02459 × (Season Total Rainfall (1Week Delay)) Clubroot Severity
Seeding Harvest Observed Predicted Deviation (Predicted –
Observed) 1999/06/01 4 26.2 -1.8 -28.0 1999/08/09 6 0.5 3.0 2.5 2001/06/01 6 22.8 0.2 -22.6 2002/05/21 5 17.2 -6.4 -23.6 2002/05/24 6 36.9 -4.0 -40.9 2002/07/24 5 19.2 7.3 -11.9 2008/05/13 5 8.7 -6.4 -15.1 2008/06/11 1 0.0 -6.9 -6.9 2008/06/11 2 1.1 -1.7 -2.8 2008/06/11 3 9.6 2.9 -6.8
182
2008/06/11 4 23.7 7.4 -16.3 2008/07/09 1 0.0 -4.8 -4.8 2008/07/09 2 2.2 0.5 -1.7 2008/07/09 4 42.1 9.1 -33.0 2008/07/09 5 48.3 11.4 -36.9 2008/08/06 1 0.0 -7.4 -7.4 2008/08/06 2 0.0 -5.9 -5.9 2008/08/06 3 0.0 -2.8 -2.8 2008/08/06 4 0.3 0.1 -0.3 2008/09/03 1 0.0 -8.2 -8.2 2009/06/11 3 29.5 2.3 -27.2 2009/07/08 3 15.9 -2.1 -18.0 2009/07/08 4 26.4 1.5 -24.8 2009/08/05 3 0.0 1.6 1.6 2009/08/05 4 0.3 3.8 3.4 2009/08/05 5 5.8 6.4 0.6 2009/09/02 1 0.0 -8.2 -8.2 2009/09/02 2 0.0 -7.4 -7.4 2009/09/02 4 0.0 -7.8 -7.8 2011/05/25 1 0.3 -1.4 -1.8 2011/05/25 3 7.3 3.2 -4.1 2011/05/25 4 10.7 5.7 -5.0 2011/05/25 7 14.8 18.2 3.5 2011/06/10 1 1.0 -0.7 -1.7 2011/06/10 4 4.5 13.3 8.8 2011/06/10 5 9.5 17.0 7.5 2011/06/10 6 15.6 21.2 5.6 2011/06/10 7 16.8 24.1 7.3 2011/06/10 8 16.9 29.0 12.1 2011/06/22 1 0.5 4.6 4.1 2011/06/22 2 10.8 12.8 2.0 2011/07/06 1 7.4 7.9 0.5 2012/05/02 3 11.2 -2.2 -13.5 2012/05/02 4 15.9 1.2 -14.7 2012/05/02 5 21.0 4.4 -16.6 2012/05/02 6 8.3 8.6 0.3 2012/05/16 1 2.8 -1.0 -3.8 2012/05/16 5 11.1 13.0 1.9 2012/05/16 8 19.3 25.1 5.8 2012/05/30 2 7.8 13.0 5.2 2012/05/30 3 37.1 17.0 -20.1 2012/05/30 5 25.2 24.3 -0.9 2012/05/30 6 48.2 26.5 -21.6 2012/05/30 7 31.5 28.1 -3.4 2012/06/13 3 37.1 15.1 -22.0
183
2012/06/13 5 38.2 20.8 -17.4 2012/06/27 1 23.8 8.4 -15.4 2012/06/27 2 14.2 11.9 -2.3 2012/06/27 3 22.9 14.1 -8.8 2012/06/27 9 49.8 33.9 -15.9 2012/07/11 3 25.9 13.6 -12.3 2012/07/11 6 0.0 24.4 -13.6 Sum -511.6 Mean bias1 -8.3 1 Mean bias = sum / 62 Table A3.3 Comparison of canola and Chinese flowering cabbage clubroot severity over time in the field in 2008, 2009, 2011, and 2012 using regression equation: Y = 4.72524 + 0.08780 × (Soil °D (1 Week Delay)) - 0.02459 × (Season Total Rainfall (1Week Delay)) Clubroot Severity
Seeding Harvest Observed Predicted Deviation (Predicted –
Observed) 1999/06/01 4 45.4 50.6 5.2 1999/08/09 6 1.9 27.9 26.0 2001/06/01 6 38.3 31.5 -6.8 2002/05/21 5 25.0 31.6 6.6 2002/05/24 6 50.0 47.5 -2.5 2002/07/24 5 24.2 35.7 11.5 2008/05/13 5 12.4 49.1 36.7 2008/06/11 4 36.0 66.7 30.6 2008/07/09 5 63.8 32.9 -30.9 2009/08/05 5 17.3 32.0 14.7 2009/09/02 4 0.0 30.9 30.9 2011/06/10 8 39.2 61.3 22.1 2011/07/06 1 16.6 66.1 49.5 2012/05/02 6 22.3 46.5 24.2 2012/05/16 8 46.0 72.3 26.3 2012/06/27 9 59.3 40.5 -18.8 Sum 225.4 Mean bias1 14.1 1 Mean bias = sum / 16
184
Table A3.4 End point stepwise regression of clubroot incidence, severity, and selected environmental variables (air and soil degree days, rainfall), during various time intervals for canola grown at the Holland Marsh, ON, 2011.
Step Parameter Partial R2 Model R2 F Value Pr > FDSI 1 Rain First 2 Weeks 0.9333 0.9333 27.99 0.0339 DI 1 Rain First 2 Weeks 0.9614 0.9614 49.86 0.0195
2 Season Total (1 Week Delay)
0.0380 0.9946 64.57 0.0788
Table A3.5 Stepwise regression of clubroot incidence, severity, and selected environmental variables (air and soil degree days, rainfall), during the growth period for canola grown at the Holland Marsh, ON, 2011.
Step ParameterPartial
R2Model
R2 C (p) † F Value Pr > FDSI 1 Soil °D (1
Week Delay) 0.6428 0.6428 1.2942 35.99 < 0.0001
2 Season Total Rainfall (1
Week Delay)
0.0711 0.7139 -0.5473 4.72 0.0426
DI 1 Soil °D (1 Week Delay)
0.6994 0.6994 3.6372 46.53 < 0.0001
2 Season Total Rainfall (1
Week Delay)
0.0786 0.7780 -0.0188 6.72 0.0178
Table A3.6 End point stepwise regression of clubroot incidence, severity, and selected environmental variables (air and soil degree days, rainfall), during various time intervals for canola grown at the Holland Marsh, ON, 2012.
Step Parameter Partial R2 Model R2 F Value Pr > F
DSI 1 Season Total Rainfall (1 Week
Delay)
0.9255 0.9255 104.51 0.0020
2 Rainfall (First 2 Weeks)
0.0486 0.9741 5.62 0.0985
DI 1 Soil Moisture (Last 2 Weeks)
0.9232 0.9232 48.05 0.0023
185
Table A3.7 Stepwise regression of clubroot incidence, severity, and selected environmental variables (air and soil degree days, rainfall), during the growth period for canola grown at the Holland Marsh, ON, 2012.
Step Parameter Partial R2 Model R2 F Value Pr > F
DSI 1 Soil °D 0.6505 0.6505 80.05 <0.0001 DI 1 Soil °D 0.4968 0.4968 42.46 <0.0001
186
Table A3.8 Linear correlations between air and soil degree days, rainfall, and soil moisture for 10 seeding dates of ‘InVigor 5030 LL’ grown at the Holland Marsh, ON, 2011 and 2012.
Parameter N Air DDSoil DD
Air DD 1W
Delay
Soil DD 1W Delay
Season Total
Season Total 1W
Delay Air DD 67 0.99 0.41 0.42 0.77 0.12
<.0001 0.0006 0.0004 <.0001 ns Soil DD 67 0.99 0.46 0.49 0.80 0.20
<.0001 <.0001 <.0001 <.0001 ns Air DD 1W Delay 67 0.41 0.46 0.99 0.29 0.67
0.0006 <.0001 <.0001 0.0161 <.0001Soil DD 1W Delay 67 0.42 0.49 0.99 0.36 0.71
0.0004 <.0001 <.0001 0.0027 <.0001Season Total 67 0.77 0.80 0.29 0.36 0.28
<.0001 <.0001 0.0161 0.0027 0.0233Season Total 1W Delay 67 0.12 0.20 0.67 0.71 0.28
ns ns <.0001 <.0001 0.0233 Rain (First 2 Weeks) 10 0.52 0.53 -0.21 -0.06 0.57 -0.18
ns ns ns ns ns ns Rain (First 3 Weeks) 10 0.57 0.60 -0.04 0.12 0.68 -0.01
ns ns ns ns 0.0309 ns Rain (Last 2 Weeks) 10 0.91 0.89 0.49 0.47 0.88 -0.08
0.0003 0.0005 ns ns 0.0008 ns Rain (Last 3 Weeks) 10 0.84 0.84 0.50 0.49 0.82 -0.08
0.0022 0.0022 ns ns 0.0034 ns Air (First 2 Weeks) 10 0.91 0.96 0.52 0.61 0.98 0.22
0.0003 <.0001 ns ns <.0001 ns Air (First 3 Weeks) 10 0.89 0.95 0.49 0.61 0.97 0.26
0.0005 <.0001 ns ns <.0001 ns Air (Last 2 Weeks) 10 0.71 0.62 0.00 -0.09 0.56 -0.57
0.0211 ns ns ns ns ns Air (Last 3 Weeks) 10 0.82 0.74 0.06 -0.01 0.69 -0.52
0.0038 0.0147 ns ns 0.0287 ns Soil (First 2 Weeks) 10 0.85 0.91 0.50 0.63 0.94 0.30
0.002 0.0002 ns ns <.0001 ns Soil (First 3 Weeks) 10 0.84 0.91 0.50 0.64 0.94 0.33
0.0025 0.0003 ns 0.0482 <.0001 ns Soil (Last 2 Weeks) 10 0.85 0.78 0.17 0.11 0.73 -0.42
0.002 0.0074 ns ns 0.0173 ns Soil (Last 3 Weeks) 10 0.91 0.86 0.20 0.16 0.81 -0.37
0.0002 0.0015 ns ns 0.0045 ns SoilM (First 2 Weeks) 6 -0.77 -0.73 -0.78 -0.70 -0.54 -0.51
ns ns ns ns ns ns SoilM (First 3 Weeks) 6 -0.71 -0.63 -0.71 -0.60 -0.41 -0.37
ns ns ns ns ns ns SoilM (Last 2 Weeks) 6 0.86 0.93 0.90 0.92 0.87 0.74
0.028 0.0074 0.0133 0.0091 0.0256 ns SoilM (Last 3 Weeks) 6 0.85 0.89 0.89 0.87 0.79 0.68
0.0316 0.0181 0.0188 0.0234 ns ns
187
Parameter N
Rain (First
2 Weeks)
Rain (First
3 Weeks)
Rain (Last 2
Weeks)
Rain (Last 3
Weeks)
Air (First
2 Weeks)
Air (First
3 Weeks)
Air DD 67 0.52 0.57 0.91 0.84 0.91 0.89 ns ns 0.0003 0.0022 0.0003 0.0005
Soil DD 67 0.53 0.60 0.89 0.84 0.96 0.95 ns ns 0.0005 0.0022 <.0001 <.0001
Air DD 1W Delay 67 -0.21 -0.04 0.49 0.50 0.52 0.49 ns ns ns ns ns ns
Soil DD 1W Delay 67 -0.06 0.12 0.47 0.49 0.61 0.61 ns ns ns ns ns 0.062
Season Total 67 0.57 0.68 0.88 0.82 0.98 0.97 ns 0.0309 0.0008 0.0034 <.0001 <.0001
Season Total 1W Delay 67 -0.18 -0.01 -0.08 -0.08 0.22 0.26
ns ns ns ns ns ns Rain (First 2 Weeks) 10 0.77 0.26 0.36 0.47 0.54
0.0091 ns ns ns ns Rain (First 3 Weeks) 10 0.77 0.51 0.40 0.66 0.64
0.0091 ns ns 0.0371 0.0465 Rain (Last 2 Weeks) 10 0.26 0.51 0.90 0.86 0.77
ns ns 0.0003 0.0015 0.0087 Rain (Last 3 Weeks) 10 0.36 0.40 0.90 0.79 0.73
ns ns 0.0003 0.0065 0.0162 Air (First 2 Weeks) 10 0.47 0.66 0.86 0.79 0.98
ns 0.0371 0.0015 0.0065 <.0001 Air (First 3 Weeks) 10 0.54 0.64 0.77 0.73 0.98
ns 0.0465 0.0087 0.0162 <.0001 Air (Last 2 Weeks) 10 0.55 0.29 0.60 0.70 0.41 0.39
ns ns ns 0.0236 ns ns Air (Last 3 Weeks) 10 0.58 0.40 0.70 0.74 0.54 0.52
ns ns 0.0229 0.0145 ns ns Soil (First 2 Weeks) 10 0.51 0.68 0.77 0.74 0.99 0.98
ns 0.0308 0.0086 0.0147 <.0001 <.0001 Soil (First 3 Weeks) 10 0.54 0.66 0.72 0.70 0.97 0.99
ns 0.039 0.0177 0.0241 <.0001 <.0001 Soil (Last 2 Weeks) 10 0.53 0.36 0.75 0.82 0.61 0.58
ns ns 0.012 0.0036 ns ns Soil (Last 3 Weeks) 10 0.57 0.45 0.81 0.83 0.70 0.68
ns ns 0.0048 0.0031 0.0239 0.0312 SoilM (First 2 Weeks) 6 0.50 0.45 -0.49 -0.59 -0.60 -0.55
ns ns ns ns ns ns SoilM (First 3 Weeks) 6 0.46 0.57 -0.41 -0.62 -0.45 -0.42
ns ns ns ns ns ns SoilM (Last 2 Weeks) 6 -0.37 -0.09 0.67 0.74 0.88 0.80
ns ns ns ns 0.0205 ns SoilM (Last 3 Weeks) 6 -0.41 -0.20 0.64 0.73 0.82 0.74
ns ns ns ns 0.0478 ns
188
Parameter N
Air (Last 2
Weeks)
Air (Last 3
Weeks)
Soil (First
2 Weeks)
Soil (First
3 Weeks)
Soil (Last 2
Weeks)
Soil (Last 3
Weeks) Air DD 67 0.71 0.82 0.85 0.84 0.85 0.91
0.0211 0.0038 0.002 0.0025 0.002 0.0002 Soil DD 67 0.62 0.74 0.91 0.91 0.78 0.86
ns 0.0147 0.0002 0.0003 0.0074 0.0015 Air DD 1W Delay 67 0.00 0.06 0.50 0.50 0.17 0.20
ns ns ns ns ns ns Soil DD 1W Delay 67 -0.09 -0.01 0.63 0.64 0.11 0.16
ns ns ns 0.0482 ns ns Season Total 67 0.56 0.69 0.94 0.94 0.73 0.81
ns 0.0287 <.0001 <.0001 0.0173 0.0045 Season Total 1W Delay 67 -0.57 -0.52 0.30 0.33 -0.42 -0.37
ns ns ns ns ns ns Rain (First 2 Weeks) 10 0.55 0.58 0.51 0.54 0.53 0.57
ns ns ns ns ns ns Rain (First 3 Weeks) 10 0.29 0.40 0.68 0.66 0.36 0.45
ns ns 0.0308 0.039 ns ns Rain (Last 2 Weeks) 10 0.60 0.70 0.77 0.72 0.75 0.81
ns 0.0229 0.0086 0.0177 0.012 0.0048 Rain (Last 3 Weeks) 10 0.70 0.74 0.74 0.70 0.82 0.83
0.0236 0.0145 0.0147 0.0241 0.0036 0.0031 Air (First 2 Weeks) 10 0.41 0.54 0.99 0.97 0.61 0.70
ns ns <.0001 <.0001 ns 0.0239 Air (First 3 Weeks) 10 0.39 0.52 0.98 0.99 0.58 0.68
ns ns <.0001 <.0001 ns 0.0312 Air (Last 2 Weeks) 10 0.98 0.33 0.31 0.97 0.93
<.0001 ns ns <.0001 <.0001 Air (Last 3 Weeks) 10 0.98 0.46 0.44 0.98 0.97
<.0001 ns ns <.0001 <.0001 Soil (First 2 Weeks) 10 0.33 0.46 0.99 0.53 0.63
ns ns <.0001 ns ns Soil (First 3 Weeks) 10 0.31 0.44 0.99 0.51 0.60
ns ns <.0001 ns ns Soil (Last 2 Weeks) 10 0.97 0.98 0.53 0.51 0.99
<.0001 <.0001 ns ns <.0001 Soil (Last 3 Weeks) 10 0.93 0.97 0.63 0.60 0.99
<.0001 <.0001 ns ns <.0001 SoilM (First 2 Weeks) 6 0.04 0.05 -0.57 -0.50 -0.31 -0.38
ns ns ns ns ns ns SoilM (First 3 Weeks) 6 -0.16 -0.13 -0.43 -0.37 -0.48 -0.54
ns ns ns ns ns ns SoilM (Last 2 Weeks) 6 -0.30 -0.33 0.84 0.78 0.04 0.09
ns ns 0.0352 ns ns ns SoilM (Last 3 Weeks) 6 -0.21 -0.24 0.78 0.71 0.14 0.20
ns ns ns ns ns ns
189
Parameter N
SoilM (First
2 Weeks)
SoilM (First
3 Weeks)
SoilM (Last 2
Weeks)
SoilM (Last 3
Weeks)
Time
Air DD 67 -0.77 -0.71 0.86 0.85 0.82878ns ns 0.028 0.0316 <.0001
Soil DD 67 -0.73 -0.63 0.93 0.89 0.31788ns ns 0.0074 0.0181 0.0088
Air DD 1W Delay 67 -0.78 -0.71 0.90 0.89 0.80777ns ns 0.0133 0.0188 <.0001
Soil DD 1W Delay 67 -0.70 -0.60 0.92 0.87 0.34723ns ns 0.0091 0.0234 0.004
Season Total 67 -0.54 -0.41 0.87 0.79 0.4894 ns ns 0.0256 ns ns
Season Total 1W Delay 67 -0.51 -0.37 0.74 0.68 0.45009
ns ns ns ns ns Rain (First 2 Weeks) 10 0.50 0.46 -0.37 -0.41 0.18078
ns ns ns ns ns Rain (First 3 Weeks) 10 0.45 0.57 -0.09 -0.20 0.48158
ns ns ns ns ns Rain (Last 2 Weeks) 10 -0.49 -0.41 0.67 0.64 0.45989
ns ns ns ns ns Rain (Last 3 Weeks) 10 -0.59 -0.62 0.74 0.73 0.24571
ns ns ns ns ns Air (First 2 Weeks) 10 -0.60 -0.45 0.88 0.82 0.2805
ns ns 0.0205 0.0478 ns Air (First 3 Weeks) 10 -0.55 -0.42 0.80 0.74 0.36347
ns ns ns ns ns Air (Last 2 Weeks) 10 0.04 -0.16 -0.30 -0.21 0.49406
ns ns ns ns ns Air (Last 3 Weeks) 10 0.05 -0.13 -0.33 -0.24 0.33982
ns ns ns ns ns Soil (First 2 Weeks) 10 -0.57 -0.43 0.84 0.78 0.40205
ns ns 0.0352 0.0667 ns Soil (First 3 Weeks) 10 -0.50 -0.37 0.78 0.71 0.4661
ns ns ns ns ns Soil (Last 2 Weeks) 10 -0.31 -0.48 0.04 0.14 0.50089
ns ns ns ns <.0001 Soil (Last 3 Weeks) 10 -0.38 -0.54 0.09 0.20 0.31475
ns ns ns ns 0.0095 SoilM (First 2 Weeks) 6 0.98 -0.86 -0.93
-0.34606
0.0008 0.0271 0.0075 ns SoilM (First 3 Weeks) 6 0.98 -0.79 -0.87
-0.29226
0.0008 0.063 0.0243 ns SoilM (Last 2 Weeks) 6 -0.86 -0.79 0.99 0.6187
0.0271 ns 0.0002 ns SoilM (Last 3 Weeks) 6 -0.93 -0.87 0.99 0.54498
0.0075 0.0243 0.0002 ns DSI 67 0.55278
<.0001 DI 67 0.5403
<.0001
190
APPENDIX 4: ANOVA TABLES FOR CHAPTER FOUR Table A4.1 Field Trial: Arcsine(CI); High inoculum 2011 and 2012, Low inoculum 2012; ‘Kilaherb’ excluded
Random effects Estimate Standard error Z value Pr>Z Block × (SiteYear) 0.00 . . . Residual 0.05 0.01 3.94 <.0001Fixed effects Numerator df Denominator df F value Pr>F Cultivar 2 22 7.81 0.0027SiteYear 2 9 40.87 <.0001 High 2011 vs 2012 (1) 9 0.22 0.6481 High vs Low (1) 9 81.74 <.0001Cultivar × SiteYear 4 22 2.39 0.0821 Table A4.2 Field Trial: Arcsine(DSI); High inoculum 2011 and 2012, Low inoculum 2012; ‘Kilaherb’ excluded
Random effects Estimate Standard error Z value Pr>Z Block × (SiteYear) 0.00 0.01 0.18 0.4293Residual 0.03 0.01 3.32 0.0005Fixed effects Numerator df Denominator df F value Pr>F Cultivar 2 22 45.12 <.0001SiteYear 2 9 92.54 <.0001 High 2011 vs 2012 (1) 9 1.87 0.2047 High vs Low (1) 9 181.12 <.0001Cultivar × SiteYear 4 22 2.34 0.0867 Table A4.3 Field Trial: Log(Yield); High inoculum 2011 and 2012, Low inoculum 2012
Random effects Estimate Standard error Z value Pr>Z Block × (SiteYear) 0.00 0.00 1.02 0.1534Residual 0.01 0.00 3.96 <.0001Fixed effects Numerator df Denominator df F value Pr>F Cultivar 3 31 55.08 <.0001SiteYear 2 9 24.06 0.0002 High 2011 vs 2012 (1) 9 0.18 0.6785 High vs Low (1) 9 48.03 <.0001Cultivar × SiteYear 6 31 10.23 <.0001
191
Table A4.4 Numerical difference and standard error of the estimate for contrast partitions of site-year means for the field trial
Parameter Difference Standard error Back
transformed difference
Arcsine(CI) High 2011 vs 2012 -0.04 0.09 -0 % Arcsine(CI) High vs Low Inoculum 0.73 0.08 44 % Arcsine(DSI) High 2011 vs 2012 0.10 0.07 1 % Arcsine(DSI) High vs Low Inoculum 0.88 0.07 59 % Log(Yield) High 2011 vs 2012 0.02 0.04 1.0 kg Log(Yield) High vs Low Inoculum -0.25 0.04 -1.8 kg Table A4.5 Root hair infection: Total root hair infection; Exp 1 only; P3 and P6; 4 DAI Random effects Estimate Standard error Z value Pr>Z Block 39.20 39.31 1.00 0.1593Residual 106.56 24.13 4.42 <.0001Fixed effects Numerator df Denominator df F value Pr>F Cultivar 2 39 0.95 0.3972Pathotype 1 39 0.49 0.4886Cultivar × Pathotype 2 39 6.18 0.0047
Table A4.6 Root hair infection: Total root hair infection; Exp 2 only; P3; 4 DAI Random effects Estimate Standard error Z value Pr>Z Block 19.82 22.94 0.86 0.1937Residual 48.42 16.14 3.00 0.0013Fixed effects Numerator df Denominator df F value Pr>F Cultivar 2 18 1.24 0.3122
Table A4.7 Root hair infection: Total root hair infection; Exp 1 and 2; P3 only; 4 DAI Random effects Estimate Standard error Z value Pr>Z Block(Repetition) 33.34 28.14 1.18 0.1181Residual 90.11 21.24 4.24 <.0001Fixed effects Numerator df Denominator df F value Pr>F Cultivar 2 36 4.84 0.0138Repetition 1 6 6.16 0.0476Cultivar × Repetition 2 36 1.14 0.3324
192
Table A4.8 Root hair infection: Primary plasmodia; Exp 1 only; P3 and P6; 4 DAI Random effects Estimate Standard error Z value Pr>Z Block 39.20 38.78 1.01 0.1561Residual 98.91 22.40 4.42 <.0001Fixed effects Numerator df Denominator df F value Pr>F Cultivar 2 39 1.15 0.3259Pathotype 1 39 0.51 0.4812Cultivar × Pathotype 2 39 6.51 0.0036 Table A4.9 Root hair infection: Primary plasmodia; Exp 2 only; P3; 4 DAI Random effects Estimate Standard error Z value Pr>Z Block 21.13 24.79 0.85 0.1970Residual 54.05 18.02 3.00 0.0013Fixed effects Numerator df Denominator df F value Pr>F Cultivar 2 18 1.00 0.3891
Table A4.10 Root hair infection: Primary plasmodia; Exp 1 and 2; P3 only; 4 DAI Random effects Estimate Standard error Z value Pr>Z Block(Repetition) 35.96 28.81 1.25 0.1060Residual 81.77 19.27 4.24 <.0001Fixed effects Numerator df Denominator df F value Pr>F Cultivar 2 36 5.05 0.0117Repetition 1 6 6.01 0.0497Cultivar × Repetition 2 36 1.30 0.2843 Table A4.11 Root hair infection: Mature sporangia; Exp 1 only; P3 and P6; 4 DAI Random effects Estimate Standard error Z value Pr>Z Block 0.04 0.13 0.27 0.3942Residual 1.49 0.34 4.42 <.0001Fixed effects Numerator df Denominator df F value Pr>F Cultivar 2 39 0.80 0.4572Pathotype 1 39 0.00 1.0000Cultivar × Pathotype 2 39 0.38 0.6876 Table A4.12 Root hair infection: Mature sporangia; Exp 2 only; P3; 4 DAI Random effects Estimate Standard error Z value Pr>Z Block 0.00 . . . Residual 54.05 18.02 3.00 0.0013Fixed effects Numerator df Denominator df F value Pr>F Cultivar 2 18 2.41 0.1181
193
Table A4.13 Root hair infection: Mature sporangia; Exp 1 and 2; P3 only; 4 DAI Random effects Estimate Standard error Z value Pr>Z Block(Repetition) 0.00 . . . Residual 1.32 0.29 4.58 <.0001Fixed effects Numerator df Denominator df F value Pr>F Cultivar 2 36 2.09 0.1382Repetition 1 6 0.02 0.9043Cultivar × Repetition 2 36 0.11 0.8960 Table A4.14 Root hair infection: Dehisced sporangia; Exp 1 only; P3 and P6; 4 DAI Random effects Estimate Standard error Z value Pr>Z Block 0.00 0.00 0.00 0.5000Residual 0.02 0.00 4.42 <.0001Fixed effects Numerator df Denominator df F value Pr>F Cultivar 2 39 1.00 0.3771Pathotype 1 39 1.00 0.3235Cultivar*Pathotype 2 39 1.00 0.3771 Table A4.15 Root hair infection: Dehisced sporangia; Exp 2 only; P3; 4 DAI Random effects Estimate Standard error Z value Pr>Z Block 0.00 . . . Residual . . . . Fixed effects Numerator df Denominator df F value Pr>F Cultivar . . . . Table A4.16 Root hair infection: Dehisced sporangia; Exp 1 and 2; P3 only; 4 DAI Random effects Estimate Standard error Z value Pr>Z Block(Repetition) 0.00 0.00 0.00 0.5000Residual 0.02 0.00 4.24 <.0001Fixed effects Numerator df Denominator df F value Pr>F Cultivar 2 36 1.00 0.3779Repetition 1 6 1.00 0.3559Cultivar × Repetition 2 36 1.00 0.3779
194
Table A4.17 Root hair infection: Total root hair infection; Exp 1 only; P3 and P6; 12 DAI Random effects Estimate Standard error Z value Pr>Z Block 18.52 20.95 0.88 0.1884Residual 84.82 19.21 4.42 <.0001Fixed effects Numerator df Denominator df F value Pr>F Cultivar 2 39 3.98 0.0267Pathotype 1 39 0.66 0.4200Cultivar × Pathotype 2 39 0.10 0.9093 Table A4.18 Root hair infection: Total root hair infection; Exp 2 only; P3; 12 DAI Random effects Estimate Standard error Z value Pr>Z Block 33.90 44.77 0.76 0.2245Residual 121.84 40.61 3.00 0.0013Fixed effects Numerator df Denominator df F value Pr>F Cultivar 2 18 0.06 0.9395 Table A4.19 Root hair infection: Total root hair infection; Exp 1 and 2; P3 only; 12 DAI Random effects Estimate Standard error Z value Pr>Z Block(Repetition) 52.97 39.90 1.33 0.0864Residual 94.99 22.39 4.24 <.0001Fixed effects Numerator df Denominator df F value Pr>F Cultivar 2 36 1.06 0.3583Repetition 1 6 0.08 0.7858Cultivar × Repetition 2 36 1.12 0.3384 Table A4.20 Root hair infection: Primary plasmodia; Exp 1 only; P3 and P6; 12 DAI Random effects Estimate Standard error Z value Pr>Z Block 6.97 11.98 0.58 0.2804Residual 90.61 20.52 4.42 <.0001Fixed effects Numerator df Denominator df F value Pr>F Cultivar 2 39 4.67 0.0152Pathotype 1 39 1.19 0.2816Cultivar × Pathotype 2 39 0.75 0.4792 Table A4.21 Root hair infection: Primary plasmodia; Exp 2 only; P3; 12 DAI Random effects Estimate Standard error Z value Pr>Z Block 11.30 19.83 0.57 0.2843Residual 74.68 24.89 3.00 0.0013Fixed effects Numerator df Denominator df F value Pr>F Cultivar 2 18 1.43 0.2645
195
Table A4.22 Root hair infection: Primary plasmodia; Exp 1 and 2; P3 only; 12 DAI Random effects Estimate Standard error Z value Pr>Z Block(Repetition) 20.47 20.75 0.99 0.1619Residual 89.69 21.14 4.24 <.0001Fixed effects Numerator df Denominator df F value Pr>F Cultivar 2 36 4.15 0.0239Repetition 1 6 7.80 0.0315Cultivar × Repetition 2 36 1.11 0.3417 Table A4.23 Root hair infection: Mature sporangia; Exp 1 only; P3 and P6; 12 DAI Random effects Estimate Standard error Z value Pr>Z Block 0.84 1.32 0.63 0.2631Residual 9.20 2.08 4.42 <.0001Fixed effects Numerator df Denominator df F value Pr>F Cultivar 2 39 0.44 0.6489Pathotype 1 39 0.51 0.4795Cultivar × Pathotype 2 39 2.71 0.0789 Table A4.24 Root hair infection: Mature sporangia; Exp 2 only; P3; 12 DAI Random effects Estimate Standard error Z value Pr>Z Block 46.28 43.35 1.07 0.1429Residual 40.47 13.49 3.00 0.0013Fixed effects Numerator df Denominator df F value Pr>F Cultivar 2 18 1.34 0.2879 Table A4.25 Root hair infection: Mature sporangia; Exp 1 and 2; P3 only; 12 DAI Random effects Estimate Standard error Z value Pr>Z Block(Repetition) 24.67 16.77 1.47 0.0707Residual 25.95 6.12 4.24 <.0001Fixed effects Numerator df Denominator df F value Pr>F Cultivar 2 36 1.74 0.1906Repetition 1 6 7.48 0.0339Cultivar × Repetition 2 36 1.26 0.2951
Table A4.26 Root hair infection: Dehisced sporangia; Exp 1 only; P3 and P6; 12 DAI Random effects Estimate Standard error Z value Pr>Z Block 0.00 0.01 0.50 0.3100Residual 0.11 0.03 4.42 <.0001Fixed effects Numerator df Denominator df F value Pr>F Cultivar 2 39 2.45 0.0995Pathotype 1 39 4.71 0.0361Cultivar × Pathotype 2 39 2.45 0.0995
196
Table A4.27 Root hair infection: Dehisced sporangia; Exp 2 only; P3; 12 DAI Random effects Estimate Standard error Z value Pr>Z Block 1.48 1.57 0.94 0.1737Residual 2.63 0.88 3.00 0.0013Fixed effects Numerator df Denominator df F value Pr>F Cultivar 2 18 1.47 0.2560 Table A4.28 Root hair infection: Dehisced sporangia; Exp 1 and 2; P3 only; 12 DAI Random effects Estimate Standard error Z value Pr>Z Block(Repetition) 0.76 0.57 1.32 0.0935Residual 1.39 0.32 4.36 <.0001Fixed effects Numerator df Denominator df F value Pr>F Cultivar 2 36 2.48 0.0982Repetition 1 6 4.06 0.0905Cultivar × Repetition 2 36 0.63 0.5382 Table A4.29 Cortical infection: Area infected; Exp 1 only; P3 and P6; 28 DAI Random effects Estimate Standard error Z value Pr>Z Block 0.00 . . . Residual 31.66 4.72 6.71 <.0001Fixed effects Numerator df Denominator df F value Pr>F Cultivar 2 87 60.79 <.0001Pathotype 1 87 2.66 0.1063Cultivar × Pathotype 2 87 0.22 0.8006
Table A4.30 Cortical infection: Area infected; Exp 2 only; P3; 28 DAI Random effects Estimate Standard error Z value Pr>Z Block 1.78 3.22 0.55 0.2906Residual 25.54 5.57 4.58 <.0001Fixed effects Numerator df Denominator df F value Pr>F Cultivar 2 42 67.31 <.0001
Table A4.31 Cortical infection: Area infected; Exp 1 and 2; P3; 28 DAI Random effects Estimate Standard error Z value Pr>Z Block(Repetition) 1.60 2.20 0.73 0.2339Residual 26.02 4.01 6.48 <.0001Fixed effects Numerator df Denominator df F value Pr>F Cultivar 2 84 95.63 <.0001Repetition 1 6 0.04 0.8473Cultivar × Repetition 2 84 3.10 0.0500
197
Table A4.32 Cortical infection: Total cells; Exp 1 only; P3 and P6; 28 DAI Random effects Estimate Standard error Z value Pr>Z Block 43.11 69.88 0.62 0.2686Residual 1010.64 153.23 6.60 <.0001Fixed effects Numerator df Denominator df F value Pr>F Cultivar 2 87 59.11 <.0001Pathotype 1 87 7.77 0.0065Cultivar × Pathotype 2 87 2.22 0.1147 Table A4.33 Cortical infection: Total cells; Exp 2 only; P3; 28 DAI Random effects Estimate Standard error Z value Pr>Z Block 72.81 116.69 0.62 0.2663Residual 826.97 180.46 4.58 <.0001Fixed effects Numerator df Denominator df F value Pr>F Cultivar 2 42 8.83 <.0001 Table A4.34 Cortical infection: Total cells; Exp 1 and 2; P3; 28 DAI Random effects Estimate Standard error Z value Pr>Z Block(Repetition) 48.41 74.07 0.65 0.2567Residual 938.01 144.74 6.48 <.0001Fixed effects Numerator df Denominator df F value Pr>F Cultivar 2 84 41.88 <.0001Repetition 1 6 1.69 0.2408Cultivar × Repetition 2 84 1.30 0.3639 Table A4.35 Cortical infection: Young plasmodia; Exp 1 only; P3 and P6; 28 DAI Random effects Estimate Standard error Z value Pr>Z Block 95.31 90.14 1.06 0.1452Residual 361.41 54.80 6.60 <.0001Fixed effects Numerator df Denominator df F value Pr>F Cultivar 2 87 11.11 <.0001Pathotype 1 87 7.94 0.0060Cultivar × Pathotype 2 87 1.33 0.2699
Table A4.36 Cortical infection: Young plasmodia; Exp 2 only; P3; 28 DAI Random effects Estimate Standard error Z value Pr>Z Block 80.83 97.67 0.83 0.2039Residual 460.18 100.42 4.58 <.0001Fixed effects Numerator df Denominator df F value Pr>F Cultivar 2 42 6.78 0.0028
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Table A4.37 Cortical infection: Young plasmodia; Exp 1 and 2; P3; 28 DAI Random effects Estimate Standard error Z value Pr>Z Block(Repetition) 61.67 55.19 1.12 0.1319Residual 444.13 68.62 6.47 <.0001Fixed effects Numerator df Denominator df F value Pr>F Cultivar 2 84 4.47 0.0143Repetition 1 6 0.07 0.7987Cultivar × Repetition 2 84 0.81 0.5500 Table A4.38 Cortical infection: Mature plasmodia; Exp 1 only; P3 and P6; 28 DAI Random effects Estimate Standard error Z value Pr>Z Block 17.97 28.68 0.63 0.2654Residual 408.18 61.89 6.60 <.0001Fixed effects Numerator df Denominator df F value Pr>F Cultivar 2 87 23.16 <.0001Pathotype 1 87 2.50 0.1175Cultivar × Pathotype 2 87 0.82 0.4425 Table A4.39 Cortical infection: Mature plasmodia; Exp 2 only; P3; 28 DAI Random effects Estimate Standard error Z value Pr>Z Block 0 . . . Residual 67.73 14.28 4.74 <.0001Fixed effects Numerator df Denominator df F value Pr>F Cultivar 2 42 13.42 <.0001 Table A4.40 Cortical infection: Mature plasmodia; Exp 1 and 2; P3; 28 DAI Random effects Estimate Standard error Z value Pr>Z Block(Repetition) 0 . . . Residual 239.62 35.72 6.71 <.0001Fixed effects Numerator df Denominator df F value Pr>F Cultivar 2 84 25.66 <.0001Repetition 1 6 5.86 0.0960Cultivar × Repetition 2 84 3.83 0.0810 Table A4.40 Cortical infection: Resting spores; Exp 1 only; P3 and P6; 28 DAI Random effects Estimate Standard error Z value Pr>Z Block 0.00 . . . Residual 101.86 15.18 6.71 <.0001Fixed effects Numerator df Denominator df F value Pr>F Cultivar 2 87 78.77 <.0001Pathotype 1 87 0.45 0.5063Cultivar × Pathotype 2 87 0.18 0.8352
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Table A4.42 Cortical infection: Resting Spores; Exp 2 only; P3; 28 DAI Random effects Estimate Standard error Z value Pr>Z Block 5.10 8.83 0.58 0.2818Residual 67.31 14.69 4.58 <.0001Fixed effects Numerator df Denominator df F value Pr>F Cultivar 2 42 66.19 <.0001
Table A4.43 Cortical infection: Resting spores; Exp 1 and 2; P3; 28 DAI Random effects Estimate Standard error Z value Pr>Z Block(Repetition) 8.26 8.38 0.99 0.1621Residual 73.90 11.40 6.48 <.0001Fixed effects Numerator df Denominator df F value Pr>F Cultivar 2 84 119.64 <.0001Repetition 1 6 0.10 0.7668Cultivar × Repetition 2 84 0.11 0.8982 Table A4.44 qPCR Log (Concentration): Exp 1 only; P3 and P6; 4 DAI Random effects Estimate Standard error Z value Pr>Z Block 0.16 0.17 0.99 0.1621Residual 0.23 0.09 2.74 0.0031Fixed effects Numerator df Denominator df F value Pr>F Cultivar 2 15 0.16 0.8564Pathotype 1 15 12.63 0.0029Cultivar × Pathotype 2 15 0.03 0.9723 Table A4.45 qPCR Log (Concentration): Exp 2 only; P3 only; 4 DAI Random effects Estimate Standard error Z value Pr>Z Block 0.04 0.17 0.24 0.4063Residual 0.15 0.17 0.87 0.1919Fixed effects Numerator df Denominator df F value Pr>F Cultivar 2 2 2.74 0.2673 Table A4.46 qPCR Log (Concentration): Exp1 and 2; P3 only; 4 DAI Random effects Estimate Standard error Z value Pr>Z Block(Repetition) 0.19 0.15 1.25 0.1058Residual 0.17 0.08 2.05 0.0203Fixed effects Numerator df Denominator df F value Pr>F Cultivar 2 8 2.91 0.1120Repetition 1 6 2.49 0.1657Cultivar × Repetition 2 8 1.60 0.2601
200
Table A4.47 qPCR Log (Concentration): Exp 1 only; P3 and P6; 12 DAI Random effects Estimate Standard error Z value Pr>Z Block 0.69 0.60 1.15 0.1256Residual 0.25 0.10 2.55 0.0054Fixed effects Numerator df Denominator df F value Pr>F Cultivar 2 13 1.04 0.3803Pathotype 1 13 19.08 0.0008Cultivar × Pathotype 2 13 1.38 0.2870
Table A4.48 qPCR Log (Concentration): Exp 2 only; P3 only; 12 DAI Random effects Estimate Standard error Z value Pr>Z Block 0.14 0.62 0.23 0.4085Residual 1.22 0.84 1.45 0.0729Fixed effects Numerator df Denominator df F value Pr>F Cultivar 2 4 0.26 0.7808 Table A4.49 qPCR Log (Concentration): Exp1 and 2; P3 only; 12 DAI Random effects Estimate Standard error Z value Pr>Z Block(Repetition) 0.84 0.61 1.37 0.0847Residual 0.55 0.25 2.15 0.0159Fixed effects Numerator df Denominator df F value Pr>F Cultivar 2 8 0.44 0.6602Repetition 1 6 0.13 0.7274Cultivar × Repetition 2 8 1.13 0.3636 Table A4.50 qPCR Log (Concentration): Exp 1 only; P3 and P6; 28 DAI Random effects Estimate Standard error Z value Pr>Z Block 0.00 . . . Residual 0.59 0.20 3.00 0.0013Fixed effects Numerator df Denominator df F value Pr>F Cultivar 2 15 2.15 0.1510Pathotype 1 15 0.22 0.6491Cultivar × Pathotype 2 15 1.67 0.2212 Table A4.51 qPCR Log (Concentration): Exp 2 only; P3 only; 28 DAI Random effects Estimate Standard error Z value Pr>Z Block 0.00 0.07 0.00 0.4996Residual 0.20 0.11 1.73 0.0416Fixed effects Numerator df Denominator df F value Pr>F Cultivar 2 6 196.85 <.0001
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Table A4.52 qPCR Log (Concentration): Exp1 and 2; P3 only; 28 DAI Random effects Estimate Standard error Z value Pr>Z Block(Repetition) 0.00 . . . Residual 0.41 0.14 3.00 0.0013Fixed effects Numerator df Denominator df F value Pr>F Cultivar 2 8 32.69 <.0001Repetition 1 6 447.81 <.0001Cultivar × Repetition 2 8 65.25 <.0001 Table A4.53 Clubroot Incidence: Exp 1 and 2; P3 and P6; 42 DAI
Cultivar N Sum of scores Expected under H0
Standard deviation under H0
Mean score
B-2819 24 902.0 876.0 80.46 37.58 Bronco 24 1426.0 876.0 80.46 59.42 Kilaherb 24 300.0 876.0 80.46 12.50
Kruskal-Wallis Test Chi-Square 65.39 DF 2 Pr > Chi-Square <.0001 1Data representative of two trials Table A4.54 Disease Severity Index: Exp 1 and 2; P3 and P6; 42 DAI
Cultivar N Sum of scores Expected under H0
Standard deviation under H0
Mean score
B-2819 24 876.0 876.0 81.46 36.50 Bronco 24 1456.0 876.0 81.46 60.50 Kilaherb 24 300.0 876.0 81.46 12.50
Kruskal-Wallis Test Chi-Square 66.37 DF 2 Pr > Chi-Square <.0001 1Data representative of two trials Table A4.55 Numerical difference and standard error of the estimate for contrast partitions of root hair infection
Parameter Difference Standard error 4 DAI Total RHI Exp 1 vs 2 12 4.9 4 DAI Primary plasmodia Exp 1 vs 2 12 5.0 12 DAI Primary plasmodia Exp 1 vs 2 12 4.2 12 DAI Mature zoosporangia Exp 1 vs 2 -10 3.8
202
Table A4.56 Numerical difference and standard error of the estimate for contrast partitions of P. brassicae gDNA means for 28 DAI
Parameter Difference Standard error
Back transformed difference
(ng/ g of root) Log(gDNA) Experiment 1 vs 2 5.53 0.03 3 × 105
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APPENDIX 5: RAW DATA FOR CHAPTER TWO Raw data for constant versus fluctuating temperature trial. Exp = experiment repetition, PP = primary plasmodia, MZ = mature zoosporangia, DZ = dehisced zoosporangia, NI = not infected, Total = total incidence. Exp Temp Range Temperature Block PP MZ DZ NI Total
1 Const 12.5 1 40 1 0 59 41 1 Const 12.5 1 36 0 0 64 36 1 Const 12.5 1 47 0 0 53 47 1 Const 12.5 2 45 10 0 45 55 1 Const 12.5 2 37 2 0 61 39 1 Const 12.5 3 46 0 0 54 46 1 Const 12.5 3 44 0 0 56 44 1 Const 12.5 4 24 0 0 76 24 1 Const 12.5 4 41 0 0 59 41 1 Const 15 1 27 0 0 73 27 1 Const 15 1 47 3 0 50 50 1 Const 15 1 36 0 0 64 36 1 Const 15 2 61 6 0 33 67 1 Const 15 2 58 0 0 42 58 1 Const 15 3 36 6 0 58 42 1 Const 15 3 38 2 0 60 40 1 Const 15 4 34 0 0 66 34 1 Const 15 4 30 0 0 70 30 1 Const 20 1 33 10 0 57 43 1 Const 20 1 54 11 0 35 65 1 Const 20 1 32 3 1 64 36 1 Const 20 2 32 27 3 38 62 1 Const 20 2 44 8 0 48 52 1 Const 20 3 51 8 2 39 61 1 Const 20 3 48 6 0 46 54 1 Const 20 4 25 12 0 63 37 1 Const 20 4 33 14 0 53 47 1 Const 25 1 22 27 3 48 52 1 Const 25 1 33 19 8 40 60 1 Const 25 1 19 29 7 45 55 1 Const 25 2 57 21 1 21 79 1 Const 25 2 34 22 0 44 56 1 Const 25 3 48 31 2 19 81 1 Const 25 3 42 26 5 27 73 1 Const 25 4 9 39 4 48 52 1 Const 25 4 31 31 3 35 65 1 Const 30 1 22 18 1 59 41 1 Const 30 1 25 25 11 39 61 1 Const 30 1 26 14 3 57 43 1 Const 30 2 37 7 0 56 44 1 Const 30 2 39 3 0 58 42 1 Const 30 3 31 6 0 63 37 1 Const 30 3 55 4 0 41 59 1 Const 30 4 37 6 1 56 44 1 Const 30 4 42 4 1 53 47 1 Flux 12.5 1 35 5 0 60 40
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1 Flux 12.5 1 31 12 0 57 43 1 Flux 12.5 1 72 12 0 16 84 1 Flux 12.5 2 39 6 0 55 45 1 Flux 12.5 2 37 0 0 63 37 1 Flux 12.5 3 51 3 0 46 54 1 Flux 12.5 3 56 0 0 44 56 1 Flux 12.5 4 45 0 0 55 45 1 Flux 12.5 4 48 1 0 51 49 1 Flux 15 1 45 9 0 46 54 1 Flux 15 1 36 8 0 56 44 1 Flux 15 1 39 5 0 56 44 1 Flux 15 2 67 6 0 27 73 1 Flux 15 2 64 1 0 35 65 1 Flux 15 3 64 3 0 33 67 1 Flux 15 3 44 0 0 56 44 1 Flux 15 4 47 1 0 52 48 1 Flux 15 4 51 2 0 47 53 1 Flux 20 1 25 16 0 59 41 1 Flux 20 1 52 3 0 45 55 1 Flux 20 1 50 14 0 36 64 1 Flux 20 2 64 5 0 31 69 1 Flux 20 2 61 8 0 31 69 1 Flux 20 3 26 18 1 55 45 1 Flux 20 3 45 7 0 48 52 1 Flux 20 4 38 15 0 47 53 1 Flux 20 4 43 14 2 41 59 1 Flux 25 1 36 11 0 53 47 1 Flux 25 1 36 38 8 18 82 1 Flux 25 1 5 50 8 37 63 1 Flux 25 2 53 13 11 23 77 1 Flux 25 2 61 10 0 29 71 1 Flux 25 3 49 5 0 46 54 1 Flux 25 3 52 3 0 45 55 1 Flux 25 4 31 23 1 45 55 1 Flux 25 4 52 8 1 39 61 1 Flux 30 1 58 5 0 37 63 1 Flux 30 1 55 5 0 40 60 1 Flux 30 2 34 0 0 66 34 1 Flux 30 2 44 10 0 46 54 1 Flux 30 3 24 6 1 69 31 1 Flux 30 3 40 15 3 42 58 1 Flux 30 4 37 19 1 43 57 1 Flux 30 4 53 4 0 43 57 2 Const 12.5 1 38 1 0 61 39 2 Const 12.5 1 35 0 0 65 35 2 Const 12.5 1 44 0 0 56 44 2 Const 12.5 2 46 8 0 46 54 2 Const 12.5 2 44 3 0 53 47 2 Const 12.5 3 45 0 0 55 45 2 Const 12.5 3 45 0 0 55 45 2 Const 12.5 4 26 1 0 73 27 2 Const 12.5 4 37 0 0 63 37 2 Const 15 1 30 0 0 70 30 2 Const 15 1 46 0 0 54 46
205
2 Const 15 1 37 3 0 60 40 2 Const 15 2 61 11 0 28 72 2 Const 15 2 57 0 0 43 57 2 Const 15 3 37 1 0 62 38 2 Const 15 3 35 1 0 64 36 2 Const 15 4 29 1 0 70 30 2 Const 15 4 35 0 0 65 35 2 Const 20 1 36 10 0 54 46 2 Const 20 1 51 11 0 38 62 2 Const 20 1 34 3 1 62 38 2 Const 20 2 30 27 3 40 60 2 Const 20 2 44 8 0 48 52 2 Const 20 3 51 7 2 40 60 2 Const 20 3 48 7 0 45 55 2 Const 20 4 25 11 0 64 36 2 Const 20 4 33 15 1 51 49 2 Const 25 1 22 27 3 48 52 2 Const 25 1 33 19 8 40 60 2 Const 25 1 19 29 7 45 55 2 Const 25 2 57 21 1 21 79 2 Const 25 2 34 22 0 44 56 2 Const 25 3 48 31 2 19 81 2 Const 25 3 42 26 5 27 73 2 Const 25 4 9 39 4 48 52 2 Const 25 4 31 31 3 35 65 2 Const 30 1 22 18 1 59 41 2 Const 30 1 25 22 11 42 58 2 Const 30 1 24 11 3 62 38 2 Const 30 2 37 7 0 56 44 2 Const 30 2 41 3 0 56 44 2 Const 30 3 31 9 0 60 40 2 Const 30 3 55 4 0 41 59 2 Const 30 4 41 6 1 52 48 2 Const 30 4 38 7 1 54 46 2 Flux 12.5 1 41 4 0 55 45 2 Flux 12.5 1 36 1 0 63 37 2 Flux 12.5 1 62 10 0 28 72 2 Flux 12.5 2 39 6 0 55 45 2 Flux 12.5 2 37 14 0 49 51 2 Flux 12.5 3 51 1 0 48 52 2 Flux 12.5 3 56 2 0 42 58 2 Flux 12.5 4 48 0 0 52 48 2 Flux 12.5 4 44 1 0 55 45 2 Flux 15 1 45 1 0 54 46 2 Flux 15 1 35 7 0 58 42 2 Flux 15 1 41 5 0 54 46 2 Flux 15 2 67 0 0 33 67 2 Flux 15 2 62 7 0 31 69 2 Flux 15 3 66 2 0 32 68 2 Flux 15 3 43 10 0 47 53 2 Flux 15 4 49 2 0 49 51 2 Flux 15 4 50 1 0 49 51 2 Flux 20 1 30 16 0 54 46 2 Flux 20 1 51 3 0 46 54
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2 Flux 20 1 51 13 0 36 64 2 Flux 20 2 59 6 2 33 67 2 Flux 20 2 56 8 0 36 64 2 Flux 20 3 31 20 1 48 52 2 Flux 20 3 43 10 0 47 53 2 Flux 20 4 40 11 0 49 51 2 Flux 20 4 43 13 0 44 56 2 Flux 25 1 39 11 0 50 50 2 Flux 25 1 30 38 1 31 69 2 Flux 25 1 11 40 6 43 57 2 Flux 25 2 60 13 9 18 82 2 Flux 25 2 53 3 1 43 57 2 Flux 25 3 49 15 4 32 68 2 Flux 25 3 31 14 0 55 45 2 Flux 25 4 48 19 7 26 74 2 Flux 25 4 55 8 1 36 64 2 Flux 30 1 58 9 1 32 68 2 Flux 30 1 54 5 0 41 59 2 Flux 30 2 34 6 1 59 41 2 Flux 30 2 44 3 0 53 47 2 Flux 30 3 24 6 1 69 31 2 Flux 30 3 40 17 1 42 58 2 Flux 30 4 37 12 0 51 49 2 Flux 30 4 53 6 1 40 60
Raw data for qPCR data. Max, Min, and Mean are temperature (°C). Concentration of pathogen in ng/g of root. Log_Concentration is the log transformation of concentration.
Exp Max Min Mean Range TRT Block Concentration Log_Concentration 1 10 10 10 Const 1 1 0.127667 0.052181 1 10 10 10 Const 1 2 0.906667 0.280275 1 10 10 10 Const 1 3 0.707457 0.23235 1 10 10 10 Const 1 4 . . 1 12.5 12.5 12.5 Const 2 1 . . 1 12.5 12.5 12.5 Const 2 2 0.218333 0.085766 1 12.5 12.5 12.5 Const 2 3 1.004786 0.302068 1 12.5 12.5 12.5 Const 2 4 8.64E-08 3.75E-08 1 15 15 15 Const 3 1 0.147666 0.059815 1 15 15 15 Const 3 2 0.495 0.174641 1 15 15 15 Const 3 3 0.177145 0.07083 1 15 15 15 Const 3 4 . . 1 17.5 17.5 17.5 Const 4 1 0.001076 0.000467 1 17.5 17.5 17.5 Const 4 2 0.030453 0.013028 1 17.5 17.5 17.5 Const 4 3 0.375333 0.138408 1 17.5 17.5 17.5 Const 4 4 0.002704 0.001173 1 20 20 20 Const 5 1 0.025826 0.011074 1 20 20 20 Const 5 2 0.44645 0.160303 1 20 20 20 Const 5 3 0.000994 0.000431 1 20 20 20 Const 5 4 0.003739 0.001621 1 22.5 22.5 22.5 Const 6 1 . . 1 22.5 22.5 22.5 Const 6 2 0.550796 0.190555 1 22.5 22.5 22.5 Const 6 3 0.015333 0.006609 1 22.5 22.5 22.5 Const 6 4 7.6E-08 3.3E-08 1 25 25 25 Const 7 1 1.293333 0.360467 1 25 25 25 Const 7 2 0.512603 0.179725 1 25 25 25 Const 7 3 . .
207
1 25 25 25 Const 7 4 . . 1 27.5 27.5 27.5 Const 8 1 0.000333 0.000145 1 27.5 27.5 27.5 Const 8 2 0 0 1 27.5 27.5 27.5 Const 8 3 0.01902 0.008183 1 27.5 27.5 27.5 Const 8 4 0.002358 0.001023 1 30 30 30 Const 9 1 0.000591 0.000257 1 30 30 30 Const 9 2 0.1385 0.056333 1 30 30 30 Const 9 3 3.78E-08 1.64E-08 1 30 30 30 Const 9 4 1.41E-07 6.14E-08 1 32.5 32.5 32.5 Const 10 1 0.0326 0.013932 1 32.5 32.5 32.5 Const 10 2 0 0 1 32.5 32.5 32.5 Const 10 3 0.000164 7.12E-05 1 32.5 32.5 32.5 Const 10 4 1.65E-08 7.17E-09 1 35 35 35 Const 11 1 1.56E-05 6.79E-06 1 35 35 35 Const 11 2 5.94E-05 2.58E-05 1 35 35 35 Const 10 3 0.00018 7.82E-05 1 35 35 35 Const 10 4 . . 1 15 5 10 Flux 16 1 0.446059 0.160186 1 15 5 10 Flux 16 2 0.952955 0.290692 1 15 5 10 Flux 16 3 1.006038 0.302339 1 15 5 10 Flux 16 4 0 1 17.5 7.5 12.5 Flux 17 1 0.763 0.246252 1 17.5 7.5 12.5 Flux 17 2 0.000528 0.000229 1 17.5 7.5 12.5 Flux 17 3 0.000528 0.000229 1 17.5 7.5 12.5 Flux 17 4 0.895192 0.277653 1 20 10 15 Flux 18 1 . . 1 20 10 15 Flux 18 2 3.57E-03 0.001549 1 20 10 15 Flux 18 3 0.025662 0.011004 1 20 10 15 Flux 18 4 . . 1 22.5 12.5 17.5 Flux 19 1 0.012 0.005181 1 22.5 12.5 17.5 Flux 19 2 0.034244 0.014623 1 22.5 12.5 17.5 Flux 19 3 . . 1 22.5 12.5 17.5 Flux 19 4 . . 1 25 15 20 Flux 20 1 0.14241 0.057822 1 25 15 20 Flux 20 2 0.001147 0.000498 1 25 15 20 Flux 20 3 0.011315 0.004886 1 25 15 20 Flux 20 4 . . 1 27.5 17.5 22.5 Flux 21 1 0.08834 0.036765 1 27.5 17.5 22.5 Flux 21 2 0.136831 0.055696 1 27.5 17.5 22.5 Flux 21 3 0.000361 0.000157 1 27.5 17.5 22.5 Flux 21 4 . . 1 30 20 25 Flux 22 1 1.394 0.379124 1 30 20 25 Flux 22 2 0.000181 7.84E-05 1 30 20 25 Flux 22 3 0.207312 0.08182 1 30 20 25 Flux 22 4 . . 1 32.5 22.5 27.5 Flux 23 1 0.01062 0.004588 1 32.5 22.5 27.5 Flux 23 2 0.00038 0.000165 1 32.5 22.5 27.5 Flux 23 3 7.8E-05 3.39E-05 1 32.5 22.5 27.5 Flux 23 4 . . 1 35 25 30 Flux 24 1 0.018627 0.008015 1 35 25 30 Flux 24 2 0.0109 0.004708 1 35 25 30 Flux 24 3 0.032263 0.01379 1 35 25 30 Flux 24 4 . . 2 10 10 10 Const 1 1 0.703196 0.231265 2 10 10 10 Const 1 2 0.703438 0.231326 2 10 10 10 Const 1 3 1.569457 0.409841 2 10 10 10 Const 1 4 . . 2 12.5 12.5 12.5 Const 2 3 0.72249 0.236157 2 12.5 12.5 12.5 Const 2 1 1.029324 0.307351 2 12.5 12.5 12.5 Const 2 2 . .
208
2 12.5 12.5 12.5 Const 2 4 . . 2 15 15 15 Const 3 2 0.177666 0.071022 2 15 15 15 Const 3 1 0.277545 0.106376 2 15 15 15 Const 3 3 . . 2 15 15 15 Const 3 4 . . 2 17.5 17.5 17.5 Const 4 4 0.020455 0.008794 2 17.5 17.5 17.5 Const 4 3 0.344282 0.12849 2 17.5 17.5 17.5 Const 4 1 . . 2 17.5 17.5 17.5 Const 4 2 . . 2 20 20 20 Const 5 3 0.000893 0.000388 2 20 20 20 Const 5 4 0.015806 0.006811 2 20 20 20 Const 5 1 . . 2 20 20 20 Const 5 2 . . 2 22.5 22.5 22.5 Const 6 2 5.4E-06 2.35E-06 2 22.5 22.5 22.5 Const 6 3 0.001809 0.000785 2 22.5 22.5 22.5 Const 6 4 0.054192 0.02292 2 22.5 22.5 22.5 Const 6 1 0.415432 0.150889 2 25 25 25 Const 7 1 0.06026 0.025412 2 25 25 25 Const 7 4 0.892358 0.277003 2 25 25 25 Const 7 2 . . 2 25 25 25 Const 7 3 . . 2 27.5 27.5 27.5 Const 8 3 0.000456 0.000198 2 27.5 27.5 27.5 Const 8 4 0.000691 0.0003 2 27.5 27.5 27.5 Const 8 1 . . 2 27.5 27.5 27.5 Const 8 2 . . 2 30 30 30 Const 9 3 0.001715 0.000744 2 30 30 30 Const 9 4 0.148088 0.059975 2 30 30 30 Const 9 1 . . 2 30 30 30 Const 9 2 . . 2 32.5 32.5 32.5 Const 10 1 0.000174 7.56E-05 2 32.5 32.5 32.5 Const 10 2 0.009872 0.004266 2 32.5 32.5 32.5 Const 10 4 0.135445 0.055166 2 32.5 32.5 32.5 Const 10 3 0 0 2 35 35 35 Const 11 2 6.93E-05 3.01E-05 2 35 35 35 Const 11 4 0.00114 0.000495 2 35 35 35 Const 11 1 0.001881 0.000816 2 35 35 35 Const 11 3 0 0 2 15 5 10 Flux 12 2 0.001322 0.000574 2 15 5 10 Flux 12 1 0.962909 0.2929 2 15 5 10 Flux 12 4 1.016838 0.304671 2 15 5 10 Flux 12 3 . . 2 17.5 7.5 12.5 Flux 13 3 0.024391 0.010466 2 17.5 7.5 12.5 Flux 13 2 0.885142 0.275344 2 17.5 7.5 12.5 Flux 13 1 . . 2 17.5 7.5 12.5 Flux 13 4 . . 2 20 10 15 Flux 14 3 0.000618 0.000268 2 20 10 15 Flux 14 4 0.032243 0.013782 2 20 10 15 Flux 14 2 1.234102 0.349103 2 20 10 15 Flux 14 1 . . 2 22.5 12.5 17.5 Flux 15 4 0.035666 0.01522 2 22.5 12.5 17.5 Flux 15 1 . . 2 22.5 12.5 17.5 Flux 15 2 . . 2 22.5 12.5 17.5 Flux 15 3 . . 2 25 15 20 Flux 16 2 0.004148 0.001798 2 25 15 20 Flux 16 1 0.010395 0.004491 2 25 15 20 Flux 16 4 0.112984 0.046489 2 25 15 20 Flux 16 3 . . 2 27.5 17.5 22.5 Flux 17 2 4.11E-05 1.78E-05 2 27.5 17.5 22.5 Flux 17 3 0.000496 0.000215 2 27.5 17.5 22.5 Flux 17 4 0.146891 0.059522
209
2 27.5 17.5 22.5 Flux 17 1 0.573807 0.196951 2 30 20 25 Flux 18 4 0.000523 0.000227 2 30 20 25 Flux 18 1 0.003773 0.001635 2 30 20 25 Flux 18 3 0.107314 0.044271 2 30 20 25 Flux 18 2 0.891884 0.276895 2 32.5 22.5 27.5 Flux 19 2 4.4E-05 1.91E-05 2 32.5 22.5 27.5 Flux 19 4 6.8E-05 2.95E-05 2 32.5 22.5 27.5 Flux 19 1 . . 2 32.5 22.5 27.5 Flux 19 3 . . 2 35 25 30 Flux 20 3 0.021264 0.009138 2 35 25 30 Flux 20 4 0.053268 0.022539 2 35 25 30 Flux 20 1 . . 2 35 25 30 Flux 20 2 . .
Raw data for qPCR trial of comparison of amplitude of temperature fluctuation Experiment Max Min Mean Range TRT Block Concentration
1 17.5 12.5 15 5 1 1 0.033175 1 17.5 12.5 15 5 1 1 0.022114 1 17.5 12.5 15 5 1 1 0.016571 1 17.5 12.5 15 5 1 2 0.012711 1 17.5 12.5 15 5 1 2 0.019548 1 17.5 12.5 15 5 1 2 0.015444 1 17.5 12.5 15 5 1 3 0.011539 1 17.5 12.5 15 5 1 3 0.004858 1 17.5 12.5 15 5 1 3 0.005615 1 17.5 12.5 15 5 1 4 0.003993 1 17.5 12.5 15 5 1 4 0.006878 1 17.5 12.5 15 5 1 4 0.012263 1 22.5 7.5 15 15 2 1 0.014139 1 22.5 7.5 15 15 2 1 0.009858 1 22.5 7.5 15 15 2 1 0.004947 1 22.5 7.5 15 15 2 2 0.004608 1 22.5 7.5 15 15 2 2 0.00534 1 22.5 7.5 15 15 2 2 0.007762 1 22.5 7.5 15 15 2 3 . 1 22.5 7.5 15 15 2 3 . 1 22.5 7.5 15 15 2 3 . 1 22.5 7.5 15 15 2 4 0.003169 1 22.5 7.5 15 15 2 4 0.006076 1 22.5 7.5 15 15 2 4 0.009692 1 17.5 17.5 17.5 0 3 1 0.003598 1 17.5 17.5 17.5 0 3 1 0.006955 1 17.5 17.5 17.5 0 3 1 0.00914 1 17.5 17.5 17.5 0 3 2 0.004852 1 17.5 17.5 17.5 0 3 2 0.009216 1 17.5 17.5 17.5 0 3 2 0.007326 1 17.5 17.5 17.5 0 3 3 0.021232 1 17.5 17.5 17.5 0 3 3 0.022417 1 17.5 17.5 17.5 0 3 3 0.010264 1 17.5 17.5 17.5 0 3 4 0.006627 1 17.5 17.5 17.5 0 3 4 0.009931 1 17.5 17.5 17.5 0 3 4 0.014811 1 20 15 17.5 5 4 1 . 1 20 15 17.5 5 4 1 4.49E-05
210
1 20 15 17.5 5 4 1 2.79E-05 1 20 15 17.5 5 4 2 0.027702 1 20 15 17.5 5 4 2 0.026669 1 20 15 17.5 5 4 2 0.047228 1 20 15 17.5 5 4 3 0.037085 1 20 15 17.5 5 4 3 0.023555 1 20 15 17.5 5 4 3 0.017461 1 20 15 17.5 5 4 4 0.003206 1 20 15 17.5 5 4 4 0.009422 1 20 15 17.5 5 4 4 0.008835 1 25 10 17.5 15 5 1 0.018714 1 25 10 17.5 15 5 1 0.003452 1 25 10 17.5 15 5 1 0.015786 1 25 10 17.5 15 5 2 0.048147 1 25 10 17.5 15 5 2 0.052068 1 25 10 17.5 15 5 2 0.053737 1 25 10 17.5 15 5 3 0.043084 1 25 10 17.5 15 5 3 0.031318 1 25 10 17.5 15 5 3 0.027746 1 25 10 17.5 15 5 4 0.008851 1 25 10 17.5 15 5 4 0.023339 1 25 10 17.5 15 5 4 . 1 20 20 20 0 6 1 0.037304 1 20 20 20 0 6 1 0.027631 1 20 20 20 0 6 1 0.005976 1 20 20 20 0 6 2 0.021784 1 20 20 20 0 6 2 0.010675 1 20 20 20 0 6 2 0.014443 1 20 20 20 0 6 3 0.021458 1 20 20 20 0 6 3 0.029633 1 20 20 20 0 6 3 0.01899 1 20 20 20 0 6 4 0.009148 1 20 20 20 0 6 4 0.010255 1 20 20 20 0 6 4 0.005899 1 22.5 17.5 20 5 7 1 0.055543 1 22.5 17.5 20 5 7 1 0.053764 1 22.5 17.5 20 5 7 1 0.057601 1 22.5 17.5 20 5 7 2 0.010498 1 22.5 17.5 20 5 7 2 0.011534 1 22.5 17.5 20 5 7 2 0.01599 1 22.5 17.5 20 5 7 3 0.019823 1 22.5 17.5 20 5 7 3 0.00843 1 22.5 17.5 20 5 7 3 0.011839 1 22.5 17.5 20 5 7 4 0.018823 1 22.5 17.5 20 5 7 4 0.006264 1 22.5 17.5 20 5 7 4 0.010754 1 27.5 12.5 20 15 8 1 . 1 27.5 12.5 20 15 8 1 2.98E-07 1 27.5 12.5 20 15 8 1 3.17E-08 1 27.5 12.5 20 15 8 2 0.026654 1 27.5 12.5 20 15 8 2 0.027305 1 27.5 12.5 20 15 8 2 0.024722 1 27.5 12.5 20 15 8 3 0.022809 1 27.5 12.5 20 15 8 3 0.017623
211
1 27.5 12.5 20 15 8 3 0.02827 1 27.5 12.5 20 15 8 4 0.005391 1 27.5 12.5 20 15 8 4 0.006702 1 27.5 12.5 20 15 8 4 . 2 17.5 12.5 15 5 1 1 10.37923 2 17.5 12.5 15 5 1 1 9.890774 2 17.5 12.5 15 5 1 1 . 2 17.5 12.5 15 5 1 2 . 2 17.5 12.5 15 5 1 2 . 2 17.5 12.5 15 5 1 2 . 2 17.5 12.5 15 5 1 3 34.69294 2 17.5 12.5 15 5 1 3 29.67921 2 17.5 12.5 15 5 1 3 26.55798 2 17.5 12.5 15 5 1 4 32.95477 2 17.5 12.5 15 5 1 4 26.81743 2 17.5 12.5 15 5 1 4 . 2 22.5 7.5 15 15 2 1 14.38577 2 22.5 7.5 15 15 2 1 16.74745 2 22.5 7.5 15 15 2 1 14.03131 2 22.5 7.5 15 15 2 2 0.071081 2 22.5 7.5 15 15 2 2 0.057623 2 22.5 7.5 15 15 2 2 0.007629 2 22.5 7.5 15 15 2 3 14.30171 2 22.5 7.5 15 15 2 3 13.2176 2 22.5 7.5 15 15 2 3 10.72456 2 17.5 17.5 17.5 0 3 1 14.43094 2 17.5 17.5 17.5 0 3 1 . 2 17.5 17.5 17.5 0 3 1 16.10023 2 17.5 17.5 17.5 0 3 2 23.807 2 17.5 17.5 17.5 0 3 2 19.45951 2 17.5 17.5 17.5 0 3 2 21.61104 2 17.5 17.5 17.5 0 3 3 . 2 17.5 17.5 17.5 0 3 3 1.247853 2 17.5 17.5 17.5 0 3 3 0.959974 2 17.5 17.5 17.5 0 3 4 39.16993 2 17.5 17.5 17.5 0 3 4 43.13716 2 17.5 17.5 17.5 0 3 4 45.88318 2 20 15 17.5 5 4 1 1.15153 2 20 15 17.5 5 4 1 2.694255 2 20 15 17.5 5 4 1 2.925955 2 20 15 17.5 5 4 2 34.63835 2 20 15 17.5 5 4 2 34.62517 2 20 15 17.5 5 4 2 32.37144 2 20 15 17.5 5 4 3 34.98695 2 20 15 17.5 5 4 3 36.93136 2 20 15 17.5 5 4 3 25.99523 2 25 10 17.5 15 5 1 0.021198 2 25 10 17.5 15 5 1 0.005239 2 25 10 17.5 15 5 1 . 2 25 10 17.5 15 5 2 0.070805 2 25 10 17.5 15 5 2 . 2 25 10 17.5 15 5 2 0.063468 2 25 10 17.5 15 5 3 7.790939 2 25 10 17.5 15 5 3 9.796631
212
2 25 10 17.5 15 5 3 9.653436 2 25 10 17.5 15 5 4 11.48388 2 25 10 17.5 15 5 4 13.38587 2 25 10 17.5 15 5 4 10.66712 2 20 20 20 0 6 1 . 2 20 20 20 0 6 1 . 2 20 20 20 0 6 1 . 2 20 20 20 0 6 2 . 2 20 20 20 0 6 2 . 2 20 20 20 0 6 2 . 2 20 20 20 0 6 3 47.71476 2 20 20 20 0 6 3 42.74463 2 20 20 20 0 6 3 42.31253 2 20 20 20 0 6 4 29.11438 2 20 20 20 0 6 4 24.34289 2 20 20 20 0 6 4 34.59466 2 22.5 17.5 20 5 7 1 . 2 22.5 17.5 20 5 7 1 . 2 22.5 17.5 20 5 7 1 . 2 22.5 17.5 20 5 7 2 . 2 22.5 17.5 20 5 7 2 2.399166 2 22.5 17.5 20 5 7 2 2.741001 2 22.5 17.5 20 5 7 3 3.190717 2 22.5 17.5 20 5 7 3 . 2 22.5 17.5 20 5 7 3 3.236677 2 22.5 17.5 20 5 7 4 4.291805 2 22.5 17.5 20 5 7 4 6.89864 2 22.5 17.5 20 5 7 4 7.385931 2 27.5 12.5 20 15 8 1 . 2 27.5 12.5 20 15 8 1 0.002918 2 27.5 12.5 20 15 8 1 0.02383 2 27.5 12.5 20 15 8 2 38.40492 2 27.5 12.5 20 15 8 2 42.95797 2 27.5 12.5 20 15 8 2 44.51147 2 27.5 12.5 20 15 8 3 2.745425 2 27.5 12.5 20 15 8 3 1.977748 2 27.5 12.5 20 15 8 3 2.64479 2 27.5 12.5 20 15 8 4 15.62738 2 27.5 12.5 20 15 8 4 16.26329 2 27.5 12.5 20 15 8 4 6.316346 2 20 15 17.5 5 4 4 27.65177 2 20 15 17.5 5 4 4 30.26978 2 20 15 17.5 5 4 4 39.04471 2 22.5 7.5 15 15 2 4 . 2 22.5 7.5 15 15 2 4 . 2 22.5 7.5 15 15 2 4 0
213
APPENDIX 6: RAW DATA FOR CHAPTER THREE Field data for canola trails. Seeding TRT Week BLOCK CI DSI
2011/05/25 1 3 1 0.00 0.00 2011/05/25 1 3 2 0.00 0.00 2011/05/25 1 3 3 4.00 1.33 2011/05/25 1 3 4 0.00 0.00 2011/05/25 1 4 1 6.00 2.00 2011/05/25 1 4 2 8.00 4.00 2011/05/25 1 4 3 22.00 7.33 2011/05/25 1 4 4 14.00 4.67 2011/05/25 1 5 1 0.00 0.00 2011/05/25 1 5 2 30.00 18.00 2011/05/25 1 5 3 24.00 8.67 2011/05/25 1 5 4 8.00 2.67 2011/05/25 1 6 1 2.00 0.67 2011/05/25 1 6 2 38.00 19.33 2011/05/25 1 6 3 42.00 19.33 2011/05/25 1 6 4 6.00 3.33 2011/05/25 1 7 1 24.00 10.67 2011/05/25 1 7 2 38.00 19.33 2011/05/25 1 7 3 46.94 26.53 2011/05/25 1 7 4 12.77 4.26 2011/05/25 1 8 1 23.33 8.89 2011/05/25 1 8 2 46.67 22.22 2011/05/25 1 8 3 66.67 35.56 2011/05/25 1 8 4 30.00 10.00 2011/05/25 1 9 1 25.00 9.72 2011/05/25 1 9 2 40.38 16.03 2011/05/25 1 9 3 56.00 26.00 2011/05/25 1 9 4 22.00 7.33 2011/05/25 1 10 1 19.23 7.05 2011/05/25 1 10 2 46.94 19.73 2011/05/25 1 10 3 55.10 24.49 2011/05/25 1 10 4 26.42 12.58 2011/06/10 2 4 1 3.77 1.26 2011/06/10 2 4 2 5.88 1.96 2011/06/10 2 4 3 1.96 0.65 2011/06/10 2 4 4 0.00 0.00 2011/06/10 2 5 1 10.00 3.33 2011/06/10 2 5 2 4.00 1.33 2011/06/10 2 5 3 5.17 1.72 2011/06/10 2 5 4 3.85 2.56 2011/06/10 2 6 1 22.58 9.68 2011/06/10 2 6 2 19.35 8.60 2011/06/10 2 6 3 10.00 3.33 2011/06/10 2 6 4 0.00 0.00 2011/06/10 2 7 1 19.61 7.19 2011/06/10 2 7 2 14.00 4.67 2011/06/10 2 7 3 12.50 5.56 2011/06/10 2 7 4 1.96 0.65 2011/06/10 2 8 1 23.08 11.54 2011/06/10 2 8 2 23.91 7.97 2011/06/10 2 8 3 41.51 13.84 2011/06/10 2 8 4 9.80 4.58 2011/06/10 2 9 1 68.75 34.72 2011/06/10 2 9 2 28.00 10.67 2011/06/10 2 9 3 43.40 15.09
214
2011/06/10 2 9 4 5.66 1.89 2011/06/10 2 10 1 62.00 29.33 2011/06/10 2 10 2 56.00 22.67 2011/06/10 2 10 3 28.00 12.00 2011/06/10 2 10 4 10.00 3.33 2011/06/10 2 11 1 42.86 14.29 2011/06/10 2 11 2 73.33 37.22 2011/06/10 2 11 3 32.50 10.83 2011/06/10 2 11 4 8.00 5.33 2011/06/22 3 5 1 2.00 0.67 2011/06/22 3 5 2 4.17 1.39 2011/06/22 3 5 3 0.00 0.00 2011/06/22 3 5 4 0.00 0.00 2011/06/22 3 6 1 49.02 18.30 2011/06/22 3 6 2 31.37 13.07 2011/06/22 3 6 3 23.53 7.84 2011/06/22 3 6 4 12.00 4.00 2011/06/22 3 7 1 61.54 23.08 2011/06/22 3 7 2 34.69 12.93 2011/06/22 3 7 3 20.00 8.48 2011/06/22 3 7 4 27.78 10.49 2011/06/22 3 8 1 64.00 29.33 2011/06/22 3 8 2 34.00 13.33 2011/06/22 3 8 3 34.00 14.00 2011/06/22 3 8 4 6.00 2.00 2011/06/22 3 9 1 86.00 45.33 2011/06/22 3 9 2 42.00 15.33 2011/06/22 3 9 3 54.00 24.67 2011/06/22 3 9 4 10.00 3.33 2011/07/06 4 5 1 23.91 8.70 2011/07/06 4 5 2 14.29 7.48 2011/07/06 4 5 3 14.58 7.64 2011/07/06 4 5 4 13.73 5.88
Seeding TRT WEEK BLOCK CI DSI 2012/05/29 1 4 1 0.0 0.0 2012/05/29 1 4 2 0.0 0.0 2012/05/29 1 4 3 4.0 1.3 2012/05/29 1 4 4 0.0 0.0 2012/06/05 1 5 1 2.0 0.7 2012/06/05 1 5 2 11.1 3.7 2012/06/05 1 5 3 8.0 2.7 2012/06/05 1 5 4 4.0 1.3 2012/06/13 1 6 1 32.0 10.7 2012/06/13 1 6 2 . . 2012/06/13 1 6 3 28.0 9.3 2012/06/13 1 6 4 24.0 8.0 2012/06/13 2 4 1 10.0 3.3 2012/06/13 2 4 2 11.5 3.8 2012/06/13 2 4 3 3.8 1.3 2012/06/13 2 4 4 2.0 0.7 2012/06/19 1 7 1 44.0 14.7 2012/06/19 1 7 2 . . 2012/06/19 1 7 3 46.0 15.3 2012/06/19 1 7 4 42.0 14.0 2012/06/19 2 5 1 28.0 9.3 2012/06/19 2 5 2 26.0 8.7 2012/06/19 2 5 3 24.0 8.0
215
2012/06/19 2 5 4 17.3 6.4 2012/06/28 1 8 1 66.0 22.7 2012/06/28 1 8 2 . . 2012/06/28 1 8 3 44.0 14.7 2012/06/28 1 8 4 40.0 13.3 2012/06/28 2 6 1 36.0 20.0 2012/06/28 2 6 2 38.0 13.3 2012/06/28 2 6 3 42.0 14.0 2012/06/28 2 6 4 16.0 5.3 2012/06/28 3 4 1 6.7 2.2 2012/06/28 3 4 2 13.3 4.4 2012/06/28 3 4 3 6.7 2.2 2012/06/28 3 4 4 3.3 1.1 2012/07/04 1 9 1 23.8 7.9 2012/07/04 1 9 2 . . 2012/07/04 1 9 3 10.0 3.3 2012/07/04 1 9 4 . . 2012/07/04 2 7 1 32.0 13.3 2012/07/04 2 7 2 36.0 16.0 2012/07/04 2 7 3 40.0 13.3 2012/07/04 2 7 4 32.0 12.0 2012/07/04 3 5 1 20.0 6.7 2012/07/04 3 5 2 26.7 8.9 2012/07/04 3 5 3 23.3 7.8 2012/07/04 3 5 4 10.0 3.3 2012/07/10 2 8 1 36.0 16.7 2012/07/10 2 8 2 28.0 9.3 2012/07/10 2 8 3 22.0 7.3 2012/07/10 2 8 4 24.0 8.0 2012/07/10 3 6 1 52.0 17.3 2012/07/10 3 6 2 60.0 20.0 2012/07/10 3 6 3 36.7 14.4 2012/07/10 3 6 4 26.7 8.9 2012/07/10 4 4 1 24.0 13.3 2012/07/10 4 4 2 22.0 7.3 2012/07/10 4 4 3 26.0 12.0 2012/07/10 4 4 4 8.0 2.7 2012/07/18 2 9 1 34.0 12.0 2012/07/18 2 9 2 66.0 26.0 2012/07/18 2 9 3 40.0 13.3 2012/07/18 2 9 4 34.0 11.3 2012/07/18 3 7 1 83.3 30.0 2012/07/18 3 7 2 86.7 33.3 2012/07/18 3 7 3 66.7 30.0 2012/07/18 3 7 4 56.7 18.9 2012/07/18 4 5 1 72.0 33.3 2012/07/18 4 5 2 54.0 39.3 2012/07/18 4 5 3 76.0 40.0 2012/07/18 4 5 4 22.0 8.0 2012/07/25 2 10 1 50.0 18.0 2012/07/25 2 10 2 54.0 22.0 2012/07/25 2 10 3 24.0 8.0 2012/07/25 2 10 4 38.0 12.7 2012/07/25 3 8 1 36.7 12.2 2012/07/25 3 8 2 93.3 47.8 2012/07/25 3 8 3 46.7 15.6 2012/07/25 3 8 4 30.0 10.0 2012/07/25 4 6 1 72.0 36.7 2012/07/25 4 6 2 84.0 57.3 2012/07/25 4 6 3 42.0 17.3
216
2012/07/25 4 6 4 26.0 10.7 2012/07/25 5 4 1 60.0 20.0 2012/07/25 5 4 2 80.0 26.7 2012/07/25 5 4 3 74.0 24.7 2012/07/25 5 4 4 20.0 6.7 2012/07/31 2 11 1 44.0 16.7 2012/07/31 2 11 2 48.0 21.3 2012/07/31 2 11 3 . . 2012/07/31 2 11 4 37.5 13.3 2012/07/31 3 9 1 70.0 38.9 2012/07/31 3 9 2 96.7 70.0 2012/07/31 3 9 3 60.0 35.6 2012/07/31 3 9 4 36.7 21.1 2012/07/31 4 7 1 82.0 40.0 2012/07/31 4 7 2 76.0 54.0 2012/07/31 4 7 3 44.0 30.0 2012/07/31 4 7 4 32.0 12.0 2012/07/31 5 5 1 44.0 20.0 2012/07/31 5 5 2 32.0 12.7 2012/07/31 5 5 3 28.0 10.0 2012/07/31 5 5 4 24.0 8.0 2012/08/08 3 10 1 33.3 11.1 2012/08/08 3 10 2 80.0 55.6 2012/08/08 3 10 3 . . 2012/08/08 3 10 4 43.3 23.3 2012/08/08 4 8 1 46.0 24.7 2012/08/08 4 8 2 78.0 52.7 2012/08/08 4 8 3 56.0 37.3 2012/08/08 4 8 4 44.0 34.7 2012/08/08 5 6 1 62.0 31.3 2012/08/08 5 6 2 46.0 26.7 2012/08/08 5 6 3 32.0 10.7 2012/08/08 5 6 4 6.0 2.0 2012/08/08 6 4 1 46.0 24.7 2012/08/08 6 4 2 12.0 4.0 2012/08/08 6 4 3 36.0 14.0 2012/08/08 6 4 4 18.0 6.0 2012/08/14 3 11 1 59.3 24.7 2012/08/14 3 11 2 80.0 40.0 2012/08/14 3 11 3 . . 2012/08/14 3 11 4 83.3 37.8 2012/08/14 4 9 1 54.0 24.0 2012/08/14 4 9 2 84.0 54.7 2012/08/14 4 9 3 78.0 49.3 2012/08/14 4 9 4 32.0 13.3 2012/08/14 5 7 1 68.0 44.0 2012/08/14 5 7 2 62.0 29.3 2012/08/14 5 7 3 26.0 12.7 2012/08/14 5 7 4 6.0 2.0 2012/08/14 6 5 1 38.0 19.3 2012/08/14 6 5 2 36.0 14.7 2012/08/14 6 5 3 30.0 14.0 2012/08/14 6 5 4 36.0 13.3 2012/08/21 4 10 1 50.0 26.0 2012/08/21 4 10 2 74.0 50.7 2012/08/21 4 10 3 62.0 38.7 2012/08/21 4 10 4 36.7 14.4 2012/08/21 5 8 1 66.0 41.3 2012/08/21 5 8 2 80.0 46.0 2012/08/21 5 8 3 68.0 38.7
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2012/08/21 5 8 4 30.0 10.0 2012/08/21 6 6 1 50.0 27.3 2012/08/21 6 6 2 44.0 24.0 2012/08/21 6 6 3 40.0 26.0 2012/08/21 6 6 4 22.0 8.0 2012/08/28 5 9 1 56.0 40.7 2012/08/28 5 9 2 84.0 56.7 2012/08/28 5 9 3 52.0 33.3 2012/08/28 5 9 4 6.0 3.3 2012/08/28 6 7 1 46.0 27.3 2012/08/28 6 7 2 56.0 34.7 2012/08/28 6 7 3 50.0 32.0 2012/08/28 6 7 4 28.0 9.3 2012/09/05 5 10 1 40.0 29.3 2012/09/05 5 10 2 68.0 46.0 2012/09/05 5 10 3 46.0 26.7 2012/09/05 5 10 4 4.0 1.3 2012/09/05 6 8 1 52.0 40.7 2012/09/05 6 8 2 64.0 45.3 2012/09/05 6 8 3 64.0 49.3 2012/09/05 6 8 4 26.0 16.7 2012/09/11 5 11 1 64.0 48.0 2012/09/11 5 11 2 64.0 49.3 2012/09/11 5 11 3 64.0 51.3 2012/09/11 5 11 4 30.0 10.7 2012/09/11 6 9 1 62.0 52.7 2012/09/11 6 9 2 48.0 30.0 2012/09/11 6 9 3 48.0 31.3 2012/09/11 6 9 4 12.0 6.0 2012/09/20 5 12 1 68.0 54.0 2012/09/20 5 12 2 74.0 52.7 2012/09/20 5 12 3 64.0 42.7 2012/09/20 5 12 4 15.0 5.0 2012/09/20 6 10 1 42.0 31.3 2012/09/20 6 10 2 56.0 41.3 2012/09/20 6 10 3 50.0 32.0 2012/09/20 6 10 4 24.0 10.0
Raw data of calculated degree days (Tbase = 14 °C) and rainfall (mm) for 2011 and 2012.
Seeding Harvest DSI CI Air DD
1W Delay
Air DD Soil DD
1W Delay
Soil DD
Season Total 1W
Delay
Season Total
2011/05/25 1 0 1 65 83 50 79 45 53 2011/05/25 2 5 13 83 128 79 114 53 83 2011/05/25 3 7 16 128 158 114 144 83 91 2011/05/25 4 11 22 158 208 144 186 91 106 2011/05/25 5 15 30 208 263 186 231 106 106 2011/05/25 6 19 42 263 329 231 292 106 110 2011/05/25 7 15 36 329 398 292 342 110 138 2011/05/25 8 16 37 398 458 342 398 138 166 2011/06/10 1 1 3 91 138 92 130 166 106 2011/06/10 2 2 6 138 192 130 174 106 106 2011/06/10 3 5 13 192 259 174 236 106 110 2011/06/10 4 5 12 259 328 236 286 110 138 2011/06/10 5 9 25 328 388 286 342 138 166 2011/06/10 6 16 36 388 435 342 388 166 213
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2011/06/10 7 17 39 435 489 388 445 213 219 2011/06/10 8 17 39 489 536 445 493 219 260 2011/06/22 1 1 2 215 284 179 238 260 138 2011/06/22 2 11 29 284 344 238 294 138 166 2011/06/22 3 14 36 344 392 294 340 166 213 2011/06/22 4 15 35 392 445 340 397 213 219 2011/06/22 5 22 48 445 492 397 445 219 260 2011/07/06 1 7 17 259 312 217 271 260 213 2012/05/02 1 0 1 35 86 11 44 49 49 2012/05/02 2 2 7 86 89 44 51 49 79 2012/05/02 3 11 32 89 138 51 93 79 93 2012/05/02 4 16 47 138 187 93 130 93 93 2012/05/02 5 21 59 187 251 130 181 93 104 2012/05/02 6 8 22 251 306 181 220 104 104 2012/05/16 1 3 8 81 126 51 90 29 44 2012/05/16 2 9 26 126 175 90 127 44 44 2012/05/16 3 16 39 175 239 127 178 44 55 2012/05/16 4 14 36 239 293 178 217 55 55 2012/05/16 5 11 29 293 341 217 261 55 72 2012/05/16 6 17 47 341 417 261 326 72 96 2012/05/16 7 16 43 417 469 326 375 96 129 2012/05/16 8 19 46 469 513 375 419 129 195 2012/05/30 1 3 9 186 240 165 217 49 55 2012/05/30 2 8 23 240 291 217 263 55 55 2012/05/30 3 37 50 291 332 263 307 55 72 2012/05/30 4 31 79 332 372 307 358 72 96 2012/05/30 5 25 59 372 406 358 392 96 129 2012/05/30 6 48 76 406 443 392 428 129 195 2012/05/30 7 31 54 443 486 428 478 195 206 2012/05/30 8 33 70 486 500 478 497 206 255 2012/06/13 1 11 24 156 215 124 176 11 28 2012/06/13 2 38 67 215 292 176 240 28 52 2012/06/13 3 37 66 292 343 240 290 52 85 2012/06/13 4 41 67 343 388 290 334 85 151 2012/06/13 5 38 60 388 454 334 396 151 162 2012/06/13 6 43 72 454 488 396 435 162 211 2012/06/13 7 38 62 488 510 435 472 211 217 2012/06/27 1 24 71 192 244 161 211 41 74 2012/06/27 2 14 35 244 288 211 254 74 140 2012/06/27 3 23 47 288 354 254 317 140 151 2012/06/27 4 31 52 354 388 317 356 151 200 2012/06/27 5 42 71 388 411 356 393 200 206 2012/06/27 6 44 64 411 459 393 435 206 210 2012/06/27 7 34 51 459 505 435 482 210 210 2012/06/27 8 50 64 505 520 482 507 210 242 2012/06/27 9 50 59 520 536 507 528 242 272 2012/07/11 1 14 31 180 238 165 221 123 134 2012/07/11 2 16 35 238 272 221 260 134 183 2012/07/11 3 26 45 272 295 260 297 183 188 2012/07/11 4 31 51 295 344 297 339 188 193 2012/07/11 5 45 60 344 389 339 386 193 193 2012/07/11 6 38 53 389 404 386 411 193 224 2012/07/11 7 35 49 404 420 411 432 224 255
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APPENDIX 7: RAW DATA FOR CHAPTER FOUR Field data for cabbage grown in 2011. Root and shoot data in grams, yield in kilograms. Harvest Cultivar Block CI DSI Root (g) Shoot (g) Head Yield (kg)
20110907 Kilaxy 1 0 0 4500 51000 2.6 20110907 Kilaxy 2 0 0 380 42000 2.1 20110907 Kilaxy 3 0 0 4200 49000 2.5 20110907 Kilaxy 4 0 0 3200 37100 1.9 20110907 Kilaton 1 0 0 3800 62000 3.1 20110907 Kilaton 2 0 0 3700 60000 3.0 20110907 Kilaton 3 0 0 3410 50200 2.5 20110907 Kilaton 4 0 0 2840 45700 2.3 20110907 Klimaro 1 100 100 0 0 0.0 20110907 Klimaro 2 100 100 0 0 0.0 20110907 Klimaro 3 100 100 3800 5800 0.3 20110907 Klimaro 4 100 100 3300 24000 1.2 20110913 Bronco 1 100 100 5200 28000 1.1 20110913 Bronco 2 100 100 3200 10300 0.4 20110913 Bronco 3 100 100 4700 15000 0.8 20110913 Bronco 4 100 98 3400 28000 1.5 20110818 Tekila 1 0 0 1550 54190 2.7 20110818 Tekila 2 0 0 1460 54880 2.7 20110818 Tekila 3 0 0 1320 57010 2.9 20110818 Tekila 4 0 0 650 28660 1.4 20110907 B-2819 1 100 90 2770 7600 0.4 20110907 B-2819 2 80 27 1680 25580 1.3 20110907 B-2819 3 87 30 3390 26500 1.3 20110907 B-2819 4 100 94 3600 15700 0.8 20110815 Kilaherb 1 0 0 1231.7 54890 2.7 20110815 Kilaherb 2 0 0 1407 52060 2.6 20110815 Kilaherb 3 0 0 1263.3 51690 2.6 20110815 Kilaherb 4 0 0 1339 46030 2.3
Field data for cabbage grown in 2012. Root and shoot data in grams, yield in kilograms. Harvest Range Cultivar Block CI DSI Root (g) Shoot (g) Head Yield (kg)
2012/08/26 4 B2819 1 100 63 186 1847 0.5 2012/08/26 4 B2819 2 100 37 172 2487 0.6 2012/08/26 4 B2819 3 100 40 178 3162 0.7 2012/08/26 4 B2819 4 90 30 155 3072 0.8 2012/08/23 4 Bronco 1 100 100 228 495 0.2 2012/08/23 4 Bronco 2 100 100 256 423 0.1 2012/08/23 4 Bronco 3 100 100 368 1682 0.0 2012/08/23 4 Bronco 4 100 93 258 2404 1.1 2012/08/26 4 Kilaherb 1 0 0 99 4557 3.1 2012/08/26 4 Kilaherb 2 0 0 106 4547 3.2 2012/08/26 4 Kilaherb 3 0 0 117 5641 3.6 2012/08/26 4 Kilaherb 4 0 0 119 5479 3.9 2012/08/23 4 Klimaro 1 100 100 255 546 0.1 2012/08/23 4 Klimaro 2 100 100 98 194 0.1 2012/08/23 4 Klimaro 3 100 100 171 328 1.3 2012/08/23 4 Klimaro 4 100 97 418 2338 0.8 2012/08/26 6 B2819 1 30 10 191 3443 0.9 2012/08/26 6 B2819 2 20 7 186 3818 1.0 2012/08/26 6 B2819 3 0 0 153 3509 1.1 2012/08/26 6 B2819 4 10 3 204 3519 1.2 2012/08/23 6 Bronco 1 100 37 127 5893 4.2 2012/08/23 6 Bronco 2 90 43 166 5354 4.0
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2012/08/23 6 Bronco 3 20 7 116 6446 4.5 2012/08/23 6 Bronco 4 50 17 127 7130 4.9 2012/08/26 6 Kilaherb 1 0 0 150 5515 4.1 2012/08/26 6 Kilaherb 2 0 0 143 6415 4.1 2012/08/26 6 Kilaherb 3 0 0 102 5696 4.3 2012/08/26 6 Kilaherb 4 0 0 145 6415 4.0 2012/08/23 6 Klimaro 1 70 37 211 3633 1.5 2012/08/23 6 Klimaro 2 60 20 187 4025 1.7 2012/08/23 6 Klimaro 3 100 47 198 3875 1.7 2012/08/23 6 Klimaro 4 30 10 192 3887 1.5
Raw data for root hair microscopy, 4 DAI and 12 DAI. Exp = experiment repetition, PP = primary plasmodia, MZ = mature zoosporangia, DZ = dehisced zoosporangia, NI = not infected, Total = total incidence
Exp Cultivar Pathotype Block Plant Harvest Date PP MZ DZ NI Total Incidence 1 B-2819 3 1 1 4 76 0 0 24 76 1 B-2819 3 1 2 4 88 7 1 4 96 1 B-2819 3 2 1 4 59 0 0 41 59 1 B-2819 3 2 2 4 60 0 0 40 60 1 B-2819 3 3 1 4 72 0 0 28 72 1 B-2819 3 3 2 4 69 0 0 31 69 1 B-2819 3 4 1 4 59 0 0 41 59 1 B-2819 3 4 2 4 51 0 0 49 51 1 B-2819 6 1 1 4 55 0 0 45 55 1 B-2819 6 1 2 4 54 3 0 43 57 1 B-2819 6 2 1 4 63 0 0 37 63 1 B-2819 6 2 2 4 52 0 0 48 52 1 B-2819 6 3 1 4 52 0 0 48 52 1 B-2819 6 3 2 4 51 1 0 48 52 1 B-2819 6 4 1 4 54 0 0 46 54 1 B-2819 6 4 2 4 57 0 0 43 57 1 Bronco 3 1 1 4 72 0 0 28 72 1 Bronco 3 1 2 4 63 0 0 37 63 1 Bronco 3 2 1 4 68 0 0 32 68 1 Bronco 3 2 2 4 78 0 0 22 78 1 Bronco 3 3 1 4 73 0 0 27 73 1 Bronco 3 3 2 4 78 0 0 22 78 1 Bronco 3 4 1 4 60 0 0 40 60 1 Bronco 3 4 2 4 56 0 0 44 56 1 Bronco 6 1 1 4 55 0 0 45 55 1 Bronco 6 1 2 4 60 0 0 40 60 1 Bronco 6 2 1 4 73 0 0 27 73 1 Bronco 6 2 2 4 74 0 0 26 74 1 Bronco 6 3 1 4 71 0 0 29 71 1 Bronco 6 3 2 4 62 3 0 35 65 1 Bronco 6 4 1 4 61 0 0 39 61 1 Bronco 6 4 2 4 41 0 0 59 41 1 Kilaherb 3 1 1 4 34 0 0 66 34 1 Kilaherb 3 1 2 4 45 0 0 55 45 1 Kilaherb 3 2 1 4 62 0 0 38 62 1 Kilaherb 3 2 2 4 75 0 0 25 75 1 Kilaherb 3 3 1 4 62 0 0 38 62 1 Kilaherb 3 3 2 4 73 1 0 26 74 1 Kilaherb 3 4 1 4 36 1 0 63 37 1 Kilaherb 3 4 2 4 49 0 0 51 49 1 Kilaherb 6 1 1 4 65 0 0 35 65 1 Kilaherb 6 1 2 4 77 0 0 23 77
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1 Kilaherb 6 2 1 4 81 0 0 19 81 1 Kilaherb 6 2 2 4 83 0 0 17 83 1 Kilaherb 6 3 1 4 46 2 0 52 48 1 Kilaherb 6 3 2 4 72 0 0 28 72 1 Kilaherb 6 4 1 4 62 0 0 38 62 1 Kilaherb 6 4 2 4 48 0 0 52 48 2 B-2819 3 1 1 4 54 1 0 45 55 2 B-2819 3 1 2 4 64 0 0 36 64 2 B-2819 3 2 1 4 40 1 0 59 41 2 B-2819 3 2 2 4 43 2 0 55 45 2 B-2819 3 3 1 4 60 0 0 40 60 2 B-2819 3 3 2 4 39 3 0 58 42 2 B-2819 3 4 1 4 63 0 0 37 63 2 B-2819 3 4 2 4 49 0 0 51 49 2 Bronco 3 1 1 4 61 0 0 39 61 2 Bronco 3 1 2 4 58 0 0 42 58 2 Bronco 3 2 1 4 48 0 0 52 48 2 Bronco 3 2 2 4 56 0 0 44 56 2 Bronco 3 3 1 4 54 0 0 46 54 2 Bronco 3 3 2 4 37 1 0 62 38 2 Bronco 3 4 1 4 50 0 0 50 50 2 Bronco 3 4 2 4 63 1 0 36 64 2 Kilaherb 3 1 1 4 57 0 0 43 57 2 Kilaherb 3 1 2 4 54 0 0 46 54 2 Kilaherb 3 2 1 4 54 0 0 46 54 2 Kilaherb 3 2 2 4 42 0 0 58 42 2 Kilaherb 3 3 1 4 40 0 0 60 40 2 Kilaherb 3 3 2 4 47 0 0 53 47 2 Kilaherb 3 4 1 4 42 1 0 57 43 2 Kilaherb 3 4 2 4 50 0 0 50 50 1 B-2819 3 1 1 12 71 0 0 29 71 1 B-2819 3 1 2 12 56 0 0 44 56 1 B-2819 3 2 1 12 92 2 0 6 94 1 B-2819 3 2 2 12 88 1 1 10 90 1 B-2819 3 3 1 12 83 5 0 12 88 1 B-2819 3 3 2 12 90 3 0 7 93 1 B-2819 3 4 1 12 81 3 0 16 84 1 B-2819 3 4 2 12 70 2 0 28 72 1 B-2819 6 1 1 12 75 1 0 24 76 1 B-2819 6 1 2 12 76 4 0 20 80 1 B-2819 6 2 1 12 85 4 0 11 89 1 B-2819 6 2 2 12 82 0 0 18 82 1 B-2819 6 3 1 12 75 8 0 17 83 1 B-2819 6 3 2 12 68 2 0 30 70 1 B-2819 6 4 1 12 84 3 0 13 87 1 B-2819 6 4 2 12 93 2 0 5 95 1 Bronco 3 1 1 12 70 1 0 29 71 1 Bronco 3 1 2 12 61 2 0 37 63 1 Bronco 3 2 1 12 79 4 0 17 83 1 Bronco 3 2 2 12 80 8 0 12 88 1 Bronco 3 3 1 12 88 0 0 12 88 1 Bronco 3 3 2 12 89 0 0 11 89 1 Bronco 3 4 1 12 50 5 0 45 55 1 Bronco 3 4 2 12 61 1 0 38 62 1 Bronco 6 1 1 12 72 4 0 24 76 1 Bronco 6 1 2 12 65 0 0 34 65 1 Bronco 6 2 1 12 82 3 0 15 85 1 Bronco 6 2 2 12 65 3 0 31 68 1 Bronco 6 3 1 12 85 6 0 9 91 1 Bronco 6 3 2 12 77 4 0 19 81
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1 Bronco 6 4 1 12 68 0 0 32 68 1 Bronco 6 4 2 12 67 6 0 27 73 1 Kilaherb 3 1 1 12 68 0 0 32 68 1 Kilaherb 3 1 2 12 63 0 0 37 63 1 Kilaherb 3 2 1 12 64 7 0 29 71 1 Kilaherb 3 2 2 12 56 12 2 30 70 1 Kilaherb 3 3 1 12 59 15 1 24 75 1 Kilaherb 3 3 2 12 67 3 1 29 71 1 Kilaherb 3 4 1 12 70 3 0 27 73 1 Kilaherb 3 4 2 12 76 2 0 22 78 1 Kilaherb 6 1 1 12 74 2 0 24 76 1 Kilaherb 6 1 2 12 85 0 0 15 85 1 Kilaherb 6 2 1 12 68 0 0 32 68 1 Kilaherb 6 2 2 12 59 0 0 41 59 1 Kilaherb 6 3 1 12 79 0 0 21 79 1 Kilaherb 6 3 2 12 80 2 0 18 82 1 Kilaherb 6 4 1 12 71 7 0 22 78 1 Kilaherb 6 4 2 12 69 3 0 28 72 2 B-2819 3 1 1 12 67 5 0 28 72 2 B-2819 3 1 2 12 67 4 0 39 71 2 B-2819 3 2 1 12 71 8 2 19 81 2 B-2819 3 2 2 12 49 22 3 26 74 2 B-2819 3 3 1 12 68 18 0 14 86 2 B-2819 3 3 2 12 67 19 0 14 86 2 B-2819 3 4 1 12 50 20 4 26 74 2 B-2819 3 4 2 12 52 29 5 14 86 2 Bronco 3 1 1 12 61 5 0 34 66 2 Bronco 3 1 2 12 74 6 0 20 80 2 Bronco 3 2 1 12 65 5 0 30 70 2 Bronco 3 2 2 12 78 5 0 17 83 2 Bronco 3 3 1 12 61 6 0 33 67 2 Bronco 3 3 2 12 49 21 3 27 73 2 Bronco 3 4 1 12 65 11 0 24 76 2 Bronco 3 4 2 12 55 27 4 14 86 2 Kilaherb 3 1 1 12 50 15 2 33 67 2 Kilaherb 3 1 2 12 59 6 1 34 66 2 Kilaherb 3 2 1 12 62 7 3 28 72 2 Kilaherb 3 2 2 12 62 9 3 26 74 2 Kilaherb 3 3 1 12 72 7 0 21 79 2 Kilaherb 3 3 2 12 47 22 1 30 70 2 Kilaherb 3 4 1 12 47 20 7 26 74 2 Kilaherb 3 4 2 12 52 32 1 15 85
Raw data of cortical sectioning trial, 28 DAI.
Exp Cultivar Pathotype Block Image Percent Young Plasmodia
Mature Plasmodia
Resting Spores
Total Cells
1 B-2819 3 1 a 10.2 90 11 0 101 1 B-2819 3 1 b 21.7 80 64 6 150 1 B-2819 3 1 c 3.5 35 0 0 35 1 B-2819 3 1 d 12.7 86 14 5 105 1 B-2819 3 2 a 4.1 58 27 4 89 1 B-2819 3 2 b 15.9 25 56 26 107 1 B-2819 3 2 c 1.8 56 23 0 79 1 B-2819 3 2 d 9.4 65 18 9 92 1 B-2819 3 3 a 0.7 26 0 0 26 1 B-2819 3 3 b 3.9 44 38 0 82 1 B-2819 3 3 c 2.0 50 16 5 71
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1 B-2819 3 3 d 14.1 32 100 14 146 1 B-2819 3 4 a 5.2 38 19 3 60 1 B-2819 3 4 b 15.8 80 46 8 134 1 B-2819 3 4 c 15.1 81 31 0 112 1 B-2819 3 4 d 12.4 56 27 0 83 1 B-2819 6 1 a 2.0 89 5 0 94 1 B-2819 6 1 b 17.2 25 60 4 89 1 B-2819 6 1 c 2.9 38 28 0 66 1 B-2819 6 1 d 7.4 66 17 5 88 1 B-2819 6 2 a 11.7 91 22 8 121 1 B-2819 6 2 b 6.9 44 34 0 78 1 B-2819 6 2 c 8.9 57 22 5 84 1 B-2819 6 2 d 3.8 66 6 0 72 1 B-2819 6 3 a 3.6 33 22 7 62 1 B-2819 6 3 b 2.9 22 9 0 31 1 B-2819 6 3 c 17.1 32 91 20 143 1 B-2819 6 3 d 5.5 56 0 0 56 1 B-2819 6 4 a 4.6 26 6 0 32 1 B-2819 6 4 b 7.1 34 9 2 45 1 B-2819 6 4 c 5.5 23 11 11 45 1 B-2819 6 4 d 6.0 25 10 0 35 1 Bronco 3 1 a 20.1 96 41 43 180 1 Bronco 3 1 b 27.0 103 46 66 215 1 Bronco 3 1 c 25.0 66 32 27 125 1 Bronco 3 1 d 26.4 80 30 47 157 1 Bronco 3 2 a 17.5 76 54 39 169 1 Bronco 3 2 b 19.0 127 73 35 235 1 Bronco 3 2 c 20.6 98 32 32 162 1 Bronco 3 2 d 17.9 73 51 36 160 1 Bronco 3 3 a 17.7 46 53 35 134 1 Bronco 3 3 b 28.3 66 15 22 103 1 Bronco 3 3 c 12.6 86 23 8 117 1 Bronco 3 3 d 14.2 41 85 14 140 1 Bronco 3 4 a 22.0 63 65 22 150 1 Bronco 3 4 b 28.3 72 62 34 168 1 Bronco 3 4 c 26.2 68 72 26 166 1 Bronco 3 4 d 14.9 60 9 8 77 1 Bronco 6 1 a 21.8 95 40 19 154 1 Bronco 6 1 b 23.2 76 22 27 125 1 Bronco 6 1 c 16.2 43 12 16 71 1 Bronco 6 1 d 8.1 39 5 0 44 1 Bronco 6 2 a 17.4 90 16 15 121 1 Bronco 6 2 b 17.7 80 16 15 111 1 Bronco 6 2 c 12.5 65 6 21 92 1 Bronco 6 2 d 10.1 90 23 18 131 1 Bronco 6 3 a 17.1 50 88 34 172 1 Bronco 6 3 b 38.5 43 72 59 174 1 Bronco 6 3 c 31.3 45 62 56 163 1 Bronco 6 3 d 25.5 39 54 47 140 1 Bronco 6 4 a 13.7 46 12 19 77 1 Bronco 6 4 b 30.6 21 68 57 146 1 Bronco 6 4 c 15.3 45 19 18 82 1 Bronco 6 4 d 25.7 55 41 25 121 1 Kilaherb 3 1 a 8.6 19 0 0 19 1 Kilaherb 3 1 b 6.0 44 3 0 47 1 Kilaherb 3 1 c 11.6 75 0 0 75 1 Kilaherb 3 1 d 9.4 53 8 0 61 1 Kilaherb 3 2 a 5.9 28 0 0 19 1 Kilaherb 3 2 b 5.6 48 0 0 47 1 Kilaherb 3 2 c 9.6 94 19 0 75
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1 Kilaherb 3 2 d 10.0 54 6 0 61 1 Kilaherb 3 3 a 5.7 63 0 0 63 1 Kilaherb 3 3 b 8.4 60 31 0 91 1 Kilaherb 3 3 c 18.9 30 0 0 30 1 Kilaherb 3 3 d 6.0 59 0 0 59 1 Kilaherb 3 4 a 5.8 46 10 0 56 1 Kilaherb 3 4 b 8.6 39 19 0 58 1 Kilaherb 3 4 c 1.7 19 0 0 19 1 Kilaherb 3 4 d 3.3 21 0 0 21 1 Kilaherb 6 1 a 2.5 33 1 0 34 1 Kilaherb 6 1 b 2.5 43 3 0 46 1 Kilaherb 6 1 c 2.0 33 6 0 39 1 Kilaherb 6 1 d 2.6 35 7 0 42 1 Kilaherb 6 2 a 4.3 46 0 0 79 1 Kilaherb 6 2 b 3.8 45 0 0 72 1 Kilaherb 6 2 c 11.4 58 10 0 38 1 Kilaherb 6 2 d 3.1 56 16 0 55 1 Kilaherb 6 3 a 10.4 70 9 0 79 1 Kilaherb 6 3 b 8.2 65 7 0 72 1 Kilaherb 6 3 c 1.6 31 7 0 38 1 Kilaherb 6 3 d 7.8 51 4 0 55 1 Kilaherb 6 4 a 14.1 51 27 0 78 1 Kilaherb 6 4 b 2.2 16 0 0 16 1 Kilaherb 6 4 c 1.6 29 6 0 35 1 Kilaherb 6 4 d 5.3 39 5 0 44 2 B-2819 3 1 a 10.1 78 30 2 110 2 B-2819 3 1 b 4.6 62 17 0 79 2 B-2819 3 1 c 14.5 80 20 3 103 2 B-2819 3 1 d 8.2 73 19 2 94 2 B-2819 3 2 a 6.9 79 17 1 97 2 B-2819 3 2 b 6.3 92 20 1 113 2 B-2819 3 2 c 2.9 60 18 0 78 2 B-2819 3 2 d 7.8 97 25 3 125 2 B-2819 3 3 a 10.9 81 0 37 118 2 B-2819 3 3 b 7.9 89 25 0 114 2 B-2819 3 3 c 4.3 69 14 0 83 2 B-2819 3 3 d 5.6 51 13 0 64 2 B-2819 3 4 a 4.0 60 14 0 74 2 B-2819 3 4 b 8.1 90 26 0 116 2 B-2819 3 4 c 4.7 81 17 0 98 2 B-2819 3 4 d 7.1 99 32 0 131 2 Bronco 3 1 a 28.4 21 7 19 47 2 Bronco 3 1 b 23.1 23 9 19 51 2 Bronco 3 1 c 13.8 15 18 21 54 2 Bronco 3 1 d 28.8 18 13 47 78 2 Bronco 3 2 a 34.7 30 22 24 76 2 Bronco 3 2 b 23.2 90 30 23 143 2 Bronco 3 2 c 10.3 65 9 15 89 2 Bronco 3 2 d 35.2 31 32 35 98 2 Bronco 3 3 a 33.0 80 31 38 149 2 Bronco 3 3 b 31.6 41 36 58 135 2 Bronco 3 3 c 30.2 23 20 29 72 2 Bronco 3 3 d 28.3 31 12 28 71 2 Bronco 3 4 a 23.5 89 29 39 157 2 Bronco 3 4 b 23.9 122 28 31 181 2 Bronco 3 4 c 17.2 56 31 36 123 2 Bronco 3 4 d 14.6 80 12 23 115 2 Kilaherb 3 1 a 8.1 55 8 0 63 2 Kilaherb 3 1 b 4.9 48 7 0 55 2 Kilaherb 3 1 c 12.7 60 10 0 70
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2 Kilaherb 3 1 d 9.6 69 9 0 78 2 Kilaherb 3 2 a 8.6 79 10 0 89 2 Kilaherb 3 2 b 4.8 51 14 0 65 2 Kilaherb 3 2 c 9.5 79 18 0 97 2 Kilaherb 3 2 d 5.2 55 4 0 59 2 Kilaherb 3 3 a 14.3 84 24 0 108 2 Kilaherb 3 3 b 4.9 27 0 0 27 2 Kilaherb 3 3 c 1.5 26 0 0 26 2 Kilaherb 3 3 d 6.2 65 4 0 69 2 Kilaherb 3 4 a 9.5 69 5 0 74 2 Kilaherb 3 4 b 3.8 59 3 0 62 2 Kilaherb 3 4 c 3.8 46 0 0 46 2 Kilaherb 3 4 d 3.6 39 0 0 39
Raw data on concentration of P. brassicae gDNA (ng/g of root).
Experiment Cultivar Pathotype Block DAI Concentration Log Concentration 1 B-2819 3 1 4 35.43638 1.561535 1 B-2819 3 2 4 2.564529 0.552002 1 B-2819 3 3 4 16.92723 1.253513 1 B-2819 3 4 4 45.9676 1.671798 1 B-2819 6 1 4 13.55444 1.162996 1 B-2819 6 2 4 0.087988 0.036624 1 B-2819 6 3 4 4.271245 0.721913 1 B-2819 6 4 4 0.286116 0.10928 1 Bronco 3 1 4 1.471869 0.393025 1 Bronco 3 2 4 1.494767 0.39703 1 Bronco 3 3 4 125.2385 2.101192 1 Bronco 3 4 4 72.10997 1.863977 1 Bronco 6 1 4 0.406872 0.148255 1 Bronco 6 2 4 0.338286 0.126549 1 Bronco 6 3 4 39.48767 1.607323 1 Bronco 6 4 4 0.054638 0.023103 1 Kilaherb 3 1 4 3.297386 0.633204 1 Kilaherb 3 2 4 1.174751 0.337409 1 Kilaherb 3 3 4 34.62682 1.551777 1 Kilaherb 3 4 4 19.7117 1.316216 1 Kilaherb 6 1 4 0.435528 0.157012 1 Kilaherb 6 2 4 0.134249 0.054708 1 Kilaherb 6 3 4 4.818577 0.764817 1 Kilaherb 6 4 4 1.021047 0.305577 2 B-2819 3 1 4 120.8858 2.085953 2 B-2819 3 2 4 . 2 B-2819 3 3 4 . 2 B-2819 3 4 4 149.2991 2.176956 2 Bronco 3 1 4 8.920257 0.996523 2 Bronco 3 2 4 12.27613 1.123072 2 Bronco 3 3 4 111.5951 2.051519 2 Bronco 3 4 4 . 2 Kilaherb 3 1 4 . 2 Kilaherb 3 2 4 62.52092 1.802917 2 Kilaherb 3 3 4 50.50547 1.711853 2 Kilaherb 3 4 4 11.67572 1.102973 1 B-2819 3 1 12 37.05801 1.580446
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1 B-2819 3 2 12 3.408717 0.644312 1 B-2819 3 3 12 1280.116 3.107589 1 B-2819 3 4 12 388.0913 2.590052 1 B-2819 6 1 12 14.63968 1.194228 1 B-2819 6 2 12 2.455106 0.538461 1 B-2819 6 3 12 35.70309 1.564703 1 B-2819 6 4 12 8.093569 0.958734 1 Bronco 3 1 12 . 1 Bronco 3 2 12 1.262012 0.354495 1 Bronco 3 3 12 1084.107 3.035473 1 Bronco 3 4 12 194.8777 2.291985 1 Bronco 6 1 12 0.255725 0.098895 1 Bronco 6 2 12 0.395735 0.144803 1 Bronco 6 3 12 4.830022 0.76567 1 Bronco 6 4 12 1.979668 0.474168 1 Kilaherb 3 1 12 8.480732 0.976842 1 Kilaherb 3 2 12 0.199603 0.079038 1 Kilaherb 3 3 12 903.4195 2.95637 1 Kilaherb 3 4 12 109.2121 2.042229 1 Kilaherb 6 1 12 0.519339 0.181655 1 Kilaherb 6 2 12 . 1 Kilaherb 6 3 12 107.1919 2.034195 1 Kilaherb 6 4 12 51.02856 1.716242 2 B-2819 3 1 12 113.273 2.057944 2 B-2819 3 2 12 951.5961 2.978909 2 B-2819 3 3 12 0.016128 0.006948 2 B-2819 3 4 12 1246.317 3.095977 2 Bronco 3 1 12 2.87443 0.588208 2 Bronco 3 2 12 135.5441 2.135273 2 Bronco 3 4 12 276.1689 2.442745 2 Kilaherb 3 1 12 23.54727 1.390003 2 Kilaherb 3 2 12 . 2 Kilaherb 3 3 12 . 2 Kilaherb 3 3 12 1180.406 3.072399 2 Kilaherb 3 4 12 274.317 2.439833 1 B-2819 3 1 28 9838105 6.993 1 B-2819 3 2 28 27850448 7.445 1 B-2819 3 3 28 131383330 8.119 1 B-2819 3 4 28 22529885 7.353 1 B-2819 6 1 28 2955080 6.471 1 B-2819 6 2 28 100191650 8.001 1 B-2819 6 3 28 8961062 6.952 1 B-2819 6 4 28 40029233 7.602 1 Bronco 3 1 28 34842068 7.542 1 Bronco 3 2 28 456311 5.659 1 Bronco 3 3 28 495421 5.695 1 Bronco 3 4 28 91269790 7.96 1 Bronco 6 1 28 2132633 6.329 1 Bronco 6 2 28 31025953 7.492 1 Bronco 6 3 28 20071799 7.303 1 Bronco 6 4 28 124894590 8.097 1 Kilaherb 3 1 28 80523875 7.906 1 Kilaherb 3 2 28 158297250 8.199 1 Kilaherb 3 3 28 82460820 7.916
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1 Kilaherb 3 4 28 15745581 7.197 1 Kilaherb 6 1 28 1263265 6.101 1 Kilaherb 6 2 28 7194831 6.857 1 Kilaherb 6 3 28 122002160 8.086 1 Kilaherb 6 4 28 8862472 6.948 2 B-2819 3 1 28 0 0 2 B-2819 3 2 28 0 0 2 B-2819 3 3 28 0 0 2 B-2819 3 4 28 0 0 2 Bronco 3 1 28 655587 5.817 2 Bronco 3 2 28 1877036 6.273 2 Bronco 3 3 28 71416 4.854 2 Bronco 3 4 28 45947 4.662 2 Kilaherb 3 1 28 0 0 2 Kilaherb 3 2 28 0 8E-04 2 Kilaherb 3 3 28 0 2E-04 2 Kilaherb 3 4 28 0 0
Raw data of final disease levels in the growth room trials, 42 DAI. Experiment Cultivar Pathotype Block CI DSI
1 B-2819 3 1 40.0 13.3 1 B-2819 3 2 40.0 13.3 1 B-2819 3 3 60.0 20.0 1 B-2819 3 4 60.0 20.0 1 B-2819 6 1 40.0 13.3 1 B-2819 6 2 40.0 13.3 1 B-2819 6 3 40.0 16.7 1 B-2819 6 4 20.0 10.0 1 Bronco 3 1 100.0 100.0 1 Bronco 3 2 100.0 100.0 1 Bronco 3 3 100.0 100.0 1 Bronco 3 4 100.0 100.0 1 Bronco 6 1 80.0 80.0 1 Bronco 6 2 100.0 96.7 1 Bronco 6 3 100.0 93.3 1 Bronco 6 4 90.0 76.7 1 Kilaherb 3 1 0.0 0.0 1 Kilaherb 3 2 0.0 0.0 1 Kilaherb 3 3 0.0 0.0 1 Kilaherb 3 4 0.0 0.0 1 Kilaherb 6 1 0.0 0.0 1 Kilaherb 6 2 0.0 0.0 1 Kilaherb 6 3 0.0 0.0 1 Kilaherb 6 4 0.0 0.0 2 B-2819 3 1 40.0 16.7 2 B-2819 3 2 60.0 26.7 2 B-2819 3 3 50.0 16.7 2 B-2819 3 4 60.0 36.7 2 B-2819 6 1 40.0 13.3 2 B-2819 6 2 60.0 20.0 2 B-2819 6 3 40.0 13.3 2 B-2819 6 4 40.0 20.0 2 Bronco 3 1 100.0 100.0 2 Bronco 3 2 100.0 100.0 2 Bronco 3 3 100.0 100.0 2 Bronco 3 4 100.0 100.0
228
2 Bronco 6 1 100.0 100.0 2 Bronco 6 2 100.0 93.3 2 Bronco 6 3 100.0 96.7 2 Bronco 6 4 100.0 93.3 2 Kilaherb 3 1 0.0 0.0 2 Kilaherb 3 2 0.0 0.0 2 Kilaherb 3 3 0.0 0.0 2 Kilaherb 3 4 0.0 0.0 2 Kilaherb 6 1 0.0 0.0 2 Kilaherb 6 2 0.0 0.0 2 Kilaherb 6 3 0.0 0.0 2 Kilaherb 6 4 0.0 0.0