The Deposition of Phakopsora Pachyrhizi Urediniospores On

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The Pennsylvania State University

The Graduate School

College of Agricultural Sciences

THE DEPOSITION OF PHAKOPSORA PACHYRHIZI UREDINIOSPORES ON

SOYBEAN

A Dissertation in

Plant Pathology

Nicholas S. Dufault

Submitted in Partial Fulfillment of the Requirements for the Degree of

Doctor of Philosophy

August 2008

The dissertation of Nicholas S. Dufault was reviewed and approved* by the following:

Scott A. Isard Professor of Aerobiology Dissertation Adviser Chair of Committee

Paul A. Backman Professor of Plant Pathology

Gary W. Moorman Professor of Plant Pathology

Shelby J. Fleischer Professor of Entomology

Erick D. De Wolf Assistant Professor of Plant Pathology

Barbara J. Christ Professor of Plant Pathology Head of the Department of Plant Pathology

*Signatures are on file in the Graduate School

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ABSTRACT

Soybean rust, caused by Phakopsora pachyrhizi Sydow, is a potentially devastating foliar disease of soybeans ( Glycine max ) that occurs throughout the United

States and the world. This obligate pathogen persists in the U.S. by overwintering on alternative hosts [i.e. kudzu ( Pueraria lobata )] in the Gulf Coast region. Predicting the risk of early season pathogen spread from southern inoculum sources to commercial soybean fields throughout the U.S. is an important process in determining proper management strategies. Increased knowledge about urediniospore dispersal will be important in improving the accuracy of current risk assessment models. This study examined the wet and dry deposition of P. pachyrhizi urediniospores into soybean canopies and the affects that environmental factors (i.e. rainfall intensity and wind speed) and cultural practices (i.e. row spacing) had on these distributions.

Rainfall simulations within soybean field plots indicated that the wet deposition of urediniospores was significantly influenced by rainfall intensity and the soybean canopy height. A larger mean proportion of (0.41) uredinia per cm 2 was observed in the middle section of the soybean canopies for rainfall events of 75 mm/hr as compared to the mean proportion (0.25) for 15 mm/hr rainfall events. For plant canopy height, a larger mean proportion of (0.40) uredinia per cm 2 was also observed in the middle section

of soybean canopies ranging in height from 30 to 38 cm when compared to the mean

proportion (0.27) for soybean canopies 71 to 85 cm in height. These results indicate that

higher proportions of urediniospores will be wet deposited into the middle sections of

soybean canopies earlier in the season during high intensity rainfall events than rainfall

events of low intensity that occur later in the season.

We examined effects of prolonged rainfall periods on the wet deposition of urediniospores on individual soybean plants with simulated rainfall. We observed that 20 to 44% of the urediniospores wet deposited on the soybean leaf tissue in the top and middle sections of the canopy could be washed off by 1 min of simulated rainfall at the intensities of 15 and 75 mm/hr. After 30 min of prolonged rainfall, 69 to 93% of the wet deposited urediniospores were removed from the soybean leaf tissue for both rainfall intensities. This indicates that prolonged rainfall will reduce the number of urediniospores deposited on the soybean leaf tissue, but not remove them completely from the leaf surface.

The dry deposition of Night-Glo® NG-20 zinc sulfide particle (DayGlo® Color

Corp., Cleveland, OH) indicated that plant canopy height and wind speed were the main factors influencing the dry deposition of P. pachyrhizi urediniospores released above a soybean canopy. The proportion of particles in the middle section of the canopy, normalized to the top canopy sample, decreased from 0.67 to 0.28 as the canopy increased in height from 69 - 71 cm to 84 - 91 cm. High wind speeds were observed to deposit larger quantities of particles farther from their source above the canopy than low wind speeds. However, even though more particles were dry deposited farther from the source than at low wind speeds, a majority the particles were still deposited within 20 m of the aerial source. It is apparent from these results that dry deposition is an effective process in depositing aerial urediniospores into middle sections of the soybean canopy close to their source.

TABLE OF CONTENTS

List of Tables ...... vii List of Figures...... ix Acknowledgements...... xiv

Chapter 1. INTRODUCTION AND RESEARCH OBJECTIVES...... 1 Soybean...... 1 Soybean Rust ...... 2 Aerobiological Concepts...... 13 Research Objectives...... 20 Literature Cited...... 21

Chapter 2. EFFECT OF LEAF AGE ON SUSCEPTIBILITY OF GLYCINE MAX TO PHAKOPSORA PACHYRHIZI AS DETERMINED BY A DETACHED SOYBEAN LEAFLET BIOASSAY ...... 27 Introduction...... 27 Materials and Methods...... 28 Results...... 32 Discussion...... 37 Literature Cited...... 40

Chapter 3. A RAINFALL SIMULATOR FOR PLANT PATHOGEN WET DEPOSITION STUDIES...... 42 Introduction...... 42 Materials and Methods...... 44 Results...... 51 Discussion...... 56 Literature Cited...... 60

Chapter 4.THE WET DEPOSITION OF PHAKOPSORA PACHYRHIZI UREDINIOSPORES WITHIN SOYBEAN CANOPIES ...... 63 Introduction...... 63 Materials and Methods...... 66 Results...... 71 Discussion...... 75 Literature Cited...... 83

Chapter 5. RAINFALL REMOVAL OF PHAKOPSORA PACHYRHIZI UREDINIOSPORES FROM SOYBEAN LEAVES...... 88 Introduction...... 88 Materials and Methods...... 89 Results...... 94 Discussion...... 94 Literature Cited...... 103

Chapter 6. THE DRY DEPOSITION OF PHAKOPSORA PACHYRHIZI UREDNIOSPORES AND PARTICLES INTO SOYBEAN CANOPIES...... 106 Introduction...... 106 Materials and Methods...... 108 Results...... 115 Discussion...... 125 Literature Cited...... 132

Chapter 7. SUMMARY ...... 135 Literature Cited...... 140

Appendix. CHAPTERS 4 AND 6 DETAILS AND DATA ...... 141

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LIST OF TABLES

Table 2-1: Manufacturer’s information about the soybean varieties evaluated for the

bioassay at the North Florida Research and Education Center, Quincy, FL...... 29

Table 2-2: The soybean variety’s growth stage information at leaf harvest for the

specified planting...... 29

Table 2-3: The concentration and average percent germination of the two spore

suspensions used to inoculate the various age groups of detached soybean leaflets...31

Table 2-4: Effect of leaflet age, cultivation method and their interaction on the square

root of disease severity based on a mixed model analysis of variance...... 34

Table 3-1: A list of component parts for one rainfall simulator...... 46

Table 3-2: The simulated rainfall characteristics of the nozzles observed within the 2 x 2

m sample area...... 47

Table 4-1: Leaf area index values recorded for the different combinations of plant height

and row spacing...... 67

Table 4-2: The Phakopsora pachyrhizi urediniospore suspensions and rainfall durations

used to evaluate the effects of rainfall deposition of urediniospores into the different

soybean canopies at the given rainfall intensities...... 67

Table 4-3: Main and interaction effects of within canopy sample height by plant height,

row spacing and rainfall intensity on the proportion of uredinia per cm 2 based on a

linear mixed model analysis of variance...... 72

Table 5-1: Characteristics about the rain simulator, soybean plants and Phakopsora

pachyrhizi urediniospores used to evaluate the effects of prolonged rainfall periods on

the removal of urediniospores from soybean leaves...... 91

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Table 5-2: Effects of rainfall intensity, prolonged rainfall and leaf sample height on

disease severity, the proportion of spores removed (S R) and the rate of spore removal

(B R) based on a linear mixed model analysis of variance...... 96

Table 5-3: The effects of rainfall intensity, leaf height and prolonged rainfall periods on

removal of urediniospores from soybean leaf tissue...... 97

Table 6-1: Dates, time, run duration and crop conditions during the 1 m dry deposition

trial runs ...... 116

Table 6-2: Average meteorological conditions recorded during the 1 m dry deposition

trial runs ...... 116

Table 6-3: Dates, time, run duration and crop conditions during the 6 m dry deposition

trial runs ...... 117

Table 6-4: Average meteorological conditions recorded during the 6 m dry deposition

trial runs ...... 118

Table 6-5: Main and interaction effects of plant height, row spacing, within canopy

sample height and distance on the normalize proportion of NG-20 particles that were

deposited within a soybean plant canopy for the 6 m trial based on a linear mixed

model analysis of variance...... 121

Table 6-6: Parameter estimates from the least squares regression fits of the power law

and exponential law models for the relationship between particle deposition trap dose

(# per cm 2) and distance from the source...... 124

Table A-1: The weather stations and sensors used to collect environmental data during

the dry deposition runs...... 142

LIST OF FIGURES

Fig. 1-1: A map showing the geographic distribution of Phakopsora pachyrhizi (soybean

rust) in the United States at the end of the growing season in November of 2007.

(Source:USDA IpmPIPE website: www.sbrusa.net ; accessed May 27, 2008)...... 4

Fig. 1-2: A map showing the geographic distribution of Pueraria lobata (kudzu) in the

United States. (Source: USDA, NRCS Plants Database; accessed May 27, 2008)...... 9

Fig. 1-3: Conceptual diagram of urediniospore dispersal through the atmosphere from

source to receptor. Spores are released from a host plant, escape the canopy,

transported horizontally and then deposited on susceptible host population. During

transport spores are exposed to may factors that can reduce their viability before

reaching the receptor. Spores are deposited by sedimentation/impaction and/or rain

washout. Sedimentation/impaction is relatively more important closer to the source

and rain washout is the primary deposition process at distances farther from the

source. Spores that do not reach a suitable host population or adhere to a substrate

may have transport reinitiated soon after landing...... 15

Fig. 1-4: Conceptual diagrams for dry and wet deposition of Phakopsora pachyrhizi

urediniospores in a soybean canopy. Solid lines represent positive spore dispersal

events and dashed lines represent spore decrease events. Splash dispersal of P.

pachyrhizi urediniospores is theoretically possible, however, no observations of

splash dispersal have been reported at this time...... 18

Fig. 2-1: Effect of soybean leaflet age on disease severity following an inoculation of

~4,000 urediniospores per ml suspension for ( A) variety DP7220RR with calendar

planting date 191; ( B) variety DP7220RR with calendar planting date 219; (C) variety

DP5915RR with calendar planting date 200. Letters above the bars represent the

Tukey grouping results ( P < 0.05) for each treatment. Error bars represent the

standard error of the mean ...... 35

Fig. 2-2: Effect of cultivation methods on disease severity following an inoculation of

~4,000 urediniospores per ml suspension for ( A) variety DP7220RR with calendar

planting date 191; ( B) variety DP7220RR with calendar planting date 219; (C) variety

DP5915RR with calendar planting date 200. Letters above the bars represent the

Tukey grouping results ( P < 0.05) for each treatment...... 36

Fig. 3-1: ( A) The rain simulator in a soybean field with the control unit. The control unit

was used to regulate both water flow and spore injection. ( B) A close up of the

control unit showing the design for injecting spores into the water flow...... 45

Fig. 3-2: The rainfall simulator’s calibration techniques of ( A) the oil method [6] used to

determine drop size and ( B) the cup method used to sample the rainfall intensity’s

uniformity over the sample area...... 49

Fig. 3-3: Comparison of the droplet frequency distribution produced by the Fulljet

3/8HH-SS24WSQ nozzle (24WSQ) and the Fulljet 1/2HH-SS50WSQ nozzle

(50WSQ) with four calculated rainfall frequency distributions [17, 24, 25] at

intensities of 1, 20, 40 and 80 mm/hr. Data for the nozzles droplet frequency

distribution was from the 150 droplets sampled using the oil method [6]. Bars

represent data from the simulator droplet distribution and lines represent theoretical

rainfall droplet distributions...... 53

Fig. 3-4: The rainfall intensity distribution of simulated rainfall collected over the 2 x 2

m sample area (5 min duration) for the ( A) Fulljet 3/8HH-SS24WSQ nozzle (CU =

86%) and ( B) Fulljet 1/2HH-SS50WSQ nozzle (CU = 85%) at 34.5 kPa operating

pressure...... 54

Fig. 3-5: The cumulative droplet volume distribution produced by the ( A) Fulljet 3/8HH-

SS24WSQ nozzle and the ( B) Fulljet 1/2HH-SS50WSQ nozzle compared to

calculated distributions of cumulative rain droplet volumes for natural rainfall

intensities. The natural rainfall volume distributions were calculated [24] between the

ranges of 0.2 to 2.6 mm for the 24WSQ, and 0.4 to 3.3 mm for the 50WSQ. The

relative droplet volume distributions for the nozzles were calculated from the 150

droplet diameters recorded for each nozzle from the oil method [6] samples...... 55

Fig. 4-1: The mean proportion of uredinia per cm 2 (± standard error) observed within the

soybean canopies at 3 sample heights for the treatments of average soybean canopy

height, row spacing and rainfall intensity. The within canopy sample heights were

adjusted relative to the average plant canopy height (ht) for the heights of low (0.3ht),

mid (0.6ht) and top (1.0ht)...... 73

Fig. 4-2: The mean proportion of uredinia per cm 2 (± standard error) observed within the

soybean canopies at 3 sample heights for the plant height range and rainfall intensities

of ( A) 30 to 38 cm and 15 mm/hr; ( B) 30 to 38 cm and 75 mm/hr; ( C) 71 to 85 cm

and 15 mm/hr; ( D) 71 to 85 cm and 75 mm/hr. Each line represents a different row

spacing treatment. The soybean canopy sample heights were adjusted relative to the

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average plant canopy height (ht) for the heights of low (0.3ht), mid (0.6ht) and top

(1.0ht)...... 74

Fig. 5-1: The mean severity (± standard error) of soybean leaves exposed to 2 min of

rainfall with urediniospores in relation to the duration of prolonged simulated rainfall

for the intensities of ( A) 15 mm/hr and ( B) 75 mm/hr. Each line represents a different

sample height for the soybean leaf tissue in relation to the plant height (ht) ...... 95

Fig. 6-1: Phakopsora pachyrhizi urediniospores and Night-Glo® NG-20 zinc sulfide

particles (DayGlo® Color Corp., Cleveland, OH)...... 110

Fig. 6-2: Strainer/funnel release apparatus used in the 1 and 6 m dry deposition trials.

The release apparatus consisted of a 100 mesh (149 µm) stainless steel strainer

connected to a 5.5 cm diameter polypropylene funnel that had a vibrating motor

affixed to its outer edge...... 111

Fig. 6-3: A schematic diagram of the sampling set-up for the 1 and 6 m deposition trials.

The grey boxes in the plan view represent the sampling points in the downwind

direction from the release point...... 113

Fig. 6-4: The linear regression relationships between the urediniospore trap dose and the

Night-Glo® NG-20 zinc sulfide particle trap dose deposited into soybean canopies 1

m from an aerial source for the different soybean canopy plant heights...... 119

Fig. 6-5: The vertical variation NG-20 particles per cm 2 (± standard error) observed

within the soybean canopies for the 6 m trial at 3 sample heights for the plant height

class and distance from the source of ( A) short (51 - 61 cm) and 2 m; ( B) short (51 -

61 cm) and 4 m; ( C) short (51 - 61 cm) and 6 m; ( D) tall (84 - 91 cm) and 2 m; ( E)

tall (84 - 91 cm) and 4 m; ( F) tall (84 - 91 cm) and 6 m. The within canopy sample

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points were adjusted relative to the average soybean canopy height (ht) for the heights

of low (0.3ht), mid (0.6ht) and top (1.0ht). The x-axis changes with distance from the

source, but does not change between the average plant canopy heights at each

distance...... 122

Fig. 6-6: Polar contour plots of the NG-20 particle dry deposition into soybean canopies

for the mid level within canopy sample height. Each ‘R’ value represents the 18

separate runs conducted in the trial (Table 3). R1 to R10 are from the average

soybean canopy height class of short (51 - 61 cm), and R11 to R18 are from the

average soybean canopy height class of tall (84 - 91 cm). Arrows indicate the

average wind direction for 1 to 5 min trials...... 123

Fig. A-1: Box plot of the raw data from the 4 repetitions of the simulated urediniospore

rainfall deposition (Chapter 4) into soybeans with a 19 cm row spacing and height

range of 30 to 38 cm at a 75 mm/hr rainfall intensity. The outlier in repetition 4 was

removed from the data set in Fig. 4-2...... 141

Fig. A-2: The linear relationship between the wind speed data record by the Model 700

WatchDog® weather station and Campbell® Scientific, WindSonic Ultrasonic wind

sensor in Pennsylvania...... 142

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ACKNOWLEDGEMENTS

I would like to acknowledge Dr. Scott A. Isard & Dr. Erick D. De Wolf for providing me with this opportunity to pursue a Ph. D. degree, & for their continued guidance & support throughout my dissertation. I also want to express my appreciation to Dr. Gary W. Moorman, Dr. Paul A. Backman & Dr. Shelby J. Fleischer for their valuable input & advice while serving on my committee & editing my dissertation. A thank you to Tim Grove for his input, friendship, & support over the last three years. A special thank you to Dr. Jim Marois, Dr. David Wright & Dr. Dario Narvaez for their contributions & support with all my research in Quincy, FL.

I want to recognize all of my family, friends, department of plant pathology colleagues, university of Illinois friends & mentors & of course everyone I met in Quincy,

Tallahassee & Paraguay for their support & friendship throughout my dissertation research & career beyond. I would especially like to thank Justin Dillon, Dawn Moore,

& Jose Santa-Cruz for all they have done for me during my whole graduate career.

My family has been a key component in all that I have done so far. Both my brother (Chris Dufault) & sister (Angie Reinhart) have been great mentors & role models to me & without them my attitude toward many things (including my dissertation) would never have been so optimistic. I want to especially thank my Mom & Dad (Steve &

Carol Dufault) for all they have done & for the love & support they have provided me for these many years of graduate school & so much more. Finally, I want to thank Ms. María

Vélez-Climent without whom I would have never been able to finish my dissertation, she is a great inspiration to me (Te amo y muchas gracias).

Chapter 1: INTRODUCTION AND RESEARCH OBJECTIVES

SOYBEAN

Soybeans [ Glycine max (L.) Merrill] are the world’s leading crop in the

production of oil and protein [1]. The diverse uses of this crop vary from food sources

for humans and animals to biofuels for automotive vehicles. In 2007, the 25 million ha

planted across the United States (U.S.) accounted for about 32% of the total soybeans

produced in the world, and their production value was $26.8 billion [73]. These values

are second only to corn for field crop production in the U.S.

The domestication of cultivated soybeans began in eastern half of northern China

before the 11 th century B.C. [42]. It is believed that the crop was then disseminated from

China to Korea about 200 B.C., and from Korea to Japan by the 3 rd century A.D. In the early 1700’s, soybean seeds were sent to Europe from various locations in the Orient, and were later described by Linnaeus in 1737. During the 1800’s, soybeans were brought into the U. S. from Europe as a specialty crop for soy sauce production, and for scientific research about its possible uses as a field crop. In 1899, the USDA published its first bulletin about the use of soybeans as a forage crop, and soybeans were evaluated for their use as an oil and protein sources in 1904. The first commercial utilization of soybeans in the U.S. began in 1907 with the establishment of an oriental bean processing plant in

England. Commercial soybean production had developed in many states by 1909, with an estimated 800 ha of soybeans being planted that year.

Since 1909, soybean production has spread from the eastern seaboard into the

central states and provinces of the U.S. and Canada [74]. The largest production areas of

the U.S. are located in the states of Iowa, Illinois, Minnesota, Indiana and Ohio [73]. In

2 general, soybeans in the U.S. are planted in early May to mid June and harvested in mid

September through mid November, but it is possible for soybeans to be planted as early as mid April and as late as mid July [8, 61, 74]. These ranges of soybean production have remained relatively unchanged in the U.S. over the past decade, however, the development of new varieties and maturity groups continue to increase the soybean growing range and change the dates of planting and harvest.

SOYBEAN RUST

Phakopsora pachyrhizi geographic distribution. P. pachyrhizi has progressively spread to almost all major soybean producing areas of the world. One of the earliest reports of the fungus was from Japan in 1902, by Torama Yoshinaga [13, 47].

The pathogen continued spreading throughout the rest of Asia being collected in Tawain by 1913, and was reported in the republic of the Philippines by 1914. In 1934, the fungus was found south of the equator in Queensland Australia [13], and the first report of soybean rust from India was made in 1951 [72]. There had been multiple reports that soybean rust was in equatorial Africa several years earlier than 1996, when it was confirmed to be in Kenya, Rwanda, and Uganda [47]. The pathogen continued to advance across the African continent as confirmations were reported from Zambia and

Zimbabwe in 1998, Nigeria in 1999, Mozambique in 2000, and South Africa in 2001 [4,

47].

In 1994, P. pachyrhizi was found in Hawaii, but was never confirmed to disseminate to the continental U.S. from this location [33]. In February of 2001, the first reappearance of this pathogen in the American continents was confirmed in Paraguay

[47]. By 2002, the pathogen had expanded its range throughout the rest of Paraguay,

3 portions of southern Brazil [78], and into limited areas of northern Argentina [65]. By

August of 2003, the pathogen spread north of the equator in Brazil and possibly into the countries of Venezuela, Guyana and Suriname [31, 78]. The pathogen finally reached the continental United States in November of 2004 [68], and by doing so had dispersed itself to every major soybean producing region of the world.

Since its introduction into the U.S. in 2004, the range for soybean rust has

extended farther north and west each year [10, 18, 71]. The observations recorded

through the U.S. Department of Agriculture’s (USDA) soybean rust surveillance and

monitoring program show that soybean rust overwinters in the Gulf Coast regions of the

U.S. from Florida to Texas, and that suitable environmental conditions can promote its

spread in the U.S. [10, 18, 29, 30, 71]. By the end of the 2007 growing season, soybean

rust had been detected in 250 counties in 19 states and in the Canadian province of

Ontario (Fig. 1-1). The spread of the pathogen in North America has generally occurred

late in the growing season, at a time when a majority of the plants are in the R3 to R4

growth stages or later [10, 18].

Economic impact. There are many diseases known to significantly reduce

soybean yields, but few are potentially more devastating than the foliar disease soybean

rust, caused by fungal pathogen Phakopsora pachyrhizi Sydow. [23, 47]. Reports from

China indicate that yield losses from soybean rust commonly range between 10 and 30%,

and in severe disease years losses have been observed to be over 50% [19]. Estimated

yield losses have ranged up to 80% for individual fields in southern Japan, and total crop

losses as high as 30% have been reported in Taiwan [77]. Field studies in Korea have

reported yield losses of 68% in susceptible varieties and 22% in tolerant cultivars [56].

Fig. 1-1: A map showing the geographic distribution of Phakopsora pachyrhizi (soybean rust) in the United States at the end of the growing season in November of 2007. (Source:USDA IpmPIPE website: www.sbrusa.net ; accessed May 27, 2008)

Yield losses of 60 to 70% were observed in Australia for research plots that had not received fungicide management applications [57].

In Africa, yield losses of 60 to 80% were observed in Zimbabwe, and from 10 to

80% in South Africa [14]. There have been reports of yield losses reaching 100% for

African producers that grow soybeans in a continuous mono-culture. The South

American countries of Brazil and Paraguay have observed yield losses ranging from 30 to

75% in fields that were not managed with fungicides [78]. The total estimated losses in

Brazil for 2001-2 and 2002-3 were 125.5 and 677 million U.S. dollars respectively.

The introduction of P. pachyrhizi into the continental U.S. poses a serious threat to its soybean industry. An economic risk analysis was conducted in 1984 that projected potential losses for producers, consumers and other sectors of the U.S. economy to exceed $7.2 billion per year, once the pathogen has become established [35]. A more recent analysis estimated that the first year of the pathogen’s establishment in the U.S. would result in a net economic loss of $640 million to $1.3 billion U.S., and that annual losses in the ensuing years would average between $240 million to $2 billion [38].

Further predictions were developed indicating yield losses would be greater than 10% for nearly all of the U.S. soybean producing regions, and up to 50% in the Mississippi delta and southeastern coastal states [63, 76, 77].

The spread of the pathogen throughout the U.S. in 2005, 2006 and 2007, occurred more gradually than was expected [18, 71]. The pathogen was not observed in the major soybean producing regions of the Midwest until late in the 2006 and 2007 growing seasons, and because of its relatively late season arrival in the Southeast only minor yield losses were observed in commercial soybean fields. Despite its late season spread,

6 researchers, producers, and industry representatives have been able to gain valuable information from field trials about the biology and control of this pathogen.

Pathogen Morphology. P. pachyrhizi (synonym: Malupa sojae ) is in the order

Uredinales which is commonly used to classify the rust fungi [28, 56]. The fungi from this order are generally obligate parasites and can have up to 5 spore types including spermagonia (pycnia), aeciospores, urediniospores, teliospores, and basidiospores [2]. Of these five spore types only the urediniospores and teliospores have been observed for P. pachyrhizi in nature [23, 47]. Basidiospores have been produced in laboratory conditions, however they are not considered a critical spore type since no alternate host has been observed for this fungus.

The urediniospores are produced in the anamorphic fruiting structures called uredinia [13, 23, 56]. The uredinia are amphigenous and mostly subepidermal with a tan to reddish brown color, and range in size from 100-200 µm in diameter. They have incurved paraphyses arising from a cellular basal peridium that forms a dome covering, with urediniospores being borne on sporophores. The paraphyses are cylindric to clavate with a hyaline to yellowish-brown color, and range in size from 25 - 50 x 6 - 14 µm. The urediniospores are released through an opening (ostiole) at the top of the dome. The urediniospores are oblate spheroids, hyaline to yellowish brown in color, and can range in size from 18 - 38 µm long by 13 - 29 µm wide. The walls of the spores are about 1 to 1.5

µm thick, and are minutely to densely echinulate.

Under appropriate environmental conditions the production of telia, which produce the teliospores, can be observed. They are often seen mixed on the leaf surfaces with the uredinia. The chestnut brown telia are subepidermal and crustose, and can range

7 in size from 150 - 250 µm. The teliospores are arranged in 2 to 7 spore layers within the telium. The spores are single-celled, oblate spheroid, with a yellowish-brown to clear color, and range in size from 15 - 26 x 6 - 12 µm. The teliospore’s walls are 1 µm thick, except for the outermost spores where the walls are thickened apically to 3 µm.

Disease Symptoms and Signs. The most common soybean rust symptom observed on soybean foliage is tan to dark brown or reddish brown angular polygonal lesions ranging in size from 0.5 to 5 mm 2 [13, 23, 47]. The darkness of the lesion is

dependent upon its age and host/pathogen genotype interactions. Lesions are generally

observed on the leaves, but can be found on petioles, pods and stems of soybean plants.

Multiple erumpent and globose uredinia develop within the lesions, on both the abaxial

and adaxial surfaces of the soybean leaf [13, 23]. Distinct signs of soybean rust are the

production of multiple uredinia in the lesion and a circular ostiole found at the top of the

uredinia. Telia are dark brown to black and are observed subepidermally among the

uredinia. High densities of lesions and uredinia can result in premature yellowing and

abscission of the leaves.

Host Range. The fungus P. pachyrhizi has been noted to infect more than 93

species in 42 genera of the family Fabaceae [13, 58]. Soybean and other related Glycine

species as well as many other legumes are included on the list [47, 66]. A complete host

range has not been clearly identified, because of the early misidentification of the two

rust species ( P. pachyrhizi and P. meibomiae ) and the multiple responses of host species

to different rust pathotypes [47, 56]. Current reported hosts in the U.S. are Glycine max

(soybeans) [68], Pueraria lobata (kudzu) [22], Desmodium tortuosum (Florida

beggarweed) [70], Phaseolus coccineus , P. lunatus and P. vulgarus [39]. P. pachyrhizi

8 requires host tissue for survival, and climatic data indicates that the winters are too severe in the central U.S. for the year-round establishment of the fungus in that region [62].

Kudzu, the primary alternative host, is widespread throughout the southern U.S. (Fig. 1-

2), and has been observed as the overwintering host in the U.S. Gulf Coast region [10, 29,

71]. The establishment of P. pachyrhizi on kudzu has provided a continual inoculum source for soybeans in the continental U.S. [71].

Infection and Reproduction. Urediniospores are the main infective spore type, and they are predominately wind dispersed to susceptible host populations [47]. The infection process of this pathogen begins with the germination of the urediniospores across the host tissue. Germination can occur within 2 hours at 20ºC in darkness when moisture is readily available [13]. The germ tube will elongate to lengths of 5 to 400 µm, with appressoria frequently forming on germ tubes of less than 100 µm in length.

Appressoria will form over anticlinal walls or over epidermal cells, but rarely develop

over stomata [9, 34]. After the appressorium is formed, the fungus will directly penetrate

through the cuticle with an appressorial penetration peg [47]. If the appressorium

develops over a stomatum, the hyphae will penetrate one of the guard cells instead of

entering through the stomatal opening. The direct penetration of the epidermis is unique

for this rust pathogen, and could be one reason for its broad host range.

Uredinia have been observed 5 days after infection, and urediniospores were

produced as early as 9 days [34, 41]. Single uredinia can produce spores for as long as 3

weeks. It is possible for sporulation to continue for 15 weeks in a single lesion while the

primary and secondary uredinia develop [47]. Temperatures between 15 and 28°C [18,

23, 41] and six hours of free moisture [13, 46] are adequate for infections to occur.

Fig. 1-2: A map showing the geographic distribution of Pueraria lobata (kudzu) in the United States. (Source: USDA, NRCS Plants Database; accessed May 27, 2008)

Maximum infection is generally observed at temperatures near 20ºC after 10 to 12 hours of free moisture.

Management. Cultural practices appear to be useful components of soybean rust management strategies [13, 14, 47]. Planting early or using an early maturing variety will have the crop maturing before conditions become favorable for disease development

[13, 47]. The destruction of alternative hosts will decrease the number of inoculum sources available for disease spread; however, the eradication of kudzu is not practical in the southern U.S. Mid-day and nighttime applications of irrigation avoid extending the periods that free moisture is available to P. pachyrhizi [14]. Research focused on the biological control of this pathogen has been limited, and is not yet seen as an effective management strategy.

Four single dominant genes have been identified as providing specific resistance to P. pachyrhizi [13, 24, 47]. These genes are named Rpp 1 [45], Rpp 2 [12], Rpp 3 [11, 12,

26] Rpp 4 [27] and provide conditioned resistance to a limited number of P. pachyrhizi

isolates [24, 41, 47]. The resistance observed from these single genes has not been

durable and is unsuccessful when challenged with multiple P. pachyrhizi isolates [24, 47].

Partial resistance, or rate reducing resistance, has been observed in soybeans, but has had

limited use in breeding programs [24]. A strategy using yield stability, or tolerance, was

developed to select genotypes that may have partial resistance. Current research is

examining more than 16,000 accessions of soybeans in the USDA germplasm collection

to identify resistance for soybean rust [24, 48, 49]. Fewer than 800 soybean accessions

have been identified as having partial resistance, but further evaluation is needed before

they can be incorporated into commercial soybean cultivars.

The control of soybean rust with protective and curative fungicides is effective and is the recommended management strategy in the U.S. [18]. One of the first fungicides observed in Asia to be an effective control for soybean rust was mancozeb

[25]. More recent trials in India [60] and southern Africa [36] have identified the triazole compounds of flusilazole, difenoconazole, and triadimenol as useful fungicides for controlling soybean rust. In 2003 and 2004, trials in South Africa and South America identified additional triazoles, including tebuconazole and tetraconazole, as well as strobilurins and strobilurin mixes of azoxystrobin, pycraclostrobin, pyrcalostrobin and boscalid, and trifloxystrobin and propiconazole as valuable fungicides for managing soybean rust [51, 52].

Data collected from the 2005 fungicide trials in Macon County, Georgia indicated that Folicur® (tebuconazole), Headline® (pyraclostrobin), Headline SBR®

(pyraclostrobin and tebuconazole), Laredo® (myclobutanil), and Stratego®

(trifloxystrobin and propiconazole) all provided acceptable control of soybean rust [71].

A greening effect was observed in trials that used pyraclostrobin and to a lesser degree azoxystrobin. This greening effect did not change the moisture content at harvest, but did create large amounts of debris in the harvested crop. Phytotoxicity was also reported in commercial fields and fungicide trials that applied tebuconazole or tebuconazole plus pyraclostrobin, however, no extra yield loss could be contributed to the injury. Current research on the effectiveness of fungicides in the U.S. and internationally continues to support the ability of triazole and strobilurin chemistries to reduce severity and reduced yield losses in treated versus control soybean plots [18, 53].

Historically, fungicides have not been used to manage soybean diseases, but

certain conditions may necessitate the use of foliar fungicides. Soybean diseases that

have been managed with fungicides include frogeye leaf spot ( Cercospora sojina ) [3],

Cercospora leaf blight ( C. kikuchii ) [69], Sclerotinia stem rot ( Sclerotinia sclerotiorum )

[54], brown spot ( Septoria glycines ) [40] and Phomopsis seed infection [75]. The

effectiveness of fungicide applications for soybean rust control is dependent upon the

severity of the disease, the duration which the chemical remains present on the host tissue,

and the length of the reproductive stages of the plant [50]. Researchers from Brazil

observed that fungicide applications were no longer effective when greater than 20 to

30% of the soybean leaves in the mid-canopy were infected, thus, early detection and

prediction of soybean rust is critical for fungicide applications [18]. Fungicides applied

during the vegetative stages can decrease the amount of disease observed on soybeans,

however, this application may not produce a positive economic or yield result. Based on

this information, a one to two spray strategy after R1 through R6 has been recommended

to U.S. producers [18].

The second important factor in proper management of a disease with fungicides is

the spray coverage on the host tissue. The method by which fungicides are applied to a

crop depends on the area that needs to be sprayed, and the equipment available to make

the application [50]. In Brazil, aerial applications of fungicides over a large soybean

acreage provided adequate control of the disease [51]. However in Africa, maximum

disease control was achieved by penetrating the spray into the soybean canopy [36]. This

type of penetration was accomplished through the use of air assist and high-pressure

lateral discharge equipment. In the U.S., effective applications of fungicides were

13 attained with a conventional sprayer and a flat-fan nozzle using high spray pressures to attain fine to medium droplet sizes [18, 59, 71].

AEROBIOLOGICAL CONCEPTS

Every day countless numbers of microbes are transported vast distances through

the atmosphere. The study of the factors and processes that influence the movement of

biota in the atmosphere is called aerobiology [21, 32]. The aerobiology process model

characterizes the manner by which organisms move through the atmosphere. The model

is separated into five components or stages of (1) preconditioning in a source area, (2)

takeoff/ascent, (3) horizontal transport, (4) descent/landing, and (5) impact at the receptor.

The ecological and environmental factors that influence organisms during each stage of

the model are important for understanding their aerial movement.

The aerial transport of spores from plant pathogenic fungi is an important process

for disease spread. However, there is limited information about the processes which

spores use to travel hundreds of kilometers and infect distant host populations [7].

Understanding the processes that influence the transport of spores over long distances

could help producers with management decisions about fungicide use, local sanitation,

and quarantines. For example, a producer’s decision whether or not to apply a fungicide

is usually dependent upon the presence or absence of fungal infective structures. If the

fungal infective structure must travel a long distance to infect a host population, than its

presence on the host population would be dependent upon aerobiological events that

occurred during its transport. Knowledge about the occurrence of likely inoculum

transport events would provide producers with valuable input for making fungicide

management decisions.

The stages of spore dispersal can be specified in more detail for P. pachyrhizi as

(1) urediniospore production on the host, (2) urediniospore escape from the canopy, (3) turbulent transport and dilution of urediniospore clouds through the atmosphere, (4) survival in transport, and (5) urediniospore deposition onto a host population (Fig. 1-3) [7,

21]. The primary focus of this research will be on the final step of urediniospore deposition.

Spore Deposition. As a cloud of spores passes over a crop canopy, some of the spores will be deposited on to the vegetation and soil [20]. The concentration (C; spores/m 3) of viable spores in a volume of wind-blown air can be determined from the release of viable spores in source areas upwind (P; spores), the fraction of spores that escape the canopy (E), the turbulent transport and dilution of spores in the volume of traveling air (T; m -3), and the fraction of spores that have survived while airborne (S) [7].

Thus,

C = P x E x T x S

It is then possible to calculate the instantaneous rate of viable spore deposition (D;

spores/m 2s) by multiplying C by the rate of spore deposition ( v; m/s). This provides the

formula:

D = v * C

The total number of viable spores deposited at a site during a transport event can be

derived by integrating D over the total duration of the spore cloud passage. The three

primary methods by which spores are deposited onto a receptor surface are impaction,

sedimentation and rainfall wash-out [21]. These three methods can be generalized into

the two fundamental classes of dry and wet deposition.

Fig. 1-3: Conceptual diagram of urediniospore dispersal through the atmosphere from source to receptor. Spores are released from a host plant, escape the canopy, transported horizontally and then deposited on susceptible host population. During transport spores are exposed to may factors that can reduce their viability before reaching the receptor. Spores are deposited by sedimentation/impaction and/or rain washout. Sedimentation/impaction is relatively more important closer to the source and rain washout is the primary deposition process at distances farther from the source. Spores that do not reach a suitable host population or adhere to a substrate may have transport reinitiated soon after landing. 15

Dry deposition events can only occur within the air layer near to the earth’s surface, and are primarily the result of sedimentation and impaction (Fig. 1-4) [21].

Sedimentation is the process by which microbes settle out of the air under the force of gravity. It is observed during nonturbulent wind conditions (laminar conditions), which in nature can occur within a few millimeters above the receptor surface [7, 16, 17, 21].

However, at night special conditions can arise that allow the atmospheric layer of nonturbulent conditions to extend up several meters in height. The sedimentation process is rare at wind speeds of 2 m/s and greater, and thus at moderate and high wind speeds impaction becomes the primary dry deposition process. Impaction is the collision of a spore with a receptor surface that is assisted by wind. The main factors that influence the impaction efficiency of a spore are wind speed, spore adhesiveness and size, and the size and trapping efficiency of the receptor surface. Wind tunnel experiments have shown that the efficiency of impaction is decreased when a small spore is propelled toward a large obstruction at low wind speeds. In contrast, large spores, such as urediniospores, have their impaction efficiency increased when propelled at high wind speeds toward small objects. However, at excessive wind speeds the efficiency of impaction can be decreased by the bounce-off and/or blow off of the spores from the receptor surface. The variability of wind speed and turbulence in both space and time can create different impaction efficiencies for spores throughout a plant canopy [5, 43]. In general, the dry deposition process is considered to be primarily responsible for the deposition of spores traveling in precipitation free air a few centimeters above the canopy, and for those spores released within the canopy.

The wet deposition of fungal spores from the atmosphere onto susceptible host tissue is primarily the result of rainfall wash-out (Fig. 1-4) [7, 21]. The rate of wet deposition ( vw) is determined by the capture efficiency of the rain drops, rate of rainfall,

duration of the rain event, depth of the precipitation layer, and height of the spore cloud

(H) [7]. Spores with diameters between 20 to 30 µm can be collected by all sizes of

raindrops, with raindrops about 2 mm in diameter having the highest collection

efficiencies [7, 21, 44]. In 1953, Chamberlain [15] concluded that a washout coefficient,

w, could be used to describe vw. This coefficient was highly dependent on the two factors of rainfall rate and spore cloud height (H). This conclusion is supported by the work of Li

[37], which showed that wet deposition of P. pachyrhizi urediniospores is highly

dependent on the rate of rainfall. This means, disregarding time, that as H and the

intensity of rain increase, so will the rate at which spores are washed out from the

atmosphere.

The relative importance of the wet and dry deposition processes changes with

distance and time from a spore source [21]. At short distances from the source, spore

clouds released during dry conditions will generally be compact and have a relatively

high concentration of spores near the earth’s surface. However, as the spore cloud

continues to increase in distance and time from the source, it will expand throughout the

atmosphere and reduce the concentration of spores found near the surface layer. The

probability of an airborne spore cloud encountering precipitation increases the longer the

spores cloud remains in the atmosphere. Rainfall has the capability of washing out a

column of spores several kilometers in height, and depositing them onto a host with the

moisture necessary for infection. Cloud cover is often associated with precipitation in the

Fig. 1-4: Conceptual diagrams for dry and wet deposition of Phakopsora pachyrhizi urediniospores in a soybean canopy. Solid lines represent events that increase the density of spores on leaves and dashed lines represent events that decrease the density of spores on foliage. Splash dispersal of P. pachyrhizi urediniospores is theoretically possible, however, no observations of splash dispersal have been reported at this time.

19 atmosphere. Clouds shield spores from harmful environmental factors, such as ultraviolet radiation, and thus sustain their viability for extended time periods and generally longer distances.

For these reasons, the majority of viable spore depositions near a source are the

result of sedimentation and impaction. As long as spores are being released, these dry

deposition processes typically persist at short distances from the source, and thus, can be

considered continuous in both time and space. The likelihood that a viable spore is

deposited by precipitation rather than by dry deposition will be greater with increased

travel time and distance. It is possible for wet deposition to occur immediately after a

spore is released, but normally it occurs after long distance transport. The washout of

spores from the atmosphere is often referred to as a wet deposition event, because this

process is generally discrete in both time and space.

Dry and wet deposition processes, as discussed above, are usually related to the

short and long distance transport of spores respectively. Theoretical models and data

from in vitro studies of these deposition processes have been well documented for many

particle and spore types [6, 7, 15-17, 21, 32, 43, 55, 64, 67]. However, empirical data

relating to these processes in different field environments are limited, especially those

concerning wet deposition. Knowledge about the effects that rainfall intensity and wind

speed have on the deposition processes of P. pachyrhizi urediniospores into soybean canopies will be important for predictions of uredinia development within a field. The effects of cultural practices, such as row spacing distance and planting date, on the deposition proportion of urediniospores throughout soybean canopies is unknown.

Increased data about these deposition processes and the factors that affect them within

20 field environments will be beneficial for understanding the spread of viable P. pachyrhizi

urediniospores and other plant pathogenic fungi.

RESEARCH OBJECTIVES

The objectives of the present research are: to determine the proportion of wet and

dry deposited urediniospores that are retained within the different levels of soybean

canopies, and to examine the effects of environmental factors and cultural practices on

urediniospore deposition throughout the soybean canopy. Before the research objectives

could be evaluated, two technical studies were accomplished to: determine the

susceptibility of different leaf age groups to P. pachyrhizi for use as a uniform bioassay substrate (Chapter 2) and design a rainfall simulator that can be used within field plots

(Chapter 3). Then the following experiments were performed to achieve the goals of: examining how rainfall intensity, row spacing and plant canopy height effect urediniospore wet deposition (Chapter 4), examining the effect of prolonged rainfall on the wet deposition distribution (Chapter 5), and finally, examining the effects of wind speed, row spacing, and plant canopy height on the dry deposition distribution of urediniospores throughout soybean canopies (Chapter 6).

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Chapter 2: EFFECT OF LEAF AGE ON SUSCEPTIBILITY OF GLYCINE MAX TO PHAKOPSORA PACHYRHIZI AS DETERMINED BY A DETACHED SOYBEAN LEAFLET BIOASSAY

INTRODUCTION

The techniques for examining the wet deposition processes of Phakopsora pachyrhizi Sydow, the causal pathogenic agent of soybean [ Glycine max (L.) Merrill] rust, urediniospores into soybean canopies requires an efficient sampling method. Detached leaf bioassays have been used in the past to study a multitude of topics, such as, culture storage [15], host resistance [2, 11-13], methods of pathogen detection [16], life cycles

[7], genetics [14], and host-parasite relationships [1]. In order to productively sample wet deposition events, a detached leaf bioassay would have to use soybean tissue that is uniformly susceptible to P. pachyrhizi . In previous research, it was observed for the soybean cultivar ‘Wayne’ that the number of uredinia/cm 2 decreased as the leaves increased in age, which suggests that younger soybean leaves are more susceptible to P. pachyrhizi [6]. Another study observed that detached leaf tissue from the soybean line

TGx 1448-2E had relatively uniform susceptibility to P. pachyrhizi , however, the

soybean line TGx 1485-1D produced more uredinia/cm 2 when the leaves were 3 to 4 weeks old compared to younger and older leaf tissues [13]. Understanding the differences in susceptibility of soybean lines and varieties to P. pachyrhizi will be

important for developing an effective detached leaflet bioassay. The objectives of the

current study were to (i) assess the susceptibility of two soybean varieties to P.

pachyrhizi , (ii) examine the effects of leaflet age and cultivation method on the varietal susceptibility, and (iii) determine an appropriate soybean leaf substrate for sampling urediniospore wet deposition.

MATERIALS AND METHODS

Host Production and Preparation. Soybean seeds of the round-up ready varieties DP7220RR and DP5915RR (Delta and Pine Land Company) (Table 2-1) were planted 1 cm below the soil surface in square plastic pots (14 x 14 x 14 cm). These pots were filled with about 2000 cm 3 of Miracle-Gro Enriched Potting Mix® (Miracle-Gro

Lawn Products, Inc.), which consisted mostly of sphagnum peat moss and contained a

0.21 - 0.07 - 0.14 proportion of nitrogen, phosphate, and potassium. The plants were cultivated both inside and outside of a 'soybean rust free' greenhouse at the North Florida

Research and Education Center (NFREC) in Quincy, Florida. The soybean plants were watered daily and fertilized once each month with 5 ml of Osmocote® Outdoor & Indoor

Smart Release Plant Food (The Scott's Company, N-P-K: 19-6-12). A trifoliate’s emergence date was labeled once each of its leaflets had completely unrolled. Before the leaf tissue was inoculated, time ranges for leaflet age were selected for each variety and planting date (Table 2-2). These ranges of leaflet age were: 13, 18 - 24, 32 - 36, and 39 -

50 days for variety DP7220RR first planting with growth stages of R3 for the inside and

R5 for the outside plants; 3 - 7, 9 - 13, and 16 - 22 days for variety DP7220RR second planting with growth stages of V8 for the inside and R1 for the outside plants; 10 - 14, 17

- 22, 31 - 35, and 37 - 41 days for variety DP5915RR with growth stages of R2 for the inside and R5 for the outside plants.

Inoculum Preparation. P. pachyrhizi urediniospores were collected from the leaves of flowering (Soybean Growth Stages: R1-R3) soybean plants (variety

DP7220RR) that were mist inoculated [4] with a Florida isolate of P. pachyrhizi and grown in a greenhouse with high relative humidity (> 90%). The spores were

Table 2-1: Manufacturer’s information about the soybean varieties evaluated for the bioassay at the North Florida Research and Education Center, Quincy, FL. Variety Flower Color Maturity Growth Habit Plant Type Plant Height DP5915RR White 5.9 Determinant Semi-bushy Short Medium DP7220RR White 7.2 Determinant Intermediate Medium

Table 2-2: The soybean variety’s growth stage information at leaf harvest for the specified planting date. Soybean Growth Stage a b c Variety Greenhouse Natural Planting Date DP7220RR R3 R5 191

DP7220RR V8 R1 219 DP5915RR R2 R5 200

a Growth stage of the plants the were cultivated inside the greenhouse. b Growth stage of the plants that were cultivated outside the greenhouse. c Dates recorded according to the calendar date.

30 vacuum collected from the leaves into 20 ml glass vials with a large single cyclone spore collector (G-R Manufacturing Co., Manhattan, KS). A concentrated spore suspension was created by adding a volume of 0.5 ml urediniospores to 100 ml of deionized water and shaking the suspension for 1 minute. Then a 1/100 dilution of the concentrated spore suspension was produced by adding 1 ml of the spore suspension to 99 ml of deionized water and shaking for 1 minute. Both of the spore suspensions were stored on ice until the time of inoculation. A hemacytometer was used to calculate the number of urediniospores per ml for both the concentrated and diluted spore suspensions.

Approximately 10 µl of each spore suspension was pipetted into the counting chambers

and the average number of spores in nine 1 mm 2 grids was recorded. These counts were conducted twice, averaged and multiplied by 10,000 to calculate the number of spores per ml for each suspension (Table 2-3). The percentage of spores that germinated before and after inoculations was determined by pipetting 1 ml of each spore suspension onto two

100-15 mm plastic Petri plates containing 10 g/L water agar (Difco Bacto). The plates were than incubated in complete darkness for 16 h at 24°C (±2°C) (3). The average germination percentage of the first 100 urediniospores observed using a microscope at

100X was recorded for each spore suspension (Table 2-3).

Inoculation and Assessment. Leaflet samples from the four plants of each variety and cultivation treatment were obtained by excising the middle leaflet of the trifoliate [13]. These leaflets were incubated on 100-15 mm plastic Petri plates containing 10 g/L water agar (Difco Bacto). Approximately, 1 ml of the inoculum suspension was applied to each detached leaflet with an atomizer attached to an air compressor with 1 kg/cm 2 (15 lb/in 2) of pressure. The leaflets were than incubated on a

Table 2-3: The concentration and average percent germination of the two spore suspensions used to inoculate the various age groups of detached soybean leaflets. Spore Suspension Spores/ml Before (%) a After (%) b Concentrated 130,000 50 49 Dilution 4,000 56 54 a Average germination percentage of the spores before leaflet inoculations. b Average germination percentage of the spores after leaflet inoculations.

32 bench exposed to natural sunlight for 12 hr with a temperature of 24ºC (±5°C) for 14 days [4]. After 14 days, the severity of each leaflet (uredinia/unit area) was recorded by counting the total number of uredinia present with a dissecting microscope (100X), and measuring the area of the leaflet with a LI-3000 Portable Area Meter (LI-COR

Environmental).

Data Analysis. A mixed linear model analysis of variance (ANOVA) was used to

examine the effects of leaf age and plant cultivation on disease severity for each variety

and planting date [5]. A square root transformation was performed on the response

variable of disease severity to manage the problems associated with non-constant

variances and non-normality [10]. The general equation for the mixed model relating to

each soybean variety and planting date is:

Yijk = R i + A j + C k + (A x C) jk + εijk (2.1)

2 in which Y is the square root of the disease severity in uredinia per cm ; R i is the effect of ith repetition ( i = 1, 2, 3, 4), considered to be a random effect with a normal distribution, a mean equal to 0 and common variance. The A j is the effect of jth leaflet age; C k is the

effect of the kth cultivation method ( k = 1, 2); εijk is the error term (variability not explained by the model with a normal distribution, a mean equal to zero and a common variance); and (A x C) jk is the interaction of A j and C k. A Tukey option procedure was

used to group the means using Tukey’s method of multiple comparisons. All analyses

were completed by using PROC MIXED in SAS version 9.1 (SAS Institute, Cary, NC).

RESULTS

Variety DP7220RR. There was a significant ( P < 0.01) leaflet age ( A) effect on the square root of disease severity ( Y) for both planting dates, but cultivation method (C)

33 only had a significant ( P < 0.05) effect on the first planting date of variety DP7220RR

(Table 2-4). No interaction effect between A and C was present, which indicates that A and C have independent effects on Y. There were no significant ( P > 0.05) effects observed for treatments A and C on Y once the inoculum concentration had been

increased to approximately 100,000 spores per ml.

The main effect of A for both planting dates is shown by the plot means in Fig. 2-

1A and B. The number of uredinia per cm 2 decreased significantly ( P < 0.01) for the first

planting between time ranges 13 - 14 days and 18 - 24 days. After this initial difference,

there is no significant difference observed between the means for the times ranging from

18 to 50 days. A significant decrease ( P < 0.05) in severity was observed for the second

planting between time ranges 3 - 7 days and 9 - 13 days, however, time range 16 - 22

days was not significantly different from either of the previous time ranges.

An effect of C was observed for the first but not the second planting date of the

variety DP7220RR (Table 2-4). Soybean plants from the first planting that were

cultivated in the greenhouse produced significantly ( P < 0.05) more uredinia per cm 2 than those that were exposed to natural environmental conditions (Fig. 2-2A).

Variety DP5915RR. Treatment A did not have a significant ( P < 0.05) effect on disease severity for the variety DP5915RR, however, treatment C did have a significant effect ( P < 0.05) on Y (Table 2-4). The effect of C was no longer observed once the inoculum level was increased to 100,000 spores per ml. Figure 2-1C shows that the average severity decreases between time ranges 10 - 14 days and 17 - 22 days, but this decrease was not significant ( P < 0.05). Plants that were cultivated in the greenhouse had

Table 2-4: Effect of leaflet age, cultivation method and their interaction on the square root of disease severity based on a mixed model analysis of variance. ~4000 Spores/ml Suspension a ~100,000 Spores/ml Suspension a b Variety Effect df n df d F Probability df n df d F Probability DP7220RR-1 Leaflet Age (A) 3 17 17.13 < 0.01 3 14 1.41 0.28 Cultivation (C) 1 17 4.87 0.04 1 14 2.14 0.17 A x C 3 17 1.50 0.25 3 14 0.63 0.61

DP7220RR-2 Leaflet Age (A) 2 13 9.27 < 0.01 2 10 2.46 0.14 Cultivation (C) 1 13 1.92 0.19 1 10 0.14 0.72 A x C 2 13 0.53 0.60 2 10 0.75 0.50

DP5915RR Leaflet Age (A) 3 20 2.16 0.13 3 16 1.18 0.35 Cultivation (C) 1 20 5.80 0.03 1 16 0.53 0.48 A x C 3 20 1.01 0.41 3 16 0.69 0.57 a The approximate concentrations of the Phakopsora pachyrhizi urediniospores and water suspension used to inoculate the soybean leaflets. b Delta and Pine Land soybean varieties examined in this study. The variety DP7220RR was planted on the two separate calendar dates of ( 1) 191 and ( 2) 219.

8 a A

6 2 4

Uredinia/cm b 2 b

0 13 to 14 18 to 24 32 to 36 39 to 50 8 B

6 2 a

4 ab b Uredinia/cm 2

0 3 to 7 9 to 13 16 to 22 8 C

2 6

4 a Uredinia/cm

a a 2 a

0 10 to 14 17 to 22 31 to 35 37 to 41 Leaflet Age Day Range Fig. 2-1: Effect of soybean leaflet age on disease severity following an inoculation of ~4,000 urediniospores per ml suspension for ( A) variety DP7220RR with calendar planting date 191; ( B) variety DP7220RR with calendar planting date 219; (C) variety DP5915RR with calendar planting date 200. Letters above the bars represent the Tukey grouping results ( P < 0.05) for each treatment. Error bars represent the standard error of the mean.

4 a A

3 b

Uredinia/cm 1

0 4 a B

3 a 2

Uredinia/cm 1

0 4 C

3 b 2

2 a Uredinia/cm 1

0 Greenhouse Natural Cultivation Methods Fig. 2-2: Effect of cultivation methods on disease severity following an inoculation of ~4,000 urediniospores per ml suspension for ( A) variety DP7220RR with calendar planting date 191; ( B) variety DP7220RR with calendar planting date 219; (C) variety DP5915RR with calendar planting date 200. Letters above the bars represent the Tukey grouping results ( P < 0.05) for each treatment.

37 a significantly ( P < 0.05) lower number of uredinia per cm 2 when compared to those that

had been cultivated in a natural environment (Fig. 2-2C).

DISCUSSION

In general, the results show that younger soybean leaves of the varieties examined

in this study are more susceptible to infections caused by P. pachyrhizi , which is similar to the trend observed by Melching et al [6]. Soybean leaflets, inoculated with approximately 4,000 spores per ml, of the variety DP7220RR had a significantly higher number of uredinia per cm 2 observed in leaflet age range 13 - 14 days, and a similar trend

was noticeable for the range 10 - 14 days of the variety DP5915RR (Fig. 2-1). However,

these effects were not significant when soybean leaflets of both planting dates and

varieties were inoculated with a spore concentration of approximately 100,000 spores per

ml (Table 2-4). These results indicate that the effect of leaflet age on severity is

dependent upon the concentration of spores inoculated.

A comparison of these detached leaflet results with those previously reported by

Twizeyimana et al. [13] indicates that soybean variety is an important factor in leaflet

susceptibility. Twizeyimana el al. [13] found that the Nigerian soybean line TGx 1485-

1D had significantly ( P < 0.05) more uredinia per cm 2 for leaflets between 21 to 28 days

old as compared to younger and older leaflets. Our results showed that leaflets 14 days

old or younger generally had more uredinia per cm 2. The reason for this difference is unknown, but there are several possible explanations: (i) different spore concentrations were used to inoculate leaflet tissue; (ii) response of individual soybean varieties and lines to P. pachyrhizi ; and (iii) the unknown effect that soybean plant growth stages have

on susceptibility. However, the average severity trend reported by Twizeyimana et al.

[13] was similar to the average severity trend reported for leaflets between 10 to 50 days old and inoculated with the higher spore concentration in this study. Overall, the conclusions from these comparisons indicate that the effects of leaflet age may be influenced by both the soybean variety and the concentration of spores used to inoculate the tissue.

A cultivation effect was observed for first plantings of ‘DP7220RR’ and

‘DP5915RR’ when inoculated with the low spore concentration (Table 2-4). Soybeans of the variety DP7220RR that had been cultivated inside the greenhouse were slightly more susceptible to P. pachyrhizi than those grown outside the greenhouse. The opposite trend was observed in the DP5915RR variety, as soybeans produced outside the greenhouse had a significantly higher number of uredinia per cm 2 than those grown inside. All of the

plants for each individual soybean variety were sown on the same date, however, those

that were cultivated outside the greenhouse were generally 2 to 3 growth stages more

advanced than those inside. These differences in the soybean plant growth stage may

have interacted with the effects of environment on host susceptibility, and could be the

reason why different trends were observed for the varieties examined. It is important to

note that the difference between the average severity observed for each cultivation

method was 1.3 uredinia per cm 2 or less, which is a relatively small difference considering possible errors made during leaf sampling and rating [3, 8, 9]. Since plants are easier to maintain inside the greenhouse, it is recommended that leaf tissue used for the bioassays come from cultivated greenhouse plants even if they may produce less uredinia per cm 2.

One of the main goals of this research was to determine an appropriate soybean leaf substrate for use in a detached leaflet bioassay. These leaflets would be used to sample simulated rainfall events with low concentrations of P. pachyrhizi urediniospores.

Our results indicate that leaflets between 18 to 50 days old from plants in growth stages

R2 to R5 of variety DP7220RR are evenly infected by P. pachyrhizi . Leaves below this age range tend to be more susceptible to P. pachyrhizi , and should not be used for

comparisons with older leaves especially in studies using low spore concentrations. In

Chapter 4, only the DP7220RR variety leaflets within the age range specified above were

used for the wet deposition detached leaflet bioassays.

LITERATURE CITED

1. Atif, A. H. and Wilcoxson, R. D. 1975. Responses of detached tissues of adult wheat plants to Puccinia graminis tritici. Phytopathology 65:318-321.

2. Burdon, J. J. and Marshall, D. R. 1981. Evaluation of Australian native species of Glycines for resistance to soybean rust. Plant Dis. 65:44-45.

3. Cooke, B. M., Jones, D. G., and Kaye, B. 2006. The Epidemiology of Plant Diseases . 2nd Ed. Springer, The Netherlands.

4. Isard, S. A., Dufault, N. S., Miles, M. R., Hartman, G. L., Russo, J. M., De Wolf, E. D., and Morel, W. 2006. The effect of solar irradiance on the mortality of Phakopsora pachyrhizi urediniospores . Plant Dis. 90:941-945.

5. Littell, R. C., Milliken, G. A., Stroup, W. W., Wolfinger, R. D., and Schabenberger, O. 2006. SAS for mixed models, 2 ed. SAS Institute, Inc. Cary, N. C.

6. Melching, J. S., Dowler, W. M., Koogle, D. L., and Royer, M. H. 1988. Effect of plant and leaf age on susceptibility of soybean to soybean rust . Can. J. of Plant Path. 10:30-35.

7. Nita, M., Ellis, M. A., and Madden, L. V. 2003. Effects of temperature, wetness duration, and leaflet age on infection of strawberry by Phomopsis obscurans. Plant Dis. 87:579-584.

8. Nita, M., Ellis, M. A., and Madden, L. V. 2003. Reliability and accuracy of visual estimation of Phomopsis leaf blight on strawberry . Phytopathology 93:995-1005.

9. Nutter, F. W. J. and Shultz, P. M. 1995. Improving the accuracy and perception of disease assessment: Selection of methods and use of computer-aided training programs. Can. J. of Plant Path. 17:174-184.

10. Ott, R. L. and Longnecker, M. 2001. An Introduction to Statistical Methods and Data Analysis, 4 ed. Duxbury. Pacific Grove, CA.

11. Pande, S., Thakur, R. P., Karunakar, R. I., Bandyopadhyay, R., and Reddy, B. V. S. 1994. Development of screening methods and identification of stable resistance to anthracnose in sorghum . Field Crops Res. 38:157-166.

12. Turechek, W. W. and Stevenson, K. L. 1998. Effects of host resistance, temperature, leaf wetness duration, and leaf age on infection and lesion development of pecan scab. Phytopathology 88:1294-1301.

13. Twizeyimana, M., Ojiambo, P. S., Ikotun, T., Paul, C., Hartman, G. L., and Bandyopadhyay, R. 2007. Comparison of field, greenhouse, and detached-leaf

evaluations of soybean germplasm for resistance to Phakopsora pachyrhizi . Plant Dis. 91:1161-1169.

14. Visker, M. H. P. W., Keizer, L. C. P., Budding, D. J., Van Loon, L. C., Colon, L. T., and Struik, P. C. 2003. Leaf position prevails over plant age and leaf age in reflecting resistance to late blight in potato. Phytopathology 93:666-674.

15. Wilcoxson, R. D., Joshi, L. M., and Saari, E. E. 1971. Some new methods and equipment for work with rust fungi in Inida. Indian Phytopath. 24:667-671.

16. Xie, G. L. and Mew, T. W. 1998. A leaf inoculation method for detection of Xanthomonas oryzae pv. oryzicola from rice seed. Plant Dis. 82:1007-1011.

Chapter 3: A RAINFALL SIMULATOR FOR PLANT PATHOGEN WET DEPOSITION STUDIES

INTRODUCTION

Plant pathogen propagules are frequently transported vast distances through the atmosphere to susceptible host populations. The movement process of these pathogens can be divided into the five stages of (i) preconditioning in a source area, (ii) release, (iii) horizontal transport, (iv) deposition, and (v) impact at the receptor area [22]. The ecological and environmental factors that influence organisms during each stage of the dispersal process are important for understanding the movement of plant pathogens and the development of plant disease epidemics [1, 24]. However, there is limited information about some of these processes, especially for the deposition of propagules that have traveled hundreds of kilometers.

Rainfall simulators have been used in studies examining factors effecting soil erosion and runoff for over 75 years [21, 28]. Experience gained throughout this time period has led to the compilation of a list of desirable rain simulator features, which includes (i) simulated rain droplet sizes, size distributions and energies similar to natural rainfall; (ii) a continuous and uniform application of rainfall be observed over the sample area; (iii) the ability to apply varying intensities and durations of rainfall; and (iv) portability for ease of plot to plot movement [26, 27, 33, 36, 37]. There have been many designs developed for rainfall simulators that meet these desirable features for soil erosion and run-off studies [21, 26, 29, 33, 36, 37].

Rainfall droplets can deposit plant pathogen propagules onto susceptible host plant surfaces and disperse them in splash droplets [35]. Past research has observed that rain droplet impaction is an important mechanism for the dispersal of many plant

43 pathogenic fungi and bacteria [7, 9, 10, 12, 13, 15, 16]. Field studies have also supported the importance of rainfall in pathogen dispersal [24]. The splash dispersal of plant pathogens has been examined with single droplet impaction methods [7] and through the use of rainfall simulators [11, 31].

The rainfall simulator designed by Fitt [11], which was similar to the designed used for soil erosion by Mutchler [29], consisted of a grouping of stainless steel needles to produce simulated rain droplets that were uniform in size. This simulator has proven effective in examining splash dispersal of plant pathogens [8, 11, 14, 18, 30], but its uniform drop size is difficult to relate to the spectrum of droplet sizes produced in natural rainfall events [19, 24, 25]. Reynolds [31] proposed a modified design that would use sprayer nozzles under specific pressure settings to create a rain droplet distribution. This design produces droplet distributions that were close to the rainfall intensities of 15, 30, and 60 mm/hr, but needed a height of 8 m to produce droplets that were similar to a natural rain event’s kinetic energy and fall speed. Rainfall simulators have provided an effective tool for understanding splash dispersal of plant pathogens, however, they have not been used to study the wet deposition of plant pathogen’s propagules into plant canopies.

This manuscript describes the construction, operation and assessment of a rainfall simulator that can be used to examine the deposition of plant pathogens in a field environment. The research focused on developing a portable plot simulator that exhibited the desirable features previously described for simulated rainfall. The nozzle simulator designs [21, 26, 31, 34] previously mentioned were adapted in this study so that plant pathogen propagules could be deposited into plant canopies.

MATERIALS AND METHODS

Simulator Design and Construction. The rainfall simulator was designed to be a portable instrument for examining wet deposition of plant pathogens into field crop canopies. It was adapted from the models described by Miller [26], Humphry [21] and

Sporre-Moeny [34] (Fig. 3-1). The frame consisted of black iron and galvanized pipe, steel cables, side outlet cross corners, plastic tarps, and PVC pipe for the spray arm

(Table 3-1). Assembly and disassembly of the simulator was simplified so that a single hexagonal wrench was required for loosening the screws at each of the corners and braces.

The spray arm is supported by black iron pipe across the top of the frame and a PVC tee is connected to a brass quick coupler on the spray arm so that nozzles can be interchanged easily.

The nozzle assemblies used for this simulator are the Spraying Systems Co.

Fulljet 3/8HH-SS24WSQ (24WSQ) and the Fulljet 1/2HH-SS50WSQ (50WSQ). These nozzles have a full cone spray characteristic and provide uniform coverage over rectangle and/or square areas (Spray Systems Co. 2007. Catalog 70 US. Wheaton, IL.). A brass quick coupler was attached to each nozzle so they can be connected to the middle of the spray arm at the top of the frame 3 m above the canopy and the arm was adjustable up to

1 m height from the top of the simulator. A pressure of 34.5 kPa (5 psi) was required for the simulator to produce droplets that are within 2% of their terminal velocity [33]. The respective flow rates for the 24WSQ and 50WSQ nozzles at this pressure are 6.8 L/min

(1.8 gal/min) and 14.0 L/min (3.7 gal/min) (Table 3-2), which were maintained during rainfall simulations by monitoring a panel mounted flow-meter. Water was supplied to the simulator from a 570 L (150 gal) tank through the use of a gas powered centrifugal

Fig. 3-1: ( A) The rain simulator in a soybean field with the control unit. The control unit was used to regulate both water flow and spore injection. ( B) A close up of the control unit showing the design for injecting spores into the water flow.

Table 3-1: A list of component parts for one rainfall simulator. Part Description Qty. Simulator Frame 2.5 cm (1 in) Diameter Black Iron Pipe (4m sections) 4 2.5 cm (1 in) Diameter Black Iron Pipe (2.5m sections) 1 2.5 cm (1 in) Diameter Galvanized Steel Pipe (2.5m sections) 8 2.5 cm (1 in) Side Outlet Cross (Speed Rail© ) 10 Steel Cables (10 m) 8 10 cm (4 in) Pipe Flanges (Simulator feet) 4 Plastic Tarps 4

Spray Arm 1.9 cm (3/4 inch) PVC Pipe (2.5m section) (Schedule 40) 1 1.9 cm (3/4 inch) Brass Quick Coupler 1 Spraying Systems Co. Fulljet 3/8HH-SS24WSQ Nozzle 1 Spraying Systems Co. Fulljet 1/2HH-SS50WSQ Nozzle 1

Accessories General Purpose Heavy Duty D-Handle Hand Truck 1 Air Compressor 1 1 x 1 m Plywood Square 1 Stainless Steel Strainer 1 In-line Flow Regulator 1 Flowmeter (Panel Mount) 1 Spray Systems Inc. 2 Liter Bottle Pressurizing Unit 1 Pressure Gauges 2 Honda™ General Purpose, Centrifugal Pump (4-Stroke Engine) 1 570 L (150 gallons) Water Tank 1

Table 3-2: The simulated rainfall characteristics of the nozzles observed within the 2 x 2 m sample area. Simulated Rainfall Intensity Natural Kolmogorov - Smirnof f Simulated Rainfall Characteristics (mm/hr) Intensity (EDF) Volume e Mean Water Match Nozzle Mean KE b Droplet Flow c Mean CU d (mm/hr) Statistic Probability Model a Diameter (J/m 2mm) Size (mm) (L/min) (mm) 24WSQ 1.0 1.6 17 6.8 45 86 15 0.028 0.18 50WSQ 1.3 2.2 23 14.0 85 85 75 0.017 0.05 a Fulljet nozzles from Spraying Systems Inc. Wheaton IL. b Kinetic Energy (KE) values were calculated from the 150 droplet diameter sample that was collected using the oil method [6] over the whole sample area. c The flow coming out of the nozzles at a pressure of 34.5 kPa. d Coefficient of uniformity as a percent. e The matching rainfall intensity was determined by comparing the sampled droplet distribution from each nozzle with estimated distributions for natural rainfall intensities for the droplet ranges produced by each nozzle at intervals of 5 mm/hr. f Empirical distribution function; Kolmogorov-Smirnof tests the null hypothesis that the measured droplet volume distribution and the theoretical natural rainfall droplet volume distribution are equal [5].

48 pump (American Honda Motor Co., Inc.). A stainless steel strainer was placed in the water flow to prevent large particles from clogging the nozzles. Pathogen propagules were injected directly into the water flow by pressurizing (70 kPa; 10 psi) a propagule suspension within a 2 L plastic bottle (Fig. 3-1).

Droplet Size. The oil method of Eigel and Moore [6] was used to determine the droplet size distribution of the simulated rainfall for both the 24WSQ and 50WSQ nozzles. This method uses a 2:1 mixture of STP® Oil Treatment (The Clorox Company,

Oakland, CA) and mineral oil to capture droplets of simulated rainfall (Fig. 3-2). Five

Petri dishes (100 x 15 mm) were filled with 90 ml of the oil solution and randomly placed within the simulators sample area. These plates were then exposed to 3 s of simulated rainfall. Droplets penetrate the less dense but more viscous oil mixture and remain suspended as spheres for a short period of time. Digital images of the Petri dishes and a background scale were collected within 2 minutes of the mixture being exposed to simulated rainfall. This process was replicated 3 times for each nozzle type examined.

The digital images of the oil solution were then enlarged, and the droplet diameters of 10 randomly selected droplets were recorded for each Petri dish (150 in total for each nozzle type) using the scale observed in the background.

Rainfall Uniformity and Intensity. One hundred, 9 cm diameter plastic cups were placed on a 10 by 10 grid (20 x 20 cm) within the 2 x 2 m sample area of the rain simulator (Fig. 3-2). Five minutes of simulated rainfall was collected in the cups for each nozzle type and repeated 3 times. The volume of water in each cup was recorded at each location on the grid. The coefficient of uniformity (CU) was derived as a percentage using Christiansen’s method [4] with the formula:

Fig. 3-2: The rainfall simulator’s calibration techniques of ( A) the oil method [6] used to determine drop size and ( B) the cup method used to sample the rainfall intensity’s uniformity over the sample area.

CU = 100*(1.0- Σd/µn) (3.1)

where n is the number of observations, µ is the mean value of the n observations, and d is

the absolute difference between each observation and µ.

The mean rainfall intensity calculation was based on the volume ( v = πr2h) of rainfall collected in each of the 100 plastic cups. For each nozzle type, the average volume of water captured in the sample area was calculated for all three replications. The mean rainfall depth was calculated by manipulating the equation above to solve for h, where h is the mean depth of rainfall in a 5 min period, v is the volume of water collected in each cup, and r is the radius of the cup (4.5 cm). Then h was transformed into mm/hr to provide the average rainfall intensity for each nozzle type across the sample area.

Kinetic Energy. The kinetic energy of the droplets produced from each nozzle type was calculated using the equation:

2 KE = 0.5m dv (3.2) where md is the mass (kg) of the droplet size recorded in each of the oil mixture sample plates, and v is the corresponding velocity (m/s) of the droplet. As stated earlier, the

droplets produced from the nozzles should be within 2% of their terminal velocity [33],

so v was considered to be the terminal velocity of the droplet. The terminal velocities of

the water droplets given by Gunn and Kinzer [17] were used for the kinetic energy

calculations. The mass of the droplets ( md) was determined using a water density value of 998 kg/m 3. The kinetic energy calculated for each droplet size was summed over the

150 droplet sample [19]. These results were used to compute the average KE value per mm of rainfall observed in the sample area for each nozzle type.

Distribution Analysis. Theoretical rainfall droplet distributions were calculated using the equation from Marshall and Palmer [25] of:

ND = N 0 exp(-λD) (3.3)

3 in which N D is the number of droplets in a unit volume (m ) of air per mm-interval of

drop diameter D, and both N 0 and λ are parameters. The λ parameter was calculated using:

λ = 4.1 r -0.21 (3.4)

in which r is the rainfall intensity (mm/hr). For D values less than 1.5 mm, N 0 was obtained from the equation proposed by Madden [24] of:

N0 = 6800 +1200(cos(4.83[D-0.2])) (3.5) and N 0 was set to 8000 for D ≥ 1.5 mm. The terminal velocity of the droplets were

calculated from the data collected by Gunn and Kinzer [17].

The Kolmogorov-Smirnof test (empirical distribution function) was used to

examine the relationship between the theoretical natural rainfall droplet volume

distribution and the measured droplet volume distribution of the rainfall simulator [5, 24].

The droplet volume distribution analysis was completed by using PROC PAR1WAY in

SAS version 9.1 (SAS Institute, Cary, NC) with the EDF statement.

RESULTS

Droplet Size and Kinetic Energy. The median droplet diameter for the 24WSQ

nozzle was 1.0 mm with a range from 0.2 to 2.7 mm (Table 3-2) and had a volume mean

diameter of 1.6 mm. The 50WSQ had a median droplet diameter of 1.3 mm with a range

of 0.4 to 3.3 mm and a volume mean diameter of 2.2 mm. For both the 24 and 50WSQ

nozzles there were no significant differences ( P > 0.05) observed between the repetitions

52 of rain droplet samples. The majority of the rain droplets produced were < 2 mm in diameter, a trend that is similar to those predicted in natural rainfall events at multiple rainfall intensities (Fig. 3-3). The average kinetic energy recorded throughout the rain simulator’s sample area was 17 J/m 2 per mm depth of rainfall with a range of 7 to 23 J/m 2

mm for the 24WSQ nozzle (Table 3-2). An average kinetic energy value of 23 J/m 2 mm

with a range of 16 to 32 J/m 2 mm was determined for the 50WSQ nozzle over the rain

simulator’s sample area (Table 3-2).

Rainfall Uniformity and Intensity. The simulated rainfall produced by the 24

and 50WSQ nozzles had CUs of 86 and 85% respectively (Table 3-2; Fig. 3-4). The

mean rainfall intensity for the 24WSQ nozzle was 45 mm/hr with a range of 32 to 76

mm/hr (Table 3-2). The 50WSQ nozzle produced a mean rainfall intensity of 85 mm/hr

with a range of 57 to 123 mm/hr (Table 3-2). Higher rainfall intensities for both nozzles

were observed at both the middle and sides of the rain simulator’s sample area (Fig. 3-4).

The cumulative droplet volume distribution (CDVD) for the 24WSQ nozzle was not

significantly different ( P > 0.05) from the droplet volume distribution calculated for the

natural rainfall intensity of 15 mm/hr over the droplet diameter range of 0.2 to 2.6 mm

(Table 3-2; Fig. 3-5A) [19, 24, 31]. No significant difference ( P > 0.05) was observed

for the 50WSQ CDVD compared to the calculated droplet volume distribution for the

rain intensity 75 mm/hr across the droplet diameter range of 0.4 to 3.3 mm (Table 3-2;

Fig. 3-5B).

Fig. 3-3: Comparison of the droplet frequency distribution produced by the Fulljet 3/8HH-

SS24WSQ nozzle (24WSQ) and the Fulljet 1/2HH-SS50WSQ nozzle (50WSQ) with four calculated rainfall frequency distributions [17, 24, 25] at intensities of 1, 20, 40 and 80 mm/hr. Data for the nozzles droplet frequency distribution was from the 150 droplets sampled using the oil method [6]. Bars represent data from the simulator droplet distribution and lines represent theoretical rainfall droplet distributions.

Fig. 3-4: The rainfall intensity distribution of simulated rainfall collected over the 2 x 2 m sample area (5 min duration) for the ( A) Fulljet 3/8HH-SS24WSQ nozzle (CU = 86%) and ( B) Fulljet 1/2HH-SS50WSQ nozzle (CU = 85%) at 34.5 kPa operating pressure.

Fig. 3-5: The cumulative droplet volume distribution produced by the ( A) Fulljet 3/8HH- SS24WSQ nozzle and the ( B) Fulljet 1/2HH-SS50WSQ nozzle compared to calculated distributions of cumulative rain droplet volumes for natural rainfall intensities. The natural rainfall volume distributions were calculated [24] between the ranges of 0.2 to 2.6 mm for the 24WSQ, and 0.4 to 3.3 mm for the 50WSQ. The relative droplet volume distributions for the nozzles were calculated from the 150 droplet diameters recorded for each nozzle from the oil method [6] samples.

DISCUSSION

Droplet Distributions. The simulator produced rainfall droplet sizes that ranged

from 0.2 to 3.3 mm in diameter which are consistent with the droplet diameter range of

0.2 to 5.0 mm recorded for natural rainfall reaching the ground [23, 24]. Droplets that are

smaller than the natural size range generally evaporate before reaching the ground and

those that are larger will break up into smaller droplets while falling. Droplets less than 2

mm in diameter are more numerous than those larger in size in natural rainfall events [19,

24]. The 24 and 50WSQ nozzles produced median droplet sizes of 1.0 and 1.3

respectively, and had a droplet distribution similar to those observed in natural rainfall

events (Fig. 3-3). The rainfall droplet distribution for both nozzles was uniformly

distributed across the sample area with coefficients of uniformity of 86 and 85% for the

24 and 50WSQ nozzles, respectively (Fig. 3-4). These values are lower than those

reported by Humphry (93%) [21] and Miller (90%) [26], but are still high for a single

nozzle over a 2 x 2 m sample area. Overall, the simulator produces uniform rain droplet

distributions with size ranges that are similar to those that have been recorded in nature.

Rainfall Intensity and Kinetic Energy. The recorded mean rainfall intensity

across the simulator’s sample area was 45 mm/hr (range: 32 to 76 mm/hr) for the 24WSQ

nozzle and for the 50WSQ nozzle it was 85 mm/hr (range: 57 to 123 mm/hr). At these rainfall intensities theoretical cumulative droplet distributions (Fig. 3-5) and droplet size frequencies (Fig. 3-3) for natural rainfall are notably different from those observed in the rainfall simulator. A similar observation was made by Reynolds et al. [31], in which, the droplet distributions produced by the simulator nozzles examined were characteristic of lower natural rainfall intensities. The cumulative droplet distributions produced by the

24 and 50WSQ nozzles were not significantly different from the theorized natural rainfall intensity events of 15 and 75 mm/hr, respectively (Table 3-2). Rainfall rates of 15 to 75 mm/hr are commonly observed in warm weather storms of Illinois [20] and throughout the year in the rest of the United States [2]. Rainstorm events will often have a range of rainfall intensities across the storms area, and in general, rainfall rates greater than 75 mm/hr will only account for a small proportion of the storms minutes [2, 20]. This suggests that the rainfall rates produced by the 2 nozzles implemented in this simulator represent the range of a warm weather rainfall rates experienced in natural precipitation.

The estimated kinetic energy (KE) values also indicate that the mean rainfall intensities of 45 and 85 mm/hr recorded for the 24 and 50WSQ nozzles, respectively are not characteristic of the simulated rain events created by each nozzle type. The KE values of the 24 and 50WSQ nozzles (Table 3-2), respectively, were approximately 60 to 65% and 66 to 82% of the estimated kinetic energy values for the natural rainfall intensities of

45 and 85 mm/hr [3, 19, 32, 38, 39]. The percent similarity of the KE values increases to

74 to 92% and 76 to 83% for the 24 and 50WSQ nozzles, respectively, when compared to the natural rainfall kinetic energy values of 15 and 75 mm/hr. These percent similarities are closer to the range of 82 to 87% reported by Humphry [21] for rain simulator with a single nozzle.

Larger rainfall droplets have the ability to deposit plant pathogen propagules deeper into plant canopies than smaller droplets, because they have higher KE values and are able to collect and retain greater concentrations of pathogen propagules. The droplet sizes produced by the two nozzles examined here ranged from 0.2 to 3.3 mm in diameter, which accounts for 98 to 99% of the total number of droplets produced in 15 and 75

58 mm/hr natural rainfall events. However, droplets within this size range will only comprise 77 to 81% of the total droplet-volume produced within the 15 and 75 mm/hr rainfall events. This indicates that the rainfall simulator will slightly underestimate the plant pathogen deposition effects of 15 and 75 mm/hr rainfall intensity events, since droplets > 3.3 mm are not represented in the volume distribution.

Increasing the size of the droplets may reduce the differences noted with the theorized natural rainfall intensities. In general, natural rainfall events produce droplets with a median size of ~ 2 mm [23], which is about 0.7 to 1.0 mm larger than the median droplets produced by the two nozzles examined in this study. Reduction in the nozzle pressure from 34.5 kPa (5 psi) to 27.5 kPa (4 psi) would result in larger droplets, and possibly shift the median droplet diameter closer to 2 mm, increase the volume mean diameter and produce more droplets greater than 3.3 mm in diameter. An increase in droplet size would lead to an increase in the estimated KE values and the propagule collection capacity of the droplets, indicating that the simulated rainfall would better quantify the natural rainfall intensity events of 15 and 75 mm/hr and their effects on plant pathogen deposition. This reduction in pressure is primarily useful as long as the rainfall remained uniform over the 2 x 2 m sample area.

Conclusions. This rainfall simulator has been used to examine the deposition of

P. pachyrhizi urediniospores into soybean canopies (Chapter 4). The 2 x 2 m sample area

was adequate to cover multiple rows of soybeans for the row spacings examined and

provided a sufficient area for sampling spore deposition. The plastic tarps attached to the

outside of the simulator frame could block air flow from wind speeds of ≤ 2 m/s, and at

higher wind speeds rain simulations had to cease. The simulator was easily transported

59 disassembled on a flatbed trailer (1.5 x 3.0 m) and could be assembled in the field within

20 min. When the winds were relatively light and the soybean canopy was 0.6 m or less in height, two people could reposition the simulator within the field. In taller soybean canopies, the repositioning of the simulator was facilitated with four people, one at each corner.

The positive features of this rainfall simulator’s design are that (i) it can produce continuous, uniform rainfall over a 2 x 2 m sample area with droplet size distributions similar to those produced natural rainfall events, (ii) a majority of the droplets reach terminal velocity [33], (iii) the pathogen propagules are easily injected into the rain water flow, (iv) the rainfall intensity matches are similar to those observed in natural rainfall events [2, 20] and (v) it can be operated efficiently within a field environment. It is apparent from the estimated KE values (Table 3-2) and the CRDVD (Fig. 3-4) that the simulated rainfall events produced by each nozzle were not characterized by the rainfall intensities of 45 and 85 mm/hr. Estimations of the rainfall intensities from the KE values and the CRDVD (Table 3-2) associate simulated rainfall events from the 50WSQ nozzle to the natural intensity of 75 mm/hr, and the 24WSQ nozzle with the natural intensity 15 mm/hr. It is apparent from these data that the rainfall simulator is an effective tool for examining the wet deposition processes of plant pathogens within a field environment.

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Chapter 4: THE WET DEPOSITION OF PHAKOPSORA PACHYRHIZI UREDINIOSPORES WITHIN SOYBEAN CANOPIES.

INTRODUCTION

Soybean rust, caused by the fungal pathogen Phakopsora pachyrhizi Sydow, is a devastating foliar disease of soybeans [Glycine max (L.) Merrill] that has produced substantial yield losses throughout the soybean producing regions of the world [8, 30, 35,

52, 54]. The pathogen is an obligate parasite that has been observed to infect over 95 species of alternative hosts from 42 genera [8]. Prior to its introduction into the United

States in 2004 [46], estimated soybean yield losses from the disease were as high as 30% in the main producing regions of the U.S. and 50% in the southern soybean producing states [53]. Since its arrival, yield losses of up to 33% have been observed in the fungicide research plots throughout Georgia [27], however, the total economic losses attributed to soybean rust have been minimal in the U.S., so far [7, 43, 48]. The extent to which substantial losses will be realized within the U.S. soybean belt is highly dependent upon infections occurring early in the growing season [41].

The overwinter survival of P. pachyrhizi is dependent upon the presence of living host tissue [35, 48]. The two primary hosts in the U.S. are soybeans and kudzu ( Pueraria lobata ) [18, 48], but multiple other host species have been discovered [6, 20, 32, 42, 47].

Analysis of climatic data from the central U.S. indicates that the winters are too severe for the establishment of P. pachyrhizi year-round in that region [42]. It has been reported through the U.S. Department of Agriculture’s (USDA) soybean rust surveillance and monitoring program that soybean rust overwinters in the Gulf Coast regions of the U.S. from Florida to Texas [7, 22, 48]. This indicates that epidemics in the

64 major U.S. soybean producing regions will be dependent upon the aerial dispersal of P. pachyrhizi urediniospores hundreds of kilometers from southern inoculum sources [41].

The aerial dispersal of P. pachyrhizi urediniospores has been documented to occur at local, regional and global scales [26, 35]. Since its discovery in North America, soybean rust has extended its growing season range farther north and west each year, but generally doing so late in the season [7, 15, 48]. A framework for quantifying the aerial dispersal of soybean rust has been implemented and used as a guide for disease management decisions at the farm, regional and continental scales [25, 26, 39]. The probability that soybean rust infections will occur is dependent upon the number of viable spores that are transported to the soybean leaf tissue at varying distances from their source [2, 11]. The ability to predict the deposition of P. pachyrhizi urediniospores onto

soybean leaf tissue is essential for determining the risk of infection within a soybean

canopy [2, 3].

Urediniospores are deposited onto host surfaces by the processes of gravitational

settling and turbulence (dry deposition) and/or rainfall washout (wet deposition) [4, 17,

24]. The wet deposition process is especially important for the initiation of a disease at

vast distances from its inoculum source. This process can washout an entire column of

spores from the atmosphere below the cloud tops and deposit a proportion of them onto

host tissue in a wet and cloudy environment that is suitable for germination and infection

[4, 24]. The rate of urediniospore wet deposition is dependent upon (i) concentration of

aerial urediniospores (ii) the rain droplet capture efficiency of spores, (iii) the rainfall rate,

(iv) the duration of rainfall, and (v) the height of the precipitation and spore cloud layers

[4, 11, 12]. Previous research has shown that spores and particles with a diameter

65 between 20 to 40 µm in size are efficiently collected by natural rainfall droplets [4, 11, 17,

33, 34, 38], and that warts (i.e. echinulate spores) may assist in puncturing the droplet’s surface tension to allow for a higher collection efficiency [10]. The urediniospores of P.

pachyrhizi are between 18 to 38 µm in diameter and are highly echinulated, which suggest that they are efficiently collected by natural rainfall droplets [8, 19]. Studies examining effects of rainfall intensity and duration found that 46 to 83% of the spores and particles in the atmosphere could be washed out within 0.5 hr of rainfall for intensities ranging from 1 to 10 mm/hr [3, 11, 21, 33]. The wet deposition of urediniospores by rainfall washout after long distance transport has been frequently documented by the collection of natural rainfall samples with spores before any local infected sources have been reported [5, 37, 45], and is considered the primary deposition mechanism for P. pachyrhizi urediniospores that spread into the northern soybean

producing regions of the U. S. [23, 28].

The importance of rainfall in the dispersal of microbes has been acknowledged

and researched since the 1880’s [16, 17]. However, there is limited information about the

wet deposition of fungal spores into plant canopies. A recent study by Carisse et al [9]

examined the deposition of Venturia inaequalis ascospores within an apple ( Malus spp. )

tree canopy during natural rainfall events. Their research indicated that ascospores were

distributed throughout the canopy by rainfall, and that the ascospore concentration in the

rain water increased with increasing height in the tree canopy. In the present study, a

rainfall simulator (Chapter 3) was used to examine the wet deposition of P. pachyrhizi

urediniospores into soybean canopies at different rainfall intensities, row spacings and

plant heights. The objectives were to (i) determine the proportion of wet deposited

66 spores that are retained within different levels of the soybean canopy and (ii) examine the effects that plant canopy height, row spacing and rainfall intensity have on the proportions of wet deposited urediniospores throughout the soybean canopy.

MATERIALS AND METHODS

Field Plots. Soybean plots of the Delta and Pine Land determinate variety

DP7220RR (Monsanto Co.) were cultivated using standard practices [51] in research fields at the North Florida Research and Education Center (NFREC) in Quincy, FL. Each plot was 12 by 15 m (40 x 50 ft) in area with a row spacing of either 19 or 76 cm (7.5 or

30 in). Two plots of each row spacing treatment were drill planted at a population density of 370,000 seeds per hectare (150,000 seeds per acre) on the Julian dates 121 and

152 in 2007. Rain simulations were not executed until the average canopy plant heights were between 71 to 85 cm (28 to 33 in) in the first planting and the seconding planting had average canopy heights ranging from 30 to 38 cm (12 to 15 in). A LAI-2000 plant canopy analyzer (LI-COR Environmental) was used to record leaf area index values within the soybean canopy 24 hr before each rainfall simulation (Table 4-1).

Soybean Leaf Sample Substrate. Plastic round pots with a diameter of 19 cm and depth of 18 cm were filled with approximately 4000 cm 3 of Miracle-Gro Enriched

Potting Mix (Miracle-Gro Lawn Products, Inc; N-P2O5-K2O proportion: 0.21-0.07-0.14.).

Seeds of the variety DP7220RR were hand planted 2 cm deep into the plastic pots and then moved into a ‘soybean rust free’ greenhouse at the NFREC. The soybean plants were managed and cultivated with the same techniques as in Chapter 2 until the leaf tissue was ready for use in the bioassay. Leaf tissue between 18 to 50 days old and growth stages R2 to R5 was used in the bioassay because it was observed to be uniformly

Table 4-1: Leaf area index values recorded for the different combinations of plant height and row spacing.

Plant Height Row Spacing Leaf Area Planting a b c d Range (cm) (cm) Index Value Date 30 to 38 19 7 152 30 to 38 76 3 152 71 to 85 19 13 121

71 to 85 76 7 121 a The average soybean canopy height ranges recorded in the field plots. b Distant between the drilled rows of soybeans. c Leaf area index values were recorded using a LAI-2000 Plant Canopy Analyzer (LI-COR Environmental). d Dates recorded according to the calendar date.

Table 4-2: The Phakopsora pachyrhizi urediniospore suspensions and rainfall durations used to evaluate the effects of rainfall deposition of urediniospores into the different soybean canopies at the given rainfall intensities. Row Rainfall Rainfall Plant Height Concentration Germ Spacing Intensity Duration (cm) a (spores/ml) d Proportion d (cm) b (mm/hr) c (min) e 30 to 38 19 15 5,500 0.48 4.4 30 to 38 19 75 4,000 0.45 4.4 30 to 38 76 15 4,500 0.61 4.7 30 to 38 76 75 4,000 0.51 4.4 71 to 85 19 15 8,000 0.72 4.5 71 to 85 19 75 6,000 0.74 5.2 71 to 85 76 15 8,000 0.59 4.5 71 to 85 76 75 11,000 0.69 4.6 a The average soybean canopy height ranges recorded in the field plots. b Distant between the drilled rows of soybeans. c Rainfall intensities produced by the rainfall simulator (Chapter 3). d The calculated mean values of the urediniospore suspension concentration and germination across the 4 replications of the experiment. e The mean duration of simulated rainfall with urediniospores over the 4 replications of the experiment.

68 infected by P. pachyrhizi (Chapter 2). The leaves were detached from the plant 1 min before each rainfall simulation to minimize dehydration and wrinkling of the leaf tissue.

Inoculum Preparation. Soybean plants of variety DP7220RR (Delta and Pine

Land Co.; Monsanto Co.) were mist inoculated [23] with a Florida isolate of P.

pachyrhizi and cultivated in a greenhouse with a relative humidity > 90%.

Urediniospores were vacuumed collected into 20 ml glass vials from infected soybean

leaves with a large single cyclone spore collector (G-R Manufacturing Co., Manhattan,

KS). The spores were weighed into allotments of 0.15 g and stored in a 1.5 ml Eppindorf

tube at 20ºC for less than 24 hr. Rainfall simulations were conducted using a

urediniospore suspension that consisted of 0.15 g of urediniospores in 2 L of deionized

water mixed in a 2 L plastic bottle for approximately 1 min. The urediniospore

suspension was mixed approximately 1 min before each rainfall simulation to prevent

possible spore germination within the suspension. Approximately, 20 ml of the

urediniospore suspension was collected before each simulation and stored on ice for

determining the germination proportion (viability) and concentration of urediniospores in

suspension (Table 4-2).

The spore viability of each suspension was determined by pipetting 1 ml of

urediniospore suspension onto three 100x15 mm plastic Petri plates containing 10 g/L

water agar (Difco Bacto), and counting the proportion of spores that germinated. These

plates were incubated in complete darkness for 16 hr at 24ºC (±5 ºC). The number

germinating spores observed in the first 100 spores examined on each plate was recorded

using a microscope (200X) and the germination proportion for each suspension was

averaged across the three Petri plates. A hemacytometer was used to calculate the spore

69 concentrations of each rainfall simulation suspension. Approximately 10 µl of

urediniospore suspension was pipetted into each of the counting chambers on the

hemacytometer and the numbers of spores in the nine 1 mm 2 grids were recorded. This process was repeated 4 times, the counts were averaged and multiplied by 10,000 to calculate the number of spores/ml for the suspension (Table 4-2).

Rain Simulation and Assessment. The rainfall simulator previously described in Chapter 3 was used in the present study. This simulator can produce droplets comparable to the natural rainfall intensities of 15 and 75 mm/hr over a 2 x 2 m experimental area. The simulator was situated in each plot so that its 2 x 2 m sample area covered 10 and 3 rows of the 19 and 76 cm row spacing treatments, respectively. Two plastic poles (height: 91 cm) containing 3 adjustable aluminum plates were randomly placed within the rows of the rainfall simulator’s sample area. These plates were positioned on the pole to prevent them from overlapping, and were adjusted relative to the plant canopy height (ht) for the three heights of low (0.3ht), mid (0.6ht) and high

(1ht). Next, the bioassay sample leaves were detached from the soybean plants, placed on plastic Petri dishes (100 x 15 mm) containing 10 g/L water agar and affixed to the aluminum plates at each height with adhesive tape. The leaves were placed on water agar to avoid dehydration and distortion of the detached leaf tissue. Once the leaves and Petri dishes were attached to the aluminum plates, the simulator’s curtains were closed and the urediniospore suspensions were mixed. Each rainfall simulation consisted of a 30 s time period of simulated rainfall without spores and then a 4 to 5 min period of simulated rainfall with spores (Table 4-2). The rainfall simulations were replicated 4 times in different sample areas for each rainfall intensity, row spacing treatment and average

70 canopy height. The Petri dishes with the sample leaves were collected from the aluminum plates, covered and stored on a bench with direct sunlight at 24ºC (±5 ºC) for

14 days. After 14 days, the disease severity on the leaves was evaluated using a microscope at 100X to count the number of uredinia per leaf. A LI-3000 Portable Area

Meter (LI-COR Environmental) was used to record the leaf area (cm 2) of each detached leaflet. The number of uredinia per cm 2 was then calculated by dividing the total number

of uredinia per leaf by its respective leaf area.

Data Analysis. Complete randomization of this experiment was impractical due

to the difficulty in changing the simulator’s nozzle and potential damage to plants in the

plot from moving the simulator between treatment applications. The limitations

associated with the ‘restricted randomization’ of the treatments were managed by

analyzing the data with a split-split-split-plot design [40]. A mixed linear model analysis

of variance (ANOVA) was used to examine the interaction effects of within canopy

sample height (SH) by plant canopy height (PH), row spacing (RS), and rain intensity

(RI) on the proportion of uredinia per cm 2. The proportion of the total uredinia per unit area (density) was calculated between the three heights on each pole and averaged between the two sample poles within the simulator’s sample area. The general equation for the mixed model is:

Yijklm = µijklm + Ri + ε*ij + ε*ijk + ε*ijkl + εijklm (3.1)

in which Y is the proportion of uredinia density observed on the ith repetition (1, 2, 3 4),

th th th th j SH level (1,2,3), k PH level (1, 2), l RS level (1, 2), and m RI level (1, 2); µijklm is

th the mean of the jklm SH, PH, RS, RI treatments and their interactions; R i is the effect of ith repetition ( i = 1, 2, 3, 4), considered to be a random effect with a normal distribution,

mean equal to 0 and common variance. The ε*ij is the R x SH effect, whole plot error;

ε*ijk is the R x SH x PH effect, subplot error; ε*ijkl is the R x SH x PH x RS effect, split- subplot error; εijklm is the R x SH x PH x RS x RI effect, split-split-subplot error; all ε

values are considered to be random with a normal distribution, mean equal to 0 and

common variance. All analysis was completed by using PROC MIXED in SAS version

9.1 (SAS Institute, Cary, NC). The TYPE3 option in the METHOD statement of the

PROC MIXED command was used to control Type I errors associated with negative

variance components estimated as zero [31].

RESULTS

Significant ( P < 0.05) effects were observed for the treatments of plant canopy

height and rainfall intensity (Table 4-3). Significantly greater proportion uredinia per

cm 2 were observed in the top (0.61) section of canopy compared to mid (0.25) and low

(0.14) sections for the rainfall intensity of 15 mm/hr (Fig. 4-1a). At a rainfall intensity of

75 mm/hr, no significant difference was observed between the top (0.44) and mid (0.40)

sections of the soybean canopy (Fig. 4-1a). A significantly ( P < 0.05) higher proportion

of uredinia per cm 2 were observed in the top (0.60) section of the soybean canopy

compared to the middle [mid] (0.27) and low (0.13) sections for plants that were between

the heights of 71 to 85 cm (Fig. 4-1b). No significant difference in the proportion of

uredinia per cm 2 was observed between the top (0.44) and mid (0.40) sections of the canopy in plants that were 30 to 38 cm tall (Fig. 4-1b). No significant effect of row spacing was identified between the treatments of 19 and 76 cm (Table 4-3). The proportion of uredinia spores per cm 2 decreased from top (0.49 for 19 cm and 0.55 for 76

Table 4-3: Main and interaction effects of within canopy sample height by plant height, row spacing and rainfall intensity on the proportion of uredinia per cm 2 based on a linear mixed model analysis of variance.

Effects df n df d F Probability Plant Height (PH) a 2 9 4.38 0.05 b Row Spacing (RS) 2 18 1.76 0.20 Rainfall Intensity (RI) c 2 36 9.17 < 0.01 PH * RS 2 18 13.81 < 0.01 PH * RI 2 36 2.15 0.13

RS * RI 2 36 7.85 < 0.01 PH * RS * RI 2 36 0.89 0.42 a The two soybean canopy height ranges of 30 to 38 cm and 71 to 85 cm. b Row spacing observed in the soybean plots of 19 and 76 cm. c Rainfall intensities produced by the simulator of 15 and 75 mm/hr.

2 Fig. 4-1: The mean proportion of uredinia per cm (± standard error) observed within the soybean canopies at 3 sample heights for the treatments of average soybean canopy height, row spacing and rainfall intensity. The within canopy sample heights were adjusted relative to the average plant canopy height (ht) for the heights of low (0.3ht), mid (0.6ht) and top (1.0ht).

Fig. 4-2: The mean proportion of uredinia per cm 2 (± standard error) observed within the soybean canopies at 3 sample heights for the plant height range and rainfall intensities of ( A) 30 to 38 cm and 15 mm/hr; ( B) 30 to 38 cm and 75 mm/hr; ( C) 71 to 85 cm and 15 mm/hr; (D) 71 to 85 cm and 75 mm/hr. Each line represents a different row spacing treatment. The soybean canopy sample heights were adjusted relative to the average plant canopy height (ht) for the heights of low (0.3ht), mid (0.6ht) and top (1.0ht).

75 cm) to low (0.15 for 19 cm and 0.14 for 76 cm) in the canopy for both row spacing treatments. Significant ( P < 0.01) interaction effects were observed between the row spacing treatments and the treatments of plant height and rainfall intensity (Table 4-3).

For both row spacing treatments of 19 and 76 cm, no significant differences were detected in the proportions of uredinia per cm 2 recorded (ranging from 0.11 to 0.65)

throughout the canopy for plants with an average canopy height range of 30 to 38 cm and

at rainfall intensities of 15 and 75 mm/hr (Fig. 4-2). For the 19 cm row spacing treatment,

a significantly ( P < 0.05) higher proportion of uredinia per cm 2 was observed in the top

(0.85) section of the soybean canopy for plant heights of 71 to 85 cm during a rainfall intensity event of 15 mm/hr than the mid (0.08) and low (0.06) samples (Fig. 4-2). No significant difference was detected between the proportion of uredinia per cm 2 in top

(0.48) and mid (0.39)section canopy samples of the 19 cm row spacing treatment at a rainfall intensity of 75 mm/hr and plant canopy heights of 71 to 85 cm. No significant difference was observed in the proportion of uredinia per cm 2 between the top (0.61 and

0.47) and mid (0.26 and 0.34) section samples in plant canopies with 76 cm row spacing, average height range of 71 to 85 cm and the rainfall intensities of 15 and 75 mm/hr.

DISCUSSION

The ability to quantify the risk of infection within a crop is dependent on a good

estimate of the deposition of pathogenic propagules into the plant canopy [3, 4, 17, 26].

Theory and observation have both indicated that rainfall washout is an important process

in the dissemination of P. pachyrhizi urediniospores to distant host populations [26, 28,

29, 49]. The wet deposition of urediniospores by simulated rainfall was examined in the present study. The research goals were to (i) determine the proportion of urediniospores

76 that are wet deposited at three heights within a soybean canopy and (ii) to examine the effects of plant canopy height, row spacing and rainfall intensity on this vertical distribution of deposited urediniospores within a soybean canopy.

Past investigations have observed that the washout rate of spores and particles from the atmosphere is highly dependent upon rainfall intensity [3, 10, 11, 44]. In the present study, the main effect of rainfall intensity was observed to be an important factor in determining the proportion of urediniospores retained within the soybean canopy at different heights (Table 4-3). As the rainfall intensity increased, so did the proportion of uredinia per cm 2 that were recorded in the middle section of the soybean canopy (Fig. 4-

1), the canopy height where a majority of the soybean rust infections are initially

observed. Thus, not only do higher rainfall intensity (75 mm/hr) events have the

potential to remove considerable numbers of urediniospores from the atmosphere, but

they will also deposit higher proportions of spores into the middle sections of both young

(height 30 to 38 cm) and mature (height 71 to 85 cm) soybean canopies as indicated by

the results observed in the present study. The average height of the plant canopy also had

a significant effect on the proportion of urediniospores retained at different levels of the

soybean canopy (Table 4-3). As the soybean plants increased in height, lower

proportions of uredinia per cm 2 were noted in the middle portions of the canopy (Fig. 4-

1). This suggests that in late season rainfalls, the proportion of urediniospores deposited will decrease in the middle sections of the soybean canopies with heights ranging from 71 to 85 cm.

No significant difference was detected in the proportions of uredinia per cm 2 observed throughout the canopy for the two row spacing treatments of 19 and 76 cm

(Table 4-3, Fig. 4-1). However, significant interaction effects were observed between the two row spacing treatments with the average plant canopy height and rainfall intensity treatments (Table 4-3). In general, as the plant canopy height increased, so did the proportion of uredinia per cm 2 recorded in the top section of the soybean canopy for the two row spacing treatments (Fig. 4-2). This effect was more noticeable for plant canopies with 19 cm row spacing than in the plant canopies with 76 cm row spacing.

One possible reason for this result could be the differences in leaf area between the average plant canopy heights and the row spacing treatments (Table 4-1). The leaf area index (LAI) value recorded within the 19 cm row spacing treatment almost doubled from

7 to 13, which indicates that more leaf tissue was present to intercept rain droplets and urediniospores. A similar doubling affect of LAI was observed in the 76 cm row spacing treatment, but the LAI values only increased from 3 to 7. This implies that dense soybean canopies with high LAI values (~ 13) will decrease the proportion of urediniospores deposited into the middle sections of canopies. These results suggest that the soybean canopy structure, more specifically variation in plant height and its interaction with row spacing, is an important characteristic that influences the depth to which urediniospores are wet deposited into soybean canopies.

The interaction of rainfall intensity with row spacing is more noticeable in the 19 cm than the 76 cm row spacing treatment (Fig. 4-2). In the 19 cm row spacing treatment, significantly higher proportions of uredinia per cm2 were recorded in the top portions of

canopy for the 15 mm/hr rainfall intensity. This suggests that a majority ( ≥ 60%) of the urediniospores deposited into a soybean canopy with 19 cm row spacing during less intense rainfall events will be retained in an environment not favorable for infection and

78 disease development. This effect will be more noticeable later in the season for soybean canopies with heights of 71 to 85 cm or greater. Despite the retention of large proportions of urediniospores in the 19 cm row spacing upper canopy, 20 to 40% of the urediniospores were still deposited into lower and middle sections, where environmental conditions can be more conducive for infections and disease development. This implies that a relatively high concentration of viable urediniospores will need to be present in the atmosphere for a significant number of them to be wet deposited into favorable canopy environments by low intensity rainfall events.

The proportion of the urediniospores that were wet deposited into the middle and lower sections of the soybean canopy was larger during 75 mm/hr rainfall than the lower intensity event for both row spacing treatments. The 75 mm/hr rainfall intensity deposited approximately 40 to 60% of the urediniospores into the lower and middle portions of the soybean canopy (Fig. 4-2). High intensity rainfall events are not only more efficient in depositing urediniospores deeper, but they are also more efficient at removing spores from the atmosphere [2, 4, 11]. This implies that higher intensity rainfall events will deposit more urediniospores into sections of the canopy that are typically favorable for infection and disease development. It also appears that the effect of plant canopy height on the distribution of urediniospores in the soybean canopy is less for higher intensity rainfall events than for lower intensity events (Fig. 4-2). No significant interaction effect was observed between rainfall intensity and average plant canopy height (Table 4-3), but it appears that higher intensity rainfalls (75 mm/hr) of short durations will deposit more urediniospores into the middle and lower portions of mature canopies then lower rainfall intensity (15 mm/hr) events of short durations.

The wet deposition of fungal spores into plant canopies has seldom been studied.

A research experiment by Carisse et al. [9] examined the wet deposition of V. inaequalis ascospores in an apple tree ( Malus spp. ) canopy. It was observed that the concentration of ascospores collected from rain water increased with increasing height in the tree canopy, which is similar to trends observed in the present study (Fig. 4-1). It was also noted that the ascospore concentration in rain water was not randomly distributed in the canopy during peak ascospore release events [9]. An examination of the results from this study suggests that the urediniospores deposited by rainfall are not randomly distributed throughout soybean canopies (Fig. 4-1, Fig. 4-2). The similarity between the results of the two studies indicates that in different plant canopies comparable trends can be observed in the wet deposition of multiple fungal spore types.

Researchers examining the penetration of foliar fungicide sprays into soybean canopies have observed trends with spray coverage that are similar to those noted here for the wet deposition of P. pachyrhizi urediniospores [14, 15, 36]. Approximately 80% or more of the fungicide spray is retained within the top portion of a 100 cm high soybean canopy, however, larger deposits of fungicide lower in the canopy have been recorded in shorter (young) and less dense soybean canopies [14, 36]. The similarities in the wet deposition of urediniospores and fungicide penetration imply that any cultural adjustments used to increase the penetration of fungicides into soybean canopies will also increase the penetration of urediniospores. It was also observed that larger concentrations of a fungicide spray could penetrate deeper into a soybean canopy at increased application rates [36] and with air assisted deposits [14]. Based on these results, it is possible that wind driven rain will deposit larger quantities of urediniospores deeper

80 into the soybean canopy than rain during calm wind conditions, but further research is needed to quantify the effects of wind on wet deposition.

Another important factor in the wet deposition of urediniospores is their adhesion to the soybean leaf tissue. Research has shown that the adhesion of the P. pachyrhizi urediniospores to soybean tissue is correlated to spore germination, and that it takes 0.5 hr in optimal environmental conditions for the germination/adhesion process to begin

[50]. This means that urediniospores deposited by rainfall may be exposed to 0.5 hr or more of prolonged rainfall before the germination/adhesion process can initiate. It has been observed that after 0.5 hr of rainfall at the intensities of 15 and 75 mm/hr as much as

90 and 93%, respectively, of the deposited urediniospores had been removed from the leaf tissue (Chapter 5). Thus, the deposition and retention of urediniospores by soybean leaf tissue will be highly dependent on both rainfall intensity and duration. Higher rainfall intensities will efficiently collect urediniospores from the atmosphere and deposit them deeper into the canopy, and shorter rainfall durations will increase the probability that the urediniospore will remain on the host tissue. For example, if 90% of the propagules (> 20 µm) are deposited in 1 mm of rain from the atmosphere [17], then a rain event of 75 mm/hr will washout a majority of the spores in less than 1 min. If the rain event continues for 0.5 hr then it is possible that 93% of the deposited spores will be removed from the soybean leaf tissue. However, if the rain event only lasts for 2 minutes or less, then approximately 74% of the spores deposited will remain on the host tissue. A similar example can be observed for the 15 mm/hr rainfall intensity, but even lower proportions of urediniospores will be deposited in the middle and lower sections of the canopy where the environment is more conducive for infection and disease development.

Overall, it is apparent that there are multiple factors affecting the wet deposition of P. pachyrhizi urediniospores into soybean canopies. Based on this study, it appears that higher rainfall intensity events (75 mm/hr) will be more effective in depositing spores deeper into the soybean canopy. Also, certain cultural practices (early planting & row spacing) produce dense canopy structures that will retain large portions (60 - 80%) of urediniospores in the upper canopy at soybean plant heights of 71 to 85 cm. This canopy structure effect is even more apparent for wet deposition events with low rainfall intensities (15 mm/hr). These results indicate that rainfall washout is an effective process in the wet deposition of P. pachyrhizi urediniospores into the middle and lower sections

of the soybean canopies.

Current models estimating the aerial dispersal of P. pachyrhizi urediniospores

calculate wet deposition as being proportional to the total observed surface precipitation

[26]. The fraction of spores that are wet deposited is calculated using the equation:

-Precip/25.4 WD f = 1.0 – e (4.2)

Thus, a precipitation event of 25.4 mm may deposit 63.2% of the urediniospore concentration in the atmosphere onto a soybean canopy. Based on the results observed here, for rainfall intensities of 15 and 75 mm/hr only 25 and 40%, respectively, of the urediniospores in the atmosphere will be deposited into the middle and lower portions of the soybean canopies after a 25.4 mm rain event. These results indicate the importance of rainfall intensity in the deposition of urediniospores into soybean canopies.

The implementation of these results into current aerial dispersal models on a national scale is a difficult task. Most meteorological data records precipitation as a depth over 1 hr periods. Radar can provide estimates of rainfall rates and durations,

82 however, there is a high degree of uncertainty in radar-rainfall estimates [1, 13]. This study showed that as much as 60% of the urediniospores deposited in a rainfall event are retained within the middle and lower portions of a soybean canopy. Estimating that 60% of the urediniospores removed from the atmosphere in a rainfall event are deposited into favorable canopy environments will generally over estimate urediniospore deposition.

However, it is possible that this value will increase the accuracy of current models by reducing error associated with the assumption that all viable urediniospores deposited from the atmosphere have a high probability of infection. Further research is needed to determine how adding this deposition parameter to the current urediniospore aerial dispersal models will improve disease risk forecasts.

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Chapter 5: RAINFALL REMOVAL OF PHAKOPSORA PACHYRHIZI UREDINIOSPORES FROM SOYBEAN LEAVES

INTRODUCTION

The long-range, aerial dissemination of Phakopsora pachyrhizi urediniospores is

an important process in the epidemiology of soybean rust in the United States [3, 13, 24,

25]. The overwinter survival of this pathogen is dependent upon the presence of an

alternative living host, such as kudzu ( Pueraria lobata ) [9, 20, 30]. Since its introduction

into the continental United States in 2004 [29], soybean rust has been observed to

overwinter in the Gulf Coast region, especially in the areas of southeastern Texas and in

southern and central Florida [5, 12, 30]. Thus, the impact of this pathogen on the major

soybean [Glycine max (L.) Merrill] producing regions in the United States is highly

dependent upon the seasonal transport of inoculum thousands of kilometers.

Successful colonization of a distant host population by a fungus is dependent

upon viable spores landing and remaining on the host tissue [3]. Urediniospores that are

transported through the atmosphere are deposited onto host surfaces by gravitational

settling, turbulence and/or rainfall [3, 8, 14]. Rainfall deposition of urediniospores is

important in long distance transport because it can deposit concentrated quantities of

spores on a potential host under favorable (wet and cloudy) environmental conditions for

disease development [3, 13]. Past research with wheat stem rust and current studies with

soybean rust indicates that rainfall deposition is important in the initiation of a disease

vast distances from its original source [21, 26, 28, 31]. However, much is still unknown

about factors effecting the rate of spore wet deposition and the effects of prolonged

rainfall on the removal of fungal spores from host tissues [3].

In the current study, a rainfall simulator was used to examine the wet deposition on and removal of P. pachyrhizi urediniospores from soybean leaf tissue. The objectives

of this study were to (i) investigate the effects of prolonged rainfall durations on the

removal of urediniospores from soybean leaf tissue; (ii) determine if the removed

urediniospores from the upper leaf canopy are re-deposited in the lower leaf soybean

canopy; and (iii) determine the duration and intensity of a rainfall event required to

remove > 90% of the urediniospores deposited on the soybean leaf tissue throughout the

canopy.

MATERIALS AND METHODS

Host Production and Preparation. Soybean [ Glycine max (L.) Merrill] seeds of

the determinate variety DP7220RR (Delta and Pine Land Company) were planted 2 cm

deep into round plastic pots (19 cm diameter and 18 cm deep) filled with approximately

4250 cm 3 of Miracle-Gro Enriched Potting Mix (Miracle-Gro Lawn Products, Inc.).

These pots were moved into a 'soybean rust free' greenhouse at the North Florida

Research and Education Center (NFREC) in Quincy, Florida. The soybean plants were watered daily and fertilized each month with 5 ml of Osmocote Outdoor & Indoor

Release Plant Food (The Scott's Company; N-P2O5-K2O: 19-6-12) until they were

between 23 to 40 cm tall and at soybean growth stages R2 to R3.

Inoculum Preparation. Inoculum was prepared in a manner similar to the

methods described in Chapter 4. Urediniospores were collected from the leaves of

flowering (R1-R3) soybean plants (DP7220RR) that were mist inoculated [15] with a

Florida isolate of P. pachyrhizi , and had been cultivated in a greenhouse with a high

relative humidity (> 90%). The urediniospores were vacuumed collected from the leaves

90 into a 20 ml glass vial with a large single cyclone spore collector (G-R Manufacturing

Co., Manhattan, KS). Then the collected urediniospores were weighed into 0.04 g cohorts and stored in 1.5 ml Eppindorf tubes at 24ºC for less than 30 hr. Urediniospore suspensions used in the rainfall simulations were created by adding 0.04 g of the urediniospores to 500 ml of deionized water in a 2 L plastic bottle and shaking for approximately 1 min. The urediniospore suspensions were mixed approximately 1 min before each rainfall simulation, so that urediniospores did not remain in suspension for more than 5 min. Approximately 20 ml of the 500 ml urediniospore suspension was collected to determine the viable proportion (based on germination) and concentration of the urediniospores in the suspension.

The spore concentrations of the rainfall simulation suspensions were calculated with a hemacytometer. Approximately 10 µl of spore suspension was pipetted into the

counting chambers, and the average number of spores in nine 1 mm 2 grids were recorded.

Four of these counts were collected, averaged and multiplied by 10,000 to calculate the number of spores/ml for each suspension (Table 5-1). The proportion of spores that germinated was determined by pipetting 1 ml of the spore suspension onto two 100x15 mm plastic Petri plates containing 10 g/L water agar (Difco Bacto). The plates were then incubated in complete darkness for 16 hr at 24°C (±5) [15]. The germination of the first

100 urediniospores observed using a microscope (200X) was recorded for each urediniospore suspension used in the rainfall simulations (Table 5-1) [15].

Rainfall Simulation and Assessment. Rainfall simulations were completed using a previously described rainfall simulator (Chapter 3). The rainfall simulator had 2

Table 5-1: Characteristics about the rain simulator, soybean plants and Phakopsora pachyrhizi urediniospores used to evaluate the effects of prolonged rainfall periods on the removal of urediniospores from soybean leaves. Rainfall Plant Spray Nozzle Soybean Concentration Germination Intensity Match Height Model a Variety c (spores/ml) d Proportion d (mm/hr) b (cm) d 24WSQ 15 DP7220RR 28 4,000 0.53 50WSQ 75 DP7220RR 35 6,000 0.53 a Fulljet nozzles from Spraying Systems Inc. Wheaton IL. b Rainfall intensity match from comparing rainfall drop distribution produced by nozzle with calculated natural rainfall distributions (Chapter 3). c Delta and Pine Land Company. Scotts, MS. d The calculated mean values for soybean plant height, urediniospore suspension concentration and urediniospore germination over the three repetitions of the experiment.

92 nozzles that produced simulated rainfall intensities of approximately 15 and 75 mm/hr over a 2 x 2 m sample area. The soybean plants were arranged into two rows of three within each half of the rainfall simulators sample area. A rainfall simulation of 2 min with urediniospores was conducted and immediately following the simulation two randomly selected plants were removed from the sample area within 30 s. Then the remaining four plants were exposed to a prolonged simulated rainfall period of either 1 or

30 min without urediniospores. For each of the three replications, this process was repeated twice so that 4 plants were exposed to the three rainfall wash-off treatments of 0,

1 and 30 min without urediniospores. The three treatments were selected to examine the effects of prolonged rainfall on spore removal before urediniospore adhesion to soybean leaf tissue [32]. Leaves were destructively sampled from the soybean plants within 5 min of exposure to each of the rainfall wash-off treatments. The height (ht) of each soybean plant from the soil to the top leaf node was recorded before the rainfall simulation (Table

5-1) and leaf samples were collected from the soil (0ht), mid-canopy (0.5ht) and upper canopy (1ht). For the soil (0ht) leaf sample, a detached, ‘rust free’ leaf was placed flat on the surface of the soil in each pot before the rainfall simulations. The detached soybean leaf samples were transferred to 100x15 mm plastic Petri plates containing 10 g/L water agar (Difco Bacto) and incubated on a bench with direct sunlight at 24°C (±5) for 14 days.

The severity of soybean rust on the leaves was evaluated using a microscope at 100X to count the number of uredinia per leaf. The area (cm 2) of each leaf was measured using a

LI-3000 Portable Area Meter (LI-COR Environmental). The number of uredinia per cm 2 was calculated on each leaf sample and used to estimate the soybean leaf urediniospore density (D) by dividing the number of uredinia per soybean leaf by its recorded leaf area.

Data Analysis. The urediniospore density (D) for the leaves exposed to prolonged rainfall (1 or 30 min [D P]) was divided by the mean urediniospore density of the leaves not exposed to prolonged rainfall periods (0 min control leaves [D C]); this proportion was subtracted from 1 to obtain the proportion of urediniospores removed by the simulated rainfall event (S R = 1 – (D P/D C) [10, 18, 22]. An exponential decline (Fig.

5.1) was assumed for the rate of urediniospore removal and was calculated by: B R =

[ln(D C) – ln(D P)]/T, where T (min) is the duration of the prolonged rainfall event [18, 22].

A mixed linear model analysis of variance (ANOVA) was used to examine the effects of rain intensity (I), prolonged rainfall duration (P) and sample height (H) on disease severity, proportion spores removed (S R) and rate of spore removal (B R) [17]. A square root transformation was performed on the response variable of proportion of spores removed to manage the problems associated with non-constant variances [23].

The general equation for the mixed linear model is:

Yijkl = µijkl + R i + εijkl (5.1)

in which Y is one of the three response variables of disease severity in uredinia per cm 2, square root of the proportion of spores removed (S r), or the rate of spore removal (B r) observed on the ith repetitions (1, 2, 3), jth I level (1, 2), kth P level (1, 2), and lth H level (1,

th 2, 3); µijkl is the mean of the jkl I, P, H treatments and their interactions. R i is the effect of ith repetition ( i = 1, 2, 3), considered to be a random effect with a mean of 0, normally

distributed, and common variance; εijkl is the error term (variability not explained by the

model) which in normally distributed with a mean of 0 and common variance. All

analysis was completed by using PROC MIXED in SAS version 9.1 (SAS Institute, Cary,

NC).

RESULTS

A significantly ( P < 0.05) higher mean number of uredinia per cm 2 (0.668) was

observed on the leaf samples for the rainfall intensity of 15 mm/hr at the time points of 0

and 1 min of prolonged rainfall than the mean number (0.283) observed for the 75 mm/hr

intensity (Fig. 5-1). However, after 30 min of prolonged rainfall no significant difference

(P > 0.05) in the mean number of uredinia per cm 2 (0.10 and 0.05, respectively) was observed between the 15 and 75 mm/hr rainfall intensities. No significant effect of rainfall intensity was observed on the proportion of spores removed ( P = 0.052) from the leaf and the rate of spore removal ( P = 0.161) after 1 and 30 min of prolonged rainfall

(Table 5-2). The duration of prolonged rainfall and leaf sample height both had significant effects ( P < 0.05) on the proportion of spores removed (S R) (Table 5-2).

There was a significant effect ( P < 0.05) of prolonged rainfall on the rate of spore

removal, but no significant effect ( P = 0.052) of leaf sample height was recorded on spore removal rate (Table 5-2). No significant difference ( P > 0.05) was observed between the sample heights of mid and top canopy leaves for both the severity and S R

(Table 5-3). Experiment repetition was not observed as a significant effect ( P > 0.05).

The rainfall intensities of 15 and 75 mm/hr removed from 69 to 93% of the P. pachyrhizi

urediniospores from the leaves within the first 30 min of rainfall (Table 5-3). The B R, however, decreased from the 1 to the 30 min of prolonged rainfall for both rainfall intensities (Table 5-3).

DISCUSSION

The depletion of fungal spores from their deposition substrate by rainfall of varying intensities has seldom been studied. Inglis et al. [10, 11] investigated the

Fig. 5-1: The mean severity (± standard error) of soybean leaves exposed to 2 min of rainfall with urediniospores in relation to the duration of prolonged simulated rainfall for the intensities of ( A) 15 mm/hr and ( B) 75 mm/hr. Each line represents a different sample height for the soybean leaf tissue in relation to the plant height (ht).

Table 5-2: Effects of rainfall intensity, prolonged rainfall and leaf sample height on disease severity, the proportion of spores removed (S R) and the rate of spore removal (B R) based on a linear mixed model analysis of variance. Response Variables Effects df n df d F Probability Severity Rainfall Intensity c 1 34 10.15 < 0.01 (uredinia/cm 2) Prolonged Rainfall d 2 34 10.25 < 0.01 Sample Height e 2 34 17.29 < 0.01

Proportion of Spores Rainfall Intensity 1 22 4.23 0.05 a Removed (S R) Prolonged Rainfall 1 22 84.66 < 0.01 Sample Height 2 22 6.00 < 0.01

Rate of Spores Rainfall Intensity 1 22 2.10 0.16 b Removed (B R) Prolonged Rainfall 1 22 14.54 < 0.01 Sample Height 2 22 3.40 0.05 a The number of urediniospores removed from the leaf after the prolonged rainfall divided by the number before prolonged rainfall. A square root transformation was performed on S R to control nonconstant variance. b The difference in ln(spores/leaf) for leaves before and after of prolonged rainfall divided by the time. Units of spores/min. c Rainfall intensities produced by the simulator of 15 and 75 mm/hr. d The duration of the prolonged rainfall event without spores. Three time points (0, 1 and 30 min) for response variable severity and two time points (1 and 30 min) for response variables S R and B R. e The height of the leaf sample relative to the plant (ht). Ground = 0ht; Mid-Canopy = 0.5ht; Top-Canopy = 1ht.

Table 5-3: The effects of rainfall intensity, leaf height and prolonged rainfall periods on removal of urediniospores from soybean leaf tissue.

Rainfall c Leaf Sample Time Uredinia Spore removal Spore removal Intensity b 2d e -1 f Position (min) per cm proportion (S ) rate (B ; min ) (mm/hr) a R R 15 Ground Leaf 1 1.40a 0.13a (0.10) 0.15 (0.12) Mid-Canopy Leaf 1 0.21b 0.44a (0.17) 0.68 (0.31) Top-Canopy Leaf 1 0.15b 0.42a (0.14) 0.59 (0.22)

15 Ground Leaf 30 0.20b 0.86b (0.04) 0.07 (0.01) Mid-Canopy Leaf 30 0.07b 0.90b (0.01) 0.08 (0.01) Top-Canopy Leaf 30 0.03b 0.77b (0.14) 0.06 (0.02)

75 Ground Leaf 1 0.47ab 0.00a (0.00) 0.00 (0.00) Mid-Canopy Leaf 1 0.19b 0.20a (0.11) 0.24 (0.14) Top-Canopy Leaf 1 0.12b 0.36a (0.16) 0.52 (0.30)

75 Ground Leaf 30 0.11b 0.79b (0.12) 0.06 (0.02) Mid-Canopy Leaf 30 0.01b 0.93b (0.04) 0.08 (0.01) Top-Canopy Leaf 30 0.01b 0.69b (0.20) 0.03 (0.01) a Rainfall intensity match of the rainfall simulator’s droplet distributions with natural rainfall event s (Chapter 3). b Leaf samples were collected relative to the plant’s height (ht). Ground = 0ht; Mid-Canopy = 0.5ht; Top-Canopy = 1ht. c The duration of the prolonged rainfall event without spores. d The mean number of uredinia per cm 2 (severity) recorded on the sample soybean leaves followed by the same letter are not significantly different based on Tukey’s HSD test (P = 0.05). e The number of uredinia removed from the leaf after the prolonged rainfall divided by the number before prolonged rainfall. Mean proportions (3 repetitions) followed by the same letter for each rainfall intensity are not significantly different based on Tukey’s HSD test ( P = 0.05). Standard errors are given in parentheses. f The difference in ln(uredinia/cm 2) for leaves before and after of prolonged rainfall divided by the duration of prolonged rainfall (time). Mean rate of three repetitions and standard errors are given in parentheses.

98 removal of conidia produced by the biological control agent Beauveria bassiana on the

leaves of potato ( Solanum tuberosum ), wheat ( Triticum aestivum ) and alfalfa ( Medicago

sativa ). They observed that rainfall intensity had significant effects on the reductions of colony forming units recorded for the different leaf types. Both Madden et al. [18] and

Ntahimpera et al. [22] observed a rainfall intensity effect on removal of multiple

Collectotrichum species conidia from inoculated strawberry ( Fragaria x ananassa ) fruits.

Rossi et al. [27] noted that as rainfall intensity increased the retention of Venturia inaequalis ascospores on apple tree ( Malus spp. ) leaves decreased. In this study, there was no significant effect of rainfall intensity on the removal of urediniospores from soybean leaf tissue (Table 5-2, S R and B R). One possible reason for the differences in rainfall intensity effects for these studies could be the variation in the initial level of spores inoculated on the host plant substrate. Past studies applied a known concentration of spores to the plant tissue, and in the present study spores were deposited onto the soybean leaf tissue through simulated rainfall. A significant difference was observed between the two rainfall intensities (15 and 75 mm/hr) for the severity levels recorded at the 0 and 1 min prolonged rainfall time points (Fig. 5-1). A consistently lower severity level was observed for the 75 mm/hr rainfall intensity, which indicates that lower numbers of urediniospores remained on the soybean leaves at this higher rainfall intensity.

A lower initial inoculum level could decrease the magnitude of the effects observed by the 75 mm/hr intensity rainfall on the proportion of urediniospores removed from the leaf surface.

Another reason for the observed difference in the rainfall intensity effects could be the durations of prolonged rainfall sustained in the studies. In past studies, rainfall

99 intensity effects were detected after prolonged rainfall durations of 15 [10] or 16 [18, 22] min. However, when the duration of prolonged rainfall was increased to 30 min, no significant effect of rainfall intensity was recorded on potato leaves [10]. In the present study, after exposure to 30 min of prolonged rainfall no significant effect was observed for rainfall intensity on the removal of urediniospores from soybean leaves. These observations indicate that the effect of rainfall intensity on the removal of spores from plant tissue is dependent upon the duration of the prolonged rainfall. For P. pachyrhizi ,

both low and high rainfall intensity events will remove equal proportions of

urediniospores from soybean leaf tissue after prolonged rainfall durations of 30 min or

greater. However, for rainfall events less than 30 min, these observations suggest that a

lower proportion of urediniospores will be removed from soybean leaves during low

intensity (15 mm/hr) rainfall events compared to high intensity (75 mm/hr) events.

An exponential decline trend was observed by Madden et al. [19] for the density

of conidia on the fruit in relation to the duration of simulated rainfall. A similar trend

was recognized in the present study for the disease severity recorded on the soybean leaf

in relation to the duration of prolonged rainfall (Fig. 5-1). This trend is also supported by

the observed decrease in rate of spore removal from 1 to 30 min prolonged rainfall

durations (Table 5-2). These results suggest that the removal of P. pachyrhizi

urediniospores from soybean leaves can be explained by an exponential decline model,

however, further research with more time points is needed to determine the parameters of

such a model.

It was observed that 69 to 93% of wet deposited P. pachyrhizi urediniospores on

soybean leaf tissue could be washed-off by 30 min of a prolonged rainfall at the

100 intensities of 15 and 75 mm/hr (Table 5-3). Even after 1 min of prolonged rainfall, 20 to

44% of the wet deposited urediniospores were removed from the soybean leaf tissue

(Table 5-3). Past research examining the wet deposition of fungal spores and pollen grains has reported that a majority of the propagules are ‘scrubbed’ from the atmosphere early in a rainfall event [4, 6, 8, 28]. For example, 96% of the propagules (> 20 µm) deposited in a rainfall event were captured within the first 1 mm (20 min) of precipitation

[8]. This indicates that a majority of the P. pachyrhizi urediniospores wet deposited within a soybean canopy will have a high probability of being exposed to a prolonged rainfall periods with low spore concentrations.

In general, fungal spores that are wet deposited on to leaf surfaces are retained either by physical leaf characteristics (leaf hairs, veins, etc.) and/or spore adhesive materials and physical characteristics [1]. A recent study observed that the adhesion of P. pachyrhizi urediniospores to soybean leaves was correlated to germination, and that the adhesion/germination process could begin as early as 30 min under ideal (21°C, RH =

100%) environmental conditions [32]. This suggests that even under ideal conditions a majority of the urediniospores deposited in wet deposition events will not adhere to the soybean leaf surface before being exposed to a considerable amount of prolonged rainfall.

Thus, the characteristics of the leaf surface will be more important than spore adhesion in determining the quantity of P. pachyrhizi urediniospores that are retained by the soybean leaves after a wet deposition event.

A framework for monitoring the aerial dispersal of P. pachyrhizi urediniospores has been implemented in the U.S. [13, 16, 24]. The model used in this framework relates the wet deposition of urediniospores to the observed surface precipitation in millimeters,

101 in which, a precipitation event of 25.4 mm results in the deposition of 63.2% of the spore cloud [13]. This method for calculating the urediniospore wet deposition does not account for the differences in spore removal observed at varying rainfall intensities and durations. For example, a 15 mm/hr rain event will take approximately 2 hr to reach a rainfall total depth of 30 mm and wash approximately 70% of the urediniospores from the atmosphere. The urediniospores deposited in the first 1.5 hr will be exposed to rainfall durations of 30 min or more. This means that approximately 90% of the spores deposited in the first 1.5 hr will be removed from the leaf tissue, and an additional 40% of the spores deposited in the last 0.5 hr will be removed as well. Thus, approximately 80% of the aerial urediniospore population deposited will be removed from the leaf tissue. In a

75 mm/hr rain event it will take less than 0.5 hours to accumulate a depth of 30 mm, so only 40% of the urediniospores deposited will be removed from the soybean leaf tissue.

This example indicates the importance of rainfall intensity and duration on the calculation of the proportion of P. pachyrhizi urediniospores deposited in soybean canopies.

The implementation of rainfall intensity and duration values into current models was previously discussed in Chapter 4. Rainfall data on a national scale is often only available in measurements of depth over an hourly time interval, and radar-rainfall estimates have a high degree of uncertainty [2, 7]. Despite the limitation mentioned for rainfall data, it is still possible to incorporate urediniospore wash-off into urediniospore aerial transport models. In the present study, it was noted that even after 1 min of simulated rainfall, approximately 20 to 40% of the urediniospores deposited on the leaf tissue were removed. Thus, it is reasonable to assume that in all significant rainfall deposition events at least 20%, if not more, of the wet deposited urediniospores will be

102 removed from the soybean leaf tissue. This is a conservative value and will probably provide an over estimation of urediniospores deposited on the soybean leaf tissue, but it will reduce errors from assuming all spores deposited are retained by the soybean canopy.

Further research is necessary to examine how a model accounting for urediniospore wash-off compares to current aerial dispersal models.

103

LITERATURE CITED

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2. Anagnostou, E. N., Krajewski, W. F., and Smith, J. 1999. Uncertainty quantification of mean-areal radar-rainfall estimates . J. of Atmospheric and Oceanic Tech. 16:206-215.

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8. Gregory, P. H. 1973. Microbiology of the Atmosphere, ed. N. Polunin. Leonard Hill Books. Aylesbury. 377.

9. Harmon, P. F., Momol, M. T., Marois, J. J., Dankers, H., and Harmon, C. L. 2005. Asian soybean rust caused by Phakopsora pachyrhizi on soybean and kudzu in Florida. Plant Health Progress. doi:10.1094/PHP-2005-0613-01-RS.

10. Inglis, G. D., Ivie, T. J., Duke, G. M., and Goettel, M. S. 2000. Influence of rain on conidial formulation on persistence of Beauveria bassiana on potato leaves and Colorado potato beetle larvae . Bio. Control 18:55-64.

11. Inglis, G. D., Johnson, D. L., and Goettel, M. S. 1995. Effects of simulated rain on persistence of Beauveria bassiana conidia on leaves of alfalfa and wheat . Biocontrol Sci. and Tech. 5:365-369.

12. Isakeit, T., Miller, M. E., Saldana, R., Barnes, L. W., McKemy, J. M., Palm, M. E., Zeller, K. A., DeVries-Paterson, R., and Levy, L. 2006. First Report of rust caused by Phakopsora pachyrhizi on soybean and kudzu in Texas . Plant Dis. 90:971.

104

13. Isard, S. A., Gage, S. H., Comtois, P., and Russo, J. M. 2005. Principles of the atmospheric pathway for invasive species applied to soybean rust . BioScience 55:851-861.

14. Isard, S. A. and Gage, S. H. 2001. Flow of life in the atmosphere: An airscape approach to understanding invasive organisms. Michigan State University Press. East Lansing.

15. Isard, S. A., Dufault, N. S., Miles, M. R., Hartman, G. L., Russo, J. M., De Wolf, E. D., and Morel, W. 2006. The effect of solar irradiance on the mortality of Phakopsora pachyrhizi urediniospores . Plant Dis. 90:941-945.

16. Isard, S. A., Russo, J. M., and De Wolf, E. D. 2006. The establishment of a national Pest Information Platform for Extension and Education. Published Online at Plant Heath Progress. doi: 10.1094/PHP-2006-0915-01-RV.

17. Littell, R. C., Milliken, G. A., Stroup, W. W., Wolfinger, R. D., and Schabenberger, O. 2006. SAS for mixed models, 2 ed. SAS Institute, Inc. Cary, N. C.

18. Madden, L. V., Yang, X., and Wilson, L. L. 1996. Effects of rain intensity on splash dispersal of Colletotrichum acutatum Phytopathology 86:864-874.

19. Madden, L. V., Wilson, L. L., Yang, X., and Ellis, M. A. 1992. Splash dispersal of Colletotrichum acutatum and Phytophthora cactorum by short-duration simulated rains . Plant Pathology 41:427-436.

20. Miles, M. R., Frederick, R. D., and Hartman, G. L. 2003. Soybean rust: is the U.S. soybean crop at risk. APSnet Feature. ( http://www.apsnet.org/online/feature/rust/) .

21. Nagarajan, S., Singh, H., Joshi, L. M., and Saari, E. E. 1976. Meteorological conditions associated with long-distance dissemination and deposition of Puccinia graminis tritici uredospores in India . Phytopathology 66:198-203.

22. Ntahimpera, N., Wilson, L. L., Ellis, M. A., and Madden, L. V. 1999. Comparison of rain effects on splash dispersal of three Colletotrichum species infecting strawberry . Phytopathology 89:555-563.

23. Ott, R. L. and Longnecker, M. 2001. An Introduction to Statistical Methods and Data Analysis, 4 ed. Duxbury. Pacific Grove, CA.

24. Pan, Z., Yang, X. B., Pivonia, S., Xue, L., Pasken, R., and Roads, J. 2006. Long- term prediction of soybean rust entry into the continental United States . Plant Dis. 90:840-846.

25. Pivonia, S., Yang, X. B., and Pan, Z. 2005. Assessment of epidemic potential of soybean rust in the United States . Plant Dis. 89:678-682.

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26. Roelfs, A. P., Rowell, J. B., and Romig, R. W. 1970. Sampler for monitoring cereal rust uredospores in rain . Phytopathology 60:187-188.

27. Rossi, V., Giosue, S., and Bugiani, R. 2003. A model simulating deposition of Venturia inaequalis ascospores on apple trees. Bulletin OEPP/EPPO 33:407 - 414.

28. Rowell, J. B. and Romig, R. W. 1966. Detection of urediospores of wheat rusts in spring rains . Phytopathology 56:807-811.

29. Schneider, R. W., Hollier, C. A., and Whitam, H. K. 2005. First report of soybean rust caused by Phakopsora pachyrhizi in the continental United States . Plant Dis. 89:774.

30. Sconyers, L. E., Kermerait, R. C., Brock, J., Phillips, D. V., Jost, P. H., Sikora, E. J., Gutierrez-Estrada, A., Mueller, J. D., Marois, J. J., Wright, D. L., and Harmon, C. L. 2006. Asian soybean rust development in 2005: A perspective from the Southeastern United States. APSnet Feature. (http://www.apsnet.org/online/feature/sbr/) .

31. Szabo, L. J. 2007. Spore trapping: technologies and results from 2007. APS National Soybean Rust Symposium. Louisville, KY. December 12 -14.

32. Velez-Climent, M. C. 2008. Adhesion of Phakopsora pachyrhizi urediniospores to soybean leaves. Dept. of Plant Pathology. Master of Science. The Pennsylvania State University: University Park. p. 153.

106

Chapter 6: THE DRY DEPOSITION OF PHAKOPSORA PACHYRHIZI UREDINIOSPORES AND PARTICLES INTO SOYBEAN CANOPIES.

INTRODUCTION

Predicting the spread of soybean rust, Phakopsora pachyrhizi Sydow, into a soybean [ Glycine max (L.) Merrill] growing region or field will help farmers determine an effective management strategy for the disease. In the United States, P. pachyrhizi has

been observed to overwinter on kudzu ( Pueraria lobata ) in the Gulf Coast region from

Texas to Florida [7, 18, 30, 32]. The aerial dispersal of P. pachyrhizi urediniospores

from southern inoculum sources northward can spread the disease into the continental

interior’s major soybean producing regions. The long distance airborne spread of this

pathogen to the Midwest U.S. has been frequently documented [19, 28], but observed to

occur late in the growing season [7, 10, 32]. The local dispersal of P. pachyrhizi

urediniospores from kudzu to soybean crops in the Gulf Coast states early in the growing

season and subsequent production of large amounts of inoculum in commercial fields are

critical factors in determining the local and long distance spread of this pathogen [30].

The ability to determine the density of viable urediniospores deposited onto

soybean leaf tissue is fundamental to predicting the probability that the disease will occur

within a field [2, 9]. The deposition of urediniospores to host surfaces occurs through the

processes of gravitational settling and turbulence (dry deposition) and/or rainfall washout

[3, 16, 20]. The dry deposition processes are relatively more important in local spread of

aerial dispersed pathogens then wet deposition processes. One reason for this is because

rainfall occurs for discrete, relatively short time intervals whereas dry deposition near a

source of inoculum can occur whenever it is not raining and deposit relatively high

concentrations of urediniospores into the soybean canopy for many more hours [3].

107

Consequently, knowledge about the dry deposition of urediniospores into soybean canopies is important in order to understand the spread of inoculum from kudzu into commercial soybean fields in the south and for deploying effective management strategies throughout the U.S. wherever there is an in-field or local inoculum source.

The dry deposition of multiple plant pathogenic fungal spores has been observed to decrease rapidly in intensity with an increase in distance from an inoculum source [8,

11, 17, 24, 26]. This decrease in intensity with distance has commonly been quantified by two empirical models called the inverse power law model [17]:

-bP y = a P s (6.1)

and the negative exponential law model [21]:

y = a E exp(-bEs). (6.2)

In both models, y is the trap dose of deposited spores (spores per cm 2) at a given distance,

s, from an inoculum source with the parameters ‘a’ and ‘b’. Throughout the current study, distance s will be in meters, which means that parameter a P is the value of y at s = 1 m and parameter a E represents the value of y at the source ( s = 0 m). Parameter ‘b’ in both models is the rate parameter that indicates the steepness of the deposition gradient. The

-1 exponent b P is dimensionless, but b E has a dimension of length (m ). The subscripts ‘P’ and ‘E’ refer to the power and exponential models, respectively, and exp is the base of the natural logarithms. These models can be used to compare the steepness of the deposition dispersal gradients and provide insight into environmental factors effecting the dry deposition of urediniospores into soybean canopies [8, 24, 26].

The local dispersal and deposition of P. pachyrhizi urediniospores is fundamental to the development of soybean rust epidemics in the U.S. Previous studies examining the

108 deposition of fungal spores near a sources and the vertical variation with height have observed steep deposition gradients [1, 4, 5, 14, 26, 29]. In the present study, the dry deposition of P. pachyrhizi urediniospores and synthetic particles into soybean canopies near a point source above the canopy was examined for different plant heights, within canopy heights, row spacing treatments and wind speeds. Synthetic particles were used to simulate urediniospore deposition, because large quantities were readily available, they are easily distinguished from other particles naturally occurring in the air and they do not produce alternative inoculum point sources through infections of the soybean crop. The objectives of the current study where to (i) determine the relationship between dry deposited urediniospores and synthetic particles, (ii) examine the effects that plant canopy height and row spacing have on the quantities of particles deposited throughout the soybean canopy, and (iii) determine the effects of wind speed on the steepness of the deposition gradient.

MATERIALS AND METHODS

In the present study, the number of dry deposited P. pachyrhizi urediniospores and Night-Glo® NG-20 zinc sulfide particles (DayGlo® Color Corp., Cleveland, OH)

(Fig. 6-1) was monitored within soybean field. The trials were conducted in plots at the

North Florida Research and Education Center in Quincy, FL during the summer of 2007.

All the soybean plots were drill planted in either 19 or 76 cm (7.5 or 30 in) row spacing treatments at a population density of 370,000 seeds per hectare (150,000 seeds per acre) and cultivated using standard soybean production practices [33]. Meteorological data was collected with a Model 700 WatchDog® weather station (Spectrum® Technologies,

Inc.) (Appendix A) that was positioned within 10 m of the plots and at a height of 2 m

109 above the ground. The WatchDog® measurements of wind speed were calibrated to a

Campbell® Scientific, WindSonic Ultrasonic wind sensor (Appendix A). A LAI-2000 plant canopy analyzer (LI-COR Environmental) was used to record the leaf area index values of the soybean canopies within 3 days of dry deposition release events.

Dry Deposition Trials. P. pachyrhizi urediniospores were vacuum collected from the leaves of infected soybean plants (DP7220RR; growth stages R1 to R2) with a large single cyclone spore collector (G-R Manufacturing Co., Manhattan, KS). The soybean plants had been mist inoculated with a Florida isolate of P. pachyrhizi and cultivated in a greenhouse with a high relative humidity (> 90%). Collected urediniospores were weighed into specified cohorts and mixed with NG-20 particles about 3 hr before all dry deposition release event. For each deposition trial, both spores and particles were released 10 cm above the soybean canopy through a 100 mesh (149

µm) stainless steel strainer (Fig. 6-2). The strainer was connected to a 5.5 cm diameter polypropylene funnel that had a vibrating motor affixed to its outer edge. The motor was connected to a 9-volt battery power supply and switch, at a distance of 5 m from the release point to minimize disruption of the natural wind flow over the soybean canopy.

1 m Deposition Trial. Two plots of the soybean variety DP7220RR (Delta and

Pine Land, Monsanto Co.) were drill planted in each row spacing treatment over an area of 12 by 5 m (40 x 16 ft) on the Julian date 162. Dry deposition release events were conducted when the average canopy height classes of the plots were between 69 to 71 cm for the short class and 84 to 91 cm for the tall class. Each sample point consisted of a plastic pole (height: 91 cm) with three adjustable metal plates bent at a 45º angle. Two sample points were established at distances of 0.5 and 1.0 m downwind from the release

110

Particle s

Urediniospore

Fig. 6-1: Phakopsora pachyrhizi urediniospores and Night-Glo® NG-20 zinc sulfide particles (DayGlo® Color Corp., Cleveland, OH).

111

Fig. 6-2: Strainer/funnel release apparatus used in the 1 and 6 m dry deposition trials. The release apparatus consisted of a 100 mesh (149 µm) stainless steel strainer connected to a 5.5 cm diameter polypropylene funnel that had a vibrating motor affixed to its outer edge.

112 point. Two more sample points were positioned at 0.5 m from the 1.0 m sample point perpendicular to the downwind direction, so that all four sample points formed the shape of a ‘T’ downwind from the release point (Fig. 6-3). The metal plates at each sample point were adjusted relative to the plant canopy height (ht) for the three heights of low

(0.3ht), mid (0.6ht) and top (1ht). Microscope slides greased with petroleum jelly were fastened to each metal plate, so that a total of 12 slides were exposed during every release event run. For each run, a 0.12 g cohort of urediniospores was mixed with 0.85 g of NG-

20 particles and placed inside the strainer/funnel release apparatus. Urediniospores and particles were released from the filter through the vibration of the motor for periods ranging from 30 to 90 seconds. The greased microscope slides were collected within 10 minutes of release from the filter, stored in a microscope slide box, and examined in the laboratory with a microscope at 100X magnification. Because of the high number of particles collected on the slides, the total number of particles (0 to 1400) and urediniospores (0 to 600) observed in one field of view (0.0314 cm 2 area) was recorded for each slide.

6 m Deposition Trial. Two plots of each row spacing treatment were planted in an area of 12 by 15 m (40 x 50 ft) on Julian date 79 for the soybean variety DP5915RR and

Julian dates of 121 and 162 for the soybean variety DP7220RR. Urediniospore and NG-

20 particle dry deposition release events were conducted when the average plant canopy height classes were 51 to 61 cm for the short and 84 to 92 cm for the tall. The sample points were constructed as described in the 1 m deposition trial. Three sample points were established in a line at 2, 4 and 6 m downwind from the release point. Two more sample point lines were established ± 45 degree from the downwind line for a total of 9

113

Fig. 6-3: A schematic diagram of the sampling set-up for the 1 and 6 m deposition trials. The grey boxes in the plan view 113 represent the sampling points in the downwind direction from the release point.

114 sample points (Fig 6-3). The metal plates were adjusted to 3 heights designated low

(0.3ht), mid (0.6ht) and top (1.0ht), with reference to the average plant canopy height (ht).

Petroleum jelly greased slides were affixed to the metal plates at each sample point for at total of 27 slides. Approximately, 10 g of the NG-20 particles were placed inside the strainer/funnel release apparatus (Fig. 6-2) and vibrated through the strainer for periods ranging from 60 to 300 seconds. After each release event run, the greased slides were collected within 25 min, stored in a microscope slide box, and examined in a laboratory with a microscope at 100X. The total number of particles (max = 15,000) observed in a

2.5 cm 2 area (1 x 2.5 cm) was recorded for each slide.

Data analysis. The relationship between the deposition of urediniospores per cm 2 and NG-20 particles per cm 2 was calculated using least squares linear regression analyses

(y = β0 + β1x + ε) with the PROC REG statement in SAS version 9.1 (SAS Institute, Cary,

NC). The differences between the slopes of the regression equations for each sample

height were compared using a T-test.

A mixed linear model analysis of variance (ANOVA) was used to examine the

effects plant height (PH), row spacing (RS), canopy sample height (CH) and distance

from the source (DS) on the normalized proportion of particles per cm 2. The number of

particles per cm 2 recorded for each slide was normalized with the top canopy sample for each distance (2, 4 and 6 m) point downwind from the source. This normalized proportion reduced problems associated with the non-constant variance of the data with distance. The general equation for the mixed linear model is:

Yijklm = µijklm + Ri + ε*ij + ε*ijk + ε*ijkl + εijklm (6.3) in which Y is normalized proportion of particles per cm 2 observed on the ith run

(1,2,…,9), jth PH level (1, 2), kth RS level (1, 2), lth CH level (1, 2, 3), and mth DS level (1,

115

th 2, 3); µijklm is the mean of the jklm , PH, RS, CH and DS treatments and their

th interactions; R i is the effect of i run ( i = 1, 2, …, 9), considered to be a random effect

with mean of 0, normally distributed and common variance. The ε*ij is the R x PH effect;

ε*ijk is the R x PH x RS effect; ε*ijkl is the R x PH x RS x CH effect, and ε*ijklm is the R x

PH x RS x CH x DS effect; all ‘ ε*’ and the ε effects are considered to be random and are normally distributed with a mean of zero and common variance. All ANOVA analysis was completed by using PROC MIXED in SAS version 9.1 (SAS Institute, Cary, NC).

All treatment comparisons were examined using a Tukey pairwise comparison test [23].

Particle deposition gradients were assessed using the data gathered from the 6 m deposition trial. Linear least squares regression analysis of the ln – ln and ln – linear transformed data were conducted to estimate the parameters of equations 1 and 2 respectively. All the parameter estimates were calculated using PROC REG command in

SAS version 9.1 (SAS Institute, Cary, NC).

RESULTS

During both the 1 and 6 m dry deposition trials, the average crop heights varied from 50 to 91 cm with leaf index values from 4 to 12 (Table 6-1 and 6-3). All the studies were conducted under a variety of meteorological conditions (Table 6-2 and 6-4). The average wind speed at 2 m above the ground ranged from 0.4 to 3.6 m/s for the 1 m trial and 0.9 to 4.1 m/s for the 6 m trial. Wind gusts as high as 5.1 and 5.5 m/s were observed in 1 and 6 m deposition trials respectively. A majority of the experiments were conducted under low to moderate relative humidity (49 – 69%) and warm air temperature conditions (29 – 34 ºC).

116

Table 6-1: Dates, time, run duration and crop conditions during the 1 m dry deposition trial runs. Time Duration Row Spacing Crop height c Run Date a b LAI (LST) release (s) (cm) (cm) 1 7/26/2007 15:09 90 19 69 7.0 2 7/26/2007 15:51 20 19 69 7.0 3 7/26/2007 16:42 20 19 69 7.0 4 7/26/2007 15:27 20 76 71 5.3 5 7/26/2007 16:12 20 76 71 5.3 6 7/26/2007 17:10 29 76 71 5.3 7 8/14/2007 17:40 40 19 84 11.2 8 8/14/2007 18:45 28 19 84 11.2 9 8/15/2007 10:31 75 19 84 11.2 10 8/15/2007 19:40 30 19 84 11.2 11 8/14/2007 17:25 45 76 91 9.1 12 8/14/2007 19:01 30 76 91 9.1 13 8/15/2007 10:17 45 76 91 9.1 a Local standard time the release trial started. b Duration of time it took for the particle and spore mixture to be released. c Leaf area index (LAI) value recorded within the canopy before the run.

Table 6-2: Average meteorological conditions recorded during the 1 m dry deposition trial runs.

a b c c Run u (m/s) Gust (m/s) RH (%) Tair (ºC) 1 2.0 3.1 58 30 2 2.1 3.1 51 31 3 2.0 4.0 49 32 4 1.7 2.8 51 31 5 2.1 4.2 50 32 6 2.0 2.6 50 32 7 1.5 2.6 50 33 8 3.6 5.1 54 30 9 0.4 1.1 66 32 10 1.2 2.0 63 31 11 1.9 3.3 49 34 12 3.0 4.6 59 29 13 0.5 1.1 69 31 a Mean adjusted wind speed measured at 2 m above the ground (Appendix Fig. A-2). b Max adjusted wind speed measured during the release period (Appendix Fig A-2). c Relative humidity (RH) and air temperature (T air ) recorded during the experiments at 2 m above the ground.

117

Table 6-3: Dates, time, run duration and crop conditions during the 6 m dry deposition trial runs. Row Crop Time Duration Run Date Spacing height LAI c (LST) a release (s) b (cm) (cm) 1 5/24/2007 16:01 180 19 51 5.1 2 5/24/2007 16:24 180 76 51 4.1 3 5/25/2007 11:23 60 19 51 5.1 4 5/25/2007 12:05 60 76 51 4.1 5 6/23/2007 15:39 60 19 61 7.3 6 6/23/2007 16:24 60 76 61 5.3 7 6/25/2007 15:43 120 19 61 7.3 8 6/25/2007 15:59 60 76 61 5.3 9 6/27/2007 14:53 60 19 61 7.3 10 6/27/2007 14:46 60 76 61 5.3 11 7/8/2007 15:30 60 19 84 12.3 12 7/8/2007 15:53 60 76 84 9.1 13 7/8/2007 17:59 180 19 84 12.3 14 7/8/2007 18:19 60 19 84 9.1 15 8/9/2007 18:29 120 76 84 10.7 16 8/9/2007 17:55 120 19 91 8.4 17 8/10/2007 12:38 240 76 84 10.7 18 8/10/2007 11:52 300 76 91 8.4 a Local standard time the release trial started. b Duration of time it took for the particle and spore mixture to be released. c Leaf area index (LAI) value recorded within the canopy before the run.

118

Table 6-4: Average meteorological conditions recorded during the 6 m dry deposition trial runs. a b c c Run u (m/s) Gust (m/s) RH (%) Tair (ºC) 1 3.2 4.3 33 29 2 2.9 5.0 33 29 3 1.7 2.9 51 26 4 2.2 2.8 52 25 5 0.9 1.1 21 36 6 1.8 2.9 23 35 7 1.5 3.1 48 28 8 1.2 2.0 50 29 9 2.0 2.5 41 33 10 1.4 1.9 41 33 11 4.1 5.5 45 33 12 4.0 5.3 47 32 13 2.4 3.0 47 32 14 2.5 3.2 45 32 15 3.9 5.0 86 26 16 1.6 2.2 71 29 17 1.8 2.5 62 33 18 1.6 2.6 72 31 a Mean adjusted wind speed measured at 2 m above the ground. b Max adjusted wind speed measured during the release period. c Relative humidity (RH) and air temperature (T air ) recorded during the experiments.

119

The relationship between the deposition of urediniospores and NG-20 particles was examined by plotting the urediniospore trap dose (number deposited per cm 2) against

the deposited NG-20 particle trap dose using the data from the 1 m deposition trial (Fig.

6-4). A linear fit was observed between the two variables for both plant canopy heights.

Linear least squares regression equation for short (69 – 71 cm) canopies was Y = 0.29X +

295 (R 2 = 0.76) and for tall (84 – 91 cm) canopies it was Y = 0.13X - 12 (R 2 = 0.64). The

slopes for the two different regression equations were significantly (t = -5.28, df = 153, P

< 0.01) different.

A mixed model analysis of variance was fit to the normalized data from the 6 m

deposition trial (Table 6-5). Significant ( P < 0.05) main and interaction effects were observed for plant height and canopy sample height variables. No significant effects were observed for the normalized proportion of particles with the main effects of row spacing and distance from the source. The normalized proportion of particles observed in the low and mid sections of the soybean canopies decreased with an increase in plant height (Fig. 6-5). On average, the normalized particle trap dose observed at 0.3 and 0.6 ht, respectively, were 0.46 and 0.67 for the short canopy height and 0.16 and 0.28 for the tall canopy height. The particle trap dose observed at all heights in the canopy decreased rapidly with distance from the source (Fig. 6-5 and 6-6). The highest particle trap doses of the mid canopy samples were observed predominantly for the sample points downwind from the point source (Fig. 6-6). Deposition of particles was observed on the sample points ±45 degrees from the downwind direction but generally in lower densities (Fig. 6-

6). These data from the 6 m deposition trial were fitted to equations 1 and 2 by ln linearizing the data and using least squares regression analysis.

120

Short plant canopy (69-71 cm)

Tall plant canopy (84-91 cm)

Fig. 6-4: The linear regression relationships between the urediniospore trap dose and the Night-Glo® NG-20 zinc sulfide particle trap dose deposited into soybean canopies 1 m from an aerial source for the different soybean canopy plant heights.

121

Table 6-5: Main and interaction effects of plant height, row spacing, within canopy sample height and distance on the normalize proportion of NG-20 particles that were deposited within a soybean plant canopy for the 6 m trial based on a linear mixed model analysis of variance.

Effects df n df d F Probability Plant Height (PH) a 1 3 16.07 0.03 Row Spacing (RS) b 1 7 3.73 0.10

Canopy Sample Height (CH) c 2 28 329.17 < 0.01 Distance from Source (DS) d 2 83 2.19 0.12 PH * RS 1 7 0.23 0.65

PH * CH 2 28 25.81 < 0.01 RS * CH 2 28 0.50 0.61 PH * RS * CH 2 28 0.54 0.59 PH * DS 2 83 1.20 0.31 RS * DS 2 83 0.64 0.53 PH * RS * DS 2 83 0.09 0.91 CH * DS 4 83 1.18 0.32

PH * CH * DS 4 83 0.61 0.66 RS * CH * DS 4 83 0.26 0.90 PH * RS * CH * DS 4 83 0.13 0.97 a The two soybean canopy height treatments were the classes of short (51 to 61 cm) and tall (84 to 91 cm.) b Row spacing treatments for the soybean plots were 19 and 76 cm. c Within canopy height sample treatments were 0.3, 0.6 and 1ht, relative to the soybean canopy’s average height (ht). d Downwind distance from the source treatments were 2, 4 and 6 m.

122

A B C

D E F

Fig. 6-5: The vertical variation NG-20 particles per cm 2 (± standard error) observed within the soybean canopies for the 6 m trial at 3 sample heights for the plant height class and distance from the source of ( A) short (51 - 61 cm) and 2 m; ( B) short (51 - 61 cm) and 4 m; ( C) short (51 - 61 cm) and 6 m; ( D) tall (84 - 91 cm) and 2 m; ( E) tall (84 - 91 cm) and 4 m; ( F) tall (84 - 91 cm) and 6 m. The within canopy sample points were adjusted relative to the average soybean canopy height (ht) for the heights of low (0.3ht), mid (0.6ht) and top (1.0ht). The x-axis changes with distance from the source, but does not change between the average plant canopy heights at each distance. 122

123

Fig. 6-6: Polar contour plots of the NG-20 particle dry deposition into soybean canopies for the mid level within canopy sample height. Each ‘R’ value represents the 18 separate runs conducted in the trial (Table 3). R1 to R10 are from the average soybean canopy height class of short (51 - 61 cm), and R11 to R18 are from the average soybean canopy height class of tall (84 - 91 cm). Arrows indicate the average wind direction for 1 to 5 min trials.

124

Table 6-6: Parameter estimates from the least squares regression fits of the power law and exponential law models for the relationship between particle deposition trap dose (# per cm 2) and distance from the source.

Power Law Model a Exponential Model b c 2 c 2 Run aP bP R (%) aE bE R (%) 1 9.62 -1.80 0.93 8.88 -0.38 0.79 2 8.47 -1.39 0.95 8.26 -0.40 0.99 3 10.20 -3.51 0.98 9.61 -0.99 1.00 4 10.67 -2.32 0.98 10.17 -0.62 0.91 5 10.53 -3.01 0.95 9.83 -0.80 0.86 6 8.61 -2.43 0.99 8.11 -0.66 0.95 7 10.68 -2.79 0.90 9.99 -0.73 0.79 8 10.23 -2.14 0.97 9.75 -0.57 0.90 9 8.02 -1.70 0.99 7.72 -0.47 0.99 10 9.98 -2.13 1.00 9.58 -0.58 0.98 11 8.39 -1.24 0.89 8.08 -0.32 0.78 12 9.38 -1.27 0.99 9.15 -0.35 0.99 13 11.95 -4.76 0.99 11.01 -1.30 0.97 14 6.85 -0.90 0.64 6.56 -0.22 0.49 15 9.07 -1.55 0.99 8.80 -0.43 0.99 16 8.44 -1.78 0.96 8.16 -0.50 0.99 17 10.26 -2.23 1.00 9.83 -0.61 0.98 18 8.54 -1.85 0.99 8.19 -0.51 0.98 a -bP y = a P s b y = a E exp(-bEs) c 2 aP = the number of particles per cm at x = 1 m d 2 aE = the number of particles per cm at x = 0 m

125

The values of parameters b P and b E were frequently greater for those experiments with average wind speeds and gusts of 5 m/s or higher (Table 6-4 and 6-6). In general, the power law model provided a better fit (average R 2 = 0.95) for the data than the exponential model (average R 2 = 0.91). The average parameter estimates for the power law model were a P = 9.44 and b P = 2.01, and for the exponential model the average parameter estimates were a E = 8.98 and b E = 0.58.

DISCUSSION

Knowledge about the dry deposition of P. pachyrhizi urediniospores into soybean

canopies is important for understanding the local spread and development of the disease.

The current study examined multiple factors effecting the dry deposition of

urediniospores into soybean canopies in a field environment. The goals of this study

were to (i) examine the relationship between the dry deposition of urediniospores and

NG-20 particles into soybean canopies, (ii) estimate the proportion of urediniospores that

are being deposited into the lower and middle canopy sections, an area of the canopy

where symptoms are first observed and the environment is potentially more conducive to

disease development than higher in the canopy, and (iii) analyze the effects of wind speed

on the deposition gradients of particles in soybean canopies.

Throughout the present study, NG-20 particles were used to simulate the dry

deposition of P. pachyrhizi urediniospores into the soybean canopies. The NG-20

particles were selected because they are of similar size (median = 20 µm) to

urediniospores (diameter range: 13 to 38 µm) (Fig. 6-1), large quantities of the particles

were readily available for each run, they were easily identifiable and because they did not

produce alternative sources of inoculum. Also, it had been indicated that urediniospores

126 and NG-20 particles behave similarly during their escape from soybean canopies [35]. In examining the dry deposition of both urediniospores and particles over a 1 m distance, strong linear relationships (R 2 = 0.76 and 0.64) were recorded for soybean canopies with heights ranging from 69 - 71 (short) cm and 84 - 91 (tall) cm and with 19 and 76 cm row spacing (Fig. 6-4). Statistical analysis of the data revealed that the slopes of the regression equations for the two plant canopy heights were different. One possible reason for this separation of the data points could be differences in wind speeds during experimental runs (Table 6-2). The tall canopy runs had wind speeds that varied from 0.4 to 3.6 m/s, but the short canopy wind speeds only ranged from 1.7 to 2.1 m/s. Only one of the tall canopy sample runs was within the recorded range of the short canopy runs.

The recorded decreases and increases in wind speed could alter the dry deposition of the spores and particles by either depositing them closer to the source or at distances beyond the 1 m sample area, respectively. These changes in deposition trends, along with the initial differences in the concentration of spores and particles, could be the reason why generally higher numbers of particles compared to spores were observed in the tall canopy versus the short canopy. Despite these differences between the plant canopies heights, the results suggest that trends observed for the dry deposition of NG-20 particles will reliably indicate the dry deposition tendencies of P. pachyrhizi urediniospores.

The dry deposition of NG-20 particles into soybean canopies was examined at 2,

4 and 6 m distances from a point source (Fig. 6-3 and 6-6). Average plant height, within

canopy sample height and their interactions were observed to have significant effects on

the proportion of particles trapped on the slides transformed relative to the top canopy

sample (Table 6-5). As the soybean canopy increased in height the proportion of

127 particles that were deposited into the lower and middle sections of the canopy decreased

(Fig. 6-5 and 6-6). A similar trend was observed in the vertical variation of Ventura inaequalis ascospores within an apple orchard [1]. Aylor reported that the concentration of spores in the air decreased rapidly with an increase in height above an ascospore ground source. The decrease in ascospore concentration with height was attributed to the rapid increase in wind speed and turbulent eddy diffusivity with height above the ground.

In the present study, however, the decrease in particle trap dose with decreasing height is more likely related to the filtration of spores by plant tissue and not turbulence [12]. In general, wind speed and turbulence rapidly decrease within field crop canopies, so much so that sedimentation is usually the primary dry deposition process within the canopy [6,

25]. The decrease in the number of particles per cm 2 observed in the lower and middle

portions of the tall soybean canopy compared to the short canopy can be related to the

documented increase in LAI values (Table 6-3). A larger LAI value implies that more

plant tissue is present to filter the particles being dry deposited throughout the canopy

than lower LAI values.

Despite the decrease in particle dry deposition concentrations with height, there

were still significant proportions of particles being collected in the low and mid canopy

samples. The average proportions of particles per cm 2 recorded at the low and mid

canopy samples, respectively, were 0.46 and 0.67 for the short soybean canopy height

and 0.16 and 0.27 for the tall soybean canopy height. These results indicate that during

dry deposition events close to a source, significant proportions of viable urediniospores

can be deposited into the lower and middle canopy sections. It is also important to note

that this effect is consistent for the two row spacing treatments, which suggests that this

128 cultural practice may not impact the spread of disease locally and within a soybean field

(Table 6-5).

The deposition intensity of fungal pathogens downwind from an inoculum source has been observed to decrease rapidly with distance [4, 11, 14, 26]. In the current study, a similar trend was apparent in the mean number of particles per cm 2 deposited into the

soybean canopy at different distances from the release point (Fig. 6-5 and 6-6).

Deposition and dispersal gradients similar to those observed here have been described by

many different empirical equations with the two most common being the power law

(equation 6.1) and exponential law models (equation 6.2) [4, 5, 8, 11, 17, 21, 24]. The

particle deposition gradients measured in the current study on average had more of the

variation explained by the power law model than by the exponential law model. Similar

model fit results have been observed for other fungi [4, 15, 31]; however, in multiple

experiments the deposition gradients of other pathogens have often been fitted equally

well by either model [13]. The better fit of the power law model in the present study can

be explained by its ability to more accurately estimate high inoculum densities closer to

the source, which would be expected when approximately 1.3 billion particles were

released from such a small area.

The parameters of the empirical models are used to describe different aspects of

the spore dispersal process. The ‘a’ parameter provides insight into the strength of a

source and is useful in comparing two separate dispersal gradients [8, 24, 29]. However,

in this study the source strength is kept relatively constant, so even though some variation

is present in the estimated ‘a’ parameters for both models, it is not plausible to use it for

comparison. The ‘b’ parameters can be used to quantify the steepness of the deposition

129 gradient [11, 24, 29]. A low value for ‘b’ indicates that the particles are dispersed at relatively vast distances from the source and a large ‘b’ value suggests that a majority of the particle deposition is occurring near the source. A wide range of ‘b’ values were estimated for both models in the current study (Table 6-5). This range of the estimated

‘b’ parameter values for both models could be reasonably explained by the average wind speed recorded for each run (Table 6-2 and 6-5). As the average wind speed increased the value of ‘b’ decreased, indicating that the concentration of particles deposited closer to the source decreased with increasing wind speed. These results suggest that at higher wind speeds, urediniospores that escape above the canopy will be deposited greater distances from their source than at lower wind speeds.

The estimated ‘b’ parameters from both models indicate that large quantities of urediniospores are required for a dry deposition event at a 1 km distance downwind from the source. For example, if 1.3 billion urediniospores were released above the canopy from a point source with a constant wind speed and direction, then considerably less than

0.05 spores per cm 2 will be dry deposited 1 km from the source using the average model

parameter values derived from measurements in this study. Even if we were only to use

the parameter estimates from the models in which the run wind speed was 4.0 m/s or

greater then only the power law model indicates that 1 spore per cm 2 would be dry deposited 1 km downwind from the source. Of the spores that are dry deposited 1 km downwind from the source, at most 67% of them will be deposited into the lower and middle portions of the canopy where the environment is more conducive for disease development than upper portions. A majority of the 1.3 billion urediniospores would be deposited at distances considerably less than 1 km, with events notably greater than 1

130 spore per cm 2 occurring at distances of 10 and 20 m for the exponential and power law model, respectively. The presence of 1.3 billion urediniospores above a field would require a sizeable number of infected soybean plants. It has been estimated that approximately 6 million spores are released per day from a heavily infected soybean plant [19, 27, 34]. Of these 6 million spores about 26% will escape from open canopies and 10% from closed canopies [35]. So, we can estimate that from a single plant roughly

600,000 to 1.5 million spores will escape from the canopy in a single day. This means that around 100,000 to 200,000 plants, roughly half a hectare (1 acre), will need to be heavily infected in order to produce an inoculum source of 1.3 billion urediniospores. In general, these examples imply that a majority of the urediniospores released above a soybean canopy will predominately be dry deposited close to the source ( ≤ 20 m),

especially early during the epidemic when low numbers of urediniospores are produced.

Plant canopy height, leaf area and wind speed were indentified as the most

important factors in determining the dry deposition and dispersal of urediniospores from

point sources above a field into soybean canopies. The likelihood that urediniospores

will be deposited into the lower two thirds of a soybean canopy, an area where initial

infections are often first observed and the environment is understood to be more suitable

for infection, is greater for younger soybeans with less leaf area then for more mature

soybeans with more leaf area. However, significant amounts of urediniospores still have

a reasonable chance (0.16 to 0.27) of being dry deposited into the lower sections of these

mature canopies. The average wind speed and prevailing wind direction will be

important factors in determining the local dispersal of urediniospores from a point source.

Higher wind speeds will spread urediniospores relatively farther than low wind speeds,

131 however, a majority of the urediniospores that are deposited will land within the first few meters from the source [22]. These results imply that effective infield and local management of soybean rust is highly dependent upon early detection of the inoculum source. The longer a source is allowed to sporulate, the more likely it is to expand infield, and increase the probability of large urediniospore concentrations being exposed to high wind speed events that will disperse inoculum to other commercial fields within the county area.

132

LITERATURE CITED

1. Aylor, D. E. 1995. Vertical variation of aerial concentration of Venturia inaequalis ascospores in an apple orchard . Phytopathology 85:175-181.

2. Aylor, D. E. 1999. Biophysical scaling and the passive dispersal of fungus spores: relationship to integrated pest management strategies . Ag. and For. Meteorol. 97:275 - 292.

3. Aylor, D. E. 1986. A framework for examining inter-regional aerial transport of fungal spores . Ag. and For. Meteorol. 38:263-288.

4. Aylor, D. E. 1987. Deposition gradients of urediniospores of Puccinia recondita near a source . Phytopathology 77:1442-1448.

5. Aylor, D. E. 1989. Aerial spore dispersal close to a focus of disease. Ag. and For. Meteorol. 47:109-122.

6. Aylor, D. E. 1975. Deposition of particles in a plant canopy . J. of Applied Meteorol. 14:52-57.

7. Bradley, C. 2007. Overview of soybean rust in North America. 2007 National Soybean Rust Symposium. Louisville, KY. December 12 -14.

8. Campbell, C. L. and Madden, L. V. 1990. Introduction to Plant Disease Epidemiology. John Wiley & Sons. New York.

9. Chamberlain, A. C. 1967. Deposition of particles to natural surfaces. Airborne Microbes. Symposium of the Society for General Microbiology . P.H. Gregory and Monteith, J.L., Editors. Cambridge University Press: Cambridge. p. 138-164.

10. Dorrance, A. E., Draper, M. A., and Hershman, D. E. eds. 2008. Using foliar fungicides to manage soybean rust. Land-Grand Universities Cooperation NCERA-208 and OMAF, Bulletin SR-2008.

11. Esker, P. D., Sparks, A. H., Antony, G., Bates, M., Dall'Acqua, W., Frank, E. E., Huebel, L., Segovia, V., and Garret, K. A. 2007. Ecology and Epidemiology in R: Modeling dispersal gradients . The Plant Health Instructor DOI:10.1094/PHI-A- 2007-1226-03.

12. Ferrandino, F. J. 2008. Effect of crop growth and canopy filtration on the dynamics of plant disease epidemics spread by aerially dispersed spores. Phytopathology 98:492-503.

13. Fitt, B. D. L., Gregory, P. H., Todd, A. S., McCartney, H. A., and Macdonald, O. C. 1987. Spore dispersal and plant disease gradients; a comparison between tow empirical models . J. Phytopathol. 118:227-242.

133

14. Frantzen, J. 1994. An epidemiological study of Puccinia punctiformis (Str.) Rohl as a stepping-stone to the biological control of Cirsium arvense (L.) Scop. New Phytol. 127:147-154.

15. Fried, P. M., MacKenzie, D. R., and Nelson, R. R. 1979. Dispersal gradients from a point source of Erysiphe graminis f. sp. tritici , on Chancellor winter wheat and four multilines. Phytopathol. Z. 95:140-150.

16. Gregory, P. H. 1973. Microbiology of the Atmosphere, ed. N. Polunin. Leonard Hill Books. Aylesbury. 377.

17. Gregory, P. H. 1968. Interpreting plant disease dispersal gradients . An. Review of Phytopathology 6:189-212.

18. Isakeit, T., Miller, M. E., Saldana, R., Barnes, L. W., McKemy, J. M., Palm, M. E., Zeller, K. A., DeVries-Paterson, R., and Levy, L. 2006. First Report of rust caused by Phakopsora pachyrhizi on soybean and kudzu in Texas . Plant Dis. 90:971.

19. Isard, S. A., Gage, S. H., Comtois, P., and Russo, J. M. 2005. Principles of the atmospheric pathway for invasive species applied to soybean rust . BioScience 55:851-861.

20. Isard, S. A. and Gage, S. H. 2001. Flow of life in the atmosphere: An airscape approach to understanding invasive organisms. Michigan State University Press. East Lansing.

21. Kiyosawa, S. and Shiyomi, M. 1972. A theoretical evaluation of the effect of mixing resistant variety with susceptible variety for controlling plant diseases. Ann. Phytopathol. Soc. Jpn. 38:41-51.

22. Legg, B. J. 1983. Movement of plant pathogens in the crop canopy . Phil. Trans. R. Soc. Lond. B 302:559-574.

23. Littell, R. C., Milliken, G. A., Stroup, W. W., Wolfinger, R. D., and Schabenberger, O. 2006. SAS for mixed models, 2 ed. SAS Institute, Inc. Cary, N. C.

24. McCartney, H. A., Fitt, B. D. L., and West, J. S. 2006. Dispersal of foliar plant pathogens: Mechanisms, gradients and spatial patterns, 2nd ed. The Epidemiology of Plant Diseases, ed. B.M. Cooke, Jones, D.G., and Kaye, B. Springer.

25. McCartney, H. A. and Aylor, D. E. 1987. Relative contributions of sedimentation and impaction to depositoin of particles in a crop canopy . Ag. and For. Meteorol. 40:343-358.

26. McCartney, H. A. and Fitt, B. D. L. 1985. Construction of dispersal models. Advances in Plant Pathology. Vol. 3. Academic Press Inc. London.

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27. Melching, J. S., Dowler, W. M., Koogle, D. L., and Royer, M. H. 1989. Effects of duration, frequency, and temperature of leaf wetness periods on soybean rust. Plant Dis. 73:117-122.

28. Miles, M. R., Frederick, R. D., and Hartman, G. L. 2003. Soybean rust: is the U.S. soybean crop at risk. APSnet Feature. ( http://www.apsnet.org/online/feature/rust/) .

29. Paysour, R. E. and Fry, W. E. 1983. Interplot interference: A model for planning field experiments with aerially disseminated pathogens . Phytopathology 73:1014- 1020.

30. Pivonia, S., Yang, X. B., and Pan, Z. 2005. Assessment of epidemic potential of soybean rust in the United States . Plant Dis. 89:678-682.

31. Roelfs, A. P. and Martell, L. B. 1984. Uredospore dispersal from a point source within a wheat canopy . Phytopathology 74:1262-1267.

32. Sconyers, L. E., Kermerait, R. C., Brock, J., Phillips, D. V., Jost, P. H., Sikora, E. J., Gutierrez-Estrada, A., Mueller, J. D., Marois, J. J., Wright, D. L., and Harmon, C. L. 2006. Asian soybean rust development in 2005: A perspective from the Southeastern United States. APSnet Feature. (http://www.apsnet.org/online/feature/sbr/) .

33. Wright, D. L., Rich, J. R., Marois, J. J., Sprenkel, R. K., and Ferrell, J. A. 2006. Soybean production in Florida. Florida Cooperative Extension Service, Institute of Food and Agricultural Sciences. ( http://edis.ifas.ufl.edu/AG185 )

34. Yang, X. B., Royer, M. H., Tschanz, A. T., and Tsia, B. Y. 1990. Analysis and quantification of soybean rust epidemics from seventy-three sequential planting experiments. . Phytopathology 80:1421-1427.

35. Zidek, J. M. 2007. Phakopsora pachyrhizi urediniospore escape from a soybean canopy. Ecology Program. Master of Science. The Pennsylvania State University: University Park. p. 115.

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Chapter 7: SUMMARY

The deposition of Phakopsora pachyrhizi urediniospores into soybean canopies is an important step in aerial dispersal of this pathogen. Past research has noted that many environmental (wind speed, rainfall intensity), physical (spore size, leaf characteristics) and cultural (row spacing, irrigation) factors can influence the deposition processes of spores and particles from the atmosphere [2-4, 7]. In the work presented here, we observed significant proportions of P. pachyrhizi urediniospores were deposited into the lower sections of soybean canopies, an area where the micro-environment is more conducive for infection, by both wet (rainfall washout) and dry

(sedimentation/impaction) deposition processes. We also observed that a number of environmental and cultural factors were important in determining the proportion of urediniospores deposited at different levels within the soybean canopy for both wet and dry deposition.

The wet deposition of urediniospores into soybean canopies was influenced by rainfall intensity and soybean canopy height. Varying the soybean row spacing distance did not produce any noticeable affects on the wet deposition of urediniospores throughout the canopy. Larger proportions of urediniospores were wet deposited into the middle sections of the soybean canopies at high rainfall intensity events (75 mm/hr) compared to low intensity events (15 mm/hr) (Fig. 4-1). Also, denser canopy structure (LAI values of

7 to 13) associated with tall soybean canopies (71 to 85 cm) retained larger proportions of urediniospores in the upper canopy sections (Fig 4-2) than the less dense canopies. The effect of the canopy structure on reducing the proportion of wet deposited urediniospores in the low and middle canopy was minimal for high intensity rainfall events and more

136 apparent in low intensity rainfall events (Fig. 4-2). These results suggest that the penetration of urediniospores into the lower sections of soybean canopies will be greater early in the season and during high intensity rainfall events than later in season during low intensity rainfall events.

The retention of wet deposited urediniospores on the soybean leaf tissue is an important factor in determining the number of infections that will occur on a soybean plant. In general, a majority of the particles in the atmosphere are washed out within the first 20 min of a rainfall event. This suggests that there is a high probability of wet deposited urediniospores being exposed to periods of prolonged rainfall. Data from the present study indicates that prolonged rainfall events will reduce the number of urediniospores deposited on the leaf tissue throughout the soybean canopy (Table 5-2).

The average number of uredinia per cm 2 measured on the middle soybean plant leaves after 1 and 30 min of prolonged rainfall events were 0.17 and 0.03. Assuming that 1 urediniospore produces 1 uredinia and that the average leaf area of the middle canopy section is 15,000 cm 2 per m 2 of ground [9], then the results from Chapter 5 suggests that

approximately 100 and 700 viable urediniospores will need to be wet deposited per m 2 to produce 1 uredinia per m 2 in the middle section of a soybean crop canopy after 1 and 30 min of prolonged rainfall, respectively. These results imply that it is possible for uredinia to be produced on the soybean leaves even after a 30 min prolonged rainfall period. The reductions in the number of uredinia per leaf throughout the canopy associated with prolonged rainfall will delay, but not limit, the epidemic onset of the disease in untreated soybean fields.

137

The dry deposition of P. pachyrhizi urediniospores into soybean canopies was simulated with a Night-Glo® NG-20 zinc sulfide particle (DayGlo® Color Corp.,

Cleveland, OH). A linear relationship (R 2 = 0.74) was detected between urediniospore and particle trap doses (per cm 2) (Fig. 6-4), indicating that trends observed for particle

deposition will also be apparent for P. pachyrhizi urediniospores. Plant canopy height

and wind speed were the main factors that influenced the dry deposition trends of

particles released above the soybean canopy (Table 6-5). As noted in the wet deposition

studies, the cultural practice of varying soybean row spacing distance did not affect dry

deposition distribution trends observed in this study. The proportion of particles recorded

in the low and middle canopy samples decreased with an increase in the soybean canopy

height, which again is similar to the trend noted for wet deposition process. Despite this

decrease, there were still significant proportions of particles recorded at the low and

middle canopy sample heights (Fig 6-5). Wind speed was an important factor in the

deposition intensity of particles with varying distances from the above canopy source.

The parameter estimates calculated from the exponential law and power law models

(Table 6-6) suggest that higher wind speeds will deposit larger quantities of

urediniospores at relatively farther distances downwind from the above canopy source.

However, both models suggest that at high wind speeds (5 m/s) a vast majority of the

urediniospores will be deposited within 20 m of the above canopy source. It is apparent

from these results that urediniospores in the air above the canopy can effectively be dry

deposited into the lower sections of the canopy close to their source.

Overall, we observed that both wet and dry deposition processes have the

capability to deposit urediniospores into the lower sections of soybean canopies.

138

Increases in leaf area and soybean canopy height reduced the effectiveness of the dry and wet deposition processes into the lower portions of the soybean canopy. For these reasons, earlier planted soybeans in regions where outside inoculum is limited (i.e. north- central U.S.), may not only escape the disease by maturing before viable urediniospores reach the region, but may also delay the epidemic onset of the disease by reducing the initial number of possible infections that can occur from a urediniospore deposition event.

This delay in the epidemic onset may allow the soybeans to mature before significant crop losses can occur from the disease.

The studies discussed in this thesis examined the deposition of urediniospores under specific environmental and cultural conditions. Past research and theoretical models indicate that other factors not examined in these studies will also have a significant effect on the deposition and spread of urediniospores within soybean canopies.

For example, it is common for fungal spores to be re-dispersed and deposited within a crop canopy by rain droplet splashes [5, 6, 8]. Preliminary investigations indicated that splash dispersal of urediniospores did not occur for the rainfall simulation conditions conducted in these studies. However, wind driven rain has been observed to spread soil particles into the lower sections of the soybean canopy, especially along the edges of the field. A practical experiment examining the splash dispersal of urediniospores within the soybean canopy may show that a proportion of urediniospores washed off the leaf surface or deposited on the soil can be re-deposited within the lower portion of the soybean canopy.

Uredinia production following a dry deposition event is an important process for disease development within a field and locally. The present research indicated that

139 significant concentrations of urediniospores can be present in the lower sections of a canopy during a dry deposition event; however, information about the trapping efficiency of the soybean leaves is absent [7]. The physical characteristics of the leaf will be important in determining the trapping efficiency of soybean leaves and may be useful in selecting varietal leaf characteristics to manage this disease [1]. Another important factor in the development of uredinia from dry deposition events will be the survival of the urediniospores on soybean leaf tissue. Urediniospores deposited into the low and middle canopy sections are believed to be in an environment conducive for infection process to begin. However, it may be several days before a urediniospore is exposed to environmental conditions that will stimulate the infection process. The ability of a urediniospore to survive and remain on the leaf surface during this time period will be critical in determining the number of uredinia that will be produced from a dry deposition event.

The deposition of fungal spores into field crop canopies is a dynamic process that is dependent upon many environmental and cultural factors. The results in this thesis can be used to understand the deposition process of P. pachyrhizi urediniospores in a soybean field and enhance the current estimations of urediniospore deposition in models predicting disease dispersal. As indicated above, there are still multiple aspects of the deposition processes that have not been addressed, and continued research on these processes will improve our understanding about fungal spore spread and disease development.

140

LITERATURE CITED

2. Aylor, D. E. 1986. A framework for examining inter-regional aerial transport of fungal spores . Ag. and For. Meteorol. 38:263-288.

3. Chamberlain, A. C. 1967. Deposition of particles to natural surfaces. Airborne Microbes. Symposium of the Society for General Microbiology . P.H. Gregory and Monteith, J.L., Editors. Cambridge University Press: Cambridge. p. 138-164.

4. Chamberlain, A. C. 1966. Transport of lycopodium spores and other small particles to rough surfaces. Proceedings of the Royal Society of London. Series A, Mathematical and Physical Sciences. p. 45-70.

5. Fitt, B. D. L., McCartney, H. A., and Walklate, P. J. 1989. The role of rain in the dispersal of pathogen inoculum . An. Review of Phytopathology 27:241-270.

6. Fitt, B. D. L., Walklate, P. J., McCartney, H. A., Bainbridge, A., Creighton, N. F., Hirst, J. M., Lacey, M. E., and Legg, B. J. 1986. A rain tower and wind tunnel for studying the dispersal of plant pathogens by rain and wind . Ann. of App. Bio. 109:661-667.

7. Gregory, P. H. 1973. Microbiology of the Atmosphere, ed. N. Polunin. Leonard Hill Books. Aylesbury. 377.

8. Madden, L. V., Wilson, L. L., Yang, X., and Ellis, M. A. 1992. Splash dispersal of Colletotrichum acutatum and Phytophthora cactorum by short-duration simulated rains . Plant Pathology 41:427-436.

9. Zidek, J. M. 2007. Phakopsora pachyrhizi urediniospore escape from a soybean canopy. Ecology Program. Master of Science. The Pennsylvania State University: University Park. p. 115.

141

Appendix. CHAPTERS 4 AND 6 DETAILS AND DATA

CHAPTER 4:

Fig. A-1: Box plot of the raw data from the 4 repetitions of the simulated urediniospore rainfall deposition (Chapter 4) into soybeans with a 19 cm row spacing and height range of 30 to 38 cm at a 75 mm/hr rainfall intensity. The outlier in repetition 4 was removed from the data set in Fig. 4-2.

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CHAPTER 6:

Table A-1: The weather stations and sensors used to collect environmental data during the dry deposition runs. Station Sensor Measurement Range Accuracy WatchDog® 700 Wind Speed 0 - 175 mph ± 5% Wind Direction 2º increments ± 7º Temperature (-)20º to 70ºC ± 0.6ºC Relative Humidity 20% too 100% ± 3% Rainfall 0.25 cm ± 2%

Campbell® Scientific WindSonic Ultrasonic 0.01 to 60m/s ± 2% Wind Sensor

Fig. A-2: The linear relationship between the wind speed data record by the Model 700 WatchDog® weather station and Campbell® Scientific, WindSonic Ultrasonic wind sensor in Pennsylvania.

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Nicholas S. Dufault 206 Buckhout Laboratory, Penn. State University, University Park, PA 16802 - [email protected]

Education 2004...... M. S. in Plant Pathology The Pennsylvania State University

2001...... B. A. in Biology Saint John’s University

Professional Experience

2001-2004 ...... Graduate Research Assistant The Pennsylvania State University

1998-2001 ...... Computer Laboratory Assistant Saint John’s University

Honors, Awards, Memberships, Etc.:

Popp Scholarship Award, Penn State Univ. (2003, 2007) Tag Along Fund, College of Agricultural Sciences, Penn State Univ. (2005) American Phytopath. Society Graduate Student Travel Award (2006) Florida Phytopath. Society, 3 rd Place Graduate Student Paper Competition (2007) Penn State Univ. College of Agricultural Sciences Student Travel Award (2007)

President and Founder of the Plant Pathology Association at Penn State (2004, 2005) Vice Chair of the American Phytopath. Society Grad Student Committee (2006-2007) Chair of the American Phytopath. Society Grad Student Committee (2007-2008)

Epidemiology Committee of American Phytopath. Society (2005 – 2008) Penn State Department of Plant Pathology Extension Committee (2002) Penn State Department of Plant Pathology Instruction & Curriculum Committee (2006)

Publications:

Dufault, N. S., De Wolf, E. D., Lipps, P. E., and Madden, L. V. 2006. Role of temperature and moisture in the production and maturation of Gibberella zeae perithecia. Plant Dis. 90:637-644.

Isard, S. A., Dufault, N. S., Miles, M. R., Hartman, G. L., Russo, J. M., De Wolf, E. D., and Morel, W. 2006. The effect of solar irradiance on the mortality of Phakopsora pachyrhizi urediniospores. Plant Dis. 90:941-945.

Tesfaye, M., Dufault, N. S., Dornbusch, M. R., Allan, D. L., Vance, C. P., and Samac, D. A. 2003. Influence of enhanced malate dehydrogenase expression by alfalfa on diversity of rhizobacteria and soil nutrient availability. Soil Biol. Biochem. 35:1103- 1113.