Influence of Rainfall Patterns on the Development of Fusarium Head Blight, Accumulation of Deoxynivalenol and Fungicide Efficacy THESIS

Transcription

1 Influence of Rainfall Patterns on the Development of Fusarium Head Blight, Accumulation of Deoxynivalenol and Fungicide Efficacy THESIS Presented in Partial Fulfillment of the Requirements for the Degree Master of Science in the Graduate School of The Ohio State University By Kelsey Faith Andersen Graduate Program in Plant Pathology The Ohio State University 2013 Master's Examination Committee: Pierce A. Paul, Advisor Laurence V. Madden Anne E. Dorrance Clay H. Sneller

2 Copyrighted by Kelsey Faith Andersen 2013

3 ABSTRACT Fusarium head blight (FHB) is a disease of wheat and other small grains primarily caused by the fungal pathogen Fusarium graminearum. It has been well established that moisture around anthesis is critical for FHB development and wheat grain contamination with deoxynivalenol (DON), a mycotoxin produced by F. graminearum. However, it is unclear how moisture patterns during this period affect these responses and the relationships between them. Because of this, the effects of several discontinuous moisture patterns on FHB development and DON accumulation were explored. Furthermore, because conditions of high moisture at anthesis are most conducive for disease development, it is during these times where a single application of a fungicide is most warranted. The effects of rainfall on the efficacy and residual life of 19% tebuconazole + 19% prothioconazole (474 ml ha -1 ) when applied to wheat spikes at anthesis, in combination with the non-ionic surfactant Induce (0.125% v/v), was also evaluated. First, two field and two mist chamber experiments were conducted to quantify the effects of pre-anthesis rainfall and post-anthesis mist, respectively, on FHB index (IND) and DON. For both sets of experiments, four rainfall or mist ii

4 treatments, one continuous and three intermittent, were applied during a 7- or -8 -day window before or after anthesis, plus an untreated check. Intermittent moisture treatments received similar duration and amounts of moisture, but the moisture pattern during the treatment window varied among the treatments. Based on results from linear mixed model analyses, rainfall and mist treatment had significant effects on arcsine-transformed IND (arcind) and log-transformed DON (logdon) in all experiments. The continuous 7- or 8-day moisture treatments consistently had the highest mean IND and DON levels, but several of the 4-day intermittent moisture treatments were not significantly different from the continuous moisture treatment. Mixed model regression analyses showed that there was a significant, positive relationship between IND and logdon in all experiments. Moisture treatment did not have a significant effect of the regression slopes (rate of logdon increase per unit increase in IND), but affected the intercepts in all cases. The height of the regression line (level of logdon at a fixed level of IND) was consistently higher for one of the intermittent rainfall/mist treatments (two days of mist or rainfall at the beginning and end of the treatment window) than the continuous moisture treatment. Generalized linear mixed models were used to estimate the risk of IND and DON exceeding critical thresholds, showing similar results in terms of estimated probabilities. Several of the intermittent treatments has comparable estimated probabilities of infection, IND > 10% and DON > 2, 5, and 10 ppm to the continuous rainfall and mist treatments. iii

5 Second, Three field experiments were conducted during 2012 and 2013 in Wooster, Ohio. Simulated rainfall of a fixed intensity and duration was applied to separate plots at five different times after the fungicide treatment (0, 60, 105, 150, and 195 minutes). Spike samples were collected at 4-day intervals after fungicide application and assayed for tebuconazole residue. A similar set of greenhouse experiments were conducted using six post-fungicide-application rainfall timing treatments (0, 15, 30, 60, 120, and 180 minutes). All experiments were inoculated at anthesis with spores of F. graminearum, and FHB index (IND) and DON were quantified. In four of the five experiments, all fungicide-treated experimental units (EUs) had significantly lower mean IND and DON than the untreated check, regardless of rainfall treatment. Among rainfall treatments, EUs that received the earliest rains tended to have the highest mean IND and DON, but were generally not significantly different from EUs that received later rain or fungicide without rain. In both years, fungicide residue on wheat spikes decreased rapidly with time after application, but the rate of reduction varied between years. Rainfall treatment did not have a significant effect on the rate of residue reduction or the level of residue at a fixed sampling time after fungicide application. In this study, 19% tebuconazole + 19% prothioconazole was fairly rainfast for a fixed set of rainfall characteristics, and tebuconazole residue did not persist very long after application on wheat spikes. iv

6 ACKNOWLEDGEMENTS There are many individuals without whom this research would not have been possible. First and foremost, I would like to thank my major advisor, Dr. Pierce A. Paul for his patient guidance throughout this process. He has proven to be an outstanding advisor who has continually challenged me to become a more thoughtful and competent scientist. I would also like to thank the other members of my SAC committee, Drs. Larry Madden, Anne Dorrance, and Clay Sneller for their assistance and support with many facets of these projects. Additionally, I would like to thank Dr. Katelyn Willyerd who devoted much time to educating me, Dr. Antonio Cabrera who mentored me in the molecular biology techniques utilized in Chapter 2, and Leslie Morris (USDA) who lead the fungicide residue quantification portion of Chapter 3. I would also like to specially thank Lee Wilson, Bob James and the farm crew, led by William Bardall, for technically supporting this work. Because applied plant pathology is a team sport, it would be remiss of me not to mention the other members of the Ohio State Cereal Disease Epidemiology Lab, namely Jorge David, Daisy, Kristen, and Jessica for their continued help and encouragement. Additionally, I thank each member of the Department of Plant v

7 Pathology at Ohio State University. I am truly honored to have been surrounded by such brilliant and energetic people during my time in Columbus and Wooster and I am confident that I have not only made colleagues here, but lasting friendships. I would like to thank my own personal team of warriors who have provided unconditional support of my dreams through every phase of my development. First, my parents (Roger and Sherlyn Andersen) and my brother (Brian Andersen). I would also like to thank Melissa, Dana, and Mary Jane for their guidance and friendship over the last few years. Finally, I could have accomplished nothing without the presence of a power greater than myself in my life, of which I have limited understanding, but to which, I owe everything. vi

8 VITA Graduated Shore Regional High School B.A. Biology, Honors in Biology Lafayette College 2011 to present... Graduate Research Associate, Department of Plant Pathology, The Ohio State University PUBLICATIONS Andersen, K. and Ospina-Giraldo, M.D Assessment of the effect of temperature on the late blight disease cycle using a detached leaf assay. Journal of the Pennsylvania Academy of Science. 85: Andersen, K. and Ospina-Giraldo, M.D Relative disease susceptibility of cultivated varieties of potato to different isolates of Phytophthora infestans. Journal of the Pennsylvania Academy of Science. 85: Major Field: Plant Pathology FIELD OF STUDY vii

9 TABLE OF CONTENTS ABSTRACT... ii ACKNOWLEDGEMENTS... v VITA... vii TABLE OF CONTENTS... viii LIST OF TABLES... x LIST OF FIGURES... xii CHAPTER 1 : Introduction... 1 CHAPTER 2 : Fusarium Head Blight Development and Deoxynivalenol Accumulation in Soft Red Winter Wheat as Influenced by Moisture Patterns Before or After Anthesis INTRODUCTION MATERIALS AND METHODS RESULTS DISCUSSION REFERENCES viii

10 CHAPTER 3 : Rainfastness of Prothioconazole+Tebuconazole for Fusarium Head Blight and Deoxynivalenol Management in Soft Red Winter Wheat INTRODUCTION MATERIALS AND METHODS RESULTS DISCUSSION REFERENCES BIBLIOGRAPHY ix

11 LIST OF TABLES Table 2.1. Probability values (levels of significance) from linear mixed model analyses (global tests) of pre-anthesis rainfall treatment effects on arcsinetransformed Fusarium head blight IND, INC, and FDK and log-transformed DON, along with probability values for pairwise comparisons of treatment means using linear contrast for field experiments conducted in Wooster, Ohio in 2011, 2012, and Table 2.2. Probability value (level of significance) from linear mixed model analyses (global tests) of post-anthesis mist treatment effects on arcsinetransformed Fusarium head blight IND, rind, and log-transformed DON, along with probability values for pairwise comparisons of treatment means using linear contrast for spray- and point-inoculated greenhouse experiments conducted in Wooster, Ohio Table 2.3. Regression coefficients from linear mixed models analyses of relationship between Fusarium head blight index (IND) as a continuous covariate x

12 and log-transformed deoxynivalenol content of harvest wheat grain as influenced by pre-anthesis and post-anthesis treatment Table 3.1. Probability values (significance level) from linear mixed model analyses of the effect of rainfall treatments on Fusarium head blight index (Index), incidence, Fusarium damaged kernels, and deoxynivalenol (ppm) control with 19% tebuconazole + 19% prothiconazole fungicide, for each year of field experiments, as well as greenhouse experiments conducted in Ohio Table 3.2. Mean Fusarium head blight (FHB) index and incidence for experiments conducted in 2013 in Wooster, Ohio to evaluate the effect of rainfall treatments on FHB control with 19% tebuconazole + 19% prothioconazole fungicide xi

13 LIST OF FIGURES Figure 2.1. Average weather conditions for each season that field experiments were conducted Figure 2.2. Mean canopy temperature ( C), A, and relative humidity (%), B, in each whole-plot (simulated rainfall treatment) and in an adjacent non-treated field (Ambient) for 2011, 2012, and Figure 2.3. Mean Fusarium head blight (FHB) incidence (A, mean proportion of diseased spike per sample of spikes), FHB index (B, mean proportion of diseased spikelets per spike), Fusarium damaged kernels (C, mean proportion of small, shriveled, discolored kernels in a grain sample), and deoxynivalenol content of harvested grain (D, ppm) for different pre-anthesis rainfall treatments applied to field plots in 2011, 2012, and Figure 2.4 Mean Fusarium head blight index (mean proportion of diseased spikelets per spike) for spray- and point-inoculated greenhouse experiments (A and B, respectively) and mean deoxynivalenol content of harvested grain for both xii

14 experiments (C) for wheat spikes subjected to different post-anthesis mist treatments Figure 2.5. Relationship between Fusarium head blight index (%) and logtransformed DON (DON+1) as influenced by pre-anthesis rainfall treatments (A and B) and post-anthesis mist treatments (C and D) for field experiments conducted in 2011 and 2012 (A) and 2013 (B) and greenhouse spray-inoculated (C) and point-inoculated (D) experiments Figure 2.6. Estimated probability of infection (P_INFECT) and Fusarium head blight index 10% (P_IND10) for field experiments conducted in 2011 and 2012 (A) and 2013 (B) and P_IND10 for assessments made at 10 (P_IND10_10DPI) and 20 (P_IND10_20DPI) in greenhouse spray-inoculated (C) and pointinoculated (D) experiments Figure 2.7. Estimated probability of deoxynivalenol contamination of harvested grain 2, 5 and 10 ppm for different post-anthesis mist treatments Figure 3.1. Mean Fusarium head blight (FHB) index (mean proportion of diseased spikelets per spike), FHB incidence (mean proportion of diseased spike per sample of 100 spikes), Fusarium damaged kernels (mean proportion of small, shriveled, discolored kernels in a grain sample), and Deoxynivalenol xiii

15 content of harvested grain (ppm) from all plots of soft red winter wheat exposed to simulated rainfall treatments at different times following the application of 19% tebuconazole + 19% prothiconazole fungicide Figure 3.2. Mean Fusarium head blight index (A) and deoxynivalenol (B) for different rainfall treatments applied at different times after the application of 19% tebuconazole + 19% prothiconazole fungicide in two different greenhouse experiments Figure 3.3. Temporal change in residue of tebuconazole fungicide active ingredient on wheat spikes from plots exposed to simulated rainfall treatments deployed at different times after the fungicide was applied in field experiment conducted in 2012 (A and C) and 2013 (B and D) xiv

16 CHAPTER 1 : Introduction IMPORTANCE OF FUSARIUM HEAD BLIGHT Fusarium head blight (FHB) is caused predominantly by the fungal pathogen Fusarium graminearum in North America and has affected global wheat (Triticum aestivum) cultivation for over a century. This disease has major implications for growers, livestock producers, processors and public health officials (Sutton 1982). Major losses have been attributed to reduced yield, decreased kernel size, reduced seed quality, and post-harvest mycotoxin contamination of grain (McMullen et al. 1997). Deoxynivalenol, also known as vomitoxin, is the most abundant and problematic mycotoxin produced by F. graminearum. Because of health concerns, the US Food and Drug Administration (FDA) has set a 2 ppm DON threshold for wheat grain and 1 ppm for finished wheat products destined for human consumption (Food and Drug Administration 2010). Understanding and being able to adequately predict DON levels has become of pressing concern because of the harmful associations that this class of mycotoxins has with human and animal health. Deoxynivalenol belongs to the trichothecene class and are generally heat stable and ph tolerant and therefore 1

17 are not broken down during food processing or during mammalian digestion (Rocha et al. 2005). The ability of these toxins to enter eukaryotic cells and inhibit protein synthesis has led to secondary symptomology in humans and mammals, including growth retardation, reproductive disorders, compromised immune function, feed refusal, and emesis (Rocha et al. 2005). Specifically, trichothecenes have been shown to target the 60S ribosomal subunit in mammals and may lead to cellular apoptosis. A gastrointestinal condition known as alimentary toxic aleukia (ATA) is a serious disease associated with trichothecene ingestion, characterized by diarrhea, vomiting, anemia and sometimes death (Foroud and Eudes 2009). Livestock feed contaminated with high levels of DON may result in decreased weight gain, particularly in nonruminant animals (Snijders 1990). Although acute symptoms are usually associated with high-dose exposure, there is also evidence of pathology associated with long term, low dose exposure to DON in the food supply (Snijders 1990). Although FHB appears to be present in all wheat growing regions of the world, epidemic levels were notably severe in the United States during the 1990s (Windels 2000). These major outbreaks were largely attributed to a lack of resistant varieties in Midwestern cropping systems as well as an adoption of reduced tillage practices, a decrease in crop diversity, and an increase in acreage, and unseasonably wet weather. (Dill-Mackay and Jones 2000; Windels 2

18 2000). During that time, economic losses due to FHB and its associated toxins were estimated at nearly three billion dollars (Windels 2000). CAUSAL AGENT Many pathogens are involved in what has been known as the Fusarium head blight disease complex (Parry et al. 1995). Seventeen species have been linked with the development of this disease, with five considered the most significant agents, notably F. graminearum Schwabe (teleomorph, Gibberella zeae) but also F. avenaceum, F. culmorum, F. poae and Microdochium nivale (t. Monographella nivalis) (Pirgozliev et al. 2003; Snijders 1990). F. graminearum is the leading cause of this disease in wheat and barley crops in the United States, Canada and other warmer wheat-growing regions (McMullen et al. 1997; Parry et al. 2005). This fungal species is also the causal agent of maize ear rot and stalk rot, important diseases of corn in terms of both economics and food safety. In cooler regions of the world, such as Europe, F. culmorum appears to be the most important pathogen involved in the development of Fusarium head blight (Parry et al 1995). F. graminearum is an Ascomycete fungus in the order Hypocreales and genus Gibberella. Characteristic morphological structures include macroconidia and chlamydospores in its anamorphic (or asexual) phase and ascospores, borne within perithecia, during its teleomorphic (sexual) phase (Bai and Shaner 1994). Based on variation in disease cycles and environmental requirements, F. 3

19 graminearum was originally divided into two groups, Group 1 and Group 2 (Bai and Shaner 1994; Sutton 1982). Group 1 isolates, more commonly associated with crown diseases of wheat, were subsequently reclassified as a distinct species, Fusarium pseudograminearum (Bushnell et al. 2003). This reclassification was based on growth rates on various media, condial morphology, and the absence of the homothallic production of perithecia in F. pseudograminearum (Aoki and O Donnell 1999). It has been well documented that F. graminearum is widespread in wheat growing regions throughout the world. To answer the question of intercontinential diversity within this species complex, a study was conducted by O Donnell et al. (2000) to compare six single-copy number nuclear genes of isolates collected from six continents. Based on genealogical concordance, the F. graminearum (Fg) clade was divided into seven phylogenetically distinct species (lineages 1-7), which are structured biogeographically (O Donnell et al. 2000). These findings suggest that a great deal of geographical speciation has taken place within this clade. Practically, it is important to understand regional species variation for disease management, breeding and also quarantine regulations (O Donnell et al. 2000). Although a great amount of species divergence exists within the Fg clade, it has been shown that toxin type is not lineage-associated (O Donnell et al. 2000). Toxin evolution within this species complex was further explored by Ward et al. (2002) by examining Tri-cluster genes. They found that the toxin 4

20 chemotypes (NIV, 3ADON and 15ADON) were polyphyletic and did not correspond to the previously described monophyletic lineages. These findings suggest that they may have evolved independently or may have been differentially conserved through balancing selection (Ward et al. 2002). In a subsequent study, O Donnell et al. (2004) explored the effect of mating-type (MAT) genes to further elucidate Fg clade phylogeny and evolutionary history. From this study, two new species lineages were added to the Fg clade, totaling nine within the complex (O Donnell et al. 2004). EPIDEMIOLOGY Several factors play into FHB and DON development in wheat. Although the pathogen is nearly ubiquitous in wheat growing regions, FHB epidemics have historically been sporadic. However, the frequency and severity of FHB epidemics have increased considerably over the last two decades (Windels 2000). This may be due in part to the widespread adoption of minimum tillage practices where wheat is planted into, or close to, fields with maize or wheat crop residue on the soil surface (Dill-Mackey and Jones 2000; Paul et al. 2004). Maize tissue serves as an overwintering site for the pathogen for later dissemination as primary inoculum. Rainfall, relative humidity, and the amount of inoculum present in crop debris from previous seasons play key roles in disease development (Lacey et al. 1999). 5

21 Studying the effect of crop rotation and tillage practices on FHB development, Dill-Macky and Jones (2000) report that disease intensity was greatest in fields where wheat was directly planted into corn residue, when compared to those planted into soybean (Glycine max) residue. A decrease in grain yields and an increase in DON contamination were found in treatments with reduced tillage and wheat following wheat or wheat following corn, when compared to conventional tillage and wheat-after-soybean treatments (Dill- Mackay and Jones 2000). This effect was further described by Schaafsma et al. (2005), who established that disease severity was directly associated with the interaction between previous crop, tillage practice, and field size. They found that large fields with no tillage and corn as a previous crop resulted in highest FHB severity and DON levels (Schaafsma et al. 2005). The association between increased inoculum and decreased tillage is largely due to the ability of F. graminearum to survive outside of the host on crop residue between growing seasons (Khonga and Sutton 1988; Schaafsma et al. 2005). Ascospores, macroconidia, chlamydospores, and hyphal fragments may all serve as infection propagules for primary inoculation, with ascospores and macroconidia being most important because of their aerial dispersal (Sutton 1982; Bai and Shaner 1994). Asexual macroconidia are housed within structures known as sporodochia, while sexually derived ascospores develop within structures known as perithecia. Default et al. (2006) elucidated the effects of both moisture and temperature on the development of perithecia in a natural substrate 6

22 (corn stalks) under artificially controlled environmental conditions. Findings from this study indicated that perithecia developed most rapidly at the highest moisture potential, with moderate temperatures (20-24 ). The rate of perithecial development, as well as perithecial abundance, are limited by high (28 ) and low (12 temperatures as well as low moisture levels. Temperatures above 30 C, and below 8 C, have been shown to completely limit perithecial production under artificial conditions (Dufault et al. 2006). This further indicates the importance of moisture events for the development of perithecia, but does not necessarily explore the subsequent effects on ascospore release or future infection potential. Ascospores are forcibly discharged from asci, borne in perithecia. Dissemination into the air and onto wheat spikes occurs via wind, rain splash, or infested insect vectors (Sutton 1982; Bai and Shaner 1994; Paul et al. 2004). Ascospore release follows specific diurnal patterns and usually favors the early evening, with aerial spore concentrations in wheat fields being highest during this time of day (Fernando et al 2000). Paulitz et al. (1996) explored the relationships among ascospore release, temperature, rainfall, relative humidity, and leaf surface wetness and found that ascospore release occurred in highest concentrations in the evening hours and was inhibited by the occurrence of high rainfall (> 5 mm) and high RH (> 80%) during the same day. However, the greatest ascospore release events occurred 1-4 days following high rainfall or RH. This study suggests that rainfall and high relative humidity may be 7

23 necessary for perithecial development, but not for ascospore release (Paulitz 1996). In fact, high leaf moisture may serve to inhibit the forced discharge of ascosprores because of the added energy required to break surface tension of standing water (Paulitz 1996). Findings were consistent with those from a classical growth chamber study conducted by Tschanz et al. (1975) to discern the factors affecting ascospore release. Specifically, the effect of different temperature and relative humidity combinations were explored. Findings of this study indicated that ascospore discharge was triggered by a period of high relative humidity directly followed by a period of atmospheric, as well as perithecial, desiccation. They also found that a prolonged period of UV irradiation was required prior to an ascospore release event (Tschanz et al. 1975). Trail et al. (2002) also explored optimal environmental conditions for ascospore release under controlled laboratory conditions and found that, somewhat dissimilar to findings in other studies, discharge events greatly increased with increased relative humidity, with the highest number of spores discharged at 100% relative humidity (Trail et al. 2002). Trail et al (2002) also found that maximum discharge occurred at 6-9 days after perithecial development. Asci contain not only ascospores but also an epiplastic fluid rich in mannitol, K+, and Cl-, which may be important in the generation of energy required for forcible discharge (Trail et al 2005). Infection of wheat spikes occurs from the flowering phase until the soft dough phase under conditions of high temperatures and prolonged surface 8

24 wetness. No infection will occur in wheat inoculated prior to anthesis (Lacey et al. 1999). In general, FHB is favored by continuous moisture because surface wetness is needed for pathogen establishment (Lacey et al. 1999; McMullen et al. 1997). After reaching the wheat spikes (infection court), spores invade via the anthers and colonize the rachis and vascular tissue of the wheat spike. While this pathogen is generally considered to be necrotrophic, studies suggest that a two day biotrophic phase of intercellular growth occurs followed by development of aerial hyphae and eventual death of plant tissue (Walter et al. 2010). DISEASE FORECASTING Rainfall has classically been used as a predictor variable in disease forecasting systems. A multi-state study published by De Wolf et al. (2003) found that moderate temperatures (between ), along with high RH during anthesis, were the best predictors of FHB epidemics. In general, models that used anthesis weather data were more accurate than those that used preanthesis weather data alone. Although pre-anthesis weather predictors tended to decrease the model accuracy, this time period is important for growers to make management decisions. This study found that a model using pre-anthesis duration of precipitation (h) and duration of moderate temperatures had 70% accuracy for FHB prediction (De Wolf et al. 2003). A similar study conducted by Hooker et al. (2002) found rainfall and temperature during specific pre- and post-anthesis windows to be important 9

25 weather variables for predicting DON accumulation. This study specifically looked at three windows surrounding anthesis, one preceding and two following (Hooker et al. 2002). They found that the number of days with rainfall exceeding 5 mm day -1 and temperatures exceeding 10 during the 4-7 days prior to heading explained a large amount of the variability in DON accumulation between fields, with rainfall alone accounting for the most variability (Hooker et al. 2002). They found that including moisture during post-anthesis windows strengthen the prediction of DON accumulation (Hooker et al. 2002) These findings are somewhat contrary to similar predictive systems which included RH as a key predictor variable, with rainfall being a less important predictor of FHB (De Wolf et al. 2003; Shah et al. 2013). Disparities between studies may be due to the breakdown in association between FHB symptoms and DON accumulation in some systems. Cowger et al. (2009) further explored the role of post-anthesis rainfall on both FHB development and DON accumulation. This controlled greenhouse study explored the effect of cultivar resistance and duration of post-anthesis moisture (0, 10, 20 and 30 days) on FHB severity, kernel damage and DON accumulation. Overall, increasing moisture durations post-anthesis lead to an increase in FHB and DON (Cowger et al. 2009). In a similar study, Culler et al. (2007) explored the effect of extended moisture during post-anthesis growth stages on DON accumulation and FHB development. Interestingly, the results from this field study indicated that DON levels generally decreased with crop 10

26 maturity and that DON levels in post-harvest grain were lower after an extended mist treatment when compared with a standard mist irrigation treatment (Culler et al. 2007). Cultivar resistance was also evaluated and appears to contribute to the effect on DON accumulation. Although classically believed that increased moisture will result in higher disease and DON levels, one explanation that the authors proposed was that high levels of standing water, post-anthesis, may actually result in toxin leaching (Culler et al. 2007). Recent studies have shown that there may be a link between postanthesis rainfall and high DON accumulation in asymptomatic grain (Cowger and Arrellano 2010). A study by Cowger and Arrellano (2010) attempted to elucidate the mechanism behind the occurrence of grain with low levels of kernel damage and high DON. Findings from this study were variable throughout years. Generally, it was found that economically significant DON levels were present in grain inoculated 10 days after anthesis, under conditions of extended misting (20 and 30 days), with DON levels increasing with increasing moisture duration. In one year, results suggested that late infection (10 and 20 days after anthesis) followed by extended moisture lead to scenarios with low kernel damage and high DON levels (Cowger and Arrellano 2010). Because these results were not consistent across years, further studies are needed to test this association. MYCOTOXIN PRODUCTION 11

27 Mycotoxins are complex organic compounds produced as secondary metabolites by some fungal species. Two species, F. graminearum and F. culmorum, are capable of producing mycotoxins in grains (Snijders 1990). The trichothecenes are the most problematic group of fungal-produced mycotoxins that accumulate in wheat (Foroud and Eudes 2009). Trichothecenes are sesquiterpenoid epoxides, classified into four types: type A, B, C and D. Fusarium species primarily produce type A or B toxins. Type A includes T-2 and HT-2 toxins as well as diacetoxyscirpenol (DAS) (Fung and Clark 2004; Foroud and Eudes 2009). Type B includes deoxynivalenol (DON), and it s derivatives, including acetyldeoxynivalenol (3-ADON and 15-ADON) and nivalenol (NIV) (Fung and Clark 2004; Foroud and Eudes 2009; Bai and Shaner 1994). Although T-2 is more toxic to mammals, DON is the most important mycotoxin associated with FHB (Foroud and Eudes, 2009). Type C and D trichothecenes are not produced by Fusarium and therefore are not important in the study of FHB. Along with trichothecenes, the esterogenic toxin, zearalenone, is also a noteworthy mycotoxin produced by F. graminearum (Bai and Shaner 1994). DON was first detected in wheat in 1981 and since that time high levels of this toxin have been found in North American wheat infected with F. graminarium (Harris et al. 1999). The development of FHB, DON and other trichotheocenes have been associated with virulence in Fusarium species (Desjardins et al. 1996; Harris et al. 1999). The role of trichothecenes in virulence has been established through the cloning of the trichodiene synthase gene, TRI5. Trichodiene 12

28 synthase plays a critical role in the biosynthesis pathway of fungal trichothecenes (Harris et al. 1999). Field studies were conducted to compare FHB development in wheat infected with TRI5 deficient mutants of F. graminarium to that infected with wild type isolates. Incidence and severity of FHB were significantly reduced and yield loss was decreased in mutant-infected wheat. This suggests that, although not required for infection, trichothecenes are an important virulence factors for the pathogen (Desjardins et al. 1996). Trichothecenes inhibit protein synthesis in eukaryotic cells which may serve to decrease host defense against fungal pathogens (Desjardins et al. 1996; Harris et al. 1999). Specifically, phytotoxic deoxynivalinol has been shown to inhibit protein production through ribosome binding (Miller and Ewen 1997). Resistant wheat varieties have shown more tolerance to this effect, when compared to varieties that are more susceptible to FHB (Miller and Ewen 1997). In general, studies have shown that genotypes with resistance to FHB have reported lower levels of DON accumulation (Culler et al. 2007). A controlled greenhouse study was conducted by Del Ponte et al. (2007) to determine the relationship between infection timing and plant phenology as it affects FHB development and DON accumulation, on a susceptible wheat variety. Results showed that the highest levels of DON accumulation occurred in kernels inoculated at watery ripe or early milk stages (Del Ponte et al. 2007). They also found negative correlations between kernel weight and DON and positive correlations between DON and visually scabby kernels (VSK). This 13

29 suggests that there is a large window of susceptibility to the pathogen and that infections during post-anthesis growth stages greatly contribute to DON accumulation. This study also suggests that management strategies must be extended through later growth stages (Del Ponte et al. 2007). Further understanding of the dynamic relationships between growth stage, susceptibility, environmental conditions and DON accumulation must be achieved. FUSARIUM BIOMASS QUANTIFICATION Specific detection and accurate quantification of F. graminearium biomass is important for many reasons, including disease diagnostics and pathogen classification. Traditionally, Fusarium identification was conducted by isolating fungal tissue from plant material, plating on selective media, and employing morphological criteria. For a number of reasons, this technique was highly subjective, lacked specificity, and was low-throughput. In the early 1990s, immunochemical assays were developed with specificity for fungal surface antigens (Niessen and Vogel 1997). These techniques can be limited in that they are growth stage-specific and can have a high rate of infidelity. Fusarium detection efficiency was much improved with the development of nucleic acid (DNA) based detection methods, notably the Polymerase Chain Reaction (PCR). The Polymerase Chain Reaction is a method that allows for the specific amplification of a gene or target sequence of interest. Early PCR methods used in differentiating Fusarium species included the random amplified polymorphic 14

30 DNA (RAPD) method and PCR using primers specific for the internal transcribed spacer (ITS) region (Niessen and Vogel 1997). Later, primers specific for F. graminearium became the most rapid and sensitive method for differentiating F. graminearum. The first F. graminearum gene studied for detection was the galactose oxidase gene (gaoa) and later the trichodine synthase (Tri5) gene became the standard for detecting F. graminearum, especially strains that produce trichotheces (Doohan et al. 1999; Schnerr et al. 2001; Li et al. 2005). Duplex and multiplex PCR methods have also been developed to simultaneously detect multiple members of the FHB species complex in a single reaction (Waalwijk et al 2003; Brandfass and Karlovsky 2006). In addition to being used for detection and classification, the development of nucleic acid based methods have been important tools in Fusarium biomass quantification. Quantification of PCR products can be performed in a number of ways, including Southern blot hybridization using radioactive or fluorescent labels (Nicholson et al. 1996). More recently, quantitative real-time PCR has become the standard for nucleic acid quantification (Schnerr et al. 2001; Fraga et al. 2012). Using fluorescence, this method allows for the real-time quantification of amplification of target sequences, before the reaction components become limiting factors (Fraga et al. 2012). Real-time PCR is a cost effective method with relatively high through-put. Shnerr et al. (2001) were first to apply the SYBR Green I dye to real-time PCR specific for Fusarium species. SYBR Green I is a dye with specificity for double stranded DNA (Schnerr et al. 2001). Waalwijk et al. 15

31 (2004) developed a real-time PCR reaction based on the TaqMan method, which exploits the Taq polymerase enzyme to visualize amplification during the reaction. This method allows for more accurate detection for diagnostics, research, and grain quality purposes. MANAGEMENT Chemical control. In recent years, fungicides have become an important component of FHB management strategies. Although important, complete control may be difficult to achieve because of the dynamic life cycles of both the pathogen and the crop, along with economic viability of a fungicide application. High levels of control may be obtained with six to eight applications of certain triazole fungicides; however this volume of chemical application would not be practical or economical for growers (Jones 2000). Application timing for FHB control is also critical and for optimal protection, applications should be made after head emergence, around anthesis (Mesterhazy 2003). Fungicide efficacy is also dependent on fungicide application rate and adequate coverage of the head and may be influenced by isolate aggressiveness and cultivar resistance (Mesterhazy 2003). Jones (2000) evaluated the efficacy of several fungicide classes against FHB and DON on both a susceptible and moderately susceptible varieties of hard red spring wheat cultivars in Minnesota, and found that both the triazole (tebuconazole) and the methyl benzimidizole carbamates (benomyl) reduced 16

32 FHB incidence and severity, when compared to a control not treated with fungicide. Applications at anthesis are necessary for FHB reduction (Jones 2000). This study also found no difference in disease intensity between a single application at Feekes 10.5 and two applications at Feekes 10.5 and 11. A similar study conducted in Italy by Haidukowski et al. (2005) evaluated several fungicide treatments for their efficacy on four susceptible varieties. Fungicides chosen for this trial included cyproconazole plus prochloraz, azoxystrobin, tebuconazole and tetraconazole. Isolates of both F. graminearum and F. culmorum were used in this study. Findings indicated that all fungicide combinations significantly reduced disease severity, incidence and DON levels and increased both yield and thousand kernel weight, when compared to non-treated controls (Haidukowski et al. 2005). Because of the imperative need to better understand fungicide efficacy in FHB management, and major discrepancies between studies due to regional variation and experimental design, uniform fungicide trials (UFT) were introduced into the United States FHB the 1990 s (Paul et al. 2007). Results from UFTs conducted between 1998 and 2005 in all major US soft red winter and spring wheat-growing regions were evaluated by Paul et al. (2007) using a randomeffects meta-analysis to determine the efficacy of the tebuconazole on FHB and DON levels in both winter and spring wheat cultivars. Tebuconazole was chosen for this study because of consistent efficacy of the triazole family of fungicides for the control of FHB and DON (Paul et al. 2007). Results showed significant 17

33 control of both FHB intensity as well as DON accumulation after application of 38.7% tebuconazole (Folicur 3.6F) at Feekes , when compared to the untreated check. This meta-analysis was also used to predict positive percent control in future studies, conducted in the same manner. This fungicide was found to be more effective at lowering FHB and DON in spring wheat, when compared to winter wheat varieties (Paul et al. 2007). Besides tebuconazole, there is evidence that other members of the triazole family of fungicides are just as or more effective against FHB and DON. Several triazole-based chemistries were evaluated for efficacy on disease and toxin control in a multivariate meta-analysis conducted by Paul et al. (2008). This study evaluated tebuconazole, propiconazole, prothioconazole, metaconazole, and a mixture of tebuconazole and prothioconazole on both spring and winter wheat cultivars, across locations, according to fairly uniform protocols. Overall, all of the fungicides tested decreased both FHB and toxin levels, when compared to untreated checks (Paul et al. 2008). Specifically, the tebuconazole+prothioconazole mix had the highest mean percent control of index (51.8%), followed by metconazole, prothioconazole, tebuconazole and propiconazole, respectively. In terms of DON reduction, metconazole, prothioconazole and tebuconazole+prothioconazole had the highest percent control. Three of the fungicide treatments (prothiconazole, metconazole, and tebuconzole+prothioconzole) performed better than tebuconazole for both FHB index and DON reduction, while one (propiconazole) resulted in higher DON and 18

34 index, when compared to tebuconazole (Paul et al. 2008). In general, all fungicide treatments performed better on spring wheat than on winter wheat varieties. Cultivar resistance. Resistance is one of the most critical components of FHB management (McMullen et al. 1997). Although imperative for management, resistance is often difficult to test because of regional variation in environmental conditions and pathogen populations. Generally, there are two main classes of resistance to Fusarium infection; type I and type II (Cowger et al. 2009). Type I resistance is the ability of the plant to deter the initial pathogen invasion, while type II resistance is the resistance to pathogen spread within the spike after initial infection (Schroeder and Christensen 1963). A third type of resistance has been proposed which is associated with the ability of the plant to degrade or prevent the synthesis of DON (Dill Macky 2003). It is believed that a combination of each type of resistance, in the form of gene pyramiding, may be the most advantageous mechanism for breeding. A spring wheat cultivar known as Sumai 3 has been rated as highly resistant and has been used as a source of resistance to FHB in many breeding programs (Rudd et al. 2001). Originating in China, Sumai 3 is considered highly stable across environments and in both spring and winter wheat, although some problems (such as susceptibility to other diseases) have been a limiting factor (Rudd et al. 2001). Cultivar Frontana and cultivar Nobeokaboouzu (from Brazil and Japan, respectively) have also been used as sources of resistance and have 19

35 shown to be durable over years (Rudd et al. 2001, Mesterhazy 1995). Along with resistant lines, agronomic traits have also been specified for optimal quantitative FHB resistance. These include a plant height around cm, no awn and a head that is not too dense (Mesterhazy 1995). Resistance genes are generally considered to have an additive effect, that is the more diverse genes present, the more durable the resistance level of the cultivar (Rudd et al. 2001). Resistance levels of cultivars of both spring and winter wheat generally fall on a continuum from susceptible to highly resistant, and resistance reaction may vary according to environmental conditions. Studies have found that the most effective management of FHB and DON occur after using a combination of a moderately resistant variety and an appropriately timed application of fungicide (Willyerd et al. 2011). Integrated management. A study conducted by Hollingsworth et al. (2008) examined the effect of cultivar resistance on the efficacy of fungicide in environmental conditions either favorable or not favorable for FHB development. Cultivars used in this study were classified as either moderately susceptible (MS) or moderately resistant (MR) and the fungicides tested included; propoiconazole applied at Feekes 2, tebuconazole applied at Feekes , and propiconazole at Feekes 2 followed by tebuconazole at Feekes (Hollingsworth et al. 2008). This study was conducted at eight different locations, which were classified into two types of environment; high FHB and low FHB. Results showed found that MS and MR cultivars responded differently, according to the type of 20

36 environment (high or low disease pressure). In environments with high FHB disease pressure, MR varieties produced a higher yield with lower DON. Generally, fungicide application decreased disease severity and index within the MR cultivars, when compared to the untreated control (Hollingsworth et al. 2008). A similar result was not found in MS cultivars. This study confirms the importance of understanding the local disease pressure, planting a MR cultivar if disease pressure is high, and also making an appropriate fungicide application. Utilizing data from multi-state uniform integrated management trials conducted from by collaborators involved in the United States Wheat and Barley Scab Initiative (USWBSI), Willyerd et al. (2012) studied the effect of integrating fungicide and cultivar resistance on FHB and DON development. Six fungicide/cultivar combinations were evaluated; susceptible (S)/untreated (UT), moderately susceptible (MS)/UT, moderately resistant (MR)/UT, (S)/ fungicide treated (TR), MS/TR, and MR/TR. The fungicide used in all trials was a tebuconazole+prothioconazole mix applied a single time, at anthesis. Overall, the MR cultivars treated with fungicide had the lowest mean index and DON levels, highest percent control (75.7%), and highest reduction in DON (71.01%), relative to the susceptible untreated check. In general, resistance combined with a single application of fungicide at anthesis outperforms moderate resistance alone, or fungicide treatment alone (Willyerd et al. 2012). The combination of moderately resistant variety and fungicide treatment was also stable across environments, with the highest mean rank when compared to the other treatments (Willyerd 21

37 2012). These results confirm the importance of integrating these too management strategies for increased FHB and DON control, however, no treatment resulted in 100% control or reduction suggesting the need for further research. 22

38 OBJECTIVES Moisture is critical for infection, colonization, Fusarium head blight (FHB) development, and grain contamination with deoxynivalenol (DON). Both the duration and amount of rainfall around anthesis are important predictors of FHB and DON. However, it is unclear how the distribution of moisture during this period affects these responses and the relationships between them. In addition, since FHB develops best under wet conditions, fungicides are most warranted for control of FHB when wet, rainy conditions occur during anthesis. However, the effects of rainfall directly following a fungicide application on efficacy against FHB and DON and the persistsnce of fungicide residue on wheat spikes are largely unknown. The effects of various moisture and rainfall treatments on FHB, DON, the FHB/DON relationship and fungicide performance against FHB and DON were evaluated through a set of field and controlled environment experiments conducted in Ohio. The specific objectives of this research were to: 1) Determine the effect of intermittent rainfall patterns deployed under field conditions prior to wheat anthesis on spore dissemination, visual symptom development and DON accumulation; 2) Evaluate the effect of intermittent post-anthesis moisture patterns on infection, colonization, disease development, and DON accumulation, under controlled conditions and; 3) Model the relationships between FHB and DON and estimate the probability of FHB and DON exceeding critical thresholds under variable moisture patterns; 23

39 4) Determine the rainfast time (the time needed between an application and a rain event for the product to maintain its effectiveness) for 19% tebuconazole + 19% prothioconazole when applied at anthesis and; 5) Evaluate the persistence of this fungicide on wheat spikes over the weeks following application. 24

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45 K., Wise, K., and Paul, P Uniform fusarium head blight integrated management trials: A 2011 Update. Pages in: Proc. Natl. Fusarium Head Blight Forum. Canty, S., Clark, A., Anderson-Scully, A. and Van Sanford, D., eds. US Wheat and Barley Scab Initiative. Windels. C. E Economic and Social Impacts of Fusarium Head Blight: Changing Farms and Rural Communities in the Northern Great Plains. Phytopathology. 90:

46 CHAPTER 2 : Fusarium Head Blight Development and Deoxynivalenol Accumulation in Soft Red Winter Wheat as Influenced by Moisture Patterns Before or After Anthesis INTRODUCTION Fusarium head blight (FHB) of wheat (Triticum aestivum), primarily caused by the fungal pathogen Fusarium graminearum (teleomorph, Gibberella zeae), and its associated toxins, is of great social and economic importance in most wheat-growing regions of North America. Losses due to FHB are primarily attributed to reductions in grain fill, yield, and seed quality, as well as mycotoxin contamination of grain. Major epidemics of FHB occurred in the 1990s, with economic losses estimated at nearly three billion dollars during that decade (Windels 2000). Such epidemics and resulting losses are often associated with widespread adoption of reduced and minimum tillage, decreased crop diversity, increased acreage of host crops, and wet, humid weather conditions (Windels 2000, Bai and Shaner 1994, Dill-Macky and Jones 2000, McMullen et al 1997, 2012). Despite research efforts, outbreaks of FHB continued annually through the 2000s, with severe epidemics in 2003 and 2010 (McMullen et al. 2012). 31

47 Deoxynivalenol (DON), also known as vomitoxin, is the most abundant and problematic mycotoxin produced by F. graminearum. It is a member of the trichothecene class of fungal-produced mycotoxins and has been shown to inhibit protein synthesis in eukaryotic cells, resulting in feed refusal, emesis, reproductive disorders and growth retardation in mammals (Rocha et al. 2005; Foroud and Eudes 2009). Because of these health concerns, the US Food and Drug Administration has set a 2 ppm DON threshold for wheat grain and 1 ppm for finished wheat products destined for human consumption (Food and Drug Administration 2010). Consequently, grain with DON levels above 2 ppm may be rejected completely or priced down at grain elevators, resulting in an economic loss for growers. Although substantial gains in our understanding of the biology and epidemiology of FHB and DON have been made in recent years, there are still major knowledge gaps. For example, the effects of moisture patterns before and after anthesis on FHB development and DON contamination of grain are still poorly understood, and thus, warrant further investigation. It is well known that weather variables, particularly rainfall and relative humidity, are important for several processes in the FHB infection cycle (Paul et al 2004, 2008, Lacey et al. 1999). Both rainfall and high relative humidity are necessary for perithecial development (Dufault et al. 2006; Paulitz 1996). However, the role of these weather variables on ascospore release is not clear. Tschanz et al. (1975) found that ascospore release was favored by periods of rainfall followed by perithecial desiccation. Similarly, Paulitz (1996) found ascospore release to be greatest

48 days after a rainfall event and inhibited by relative humidity >80% and rainfall >5 mm. However, interestingly, Trail et al. (2002) found ascospore release to be greatest under conditions of high (~100%) relative humidity. Both the duration and amount of rainfall before, during and following anthesis have been examined for their relationships with disease development, DON accumulation, and spore dissemination within the wheat canopy. For instance, Culler et al. (2007) observed that extended post-anthesis irrigation patterns resulted in lower DON levels in grain than anticipated. Contrastingly, also investigating post-anthesis moisture effects on DON, Cowger et al. (2009) reported that increasing moisture durations post-anthesis led to an increase in FHB and DON. Paul et al. (2004, 2007) reported on the relationship between spore abundance on individual spikes and a number of weather predictors, and found relative humidity, wetness duration, temperature, and rainfall over the previous 8 days to be important predictors. This type of information is particularly useful for developing FHB and DON predictive models to help guide FHB management and grain marketing decisions. DeWolf et al. (2003) found that duration of precipitation and moderate temperatures during 7 days pre-anthesis were predictive of major FHB epidemics (defined as >10% disease index). The accuracy of the predictive models they developed increased somewhat when post-anthesis weather variables were included as predictors. Investigating the role of weather patterns during specific pre- and post-anthesis windows on DON accumulation, Hooker et al. (2002) also observed that rainfall during the 4-7 days prior to anthesis was the most 33

49 important predictor of DON. More recently, Shah et al. (2013) attempted to further understand the role of pre- and post-anthesis weather variables on FHB prediction using a large dataset across 15 state and 27 years and an expansive set of weather-related predictors. Out of 380 weather-based predictors across several pre-and post-anthesis time windows, 21 were selected and incorporated into 15 different logistic models. These models varied in the length of the pre- or post-anthesis window, along with exact conditions of RH, temperature or rainfall. The pre-anthesis model that had the least misclassifications of epidemics included the number of hours with temperature between 15 C and 30 C where RH was also greater than 80%. This model also included the mean temperature per day along with the number of hours with a temperature greater than 9 C (Shah et al. 2013). Rainfall generally was not significant for predicting risk of epidemics in this study. It is very clear from empirical observations and designed experiments that extended periods of high relative humidity during anthesis are critical for FHB development and DON accumulation. Although rainfall often is associated with this disease and toxin, the role of rainfall is less certain. In fact, FHB may still develop, and most importantly, DON may still exceed the 2 ppm threshold in years with infrequent rainfall or discontinuous moisture patterns. Moreover, the disparity between FHB visual symptoms and corresponding DON accumulation (Cowger and Arrellano 2010, Sneller et al. 2012), sometimes observed in years with less-than-ideal conditions for major FHB epidemics, may also be associated with variable moisture patterns (Cowger et al 2009, Culler et al 2007). A series of 34

50 experiments were designed to better understand the role of variable pre- and post-anthesis moisture patterns from either mist or simulated rain on pathogen biology, spike colonization, FHB development, DON accumulation, and the association between FHB and DON. The specific objectives were to: i) determine the effect of simulated intermittent rainfall patterns deployed under field conditions during the week prior to wheat anthesis on visual symptom development and DON accumulation (KFB); ii) evaluate the effect of intermittent post-anthesis moisture patterns on infection, colonization, disease development, and DON accumulation, under controlled conditions and; iii) model the relationships between FHB and DON and estimate the probability of FHB and DON exceeding critical thresholds under variable moisture patterns. MATERIALS AND METHODS Establishment and design of experiments. Pre-anthesis rainfall effect on FHB development, and DON accumulation. Three field experiments were conducted at the Ohio Agricultural Research and Development Center Snyder Farm near Wooster, OH, during the 2010/11, 2011/12 and 2012/13 growing seasons. Fields were managed in terms of land preparation, fertilizer application, and pest management according to standard wheat production practices for Ohio (Paul et al 2008). The experiments were set up as a randomized complete block, with a split-split plot arrangement of treatment factors. Simulated rainfall treatment was used as the whole plot, with planting date or planting date x cultivar combination as sub-plot, and inoculum source as sub-sub-plots. 35

51 Four simulated pre-anthesis rainfall treatments, one daily and three discontinuous, plus an untreated check, were evaluated. During 2011 and 2012, rainfall treatments were applied to plots during a 7-day pre-anthesis window as: 1) rain on each of the 7 days (Rain1); 2) rain only on the first and last 2 days of the window (Rain2); 3) rain only on the middle 3 days, and no rain on the first and last 2 days of the window (Rain 3); and 4) rain every other day, beginning on the first day of the 7-day window (Rain 4). Plots subjected to Rain1 received 7 days of simulated rain, those under Rain2 and Rain4 received 4 days of rain, while those assigned to Rain3 received 3 days of rain. In 2013, the protocol was modified slightly so that all discontinuous rainfall treatments (Rain2, Rain3, and Rain4) received the same number of days and amount of rain (8 h/day for 4 days at an intensity of approximately 3.3 to 4.5 mm/h). This was accomplished by increasing the treatment window by a day so that plots assigned to Rain1 received rain on each of 8 consecutive days prior to anthesis and those assigned to Rain3 received rain on the middle 4 days and no rain on the first and last two days of the 8-day treatment window. There were 2 replicate blocks of each rainfall treatment in In each year, to simulate rainfall, full circle pattern rotary nozzles (R13-18F, Rain Bird Corporation, Azusa, CA) were mounted on 5- m risers in each whole plot and programed using timers to deliver rainfall treatments for 4 min. every 12 min. in the morning between 500 and 900 h, and again in the evening between 1700 and 2100 h. A day was considered as the 24 h period between 1700 and 1600 h. 36

52 Each whole-plot was divided into three sub-plots planted with one or two susceptible soft red winter wheat (SRWW) cultivars (cv. Hopewell and/or Bravo) at a row spacing of 191 mm and seeding rate of approximately 4 million seeds/ha. A Kincaid planter was used to establish all plots. Three planting dates or cultivar x planting data combinations were used in each year to better capture the pre-anthesis window and increase the likelihood of some of the plots receiving the rainfall treatments at the designated time. For the 2011 and 2013 experiments, this was accomplished by planting the same cultivar (Hopewell) on three different dates (25 September, 4 October, and 13 October 2010, and 25 September, 4 October, and 16 October 2012), 7-10 days apart, beginning on the Hessian Fly-safe date (25 September), whereas for the 2012 experiment, two cultivars of different maturity were planted on two separate planning dates (earlymaturing Bravo on 7 October and mid-season Hopewell on 7 and 17 October, 2011). Sub-plots were then further divided into sub-sub plots (1.5 x 6 m) to which three different in-field sources of inoculum were assigned; corn spawn (sterilized corn kernels artificially infected with G. zeae as described by Sneller et al. 2011), corn stalks naturally infected with G. zeae, and natural, ambient inoculum. Post-anthesis moisture effects on FHB development and DON accumulation. Spray-inoculated and point-inoculated (described below) controlled-environment studies were performed in mist and growth chambers. The spray inoculation method was used to assess the effects of post-anthesis moisture on infection and subsequent disease development, whereas the point inoculation method was used to assess the effects of the moisture patterns on 37

53 spike colonization, FHB development, and DON accumulation, assuming an infection has already occurred. The two types of experiments were run either simultaneously or consecutively, depending on how the plants developed in the growth chamber, and were repeated twice between 1 March and 16 April, Seeds of awnless, FHB susceptible soft red winter wheat cultivar Hopewell were sown on 10 December 2013, in plastic trays with 10-cm row spacing, and allowed to germinate in the greenhouse for approximately 2 weeks. After germination, plants were fertilized with Osmocote (Scotts Miracle-Grow, Columbus, OH) and allowed to vernalize in a cold (3 C) room for10 wks. Plants were then transplanted to individual containers (Stuewe and Sons, Inc. Corvallis, OR) containing autoclaved silt loam and transferred to the greenhouse (average temperature 26 C) until inoculated. At approximately Feekes growth stage 7, plants were tied to bamboo stakes and excess tillers were trimmed to enhance proper spike development. At Feekes growth stage 8, plants were moved to a walk-in growth chamber (Conviron BDW40, Winnipeg, Manitoba, Canada) and placed under controlled conditions of (23.6 C, 78.9% relative humidity, and fixed light intensity of 16 h light and 8 h dark) to help stimulate uniform development. All plants were either spray- or point-inoculated at anthesis with a spore suspension of highly aggressive isolates of F. graminearum collected from wheat fields in Ohio (J. D. Salgado, personal communication). Inoculum was produced by aseptically transferring mycelial plugs from Komada selective media to Mung Bean Agar, and incubating plates at room temperature under ultraviolet lights, with a 12-hour photoperiod. After approximately 10 days, mycelia were removed 38

54 and plates were transferred to a dark room for macroconidia development. After 2 wk, macroconidia were harvested by adding 500 µl of sterile deionized water to each plate and agitating using a rubber policeman to dislodge spores. Spore concentration was estimated using a hemacytometer, and diluted with deionized water to appropriate concentrations for immediate usage or storage at 0 C. For each replicate of each pair of experiments, a set of 100 plants were chosen at anthesis (Feekes ) of which 50 were spray-inoculated with a spore suspension containing 50,000 spores/ml, using a hand-held sprayer, with approximately 3.6 x 10 3 spores applied to each spike. The other 50 plants were inoculated using a point inoculation method (Engle at al. 2003). Each pointinoculated spike received 10 µl of a F. graminearum macroconidia suspension (10,000 spores/ml), directly pipetted into the central spikelet of the spike. Separate groups of inoculated plants were immediately exposed to one of five mist treatments. Controls (no-mist) plants were placed in a growth chamber with an average temperature of 23.6 C and average RH of 79%. The experimental design was a randomized complete block (with blocking in time), with experimental units replicated over three periods (blocks defined by batches of plants and cohorts of spikes at anthesis). There were five postanthesis mist treatments: 1) mist every day (Mist1); 2) 2 day mist, 4 day no mist, 2 day mist (Mist2); 3) 2 day no mist, 4 day mist, 2 day no mist (Mist3); 4) mist every other day (Mist4); and 5) no mist (Mist 5). On a misting day, plants were placed in a mist bench (101 x 220 cm) and received 12 hours of mist deployed at 90s intervals, with an intervening 10 min of no mist. These 12 hours of high RH 39

55 were followed by 12 hours of no mist, to complete a full day. The average RH during these 12 hours of mist was 89%, with the maximum RH being 92% and the minimum being 84% (average temperature = 20.5 C). On days when a treatment required no mist, plants were placed in the same growth chamber as the control plants (at an average temperature of 23.6 C and average RH of 79%). At the end of the 8-day mist cycle, all plants were moved to a greenhouse (average temperature 27 C) to await symptom development. Monitoring of environmental conditions and disease and DON quantification. In all field experiments, a Campbell Scientific unit (CR10X datalogger; Campbell Sci., Logan UT) deployed approximately m away from the research plots was used to collect ambient temperature, relative humidity, surface wetness, rainfall, wind speed, and solar radiation data at 30- min intervals from Feekes GS 7 (stem elongation) to harvest. Watchdog (WatchDog 1450 Micro Station, Spectrum Technologies, Inc., Plainfield, IL) and Hobo (Hobo Micro Station, Onset Computer Corp. Bourne, MA) data loggers and sensors, equipped with tipping bucket rain gauges were used to monitor temperature, RH, and rainfall within each whole-plot. An additional weather unit was placed in a plot outside the trial area to monitor ambient conditions during the 7-8-day rainfall-simulation treatment window. WatchDog and Hobo loggers and sensors were also used, along with built-in sensors, to monitor conditions in mist and growth chambers, and in the greenhouse. In the field, FHB index (mean proportion of diseased spikelets per spike) and incidence (mean proportion of diseased spikes) were evaluated during the 40

56 soft dough stage (Feekes 11.2, approximately 21 days after anthesis) on 20 spikes in five arbitrarily selected clusters within each plot, for a total of 100 spikes/plot. For the greenhouse experiments, index was rated (as described above) on all ten spikes in each experimental unit at 10 and 20 days after inoculation; i.e., 2 and 12 days, respectively, after the end of the mist cycle. All field plots were harvested with a research plot combine (SPC 20, Almaco, Nevada, IA), and a sample of grain from each plot were rated for percent Fusarium damaged kernels (FDK) and assayed for DON and fungal biomass. FDK was quantified as the proportion of visibly small, shriveled, and discolored kernels in each sample, with the aid of a diagrammatic chart (Jenkins and Jones, University of Minnesota, St. Paul, MN). In the greenhouse, all individual spikes from each experimental unit were hand-harvested, threshed using a table-top thresher, and separate sub-samples of the grain were prepared and assayed for DON and fungal biomass. Field and greenhouse grain samples were ground using a laboratory mill (Laboratory Mill 3033, Perten Instruments, Springfield, IL) and a subsample of the ground grain was sent to the US Wheat and Barley Scab Initiative-funded mycotoxin testing laboratory at the University of Minnesota for DON analysis. Data Analysis. Pre-anthesis rainfall effect on FHB index and DON. Data from the 2011 and 2012 field experiments (which were conducted without true replicate blocks) were pooled for analysis as a single experiment (with year as the blocking factor), whereas data from the 2013 experiment (in which there were replicate blocks) were considered as a separate experiment. Since the primary 41

57 objective of the field experiments was to evaluate the effect of rainfall patterns during 7- or 8-day pre-anthesis windows on FHB and DON, for each year, only the planting date for which the wheat crop reached anthesis 8 (planting date 3 in 2011 and planting date 1 in 2012) or 9 (planting date 1 in 2013) days after the beginning of the simulated rainfall treatments were considered for analysis. The other planting dates were not used. The experimental design was therefore reduced to a randomized complete block (with year as the blocking factor for the pooled data), with a split-plot arrangement of rainfall treatment (whole-plot) and inoculum source (sub-plot). Prior to the analysis, disease index and incidence data were arcsinesquare-root-transformed (arcind and arcinc) and DON data were logtransformed (logdon+1) to stabilize variances. Separate linear mixed models were then fitted to the transformed data using the GLIMMIX procedure of SAS (SAS, Cary, NC) to determine the effects of rainfall treatment on each of the measured responses. In all cases, the model fitted to data could be written as:, (1) where is the response (dependent variable; arcind, arcinc or logdon) for the i-th treatment and j-th inoculum source in the k-th block, is the constant (intercept), i is the fixed effect of the i-th rainfall treatment, is the fixed effect of the j-th inoculum source, is the fixed effect of the i-th rainfall treatment x j- th inoculum source interaction, is the random effect of the k-th block, is the the whole-plot error term, and is the residual (the sub-plot error term). 42

58 Pairwise comparisons between treatments were done using contrast and lsmestimate statements in proc GLIMMIX. Post-anthesis mist effect on FHB index and DON. For the greenhouse experiments, the data were analyzed in a manner similar to that described above for the field experiments. Linear mixed model analyses were performed to determine the effect of post-anthesis moisture on IND estimated at 10 and 20 days after inoculation (IND_10 and IND_20), DON, and the rate of disease spread within the spike. The latter response, abbreviated as rind (IND/day), was defined as the different between IND_10 and IND_20, divided by the number of days between the two assessments ( 10). IND and DON data were again arcsine-square-root- and log-transformed, respectively. For all analyses, equation 1 was modified by removing all terms related to inoculum source, as:, (2) where all terms were defined as above. Separate analyses were performed for IND 10, IND 20, DON, and rind for the spray- and point-inoculated experiments, and treatment means were again compared using contrast and lsmestimate statements in proc GLIMMIX. Pre-anthesis rainfall and post-anthesis mist effects on IND/DON relationships. Relationships between DON and IND, as influenced by rainfall and mist treatments, were modeled for both field and greenhouses experiments. IND was used as a continuous covariate and treatment as a class variable, and linear mixed model regression analyses were performed using Proc GLIMMIX of SAS. 43

59 The effect of treatment and the continuous variable are correlated and there is no unique ordering of variables within the mixed model. Thus, Type 3 hypothesis tests were used. Since different residue sources were used in the field experiments primarily as a means of generating a range of IND and DON levels (required for modeling the IND/DON relationship), residue was not used as a fixed effect in the analyses of the field data. However, all random effect terms from equation 1 for the field experiments and equation 2 for the greenhouse experiments were maintained. The equations were then modified by adding terms for IND and IND x treatment interaction. The generic model fitted to the different datasets was: α, (3) where is the response (logdon), is the intercept, is the effect of i-th rainfall or mist treatment, is the j-th observation of the covariable disease index (IND_20 for greenhouse experiments), is the (main) effect of the covariable, is the interaction effect of the covariable (effect of the rain or mist treatment on the relationship between and ), is the effect of the k-th block, and is the residual. Here, the subscript refers to each unique covariable observation within a block (e.g., for field study, this represents the two inoculum sources). Pre-anthesis rainfall effect on the risk of infection and FHB epidemic. Analyses were performed to estimate the expected probability of infection (P_INFECT) and the expected probability of FHB index exceeding 10% (P_IND10; used here and in FHB risk assessment as the definition of an FHB epidemic [Shah et al 2013]) under the influence of different simulated rainfall 44

60 treatments. For the purpose of these analyses, data from 2011 and 2012 were again pooled (with year as a blocking effect), while data from 2013 were kept separate. The disease status of each of 80 to 100 spikes rated per experimental unit (plot) was used to estimate the response variable, Y (instead of arcsinetransformed plot means). For both P_INFECT and P_IND10, Y was defined as D/N, where D = number of diseased spikes per plot for P_INFECT and the number of spikes per plot with IND 10% for P_IND10, and N = total number of spikes rated. Y was assumed to have a conditional binomial distribution; therefore, generalized linear mixed models (GLMM) were fitted to the data as described by Gbur et al (2012). Models were fitted to the logit link function (, also known as the linear predictor) of Y as: 1 (4) where 1 is the logit link function, is the probability of infection or IND 10% given the i-th treatment and the j-th inoculum source, and all other terms in the model are the same as defined above. After fitting the models, treatments were compared on the logit scale (differences between log odds), and then P_INFECT and P_IND10 were estimated for each rainfall treatment from the corresponding value, using the inverse link function: 1 (5) Post-anthesis mist effect on the risk of IND and DON exceeding critical thresholds. Separate generalized linear mixed models were fitted to the greenhouse data as described above to estimate P_IND10 for point- and sprayinoculated experiments, and P_DON2, P_DON5, and P_DON10 (probabilities of 45

61 DON exceeding 2, 5, and 10 ppm, respectively), for the spray-inoculated experiment. Again, the disease level of each individual spike was used, and Y, the response variable, was estimated as D/N for each experimental unit as described above, where N = 10 (the numbers of spikes rated per block) and D = the number of spikes with FHB-affected area 10%, at 10 and 20 days after inoculation (D_10 and D_20, respectively). For P_DON2, P_DON5, and P_DON10, the DON concentration of the grain harvested from each of the 10 individual spikes from each experimental unit was quantified and used to obtain separate sets of D values (and correspondingly, Y values) for DON 2, 5, and 10 ppm (number of spikes in each experimental unit with DON concentration 2, 5, and 10 ppm, respectively). Models with a logistic link were then fitted and probabilities estimated as described above. RESULTS Summary of field conditions. Weather conditions for the days preceding, during, and following rainfall treatment application are summarized in Figure 1. In 2011, 2012, and 2013 simulated rainfall treatments were deployed from 26 May - 1 June, 5 May - 11 May, and 19 May - 26 May, respectively (Fig. 1). The utilized planting date (PD) reached approximately 50% anthesis (Feekes ) on 2 June 2011 (PD3), 12 May 2012 (PD1), and 27 May 2013 (PD1). For each day in which a simulated rainfall event occurred, exposed plots received 8 hours of rain, at intensities ranging from 3.3 to 4.5 mm/h, giving a total precipitation of approximately 32, 26, and 36mm in 2011, 2012, and

62 Treatment-specific average RH and temperature for each year is summarized in Figure 2. Mean RH and temperature varied among years, but were similar across rainfall treatments within each year. In 2011, average temperature and total rainfall during the 28 days preceding the commencement of simulated rainfall treatment were 14 C, and 186 mm, respectively (Fig. 1A). There was an average relative humidity (RH) of 77% during that period, with 222 total hours of RH > 90% (Fig. 1B). The corresponding values for the post-anthesis period were 20 C for mean temperature, 83 mm total precipitation, 83% for mean RH, and 201 hours with RH > 90% (Fig. 1A and B). During the 7-day simulated rain cycle, average ambient temperature and total rainfall were 21 C and 34 mm, respectively (Fig. 1A). Ambient RH during this same period was 78%, with 48 total hours where RH exceeded 90% (Fig. 1B). In 2012, conditions were cool and dry, relative to During the 28 days prior to the simulated rainfall treatment window, average daily temperature and total rainfall were 10 C and 44 mm, respectively (Fig. 1C). Average RH was 68%, with 96 total hours with RH > 90% (Fig. 1D). Mean weather conditions for the post-anthesis period were 18 C for mean temperature, 28 mm total precipitation, 68% for mean RH, and 106 hours with RH > 90% (Fig. 1A and B). Ambient conditions during the rainfall treatments window in 2012 were cool and dry, with average daily temperature, and total rainfall of 15 C and 27 mm, respectively. Average RH during those 7 days was 73%, with 48 total hours of RH > 90% (Fig. 1D). 47

63 In 2013, in the 28 day window preceding the simulated rain regimen, there was an average daily temperature of 13.5 C and total rainfall of 52 mm (Fig. 1E). Average RH was 68% and there were 133 hours with RH > 90% (Fig. 1F). The corresponding values for the post-anthesis window were 19 C, 97 mm, 78%, and 220 hours (Fig. 1A and B). During the 8-day temporal window when the rainfall treatments were deployed, there was an average ambient temperature of 16 C, with 3.2 mm of total rainfall (Fig. 1E). Average RH during this period was 76.94%, with 51 hours of RH > 90% (Fig. 1F). Pre-anthesis rainfall effect on FHB and DON. Averaged across treatments, mean FHB incidence (INC) and index (IND) were highest in 2013 and lowest in These differences can largely be attributed to variation in ambient conditions. In 2011, averaged across inoculum source, mean INC for Rain1, Rain2, Rain3, Rain4 and the Check were 24.5, 17.3, 13.2, 17.5, and 10.3%, respectively (Fig. 3A). Rain1 also had the highest IND in 2011, with treatment means of 6.6, 3.5, 2.4, 4.0, and 3.6%, respectively (Fig. 2B). Interestingly, Rain2 had the highest mean DON (4.13), followed by Rain1, Rain3, Rain4 and the check (1.90, 1.86, 2.61 and 0.38, respectively) (Fig. 3D). FDK in 2011 was also highest for Rain1 and lowest for the check, with treatment means of 24.50, 17.33, 13.17, and 10.25, respectively (Fig. 3C). In 2012, mean FHB intensity, DON and FDK were much lower than in 2011, however, the trends were very similar. Low disease intensity during 2012 may be attributed to unseasonably dry weather during the month prior to anthesis. Mean INC for Rain1, Rain2, Rain3, Rain4 and the Check were 14.8, 48

64 12.5, 3.8, 9.3, and 1.8%, respectively (Fig. 3A). The corresponding means for IND were 5.1, 4.0, 1.1, 3.1 and 0.3% (Fig. 3B). As in 2011, in 2012 DON was highest for the Rain2 treatment (0.82), followed by with Rain1, Rain3, Rain4 and the check (0.63, 0.36, 0.35 and 0.06 ppm, respectively) (Fig. 3D). FDK in 2012 was very low for all treatments, ranging from 0 (Check) to 2% (Rain1) (Fig. 3C). FHB intensity was highest in 2013, compared to 2011 and Interestingly, mean INC was numerically highest in Rain4 (62.2%), with Rain1, Rain2, Rain3 and the Check having means of 60.2, 61.4, 61.5, and 13.6%, respectively (Fig. 3A). Rain2 had the highest mean IND (17.5%), followed by Rain1 (13.2), Rain3 (15.3), Rain4 (15.6) and the check (4.0%) (Fig. 3B). As was the case in the previous 2 years, mean DON was numerically highest for Rain2 (6.95 ppm), followed by Rain1, Rain3, Rain4 and the Check (5.55, 6.50, 6.12, and 2.14 ppm, respectively) (Fig 3D). Rain2 also had the highest mean FDK in 2013 (33.7%), with Rain1, Rain3, Rain4 and the check having means of 29.7, 32.5, 29.2, and 7.0%, respectively (Fig. 3C). Linear mixed model analysis was performed to determine the effect of rainfall treatment and inoculum source on transformed IND, INC, FDK, and DON (arcind, arcinc, arcfdk, logdon). The effects of inoculum source and its interaction with rainfall treatment were not significant (or were only marginally significant). Therefore, since the main focus of this research was the rainfall treatment, mean responses, averaged across inoculum sources, were compared. Results from global tests and lsmestimate statements for treatment comparisons 49

65 with the untreated check (Check) and the continuous rainfall control (Rain1) are presented in Table 1 for pooled data from 2011 and 2012, as well as From the global test in each year, the effect of treatment was statistically significant for arcind, arcinc, arcfdk, and logdon (P < 0.05; Table 1). In most cases, Rain1 (daily rain) and Rain2 (rain at the beginning and end of the treatment window and rain every other day) had significantly higher levels of FHB index and DON (on the transformed scale) than the untreated check. In general, Rain2 and Rain4 were not statistically different from Rain1 (the daily rainfall reference treatment) for any of the measured responses. In 2013, all rainfall treatments were significantly different from the check for all responses, and Rain2, Rain3 and Rain4 were not significantly different from Rain1 for any response. In 2011/12, Rain3 was not statistically different from the check for any response, and resulted in significantly lower mean arcind and arcinc, but not arcfdk and logdon than Rain1. In that same year, Rain4 resulted in significantly higher arcind and arcinc than the check, but did not have a significant effect on arcfdk and logdon (Table 1). Post-anthesis moisture effect on FHB and DON. At 10 days post inoculation (DPI), for the experiment utilizing the spray inoculation method, Mist1 displayed the highest mean IND (34.3%), while Mist2 had the highest mean IND (19.3%) among the three treatments that employed an intermittent mist regimen (Fig. 4A). Mist3, Mist4 and the non-misted check had mean IND of, 14.9, 13.4, and 5.0%, respectively (Fig. 4A). At 20 DPI, a similar numerical trend was observed, with Mist1, Mist2, Mist3, Mist4 and check having mean IND of 50.5, 50

66 43.3, 40.1, 39.3, and 22.1, respectively (Fig. 4A). Interestingly, for this experiment, although not having the highest visual symptoms, Mist2 had the highest mean DON (75.01 ppm), followed by Mist1, Mist3, Mist4 and the check, with means of 59.53, 23.97, 23.23, and 8.75 ppm, respectively (Fig. 4C). For the point-inoculation experiment, Mist1 and Mist2 had the highest mean IND at 10d DPI (19.3% and 19.4%, respectively) (Fig. 4B), while Mist3, Mist4 and un-misted check had means of 14.0, 17.9, and 12.8%, respectively (Fig. 4B). A slightly different trend was observed among treatments at 20 days after inoculation. Mist2 and Mist4 had the highest mean IND_20 (40.3% and 37.6%, respectively) with Mist1, Mist3 and the un-misted check having means of 30.0, 31.0, and 35.2%, respectively (Fig. 4B). Interestingly, as with the spray inoculation experiment (and the pre-anthesis rainfall field experiments), the highest mean DON was seen in plants exposed to Mist2 (15.70 ppm) (Fig. 4C). Mist1, Mist3, Mist4 and the check had mean DON levels of 4.96, 8.12, 8.62, and 6.05, respectively (Fig. 4C). Linear mixed model analysis was performed to assess the effects of postanthesis mist treatment on transformed IND_10, IND_20, and DON (arcind10, arcind20, logdon), and rind (the rate of disease spread within the spike). Pairwise treatment comparisons were carried out using the lsmestimate statements and are summarized in Table 2. All treatments were contrasted with both the un-misted check (Check) and the daily mist reference treatment (Mist1) for both the point and spray inoculation methods. For the spray inoculation experiment, the effect of rainfall treatment (global test) was significant for all but 51

67 one measured response (rind), with all mist treatments having significantly higher arcind10, arcind20, logdon then the un-misted check. When compared to Mist1, all three of the discontinuous mist treatments had significantly lower arcind10, but arcind20 and logdon were not significantly different between daily and discontinuous post-anthesis mist treatments. Interestingly, for the point inoculation experiment, there were no significant treatment effects on arcind10, arcind20, rind, or logdon (Table 2) Pre-anthesis rainfall and post-anthesis mist effect on FHB/DON relationships. The above analysis shows the direct and indirect influence of treatment on logdon and one cannot tell if a change in DON is due to the treatment effect on disease intensity with corresponding change in DON, or due to direct effect of the treatment on toxin levels (that does not involve change in disease intensity). Therefore, linear mixed model regression analyses were used to model relationships between FHB index (IND) and logdon as influenced by pre-anthesis rainfall and post-anthesis mist treatments (Table 3, Fig. 5). In all cases, logdon was significantly affected by rainfall or mist treatments, and logind (as a covariate), but not by interactions between the covariate and the qualitative treatment factors. The P values (levels of significance) for rainfall treatment x IND interactions were for 2011/2012 and for 2013 field experiments; for the mist treatment x IND interactions, the P values were and for the spray- and point-inoculated greenhouse experiments, respectively. Without statistically significant interactions, a generic model for the relationship between expected [E(logDON)] and IND can be written as: 52

68 , (5) Where E(logDON) is the expected value, = intercept for each treatment and = common slope (rate of logdon increase per unit increase in IND) for each experiment (Table 3). Estimated intercepts (height of regression lines) were compared among treatments, with emphasis on comparisons between daily rainfall or mist treatments and discontinuous treatments (Fig. 5). For the 2011/2012 preanthesis rainfall field experiment, Rain2 (simulated rain only on the first and last two days of the 7-day pre-anthesis treatment window) had a significantly higher intercept (height of the regression line; level of logdon at a fixed level of IND) than Rain1 (simulated rain on all 7 days) (Fig. 5A). For the 2013 field experiment, all treatments, except for Rain3, had significantly higher intercepts than the check, but the difference between Rain1 and Rain2 was not statistically significant (Fig. 5B). Interestingly, a similar trend to that observed in the 2011/2012 field experiment was observed in the greenhouse experiments with post-anthesis misting. The intercept was significantly higher for Mist 2 (mist only on the first and last 2 days of the 8-day post-anthesis treatment window) than Mist1 (mist on all 8 days) at the 5significance P 0.05 for the spray-inoculated experiment (Fig. 5C) and at P 0.10 level of significance for the point-inoculated experiment (Fig. 5D). Pre-anthesis rainfall and post-anthesis mist and risk of FHB and DON exceeding critical thresholds. Generalized linear mixed models were fitted to the data from all experiments and probabilities were estimated as described 53

69 above as measures of the risk of IND and DON exceeding critical levels. In all cases, the lsmestimate statements in Pro GLIMMIX were used to compare treatments on the link scale (logit, which is an estimate of the log odds) and then the results were reported on the data scale (probability) following conversion using the inverse link function (Fig. 6). For the pre-anthesis rainfall experiments, estimated probability of infection (P_INFECT) or index > 10% (P_IND10) for 2011/12 pooled data and for 2013 data are presented in Figure 6. In general, P_INFECT and P_IND10 were lower ( ) in 2011/12 than in 2013 (> 0.60 for rainfall treatments, > 0.20 for check) (Fig. 6A, B). In 2011/12, Rain1 had a significantly higher probability of infection then the intermittent rainfall treatments and the check (Fig. 6A). Rain3 had significantly lower P_INFECT and P_IND10 values than Rain1, Rain2 and Rain4, but was not significantly different from the check (Fig. 6A). Corresponding to the higher disease and DON levels in 2013; there was no difference in P_INFECT or P_IND10 among rainfall treatments in 2013, but all treatments had significantly higher probabilities than the untreated check (Fig. 6B). For the post-anthesis greenhouse experiments, P_IND10 was evaluated at both 10 and 20 DPI (Fig. 6 C, D). As would be expected, for both experiments, P_IND10 was higher at 20 DPI than at 10 DPI (Fig. 6C, D). For the sprayinoculation experiment, there was a significant treatment effect on P_IND10 (on the logit scale) at both 10 DPI and 20 DPI (P < 0.001), with all mist treatments having higher P_IND10 values than the check (Fig. 6C). Among the mist treatments, Mist1 and Mist4 were significantly different from Mist2 and Mist3 at 54

70 10 DPI, but the mist treatments were not significantly different from each other at 20 DPI. All mist treatments had between a 45 and 86% chance of resulting in index greater than 10% at 10 DPI, and greater than 78% chance at 20 DPI (Fig. 6C). Trends in the point-inoculated experiment were very similar to those observed in the spray-inoculated experiment, however, the effect of treatment was only statistically significant at 10 DPI (P = 0.019) but not at 20 DPI (P = 0.110). At 10 DPI, the check had the lowest P_IND10 (0.61), whereas all the mist treatments had P_IND10 > 0.70 (Fig. 6D). All mist treatments as well as the check had P_IND10 > 0.80 at 20 DPI. Probability of DON exceeding thresholds of 2 (P_DON2), 5 (P_DON5), and 10 ppm (P_DON10) for the spray inoculation experiment was also evaluated. For all thresholds, the effect of mist treatment was statistically significant (Fig. 7). In all cases, the check had the lowest estimated probabilities (<0.20). Mist1, Mist2, Mist3 and Mist4 all had mean P_DON2 values > 0.60, with no statistical difference between Mist1, Mist2 or Mist3 (Fig. 7). A similar trend was apparent for P_DON5 and P_DON10. Interestingly, for all threshold levels, Mist1 had the highest estimated probability, with Mist2 not being significantly different. This suggests that this particular post-anthesis mist pattern that received four days less mist than Mist1 may still yield equally high probabilities of exceeding critical DON thresholds (Fig 7). 55

71 DISCUSSION To our knowledge, these experiments are the first to look into the effects of specific intermittent rainfall patterns pre- and post-anthesis on FHB visual severity and DON. From the field experiments, it was clear that in the warmer drier years, the amount and pattern of pre-anthesis rainfall did have an effect on both FHB visual symptoms and DON accumulation in harvested grain. However, in the cooler, wetter year, four days of pre-anthesis simulated rainfall, regardless of pattern, had comparable effect on DON as 8-day continuous pre-anthesis rainfall. This suggests that the amount and duration (h) of pre-anthesis rainfall are important contributors to disease development and DON accumulation, and that the rainfall effect is influenced by the overall ambient conditions. Results from the controlled-environment studies showed that postanthesis mist (amount and pattern) had an effect on IND and DON in the sprayinoculated experiment but had little discernable effect in the point-inoculated experiment. In addition, for the spray-inoculation experiment, differences in mean IND and DON between the continuous 8-day mist treatment and the 4-day mist treatments, regardless of the mist pattern, were only significant at 10 days post inoculation (DPI); by 20 DPI the mist treatment effect had disappeared. This suggests that post-anthesis moisture likely had an effect on the infection process, which takes place around anthesis. Once infection had occurred, mimicked by the point-inoculation process (where the necessary spore germination and penetration processes were bypassed), 4-days of post-anthesis mist, with any 56

72 pattern, was sufficient to yield comparable levels of disease and DON to 8 days of daily post-anthesis mist. Linear mixed models subsequently fitted showed that there was a significant positive linear relationship between IND and DON (on the logtransformed scale), with DON levels increasing as IND increased. This significant relationship was consistent with previous studies (Bai et al. 2001, Mesterhazy 2002, Paul et al. 2005, 2006). In none of the experiments were the rates (slopes of the regression line) at which DON increased per unit IND increased influenced by the rainfall/mist treatments, but the intercepts (height of the regression line) were. This suggests that whatever the influence of the pre- and post-anthesis moisture conditions was, it likely affected the initial infection and early DON, which then increased at a constant rate after the treatments subsided. This is understandable since all plants were exposed to the same set of post-treatment conditions during the time that DON could be produced. The height of the regression line was generally higher for one particular type of intermittent moisture pattern - the one with two consecutive days of simulated rainfall or mist, followed by four consecutive days without the supplemental moisture, and then another two days of rainfall/mist (the Rain2 and Mist2 treatment). However, this particular moisture effect on the height of the regression line was not observed in 2013, the wettest of the field seasons. This could be attributed to the fact that the influence of pre-anthesis rainfall was likely overridden by the prevailing highly favorable in-field atmospheric conditions in that year. 57

73 The finding that both Rain2 (rain for 2 days, followed by a period of dry, and then 2 days of rain), applied pre-anthesis, and Mist2, applied post-anthesis, resulted in significantly higher levels of DON at fixed levels of index, when compared to other treatments, was particularly surprising. This suggests that this particular pattern, when occurring both prior to and after anthesis, affected some biological process which resulted in increased DON even at a fixed level of visual symptoms. Quite often differences in DON among treatment groups (fungicides, cultivars, locations) are attributed to differences in disease levels or grain colonization (Paul et al. 2010; Wise 2012; Willyerd et al. 2012). However, since the analytical approach used in this study allowed us to control for the confounding effects of visual symptoms on DON response to moisture treatment, it would be reasonable to assume that the observed differences in DON were not necessarily solely due to difference in disease severity. In fact, Rainfall2 and Mist2 were not always the treatments with the highest mean index, yet these treatments had the highest mean logdon at a fixed level of index. This is because differences in the intercepts reflect differences in logdon at the same index level. Rainfall-influencing pre-anthesis factors and post-anthesis moistureinfluencing factors likely contributed to DON increase in a manner that was somewhat independent of changes in visual symptoms. Differential inoculum production by F. graminearum, in association with asymptomatic infections occurring latter in the season, may explain the DON response to Rain2 and other simulated rain treatments under field conditions. It may be that a period of wetting followed by days of drying resulted in enhanced 58

74 perithecial development within the in-field inoculum sources. It may also be the case that perithecial development occurred during the period of high moisture, as suggested by Trail et al. (2002), but ascospores were ejected during the subsequent dry period. This would be consistent with studies conducted by Tschanz et al. (1975) and Paulitz et al. (1996) which showed that ascospore release was greatest during the 4 days following a rainfall event. However, the fact that Rain2 was not always the treatment with the highest level of visual symptoms suggests that even if more spores are produced and released under Rain2, these may have persisted to result asymptomatic late season infections. This may in part explain higher DON for a given level of visual symptoms under Rain2, since asymptomatic infects have been shown to be associated with DON accumulation (Paulitz et al. 1997; Lacey et al. 1999; Del Ponte et al. 2007; Yoshida and Nakajima 2010). In terms of post-infection conditions, we hypothesize that mist treatment effect after infection are related to stress responses of the pathogen, That is, in the presence of continuous moisture and surface wetness following inoculation, the pathogen is less likely to undergo a stress response and can complete infection and colonization. However, the introduction of a fairly extended dry period following an initial wet period may trigger a stress response and cause the pathogen to increase deoxynivalenol production to facilitate colonization. The effect of post-inoculation wet/dry cycle may have some similarity to that seen with the stress response to temperature cycles described by Ryu and Bullerman (1999). Results from thir study showed that in vitro DON production by F. 59

75 graminearum was highest when an extended period of cold stress followed a period of moderate temperatures. The DON:IND results could also be due to the initial enhancement of DON biosynthesis during the infection timeframe. Hallen-Adams et al. (2011) found that expression of Tri5 (the gene complex most widely associated with DON biosynthesis) was greatest in asymptomatic tissue during the very early stages of infection. Expression levels remained enhanced throughout tissue associated with the infection front (above and below the infection point) but diminished at the initial infection point. Tri5 expression, although diminished, had not ceased in any tissue by 21 days post infection (Hallen-Adams et al. 2011). This research suggests that very early infection is the critical timeframe for DON biosynthesis. Mist2 may have contributed to an enhancement in Tri5 expression early in this critical infection period, which would explain the higher than expected regression intercept. In this study we also evaluated the estimated risk of infection, IND >10% and DON > 2, 5, and 10 ppm as influenced by both pre- and post-anthesis moisture patterns. These thresholds were based on the IND used to denote an epidemic in the current disease forecasting system (wheatscab.psu.edu, Shah et al 2013) and DON limits set by the FDA for raw grain (2 ppm) and grain destined for livestock feed (5 ppm and 10 ppm). In the 2011/12 experiment, three out of the four rain treatments had equally high probabilities of infection and IND >10%. In that experiment, Rain3 had as low a probability as the check. This may have been an artifact of the experimental design in those 2 years (which was modified 60

76 for 2013) in which Rain3 received three days of rain in the middle of the 7 day window, as opposed to 4 days in an 8 day window for As such, Rain3 received one less day rain in the first 2 years, which may have resulted in lower disease compared the other rainfall treatments. Interestingly, then this was modified to 4 days in 2013, the probability became comparable to those of the other intermittent treatments and the continuous rainfall control. This may suggest that there may be a minimum number of days of free moisture that must occur during that window for infection and IND to exceed critical levels. It was, nevertheless, interesting to see that there was an equal probability of all of the rainfall treatments exceeding IND 10%, compared to the no rain control. Also, those mean estimated probabilities were relatively high at >60%. Under controlled conditions, probabilities of IND>10% were estimated at both 10 and 20 DPI, for both the point and spray inoculation types. For the spray inoculation experiment, at 10 DPI, all treatments had significantly higher probability (>40%) of exceeding this threshold than the check. Mist1 had the highest probability, with Mist2 and Mist3 having equaling high probabilities. By 20 DPI, all intermittent treatments (either continuous mist or some variation of 4 days of mist), had equally high (80-100%) probabilities of exceeding IND >10%. Although these post-anthesis mist treatments appeared to differentially affect initial pathogen establishment and visual disease development, they all led to a relatively high chance of exceeding the critical disease threshold evaluated here. For the point inoculation experiment, which bypassed several critical infection events, by 10 DPI all treatments (including the check) had >60% chance of 61

77 exceeding IND>10%, and a >80% chance at 20 DPI. It is possible that these moisture patterns differentially affect the infection stage of disease development (as seen with the spray inoculation experiment) but once the pathogen is already established, the pattern of moisture is less important for disease progression. It is important to note that under these artificial conditions, there was a fixed, a fairly high, concentration of spores used. Future studies may consider the impacts these mist patterns on different spore concentrations. DON thresholds (2, 5, 10ppm) were also evaluated for individual spikes from the spray inoculation experiment. At 2ppm Mist1, Mist2 and Mist3 each had an equally high probability of exceeding this threshold, and all four intermittent treatments had >60% probability of exceeding this threshold. In all cases, Mist2 (mist for four days in the middle of the window) had an equally high probability of exceeding the threshold as Mist1 (continuous). This trend was similar for the 5 and 10 ppm thresholds as well, when compared to the check which had >20% chance in all cases. These findings suggest that even if moisture is discontinuous after rainfall, if there are a sufficient number of days of moisture (4) then DON may exceed economically important limits. Furthermore, if there is a pattern similar to our Mist2 treatment, with two days of drying followed by four of mist, this probability may increase. This information is critical for those who are making Fusarium head blight management decisions. Key findings from this study were that (1) pre-anthesis rainfall amount and pattern had a greater effect on IND and DON, the risk of infection, and probability of IND and DON exceeding critical thresholds in years with relatively 62

78 dryer post-anthesis conditions than those observed in 2013; (2) post-anthesis moisture patterns, within the 4 to 8-day range tested here, was likely important for infection (based on spray inoculatons), but once infection had occurred, IND may be similar regardless of amount and pattern of mist; (3) Both intermittent pre- and post-anthesis moisture patterns, particularly if wet days are separated by dryer days in the middle of the period, could lead to higher levels of DON at a fixed level of IND, compared with continuous moisture during the 8-day pre- and post-anthesis windows; (4) The risk of IND and DON exceeding critical thresholds may be just as high for 8-day continuous moisture and 4-day discontinuous moisture. These responses were observed despite the fact that temperature and RH were similar among the different rainfall and mist treatments. In the case of the IND/DON relationship, this suggests that the observed differences in the heights of the regression lines (intercepts) were indeed due to the rainfall/mist pattern and not due to other ambient environmental conditions. As found in 2013, very favorable late-season conditions could moderate the IND/DON relationship in such a way that all rainfall treatments had similar effects. This hypothesis is supported by results from studies conducted by Cowger et al. (2009) and Culler et al. (2007) which showed that the amount and duration of rainfall/moisture during windows beyond those investigated in this study (past Feekes 11.0) may also affect DON accumulation. With additional location-years of data, it would be of value to refit models to evaluate the effect of pre-anthesis rainfall pattern in combination with prevailing local conditions (both genotypic and environmental) on DON response 63

79 at fixed levels of IND. In addition, future research such as in-field spore sampling, fungal biomass analysis (through qpcr for instance) of asymptomatic grain, and Tri5 gene expression profile under differ moisture treatments may help to further explain why some intermittent rainfall and moisture patterns led to elevated DON relative to continuous moisture within the treatment windows evaluated in this study. 64

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84 Table 2.1. Probability values (levels of significance) from linear mixed model analyses (global tests) of pre-anthesis rainfall treatment effects on arcsine-transformed Fusarium head blight IND, INC, and FDK and log-transformed DON, along with probability values for pairwise comparisons of treatment means using linear contrast for field experiments conducted in Wooster, Ohio in 2011, 2012, and Comparison arcind arcinc arcfdk logdon arcind arcinc arcfdk logdon a 2011/2012 b 2013 b Rain1 vs. Check <0.001 < <0.001 < <0.001 Rain2 vs. Check <0.001 <0.001 <0.001 <0.001 Rain3 vs. Check <0.001 <0.001 <0.001 Rain4 vs. Check <0.001 < <0.001 Rain2 vs. Rain Rain3 vs. Rain Rain4 vs. Rain Global test < <0.001 <0.001 <0.001 <0.001 a Plots were subjected to simulated rainfall 4 min every 12 min in the morning between 500 and 900 h and between 1700 and 2100 h during a 7- day pre-anthesis window in 2011 and 2012, and an 8-day window in Rain1 = simulated rain on each of 7 or 8 consecutive days before anthesis; Rain2 = simulated rain only on the first and last 2 days of the treatment window; Rain3 = simulated rain only on the middle 3 or 4 days, and no rain on the first and last 2 days of the treatment window; and Rain4 = rain every other day, beginning on the first day of the treatment window. Check = plots that received no simulated rainfall. b Data from 2011 and 2012 were pool for analysis, while data from 2013 were analyzed as a separate experiment. IND = Fusarium head blight index (mean proportion of diseased spikelets per spike), INC = Fusarium head blight incidence (mean proportion of diseased spike per sample of spikes), FDK = Fusarium damaged kernels (mean proportion of small, shriveled, discolored kernels in a grain sample), and DON = deoxynivalenol content of harvested grain (ppm) 69

85 Table 2.2. Probability value (level of significance) from linear mixed model analyses (global tests) of post-anthesis mist treatment effects on arcsine-transformed Fusarium head blight IND, rind, and log-transformed DON, along with probability values for pairwise comparisons of treatment means using linear contrast for spray- and point-inoculated greenhouse experiments conducted in Wooster, Ohio 70 Comparison arcind10 arcind20 rind logdon arcind10 arcind20 rind logdon a Spray b Point b Mist1 vs. Check < < Mist2 vs. Check < Mist3 vs. Check Mist4 vs. Check Mist2 vs. Mist Mist3 vs. Mist Mist4 vs. Mist Global test < < a Experimental units were treated with 90 sec of mist every 10 min for 12 h per day during 8 days after anthesis. Mist1 = mist treatment applied on each of eight consecutive days after anthesis; Mist2 = mist applied only on the first and last two days of the 8-day window; Mist3 = mist applied only on the middle four days, and no mist on the first and last two days of the treatment window; and Mist4 = mist applied every other day, beginning on the first day of the treatment window. Check = no mist. b Spray = spikes were spray-inoculated with a Fusarium graminearum macroconidia suspension containing 50,000 spores/ml, Point = spikes were point-inoculated by pipetting 10 µl of a macroconidia suspension containing 10,000 spores/ml into the central spikelet of each spike. IND10 = Fusarium head blight index (mean proportion of diseased spikelets per spike) rated at 10 days after anthesis, IND = index rated at 20 days after anthesis, rind = rate of index increase (% IND/day) between 10 and 20 days after anthesis ([IND20-IND10]/10), and DON = deoxynivalenol content of harvested grain (ppm) 70

86 Table 2.3. Regression coefficients from linear mixed models analyses of relationship between Fusarium head blight index (IND) as a continuous covariate and log-transformed deoxynivalenol content of harvest wheat grain as influenced by preanthesis and post-anthesis treatment 71 Factor b 2011/2012 a 2013 a Spray a Point a Intercept c se Intercept Se Intercept se Intercept se Treatment Treatment Treatment Treatment Check Slope c a Field experiments conducted in 2011/2012 and in 2013 and spray- and point-inoculated greenhouse experiments. Spikes were spray-inoculated with a Fusarium graminearum macroconidia suspension containing 50,000 spores/ml or point-inoculated by pipetting 10 µl of a macroconidia suspension containing 10,000 spores/ml into the central spikelet of each spike. b For field experiments, Treatment1 = simulated rainfall on each of 7 or 8 consecutive days before anthesis; Treatment2 = simulated rain only on the first and last two days of the treatment window; Treatment3 = simulated rain only on the middle three or four days, and no rain on the first and last two days of the treatment window; and Treatment4 = rain every other day, beginning on the first day of the treatment window, whereas for the greenhouse experiments, Treatments1, 2, 3 and 4 correspond to mist treatments applied following the same patterns used for the field experiments. Check = experimental units that received no simulated rainfall or mist treatment. c Intercepts (logdon when IND = 0) and slopes (the rate of increase in logdon per unit increase in IND) were estimated from linear mixed model covariance analysis. 71

87 Temperature Rainfall A RH RH > 90 B Average temperature ( o C) /2/11 5/16/11 5/30/11 6/13/11 6/27/11 C 4/9/12 4/23/12 5/7/12 5/21/12 6/4/12 E Total rainfall (mm) Average relative humidity (%) /2/11 5/16/11 5/30/11 6/13/11 6/27/11 D 4/9/12 4/23/12 5/7/12 5/21/12 6/4/12 F Relative humidity > 90 (h) /22/13 5/6/13 5/20/13 6/3/13 6/17/ /22/13 5/6/13 5/20/13 6/3/13 6/17/13 0 Figure 2.1. Average weather conditions for each season that field experiments were conducted. Average temperature (solid black lines) and total rainfall (black bars) (A, C and E) and average relative humidity (broken clack lines) and number of hours with relative humidity greater than 90% (white bars) (B, D, and F) from April 28 th -June 29 th, 2011, April, 7 th - June 8 th, 2012, and April 21 st - June 23 th, 2013 (E). Vertical lines show pre-anthesis window during which simulated rainfall treatments were applied. Plots reached 50% anthesis on June 2 nd, 2011, May 12 th, 2012, and May 27 th,

88 30 Average temperature ( o C) A Average relative humidity (%) B Rain1 Rain2 Rain3 Rain4 Ambient Figure 2.2. Mean canopy temperature ( C), A, and relative humidity (%), B, in each whole-plot (simulated rainfall treatment) and in an adjacent non-treated field (Ambient) for 2011, 2012, and Treated plots were subjected to simulated rainfall four minutes every 12 minutes between 500 and 900 h and between 1700 and 2100 h during a 7-day pre-anthesis window in 2011 and 2012, and an 8-day window in Rain1 = simulated rain on each of 7 or 8 consecutive days before anthesis; Rain2 = simulated rain only on the first and last two days of the treatment window; Rain3 = simulated rain only on the middle three or four days of the treatment window; and Rain4 = rain every other day, beginning on the first day of the treatment window. 73

89 70 65 A B Incidence (%) Index (%) Fusarium damaged kernels (%) C Rain1 Rain2 Rain3 Rain4 Check Deoxynivalenol (ppm) D Rain1 Rain2 Rain3 Rain4 Check Figure 2.3. Mean Fusarium head blight (FHB) incidence (A, mean proportion of diseased spike per sample of spikes), FHB index (B, mean proportion of diseased spikelets per spike), Fusarium damaged kernels (C, mean proportion of small, shriveled, discolored kernels in a grain sample), and deoxynivalenol content of harvested grain (D, ppm) for different pre-anthesis rainfall treatments applied to field plots in 2011, 2012, and Treated plots were subjected to simulated rainfall four minutes every 12 minutes between 500 and 900 h and between 1700 and 2100 h during a 7-day pre-anthesis window in 2011 and 2012, and an 8-day window in Rain1 = simulated rain on each of 7 or 8 consecutive days before anthesis; Rain2 = simulated rain only on the first and last two days of the treatment window; Rain3 = simulated rain only on the middle three or four days of the treatment window; and Rain4 = rain every other day, beginning on the first day of the treatment window. 74

90 A IND_10 IND_20 Fusarium head blight index (%) B Deoxynivalenol (ppm) C Spray Point 0 Mist1 Mist2 Mist3 Mist4 Check Figure 2.4. Mean Fusarium head blight index (mean proportion of diseased spikelets per spike) for spray- and point-inoculated greenhouse experiments (A and B, respectively) and mean deoxynivalenol content of harvested grain for both experiments (C) for wheat spikes subjected to different post-anthesis mist treatments. Spikes were exposed to 90 seconds of mist every 10 minutes for 12 hours per day during 8 days after anthesis. Mist1 = mist treatment applied on each of eight consecutive days after anthesis; Mist2 = mist applied only on the first and last two days of the 8-day window; Mist3 = mist applied only on the middle four days; and Mist4 = mist applied every other day, beginning on the first day of the treatment window. Check = no mist. 75

91 A B Deoxynivalenol (log[don+1]) Rain1 1.0 Rain2 Rain3 0.5 Rain4 Rain1 vs Rain2 (P = 0.02) Check Rain1 vs Rain2 (P = 0.76) C Mist1 Mist2 1.0 Mist3 Mist4 0.5 Mist1 vs Mist2 (P = 0.08) Check Mist1 vs Mist2 (P = 0.04) Fusarium head blight index (%) D Figure 2.5. Relationship between Fusarium head blight index (%) and logtransformed DON (DON+1) as influenced by pre-anthesis rainfall treatments (A and B) and post-anthesis mist treatments (C and D) for field experiments conducted in 2011 and 2012 (A) and 2013 (B) and greenhouse spray-inoculated (C) and point-inoculated (D) experiments. For field experiments plots were subjected to simulated rainfall four minutes every 12 minutes between 500 and 900 h and between 1700 and 2100 h during a 7-day pre-anthesis window in 2011 and 2012, and an 8-day window in Rain1 = simulated rain on each of 7 or 8 consecutive days before anthesis; Rain2 = simulated rain only on the first and last two days of the treatment window; Rain3 = simulated rain only on the middle three or four days of the treatment window; and Rain4 = rain every other day, beginning on the first day of the treatment window. For greenhouse experiments spikes were exposed to 90 seconds of mist every 10 minutes for 12 hours per day during 8 days after anthesis. Mist1 = mist treatment applied on each of eight consecutive days after anthesis; Mist2 = mist applied only on the first and last two days of the 8-day window; Mist3 = mist applied only on the middle four days; and Mist4 = mist applied every other day, beginning on the first day of the treatment window. Check = no simulated rain or mist. 76

92 Figure 2.6. Estimated probability of infection (P_INFECT) and Fusarium head blight index 10% (P_IND10) for field experiments conducted in 2011 and 2012 (A) and 2013 (B) and P_IND10 for assessments made at 10 (P_IND10_10DPI) and 20 (P_IND10_20DPI) in greenhouse spray-inoculated (C) and pointinoculated (D) experiments. For field experiments plots were subjected to simulated rainfall four minutes every 12 minutes between 500 and 900 h and between 1700 and 2100 h during a 7-day pre-anthesis window in 2011 and 2012, and an 8-day window in Rain1 = simulated rain on each of 7 or 8 consecutive days before anthesis; Rain2 = simulated rain only on the first and last two days of the treatment window; Rain3 = simulated rain only on the middle three or four days of the treatment window; and Rain4 = rain every other day, beginning on the first day of the treatment window. For greenhouse experiments spikes were exposed to 90 seconds of mist every 10 minutes for 12 hours per day during 8 days after anthesis. Mist1 = mist treatment applied on each of eight consecutive days after anthesis; Mist2 = mist applied only on the first and last two days of the 8-day window; Mist3 = mist applied only on the middle four days; and Mist4 = mist applied every other day, beginning on the first day of the treatment window. Check = no simulated rain or mist. 77

93 Figure 2.7. Estimated probability of deoxynivalenol contamination of harvested grain 2, 5 and 10 ppm for different post-anthesis mist treatments. Spikes were spray inoculated and then exposed to 90 seconds of mist every 10 minutes for 12 hours per day during 8 days after anthesis. Mist1 = mist treatment applied on each of eight consecutive days after anthesis; Mist2 = mist applied only on the first and last two days of the 8-day window; Mist3 = mist applied only on the middle four days; and Mist4 = mist applied every other day, beginning on the first day of the treatment window. Check = no mist 78