University of Florida Thesis Or Dissertation

FIELD EFFICACY OF QUINONE OUTSIDE INHIBITOR AND DEMETHYLATION INHIBITOR FUNGICIDES ON PEANUT FOLIAR DISEASES IN FLORIDA

WAEL M. ELWAKIL

A THESIS PRESENTED TO THE GRADUATE SCHOOL OF THE UNIVERSITY OF FLORIDA IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF MASTER OF SCIENCE

UNIVERSITY OF FLORIDA

2018

To my Mom

ACKNOWLEDGMENTS

I thank my family for their emotional support and encouragement during this journey of pursuing my master’s.

I owe a great debt of gratitude to my masters and doctorate advisor Dr. Nicholas

S. Dufault of the Plant Pathology Department at the University of Florida for his continuous support, advice, and mentoring. Dr. Dufault is undeniably one of the most respectable and generous people that I have ever encountered in my life.

I would like also to acknowledge Mrs. Kristin Beckham for her help during the various steps of this project. I also thank Dr. Rebecca Barocco for assisting with the field trials setup, maintenance, and disease ratings.

TABLE OF CONTENTS

page

ACKNOWLEDGMENTS ...... 4

LIST OF TABLES ...... 7

LIST OF FIGURES ...... 8

LIST OF ABBREVIATIONS ...... 9

ABSTRACT ...... 10

CHAPTER

1 LITERATURE REVIEW ...... 12

Peanut (Host and Production) ...... 12 Peanut Diseases in U.S...... 13 Peanut Foliar Diseases ...... 14 Early Leaf Spot (ELS) ...... 15 Late Leaf Spot (LLS) ...... 16 Rust ...... 17 Foliar Disease Management ...... 18 Quinone Outside Inhibitor Fungicides (QOI) ...... 20 DeMethylation Inhibitor Fungicides (DMI)...... 21 Hypothesis and Objectives ...... 22

2 RELATIVE TREATMENT EFFECT OF QOI AND DMI FUNGICIDES ON PEANUT EARLY LEAF SPOT, LATE LEAF SPOT, AND RUST ...... 24

Introduction ...... 24 Materials and Methods...... 26 Plot Establishment and Experimental Design: ...... 26 Fungicide Treatments and Applications ...... 26 Disease Assessment ...... 27 Yield Collection ...... 28 Data Analysis ...... 28 Results ...... 29 Disease Incidence and Environment ...... 29 Disease Management ...... 30 Discussion ...... 31

3 THE EFFICACY OF FUNGICIDE MIXTURES AND ALTERNATIONS ON PEANUT FOLIAR DISEASE MANAGEMENT IN FLORIDA ...... 43

Introduction ...... 43

Materials and Methods...... 44 Plot Establishment and Experimental Design ...... 44 Treatments and Fungicide Applications...... 45 Disease Assessment ...... 46 Data Analysis ...... 47 Results ...... 48 Disease Progression and Incidence ...... 48 Disease and Yield ...... 48 Discussion ...... 50

4 CONCLUSION ...... 64

APPENDIX: EXAMINATION OF PRIMERS FOR SINGLE POINT MUTATIONS RELATED TO FUNGICIDE RESISTANCE ...... 66

Introduction ...... 66 Experimental Notes ...... 66 Concluding Thoughts ...... 67

LIST OF REFERENCES ...... 70

BIOGRAPHICAL SKETCH ...... 75

LIST OF TABLES

Table page

2-1 A list of the commercial fungicide products used in this study with their respective rates for the seven seasonal applications from 2014 to 2017...... 35

2-2 Effect of the various fungicide treatments on peanut foliar disease intensity and average yield for each year of the experiment based on a mixed model analysis of variance...... 36

2-3 Summary of total rainfall (cm) during the four years of this study. This data was collected from the Florida Automated Weather Network located in Citra, FL...... 37

2-4 Average monthly temperature (ºC) during the four years of this study. This data was collected from the Florida Automated Weather Network located in Citra, FL...... 38

3-1 Summary of the seven spray application programs tested in this study during the 2017 peanut season...... 54

3-2 Summary of the total fungicide (active ingredient) seasonal application rates in the programs tested in this study during the 2017 peanut season...... 55

3-3 Economic/cost estimate of the various spray application programs tested in this study during the 2017 peanut season ...... 56

3-4 Summary of the foliar disease variables standardized area under the disease progression curve (sAUDPC), FL 1 to 10 intensity rating and relative treatment effects ...... 57

3-5 Summary of average yields recorded at the Plant Science Research and Education Unit in Citra, FL and the North Florida Research and Education Center in Mariana and Live Oak, FL during 2017...... 58

LIST OF FIGURES

Figure page

1-1 Foliar pathogens of peanut, early leaf spot (ELS), late leaf spot (LLS) and rust...... 23

2-1 Disease progression curves of the untreated controls using the FL 1-10 ratings from the trials conducted between 2014 to 2017 at the Plant Science Research and Education Unit, Citra, FL ...... 39

2-2 Disease progression curves based on FL 1-10 scale ratings for the various fungicide treatments indicated in the legend (Table 2-2). The results are from four years in Citra, FL (PSREU) ...... 40

2-3 Final leaf spot FL 1-10 disease intensity rating and corresponding relative treatment effect for the treatments from 2014 to 2017 at the Plant Science Research and Education Unit, Citra, FL...... 41

2-4 Disease incidence percentages in the untreated plots for the trials from 2014 to 2017 at the Plant Science Research and Education Unit, Citra, FL ...... 42

3-1 Disease progression curves of the untreated control treatments during 2017 in the three locations Citra, FL (PSREU), the North Florida Research and Education Centers in Mariana (NFRECm) and Live Oak (NFRECl), FL ...... 59

3-2 Disease progression curves based on FL 1-10 scale ratings for the various fungicide treatments indicated in the legend (Table 3-1) ...... 60

3-3 Disease incidence percentages in the untreated control treatments ...... 61

3-4 Relative disease intensity rating (RDIR) for the various fungicide treatments (X-axis) listed in Table 3-1. RDIRs were calculated by dividing the treatments mean final FL 1 to 10 rating by the mean rating for the untreated control ...... 62

3-5 Relative yield increase (RYI) for the various fungicide treatments listed in Table 3-1. RYIs were calculated by dividing the treatments mean yield by the mean yield for the untreated control ...... 63

LIST OF ABBREVIATIONS

AUDPC Area under the disease progression curve

DAP Days after planting

DMI DeMethylation inhibitor

ELS Early leaf spot

LLS Late leaf spot

MBC Methyl Benzimidazole Carbamates

MOA Mode of action

NCBI National Center for Biotechnology Information

QOI Quinone outside Inhibitor

RDIR Relative disease intensity rating

RUTF Ready-to-use therapeutic food

RYI Relative yield increase sAUDPC Standardized area under the disease progression curve

SBI Sterol biosynthesis inhibitors

SDHI Succinate-dehydrogenase inhibitors

Abstract of Thesis Presented to the Graduate School of the University of Florida in Partial Fulfillment of the Requirements for the Degree of Master of Science

FIELD EFFICACY OF QUINONE OUTSIDE INHIBITOR AND DEMETHYLATION INHIBITOR FUNGICIDES ON PEANUT FOLIAR DISEASES IN FLORIDA

Wael M. Elwakil

August 2018

Chair: Nicholas S. Dufault Major: Plant Pathology

Disease management of rust and early and late leaf spot is critical to Florida peanut production. Fungicides are an important component of peanut foliar disease management. Understanding fungicide foliar disease efficacy, especially under different application strategies, is critical to peanut production.

There are many fungicides available for peanut disease control, and common classes include quinone outside inhibitors (QOI) and deMethylation inhibitors (DMI), which are identified as high-risk and medium-risk of resistance selection, respectively.

Azoxystrobin, pyraclostrobin, and tebuconazole fungicides efficacies were tested in solo peanut foliar disease spray programs from 2014 to 2017. The tested QOI and DMI fungicides showed reduced leaf spot field efficacy compared to chlorothalonil. This suggests that strains of the leaf spot pathogens with reduced sensitivity to the fungicides are present, and that alternative use strategies should be considered for these products.

Two common fungicide resistance management strategies are altering different modes of action (alternations) and mixing different modes of action (mixtures). In this

study both alternation and mixture programs were compared to single fungicide programs at three locations during 2017. Alternations of azoxystrobin, pyraclostrobin and tebuconazole with chlorothalonil did show improved disease control and yield increase in multiple field and disease situations. Mixtures of these fungicides, however, were either equal to or better than alternation treatments across all the sites tested in this study. These results indicate that both alternations and mixtures will help maintain the usefulness of these at risk fungicides, however, mixtures appear to be the optimal choice for resistance and disease management.

CHAPTER 1 LITERATURE REVIEW

Peanut (Host and Production)

Peanut (Arachis hypogaea L.) also known as groundnut is a legume crop in the family Fabaceae. It is grown for its edible seeds and its high oil content. Peanut seeds can be used or processed in a several different ways; roasted or boiled peanut seeds, peanut oil for cooking and frying, peanut butter, peanut flour, and many different snacks and food recipes. Peanut is a valuable source of protein and contains many nutrients including high levels of fat, fiber, vitamin E, folate, iron, magnesium, potassium and many others (The-Peanut Institute, http://www.peanut-institute.org/). Since it contains such valuable nutrients, peanut seed is used in a ready-to-use therapeutic food (RUTF) that is formulated to provide a nutritional supplement for children at risk of starvation

(MANA, https://mananutrition.org/). Some peanut products are iconically associated with the American culture, such as peanut butter used in snacks and the well know peanut butter and jelly sandwich, also boiled peanuts is a common snack in most of the southern states.

The United States (U.S.) has been the fourth world producer of peanut for several years after China, India, and Nigeria, respectively, with an average annual production of 2.14 million tonnes over an average harvested area of 530.11 thousand hectares and average 4.24 tonnes per hectares during the period of 2006-2016 (FAO,

2017). According to the U.S. national peanut board, the major peanut producing states are grouped into three regions; Southeast region (Georgia, Florida, Alabama, &

Mississippi), Virginia-Carolina region (Virginia, North Carolina, & South Carolina), and

Southwest region (Texas, Oklahoma, New Mexico, & Arkansas). The U.S. is one of the

leading peanut exporting countries with approximately 80% of its exports going to

Canada, Mexico, Europe and Japan (American Peanut Council, https://www.peanutsusa.com/).

Peanut or groundnut (Arachis spp.) is native to South America (Moss and Rao,

1995). There are only six species that are grown either for their edible seed, forage, or ground cover out of sixty-nine species identified in the genus Arachis. These six species are A. hypogaea L., A. villosulicarpa Hoehne, A. stenosperma Krapov. & W. C. Gregory,

A. repens Handro, A. pintoi Krapov. & W. C. Gregory, and A. glabrata Benth. The cultigen A. hypogaea L. is the most commonly cultivated worldwide in tropical and sub- tropical areas (Kokalis-Burelle et al., 1997; Stalker and Simpson, 1995).

The peanut plant is erect, sparsely hairy, annual herbaceous legume that ranges in height from 15-60 cm (Moss and Rao, 1995). Its root system has minimal root hairs and its taproot has many lateral roots. Leaves are pinnate (quadrifoliate) with two leaflet pairs. Leaflets size ranges from 3 to 5 cm in length. The peanut plant produces aboveground self-pollinating flowers at 4 to 6 weeks after planting. After pollination and flower withering within a week of pollination, the carpophore (needle-like structure), which is commonly known as the “peg” is formed, then it elongates and penetrates the top 7 cm of the soil surface orienting itself horizontally. The ovaries behind the tip of the peg expand rapidly as pods form and grow (Kokalis-Burelle et al., 1997).

Peanut Diseases in U.S.

There are many foliar and soilborne peanut pathogens of concern to growers in the U.S. Foliar peanut pathogens can greatly reduce the percent of photosynthetic area by forming lesions, causing necrosis or premature leaf senescence (defoliation) (Nutter and Shokes, 1995). Soilborne pathogens damage the root system and pods causing 13

significant annual yield losses. Thus, peanut is heavily managed to ensure desired levels of production. Some strategies used to manage peanut diseases include rotating to non-host crops (e.g. cotton, corn) for 2 or 3 years, planting disease resistant/tolerant cultivars using certified disease free seeds to ensure good germination and plant vigor, and avoiding early planting (April and early May) to reduce chances of white mold

(Southern stem rot, Sclerotium rolfsii) and tomato spotted wilt (TSWV) diseases.

However, foliar pathogens can develop from small amounts of initial inoculum and cause severe epidemics. Thus, applying fungicides for fungal diseases are required in most years (Nutter and Shokes, 1995; Smith and Littrell, 1980). A typical peanut crop receives from 7 to 10 fungicide spray applications starting a month after planting.

Product usage varies by region as well as planting date, but conventional peanut producers focus a majority of their fungicide application on foliar disease management

(Wright et al., 2016).

Peanut Foliar Diseases

Early leaf spot (ELS), late leaf spot (LLS) and rust (Fig. 1-1) caused by

Cercospora arachidicola Hori, (1917), Cercosporidium personatum (Berk. & M.A. Curtis)

Deighton, (1967) and Puccinia arachidis Speg, respectively. (1884), respectively are the major foliar peanut diseases in Florida and the southeastern U.S. In addition, ELS and

LLS diseases can be devastating worldwide to peanut production. These three diseases may occur individually on peanut leaves and stems or combined (Elwakil and Dufault

2016; Nutter and Shokes, 1995). The presence of one or more of these diseases on

Florida peanut depends on several factors including planting date, peanut variety, initial inoculum, and environmental conditions such as rainfall, temperature, and relative humidity (Damicone 2017; Elwakil et al., 2017a; Jacobi et al., 1995). Early and late leaf 14

spot diseases are reported to contribute to pod yield losses up to 65% if left unmanaged properly (Nutter and Shokes, 1995). Rust alone can also cause up to 70% peanut yield losses, and up to 86% when its effects are combined with the leaf spot pathogens, but its impact on yield often varies across the Southeastern U.S. depending on environment and inoculum (Damicone 2017; Elwakil et al., 2017b; Hagan 1998; Kokalis-Burelle et al.,

1997). These foliar peanut pathogens have a major impact on peanut production in the

U.S. and the world, and continue to be a major focus of peanut management programs

(Kemerait et al. 2018).

Early Leaf Spot (ELS)

Early leaf spot disease symptoms are usually brown round spots with a yellow hallow. The lesion size, color, and yellow hallow presence/absence vary depending on peanut cultivar and environmental conditions, and their sizes can range from 1-10 mm in diameter (Kokalis-Burelle et al., 1997, McDonald et al., 1985). Infected leaves with

ELS can defoliate rapidly. Hyaline to light brown colored conidiophores and conidia are observed on the upper leaflet surface at magnifications of 40X or greater. The anamorph Cercospora arachidicola forms dark brown stromata of pale olivaceous or yellowish brown dense conidiophore bundles. Conidia are subhyaline, obclavate and slightly curved multi-celled with 3–12 septa, truncate bases, and subacute tips.

Conidiophore size range of C. arachidicola is 15–45 X 3–6 μm and conidia size range is

35–110 X 3–6 μm. Early leaf spot disease may occur any time during the peanut growing season, but is usually favored by moderate temperatures (20 to 24°C) and extended periods of high moisture (e.g. RH ≥ 95%). The main source of initial inoculum is conidia on crop residue. Ascospores, chlamydospores and mycelia can also cause

infection. The teleomorph (Mycosphaerella arachidis Deighton) does not normally occur on peanut (Kokalis-Burelle, 1997; McDonald et al., 1985).

Taxonomic tree: Domain: Eukaryota

Kingdom: Fungi

Phylum: Ascomycota

Subphylum: Pezizomycotina

Class: Dothideomycetes

Subclass: Dothideomycetidae

Order: Capnodiales

Family: Mycosphaerellaceae

Genus: Mycosphaerella

Species: Mycosphaerella arachidis

Late Leaf Spot (LLS)

Late leaf spot disease symptoms are dark brown to black roundish spots, usually with no hallow around them, and they are close in size to those caused by C. arachidicola (1–10 mm). Infected leaves with LLS can defoliate rapidly (Subrahmanyam et al., 1982). The lower leaflet surface of sporulating LLS spots has a rough appearance due to the dense pseudoparenchymatous stromata that are up to 130 μm in size formed by the anamorph Cercosporidium personatum. Conidiophores range in size from 10–

100 X 3–6.5 μm, they bear olivaceous medium size conidia that are cylindrical, obclavate and straight or slightly curved. Conidia have round apexes not constricted with 1-9 septa and their size range is 20–70 X 4–9 μm. Late leaf spot disease usually occurs later in the peanut growing season when temperatures are cooler (optimum temperature is around 20°C) for extended periods of time (>12 hrs.) of high moisture 16

(e.g. RH ≥ 93%). Similar to C. arachidicola, conidia are the primary source of inoculum of C. personatum. The teleomorph (Mycosphaerella berkeleyi Jenk.) does not normally occur on peanut (Chiteka et al., 1988; Kokalis-Burelle, 1997; McDonald et al., 1985).

Taxonomic tree: Domain: Eukaryota

Kingdom: Fungi

Phylum: Ascomycota

Subphylum: Pezizomycotina

Class: Dothideomycetes

Subclass: Dothideomycetidae

Order: Capnodiales

Family: Mycosphaerellaceae

Genus: Mycosphaerella

Species: Mycosphaerella berkeleyi

Rust

Peanut rust symptoms appear in the form of yellowish green flecks on the upper leaf surface with small orange raised pustules on the lower leaf surface

(Subrahmanyam et al., 1985). Infected leaves with rust may remain attached to the plants contrary to the rapid defoliation caused by ELS and LLS. The Puccinia arachidis

Speg. uredinial stage is the most common on peanut. Circular uredinia (orange rust pustules) are 0.5–1.4 mm in diameter, appear scattered on the foliar surfaces as sub- epidermal blister-like structures at immature stages. As the pustules mature, the thin periderm layer covering the pustule ruptures exposing and releasing reddish brown masses of urediniospores to infect healthy foliage. Urediniospores are obovoid shaped

(size range is 23–29 X 16–22 μm), and they have 1–2.2 μm thick brown walls finely 17

echinulate with spines that are 2–3 μm apart. Telia are less commonly noticed rust structures on peanut. The telial stage also appears on the lower side of the leaves as chestnut colored and scattered pustules that turn grayish in color as teliospores germinate after the pustules rupture at maturity. Teliospores are oblong, ellipsoid to ovate shaped with thickened rounded to acute apex. Teliospores are constricted in the middle and usually are two celled or may have three to four cells. They have light to golden yellow or chestnut brown color with size range is 38–42 X 14–16 μm.

Temperature ranging from 20 to 24°C with relative humidity (≥ 87%) and intermittent rain are considered optimal for urediniospores germination, while they can survive in below zero temperatures (-16°C) for several months, they can only survive for 5 day or less at high temperatures (≥40°C) (Bulter and Jadhav, 1991; Kokalis-Burelle, 1997;

Mondal and Badigannavar 2015).

Taxonomic tree: Domain: Eukaryota

Kingdom: Fungi

Phylum: Basidiomycota

Subphylum: Pucciniomycotina

Class: Pucciniomycetes

Order: Pucciniales

Family: Pucciniaceae

Genus: Puccinia

Species: Puccinia arachidis

Foliar Disease Management

Fungicides are an essential tool for managing the foliar diseases ELS, LLS, and rust in the southeastern region of the U.S. (Monfort et al., 2004; Nutter and Shokes, 18

1995; Smith and Littrell, 1980). Typically, 7 to 10 fungicide spray applications are made starting 30 days after planting for peanut disease management including 5 to 8 sprays focused mainly on foliar diseases. Thus, fungicide selection is a critical component of disease management. Methyl Benzimidazole Carbamates (MBC), Succinate- dehydrogenase inhibitors (SDHI), Quinone Outside Inhibitors (QOI), and DeMethylation

Inhibitors (DMI) are single mode of action active (MOA) ingredients in addition to multi- site contact activity active ingredients (e.g. chlorothalonil) have been used to manage these foliar diseases since the early 1990’s. However, there is great risk of resistance development in pathogens to these single modes of action fungicides, especially those classified as high-risk of resistance development by the Fungicide Resistance Action

Committee (FRAC http://www.frac.info/). The groups MBC and QOI fungicides are considered high-risk modes of action, SDHI is medium to high-risk and DMI are medium-risk. On the contrary, the multi-site inhibitors modes of action such as M5 (e.g. chlorothalonil) are considered low-risk for resistance developing. Resistance in many fungal plant pathogens species has been reported against those high-risk modes of action (Gisi 2000; Ishii 2010; Ma and Michailidides 2005; Vincelli 2002). This resistance in pathogen populations usually occurs due to the excessive and/or the repeated use of these single modes of action active ingredients that leads to high selection pressure for resistant pathogen strains (Gisi 2000; Via 1986). Resistance usually develops at a fast rate in polycyclic pathogens, where the selection for resistance genes occurs multiple times in the growing season with multiple applications of these high-risk fungicides

(Rebollar-Alviter and Nita 2011; Via 1986; Vincelli 2002).

Resistance (reduced sensitivity) to fungicides can be reflected in higher disease incidences, severities and defoliation levels resulting in significant yields losses as products tend to fail in their disease control (van den Bosch et al., 2014; Elderfield et al.,

2018). Thus, it is critical to understand the impacts of resistant populations in the different pathosystems to better strategize for minimizing the risk of resistance developing and to prolong the life of a fungicide product. Alternation of these fungicide classes with different MOAs (preferably multi-site fungicides) and mixing more than one mode of action are generally good strategies to reduce the risk of selection for resistance (van den Bosch et al., 2014; Ishii and Hollomon, 2015).

Quinone Outside Inhibitor Fungicides (QOI)

Quinones outside Inhibitor fungicides are single mode of action chemicals inhibiting the cytochrome b (cyt b) region of the mitochondrial respiration mechanism

(Gisi et al., 2002; Ishii and Hollomon, 2015). Quinone outside inhibitors are classified as group 11 according to FRAC. They are considered a high-risk of resistance selection and cross-resistance mode of action fungicides by FRAC. Examples of QOI active ingredients used for foliar peanut disease management include azoxystrobin and pyraclostrobin. Known resistance against QOI fungicides is primarily due to mutations in the cyt b gene: G143A (complete resistance mutation) and F129L & G137R (partial resistance mutations) (Fernández-Ortuño et al., 2008; Gisi et al., 2000; Gisi et al.,

2002). The latter two mutations cause less conspicuous resistance or reduced sensitivity meaning that the pathogen is still partially sensitive to the QOI fungicides but they may not be able to provide complete inhibition. The reduction of QOI fungicide efficacy has been noticed in many pathogens more rapidly due to the expiration of product patents and availability of cheaper generic products containing QOI active 20

ingredients such as azoxystrobin. Resistance against QOI fungicides has been reported in several plant pathogens including the genera (Alternaria, Botrytis, Cercospora,

Cladosporium, Colletotrichum, Corynespora, Stagonosporopsis, Erysiphe,

Mycosphaerella, Plasmopara, Pseudoperonospora, Pyricularia, Pythium, Rhizoctonia,

Stemphylium, etc.) (Heaney et al., 2000; Kim et al., 2003; Gisi et al., 2002; Ishii and

Hollomon 2015, Sierotzki et al., 2000; Torriani et al., 2008).

DeMethylation Inhibitor Fungicides (DMI)

DeMethylation inhibitor fungicides are single mode of action targeting the 14 α- demethylase enzyme in pathogens (Gisi et al., 2000; Ishii and Hollomon, 2015).

DeMethylation inhibitor fungicides are classified as group 3 according to FRAC and there are also known as sterol biosynthesis inhibitors (SBIs). Examples of DMI active ingredients used in peanut disease management include tebuconazole and prothioconazole. There is no reported cross-resistance with other SBI fungicide classes.

The Fungicide Resistance Action Committee considers DMI fungicides medium-risk for resistance selection. Insensitivity of pathogens to this group of fungicides is qualitative, which requires stacking multiple resistance mutations to become completely insensitive, thus, they are considered medium-risk fungicides. This is contrary to the G143A complete (quantitative) resistance mutation to QOI active ingredients, which make them high-risk for resistance selection. Resistance to DMI fungicides is contributed to several mechanisms including – most commonly – amine substitutions of the cytochrome P450

14 α-demethylase enzyme encoded by ERG11 (CYP51) and overexpression of the

ERG11 gene (Gisi et al., 2000; Sanglard et al., 1998). DMI resistance has been observed as reduced sensitivity of pathogens to theses fungicides or erosion of products efficacy to control diseases. The Resistance to DMIs has been reported in 21

some genera including Botrytis, Monilinia, Mycosphaerella, and Podosphaera (Ishii and

Hollomon 2015).

Hypothesis and Objectives

Quinone outside inhibitor and DeMethylation inhibitor fungicides have been observed to reduce efficacy on peanut foliar disease in the southeastern U.S. fungicide trials (Dufault and Culbreath personal communication). Inconsistent fungicide efficacy results were also observed due to variation in pathogens controlled. Thus, monitoring peanut leaf spot pathogens is crucial to address the concerns raised from the possibility of losing valuable fungicide products in peanut disease management.

We hypothesize that resistant populations of ELS and LLS are reducing the efficacy of the fungicides tebuconazole, azoxystrobin and pyraclostrobin. The objectives of this study are to: (1) monitor the presence of ELS, LLS and rust across different environments in Florida; (2) assess how foliar diseases progress in late planted

Florida peanuts; (3) examine the efficacy of chlorothalonil, tebuconazole, azoxystrobin and pyraclostrobin in a field environment; and (4) determine how alternations and mixtures of the previous fungicides impact peanut foliar disease management and yield.

A B C

Figure 1-1. Foliar pathogens of peanut, early leaf spot (ELS), late leaf spot (LLS) and rust. (A) ELS symptoms are brown lesions usually with yellow halo around them on the upper leaf surface, photo courtesy of W. M. Elwakil; (B) LLS symptoms are dark brown to black rough/raised lesion on the lower leaf surface, photo courtesy of N. S. Dufault; (C) Rust symptoms are reddish orange rust pustules typically on the lower leaf surface, photo courtesy of N. S. Dufault. Pustules break open releasing large numbers of urediniospores.

CHAPTER 2 RELATIVE TREATMENT EFFECT OF QOI AND DMI FUNGICIDES ON PEANUT EARLY LEAF SPOT, LATE LEAF SPOT, AND RUST

Introduction

Foliar peanut diseases are a major concern for peanut producers worldwide

(Kokalis-Burelle et al., 1997, Nutter and Shokes 1995, Pattee and Stalker 1995). Early leaf spot (ELS), late leaf spot (LLS), and rust cause by Cercospora arachidicola S. Hori

(teleomorph Mycosphaerella arachidis Deighton), Cercosporidium personatum (Berk. &

M. A. Curtis) Deighton (teleomorph Mycosphaerella berkeleyi Jenk), and Puccinia arachidis Speg., respectively, are common foliar diseases of peanut in the Southeastern

U.S. (Smith and Littrell 1980). ELS and LLS have been reported to cause annual yield losses up to 30% if left unmanaged (Hagan 1998). However, research trials in Florida have demonstrated yield losses up to 80%, especially in later plantings occurring from late May to early June from these leaf spot diseases and rust (Elwakil et al., 2017). The damage caused by these diseases is from reduction in healthy green leaf tissues due to the spread of the ELS and LLS lesions, and rust pustules, which can also cause significant levels of defoliation. These diseases often occur at the same time resulting in devastating effects on peanut development under favorable environmental conditions.

Thus, foliar peanut disease management with fungicides must be broad enough to manage all three of these pathogens.

Fungicides have been successfully used to control these peanut diseases, and are commonly applied by peanut producers (Kemerait et al., 2018, Wright et al., 2016).

Producers in the Southeastern U.S. typically apply fungicides 7 to 9 times in a season with 5 to 7 of those applications being directed at foliar diseases. Application programs often consist of spray mixtures or alternating sprays using several fungicide chemistries

and modes of action. Producers typically rely on chlorothalonil, a broad-spectrum protectant fungicide, for control of leaf spot diseases. However, more specific modes of action have been used for disease management, as they tend to improve disease control and yield savings for foliar and soilborne diseases.

There are many fungicide modes of action available for foliar disease management, but typically the specific modes of action are demethylation inhibitors

(DMI), quinone outside inhibitors (QOI) and succinate dehydrogenase inhibitors (SDHI).

The DMI and QOI fungicides have been used in peanut disease management for over

20 years (Culbreath et al. 2002), and recently there have been some indications that these fungicides are not as effective as previously reported. However, direct comparisons of these fungicide products in field trials are rare, especially in situations where expected disease pressure is high. Field comparisons of these products are needed to better understand the possibility of resistance in current pathogen populations as well as their field efficacy.

It has been observed in the past several years that the fungicides azoxystrobin, pyraclostrobin, and tebuconazole are showing reduced efficacy in peanut foliar disease control (Culbreath; personal communication, July 2015). We hypothesized that the fungicides azoxystrobin, pyraclostrobin and tebuconazole will not decrease leaf spot incidence and corresponding defoliation compared to non-treated peanuts. The objectives of this study were to (1) monitor peanut foliar disease epidemics across multiple environments in Florida and (2) assess the efficacy of the QOI fungicides azoxystrobin and pyraclostrobin as well as the DMI fungicide tebuconazole for foliar peanut disease control.

Materials and Methods

Plot Establishment and Experimental Design:

Field experiments were established in 2014, 2015, 2016 and 2017 at the Plant

Science Research and Education Unit in Citra, FL. Research plot soil type was

Gainesville loamy sand (85 % Gainesville and 15 % minor components), and the field had been continuously planted with peanut since 2011 using conventional tillage practices and having a grass cover crop in the winter (Wright et al. 2016). The peanut cultivar Georgia-06G was planted at a rate of 1 seed per 5 cm on 5, 4, 9 and 5 May during 2014, 2015, 2016 and 2017 seasons, respectively. The seeds were treated with the fungicide product Dynasty® PD (Syngenta Crop Protection, Greensboro, NC) prior to planting. The field was visually inspected for drought stress and soil moisture levels to determine the need for irrigation. If irrigation was required, the plots were overhead irrigated with 12.7-19.1 mm per application twice a week until germination and then once a week until starting peg formation. Treatment plots consisted of two single-rows with one untreated border row on both sides. Plot length was 7.62 m and spacing between rows was 0.9 m. Randomized complete block design with four blocks was used in all experiments. Blocks were separated by 3.1 m wide fallow alleys, which were used to apply recommended maintenance insecticides and herbicides sprays for Florida peanut production (Wright, et al. 2016).

Fungicide Treatments and Applications

Fungicide treatments consisted of azoxystrobin, pyraclostrobin, tebuconazole and chlorothalonil commercial formulations applied at maximum labeled rates and an untreated control (Table 2-1). A total of seven fungicide product applications were made throughout the season at approximately 14-day intervals. Applications started at 30

days after planting (DAP) and were completed by 128 DAP. Fungicides were applied using a CO2 pressurized backpack sprayer consisting of a boom with two flat fan nozzles (XR-4008, TeeJet Technologies Illinois, LLC) spaced at 91.4 cm apart. The sprayer was calibrated for application rate of 187 L/ha delivered at approximately 28

PSI.

Disease Assessment

Foliar disease pressure was visually assessed on the two middle treatment rows of the plot using the Florida 1-10 (FL 1-10) scale; where 1 = no disease, 2 = very few lesions in canopy (none on the upper canopy), 3 = few lesions (very few on upper canopy), 4 = some lesions with more on upper canopy than for rank 3 and slight defoliation, 5 = lesions noticeable even on the upper canopy with noticeable defoliation,

6 = lesions numerous and very evident on the upper canopy with significant defoliation

(≥ 50 %), 7 = lesions numerous on the upper canopy with much defoliation (≥ 75 %), 8 = upper canopy covered with lesions with high defoliation (≥ 90 %), 9 = very few remaining leaves and those covered with lesions (some plants completely defoliated), and 10 = plants are defoliated or dead (Chiteka et al., 1988). These ratings were made approximately every 14 days; one week after each spray application starting 37 DAP.

The FL 1-10 scale provides observation data about disease incidence and severity, and also provides a logistic response for defoliation of peanut plants (Li et al., 2012). Area under the disease progression curve (AUDPC) was calculated using the FL 1-10 scale ratings, which was standardized by the rating period for each year (Campbell and

Madden, 1990)

In each year, leaflet sampling was conducted by randomly collecting 12 leaflets from the upper, middle, and lower peanut canopy in the two treatment rows for a total of 27

48 leaflets per treatment. This sampling was done at the same time as FL 1 - 10 scale ratings were collected and ended once canopy defoliation was greater than 80%. The collected samples were then kept cool (< 5°C) for up to 10 days, when they were evaluated for foliar disease pathogen percent incidence and severity. Only ELS, LLS, and rust ratings were recorded on the leaflet samples. The pathogens were visually confirmed by symptoms and signs, which were the presence of stroma for LLS, sporulation of ELS on the adaxial surface of the leaf, and pustule formation for rust. The average seasonal disease incidence and the percentage of leaflets that had the disease, was calculated for each pathogen across each year to compare foliar disease presence. Each disease incidence was assessed separately from the other diseases (0-

100% range) (Fig. 2-3).

Yield Collection

The middle two treatment rows in each plot were dug and inverted approximately

140 DAP in 2014, 2016, and 2017, and at 130 DAP in 2015. Plots were harvested mechanically 2 to 4 days after being dug and inverted. Yield was determined by weighing pods after being dried to 10% (wt/wt) moisture approximately 5 to 7 days after harvest.

Data Analysis

All statistical analysis was completed using SAS University Edition (SAS Institute

Inc., Cary, NC, USA). A nonparametric analysis was performed on final foliar disease ratings that were recorded using the FL 1-10 scale ratings. The ratings of each experiment were analyzed individually, and as relative treatment effects with their respective confidence intervals using LD_CI macro for SAS (Burnner et al., 2002, Shah and Madden 2004). Relative treatment effect ( ̂푝푖) of each treatment was estimated from 28

the mean ranks (푅̅푖•) of all observation (푁). Relative treatment effect has a range of 0 to

1.0.

1 1 푝̂ = (푅̅ − ) 푖 푁 푖• 2 (2-1)

A mixed model analysis of variance was used to examine the effects of year on disease and yield using the Proc GLIMMIX procedure. Based on the full model results a reduced model was run for each year to determine the effects of fungicide treatment on yield and standardized area under disease progression curve (sAUDPC) using Proc

GLIMMIX procedure (SAS Institute Inc. 2015). Fungicide treatments were considered fixed effects and replicates (blocks) were random effects. Significance among the different least square treatment means was determined using Tukey-Kramer grouping

(Tukey’s HSD; α = 0.05).

Results

Disease Incidence and Environment

Southern stem rot (Sclerotium rolfsii) and Rhizoctonia limb rot (Rhizoctonia solani

AG-4) were not observed in this study. The average maximum foliar disease intensity ranged from scale values of 8.5 to 10 (90% or greater defoliation) for all trials (Fig. 2-1).

Average disease incidence ranged from 24 to 78% for ELS, 4 to 63% for LLS and 0 to

49% for rust. (Fig. 2-4). The highest average incidence of ELS, LLS and rust was recorded in 2017, 2016 and 2014, respectively. The LLS was prominent (> 45% incidence) in 2014 and 2015, ELS was in 2105, 2016 and 2017 and rust was only prominent in 2104. The average daily temperature only varied by 0.35°C across the 4 years of this study (Table 2-4). Rainfall varied by as much as 51.9 cm between the 2016

and 2017, with more 74.83 cm of rainfall recorded in 2015 and 2017 and less than 50 cm of rainfall recorded in 2014 and 2016 (Table 2-3).

Disease Management

In 2015 and 2017, the foliar disease epidemics started between 30 and 50 DAP and reached a scale rating between 9 and 10 approximately 120 DAP (Fig. 2-1). The foliar disease epidemics in 2014 and 2016 started between 60 and 90 DAP and reached a scale rating of 10 by 136 DAP. Year effects on final leaf spot intensity, sAUDPC and yield were significant (p < 0.05), thus each year was analyzed separately.

In 2015, none of the fungicide treatments had a different final FL 1-10 rating, while in 2014 and 2016 chlorothalonil was lower than all other treatments (Fig. 2-2 & 2-

3). Azoxystrobin and pyraclostrobin were not different form the untreated control in any of the years. Tebuconazole was only lower in 2017. Relative treatment effects showed similar patterns to the FL 1 to 10 scale ratings with no treatment differences present in

2015. Chlorothalonil had a lower relative treatment effect than azoxystrobin and pyraclostrobin in 2014, 2016 and 2017. Tebuconazole was only different in 2017.

Relative treatment effects of azoxystrobin and pyraclostrobin were not different from the untreated control in all years of the study (Fig. 2-2 & 2-3).

That there was a treatment effect in sAUDPC in 2014 (F-value = 39.29, p < 0.05),

2015 (F-value = 2.05, p = 0.14), 2016 (F-value = 53.95, p < 0.05) and 2017 (F-value =

34.09, p < 0.05). The sAUDPC values were consistently lower than the untreated check in the chlorothalonil and tebuconazole treated plots. In 2014, sAUDPC values of all treatments were lower than the untreated control, with tebuconazole, azoxystrobin, and pyraclostrobin having higher sAUDPC than chlorothalonil. No differences were observed among the treatments in 2015, with azoxystrobin and pyraclostrobin having 30

numerically higher sAUDPC values than the untreated control. Chlorothalonil, tebuconazole and pyraclostrobin were all lower than the control in 2016. The azoxystrobin treatment in 2016 was not different from either the control or pyraclostrobin treatments. SAUDPC values of azoxystrobin and pyraclostrobin were not different from the untreated control in 2017, while both chlorothalonil and tebuconazole had lower sAUDPC values (Table 2-2).

There was a treatment effect on yield in 2014 (F-value = 29.64, p < 0.05), 2015

(F-value = 3.58, p = 0.04), 2016 (F-value = 12.48, p < 0.05) and 2017 (F-value = 17.30, p < 0.05). Yield results tended be similar to sAUDPC data, however values were much more variable (Table 2-2). In 2014, azoxystrobin was not different from the untreated control, while yields of the rest of the fungicide treatments were all higher than the untreated control. However, there were no differences among the treatments tebuconazole, azoxystrobin and pyraclostrobin in 2014. Pyraclostrobin had a lower yield than tebuconazole in 2015, while the rest of the fungicide treatments were not different from each other. In 2016, yield in the chlorothalonil and tebuconazole treatments were higher than the untreated control, and tebuconazole was not different from chlorothalonil, azoxystrobin and pyraclostrobin. In the year 2017, yields in all treatments were higher than the untreated control, and chlorothalonil was only higher than azoxystrobin and pyraclostrobin (Table 2-2).

Discussion

The FL 1-10 ratings from this trial and disease incidence surveys indicate that azoxystrobin and pyraclostrobin resistant populations of ELS and LLS were factors in all our tests. Resistance to QOI fungicides by Mycosphaerella spp. has been previously reported (Amand et. al, 2003; Sierotzk et. al, 2000), and resistance has been attributed 31

to recurring independent mutations (Torriani et. al, 2008). These resistant haplotypes then increase in frequency usually due to intensive use of QOI fungicides. This is a concern for peanut leaf spot control since the availability of azoxystrobin has increased due to expiration of its patent and the increase of this chemistry in generic fungicide products. While resistant populations appear to be present, sensitive populations also appear to be present which means it will be critical to manage these products carefully in the future to prolong their usefulness (Ishii and Hollomon 2015, Hollomon 2015, van den Bosch et. al, 2014).

Our results also indicate that while tebuconazole appears to be more efficacious than the QOI fungicides, it also has apparent failures when LLS is present. This can be seen most evidently in the 2014 and 2016 data in which the fungicide’s overall performance was similar to the QOI products and less than chlorothalonil. There have been some indications that tebuconazole is not as effective against late leaf spot

(Culbreath et. al, 2008), and our results support this observation. It is likely that tebuconazole will be an effective leaf spot fungicide if mixed or alternated with other leaf spot products (Elderfield et. al, 2018; Hobbelen, et. al, 2013). However, in areas where

LLS is the dominant pathogen, the use of this fungicide should be avoided for foliar disease control.

Tebuconazole, azoxystrobin and pyraclostrobin showed levels of rust control in our trials, which were not achieved by chlorothalonil alone. This result is similar to reports by Besler et al., 2006 and Hagan et al., 2006 in which these products significantly reduced rust levels compared to chlorothalonil. Thus, in years where rust is dominant, these products would provide good protection. However, if ELS or LLS were

present with rust, they will likely fail to provide acceptable foliar disease control. This is because their rust reduction benefits would most likely be masked by their reduced efficacy on ELS and LLS. This could be a reason that these products have provided inconsistent foliar disease management from one year to another. The presence of 2 or more of these pathogens, which is not uncommon, will significantly affect how these products control disease and their apparent efficacy. It is likely that adequate control can be attained through fungicide mixtures or alternation of these chemistries while prolonging the life of these products (Elderfield et. al, 2018).

The predominant foliar pathogen present within a season seemed to be related to disease onset. For example, in 2015 and 2017 when disease onset occurred before

50 DAP, ELS was the predominant. When disease onset occurred 60 DAP or later, as in 2014 and 2016, either LLS and Rust or LLS and ELS were dominant. Onset also seemed to affect the total yield as the untreated plots had lower yield in 2015 and 2017 than the other two years. It is possible that the predominant pathogens were important for final yield too, however 2016 and 2017 had similar pathogen incidence and yields varied greatly between these two years. Further information about pathogen incidence throughout the year is needed to better understand the importance of early disease onset in pathogen presence and attainable yields. Knowing the dominant pathogen can be important in determining the proper fungicide control (e.g. tebuconazole and rust), and our results indicate that early disease onset is associated with ELS and late onset with LLS for peanuts planted in June or later.

It is obvious from this study that chlorothalonil alone continues to be an effective fungicide for peanut foliar disease control. Chlorothalonil is a regularly recommended

product in peanuts disease management; however, its inability to control soilborne pathogens is a problem for many peanut producers. This product is used primarily as a protectant fungicide and often its efficacy is decreased if disease is present before it is applied. Our data from the 2015 season indicates that this early disease onset led to reduced disease control and low yields. However, the importance of chlorothalonil in a foliar peanut spray program is well supported by our results, and combining this product with others will be important for both fungicide resistance management and disease control.

In conclusion, it is apparent from these results that the efficacy of azoxystrobin, pyraclostrobin and tebuconazole is dependent on the leaf spot pathogen present. In many cases, azoxystrobin and pyraclostrobin programs did not have significantly different yields than the control, which is different from what has been previously reported (Culbreath et al., 2002). This lack of a yield difference is strong evidence that the pathogen population has resistant isolate(s) within them. Tebuconazole also appears to have resistance developing in LLS populations as the fungicides efficacy was significantly reduced when this disease was dominant. While there are concerns related to these fungicides, the multi-site product chlorothalonil continued to perform well. It seems that alternating or mixing azoxystrobin, pyraclostrobin and tebuconazole with this fungicide would be beneficial. However, more research is needed to better understand the utility of these techniques in disease management and how they might be altered related to pathogen presence and disease onset.

Table 2-1. A list of the commercial fungicide products used in this study with their respective rates for the seven seasonal applications from 2014 to 2017. Application Max Label Rate Used Rate Common Name Rate Trade Name a FRAC # b (kg ai/ha/season) (kg ai/ha/season) (kg ai/ha) Chlorothalonil 1.26 10.09 8.83 Chloronil 720 M5 Tebuconazole 0.23 0.90 1.59 TebuStar 3.6 3 Azoxystrobin 0.33 0.90 2.30 Abound 2.08F 11 Pyraclostrobin 0.33 0.82 2.31 Headline 250 SC 11 a Chloronil 720 (Syngenta, Greensboro, NC), TebuStar 3.6L (Albaugh, Ankeny, IA), Abound 2.08F (Syngenta, Greensboro, NC), and Headline 250 SC (BASF, Florham Park, NJ) b Fungicide Resistance Activation Committee (FRAC) number which represents mode of action of the fungicide (fracinfo.org).

Table 2-2. Effect of the various fungicide treatments on peanut foliar disease intensity and average yield for each year of the experiment based on a mixed model analysis of variance. Disease intensity was measured using the Florida 1 to 10 scale. sAUDPC y Yield (kg/ha) x Treatment z 2014 2015 2016 2017 2014 2015 2016 2017 Untreated 8.81 a 5.66 a 6.15 a 4.44 a 572.89 c 288.29 ab 774.64 c 218.77 c Chlorothalonil 4.92 c 5.14 a 3.94 d 3.24 b 4556.74 a 349.86 ab 2876.09 a 626.24 a Tebuconazole 6.33 b 5.09 a 4.61 c 3.41 b 1796.42 b 684.12 a 1903.68 ab 505.93 ab Azoxystrobin 7.02 b 5.98 a 5.62 ab 4.29 a 1668.45 bc 284.03 ab 1355.47 bc 422.79 b Pyraclostrobin 6.85 b 5.74 a 5.53 b 4.25 a 2010.65 b 251.12 b 1413.36 bc 409.45 b z Fungicide products listed were applied seven times throughout at the rates listed in Table 2-1, using commercial formulations. y sAUDPC = standardized area under the disease progression curve which was based on FL 1 to 10 scale ratings; treatments within a column labeled with the same letter are not significantly different (Tukey’s HSD, P < 0.05). x Average yield estimated from plot yields which consisted of two, 25-ft rows; treatments within a column labeled with the same letter are not significantly different (Tukey’s HSD, P < 0.05).

Table 2-3. Summary of total rainfall (cm) during the four years of this study. This data was collected from the Florida Automated Weather Network located in Citra, FL. Year June July August September Total z 2014 9.1 16.3 11.7 11.7 48.8 2015 8.6 28.9 25.8 11.4 74.8 2016 2.4 4.4 13.3 18.3 38.3 2017 26.9 16.7 19.6 27.0 90.3 z Total equals the complete amount of rainfall that was recorded from June to September.

Table 2-4. Average monthly temperature (ºC) during the four years of this study. This data was collected from the Florida Automated Weather Network located in Citra, FL. Year June July August September Total z 2014 26.2 26.6 27.3 25.2 26.4 2015 27.2 26.9 26.8 25.7 26.8 2016 27.6 28.3 27.2 26.2 27.3 2017 25.9 27.1 27.3 25.9 26.6 z Total equals the average temperature from June 1st to September 30th.

10 2014

9 2015

2016 8 2017

6 10 10 ratings

- 5 FL1 4

1 35 50 65 80 95 110 125 140 Days after planting

Figure 2-1. Disease progression curves of the untreated controls using the FL 1-10 ratings from the trials conducted between 2014 to 2017 at the Plant Science Research and Education Unit, Citra, FL. Ratings were collected starting around 30 days after plantings but only the values starting at disease onset until reaching 10 on the scale or until harvest time (whichever occurs first) are shown.

Days After Planting

Figure 2-2. Disease progression curves based on FL 1-10 scale ratings for the various fungicide treatments indicated in the legend (Table 2-2). The results are from four years in Citra, FL (PSREU). The ratings started at disease onset until reaching a 10 on the scale or harvest time. Days after planting represent the number of days that have passed since the peanut crop was planted.

Figure 2-3. Final leaf spot FL 1-10 disease intensity rating and corresponding relative treatment effect for the treatments from 2014 to 2017 at the Plant Science Research and Education Unit, Citra, FL. Treatments included untreated control (Untreated) chlorothalonil (Chloro), tebuconazole (Teb), azoxystrobin (Azoxy), and pyraclostrobin (Pyrac) fungicides applied 7 times throughout the various seasons. The relative treatment effect was calculated based on the mean rank of all observations in each treatment using the FL 1 to 10 ratings.

Figure 2-4. Disease incidence percentages in the untreated plots for the trials from 2014 to 2017 at the Plant Science Research and Education Unit, Citra, FL. ELS, LLS, and rust were rated individually on leaflet samples (48 leaflets/treatment) on 3 to 6 different occasions throughout the year. Disease incidence shown represents average seasonal incidence recorded all the rating time points.

CHAPTER 3 THE EFFICACY OF FUNGICIDE MIXTURES AND ALTERNATIONS ON PEANUT FOLIAR DISEASE MANAGEMENT IN FLORIDA

Introduction

Understanding the efficacy of various fungicide mixtures and alternations on early and late leaf spot and rust disease development will be critical to establishing quality fungicide application programs (Culbreath et al., 2002a). Fungicide mixture is adding more than one mode of action fungicide in the same application, while alternation involves the rotation or substitution of different modes of action among different fungicide applications in a spray program (Hollomon 2015; Ishii and Hollomon

2015). Multiple groups of fungicides, tebuconazole from the DMI group, and azoxystrobin and pyraclostrobin from the QOI group, have shown reduced field efficacy to leaf spot pathogens (Chapter 2). This reduced efficacy indicates that resistant isolates are most likely present in the pathogen populations, and that adjustments are needed to application programs to prolong the life of current products (Hollomon, 2015).

Alternating or mixing fungicide modes of action (MOA) when multiple applications are made in a single season are two strategies used to reduce the selection pressure for resistance in pathogen populations and prolong a fungicide’s life (Ishii and Hollomon

2015; Ishii et al., 2001; van den Bosch et al., 2014). Several publications indicate that fungicide mixtures usually outperform alternations (Elderfield et al., 2018; Ishii and

Hollomon 2015; van den Bosch et al., 2014). This is primarily because alternations will not work unless the number of high-risk fungicide applications is reduced in the program to reduce the selection pressure for resistance (van den Bosch et al., 2014). Another problem with alternations is that when a resistant pathogen population is high, these single MOA fungicide sprays are often non-effective leading to disease increase that

make it difficult to manage with other products. Thus, when resistance is identified in a pathogen population it is recommended that only mixtures be considered for fungicide management.

Mixtures of different MOA fungicides are more likely to manage the pathogen population that is resistant to the high-risk fungicide, thus reducing sensitive and resistant populations at relatively similar rates. This is typically achieved by adding a low-risk fungicide to an application mixture, which can have broad spectrum activity

(Elderfield et al., 2018). However, mixtures of two high-risk fungicides can be beneficial when the pathogen strain resistant to both modes of action is present at low initial frequencies (Hobbelen et al., 2013). By dose-splitting the fungicide MOA, a mixture can give better background disease control and will be the best resistance management tactic in a range of situations (Elderfield et al., 2018, van den Bosch et al., 2014).

However, these resistance management tactics should be further examined in various pathosystems as many different variables can affect them (e.g. disease onset, number of pathogens, number of fungicide applications and application timing). We hypothesize that alternations and mixtures of fungicide MOA will lower leaf spot intensity and save more yield compared to single MOA application programs. The main objectives of this study were to: (1) monitor the peanut foliar disease epidemics across three geographically different locations in Florida; and (2) examine the efficacy of alternation and mixture of medium-risk and high-risk fungicides with a low-risk fungicide.

Materials and Methods

Plot Establishment and Experimental Design

Field trials were established in 2017 in three Florida locations. These locations were the Plant Science Research and Education Unit (PSREU) in Citra, FL, the North

Florida Research and Education Center (NFRECm) in Marianna, FL, and the North

Florida Research and Education Center (NFRECl) in Live Oak, FL. The PSREU had a soil type of Gainesville loamy sand (85 % Gainesville and 15 % minor components),

NFRECm was Chipola loamy sand (80 % Chipola and 20 % minor components) and

NFRECl was Hurricane, Albany, and Chipley (39 % Hurricane, 32 % Albany, 26 %

Chipley, and 6 % minor components). The peanut cultivar Georgia-06G was planted at a rate of 1 seed per 5 cm on 5 June at PSREU and 9 June at NFRECm and NFRECl.

The seeds were treated with the fungicide product Dynasty® PD (Syngenta Crop

Protection, Greensboro, NC) prior to planting. The fields at PSREU and NFRECl were prepared for planting using conventional tillage, while NFRECm fields were prepared using a reduced/conservation tillage system. Plot lengths were 7.62 m and spacing between rows was 0.9 m with 3.1 m wide fallow alleys to separate blocks. Randomized complete block design with four blocks was used in all experiments. Treatment plots consisted of two single-rows with one untreated border row on both sides. The field was inspected for drought stress and soil moisture levels to determine the need for irrigation.

If irrigation was required, the plots were overhead irrigated with 12.7-19.1 mm per application twice a week until germination and then once a week until starting peg formation.

Treatments and Fungicide Applications

The fungicide products examined in this study are chlorothalonil (FRAC # M5, multi-site fungicide), tebuconazole (FRAC # 3, single site fungicide), azoxystrobin and pyraclostrobin (FRAC # 11, single site fungicide). The commercial formulations used for these products were Chloronil 720 (Syngenta, Greensboro, NC) TebuStar 3.6L

(Albaugh, Ankeny, IA), Abound 2.08F (Syngenta, Greensboro, NC), Headline 250 SC 45

(BASF, Florham Park, NJ). In all spray programs chlorothalonil was the low resistance risk, multi-site fungicide used for alternations and mixtures. Fungicide programs were divided into 3 application cohorts with treatments 2 to 5 representing the individual products applied alone, treatments 6 to 8 the alternation programs of the multi-site and single site products, treatments 9 to 11 the multi-site and single site product mixture programs, and treatments 12 and 13 the single site and single site product mixture programs. Due to space limitations, treatments 3, 4, and 5 were not tested in the

NFRECl trial. The various fungicide products were applied at rates of 1.26 kg ai/ha, 0.23 kg ai/ha, 0.33 kg ai/ha, and 0.16 kg ai/ha for chlorothalonil, tebuconazole, azoxystrobin and pyraclostrobin, respectively (Table 3-1 & 3-2). Fungicide applications were made on a 14-day schedule starting 30 days after planting (DAP) for a total of seven applications.

These applications were delivered using a hand-held boom with two XR-4008 TeeJet nozzles (TeeJet Technologies Illinois, LLC) spaced at 90.4 cm apart and pressurized at

28 PSI with CO2 backpack sprayer calibrated to deliver 187 L/ha.

Disease Assessment

The FL 1-10 scale was used to assess the intensity of the foliar diseases early leaf spot (ELS), late leaf spot (ELS), and rust, where, 1= no disease and 10= plants are defoliated and dead (Chiteka et al., 1988). These foliar disease assessments were collected approximately every 14-days starting 37 DAP. The FL 1-10 scale provides observational data diseases incidence and severity, and provides a logistic response curve for defoliation. Area under the disease progression curve (AUDPC) was calculated using the FL 1-10 ratings, and standardized (sAUDPC) by the rating period for each trial (Table 3-4) (Campbell and Madden, 1990).

At each trial a 12-leaflet sample was randomly collected from upper, middle, and lower canopy of treatment plots with total 48-leaflets samples obtained from each treatment. The leaflet sampling was conducted at the same time the FL 1-10 ratings were collected. Collected leaflets were stored (< 5ºC) for up to 10 days until they were evaluated for ELS, LLS, and rust incidence and severity. The pathogens were visually confirmed by symptoms including conidial sporulation in ELS on the adaxial leaf surface, presence of stroma in LLS on the abaxial leaf surface, and rust pustule formation.

The plants in the middle two treatment rows were inverted mechanically at 141

DAP for PRSEU, 140 DAP for NFRECm and NFRECl. Treatment rows were harvested mechanically after 2-4 days of inversion. Pods were dried to 10% (wt/wt) moisture and pod yield was measure for each plot within 7 days after harvest.

Data Analysis

Statistical analysis was complete using SAS University Edition (SAS Institute

Inc., Cary, NC, USA). Relative treatment effect was calculated using the mean rank of the final FL 1-10 ratings in a nonparametric analysis. FL 1-10 ratings of each experiment were analyzed individually, and relative treatment effects and their respective confidence intervals were calculated using LD_CI macro for SAS (Burnner et al., 2002, Shah and Madden 2004) as described in Chapter 2.

A mixed model analysis of variance was used to examine the effects of site

(PSREU and NFRECm) on disease and yield using the Proc GLIMMIX procedure (SAS

Institute Inc. 2015). Based on the full model results a reduced model was run for each site including NFRECl with its reduced number of fungicide treatments. This model was designed to determine the effects of fungicide treatment on yield and sAUDPC and was 47

analyzed using Proc GLIMMIX procedure (SAS Institute Inc. 2015). Fungicide treatments were considered fixed effects and replicates (blocks) were random effects.

Significance among the different least square treatment means was determined using

Tukey-Kramer grouping (Tukey’s HSD; α = 0.05).

Results

Disease Progression and Incidence

Disease was first observed in the untreated control at PSREU and NFRECl at 49 and 54 DAP, respectively (Fig. 3-1 & 3-2). Disease progressed continually at these sites reaching a maximum severity of 9 at PRSEU 127 DAP and 10 at NFRECl 131 DAP.

NFRECm disease was observed in the untreated control at 69 DAP and reached a final scale rating of 10 at 131 DAP. NFRECm disease ratings tended to be lower than the other two sites until 118 DAP when it increased from a 5 to 9.

The disease incidence percentages were visually different for each trial examined in this study (Fig. 3-3). Early leaf spot was prominent (>45% incidence) at

PSREU location and LLS was prominent in Marianna and Live Oak. Both ELS and LLS were found at all locations, but rust was only identified at PRSEU. The ELS incidence was negligible (<5%) at the NFRECm and was not identified on many of the samples collected there.

Disease and Yield

There was a treatment effect on sAUDPC in Citra (F-value = 20.33, p < 0.05),

Marianna (F-value = 17, p < 0.05), and Live Oak (F-value = 7.23, p < 0.05). Application programs using individual products (treatments 2 to 5) tended to have higher intensity ratings (6 to 10) and higher sAUDPC values (3.41 to 5.54) except for the chlorothalonil only treatment (Table 3-4). The tebuconazole individual program at the PSREU had

lower disease than the untreated control (Table 3-4). The azoxystrobin treatment sAUDPC was not different from the untreated control at both the PSREU and NFRECm.

Pyraclostrobin treatment was not different from the untreated control at PSREU location. All the alternation (treatments 6 to 8) and mixture (treatments 9 to 13) product application programs were not different from the chlorothalonil only program at all three locations. The mixture programs of chlorothalonil with tebuconazole, azoxystrobin and pyraclostrobin had lower disease compared to the untreated control at the three locations (Fig. 3-2 & 3-4).

There was a treatment effect on yield at PSREC (F-value = 13.82, p < 0.05),

NFRECm (F-value = 22.38, p < 0.05), and NFRECl (F-value = 2.66, p = 0.02). Yields of azoxystrobin and pyraclostrobin individual spray programs were not different from the untreated control at the PSREU and NFRECm locations (Table 3-5). The tebuconazole individual program was higher from the untreated control at the PSREU, and not different from the control at the NFRECm. The individual chlorothalonil spray program was higher than the untreated control at all three locations. All the alternation and mixture spray programs (treatments 6 to 13) were not different from each other across the three locations. These programs had higher yields than the untreated control at all locations except for treatments 6, 8 and 9 at NFRECl. None of the alternations or mixture programs produced yields that were different from the chlorothalonil individual program. Despite these lack of significant differences, all the mixture treatments had numerically higher yields than the chlorothalonil individual program and alternation programs at the PSREU locations (Table 3-5, Fig. 3-5). It was observed at NFREC

locations that treatment 8 (alternation of chlorothalonil with pyraclostrobin) had the lowest yield increase out of any of alternation or mixture treatments.

Fungicide products’ price estimates in U.S. dollars during 2017 of the various tested spray programs are presented in table 3-3. Alternation programs cost estimates ranged from $14.14 to $27.78, while mixture programs ranged from $19.19 to $32.83.

Mixture average program cost was about $5.4 per hectare more than the average cost of the alternation programs.

Discussion

Fungicide mixtures have been considered the optimal management strategy for sustaining the effectiveness of high-risk fungicides while typically providing optimal disease control (Ishii and Holloman, 2015; Hobbelen et. al, 2013; van den Bosch et. al,

2014). Our results support this theory, where fungicide mixtures tended to provide similar, if not better, yield benefits when compared to alternation programs and a low- risk fungicide program of chlorothalonil alone. It is important to note that all of the study’s mixture and alternation programs had split applications with a low-risk fungicide, which is relatively important to their success (Elderfield et. al, 2018). As new chemistries become available for peanut disease control, program recommendations have focused on increasing the use of high-risk product mixtures while reducing the low-risk/multi-site fungicide dosage. The impact this has on peanut disease management when resistant populations are present will be important to explore in the future.

Alternation programs did provide adequate disease control that was often comparable to the mixture programs. This could be related to a dose-splitting effect that comes from having an alternation partner (i.e. chlorothalonil) in a majority of the applications that effectively suppresses disease (Elderfield et. al, 2018). However, one 50

alternation spray program treatment (chlorothalonil and pyraclostrobin) tended to have less yield increase when LLS was dominate, and all these programs did not do as well as mixtures when all three foliar pathogens were present. These inconsistencies support the theory that this tactic is not the best fungicide resistance management strategy for a range of disease and pathogen situations (Elderfield et. al, 2018). It is also apparent that when a significant resistant population is present, which is believed to be the case with pyraclostrobin and LLS (Culbreath et al., 2002b), the dose reductions of the effective alternation product led to disease control losses. Alternations are still effective programs for disease management, but these concerns along with the theory that alternations do not provide the best resistance management, indicate that they should not be the primary recommendation for peanut foliar disease management.

The pathogen population present and disease progression impacted the effectiveness of all the resistance management tactics examined except for chlorothalonil alone. Early disease onset (~40 DAP) led to drastically lower yields, but it appears that the predominant pathogen had more impact on the tactics general efficacy.

For example, in the location where ELS was predominant the mixtures were more effective at disease control and saving yields. However, in areas where LLS was predominant, this tactic was no more effective than alternations and chlorothalonil alone programs. The LLS pathogen is believed to have resistant populations present to the three fungicides examined in this study. It is possible that these populations are relatively high and thus the application of the high-risk products has little effect on disease control. More detailed information is needed about the pathogen populations present before and after the various fungicide applications to better understand this

issue. Regardless, the predominant pathogens as well as disease onset are important factors to consider when examining disease control tactics.

Fungicide mixtures should be the tactic recommended to peanut growers for managing foliar diseases. While chlorothalonil appears to be a good product on its own when compared to mixtures, it lacks the ability to manage soilborne diseases (e.g. southern stem rot), which commonly appear in peanut production. Thus, those high-risk fungicides are needed for broad-spectrum disease control from the leaves to the roots.

The recommendation from FRAC is that low-risk fungicides should be applied in tank mixtures with high-risk fungicides (e.g. DMI, QOI), which is strongly supported by these results for foliar disease management in peanuts.

The spray program cost is another important factor to peanut growers for selecting an affordable option to maximize their returns. Cost estimate of the tested spray programs showed overlapping costs of the alternation and mixture programs. Also the increase in average cost from alternation to mixture programs is relatively low (only

$5.4 per hectare), thus favoring the use of the mixture programs considering the added benefits of reducing selection for resistance and prolonging the life of the high-risk mode of action fungicides.

Future research is needed to better understand how low-risk fungicide dose- splitting is affected by increased used of high-risk mixtures in peanut spray programs.

Currently, many industry program recommendations either reduce a low-risk fungicide’s dosage or eliminate the fungicide spray from certain application dates. Our results indicate that this strategy may be effective, but more than half the applications in this study consisted of a low-risk fungicide. Another question that arises is how cultivars

respond to these programs and if they will have an effect on the efficacy results

(Woodward et al., 2008). A future project examining various dose reduction of low-risk products with increased use of high-risk mixtures on various cultivars is being developed.

Table 3-1. Summary of the seven spray application programs tested in this study during the 2017 peanut season. These programs were tested at the Plant Science Research and Education Unit in Citra, FL and the North Florida Research and Education Center in Marianna and Live Oak, FL. Treatment Application Numbers (1, 2, 4, 6, & 7) Application Numbers (3 & 5) Number Code DAP x (±1); 30, 44, 72, 100, 114 DAP x (±1); 58, 86 1 Untreated No application No application 2 C Chlorothalonil y Chlorothalonil 3 T Tebuconazole z Tebuconazole 4 A Azoxystrobin v Azoxystrobin 5 P Pyraclostrobin w Pyraclostrobin 6 C&T Chlorothalonil Tebuconazole 7 C&A Chlorothalonil Azoxystrobin 8 C&P Chlorothalonil Pyraclostrobin 9 C&CT Chlorothalonil Chlorothalonil + Tebuconazole 10 C&CA Chlorothalonil Chlorothalonil + Azoxystrobin 11 C&CP Chlorothalonil Chlorothalonil + Pyraclostrobin 12 C&TA Chlorothalonil Tebuconazole + Azoxystrobin 13 C&TP Chlorothalonil Tebuconazole + Pyraclostrobin x Days after planting of the fungicide applications ±1 day depending on weather. y Chlorothalonil applied at a rate 1.26 kg ai/ha (Maximum label rate 10.09 kg ai/ha/season) (Chloronil 720, Syngenta, Greensboro, NC) z Tebuconazole applied at a rate 0.23 kg ai/ha (Maximum label rate 0.9 kg ai/ha/season) (TebuStar 3.6L, Albaugh, Ankeny, IA) v Azoxystrobin applied at a rate 0.33 kg ai/ha (Maximum label rate 0.9 kg ai/ha/season) (Abound 2.08F, Syngenta, Greensboro, NC) w Pyraclostrobin applied at a rate 0.16 kg ai/ha (Maximum label rate 0.82 kg ai/ha/season) (Headline 250 SC, BASF, Florham Park, NJ)

Table 3-2. Summary of the total fungicide (active ingredient) seasonal application rates in the programs tested in this study during the 2017 peanut season. These programs were tested at the Plant Science Research and Education Unit in Citra, FL and the North Florida Research and Education Center in Marianna and Live Oak, FL. Treatment Total Application Rate (kg ai/ha/season) x Number Code Chlorothalonil y Tebuconazole z Azoxystrobin v Pyraclostrobin w 1 Untreated ------2 C 8.83 ------3 T -- 1.59 -- -- 4 A -- -- 2.30 -- 5 P ------2.31 6 C&T 6.31 0.45 -- -- 7 C&A 6.31 -- 0.66 -- 8 C&P 6.31 -- -- 0.66 9 C&CT 8.83 0.45 -- -- 10 C&CA 8.83 -- 0.66 -- 11 C&CP 8.83 -- -- 0.66 12 C&TA 8.83 0.45 0.66 -- 13 C&TP 8.83 0.45 -- 0.66 x Total fungicide seasonal application rate (kg ai/ha). y Chlorothalonil applied at a rate 1.26 kg ai/ha (Maximum label rate 10.09 kg ai/ha/season) (Chloronil 720, Syngenta, Greensboro, NC) z Tebuconazole applied at a rate 0.23 kg ai/ha (Maximum label rate 0.9 kg ai/ha/season) (TebuStar 3.6L, Albaugh, Ankeny, IA) v Azoxystrobin applied at a rate 0.33 kg ai/ha (Maximum label rate 0.9 kg ai/ha/season) (Abound 2.08F, Syngenta, Greensboro, NC) w Pyraclostrobin applied at a rate 0.16 kg ai/ha (Maximum label rate 0.82 kg ai/ha/season) (Headline 250 SC, BASF, Florham Park, NJ)

Table 3-3. Economic/cost estimate of the various spray application programs tested in this study during the 2017 peanut season. These programs were tested at the Plant Science Research and Education Unit in Citra, FL and the North Florida Research and Education Center in Marianna and Live Oak, FL. Treatment Total Application Rate (kg ai/ha/season) x Total Program Cost Number Code Chloronil 720 y TebuStar 3.6L z Abound 2.08F v Headline 250 SC w Estimate ($/ha) s 1 Untreated ------2 C 8.83 ------17.68 3 T -- 1.59 -- -- 5.30 4 A -- -- 2.30 -- 53.03 5 P ------2.31 50.20 6 C&T 6.31 0.45 -- -- 14.14 7 C&A 6.31 -- 0.66 -- 27.78 8 C&P 6.31 -- -- 0.66 26.97 9 C&CT 8.83 0.45 -- -- 19.19 10 C&CA 8.83 -- 0.66 -- 32.83 11 C&CP 8.83 -- -- 0.66 32.02 12 C&TA 8.83 0.45 0.66 -- 29.29 13 C&TP 8.83 0.45 -- 0.66 28.48 x Total fungicide seasonal application rate (kg ai/ha) y Chlorothalonil applied at a rate 1.26 kg ai/ha (Maximum label rate 10.09 kg ai/ha/season) (Chloronil 720, Syngenta, Greensboro, NC) z Tebuconazole applied at a rate 0.23 kg ai/ha (Maximum label rate 0.9 kg ai/ha/season) (TebuStar 3.6L, Albaugh, Ankeny, IA) v Azoxystrobin applied at a rate 0.33 kg ai/ha (Maximum label rate 0.9 kg ai/ha/season) (Abound 2.08F, Syngenta, Greensboro, NC) w Pyraclostrobin applied at a rate 0.16 kg ai/ha (Maximum label rate 0.82 kg ai/ha/season) (Headline 250 SC, BASF, Florham Park, NJ) s Total program cost estimate per hectare in US dollars based on prices during 2017

Table 3-4. Summary of the foliar disease variables standardized area under the disease progression curve (sAUDPC), FL 1 to 10 intensity rating and relative treatment effects recorded at the Plant Science Research and Education Unit in Citra, FL (C) and the North Florida Research and Education Center in Mariana (M) and Live Oak (L), FL. Treatment x sAUDPC y FL 1-10 (Final Rating) z Relative Treatment Effect w Number Code C M L C M L C M L 1 Untreated 4.44 a 5.96 a 4.80 a 9 10 10 0.91 0.92 0.90 2 C 3.24 b 3.80 d 3.50 b 5 6 7 0.15 0.11 0.28 3 T 3.41 b 4.99 bc - 6 9 - 0.56 0.81 - 4 A 4.29 a 5.54 ab - 8 10 - 0.88 0.88 - 5 P 4.25 a 4.99 bc - 8 9 - 0.86 0.77 - 6 C&T 3.22 b 4.37 cd 3.75 b 6 7 8 0.41 0.50 0.43 7 C&A 3.60 b 4.20 cd 3.94 b 7 7 8 0.60 0.44 0.59 8 C&P 3.47 b 4.32 cd 3.83 b 6 7 8 0.52 0.44 0.47 9 C&CT 3.22 b 3.85 d 3.56 b 5 7 8 0.28 0.27 0.43 10 C&CA 3.34 b 3.76 d 3.54 b 5 7 8 0.21 0.27 0.51 11 C&CP 3.18 b 3.97 d 3.60 b 5 7 7 0.27 0.27 0.28 12 C&TA 3.32 b 4.11 d 3.82 b 6 7 8 0.41 0.44 0.55 13 C&TP 3.38 b 4.40 cd 3.71 b 6 7 8 0.41 0.36 0.55 x Treatment number and code based on applications for the fungicide programs listed in Table 3-1. y Standardized area under the disease progression curve values labeled with the same letter within a column are not significantly different (Tukey’s HSD, P<0.05). Different letters (a, b, c, & d) represent significant differences among treatments y Final FL 1-10 rating in the season at 127, 131, and 131 DAP in Citra, Marianna, and Live Oak. w Relative treatment effect calculated based on the final mean rank calculated from the Final FL 1-10 ratings (Burnner et al., 2002, Shah and Madden 2004).

Table 3-5. Summary of average yields recorded at the Plant Science Research and Education Unit in Citra, FL and the North Florida Research and Education Center in Mariana and Live Oak, FL during 2017. Treatment x Yield y Number Code Citra Marianna Live Oak 1 Untreated 216.16 d 1587.12 c 107.08 b 2 C 618.47 ab 4617.90 a 306.23 a 3 T 499.38 bc 2274.48 c - 4 A 417.32 cd 1814.66 c - 5 P 404.31 cd 2626.99 bc - 6 C&T 587.45 ab 4623.23 a 282.21 ab 7 C&A 591.45 ab 4885.50 a 295.22 a 8 C&P 679.52 ab 3777.26 ab 231.18 ab 9 C&CT 744.56 ab 4533.84 a 271.21 ab 10 C&CA 798.61 a 4772.58 a 298.23 a 11 C&CP 838.64 a 4769.78 a 311.24 a 12 C&TA 806.61 a 4746.80 a 304.23 a 13 C&TP 814.42 a 4674.79 a 300.23 a x Treatment number and code based on applications for the fungicide programs listed in Table 3-1. y Average yields (kg/ha) labeled with the same letter within a column are not significantly different (Tukey’s HSD, p < 0.05). Different letters (a, b, c, & d) represent significant differences among treatments.

Citra LiveOak Marianna 10

6 10 10 Ratings

- 5 FL1 4

1 35 50 65 80 95 110 125

Days After Planting

Figure 3-1. Disease progression curves of the untreated control treatments during 2017 in the three locations Citra, FL (PSREU), the North Florida Research and Education Centers in Mariana (NFRECm) and Live Oak (NFRECl), FL using the FL 1-10 ratings starting at disease onset until reaching 10 on the scale or until harvest time (whichever occurs first) across time on the x-axis.

Days After Planting

Figure 3-2. Disease progression curves based on FL 1-10 scale ratings for the various fungicide treatments indicated in the legend (Table 3-1). Results are from the Plant Science Research and Education Unit in Citra, FL (PSREU), the North Florida Research and Education Centers in Mariana (NFRECm) and Live Oak (NFRECl), FL. Rating started at disease onset until reaching a 10 on the scale or harvest time. Days after planting represent the number of passed days since the crop was planted.

Figure 3-3. Disease incidence percentages in the untreated control treatments in the Plant Science Research and Education Unit in Citra, FL (PSREU), the North Florida Research and Education Centers in Mariana (NFRECm) and Live Oak (NFRECl), FL. ELS, LLS, and rust were rated individually on leaflet samples (48 leaflets/treatment). Disease incidence shown represents average seasonal incidence recorded in the three locations during 2017.

Citra Marianna Live Oak 1.2 1.2 1.2

1.1 1.1 1.1 1 1 1 0.9 0.9 0.9 0.8 0.8 0.8 0.7 0.7 0.7 0.6 0.6 0.6 0.5 0.5 0.5 0.4 0.4 0.4

Relative Disease Intensity Rating 0.3 0.3 0.3 2 3 4 5 6 7 8 9 10 11 12 13 2 3 4 5 6 7 8 9 10 11 12 13 2 3 4 5 6 7 8 9 10 11 12 13

Fungicide Treatment Number Fungicide Treatment Number Fungicide Treatment Number

Figure 3-4. Relative disease intensity rating (RDIR) for the various fungicide treatments (X-axis) listed in Table 3-1. RDIRs were calculated by dividing the treatments mean final FL 1 to 10 rating by the mean rating for the untreated control. A value of 1 on Y-axis means the two ratings were the same, while a value of 0.5 means the treatment had half the rating of the untreated control. Each line represents a different location, which were the Plant Science Research and Education Unit in Citra, FL (PSREU), the North Florida Research and Education Centers in Mariana (NFRECm) and Live Oak (NFRECl), FL.

Citra Marianna Live Oak 4 4 4

3.5 3.5 3.5

3 3 3

2.5 2.5 2.5

2 2 2

1.5 1.5 1.5

Relative Yield Increase 1 1 1

0.5 0.5 0.5 2 3 4 5 6 7 8 9 10 11 12 13 2 3 4 5 6 7 8 9 10 11 12 13 2 3 4 5 6 7 8 9 10 11 12 13

Fungicide Treatment Number Fungicide Treatment Number Fungicide Treatment Number

Figure 3-5. Relative yield increase (RYI) for the various fungicide treatments listed in Table 3-1. RYIs were calculated by dividing the treatments mean yield by the mean yield for the untreated control. A value of 1 means the two yields were equal. Each line represents a different location, which were the Plant Science Research and Education Unit in Citra, FL (PSREU), the North Florida Research and Education Centers in Mariana (NFRECm) and Live Oak (NFRECl), FL.

CHAPTER 4 CONCLUSION

The foliar peanut pathogens, early leaf spot (ELS), late leaf spot (LLS), and rust, are devastating diseases to peanut production worldwide. Peanut growers rely mainly on fungicides for foliar disease management, applying 7-10 spray application in a growing season with the first application made at around 30 days after planting (Hagan

1998; Kokalis-Burelle et al., 1997; McDonald et al., 1985; Wright et al., 2016). Because they use so many applications, producers typically rely on having a large diversity of products for managing these foliar diseases.

Quinone Outside Inhibitor fungicides (QOI) and DeMethylation Inhibitor fungicides

(DMI) have been classified as high and medium-risk for resistance selection in pathogens populations, respectively (Ishii and Hollomon 2015). The single mode of action fungicides azoxystrobin and pyraclostrobin (QOIs) and tebuconazole (DMI) have shown reduced efficacy in peanut foliar pathogens management in Florida across multiple years (2014-2017) and multiple locations when used alone compared to the low-risk active ingredient chlorothalonil (multi-site mode of action product). Thus, using these high-risk fungicides alone is ill-advised. Strategies need to be developed to reduce the resistance selection pressure of these products and provide adequate disease control and yield savings. Alternations and mixtures of different modes of action are some strategies that may allow us to prolong the life of these single modes of action active ingredients that are failing to effectively control peanut foliar diseases.

Our research showed that mixing different fungicide modes of action resulted in improved disease control and yield increase in one of the three sites examined.

However, the other two sites did not see this increase related to mixing compared to

chlorothalonil alone or the alternation programs. It was apparent from these results that disease onset and pathogen presence were also critical to the success of these strategies. Thus, an improved understanding of the peanut foliar pathogens epidemiology is needed to apply the appropriate mode of action fungicides for optimum disease management (Elderfield et al., 2018; Hagan et al., 2006). It should also be noted that disease assessment using the FL 1-10 scale is useful to monitor disease onset, however it does not provide any information about the pathogens present. It is critical that future fungicide efficacy studies gather information about pathogen incidence to better improve our understanding of product success and failures

(Kemerait et al., 2018). Overall, mixing and alternating fungicides are useful strategies for disease control and yield savings, however, it does appear that mixing products is the optimal strategy for multiple disease situations (Elderfield et al., 2018). It will be important to continue to examine these strategies in the future as our results indicate that reduced sensitivity of peanut leaf spot pathogen populations to three key fungicide products, in addition to investigating possible genetic resistance mutations.

APPENDIX EXAMINATION OF PRIMERS FOR SINGLE POINT MUTATIONS RELATED TO FUNGICIDE RESISTANCE

Introduction

Preliminary experiments were done to trying to identity primers capable of identifying QOI single point mutations in the cytochrome b region. The single point mutations of interest were G143A, F129L, and G137A, which have not been reported in either Cercospora arachidicola or Cercosporidium personatum.

Experimental Notes

1. Multiple isolates (> 50) of both pathogens, C. arachidicola and C. personatum, were collected from 2014 to 2017 from multiple locations. The pathogens were single spore isolated and maintained on a peanut hull extract media (Starkey, 1980).

2. Fungal DNA was extracted using the DNeasy Qiagen® DNA extraction kit (QIAGEN Inc., Germantown, MD).

3. The extracted DNA was PCR amplified with the universal barcode ITS 4 and ITS 5 primers for species identification. PCR products were purified, sequenced, and queried against the NCBI database using BlastN. The results confirmed the morphological and symptomatic identification of the pathogens.

4. Multiple primers (Table A-1) were designed using the software Geneious® (Biomatters Inc., Newark, NJ) to amplify the cytochrome b (Cyt b) region in the mitochondrial genome. These included primers to test for point mutations (G1432A, F129L, and G137A) commonly associated with fungicide resistance. All primers were based on Cyt b genes (Fig. A-1) from closely related species (e.g. Cercospora beticola, Cercospora sojina, and Cercospora kikuchii) since the sequence information for C. arachidicola and C. personatum was not available.

5. None of the designed primes in step 5 amplified their target regions, so further steps were taken to optimize them. Different DNA polymerases were utilized; Qiagen® (QIAGEN Inc., Germantown, MD), Dream Taq (Thermo Fisher Scientific, Waltham, MA), and Phusion® high-fidelity (New England Biolabs, Ipswich, MA). PCR annealing temperature was optimized using Qiagen polymerase (temperature range was 40 – 55°C) and Phusion polymerase (temperature range 45 – 72°C). None of the optimized PCR reactions had positive results.

6. The previous results indicated differences in the cyt b region from the isolated pathogens to the closely related published species.

Concluding Thoughts

Since none of the primers were able to amplify the region of interest, further sequence data of the cytochrome b region is needed. Spore assays are also a possibility, however, preliminary results from this study indicated that sporulation is limited on single spore cultures after about 60 days of incubation. Renewed cultures grow vegetatively or with reduced sporulation, limiting the ability of testing spores growth on poison agar media (fungicide amended media). Thus, bulk spore assays could be used, however, their utility will depend on the fungicides used within a plot and the environment for leaf spot production. Our field assays results indicate that ELS and

LLS pathogen populations likely have resistance presence. However, proving this with molecular and laboratory based assays is difficult and will require further understanding of the pathogen’s mitochondrial DNA and novel culturing techniques.

Table A-1. List of primers designed to amplify the cytochrome b region including the potential point mutation G143A, F129L, and G137A in both Cercospora arachidicola and Cercosporidium personatum. Forward Primers Reverse Primers x 5’- CAAAGCACCTAGAACAC -3’ 5’- CCTGCGCTATCATGTAAAGC -3’ 5’- TAATACAGCTTCAGCATTTTTCTTCT -3’ 5’- CTCATTAAATTAGTAATAACTGTGGCCG -3’ 5’- GGTTCACTATTAGGATTTTGTCTTGTA -3’ 5’- CTCATTAAATTAGTAATAACTGTGGCCC -3’ 5’- TTACACGTAGGAAGAGGTCTATACTATG -3’ 5’- CTAATGCAGCTAATACGAAAGGTA -3’ 5’- GATTCACCTCAGCCTTCAAA -3’ 5’- CTCAACTATGTCCTGTCCTACTCA -3’ 5’- GCTACAGCCTTCTTGGGTTATG -3’ 5’- ACCTAGGTCTGTGAAAGGCA -3’ 5’- GCTACAGCCTTCTTGGGTTATG -3’ 5’- CCTAGGTCTGTGAAAGGC -3’ 5’- CTACGGGTCTTACAAGGCACC -3’ 5’- GCAGGAGGTGTTTGCATTGG -3’ x Reverse primers in the right column correspond with the forward primers in the left column in the same row

‘5_GAGGTTTGTACTATGGTTCTTACAAAGCACCTAGAACACTAGTATGAACTATACGTACTGTTATA TTAGTTTTAATGATGGCTACAGCCTTTCTGGGTTATGTTTTACCTTATGGACAAATGTCTTTATGAG GTGCTACAGTTTATTACTAATTTAATGAGTGCTATTCCATGAGTAGGACAAGACATAGTTGAGTTCT TATGAGGAGGTTTCTCTGTAAACAATGCAACATTAAATAGATTTTTTGCATTACATTTGTATTACCTT TCGTATTAGCTGCTTTAGCTTTAATGCACTTAATTGCTTTACATGATAGCGCAGGGTCAGGTAATCC CTTAGGAGTTTCTGGTAATTATGACAGACTTCCTTTTGCACCTTACTTCATATTTAAGGATTTAATAA CTATATTCTTATTTAGTATTATCAGTCTTCGTTTTCTTTATGCCTAATGTATTAGGTGATAGTGAAAA CTACGTTATGGCTAATCCAATGCAAACTC_3’

Figure A-1. Cytochrome b region consensus of closely related species to Cercospora arachidicola including Cercospora beticola, Cercospora sojina, and Cercospora kikuchii. Data obtained from National Center for Biotechnology and Information (NCBI, https://www.ncbi.nlm.nih.gov/).

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BIOGRAPHICAL SKETCH

Wael M. Elwakil was born in 1987 in Mansoura, Egypt. He received his bachelor’s in plant pathology form Mansoura University, Egypt in 2008. During the period from 2008 to 2011, he had experience working with agrochemical and pesticide producing company and vegetable seed producing company. He then received both his master’s in plant pathology and Doctor of Plant Medicine (DPM) degrees from the

University of Florida in 2018.