1 Assessment of Root-Knot Nematode Presence in Tomatoes in Ohio

Assessment of Root-Knot Nematode Presence in Tomatoes in Ohio, Yield Loss, and

Biocontrol

THESIS

Presented in Partial Fulfillment of the Requirements for the Degree Master of Science in

the Graduate School of The Ohio State University

Marlia Bosques Martínez

Graduate Program in Plant Pathology

The Ohio State University

2020

Thesis Committee

Dr. Sally Miller, Co-Advisor

Dr. Christopher G. Taylor, Co-Advisor

Dr. Pierce Paul

Copyrighted by

Marlia Bosques Martínez

2020

Abstract

Tomato is one of the most economically important vegetables in the world, with a value of $1.67 billion in the United States. Globally, Meloidogyne spp., also known as root- knot nematode (RKN), is the most widespread and yield-limiting plant-parasitic nematode of tomato production. A soilborne disease complex consisting of corky root rot

(Pyrenochaeta lycopersici), black dot root rot (Colletotrichum coccodes), Verticillium wilt

(Verticillium dahliae), and RKN (Meloidogyne incognita and M. hapla) is an emerging problem in tomato high tunnel production in Ohio. A survey was conducted to determine the presence and distribution of RKN in order to provide growers with practical management strategies. Farmers were provided with a soil sampling guide and asked to submit samples to regional produce auctions or the OARDC. A bioassay was conducted to assess the presence and severity of RKN galling. DNA was extracted from galls, and soil samples and species-specific primers were used to identify each pathogen. RKN was detected in 32 of 68 high tunnels (45%) on 19 of 34 farms (56%). All of the 32 samples were identified as M. hapla and two samples were co-infected with M. incognita.

Due to the emerging threat of RKN, trials are needed to evaluate effects of different initial densities of RKN species on tomato yield under high tunnel conditions. The objective of this study was to measure the yield of tomatoes under varying densities of M. hapla and M. incognita and determine final RKN root severity. In the second year of field

ii trials, water stress was also used as a treatment effect. In the 2018 and 2019 trials, there were no significant differences in total yield/plant or number of fruits/plant among tomato plants inoculated with different RKN population densities and non-inoculated control plants. In the 2018 trial, tomato plants inoculated with M. hapla tomato host isolate and M. incognita isolate had significantly higher number of eggs/g than plants inoculated the M. hapla lettuce host isolate. In the 2019 trial, the odds of having higher RKN severity rating was highest for plants inoculated with a Pi of 50,000 M. hapla with water stress versus non- inoculated water stress control plants.

One of the main methods used for RKN management is the application of chemical pesticides; however, concerns over environmental contamination and toxic effects to non- target organisms have restricted the widespread use of various synthetic products, and most have been discontinued from use. Additionally, other diseases such as C. coccodes

(anthracnose) and Pseudomonas syringae pv. tomato (bacterial speck) can also severely curtail production, and effective control tactics for their control are also limited due to similar reasons. Growers are seeking new alternatives such as biocontrol that can be integrated into a more sustainable management program. Biocontrol products are available on the market; however, few studies have been conducted to test these products in independent efficacy trials. Further testing is also needed to find new potential biocontrol agents. Numerous studies have reported antagonistic activity by Pseudomonas spp. against bacterial, fungal, and plant-parasitic nematode diseases. This study aimed to assess the efficacy of several strains of Pseudomonas spp. against P. syringae pv. tomato, C. coccodes, and M. incognita. In vitro assays were used to assess the antagonistic activity of

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Pseudomonas spp. towards the aforementioned pathogens. Soil drench application under greenhouse conditions was also used to assess Pseudomonas spp. activity towards M. incognita. In vitro screening of Pseudomonas spp. demonstrated that several strains significantly reduced growth of P. syringae pv. tomato (eight strains), C. coccodes

(seventeen strains decreased growth by >50%), and M. incognita (eighteen strains decreased hatching by >80%). However, the selected Pseudomonas spp. did not significantly reduce tomato RKN (M. incognita) in soil drench application assays.

The second objective was to evaluate the efficacy of nine commercially available biocontrol products in controlling M. hapla under greenhouse conditions and their effects on yield and quality under high tunnel conditions. Under greenhouse conditions, plants treated with Actinovate® and Bio-Activate® significantly reduced the number of eggs/g of root by 30% when compared to the non-treated-inoculated control plants. Under high tunnel conditions, there were no significant differences in mean yield/plant, mean number of fruits/plant, and percentage of marketable fruits among treated plants and non-treated- inoculated control plants. Moreover, there were no significant differences in odds ratios among treated plants versus non-treated-inoculated plants. Application rate, application frequency, and media should be evaluated in order to elucidate the best method for testing

Pseudomonas spp. and commercial biocontrol products.

Dedication

This work is dedicated to my makers, my mother Gladys Z. Martínez Guevara and father

Juan R. Bosques Caro. Everything I am is because of you. Every sacrifice you’ve made is deeply embedded in me. Dad, thank you for your unconditional support and love. You make me feel safe. Mom, thank you for being my best friend and pillar. You are my ultimate inspiration. To my stunning sister, Amara Bosques Martínez, you define kindness and love. I will never be alone because of you. To my aunt Evelyn Martínez Guevara, my second mother, thank you for always listening and bringing light and hope to every situation. The world is better because of you. To my grandparents, Gladys Guevara and

Augusto Martínez, you raised me, fed me, and loved me. You are the reason I have hope.

Finally, I dedicate this to Puerto Rico. You inspire me every day to keep fighting and to see the beauty in the darkness.

Acknowledgments

I want to thank my advisors Dr. Christopher G. Taylor and Dr. Sally A. Miller, for their guidance and support, and for providing the opportunities to develop my professional and research skills. I also thank Dr. Pierce Paul for providing critical comments and helping me with the data analysis. Thanks to Dr. Anna L. Testen for your constant encouragement, guidance, and teaching. Being able to work and learn from you was inspiring, and I am proud to see what you have achieved.

A huge debt of gratitude is owed to the Summer Research Opportunities Program at OSU for providing my first experience in research and encouraging me to continue graduate school. I will also like to thank the present and past members of the Taylor and

Miller Labs: Dr. Timothy Frey, Dr. Krystel Navarro Acevedo, Cecilia Chagas de Freitas,

Therese Miller, Rebecca Kimmelfield, Leslie Taylor, Edwin Navarro, Madeline Horvath,

Dr. Francesca Rotondo, and Angela Nanes. To all, thank you for your knowledge, constant support, and technical assistance. I would also like to acknowledge Bob James and Cindy

Eshler; they are exceptional people that were very welcoming and are instrumental to the success of the department.

To my friends and partner: Ana Vázquez, Noelymar Maldonado, Andrea Lugo, Rey

Cotto, Guillermo Valero, Otto Oppenheimer, and Francisco Tirado, respectively. Thank

vi you for your friendship, for always believing in me, for your advice, and for always pushing me forward. I could not have done it without you guys.

Salaries and research support were provided in part by State and Federal Funds appropriated to the Ohio Agricultural Research and Development Center, The Ohio State

University. The project was funded in part by a USDA Specialty Crop Block Grant administered by the Ohio Department of Agriculture, and by the OSU Graduate School’s

Matching Tuition and Fee Award Program.

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Vita

August, 1994……………………………………………………..Born- Moca, Puerto Rico

2017……………………………………..B.S. Crop Protection, University of Puerto Rico

2017………..…………………………………..Graduate Research Associate, Department

of Plant Pathology, The Ohio State University

Publications

Anna L. Testen1, Marlia Bosques Martínez1, Alejandra Jiménez Madrid1, Loïc Deblais1,2, Christopher G. Taylor1, Sally A. Miller1. On-farm evaluations of anaerobic soil disinfestation and grafting for management of a widespread soilborne disease complex in tomato in Ohio. In preparation.

Fields of Study

Major Field: Plant Pathology

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Table of Contents

Abstract ...... ii Dedication ...... v Acknowledgments ...... vi Vita ...... viii List of Tables ...... xi List of Figures ...... xii Chapter 1. Literature Review ...... 1 Introduction ...... 1 High tunnel tomato production ...... 4 Important diseases of tomato ...... 6 Black dot root rot (Colletotrichum coccodes) ...... 8 Bacterial speck (Pseudomonas syringae pv. tomato) ...... 9 Root-knot nematodes ...... 10 Management methods for RKN ...... 14 Pseudomonas spp. as possible biocontrol agents for root-knot nematode ...... 20 Commercial biocontrol products for root-knot nematode ...... 23 Limitations of biocontrol use for RKN ...... 26 References ...... 28 Chapter 2. RKN prevalence and yield loss assessment in tomato high tunnels in Ohio .. 39 Introduction ...... 39 Material and Methods ...... 43 Soilborne disease survey sample collection...... 43 Meloidogyne spp. survey bioassays...... 44 Meloidogyne spp. survey PCR assays...... 44 Meloidogyne spp. yield loss assessment ...... 45 Statistical Analysis ...... 49 Results ...... 50 High tunnel tomato RKN survey ...... 50 Effects of initial densities of Meloidogyne species on tomato yield ...... 50

Discussion ...... 52 References ...... 70 Chapter 3. Evaluation of Pseudomonas spp. against Tomato Pathogens ...... 75 Introduction ...... 75 Material and Methods ...... 79 Nematode culture ...... 79 Pseudomonas strains and culture conditions ...... 80 Pseudomonas spp. in vitro antagonistic activity against Pseudomonas syringae pv. tomato ...... 81 Pseudomonas spp. in vitro antagonistic activity against Colletotrichum coccodes .. 81 Pseudomonas spp. in vitro antagonistic activity against Meloidogyne incognita .... 82 Pseudomonas spp. antagonistic activity against Meloidogyne incognita under greenhouse conditions ...... 84 Statistical Analysis ...... 86 Results ...... 86 Pseudomonas spp. in vitro antagonistic activity against Pseudomonas syringae pv. tomato ...... 86 Pseudomonas spp. in vitro antagonistic activity against Colletotrichum coccodes .. 87 Meloidogyne incognita egg hatching after exposure to Pseudomonas spp. volatile organic compounds...... 87 Pseudomonas spp. antagonistic activity against Meloidogyne incognita under greenhouse conditions ...... 88 Discussion ...... 90 References ...... 112 Chapter 4. Efficacy of commercial biocontrol products against Meloidogyne hapla ..... 118 Introduction ...... 118 Material and Methods ...... 120 Results ...... 125 Discussion ...... 126 References ...... 136 Bibliography ...... 139

List of Tables

Table 2.1. Primers used in this study and their original publications...... 61 Table 2.2. Assessment of Meloidogyne hapla (Mh) and Meloidogyne incognita (Mi) presence in Ohio tomato high tunnel soils through bioassays and PCR assays...... 62 Table 2.3. Effects of initial densities of Meloidogyne species on tomato yield in high tunnel microplot trial (2018 trial)...... 64 Table 2.4. Effects of root-knot nematode (RKN; Meloidogyne spp.) isolates and initial egg densities on tomato root damage in high tunnel microplot trial (2018 trial)...... 65 Table 2.5. Effects of initial densities of Meloidogyne species and water stress on tomato yield in high tunnel microplot trial (2019 trial)...... 66 Table 2.6. Effects of initial densities of Meloidogyne species and water stress on tomato root galling severity in high tunnel microplot trial (2019 trial)...... 67 Table 3.1. Pseudomonas species and strains used in this study and source of origin of each strain...... 97 Table 3.2. Pseudomonas spp. in vitro antagonistic activity against Pseudomonas syringae pv. tomato ...... 99 Table 4.1 List of commercial products used in high tunnel and greenhouse assays...... 131 Table 4.2. Effects of commercial biocontrol products on yield parameters in soil infected with M. hapla in high tunnel tomato microplots...... 132 Table 4.3. Nematicidal activity of commercial biocontrol products against M. hapla in high tunnel tomato microplots...... 133

List of Figures

Figure 2.1. Locations of counties and produce auctions participating in soilborne disease survey and anaerobic soil disinfestation trial sites. Farmers participating in the soilborne disease survey were located in seventeen counties (shaded) and participating produce auctions (squares) were located in the following counties 1: Hardin, 2: Pike, 3: Morrow, 4: Medina, 5: Holmes, 6: Tuscarawas...... 68 Figure 2.2. Results of high tunnel tomato soilborne disease survey in Ohio. Dark shaded barns or high tunnels represent farms and high tunnels in which Meloidogyne spp. were found...... 69 Figure 3.1. (A) Pseudomonas spp. antagonistic activity against Colletotrichum coccodes (CcOH19-01). The dual culture technique was used. Rate of inhibition was determined after nine days. (B) Pseudomonas spp. antagonistic activity against Pseudomonas syringae pv. tomato. Zones of inhibition were measured after 24 hours of co-culturing. (C) Pseudomonas spp. antagonistic activity assay against Meloidogyne incognita. Number of hatched J2 were counted after five days of exposure to volatile organic compounds of Pseudomonas strains. 48G9 = P. chlororaphis; 90F12-1 = P. rhodesiae; 15H3 = P. protogens; 48C10 = Pantoea agglomerans...... 100 Figure 3.2. (A) A greenhouse study to evaluate nematicidal effects of Pseudomonas spp. against Meloidogyne incognita. Image represents all replicates of one experiment. (B) Development of Meloidogyne incognita in tomato after Pseudomonas soil drench application. Image includes non-treated inoculated control root and roots treated with P. protegens 38G2 or P. protegens 15H3...... 101 Figure 3.3. Pseudomonas spp. in vitro antagonistic activity against Colletotrichum coccodes. Fungal growth was measured after nine days of co-culture and percent inhibition was calculated using the formula %= A1-A2A1×100. Results of two independent experiments are shown. Columns in red indicate 50% or more of growth inhibition. Vertical bars indicate standard error of the mean. Means with same letter are not significantly different (P < 0.05, least squares means option of the GLIMMIX procedure of SAS). Means of non-transformed data are presented for clarity...... 102 Figure 3.4. Pseudomonas spp. in vitro antagonistic activity against Colletotrichum coccodes. Fungal growth was measured after nine days of co-culture and percent inhibition was calculated using the formula %= A1-A2A1×100. Results of two independent experiments are shown. Bars in red indicate 50% or more of growth inhibition. Vertical bars indicate standard error of the mean. Means with same letter are not significantly different (P < 0.05, least squares means option of the GLIMMIX procedure of SAS). Means of non-transformed data are presented for clarity...... 103

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Figure 3.5. Meloidogyne incognita (RKN) egg hatching after exposure to Pseudomonas spp. volatile organic compounds. Results of two independent experiments are shown. Each treatment was represented by three LB plates containing 30µl of a Pseudomonas strain that was inverted and placed on top of a plate containing 500 M. incognita eggs in a hatch chamber. Vertical bars indicate standard error of the mean. Means with same letter are not significantly different (P < 0.05, least squares means option of the GLIMMIX procedure of SAS)...... 104 Figure 3.6. Development of Meloidogyne incognita (RKN) in tomato after Pseudomonas spp. soil drench application at a 1:100 dilution (108 CFU/ml). Number of eggs/g of root was calculated 45 days after inoculation. Control A is Luria-Bertani inoculated plants. Control B is non-treated inoculated plants. Results of three independent experiments are shown. Each treatment was represented by ten plants. Vertical bars indicate standard error of the mean. ANOVA was used to test significant differences at P <0.05 (GLIMMIX procedure of SAS)...... 105 Figure 3.7. Development of Meloidogyne incognita (RKN) in tomato after Pseudomonas spp. soil drench application at a 1:100 dilution (108 CFU/ml). Number of eggs/g of root was calculated 45 days after inoculation. Control A is Luria-Bertani inoculated plants. Control B is non-treated inoculated plants. Results of four independent experiments are shown. Each treatment was represented by ten plants. Vertical bars indicate standard error of the mean. ANOVA was used to test significant differences at P <0.05 (GLIMMIX procedure of SAS)...... 106 Figure 3.8. Development of Meloidogyne incognita (RKN) in tomato after Pseudomonas spp. soil drench application at a 1:100 dilution (108 CFU/ml). Number of eggs/g of root was calculated 45 days after inoculation. Control A is Luria-Bertani inoculated plants. Control B is non-treated inoculated plants. Results of one independent experiment is shown. Each treatment was represented by ten plants. Vertical bars indicate standard error of the mean. ANOVA was used to test significant differences at P <0.05 (GLIMMIX procedure of SAS)...... 107 Figure 3.9. Development of Meloidogyne incognita (RKN) in tomato after Pseudomonas spp. soil drench application. Three different dilutions (108 CFU/ml) of Pseudomonas protegens 38G2 and Pseudomonas protegens 15H3 were evaluated at an initial density of 1,000 M. incognita eggs. Results of one independent experiment is shown. Number of eggs/g of root was calculated 45 days after inoculation. Control is non-treated inoculated plants. Each treatment was represented by ten plants. Vertical bars indicate standard error of the mean. ANOVA was used to test significant differences at P <0.05 (GLIMMIX procedure of SAS)...... 108 Figure 3.10. Development of Meloidogyne incognita (RKN) in tomato after Pseudomonas spp. soil drench application. Three different dilutions (108 CFU/ml) of Pseudomonas protegens 38G2 and Pseudomonas protegens 15H3 were evaluated at an initial density of inoculation of 2,000 M. incognita eggs. Results of one independent experiment is shown. Number of eggs/g of root was calculated 45 days after inoculation. Control is non-treated inoculated plants. Each treatment was represented by ten plants. Vertical bars indicate standard error of the mean. ANOVA was used to test significant differences at P <0.05 (GLIMMIX procedure of SAS)...... 109 xiii

Figure 3.11. Development of Meloidogyne incognita (RKN) in tomato after Pseudomonas spp. soil drench application. Three different dilutions (108 CFU/ml) of Pseudomonas rhodesiae 88A6 and Pantoea agglomerans 48C10 were evaluated at an initial density of inoculation of 2,000 M. incognita eggs. Results of one independent experiment is shown. Number of eggs/g of root was calculated 45 days after inoculation. Control is non-treated inoculated plants. Each treatment was represented by ten plants. Vertical bars indicate standard error of the mean. ANOVA was used to test significant differences at P <0.05 (GLIMMIX procedure of SAS)...... 110 Figure 3.12. Development of Meloidogyne incognita (RKN) in tomato after Pseudomonas spp. soil drench application. Three different dilutions (108 CFU/ml) of Pseudomonas rhodesiae 88A6 and Pantoea agglomerans 48C10 were evaluated at an initial density of inoculation of 4,000 M. incognita eggs. Number of eggs/g of root was calculated 45 days after inoculation. Control is non-treated inoculated plants. Graph A represents one independent experiment. Graph B represents one independent experiment. Data was not pooled together for analysis due to inconsistency between independent experiments in mean number of eggs/g of root on plants treated with Pseudomonas strains. Each treatment was represented by ten plants. Vertical bars indicate standard error of the mean. Means with same letter are not significantly different (P < 0.05, least squares means option of the GLIMMIX procedure of SAS)...... 111 Figure 4.1. Biocontrol assays for RKN management. (A) A high tunnel microplot system was used to evaluate the efficacy of commercial biocontrol products against Meloidogyne hapla. Overview of study with tomato plants. (B) A tomato microplot. (C) One block of a greenhouse study to evaluate the nematicidal activity of commercial biocontrol products against M. hapla. (D) Development of M. hapla in tomatoes from greenhouse (Left) negative control (non-treated), (middle) positive control (oxamyl), and (right) Actinovate AG®...... 134 Figure 4.2. Development of Meloidogyne incognita (RKN) in tomato after commercial biocontrol soil drench application in a greenhouse assay. The number of eggs per gram of root was determined 45 days after inoculation. The negative control was non-treated, inoculated plants (H2O control) and the positive control was plants treated with oxamyl. Data were pooled from two independent experiments. Vertical bars indicate standard error of the mean. Means with same the letter(s) are not significantly different at P<0.05 (Least squares means option of the GLIMMIX procedure of SAS)...... 135

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Chapter 1. Literature Review

Introduction

Tomato is one of the most widely produced and economically important vegetables in the world, both for the processed food and fresh market industries (Naika et al., 2005;

Stevens, 2012; Heuvelink and Costa, 2018). Tomatoes belong to the Solanaceae family, which includes other important species such as potato, pepper, eggplant, tobacco, and many garden ornamentals. The taxonomic classification of tomatoes has been debated for several decades. However, with DNA sequence evidence, the species name was changed to

Solanum lycopersicum L., although Lycopersicon esculentum is still sometimes used

(Stevens, 2012). Tomatoes were first domesticated in Mexico and was introduced to several European countries. In the 18th century, tomato was introduced in the USA where production and consumption proliferated. By the end of the 19th-century, tomato was regularly consumed in processed products such as juice, sauces, and ketchup (Naika et al.,

2005; Heuvelink and Costa, 2018).

The tomato, a fruit generally treated as a vegetable, is, after the potato, the most consumed vegetable in the world. The demand for tomatoes comes from the fact that they can be eaten fresh or in processed form (Blancard, 2012). Processed tomato products include tomato preserves, dried tomatoes, and tomato-based foods such as soup, sauces, and ketchup (Gould, 1992). It is an essential source of vitamins, phenolics, and antioxidants 1 in the diet of many countries. In the United States (US) tomatoes account for approximately

10% of total dietary intake of vitamin C. Tomatoes are also an important source of Vitamin

A, carotenoids B-carotene, and lycopene. Vegetatively, tomatoes are classified in two forms of growth habit: determinate or indeterminate (Stevens, 2012). Determinate cultivars are primarily used in open field production, and they flower within a preset period. Once fruits are formed, they are harvested at one time and are amenable to mechanical harvesting. Indeterminate varieties have non-restricted flowering, a more extended harvest period, and are commonly used for small scale farms or greenhouse production (Blancard,

2012).

Different management methods are practiced in the field, depending on the end- use of the fruit. In the fresh market industry, tomatoes are usually hand-picked when they reach the mature green stage. On the other hand, for processing tomatoes, a high percentage of growers apply a plant growth regulator to provide more uniform ripening of the fruit at the time of harvest. A few days after the plant growth regulator application, the tomatoes are harvested using a mechanical harvester (Naika et al., 2005). A daily maximum temperature of 25-35°C is optimal for growth and reproduction (Stevens, 2012). The production of tomatoes has substantially evolved in recent decades, especially in the form of cultivation. Traditionally, tomatoes are produced in conventional land farms. Currently, alternative production systems are becoming more popular, such as greenhouse and high tunnel production for fresh-market tomatoes.

The annual worldwide production of tomatoes has grown considerably in the past three decades. In 2016, tomato production was estimated at 177 million tons with a total production area of about 4.8 million ha (FAO, 2017). China and India are the leading producers, followed by the US, Turkey, Italy, and Iran (Guan et al., 2017). In the United

States, production of tomatoes in 2017 occupied approximately 311,500 acres and yielded

15.62 million tons of produce, accounting for approximately $1.67 billion (FAO, 2017;

USDA-AMS, 2017). California is the leading state in processing tomatoes, accounting for approximately 95% of the US supply and a large share of the world supply (USDA-AMS,

2017). Florida and California are the primary fresh market tomato-producing states. Other states with significant tomato production are Michigan, Virginia, Georgia, and Ohio (Guan et al., 2017).

In 2018, Ohio ranked as the 4th leading US tomato producer and had a total of

5,500 acres of tomatoes accounting for a total of $52 million dollars in production (USDA-

NASS, 2018). Total production of tomatoes in protected systems in Ohio was 8,841 cwt with a total value of approximately $1.16 million in 2013, the latest date figures are available (USDA-NASS, 2014). This amount has likely increased since then with the opening of three large-scale hydroponic tomato facilities and continued adoption of high tunnels throughout the state. Approximately 70% of open field tomatoes are used by the processing industry. Fresh market tomato production for direct consumer use is spread out across Ohio; however, the highest production is concentrated in the southeast (Washington and Meigs Counties) and northwest regions of the state (Welty et al., 1999). Processing tomatoes are predominantly grown in the counties of northwestern Ohio. In Ohio, fresh 3 market and processing tomatoes are mostly grown from transplants produced within the state or from the southern U.S. (Welty et al., 1999).

High tunnel tomato production

High tunnels, also called hoop houses, are a type of structure that can help farmers extend their growing season in temperate regions. High tunnels are polyethylene, polycarbonate (plastic), or fabric-covered structures with manually operated vents and solar heating, which significantly increase average daily temperature. Besides extending the growing season, high tunnels also protect plants from wind, snow, rain and some insects and diseases (Kaiser and Ernst, 2017). High tunnels crops are grown directly in the soil either by tilling the soil or by installing raised beds. High tunnel systems differ from greenhouses because they are seasonal and are considered temporary structures (Lewis,

2004). Most high tunnels have roll-up sidewalls and detachable end walls to manually or mechanically adjust air movement, humidity, and temperature (Kaiser and Ernst, 2017).

Crops grown in high tunnels tend to have higher yields and better quality fruit than plants grown in open fields. The use of high tunnels for tomato production can increase yield when the right management practices are used and environmental conditions are met

(Kleinheinz, pers. comm, O’Connell et al., 2012). Production of fresh market tomatoes is shifting from open field to high tunnel systems in the Midwestern states. High tunnel production continues to increase every year in Ohio because it is considered a consistently reliable system that reduces some of the risks of production such as excessive rain, wind, and freezing temperatures (Kleinheinz, pers. comm.). In Ohio tomatoes are grown in high 4 tunnels from April to September, often followed by leafy greens or flowers or fallowing; however, most growers continue to grow tomatoes within the high tunnel structure year after year. Another advantage of producing tomatoes in a high tunnel system is the reduction of certain foliar diseases, such as Septoria leaf spot (Septoria lycopersici), bacterial spot (Xanthomonas spp.) and bacterial speck (Pseudomonas syringae pv. tomato)

(Testen et al., unpublished). These foliar diseases are spread by rain splash; thus, the protection of the high tunnel reduces the development and dissemination of these pathogens.

However, high tunnel systems also have several disadvantages. Structures do not have the strength of greenhouses and can be destroyed by high winds, snow, and ice. Other difficulties include the lack of exhaust fans for venting during hot weather, and the fact that most high tunnels cannot be easily moved (Pool and Stone, 2014). Not being able to move high tunnels into a new location usually results in growing the same crop in the same location every year. Additionally, protected cultures creates warmer soil conditions with limited winter freezes and increases daily humidity and temperature. These characteristics create conditions conducive for the growth and survival of soilborne pathogen populations and the development of soilborne disease complexes. Soilborne diseases can significantly reduce yield and often go unnoticed unlike leaf or fruit disease because they are challenging to detect and identify (Campbell and Hall, 1982).

Important diseases of tomato

A wide range of disorders, insects, and diseases may cause problems during any time in the tomato growing season, which may damage the crop and reduce quality and yield. Tomatoes are susceptible to a variety of diseases; in fact, there are nearly 200 known diseases, each with different effects and economic relevance (Chetelat, 2014). The first step for managing a disease is a diagnosis. However, diagnosis is made difficult not only because there are so many diseases but also because individual plants can be infected by more than one pathogen. Nevertheless, successful management of a disease requires incorporating cultural, chemical and biological control tactics, as well as host resistance into a program of integrated pest management.

Diseases can occur both in open field production and enclosed structures such as high tunnels. However, while some diseases are common in the field, other diseases are more common in enclosed structures because of the particular environmental conditions these structures create (Miller, 2017). Consequently, disease management strategies for open field production are slightly different than those for enclosed structures. Cultural methods such as the use of pathogen free seeds, resistant cultivars, and crop rotation are recommended and practiced in both types of production. Both production systems use pesticides, although application methods or rate may be different for each system. One significant difference is that in enclosed structures, the environmental conditions can be more easily altered than in open fields. In greenhouses or high tunnels growers have better control over irrigation, temperature, and humidity. For instance, for diseases in

6 greenhouses, growers can increase the daily temperature and decrease air humidity to decrease the reproduction and spread of pathogens. In high tunnels, the plastic side walls can be rolled up to increase air circulation and manage temperature. Irrigation can also be altered to avoid excess humidity. Crops in enclosed structures generally are more intensively managed than crops grown in open fields, thus practices such as pruning are more heavily relied on to increase air flow between plants.

Tomato crops in Ohio are commonly affected by several fungi, bacteria, nematodes, and viruses. Some of the common foliar diseases in Ohio open field production are bacterial spot (Xanthomonas spp.), bacterial speck (Pseudomonas syringae), Septoria leaf spot

(Septoria lycopersici), and tomato pith necrosis (Pseudomonas spp. and Pectobacterium caratovorum subsp. carotovorum). Diseases such as bacterial speck and bacterial spot are economically significant; however, their management is challenging due to the limited number of reliable options (Louws et al., 2001). High relative humidity and free moisture are common characteristics encountered in enclosed structures that favor pathogen growth in these systems. In high tunnels and greenhouses, several of the recurrent diseases are powdery mildew (Leveillula taurica, Oidium neolycopersici or Erysiphe orontii), leaf mold

(Passalora fulva), Sclerotinia white mold (Sclerotinia sclerotiorum), gray mold (Botrytis cinerea) and tomato mosaic (Tomato mosaic virus or Tobacco mosaic virus). Powdery mildew is one of the most common tomato diseases in enclosed structures, and losses in production can reach up to 50% (Vallad et al., 2017). Gray mold and white mold are common diseases in greenhouses and high tunnels that have a very broad host range and gray mold can also affect the fruit post-harvest (Rodríguez et al., 2014). 7

In recent years, soilborne diseases have become a greater concern in high tunnel production. Lack of crop rotation and increase in soil temperature has created a favorable environment for soilborne pathogen development. A soilborne disease complex consisting of corky root rot (Pyrenochaeta lycopersici), black dot root rot (Colletotrichum coccodes),

Verticillium wilt (Verticillium dahliae), and root-knot nematode (Meloidogyne incognita and M. hapla) has emerged in tomato high tunnel production in Ohio. The disease complex was first identified in several Ohio high tunnels in 2015 (Vrisman et al. 2017). Pathogens in these complexes are difficult to manage (Colla et al. 2012) and have broad host ranges

(Inderbitzin, 2013). In Ohio, both northern root-knot nematode (NRKN, M. hapla) and southern root-knot nematode (SRKN, M. incognita) are present in tomatoes. Barker et al.

(1976) reported that M. hapla and M. incognita can reduce tomato production by 50% and

85%, respectively. Verticillium wilt and corky root rot are also known to cause considerable damage (Ashworth et al., 1979; Workneh et al., 1993). Individually these pathogens can cause devastating yield loss; therefore, occurrence of co-infections may produce higher yield losses (Siddiqui and Ehteshamul-Haque, 2001).

Black dot root rot (Colletotrichum coccodes)

Colletotrichum coccodes (Wallr.) S.J. Hughes is the causal agent of two critical diseases of tomato, anthracnose on the fruit and black dot root rot. Anthracnose can cause serious economic losses by reducing yield up to 50% (Gutierrez-Chapin et al., 2006). This fungus can attack almost every part of the tomato plant, but the fruit and roots are where most symptoms are seen (Dillard and Cobb, 1998). On fruits, symptoms of anthracnose

8 initially appear as small light brown lesions, and as the disease progresses the lesions enlarge and become circular and sunken. During moist weather, masses of salmon-colored spores are produced, and mature lesions release small black sclerotia. Black dot root rot symptoms appear as honey-brown to grayish-brown root discoloration covered with small black sclerotia. Black dot root rot symptoms are primarily seen in soil-based greenhouse production or other protected culture systems because environmental conditions are conducive for C. coccodes development (Stevenson and Pernezny, 2014). Successful management of these diseases requires the implementation of several management strategies such as pathogen-free seeds, sanitation, soil disinfestation, using grafting and resistant cultivars, and using 3- to 4-year crop rotations (Farrar, 2012). Hence, there is a pressing need to develop alternative management methods such as biocontrol that can integrate into existing management programs and provide control for soilborne diseases.

Bacterial speck (Pseudomonas syringae pv. tomato)

Pseudomonas syringae van Hall pv. tomato (Okabe) Young, Dye and Wilkie is the causal agent of bacterial speck in tomatoes. Bacterial speck is a widespread and globally significant disease in tomato production. On leaflets, symptoms include small, black leaf spots with a yellow halo around the lesion that develops with time. Speck lesions in fruits are tiny black spots, mainly on green fruits. The lesions are slightly raised, and a wet green halo often surrounds them (Miller and Jones, 2014). This disease is frequently encountered in temperate regions such as the upper Midwest since it is favored by cool temperatures and high moisture. Bacterial speck is seedborne, thus for efficient management, high-

9 quality pathogen-free seed and transplants, rigorous sanitation strategies, resistant cultivars and timely application of copper-based fungicides are required (Pernezny et al., 2012).

Biocontrol of bacterial speck is another alternative management tactic that has been studied in recent years. More research is needed to provide a more comprehensive selection of biocontrol alternatives that can be implemented in an integrated pest management (IPM) program.

Root-knot nematodes

Root-knot nematodes (RKN; Meloidogyne spp.) were discovered during the 1850s on cucumber by Miles Joseph Berkeley. The genus Meloidogyne contains around 100 species described to date (Perry et al., 2009). Worldwide, these species attack a vast range of species of plants, causing annual losses approaching 100 million dollars. The distribution of RKN is worldwide, and hundreds of plant species are a host of this pathogen

(Mitkowski and Abawi, 2003). Although over 34 species of RKN have been reported in tomato, there are four species of particular economic importance to tomato production: M. incognita (SRKN), M. javanica (javanese root-knot nematode), M. arenaria (peanut root- knot nematode), and M. hapla (NRKN; Noling, 2014).

On a global basis, RKN are the most widespread and economically devastating plant-parasitic nematodes that attack tomato (Noling, 2014). RKN have a vast host range; thus, it limits growers’ options for crop rotation. In vegetable production infected with

RKN, reports indicate an average of 10% reduction in yield (Collange et al., 2011).

However, depending on the species, race, population density, crop susceptibility, and 10 environmental factors, yield losses can be much higher. For instance, Barker et al. (1976) reported M. incognita and M. hapla caused yield losses on tomatoes of 85% and 50%, respectively.

Many of the most economically important RKN species reproduce parthogenically

(fertilization is not required), and males are usually rare; however, certain races are amphimictic (fertilization of the female by the male). Parthenogenesis is usually present in

M. incognita, M. javanica, M. arenaria, and some populations of M. hapla (Perry et al.,

2009). A high number of males can occur under suboptimal conditions or high population density when sex reversal is activated in early infecting juveniles (Mitkowski and Abawi,

2003). Species of RKN exhibit sexual dimorphism; mature females are pyriform (pear) or saccate (pouchlike) in shape and become sedentary. Adult males are vermiform (worm- shaped) and leave the root without feeding to return to the soil and eventually die (Perry et al., 2009; Jones et al., 2013). Under optimal conditions, RKN can complete its life cycle within three to four weeks, and in one growing season, five to eight generations may develop (Noling, 2014).

The life cycle of RKN species starts with deposition of eggs into a gelatinous matrix that protects and holds them together. The gelatinous matrix may hold from 300-1,000 eggs that usually protrude from the “butt” end of the nematode and protrude to the surface of the root (Perry et al., 2009). A fully formed larva develops from a single cell inside the egg. The developing nematode undergoes numerous rounds of mitosis leading to a fully formed infective juvenile complete with a visible stylet and molt its first time within the

11 egg. While still inside the egg, second-stage juveniles (J2) secrete enzymes (lipases and proteases) to induce hatching (Perry et al., 1992). The hatched J2 moves through the soil in search of a root to feed on, moving directly to the tip and invading the root tissue (Jones et al., 2013). To locate the root tissue, J2 use physical and chemical cues. Plant attractants

such as CO2 and ethylene play a key role in chemotaxis of RKN (Robinson and Perry 2006,

Fudali et al., 2013). Once a J2 finds the root, it secretes enzymes and uses its stylet to penetrate the root. Eventually, the J2 moved between the root cells and finally come to rest by establishing a permanent feeding site in the vascular cylinder. Secretions from the esophageal glands create the feeding site in the cells walls, which cause enlargement of cells in the vascular tissue and higher rates of cell division in the pericycle. The feeding site consist of four to eight multinucleated giant cells that form as a result of hypertrophy

(enlargement of cells; Perry et al., 2009; Jones et al., 2013). As the second stage larvae continue feeding, its body size begins to increase. Bodies then become flask-shaped and undergo two additional molts. The third and fourth stage juveniles lack a functional style and do not feed. In the last molt, the adult female is formed containing a functional stylet and median bulb, a uterus and a vagina (Perry et al., 2009; Jones et al., 2013).

In fields where growers practice fallowing, RKN can survive longer in colder climates than in hotter climates. M. hapla, also known as the northern root-knot nematode, is favored by moderate to cool weather, and M. incognita prefers a more temperate environment (Mitkowski and Abawi, 2003; Blancard, 2012). In general, the optimal soil temperature for NRKN development ranges from 20-25°C, while SRKN can thrive until

27°C (Noling and Ferris, 2012). 12

Tomato symptoms induced by RKN generally result from root dysfunction and from large galls or "knots" throughout the root system. Galls produced by M. hapla are small and affect a smaller area of the roots because it infects the apical meristems of the primary roots. In contrast, M. incognita and M. javanica produce rather large galls that usually affect the entire root system (Noling and Ferris, 2012). Tomato symptoms in the foliage include stunting, premature wilting, slow recovery after irrigation or rainfall and leaf chlorosis. Also, the fruit usually mature early but not uniformly. Symptoms in the field occur in clusters of plants (foci), rather than across an entire field because nematodes move slowly through the soil. Thus, symptoms progressively spread from the initial point of infection (Mitkowski and Abawi, 2003; Blancard, 2012).

Tomato thresholds of populations of nematodes vary within species; thresholds for

M. arenaria and M. incognita range 2 to 100 juveniles per 6 to 12 cubic inches. The M. hapla threshold ranges from 20 to 100 juveniles per 6 to 12 cubic inches (Noling and Ferris,

2012). Significant crop losses in tomato have also been reported by disease complexes that involve root-knot nematode and other pathogens. The most observed complex is M. incognita and Fusarium oxysporum infection. This complex is commonly found in wilt- susceptible cultivars, especially in sandy soils (Noling, 2014). Hence, the broad host range, the co-occurrence with other pathogens, and the high reproduction rate of RKN create complex challenges in developing efficient management strategies in tomato.

Management methods for RKN

Efficient RKN management depends on IPM: a holistic, multi-tactic approach that combines the use of chemical, cultural, and biological strategies. The IPM approach considers the growth and development of the present and future crop and the entire pest complex including present and future interactions in the tomato and soil community.

Another important pre-plant factor to consider is weeds since several weeds are an alternate host of RKN and other economically important tomato pathogens. As previously indicated, the host range of root-knot nematodes is very wide, so a minimum of one to two years with a non-host crop is required for efficient management in tomato (Noling, 2016).

Using cover crops is a reliable management alternative to reduce nematode pathogen populations by growing cover crops between cash crop cycles or incorporating them with cash crops to cover the soil. Cover crops are known to promote the development of disease suppressive soils as well as providing soil health benefits such as reducing soil erosion, decreasing nitrogen leaching, and increasing organic matter (Wen et al., 2017).

Hence, cover crops improve the overall health and vigor of cash crops. In general, for winter rotation, cereal cover crops such as rye (Secale cereale) and oat (Avena sativa) provide better nematode suppression than most leguminous cover crops. Summer cover crops that are commonly used to keep RKN populations at lower levels include cowpea

(Vigna unguiculata), sunn hemp (Crotalaria juncea), marigold (Tagetes spp.), and sorghum-sudangrass (Sorghum bicolor × S. sudanense). Marigold also exhibits another mechanism of suppression by producing an allelochemical called alpha-terthienyl which

14 provides nematicidal activity against several plant-parasitic nematodes (Gill and McSorley,

2017).

Other cultural measures that tomato growers use are sanitation, pathogen-free transplants, and soil solarization. Sanitation practices are implemented to prevent the spread of the pathogen to other fields and decrease secondary infestations in fields with

RKN present. Whether open field or greenhouse production, all tools, pots, and equipment should be thoroughly disinfected before reuse. If a small area is infested, carefully removing all plants with visible symptoms should decrease the nematode population.

Purchasing transplants from reliable nurseries is another practice used to prevent early infestation of plants. Solarization is the process of covering the desired field with clear plastic tarp for four to six weeks during the sunniest part of the year. This process allows the heat to kill unwanted organisms such as weeds, plant pathogenic fungi, bacteria, and nematodes. To effectively decrease RKN population soil temperature needs to exceed

125°F for 30 minutes (Perry and Ploeg, 2010). This practice is successful in southern states, however, states with a cooler climate rely on other cultural practices.

Since RKN has a wide host range, crop rotation can sometimes be difficult to achieve. Fallowing is another option that can decrease RKN populations and other plant pathogens. One year of fallowing will decrease RKN populations sufficiently to grow a susceptible crop the following year. For efficient fallowing, growers must control weeds and soil must be kept moist so eggs will hatch and have nothing to feed on. Weed control is also critical during the growing season since many common weeds host RKN. Proper

15 management of weeds also includes eliminating weeds in the margin of fields because they can serve as a source of inoculum for future infestations (Schroeder et al., 2005).

Different types of soil amendments are also being studied, such as green manure, compost, and anaerobic soil disinfestation (ASD). Brassica species are a cultural alternative used as a green manure amendment before planting. Brassica species have nematicidal activity against several RKN species (Monfort et al., 2007). Brazilian wild mustard reduced the number of galls, egg masses, and eggs on tomato plants by 90%

(Oliveira et al., 2010). Nematicidal activity of plants in this genus is due to the production of secondary metabolites known as glucosinolates that synthesize hydrolytic products such as allyl, benzyl, and isothiocyanate (Dutta et al., 2011). Compost produced from organic matter such as poultry litter or municipal waste has also been studied as a soil amendment option for controlling phytoparasitic nematodes. Compost amendments may suppress nematodes by direct inhibition of nematode growth or indirectly by enhancing beneficial microbes and improving soil quality. For instance, application of 10% poultry litter compost on cacao seedlings reduced the number of M. incogonita eggs produced by 72.5%

(Meyer et al., 2011). ASD is a relatively new management tool that is used for soilborne diseases in vegetable crop production. ASD is characterized by the incorporation of carbon source amendments, irrigation to increase soil moisture content, and tarping with plastic mulch for several weeks (Testen and Miller, 2017). Ohio on-farm evaluations of ASD for a tomato soil borne disease complex demonstrated that RKN galling was significantly lower in tomato plants grown in ASD-treated soils (Testen et al., unpublished). Moreover,

ASD was effective in reducing M. hapla galling in lettuce grown on muck soils in Ohio 16 on-farm trials (Testen and Miller, 2019). The use of soil amendments with other management strategies may become an integral component in vegetable production as research and application technology advances.

Another alternative management tactic that is widely adopted by growers is the use of RKN resistant tomato cultivars. RKN-resistant rootstocks are also used worldwide in tomato production, particularly in small-scale farms with limited options (Barrett, Zhao, and McSorley 2012). Resistance in tomato cultivars is based on the presence of a single dominant gene named Mi that was transferred through introgression from a wild tomato species, Lycopersicon peruvianum (López-Pérez et al., 2006). In resistant cultivars, resistance is triggered by a hypersensitive response leading to localized cell necrosis near the site where feeding would be initiated (Jacquet et al., 2005). One of the concerns of using resistant cultivars is that resistance breaks down under high soil temperatures (82°F)

(López-Pérez et al., 2006). Other concerns arise from the pathogenic variability of RKN, which could result in overcoming resistance conferred by the Mi-gene and has already been encountered (Eddaoudi et al., 1996 Jacquet et al., 2005). The Mi-gene confers resistance to

M. arenaria, M. incognita, and M. javanica; however, it does not confer resistance to other

RKN species. M. hapla is an emerging tomato constraint in Ohio, which raises concern on the limited amount of options available to control this species.

When high-density populations of nematodes are already established in the field, and other cultural practices have not reduced populations, the use of chemical control tactics may be needed for nematode management. Chemical nematicides have played a key

17 role in managing nematodes in vegetable production. However, environmental concerns have restricted the broad use of these products, and some have already been discontinued due to their effect on non-target organisms (Noling and Ferris, 2012). Nematicides have be widely classified as fumigant or non-fumigant based on their volatility in the soil.

Fumigants nematicides provide broad-spectrum control against most nematodes, pathogens, and many weed species. Fumigants are usually applied as a solid or liquid that rapidly turns into a gas once in the soil. The fumigant is usually trapped in the soil by a sealed plastic tarp and application must be done prior to planting under appropriate conditions. Fumigants available for vegetable use are grouped according to their mode of action including halogenated hydrocarbons such as 1,3 dichloropropene (Telone II®), chloropicrin (Chlor-O-Pic®) and methyl bromide. Another important group is isothiocyanate fumigants such as sodium methyl dithiocarbamate (K-Pam®, Vapam®) and allyl isothiocyanate (Dominus®). Methyl bromide was one of the most common soil fumigants previously used by growers because of its efficacy against a wide range of pathogens and pests. However, methyl bromide phaseout was initiated in 2005 due to concerns of its ability to deplete ozone (Hajihassani, 2018).

Non-fumigant nematicides include a variety of chemical compounds that directly affect the biology of the nematode. The most widely developed and used nematicides were carbamates and organophosphates that directly affected the nervous system of the nematode. However, for tomato production there is a limited number of non-fumigants that can be used, and it varies by state. The only carbamate that is currently available for tomato production in Ohio is Oxamyl (Vydate®, DuPontÔ, Wilmington, DE). Other non- 18 carbamate and organophosphate nematicides developed include Fluensulfone (Nimitz®) a recently added nematicide with unknown function that has been shown to control several plant-parasitic nematodes and is registered for most vegetable crops; and Fluopyram

(Velum Prime®) a nematicide that targets the succinate dehydrogenase enzyme in nematodes and is registered for use on tomato with systemic properties that provide control for plant-parasitic nematodes and fungal diseases (Hajihassani, 2018). However,

Nonetheless, most growers prefer broad-spectrum fumigants because they are less expensive and have more consistency in reducing nematode populations. Another factor to consider is that organic tomato production is increasing, so growers are seeking new sustainable alternatives that do not rely on synthetic chemistries to manage nematode populations.

Biological control or biocontrol is a promising method of control for soilborne diseases that has been researched for several decades. This management tactic uses one or more beneficial organisms to control a pathogenic organism. Biocontrol is best implemented as a preventive treatment against pathogens rather than as a curative method and should not be used as a stand-alone tactic. Biocontrol products are used in different crop systems and several provide control against a number of pathogens. Natural enemies such as fungi and bacteria have been extensively studied for RKN management. The most studied and effective egg-parasitic fungi are Paecilomyces lilacinus, and Pochonia chlamydosporia (Hoedekie et al., 2005, Goswami and Mittal, 2004). The most studied nematode-trapping fungi are Arthrobotrys spp. (Azad and Devi, 2007; Collange et al.,

2011). Even though these fungi demonstrate high antagonistic activity, there is only 19 commercial products of P. lilacinus that is commercially available. Among bacterial organisms, Pasteuria penetrans is one of the most antagonistic against RKN. In tomato microplot studies, P. penetrans reduced nematode reproduction by 88% and increased the tomato yield 44% compared to the non-treated inoculated control (Kamran et al., 2014).

Commercial products containing P. penetrans are not available due to the inability to culture it outside its host and its host specificity (Davies, 2009). Hence, more research is needed to formulate a more viable product. Pseudomonas spp. are the second most researched bacteria for the control of RKN. Numerous studies have reported pseudomonads’ antagonistic activity against several plant-parasitic nematodes, especially

RKN species. Factors limiting biocontrol research are that most studies are conducted in vitro or under greenhouse conditions, and field trials are limited and usually inconsistent

(Dong and Zhang, 2006). A better understanding of the complex interactions between the microbial agent, soil, plant, pathogen, and the environment will lead to more commercialization of microbial products for RKN. Integrating biocontrol with other management tactics will be critical to more sustainable and successful control of RKN.

Pseudomonas spp. as possible biocontrol agents for root-knot nematode

Pseudomonas spp. are gram-negative bacteria that are ubiquitous in all major natural environments and are functionally diverse. These bacteria possess many attributes that make them potential bioremediators, plant-growth-promoters, and biocontrol agents.

Pseudomonads grow fast, consume root exudates, produce a vast range of metabolites, actively compete with other microbes, quickly colonize the rhizosphere, and readily adjust

20 to abiotic stresses (Weller, 2007). Their biocontrol effects may be due to several modes of action such as induced systemic resistance (van Peer et al., 1991; Wei et al., 1991), antibiotic production (Thomashow et al., 1990; Corbell et al., 1995), and competitive niche and nutrient exclusion (Dhingani et al., 2013). They are capable of secreting multiple antibiotic compounds, including phenazines, pyrrolnitrin, pyoluteorin, 2,4- diacetylphloroglucinol (DAPG), and volatile hydrogen cyanide (Weller, 2007; Panpatte et al., 2016).

Pseudomonas spp. have demonstrated activity against bacterial (Kuarabachew et al., 2007; Lanteigne et al., 2012; Mishra and Arora, 2012), fungal (Bolwerk et al., 2003;

Bardas et al., 2009; Erdogan et al., 2010) and plant-parasitic nematode diseases. Numerous studies have reported antagonistic activity by Pseudomonas spp. towards plant-parasitic nematode species belonging to the genus Globodera, Rotylenchulus, and Criconemella

(Cronin et al., 1997; Jayakumar et al., 2002; Westcott and Kluepfel, 1993). Most studies have evaluated pseudomonads’ activity against RKN species. P. fluorescens seed and soil application on rice significantly suppressed M. graminicola and enhanced plant growth under greenhouse and field settings compared to untreated controls (Seenivasan, 2011;

Seenivasan et al., 2012). P. chlororaphis O6 wettable powder application also has antagonistic activity against M. hapla in tomato and melon (Nam et al., 2018). However, no studies have been done on M. hapla with a formulated product of Pseudomonas spp.

Several species of Pseudomonas have also significantly reduced M. javanica populations as compared to the untreated controls (Ali et al., 2002; Samaliev et al., 2000; Siddiqui,

2002). Tomato greenhouse trials using a seedling treatment of Pseudomonas spp. showed 21 effective disease control against M. incognita as compared to the untreated control seedlings (Santhi and Sivakumar, 1987; Siddiqui and Futai, 2009). Similarly, lower M. incognita populations were found when applying P. fluorescens on sugar beet under field conditions (Kavitha et al., 2007). Moreover, pea, chilis, and mulberry have been other crops for which Pseudomonas spp. significantly decreased M. incognita damage (Siddiqui et al.,

2009; Muthulakshmi et al., 2010; Thiyagarajan and Kuppusamy, 2014).

Research has also been focused on understanding the mode of action of these rhizobacteria on RKN in tomatoes; findings include P. aeruginosa parasitism of eggs and colonization of roots (Siddiqui et al., 2000), P. chlororaphis production of hydrogen cyanide (Kang et al., 2018), and P. fluorescens secretion of extracellular protease and induction of systemic resistance (Samiyappan et al., 2004; Siddiqui et al., 2005). These results may indicate that Pseudomonas spp. utilize different modes of action against RKN, although more research is needed to have a better understanding of Pseudomonas-RKN interactions.

Even though multiple studies have demonstrated the antagonistic activity of

Pseudomonas spp. on a broad spectrum of diseases, currently, there are only three products on the market containing pseudomonads as their active ingredient. Bio-save® 10 L.P. (Jet

Harvest Solutions, Longwood, FL) is a bioproduct whose active ingredient is Pseudomonas syringae ESC-10, and it is marketed as a post-harvest application for control of several fungal diseases. Blightban® A506 (Nufarm America Inc., Alsip, IL) is a biocontrol product used for frost damage reduction and fire blight disease control containing Pseudomonas

22 fluorescens A506 as the active ingredient. Howler® (a.i. Pseudomonas chlororaphis strain

AFS009; Ag Biome Innovations, Durham, NC) is a biocontrol product used to control plant diseases including those caused by Rhizoctonia spp., Pythium spp., Fusarium spp., and several other fungal pathogens. Pseudomonas spp. have great potential for commercial products; thus, there is a need to conduct more field trials to test performance and consistency and evaluate formulations that contribute to a long shelf life.

Commercial biocontrol products for root-knot nematode

Commercial biocontrol products for plant diseases, especially for RKN, are relatively new and still emerging. In the past few decades, several commercial biocontrol products for nematodes have been developed and marketed. Even though products are commercially available, few studies have been conducted to test the efficacy of these products in a field setting. Majestene® (Marrone Bio Innovations, Davis, CA), a biocontrol product containing killed cells of Burkholderia spp. strain A396 and spent fermentation media, is marketed for the control of Meloidogyne spp., Pratylenchus spp.,

Tylenchorhynchus spp., Bursaphelenchus spp., and Rotylenchulus spp. A non-viable

Burkholderia cepacia strain Bc-2 was evaluated for antagonistic activity against M. incognita, and suppressed eggs and J2/g of roots by 29% in a greenhouse test (Meyer et al., 2000). B. cepacia has also been evaluated for control of Rhizoctonia stem and root rot and reduced both diseases significantly, proving that it has the potential to be used for management of several pathogens (Hwang and Benson, 2002). The active ingredient of

Actinovate AG® (Bayer AG, Whippany, NJ) is also a bacterium (Streptomyces lydicus

23 strain WYEC 108). Although this product is marketed as a biological fungicide, a greenhouse study on tomatoes demonstrated that Actinovate® in combination with chitin at 1.0% significantly decreased the survival of M. hapla juveniles by 85% compared to untreated controls. However, due to the variability of results and high costs of application, researchers concluded that Actinovate® is not a viable alternative for M. hapla management

(Bélair et al., 2011).

Although bacterial products are available for nematode control, most commercial products are fungal-based formulations. DiTera® (Valent BioSciences Corp., Libertyville,

IL), is a relatively new biological nematicide obtained from fermentation of Myrothecium verrucaria strain AARC-0255. Application of DiTera® to soil led to increased antagonism of M. incognita by dual activity of killing nematodes directly on contact and by enhancing the fungal and bacterial rhizosphere community (Fernández et al., 2001). More recently,

Dong et al. (2015) showed that M. verrucaria strain X-16 was able to parasitize M. hapla and significantly reduce J2 nematodes in greenhouse assays. Similarly, Melocon® W.G.

(Certis USA, Columbia, MD) is a biological nematicide for which the active ingredient is

Paecilomyces lilacinus strain 251. Several studies have demonstrated that P. lilacinus can significantly reduce M. incognita root galling (Acosta et al., 1996; Kiewnick and Sikora,

2006; Siddiqui and Futai, 2009). Even though these products show nematicidal activity, more research is needed to test their performance on tomatoes under different environmental and field conditions.

Other fungal-based products are marketed as a biofungicide or plant growth promoter (PGR); however, studies show that several of these products also have biocontrol activity against RKN. MycoApply Endomaxx® (Valent BioSciences Corp., Libertyville,

IL) is a biocontrol product containing four endomycorrhizal fungi (Glomus intraradices,

G. mosseae, G. aggregatum, and G. etunicatum) that colonize the rhizosphere of plants in a symbiotic manner. This product is marketed as a PGR; however, Talavera et al. (2001) showed that early mycorrhizal inoculation (G. mosseae) of tomato plants reduced soil densities of M. incognita by 85%. Similarly, G. intraradices has also been evaluated for nematode management, showing an increase in tomato biomass and a decrease in the number of galls of Nacobbus aberrans (Marro et al., 2014). Rootshield® W.P. (BioWorks,

Inc., Victor, NY) is another alternative on the market as a biological fungicide (A.I.

Trichoderma harzianum Rifai strain KRL-AG2). Even though most studies evaluate T. harzianum fungistatic activity, several studies have demonstrated that this fungus can significantly reduce plant-parasitic nematode (M. incognita, M. javanica, M. arenaria, and

P. penetrans) populations by penetrating the egg mass matrix or inducing systemic resistance in the plant host (Sharon et al., 2001; Al-Fattah et al., 2007; Lamondia and

Cowles, 2002). These results indicate that these commercial products have the potential to be marketed as biological nematicides. However, more trials are needed to justify their efficacy as a nematicide.

Plant and seaweed extracts are other emerging alternatives for sustainable management of nematodes. Promax® (Bio Huma Netics, Inc, Gilbert, AZ) is a biocontrol sold as a fungicide and nematicide with thyme oil (Thymus vulgaris L.) as its active 25 ingredient. Perez et al. (2006) tested 3.5% thyme oil formulated as Promax and found it reduced population growth of all plant-parasitic nematode genera on English boxwood.

The essential oil of thyme has also been tested for control of M. javanica and showed significant nematicidal activity in in vitro and greenhouse tests (Oka et al., 2000). Monterey

Nematode Control® (Monterey Lawn and Garden Products, Fresno, CA) is a biocontrol product marketed for nematode control in organic gardening containing saponins of

Quillaja saponaria. Giannakou (2011) investigated the use of a commercial product (Q.L.

Agri® 35) based on the plant extract from Q. saponaria and showed that it controlled M. incognita and prevented nematode increase in soil. Another potential option is Bio-

Activate® (J.H. Biotech, Inc., Ventura, CA), which is a commercial product sold as a PGR derived from seaweed extracts (Ascophyllum nodosum). Algaefol® (a.i. Ascophyllum nodosum) reduced root galling by M. incognita by 87% (Radwan et al., 2012). The wide range of commercially available products shows promise for RKN management. Hence, evaluating these products under Ohio high tunnel conditions will provide growers information on what products are most suited for RKN.

Limitations of biocontrol use for RKN

More and more biocontrol products are marketed every year, and countless publications in the literature show the potential of biocontrol for a wide range of diseases.

One of the main challenges is the lack of regulation of efficacy tests conducted on commercially available biocontrol products. Environmental Protection Agency’s role is to

26 test for the safety and hazards of the products; however, there is no specific entity that regulates product efficacy standards.

A major challenge in adopting new biocontrol products is the lack of consistency and efficacy when used under field conditions. These limitations may be due to the stability of the product, the amount of material reaching the plant target, degradation of the active ingredient, and shelf-life of the formulation (Jones and Burges, 1998). Degradation of the active ingredient could be associated with reduced survival and colonization of plants. The active ingredient could also have high sensitivity to abiotic stresses such as pH, low or high temperature, drought, and so forth. There is still much work to be done to gain a better understanding of bacterial biocontrol interactions, their role in the environment, and how different climatic and soil factors affect their efficacy. The vast literature demonstrates that biocontrol should be included in an integrated pest management program. Nonetheless, biocontrol should not be considered a stand-alone tactic; it should be part of a holistic and sustainable approach that benefits not only the crop but also the environment and future generations.

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Chapter 2. RKN prevalence and yield loss assessment in tomato high tunnels in Ohio

Introduction

High tunnels are structures used to modify the crop growing environment and extend the production season. By extending the production season, growers in temperate regions can provide fresh market tomatoes when they are traditionally off-season and at a premium price. Besides the added benefit of season extension, this system also includes crop risk reduction, such as protection against weather and certain foliar diseases. High tunnel popularity is rapidly expanding across the US. In the Midwest, tomatoes are the most commonly grown crops in high tunnels. High levels of adoption of high tunnels for fresh market tomato production is also related to an increase in consumer demand for both organically grown and heirloom varieties (O’Connell et al., 2012).

In the Midwest, many farmers are shifting from open-field production to soil-based protected culture systems, and its use continues to increase every year in Ohio (A. Testen, personal communication). Under appropriate management, tomato production in high tunnel systems results in increased yields and significantly higher revenue than in open field systems (O’Connell et al., 2012). Tomato production under protection in Ohio, such as high tunnels and greenhouses, continues to increase every year and had a total value of approximately $1.16 million in 2014, the last year for which data are available (USDA-

NASS, 2014). 39

Nonetheless, using high tunnel systems create certain disadvantages. The use of high tunnels limits the potential of using crop rotation to manage pests or plant pathogens.

Moreover, average soil temperature increases; thus, it decreases winter freezes that reduce pest and pathogen survival. The buildup of pathogen populations is highly concerning because most Ohio farmers grow tomatoes every season without crop rotation. The protected environment within high tunnels creates conditions conducive to the buildup of soilborne pathogen populations and the development of soilborne disease complexes (Last et al. 1969). Infection of root-knot nematode (RKN) predisposes tomatoes to infection of other soilborne fungal pathogens leading to the development of a disease complex (Mai and Abawi, 1987). A disease complex of RKN with other soilborne fungi can lead to higher losses than caused by the pathogen alone (Siddiqui and Ehteshamul-Haque, 2001). Several disease complexes involving RKN in tomato have been reported. The most common example is Fusarium wilt-RKN disease complex in tomatoes. Ongoing efforts currently involve researching management tactics for this complex (Kassie, 2019).

A soilborne disease complex consisting of corky root rot (Pyrenochaeta lycopersici), black dot root rot (Colletotrichum coccodes), Verticillium wilt (Verticillium dahliae), and RKN (Meloidogyne incognita and M. hapla) is an emerging problem in tomato high tunnel production in Ohio. The disease complex was first identified in several

Ohio high tunnels in 2015 (Vrisman et al., 2017). Pathogens in these complexes are difficult to manage (Colla et al., 2012) and have broad host ranges (Inderbitzin et al. 2013).

Another constraint is that these diseases are often not noticed until growers observe visible above-ground plant symptoms or significant reduction in yield. Thus, there is a crucial need

40 to identify the distribution of these diseases and to develop reliable diagnostic methods that would provide rapid answers to growers. This information will also improve grower’s awareness of soilborne disease risks and management options.

Although these diseases threaten to significantly reduce tomato yield exist, no information was available on the presence and distribution of these diseases in Ohio high tunnels. A survey was designed and conducted by Dr. Anna Testen in Dr. Sally Miller’s laboratory (The Ohio State University, Department of Plant Pathology) to determine the distribution of these diseases within Ohio high tunnels. This information was used to provide farm-specific management recommendations for soilborne diseases. In this chapter, we focus on explaining the distribution of RKN. The objective of this study was to determine the incidence of RKN in Ohio high tunnels and identifying what species were present.

RKN is an emerging threat to tomato production in protected culture systems in

Ohio. To our knowledge, M. hapla and M. incognita have been the only RKN species reported on tomatoes in Ohio. Besides reducing plant vigor and yield, RKN predisposes plants to infection by secondary pathogens (Siddiqui and Ehteshamul-Haque, 2001). Yield losses due to RKN depend on the nematode species, initial population density, crop variety, and environmental factors. Using resistant cultivars and rotating tomato with nonhost crops are strategies commonly used to limit yield loss in open-field tomato production systems.

However, most growers using high tunnel systems do not rotate tomato with nonhost crops, and RKN resistant tomato cultivars do not confer resistance to M. hapla and are temperature-sensitive, losing their effectiveness at high temperatures.

The population density at which M. incognita and M. hapla cause yield loss varies significantly in tomato. Yield losses in tomatoes by M. hapla and M. incognita up to 50% and 85%, respectively, have been reported (Barker et al., 1976). Initial densities of 27,950

M. hapla juveniles/kg of soil reduced tomato production by 40% (Olthof and Potter, 1977).

Similarly, Reddy (1985) reported that M. incognita caused a 40% yield loss in tomatoes; however, initial population densities were slightly lower (20,000 larvae/kg of soil) compared to M. hapla. In processing tomato areas of Turkey, Meloidogyne spp. caused yield losses of up to 80% (Kaskavalci, 2007). Moreover, in Spain, M. javanica initial densities of 4,750 juveniles per 250cm of soil reduced yield up to 61% in tomato (Verdejo-

Lucas et al., 1994). The wide variation in yield loss reflects the vast difference in tomato response to RKN infection as well as the influence of biotic and environmental factors and soil texture.

Besides soil texture, soil moisture levels can also affect nematode movement in the soil and concomitantly, nematode population densities. O’Bannon and Reynolds (1965), found that when soil moisture was not maintained at field capacity, M. incognita-infested cotton plants had reduced water consumption and plant growth. Evaluating the relationship between the amount of water received by tomato and the amount of damage caused to the crop by RKN could provide a better understanding of how abiotic factors influence RKN infestation in a tomato production system.

More extensive studies of varying densities of RKN species are needed to provide a better understanding of the direct and indirect roles of RKN under high tunnel conditions.

RKN yield loss data will provide general guidelines for further experiments and provide

42 threshold standards for when control is required. Ultimately, this knowledge will increase

Ohio growers’ awareness of this emerging threat. The objective of this study was to measure the yield of tomatoes under varying densities of M. hapla and M. incognita and determine final damage by the degree of root galling under high tunnel conditions. In the second year of the field trial, water stress was also used as a variable. The objective was to determine whether the combined effects of RKN and water stress creates higher yield loss.

Material and Methods

Soilborne disease survey sample collection.

Soil samples were collected from farmers through six produce auctions across Ohio or through direct submission to The Ohio State University Vegetable Pathology Lab.

Farmers were provided with a soil sampling guide explaining how to collect samples and

Ziploc bags (33cm x 38-13mm/20mm, S.C. Johnson and Son, Inc., Racine, WI). Farmers were asked to collect soil samples from 10-20 locations within plant rows in an individual high tunnel at a depth of 15 cm. Soil samples were collected from produce auctions within

2 days of sampling and brought to The Ohio State University OARDC in Wooster, OH

(Figure 2.1). Soil samples were homogenized, and large clumps broken by hand in a clean five-gallon bucket. A 20 ml subsample of soil was placed in a coin envelope and stored at

-20°C for future PCR assays. Soil samples for bioassays were stored no longer than one month in a 4°C cooler until processed.

Meloidogyne spp. survey bioassays.

A bioassay was conducted to assess the presence of root-knot nematodes. Tomato

‘Moneymaker’ seeds were sown in pots (Deepots D16H, Steuwe and Sons, Inc. Tangent,

OR) containing 250ml of soil from an individual high tunnel. One pot was filled per high tunnel, and tomatoes were grown in the greenhouse for nine weeks. Roots were washed and assessed for root-knot nematode galling (number of galls per root system). The bioassay was completed twice for each soil sample.

Meloidogyne spp. survey PCR assays.

One soil DNA extraction was performed for each high tunnel sample using the

PowerSoil DNA extraction kit (MoBio Laboratories, Carlsbad, CA) following the manufacturer's instructions. The second approach was dissecting approximately 5-10 galls

(sterilized with 70% ethanol) from the bioassay, from which DNA was extracted using the

PowerSoil DNA extraction kit. All PCR assays were conducted in 25µL reaction volumes using GoTaq Green Master Mix (Promega Corporation, Madison, WI) and 2µL of template for each PCR reaction. Primers JMV1/JMV2/JMV (0.4µM) hapla were used for detecting

M. hapla, M. chitwoodi, or M. fallax (Table 2.1). Primers Mi-F/Mi-R (0.4µM) were used for M. incognita detection. A multiplex PCR reaction was used for detection of M. hapla

(Wishart et al., 2002) using cycling conditions of denaturation at 94°C for 2 min, followed by 40 cycles of 94°C for 30 s, 55°C for 30 s, and 72°C for 90 s, and ending with an extension step at 72°C for 7 min. A single PCR reaction was used for detection of M. incognita (Meng et al., 2004) using the same cycling conditions for M. hapla detection,

44 with the exception that the annealing temperature was 62°C. PCR products were run on

1.5% agarose gels and visualized using ethidium bromide. All PCR assays were scored for presence or absence of expected amplicons and were run once for each high tunnel sample.

Meloidogyne spp. yield loss assessment

Nematode inocula. The root-knot nematodes used in this study were M. hapla isolate HHT19 collected from tomato (Highland county), M. hapla isolate HPL19 collected from lettuce (Portage County), and M. incognita isolate IPT19 collected from tomato in

Ohio (Pike County). Isolates HHT19 and IPT19 were maintained and increased individually on tomato (‘Moneymaker’) in the greenhouse. Isolate HPL19 was maintained and increased individually on lettuce (‘Tropicana’, Johnny's Selected Seed, Winslow, ME) in the greenhouse. Maintenance procedures are described in chapter three. Tomato or lettuce roots were washed free of adhering soil. Roots were cut into small pieces and placed in a beaker. A 0.5% sodium hypochlorite solution was then added for 4.5 minutes, followed by egg collection using a 500-mesh sieve. The sieve was rinsed for at least five minutes to eliminate bleach residues. Eggs were collected by pouring the eggs from the sieve into a beaker with H2O. The number of eggs was then counted under a Nikon SMZ645 dissecting microscope in three 10µl droplets. The egg concentration for assays was adjusted to the inoculum density of the specific treatment combination.

High tunnel experimental procedures. Experiments were done in a 30m by 9.1m high tunnel structure located at the Fry Farm of the Ohio Agricultural Research and

Development Center (OARDC), Wooster, OH. Microplots within the high tunnel consisted

45 of 25cm by 30cm long (3cm width) polyvinyl chloride (PVC) pipes filled with topsoil from

Rock Shop in Wooster, Ohio. The high tunnel floor was covered with black ground cover

(DeWitt©, Sikeston, MO) to maximize weed control within the structure. PVC pipes were installed by digging holes in the soil with a 30cm auger drill and securing the pipes vertically within the holes using loose soil. There were five rows of 21 microplots placed

0.75m apart in total and each row 1.5m apart. Seeds of tomato (‘Red Deuce’, Seedway,

Hall, NY; resistant to Verticillium wilt, Fusarium wilt (races 1 and 2), and Tobacco mosaic virus) were planted in a 50-cell square transplant tray with Promix in a greenhouse. Plants were watered daily and fertilized once weekly with a 20-20-20 fertilizer (Peter’s

Professional; ICL Fertilizers, Ltd., Beer Sheva, Israel). After six weeks, a single 10-15cm high tomato seedling was transplanted into each microplot. Six-week-old plants were pruned once by removing the lower three to five suckers. Tomato staking was done once a week for one month when plants were three months old by tying twine around a 1.4m pine stake (Yoder’s Produce Inc, Fredericksburg, OH) placed just outside of the microplot. Soil test analysis (Spectrum Analytics, Fayette County, Ohio) of topsoil was used as a guide for creating a fertilization program. Fertilizer (20-5-5, Miller®, Hanover, PA) was applied once a week through the drip irrigation system with microtubes that provided water directly to each microplot. First 2-3 weeks after transplanting, fertilization was the equivalent of 340g of nitrogen/A/day. At week four, fertilization was increased to 566g of nitrogen/A/day.

High tunnel sidewalls were rolled up when temperatures exceeded approximately 10-15°C or down when temperatures were expected to be below approximately 10-15°C. In 2018, plants were sprayed with Dipel DF® on four occasions for control of yellow-striped

46 armyworm (Spodoptera ornithogalli). In 2019, Dipel DF® (Valent BioSciences LLC,

Libertyville, Il) and Cease® (Bio Works, Inc., Victor, NY) were applied every 14 days beginning on May 24 to prevent pests and fungal diseases. Milstop® (Bio Works, Inc.,

Victor, NY) was applied every 14 days beginning on May 31.

2018 trial. The experimental design was a randomized block; each treatment was replicated 20 times. The experimental treatments were the following: (1) non-inoculated control; (2) M. hapla tomato isolate 500 eggs; (3) M. hapla tomato isolate 5,000 eggs; (4)

M. hapla tomato isolate 25,000 eggs; (5) M. hapla lettuce isolate 500 eggs; (6) M. hapla lettuce isolate 5,000 eggs; (7) M. hapla lettuce isolate 25,000 eggs; (8) M. incognita 500 eggs; (9) M. incognita 5,000 eggs; and (10) M. incognita 25,000 eggs. Seedlings were transplanted into microplots on June 18, 2018. Depending on weather conditions, plants were irrigated for 2-3 hours one to three times a week. The nematode inoculum was added one month after transplanting to give the desired range of initial densities for a given nematode isolate. The inoculum was added by pouring a 50ml nematode egg suspension into one hole made to a depth of 2.5-5cm below the soil surface around the base of the plants. Average maximum temperatures for 18-30 June, July, August, September, and 1-

13 October were 27.9, 28.9, 26.5, 25.6, and 23.6°C; average minimum temperatures were

17.5, 16.4, 17.5, 15.4 and 13.0°C, respectively.

2019 trial. The experimental design was a randomized block; each treatment was replicated 12 times. The experimental treatments were the following: (1) non-inoculated control; (2) non-inoculated control with water stress; (3) M. hapla 10,000 eggs; (4) M.

47 hapla 50,000 eggs; (5) M. hapla 50,000 eggs with water stress; (6) M. incognita 10,000 eggs; (7) M. incognita 50,000 eggs; and (8) M. incognita 50,000 eggs with water stress.

Seedlings were transplanted into microplots on May 8, 2019. Water stress effects were started after the first flower bud formed. Water stress consisted of watering every two weeks while non-water stressed treatments were watered as needed (once or twice a week).

The nematode inoculum was added one week after transplanting to give the desired range of initial densities for a given nematode isolate. The inoculum was added by pouring a

50ml nematode egg suspension into one hole made to a depth of 2.5-5cm below the soil surface around the base of the plants. Average maximum temperatures for 8-30 May, June,

July, and 1-21 August were 22.8, 25.3, 29.5, and 28.3°C; average minimum temperatures were 10.6, 14.1, 17.9, and 16.4°C, respectively.

Yield and disease assessment. In the 2018 trial, harvest started on September 9. In the 2019 trial, harvest started on August 1. Twelve weeks after transplanting, ripe fruits were picked weekly. Fruits were individually weighed from each plant in the 2018 trial.

Fruits were collectively weighed by plant in the 2019 trial. The number of fruits and the number of marketable and unmarketable fruits were counted individually per plant. At the end of the picking period (6 weeks in 2018; 4 weeks in 2019), all remaining green fruits were removed and weighed collectively per plant. The root system from each tomato plant was uprooted, washed with running tap water, and weighed. The nematode damage was assessed by rating the root galling on the 0-10 scale of Bridge and Page (1980).

In the 2018 trial, each root system was cut into small pieces, and the number of

48 eggs per root system was determined. One-hundred milliliters of a 0.5% sodium hypochlorite solution was added to the complete root system of each plant, which was then shaken for 15 minutes on a Lab-Line Thermal Rocker (6 speed). Six 50µl aliquots were taken for each sample and eggs were counted using a Nikon SMZ645 dissecting microscope. For each replicate, the total number of eggs per 100ml 0.5% sodium hypochlorite solution per gram of roots was calculated.

Statistical Analysis

The galling rating scale (0-10; Bridge and Page, 1980) was modified to calculate the odds ratio according to a 1-4 scale: 1 = 0-4 galled roots, 2= 4-6, 3=7, and 4 = 8-10. A cumulative logit proportional-odds model was used to assess treatment effects and calculate odds ratios using the GLIMMIX procedure of SAS statistical software (SAS

Institute, Cary, NC). For all count data (number of fruits, number of eggs/grams of root) a generalized linear mixed model was fitted assuming a Poisson distribution. Data expressed as percent were subjected to arcsine transformation to stabilize variance. However, non- transformed data are presented. In the 2018 trial, analysis of variance was used to test whether RKN isolates (M. incognita, M. hapla tomato host, and M. hapla lettuce host), initial egg density (500, 5,000, and 25,000) or the interaction of these factors influenced the number of eggs per gram of root. However, yield/plant, number of fruits/plant, and percentage of marketable fruits were compared by analysis of variance by combining factors into nine unique RKN isolate x initial egg density treatments combinations. In the

2019 trial, treatments were compared by analysis of variance. All data was analyzed using

49 the GLIMMIX procedure of SAS software program and means were separated with least squares estimates of marginal means.

Results

High tunnel tomato RKN survey

RKN was detected in 32 of 71 high tunnels (45% incidence) and 19 of 34 farms

(56% incidence) that came from 14 counties (Table 2.2; Figure 4.2.). The counties in which

RKN was present were Ashland (1/1 farm), Wayne (2/2 farms), Knox (1/2 farms), Marion

(1/1 farm), Medina (1/2 farms), Wayne (3/3 farms), Guernsey (3/5 farms), Holmes (1/1 farm), Columbiana (1/1 farm), Highland (2/2 farms), Jackson (1/1 farm), Hardin (1/1 farm),

Pike (1/1 farm), and Cuyahoga (1/1 farm). The M. hapla PCR assay of DNA from gall samples obtained from the bioassay was positive for 26 of 26 samples, and the M. incognita

PCR assay produced positive amplicons for two of 26 RKN gall samples (both samples were co-infected with M. hapla). PCR amplification of DNA from high tunnel soil samples yielded six additional samples that were positive for M. hapla but were not identified in the bioassay. No additional M. incognita positives were detected in PCR amplifications of soil DNA.

Effects of initial densities of Meloidogyne species on tomato yield

2018 trial. Non-inoculated plants and plants inoculated with different initial egg inoculum (Pi) of RKN isolates did not significantly differ in mean yield/plant, mean number of fruits/plant and percentage of marketable fruits (Table 2.3). Similar results were observed when adding the yield of green fruits (collected in the last harvest) to the analysis 50

(data not shown). Plants inoculated with a Pi of 500 M. hapla lettuce host (MHLH), a Pi of

25,000 MHLH, and a Pi of 25,000 M. incognita (MI) had significantly lower percentage of marketable fruits than plants inoculated with a Pi of 5,000 M. hapla tomato host (MHTH), a Pi of 25,000 MHTH, and a Pi of 500 MI, respectively. There was a significant interaction between root-knot nematode isolate and Pi (Table 2.4). Tomato plants inoculated with M. hapla tomato host isolate and M. incognita isolate had significantly higher number of eggs/g than plants inoculated the M. hapla lettuce host isolate. Plants inoculated with a Pi of 5,000 and 25,000 had significantly higher number of eggs/g than plants inoculated with

Pi of 500.

2019 trial. Non-inoculated plants and non-inoculated plants with water stress did not significantly differ in mean yield/plant, mean number of fruits/plant and percentage of marketable fruits (Table 2.5). Non-inoculated plants had numerically higher yield/plant and number of fruits/plant than the non-inoculated control water stress control and all RKN infested plants, but the differences were not significant. Non-inoculated plants, non- inoculated water stress plants, and plants inoculated with different initial egg inoculum (Pi) of RKN species did not significantly differ in mean yield/plant and mean number of fruits/plant. The same trends were observed when including the mean yield/plant of green fruits (collected in the last harvest) to the analysis (data not shown). Plants inoculated with a Pi of 50,000 M. hapla had significantly higher percentage of marketable fruits than plants inoculated with a Pi of 10,000 M. hapla and plants inoculated with a Pi of 10,000 M. incognita. Moreover, plants inoculated with a Pi of 50,000 M. hapla had significantly higher percentage of marketable fruits than non-inoculated control plants and non- 51 inoculated control plants with water stress. Plants inoculated with a Pi of 50,000 M. incognita with water stress and Pi of 50,000 M. hapla with water stress had significantly higher percentage of marketable fruits than plants inoculated with a Pi of 10,000 M. incognita. All nematode-infested tomatoes were galled severely. The odds of having higher root-knot nematode severity rating was highest (155 times more likely) for plants inoculated with a Pi of 50,000 M. hapla with water stress versus non-inoculated water stress control plants (Table 2.6). The odds of having higher RKN severity rating was 66 and 39.2 times more likely for plants inoculated with a Pi of 50,000 M. incognita with water stress and a Pi of 50,000 M. incognita versus non-inoculated water stress control plants, respectively. There were no significant differences in odds ratio in plants inoculated with a Pi of 10,000 M. incognita and a Pi of 10,000 M. hapla versus non-inoculated water stress control plants.

Discussion

The objective of the high tunnel soil survey was to determine the prevalence of members of the soilborne disease complex across Ohio, including P. lycopersici, C. coccodes, V. dahlia, and Meloidogyne spp. (A. Testen, unpublished). Here I will focus on the prevalence of Meloidogyne spp. and briefly describe occurrence of co-infestations.

RKN outbreaks can lead to a reduction in yield and fruit quality and to infection of secondary pathogens (Manzanilla-López and Starr, 2009). M. hapla (northern root-knot nematode) is widely distributed in temperate regions, specifically in regions of the US north of 39ºN (Taylor and Buhrer, 1958). However, M. hapla has also been reported in southern regions of the US such as Hawaii and Texas (Handoo et al., 2005; Wheeler et al.,

2000). M. incognita is adapted to warm, subtropical climates of the southeastern and southwestern US, nonetheless, it has also been reported in the north central regions of the

US (Walters and Barker, 1994). In Ohio, only M. incognita and M. hapla have been reported parasitizing tomato in open field production and now in covered production systems.

Efficient nematode management requires species identification since management may differ depending on the species identified. Our study confirmed the presence of M. hapla in 14 counties in Ohio, suggesting an emerging threat to vegetable production in the state. M. hapla is the most common species; M. incognita was only found in a mixed infection with M. hapla in two samples from Pike county. These results indicate that M. hapla is more prevalent than M. incognita in tomato high tunnel soils in Ohio. Co-infection of Meloidogyne species has been found in other RKN studies such as M. incognita co- infecting with M. hapla, M. javanica or M. enterolobii (Carillo-Fasio et al., 2018; Johnson and Nusbaum, 1970; Khan and Haider, 1991; De Araujo-Filho et al., 2016; Kolombia et al., 2017). The dominance of one Meloidogyne species over other species has been previously reported (De Araujo-Filho et al., 2016; Kolombia et al., 2017). For instance, a survey of tomato production soils in Mexico found that M. enterolobii was the most common species of RKN followed by M. incognita and mixed infection with both nematodes (Carillo-Fasio et al., 2018). The dominance of M. hapla in Ohio soil samples can be attributed to different factors such as temperature and host cultivar. The low prevalence of M. incognita in tomato high tunnel soils in Ohio may be highly influenced by overwintering temperatures. M. incognita cannot survive if temperature averages are

53 below 3°C during the coldest month (Walters and Barker, 1994). Moreover, M. hapla is mainly distributed in cool areas, whereas M. incognita is found in hotter climates. The use of cool weather crops may have also led to the increase of prevalence of M. hapla at the sampled sites.

Furthermore, these findings could be the result of competition among Meloidogyne species, and after several generations, one of the species dominates (Manzanilla-López and

Starr, 2009). The lack of species diversity could also be due to a decrease in crop diversity since most growers do not rotate tomato with other crops. In addition, a high percentage of growers who participated in the survey were using tomato cultivars with the Mi-gene, which confers resistance to M. incognita but not to M. hapla. Nonetheless, the prevalence of species may change in the future because the average soil temperature may increase due to global warming. Thus, frequent monitoring would be necessary for the appropriate management of this pathogen. The absence of M. chitwoodi and M. fallax from all samples analyzed is not surprising as they have not been reported in the US Midwest. M. chitwoodi has only been reported in the central and western regions of the US, and M. fallax has not been reported in the US.

Our findings indicate that 56% of sampled farms had RKN in high tunnel soil. Even so, we may have underestimated the incidence of RKN in high tunnels because the survey only consisted of soil samples. Previous researchers conducting RKN surveys used either root samples (Zeng et al., 2018; Carillo-Fasio et al., 2018) or a combination of root and soil samples (Anwar and Mckenry, 2010). Future surveys should also include root samples because of RKN variability in the soil. The diagnostic bioassay for the detection of RKN

54 provided more RKN positives and more consistent results than the PCR assay using soil samples (0.25 grams). A possible explanation is that the amount of soil used in the diagnostic bioassay (250 mL) is much higher, increasing the probability of having eggs or juveniles in the soil. Additionally, if soils are not handled or stored properly following sampling, viable RKN could be killed, leading to false negatives in the diagnostic bioassay.

Using DNA extracted from the soil samples, six additional M. hapla positive samples that were not detected in the diagnostics bioassay were identified. Therefore, the best approach for RKN detection is to combine both methods. Further studies could conduct PCR sensitivity tests to assess the minimum number of eggs or juveniles that could be detected in a soil sample. Furthermore, PCR sensitivity studies could also assess if DNA extracted from non-viable eggs could have led to false positives.

Compared with other molecular diagnostic methods, this strategy only requires routine PCR and electrophoresis and is fast, affordable, and relatively simple, without further DNA sequencing and analysis. Methods for the detection of Meloidogyne spp. directly from tomato galls have also been reported (Hu et al., 2011). Moreover, several researchers have used M. incognita-specific primers (Mi-F/Mi-R) and M. hapla-specific primers (JMV1/JMV2/JMV hapla) for detection of Meloidogyne spp. (Carillo-Fasio et al.

2018; Zeng et al., 2018; Joseph et al., 2016; Adam et al.; 2007). Thus, based on previous studies and our results, it is clear that species-specific primers allow for reliable detection of RKN at the species level and have the potential for use in routine diagnostic procedures.

In the survey, all four pathogens were detected in five tunnels (Testen et al., unpublished). The most common triple infection was Pyrenochaeta-Colletotrichum-RKN,

55 and the most common double infection was Colletorichum-RKN. RKN was not found alone in any high tunnel. The co-infections may be the result of RKN predisposing tomato plants to infection of other soilborne pathogens by physical wounding of the plant (Siddiqui and Ehteshamul-Haque, 2001). Thus, if RKN is detected in a field it is necessary to assess the prevalence of other soilborne pathogens. This study is, to the best of our knowledge, the first report of RKN distribution in Ohio tomato high tunnel production systems.

Findings in this study, in conjunction with knowledge generated in the remainder of the survey, will aid in understanding the distribution of these soilborne pathogens. Moreover, it will increase growers' awareness of these pathogens, and the potential adoption of effective management tactics.

Soilborne diseases can have a significant impact on tomato production (Dillard and

Cobb, 1998; Last and Ebben, 1966). Several studies have demonstrated the damage potential of Meloidogyne spp. on tomatoes. Tomato cultivars differ considerably in the level of susceptibility to Meloidogyne spp. (Seid et al., 2015). Damage and yield loss studies on different tomato cultivars have shown different degrees of susceptibility towards different populations of the same species of Meloidogyne. Previous studies were conducted under different conditions with different experimental approaches making it challenging to extrapolate the results to this study. Thus, due to the emerging constraints imposed by RKN on protected culture tomato production in Ohio, an RKN yield loss study was conducted under high tunnel conditions. ‘Red Deuce’ was chosen in this study because of its widespread use in high tunnel production and its lack of the Mi-gene.

In 2018 and 2019 trial, several of the RKN infested plants had significant difference in percentage of marketable fruits. In the 2018 trial, RKN infested plants did not significantly differ from the non-inoculated control plants in percentage of marketable fruits. Similarly, in the 2019 trial, most RKN infested plants did not significantly differ from the non-inoculated plants. In addition, there were no relationships in percentage of marketable fruits when comparing RKN infested plants by the different isolates or initial egg inoculum. Thus, no explanations can be made in the differences in percentage of marketable fruits in plants inoculated with different Pi of RKN isolates.

In both trials, the yield/plant and number of fruits/plant did not differ significantly between RKN infested tomato plants and non-inoculated control plants. A study by Good and Steele (1958) reported that low M. incognita infestations did not significantly reduce the final yield of processing tomato. Moreover, Sayre and Toyama (1963) indicated that in field tests, yields of processing tomato were not significantly reduced by slight to moderate infestations of M. javanica and M. hapla as well. Even though we observed similar results, the latter studies cannot be directly compared with the present one as they used a different cultivar, experimental setup, and initial egg densities.

Another possible explanation of the lack of differences could be that the plants inoculated with different isolates of Meloidogyne spp. stimulated the growth of many adventitious roots. While the presence of RKN is generally considered to be harmful to plants, several studies have reported a slight increase in tomato yield (Chitwood, 1951;

Olthof and Potter, 1977). For instance, Barker et al. (1976) reported that an initial density of 2,000 nematodes/kg of soil or lower increased total tomato yield as compared to the

57 control. Moreover, Wallace (1971) showed that differences in the response of host plants to infection by M. javanica are the result of the interaction between inhibitory and stimulatory processes in the plant. According to previous studies, another contributing factor to the lack of differences in yield may be due to the initial densities tested. The initial density at which most studies observed significant losses had higher population densities than the densities tested in both of our trials (Ekanayake and Di Vito, 1984; Olthof and

Potter, 1977; Barker at al., 1976). Future assays should evaluate higher initial densities of

RKN and sterilize the soil prior to planting to establish a better yield loss threshold under high tunnel conditions.

In our study, the use of unsterilized amended topsoil may have also contributed to the lack of differences in yield/plant, number of fruits/plant and percentage of marketable fruits between RKN infested plants and non-inoculated control plants. Unsterilized soils with high beneficial microbe populations may have promoted plant growth and health

(Berg, 2009), and concomitantly, decreased RKN effects. Therefore, the use of amended topsoil may partially explain why significant losses did not occur. In addition, most RKN yield loss studies are conducted in sterilized or fumigated soils (Barker et al., 1976; Di Vito et al., 1991; Charegani et al., 2012). Hence, future experiments should be conducted in fumigated soils to decrease any possible effect of the microbial communities.

In the 2018 trial, plants infested with M. hapla tomato isolate and M. incognita isolate did not significantly differ in final population densities but had significantly higher number of eggs/g of root than M. hapla lettuce isolate. Although M. hapla have a wide host range, different isolates may have differences in host range and in pathogenicity on

58 varieties of a host species (Griffin and McKenry, 1989; Carillo-Fasio; et al., 2018). M. hapla lettuce isolate was originally isolated from commercially grown lettuce versus M. hapla tomato isolate was originally isolated from commercially grown tomato. Thus, M. hapla isolates may have differences in host pathogenicity; however, further analysis is needed to understand the isolates differences at the genome level. Plants infested with a Pi of 5,000 RKN and a Pi of 25,000 RKN and did not significantly differ in the number of eggs/g of root; however, plants inoculated at both initial concentrations had significantly

higher number of eggs/g of root than plants inoculated with a Pi of 500 RKN. These results may indicate that a fivefold increase in initial egg inoculum may not be sufficient to observe differences in final number of eggs/g of root. Hence, future experiments should have a tenfold increase in initial egg inoculum to obtain significant differences in final number of eggs/g of root.

Drought also affects RKN development in tomato. Ficht (1939) reported that tomato yield losses were highest under drought conditions. Moreover, O’Bannon and

Reynolds (1965) reported that when plants are irrigated at intervals, and soil moisture was not maintained at field capacity, cotton roots infected with RKN did not transmit sufficient water to the plant, thus, decreasing plant growth. In the 2019 trial, there were no significant differences in yield/plant and number of fruits/plant among RKN infested plants with water stress and RKN infested plants without water stress. However, the odds of having higher

RKN severity rating was more likely for plants inoculated with a Pi of 50,000 M. hapla with water stress (155 OR) and a Pi of 50,000 M. incognita with water stress (66 OR) versus plants inoculated with a Pi of 50,000 M. hapla without water stress (27 OR) and a Pi of

50,000 M. incognita without water stress (39.2 OR), respectively. These results indicate that water stress in tomato plants may increase RKN severity. Similarly, Davis et al. (2014) reported that cotton plants with a low irrigation level had significantly higher number of

M. incognita galling severity than plants with high irrigation. Future studies with a factorial treatment design could be conducted to understand if the interaction between RKN and water stress are additive, synergistic, or antagonistic.

Chapter 2. Tables and Figures

Table 2.1. Primers used in this study and their original publications.

Primer Expected Target Sequence (5’ to 3’) Reference Name amplicon size Meloidogyne hapla (Mh),

M. chitwoodi (Mc), and M. Mh: 440-bp JMV1 GGATGGCGTGCTTTCAAC Wishart et al. 2002 fallax (Mf) Mc: 560-bp Meloidogyne chitwoodi, and TTTCCCCTTATGATGTTTACCC Wishart et al. 2002 JMV2 M. fallax Mf: 670-bp JMV Meloidogyne AAAAATCCCCTCGAAAAATCCACC Wishart et al. 2002 hapla hapla Meloidogyne GTGAGGATTCAGCTCCCCAG Meng et al. 2004 Mi-F incognita 999-bp Meloidogyne Mi-R ACGAGGAACATACTTCTCCGTCC Meng et al. 2004 incognita

Table 2.2. Assessment of Meloidogyne hapla (Mh) and Meloidogyne incognita (Mi) presence in Ohio tomato high tunnel soils through bioassays and PCR assays.

Number of galls/plant Sample County Gall Detectiony Soil Detectionz Bioassay 1x Bioassay 2 1 Ashland 17 49 Mh Mh 2 Wayne 1 1 Mh NDz 3 Wayne 39 97 Mh Mh 4 Hardin 0 0 ND 5 Hardin 0 0 ND 6 Knox 51 39 Mh Mh 7 Knox 0 14 Mh Mh 8 Morrow * * ND 9 Knox 0 0 ND 10 Marion 5 4 Mh ND 11 Marion 0 0 ND 12 Marion 0 0 ND 13 Hardin 0 0 ND 14 Holmes 0 0 ND 15 Holmes 0 0 ND 16 Lorain 0 0 ND 17 Medina 0 0 Mh 18 Medina 0 0 ND 19 Medina 0 0 ND 20 Medina 3 2 Mh ND 21 Medina 0 0 Mh 22 Wayne 0 0 ND 23 Wayne 22 37 Mh ND 24 Wayne 11 11 Mh ND 25 Wayne 4 6 Mh ND 26 Wayne 0 0 ND 27 Franklin 0 0 ND 28 Franklin 0 0 ND 29 Franklin 0 0 ND 30 Franklin 0 0 ND 31 Franklin 0 0 ND 32 Franklin 0 0 ND 33 Franklin 0 0 ND 34 Franklin 0 0 ND 35 Guernsey * 0 ND 36 Guernsey 0 0 ND 37 Guernsey 42 34 Mh Mh 38 Guernsey * 65 Mh Mh 39 Guernsey 0 * ND

Table 2.2 continued

Number of galls/plant Gall Soil Sample County Bioassay 1 Bioassay 2 Detection Detection 40 Wayne 113 114 Mh ND 41 Wayne 3 5 Mh ND 42 Wayne 30 57 Mh ND 43 Wayne 5 24 Mh ND 44 Medina 0 0 ND 45 Medina 0 0 ND 46 Holmes 0 * ND 47 Holmes 0 0 ND 48 Holmes 0 0 Mh 49 Columbiana 0 1 undetermined ND 50 Cuyahoga 0 0 ND 51 Cuyahoga 0 0 ND 52 Highland 0 * ND 53 Highland * 11 Mh ND 54 Highland 0 0 ND 55 Highland 0 1 Mh ND 56 Highland 0 0 ND 57 Pike 0 0 ND 58 Pike 0 0 ND 59 Highland 1 8 Mh ND 60 Highland 12 7 Mh ND 61 Jackson 6 4 Mh Mh 62 Jackson * 14 Mh Mh 63 Pike 12 24 Mh, Mi ND 64 Pike 30 10 Mh, Mi ND 65 Pike 0 0 ND 66 Pike 0 0 ND 67 Pike 0 0 Mh 68 Hardin 21 9 Mh Mh 69 Ross 0 0 Mh 70 Cuyahoga 0 0 Mh 71 Cuyahoga 0 0 ND xDiagnostic bioassays conducted to assess the presence of root-knot nematodes (RKN) in soils obtained from tomato high tunnel production. Number of galls per root system were counted. *= Plants died during bioassay. yUse of species-specific primers for detection of RKN at the species level with DNA extracted from RKN galls obtained from the diagnostic bioassay. zUse of species-specific primers for detection of RKN at the species level with 0.25g of soil subsample. ND=Samples that Mh or Mi were not detected. 63

Table 2.3. Effects of initial densities of Meloidogyne species on tomato yield in high tunnel microplot trial (2018 trial).

Initial RKN Yield Number of % Marketable eggs/microplotx (kg/plant) Fruits/Plant Fruitsy 500 6.4 ±0.10 22.6 ±0.40 63.9 ±1.19ab M. hapla 5,000 6.0 ±0.10 21.8 ±0.38 69.4 ±0.90a tomato host 25,000 6.4 ±0.12 24.9 ±0.44 71.3 ±0.88a 500 6.7 ±0.15 25.9 ±0.57 61.1 ±1.21b M. hapla 5,000 5.7 ±0.11 20.4 ±0.44 63.3 ±1.15ab lettuce host 25,000 6.7 ±0.14 23.6 ±0.59 58.0 ±1.00b 500 6.8 ±0.16 24.8 ±0.82 69.8 ±1.11a M. incognita 5,000 6.6 ±0.14 23.7 ±0.59 64.4 ±1.18ab 25,000 7.0 ±0.12 24.8 ±0.49 61.0 ±1.38b Non-inoculated Control 6.0 ±0.15 21.7 ±0.60 64.6 ±0.97ab P-value NSz NS 0.0165 xResults of one independent experiment are shown; values are means of data from twenty replicate plants + standard error of the mean. yMeans followed by the same letter(s) did not significantly differ according to least squares means option of the GLIMMIX procedure of SAS (P=0.05). Means of non- transformed data are presented for clarity. zNS= non-significant differences.

Table 2.4. Effects of root-knot nematode (RKN; Meloidogyne spp.) isolates and initial egg densities on tomato root damage in high tunnel microplot trial (2018 trial).

RKN isolatex Eggs/g of rooty M. hapla tomato host 429.7 ±37.60a M. hapla lettuce host 14.0 ±2.46b a M. incognita 364.0 ±32.83 Initial egg density Eggs/g of root 500 64.8 ±34.52a 5,000 308.1 ±35.11b 25,000 435.0 ±34.78b ANOVA P-value RKN isolate 0.0005 Initial egg density <.0001

RKN isolate*initial <.0001 egg density xResults of one independent experiment are shown; values are means of data from twenty replicate plants + standard error of the mean. Analysis of variance was used to test whether RKN isolates (Meloidogyne incognita, M. hapla tomato host, and M. hapla lettuce host), initial egg density (500, 5,000, and 25,000), or the interaction of these factors influenced the number of eggs per gram of root. yMeans followed by the same letter(s) did not significantly differ according to least squares means option of the GLIMMIX procedure of SAS (P=0.05). zNumber of eggs/g of root was calculated after the last tomato harvest (6 weeks after seedlings were transplanted).

Table 2.5. Effects of initial densities of Meloidogyne species and water stress on tomato yield in high tunnel microplot trial (2019 trial).

Initial RKN Yield Number of % Marketable eggs/microplotw (kg/plant) fruits/plant Fruitsy

10,000 6.8 ± 0.18 34.2 ± 0.6 83.9 ± 0.64bc a M. hapla 50,000 7.0 ± 0.24 29.5 ± 1.09 89.1 ± 1.02 50,000 WSx 6.5 ± 0.21 30.7 ± 1.09 85.6 ± 1.02ab 10,000 7.3 ± 0.18 30.9 ± 0.61 77.2 ± 1.17c M. incognita 50,000 7.7 ± 0.22 32.6 ± 0.64 85.3 ± 0.64abc

50,000 WS 7.2 ± 0.21 34.3 ± 0.77 85.9 ± 0.87ab bc Non-inoculated Control 9.0 ± 0.32 36.4 ± 0.57 83.4 ± 0.86 Non-inoculated WS Control 6.9 ± 0.18 33.6 ± 0.60 78.9 ± 0.77bc P-value NSz NS 0.0203 wResults of one independent experiment are shown; values are means of data from eight replicate plants + standard error of the mean. xWS=Treatments of combined effects of RKN isolate and water stress. yMeans followed by the same letter(s) did not significantly differ according to least squares means option of the GLIMMIX procedure of SAS (P=0.05). Means of non- transformed data are presented for clarity. zNS= non-significant differences.

Table 2.6. Effects of initial densities of Meloidogyne species and water stress on tomato root galling severity in high tunnel microplot trial (2019 trial).

Gall Odds Initial RKN eggs/microplotw P-value indexy ratioz

10,000 2.0 ±0.0 0.0 0.9958 M. hapla 50,000 2.8 ±0.60 27.1 0.0008 50,000 WSx 3.7 ±0.48 155.0 <.0001 10,000 2.0 ±0.0 0.0 0.9934

M. incognita 50,000 3.3 ±0.48 39.2 0.0004 50,000 WS 3.6 ±0.66 66.0 0.0003 Non-inoculated Control 1.0 ±0.0 1.0 1

Non-inoculated WS Control 1.0 ±0.0 - - wResults of one independent experiment are shown; values are means of data from twelve replicate plants + standard deviation. xWS=Treatments of combined effects of root-knot nematode (RKN) isolate and water stress. yThe galling index scale developed by Bridge and Page (1980) was used to rate RKN damage. The original galling index scale (0-10) was modified according to a 1-4 galling scale: 1 = 0-4, 2= 4-6, 3=7, and 4 = 8-10. zProportional odds ratios are shown, along with p-values, for the comparison of root knot- nematode gall ratings for non-inoculated waters stress control plants compared to plants grown in different initial densities of RKN isolates.

4: Medina, 5: Holmes, 6: Tuscarawas.

Meloidogyne spp.

Figure 2.2. Results of high tunnel tomato soilborne disease survey in Ohio. Dark shaded barns or high tunnels represent farms and high tunnels in which Meloidogyne spp. were found.

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Chapter 3. Evaluation of Pseudomonas spp. against Tomato Pathogens

Introduction

Tomato quality and yield depend on soil health, abiotic factors, and pest and disease management. Root-knot nematode (RKN) caused by Meloidogyne spp., black dot root rot caused by Colletotrichum coccodes, and bacterial speck caused by Pseudomonas syringae pv. tomato are economically important diseases of tomato (Dillard and Cobb, 1998;

McCarter et al., 1983; Noling, 2014). Bacterial speck is a common disease in open field production of tomato, which may be seedborne or endemic (McCarter et al., 1983). RKN and black dot root rot are soilborne diseases commonly found in enclosed structures.

Bacterial speck is primarily managed with copper-based fungicides; however, weather conditions in the Midwest have made management more challenging. Moreover, few chemical options are available for RKN and black dot root rot, and the use of resistant cultivars is often inadequate. Currently, they are no commercial cultivars or rootstocks used for black dot root rot resistance (Gilardi et al., 2014). RKN resistant cultivars are widely used in tomato production; however, resistance breaks down under high soil temperatures

(López-Pérez et al., 2006). In addition, the Mi-gene only confers resistance to M. arenaria,

M. incognita, and M. javanica. Thus, management of these diseases is difficult due to limited options of efficient management strategies.

Concomitantly, there is a need to provide new alternatives that can be integrated into existing management programs. Biocontrol could be one of these alternatives. Bacillus 75 thuringiensis and Beauveria bassiana have been successfully deployed for the management of pests in greenhouse and high tunnel structures (Thomas and Burkhart,

2004). Antagonistic bacteria have also been studied as an alternative or complementary approach to chemical control tactics of plant pathogens. For example, Pantoea agglomerans is a well-known plant-associated bacterium that has been reported to suppress fungal, bacterial, and plant-parasitic nematode diseases of plants (Nunes et al., 2001; Braun et al., 2000; Munif et al., 2013; Zhou et al., 2016). Mechanism for P. agglomerans activity as a biocontrol agent range from iron scavenging/competition to production of antibiotic compounds to activation of induced systemic resistance in the host plant (Braun et al.,

1998).

Members of the plant growth-promoting rhizobacteria genus Pseudomonas have been extensively investigated for their potential as biocontrol agents. Members of the

Pseudomonas genus have been shown to promote plant growth and possess antagonistic traits that can reduce the impact of plant pathogens on crop plants (Weller, 2007). Some of these antagonistic traits include nutrient exclusion (Dhingani et al., 2013), the production of antibiotics, and induced systemic resistance (Wei et al., 1991; Thomashow et al., 1990;

Corbell et al., 1995).

Pseudomonas spp. have also been extensively evaluated for their ability to limit fungal and bacterial diseases. For example, Wilson et al. (2007), reported that P. syringae

Cit7 reduced bacterial speck in tomato by 28% over ten different field experiments.

Another study evaluated P. chlororaphis PCL 1391 and P. fluorescens WCS365 as

76 biocontrol agents of C. lindemuthianum (anthracnose) in bean. P. chlororaphis PCL 1391 significantly reduced severity of anthracnose as well as promoted plant growth. When P. chlororaphis PCL 1391 and P. fluorescens WCS365 were used in combination rather than individually they provided higher levels of disease control and improvement in plant growth (Bardas et al., 2009). Similarly, P. fluorescens WCS365 and P. chlororaphis

PCL1391 were more effective against F. oxysporum on tomato when used in combination than individually (Dekkers et al., 2000). Moreover, previous studies have demonstrated the suppression of Rhizoctonia solani on tomatoes by P. fluorescens (Berta et al., 2005; Rini and Sulochana, 2007). The antagonistic activity of Pseudomonas spp. against a variety of pathogens adds to its practical value as a biocontrol agent.

Pseudomonas spp. have also been shown to be promising biocontrol agents against plant-parasitic nematodes. In greenhouse studies P. chlororaphis O6 displayed preventative and curative control of M. hapla in tomato (Nam et al., 2018). In field experiments, P. aeruginosa significantly suppressed a root rot - root-knot (M. javanica) disease complex of tomato (Siddiqui et al., 2000). Moreover, P. putida and P. fluorescens application as a soil drench reduced M. incognita populations in tomato (Santhi and

Sivakumar, 1987; Siddiqui and Futai, 2009). Modes of action of Pseudomonas spp. as biocontrol agents of RKN on tomatoes include parasitism of eggs and colonization of roots

(Siddiqui et al., 2000), production of hydrogen cyanide (Kang et al., 2018), secretion of extracellular protease, and induction of systemic resistance (Samiyappan et al., 2004;

Siddiqui et al., 2005). These studies demonstrate the possibilities of developing a

77 formulated product of Pseudomonas spp. and launching new alternatives to the market for the management of RKN.

The Taylor laboratory has a collection of Pseudomonas and Pantoea strains that have been sequenced and annotated and were isolated from water, soil, and plant samples collected from around the US (Table 3.1). Strains from this collection have been used to reduce diseases by bacterial and fungal plant pathogens. For instance, several strains of

Pseudomonas from this collection have been shown to reduce the severity of tomato bacterial wilt caused by Ralstonia pseudosolanacearum (Subedi et al., 2019). These strains have also been shown to reduce the incidence of pathogens in wheat. Mavrodi et al. (2013) reported that strains 15G2, 39A2, 48G9 and Wood3 are biological control candidates for the management of Rhizoctonia and Pythium root rot. These strains suppressed seedling damage by both R. solani AG-8, and Pythium ultimum and inhibited multiple Rhizoctonia and Pythium spp. in vitro.

With this in view and the importance of providing new alternatives for tomato disease management, the present study was carried out. Strains from the Taylor laboratory collection were selected to explore their potential as biocontrol agents of P. syringae pv. tomato, C. coccodes, and M. incognita. The objective of this study was to assess the in vitro antagonistic activity of the Taylor laboratory collection against P. syringae pv. tomato, C. coccodes and M. incognita. Our second aim was to test Pseudomonas spp. antagonistic activity against M. incognita on tomatoes under greenhouse conditions.

Material and Methods

Nematode culture

Greenhouse culture. The Meloidogyne incognita isolate IAT19 was collected from

Athens, Georgia. Isolate IAT19 was previously identified using morphological and molecular techniques. Root-knot nematode populations were maintained on tomato

(Solanum lycopersicum ‘Moneymaker’; Everwilde Farms, Bloomer, WI) grown in a sand/TurfaceTM (1:1 ratio) medium. Plants were kept in a greenhouse at 18-35°C and irrigated twice a day and fertilized initially by mixing 5 grams of Osmocote® (14-14-14,

Bloomington Brands LLC., Bloomington, IN) per liter of medium. M incognita isolate

IAT19 were propagated every 8-10 weeks as follows: plants were uprooted, washed, and freed of adhering soil. Roots were cut into small pieces and placed in a beaker. A 0.5% sodium hypochlorite solution was then added for 4.5 minutes, followed by egg collection using a 500-mesh sieve. The eggs were rinsed in the sieve under running tap water for at least 5 minutes to eliminate bleach residues. Eggs were transferred from the sieve into a beaker with H2O. The number of eggs was then counted under a Nikon SMZ645 dissecting microscope (Nikon Inc., Melville, NY) in three 10µl droplets, and concentration was adjusted to 4,000 eggs/ml. Plants were inoculated by pipetting 4,000 nematode eggs into a

2.5-5cm hole around the base of a 2-week-old tomato seedling in a 20cm plastic pot.

Laboratory culture. Arabidopsis thaliana ecotype Columbia seeds (300-400 seeds) were sterilized by incubating them in 1ml of a 70% ethanol and 0.05% triton x-100 solution

(Sigma-Aldrich, St. Louis, MO, USA) at 15 rpm for 30 minutes on a roller drum (New

Brunswick TC-7, Fisher Scientific Hampton, NH, USA). The solution was then removed 79 and replaced with 1ml of 100% ethanol. Seeds were then incubated again for 15 minutes on the roller drum and then allowed to air dry in a laminar flow hood for approximately 2 hours. All seeds were germinated on Gamborg’s medium [2% D-sucrose (Phytotech,

Shawnee Mission, KS, USA), 0.3% Gamborg's basal salts (Phytotech), 0.6% phytagel

(Sigma-Aldrich, St. Louis, MO, USA), pH 6.1] before being transferred to fresh

Gamborg’s plates five 1-week-old seedlings per plate). Plates were wrapped with micropore tape (3M™ Micropore™, Maplewood, MN) and placed in clear polycarbonate boxes at an approximate 40° angle, and sterile felt was placed on the bottom of the boxes to absorb water from condensation. Seedlings were incubated in a Percival (Perry, IA,

USA) growth chamber under the following conditions: 8 hours light/16 hours dark, 23°C.

After 3 weeks 1,000 M. incognita eggs in 1ml of H2O per plate were pipetted on the roots of Arabidopsis seedlings.

Pseudomonas strains and culture conditions

Pseudomonas strains were collected by the Taylor and Brian McSpadden Gardener

(OSU) laboratories from river water, soil, and plant samples from across the US (Mavarodi et al., 2013; Subedi et al., 2019). The origin and species of each strain are shown in Table

3.1. Species designation is based on the 16S rRNA gene sequence. Pseudomonas strains were grown in Luria-Bertani (LB) broth or on LB agar medium (Sambrook et al., 1989) for 18 hours at 28°C, and the final bacterial concentration was adjusted to 108 CFU/ml.

Subsequently, bacteria were stored long term as glycerol stocks at -80°C. The stock was made by combining 500µl of Pseudomonas sp. (108 CFU/ml) in LB broth and 500µl of

50% glycerol. Fresh cultures were maintained on LB agar medium plates at 28°C. 80

Pseudomonas spp. in vitro antagonistic activity against Pseudomonas syringae pv. tomato

The antagonistic activity in vitro experiment was examined using a zone of growth inhibition assay. The pathogen Pseudomonas syringae pv. tomato strain SM94-08 (Pst

SM94-08; Location Ohio; OSU, Sally Miller Laboratory) was used for this study. Potential biocontrol Pseudomonas strains from the OSU collection and PstSM94-08 were grown in

LB broth for 18h on a shaker at 28°C. The final bacterial concentrations were adjusted to

108 CFU/ml. A top agar solution containing PstSM94-08 was prepared as follows: First,

1L of LB (0.7% agar) medium was inoculated with 1ml of PstM94-08 and 7ml were pipetted over LB agar medium in a 9cm diameter plastic Petri plate. Subsequently, 6mm sterile filter paper disks were placed on the top of the plate, and 10µl of each putative biocontrol Pseudomonas strain was pipetted onto each filter paper disk. Each plate had two filter paper disks, each inoculated with the same Pseudomonas strain. Each plate was sealed with two layers of parafilm. Each treatment was replicated six times, and LB only was used as a negative control. The plates were arranged in a completely randomized design on a laboratory bench. The plates were kept for 48 hours at ambient temperature and light conditions, after which the zones of growth inhibition (mm) were taken by measuring the vertical and diagonal diameter across the zone of inhibition using a ruler. Each strain was tested in two independent experiments.

Pseudomonas spp. in vitro antagonistic activity against Colletotrichum coccodes

Colletotrichum coccodes isolate OH19-01 (CcOH19-01), isolated from tomato in

Ohio, was provided by the OSU Miller laboratory. Three-mm plugs taken from the margin of a 10-day-old CcOH19-01culture were placed in the center of 9cm diameter plastic Petri plates containing 20±0.5 ml potato dextrose agar (PDA, VWR International, LLC).

Pseudomonas strains were grown in LB medium for 24 hours at 28°C. Single colonies of

Pseudomonas cultures were streaked in two parallel lines, each 2.1cm apart from the plug.

Each plate was sealed with two layers of parafilm. Colletotrichum coccodes on PDA only was used as negative control. Each treatment was replicated three times. The plates were arranged in a completely randomized design on a laboratory bench and were kept at ambient temperature and light conditions. After 9 days, mycelial growth was recorded by measuring the vertical and diagonal diameter (cm) of each CcOH19-01 colony. Each strain was tested in two independent experiments. Percentage (%) of mycelial growth inhibition was calculated according by the following formula.

A -A (%)= 1 2 ×100 A1

Where, A1 = C. coccodes colony growth (cm) on PDA only, A2 = C. coccodes colony growth (cm) when co-cultured with Pseudomonas spp.

Pseudomonas spp. in vitro antagonistic activity against Meloidogyne incognita

Nematode egg isolation. Six weeks after inoculation, stem and leaves of 20 RKN- infested Arabidopsis plants were removed and discarded using a sterilized scalpel blade.

Roots containing RKN egg masses were collected using sterilized tweezers from approximately 60 plants and placed in two 50ml centrifuge tubes (Falcon, Corning, NY,

USA). Forty-five ml of 0.5% sodium hypochlorite was added to the tubes, which were then shaken vigorously by hand for two minutes. The root material was poured over a 2mm coarse filter into a new 50ml centrifuge tube, which then contained most of the eggs. The tubes containing the eggs and solution were centrifuged for five minutes at 1000rpm. After centrifugation, approximately 40ml of the solution was poured out of the centrifuge tubes and replaced with approximately 40ml of ddH2O. The centrifugation and pouring off steps were then repeated three more times. The number of eggs was then counted under a Nikon

SMZ645 dissecting microscope in three 10µl droplets and the concentration adjusted to

500 eggs/100 µl dH2O.

Inhibition of egg hatching by Pseudomonas strains. Pseudomonas strains were grown in LB broth for 18 hours at 28°C, and the final bacterial concentration adjusted to

108 CFU/ml. Thirty microliters of a Pseudomonas strain was pipetted onto a 35mm plastic

Petri plate containing 3±0.3mL of LB medium. The suspension was spread using a sterilized loop. On a separate plate of the same diameter, 2mL of sterile dH2O was pipetted, and a hatch chamber was placed in direct contact with the sterile dH2O. Hatch chambers were made using 25µm wire transmembrane held in place by a ring of plastic tubing surrounding a 1ml pipette tip (1.5cm long; Figure 3.2). Approximately 500 eggs/100µl dH2O of aseptically propagated M. incognita were pipetted in the hatch chamber. For each replicate an LB plate containing a Pseudomonas strain was inverted and placed on top of a plate containing the M. incognita eggs in a hatch chamber. Each plate was sealed with three layers of parafilm to trap all volatiles within the system. M. incognita with LB only and M. incognita without medium were used as negative controls. Each treatment was replicated 83 three times. Plates were kept at ambient temperature and light conditions on a laboratory bench and arranged in a completely randomized design. After 5 days, the number of hatched juveniles in each plate was counted using a Nikon SMZ645 dissecting microscope.

Pseudomonas spp. antagonistic activity against Meloidogyne incognita under greenhouse conditions

Organisms and general methods. All bacterial strains used in this study are listed in Table 3.1. LB medium was used for routine culturing. For inoculum preparation, the bacterial strains were cultured in LB broth medium at 28°C with constant shaking at 220 rpm. After three days, cell concentrations were adjusted to 108 CFU/mL using LB to dilute the cultures. The root-knot nematode used in this study was Meloidogyne incognita collected from Georgia. Isolates were maintained and increased individually on tomato

(‘Moneymaker’) in the greenhouse. For egg extraction, the same procedure as describe in the greenhouse culture section were conducted.

Greenhouse experimental procedures. Greenhouse experiments were conducted in

Selby Hall, OSU Wooster Campus. Tomato ‘Money Maker’ was used in all experiments.

Approximately 200 seeds were sown in Promix (Premier, Red Hill, PA) contained in two

15cm plastic pots (Dillen Products, Inc., Middlefield, OH). Two weeks after seed germination, seedlings were washed and one seedling was transferred per 10cm plastic pot

(Dillen Products, Inc., Middlefield, OH) containing a sand/TurfaceTM substrate 1:1

(vol./vol.) (Figure 3.1). Pots were arranged in a completely randomized block design on a greenhouse bench. Plants were maintained at 25 to 35°C under natural and supplemental

84 lighting (16 continuous hours of light per day, 1,000-watt high pressure sodium lamp,

DigiLamp, Allstate Garden Supply, Inc., Ontario, CA). Plants were irrigated overhead twice a day and fertilized initially by mixing 5 grams of Osmocote® (14-14-14) per liter of medium. Each treatment had ten single plant replicates.

Bacterial Application and Meloidogyne incognita inoculation. Two weeks after transplanting, 50ml of a Pseudomonas strain suspension (mixture of 10 ml of 108 CFU/ml of Pseudomonas sp. with 990ml of H2O to make a 1:100 dilution) was applied to the potting medium in each replicate pot by applying the suspension around the base of the plant.

Drenched plants were separated from other treatments, and gloves were changed between each treatment to minimize cross-contamination. The bacterial isolates were applied three days prior to inoculation with M. incognita to allow their prior establishment. All pots were inoculated with 4,000 nematode eggs by pipetting the nematode suspension into a hole made to a depth of 2-4cm below the soil surface around the base of the plants.

Disease assessment. The experiments were terminated 45 days after inoculation.

The root system of each replicate plant was uprooted, washed, and cut into small pieces, and weighed. The number of eggs per root system was determined by 100ml of a 0.5% sodium hypochlorite solution was added to the complete root system of each plant for fifteen minutes while shaking on a Lab-Line Thermal Rocker (6 speed). Six 10µl aliquots were taken for each sample, and eggs were counted using a Nikon SMZ645 dissecting microscope. For each replicate, the total number of eggs per 100ml 5% sodium hypochlorite solution per gram of roots was calculated.

Statistical Analysis

For the in vitro assay of growth inhibition by Pseudomonas spp. against PstSM94-

08, measurements of strains that produced a zone of inhibition were subjected to analysis of variance (ANOVA) and means were separated using Fisher's least significant difference test (a=0.05). Analysis was performed in Minitab statistical software version 19 (State

College, PA). To assess differences in growth inhibition by Pseudomonas strains against

C. coccodes, data were analyzed using the Students’ t-test at P≤0.05. The analyses were conducted with the Analysis Tool of Microsoft Excel.

For all count data (number of fruits, number of eggs/g of root) a generalized linear mixed model was fitted assuming a Poisson distribution. Data expressed as percent were subjected to arcsine transformation to stabilize variance. However, all results and graphs are presented on the original data scale. All data were analyzed by ANOVA using the

GLIMMIX procedure of SAS software program (SAS Institute, Cary, NC) and means were separated with least squares estimates of marginal means.

Results

Pseudomonas spp. in vitro antagonistic activity against Pseudomonas syringae pv. tomato

Of the 44 Pseudomonas strains tested in vitro for antagonism against P. syringae pv. tomato, seven strains (P. protogens 1B1, P. protogens Darke, P. protogens 15H3, P. protogens IF2, P. frederiksbergensis 37A11, P. protogens 15H10, and P. protogens 15G2), inhibited growth of the pathogen (Table 3.2 and Figure 3.2). Mean zones of inhibition

86 varied between 30.4mm and 13.5mm. P. protegens Darke was the most inhibitory, followed by P. protegens 1B1 and P. frederiksbergensis 37A11. Two independent experiments showed the same differences with a low standard error of the mean.

Pseudomonas spp. in vitro antagonistic activity against Colletotrichum coccodes

Of the 43 Pseudomonas strains tested in vitro for antagonism against C. coccodes,

17 strains inhibited more than 50% of the fungal growth (Figure 3.2, Figure 3.3, and Figure

3.4). P. protegens strains had the most activity among the 17 strains that inhibited C. coccodes growth by 50% or more. In the first in vitro experiment, P. brassicacearum Wood

3, P. chlororaphis 14B11, P. protogens 38G2, P. brassicacearum 93G8, P. chlororaphis

48G9, P. poae 36C8, P. protogens 1B1, and P. protogens Darke were among the strains with highest inhibitory activity (Figure 3.3). In the second in vitro experiment, the strains with the highest inhibitory activity were: P. protogens 15G6, P. protogens Clinton, P. protogens Wayne, P. protogens 12H11, and P. chlororaphis 48B8 (Figure 3.4). The highest inhibitory strains of both experiments did not significantly differ in percentage of growth inhibition according to T-test analysis.

Meloidogyne incognita egg hatching after exposure to Pseudomonas spp. volatile organic compounds.

All 21 Pseudomonas strains evaluated in vitro for nematicidal activity against M. incognita significantly reduced the number of hatched juveniles as compared with the non- treated H2O control and LB control (Figure 3.5). H2O only control had significantly higher number of hatched juveniles than the LB only control, indicating that medium influenced 87 hatching. Of the 21 Pseudomonas strains that significantly reduced number of hatched juveniles, 18 strains we able to reduce 80% or more of juvenile hatching. P. fluorescens strains had the most activity among the 18 strains that decrease juvenile hatching by 80% or more. Pseudomonas brassicacearum 93G8, P. protogens 15G2, P. chlororaphis 14D6,

P. chlororaphis 14B11, P. chlororaphis 48G9, P. rhodesiae 88A6, P. brassicacearum

37D10 and P. protogens 38G2 were among the strains with the highest inhibitory activity.

Pseudomonas spp. antagonistic activity against Meloidogyne incognita under greenhouse conditions

Pseudomonas brassicacearum Wood 3, P. protogens 15G2, P. chlororaphis 48G9,

P. fluorescens 89F1, and P. fluorescens 24D3 evaluation. When comparing plant replicates of treatments and control between the three independent experiments there was no consistency in number of eggs/g of root (Figure 3.6). There were no significant differences among plants treated with any of the Pseudomonas strains and the non-treated controls in number of eggs/g of root.

P. protogens Clinton, P. protogens Wayne, P. fluorescens 36G2, P. protogens

38G2, and P. protogens 15H3 evaluation. Plants treated with P. protogens 15H3 had significantly lower amount of eggs/g of root in one out of the four independent experiments

(Figure 3.7). However, when data was pooled P. protogens 15H3 treated plants had numerically lower number of eggs/g of root but there were no significant differences as compared to non-treated control plants. There were no significant differences in number of

88 eggs/g of root among plants treated with any of the Pseudomonas strains and non-treated control plants.

P. frederiksbergensis 39A2, P. fluorescens 24D3, P. protogens IC5, and P. brassicacearum 37D10 evaluation. The number of eggs/g of root was not consistent between plant replicates (Figure 3.8). The non-treated control plants had numerically lower number of eggs/g of root when compared to pseudomonads treated plants. There were no significant differences in number of eggs/g of root among plants treated with any of the

Pseudomonas strains and non-treated control plants.

Application rate evaluation of P. protogens 15H3 and P. protogens 38G2. At an initial inoculum of 1,000 RKN eggs, there were no significant differences in number of eggs/g of root among plants treated with any of the Pseudomonas strains and non-treated control plants (Figure 3.9). At an initial inoculum of 2,000 RKN eggs, all plants treated with Pseudomonas spp. had numerically lower number of eggs as compared to the non- treated control plants (Figure 3.10). However, there were no significant differences in number of eggs/g of root among plants treated with any of the Pseudomonas strains and non-treated control plants.

Application rate evaluation of P. rhodesiae 88A6 and P. lini 48C10. At an initial inoculum of 2,000 RKN eggs, there were no significant differences in number of eggs/g of root among plants treated with any of the Pseudomonas strains and non-treated control plants (Figure 3.11). In the first independent experiment, plants treated with P. rhodesiae

88A6 had the lowest number of eggs/g of root as compared to the non-treated control plants

(initial inoculum of 4,000 RKN eggs, Figure 3.12.A). However, plants treated with the 89 same strain had among the highest number of eggs/g of root in the second independent experiment as compared to the non-treated control plants (Figure 3.12.B). Data was not pooled together for analysis due to inconsistency between independent experiments in mean number of eggs/g of root on plants treated with Pseudomonas strains.

Discussion

This study reports the in vitro antagonistic activity of Pseudomonas spp. against P. syringae pv. tomato, C. coccodes, and M. incognita. The use of pseudomonads as a bacterial disease treatment has been investigated and reported in the literature. For instance, foliar application of P. syringae Cit7 resulted in a significant reduction of bacterial speck severity in tomato studied under greenhouse and field conditions (Wilson et al., 2002; Ji et al., 2006). Disk diffusion experiments are routinely used to screen microbes for antimicrobial activity and are usually the first step in assessing inhibitory activity. In order to identify Pseudomonas spp. in the Taylor Lab collection inhibitory to P. syringae pv. tomato this type of in vitro assay was used. Seven Pseudomonas strains inhibited the growth of P. syringae pv. tomato, indicated by a zone of inhibition of the pathogen.

Interestingly, six of the seven inhibitory strains were P. protogens (Table 3.2). Moreover, four of the seven inhibitory strains have been analyzed for potential biocontrol and plant- growth promoting traits. All four strains have the presence of the DAPG gene cluster and

HCN gene cluster (X. Y. Tao, personal commun.). P. protegens (ex-fluorescens) is a well- known nitrogen-fixing bacterium capable of synthesizing antimicrobial compounds such as DAPG (Haas and Défago, 2005). Presumably volatile or diffusible compounds

(Hernández-León et al., 2015) synthesized by Pseudomonas spp. may have suppressed P.

90 syringae pv. tomato growth. These results are in accordance with those of Subedi et al.

(2019). Strains that inhibited P. syringae pv. tomato in our study also had in vitro antagonistic activity against R. pseudosolanacearum. Previous studies using the disk diffusion method and greenhouse assays to evaluate non-pathogenic P. syringae strains against the pathogen P. syringae pv. glycinea showed that eight strains had in vitro and in planta inhibitory activity (Völksch and May, 2001). However, five strains did have in planta activity but no in vitro activity. These assays provided insight into strains that could be further investigated and potentially become an integrated control tactic.

We evaluated antagonistic activity of Pseudomonas spp. in vitro against C. coccodes using the dual culture technique. Seventeen strains inhibited 50% or more of the colony growth of this fungus (Figure 3.3 and Figure 3.4). Among these 17 strains nine are

P. protegens, demonstrating the most activity out of all the species evaluated. These results align with those of the P. syringae pv. tomato disk diffusion assay in which the majority of antagonistic strains were P. protegens. Other strains also had inhibitory activity against

C. coccodes, but, they did not inhibit fungal growth by 50% or more. Mavrodi et al. (2012) reported that several strains of the OSU Pseudomonas collection had high in vitro inhibitory activity against R. solani AG-8 and P. ultimum. The antifungal activity of

Pseudomonas spp. could be due to the production of one or several antibiotic compounds such as pyrrolnitrin or pyoluteorin (Haas and Défago, 2005; Weller, 2007; Panpatte et al.,

2016). Bardas et al. (2009) conducted a similar study evaluating P. chlororaphis CL139 and P. fluorescens WCS365 using the dual culture technique against C. lindemuthianum.

They found that application of pseudomonads alone or in combination reduced fungal growth, sporulation, and conidial germinability.

Hence, a similar in vitro study is necessary to unravel Pseudomonas spp. effects on sporulation and conidial germinability of C. coccodes. Moreover, more testing is necessary to determine whether Pseudomonas spp. volatile compounds play a role in C. coccodes suppression. Reduction of conidial germination was also observed in a study evaluating P. putida against C. acutatum (Moreira et al., 2014). However, P. putida did not reduce mycelial growth compared to the control. For these reasons, due to possible variations in antifungal activity, a combination of different in vitro assays would provide a better understanding of the effects of Pseudomonas spp. The evaluation of Pseudomonas spp. against C. coccodes showed promising results. Nonetheless, greenhouse and field experiments are needed for efficacy evaluation and to justify their use for control of black dot root rot on tomato.

In order to assess if Pseudomonas spp. volatile compounds inhibit M. incognita, an in vitro assay was developed. Previous findings from the Taylor Laboratory indicated that multiple Pseudomonas strains produce volatile(s) lethal to Caenorhabditis elegans and H. glycines. In addition, a study conducted in the Taylor Laboratory using spectrophotometry to quantify hydrogen cyanide demonstrated that 78% of the Pseudomonas spp. collection produced varying levels of hydrogen cyanide (R. Kimmelfield, personal commun.). In the literature it is well documented that Pseudomonas spp. produce hydrogen cyanide, a relevant volatile compound for biocontrol (Haas and Défago, 2005). Thus, our results may be due at least in part to the production of this nematicidal volatile. In our findings, eighteen

92 strains we able to reduce 80% or more of juvenile hatching. Fourteen of these eighteen strains have the presence of the HCN gene cluster and produced quantifiable levels of hydrogen cyanide in other experiments (X. Y. Tao, personal commun. and R. Kimmelfield, personal commun.). This indicates that hydrogen cyanide may be a critical factor in hindering nematode hatching and survival. These results are in accordance with those of

Hegazy et al. (2019) who tested P. aeruginosa in vitro antagonistic activity against M. hapla, showing 98% reduction of hatching of M. incognita juveniles. Similar results were also reported by Mohamed et al. (2009) showing 100% mortality of M. incognita after exposure to P. fluorescens. Further in vitro analyses of the effects of contact by pseudomonads in aqueous suspension or by cell-free Pseudomonas supernatants on M. incognita eggs are needed. Future studies will provide a better comprehension of the possible modes of action of Pseudomonas spp. against M. incognita.

After testing Pseudomonas spp. in vitro activity against M. incognita, we tested their efficacy under greenhouse conditions. Fifteen Pseudomonas strains were evaluated in three separate assays. Of the fifteen strains evaluated, there were no statistical differences number of eggs/g of root among Pseudomonas spp. treated plants and non-treated control plants. The mean number of eggs recovered from tomato roots in treated and control plants was not consistent among assays. There was a trend in some experiments indicating fewer eggs collected from plants treated with P. protegens 15H3 and P. protogens 38G2, but these differences were not significant when data from all experiments was pooled (Figure

3.7). Both strains inhibited hatching in the in vitro assay; however, P. protegens 15H3 only significantly decreased hatching by 20%. P. protegens 15H3 and P. protegens 38G2 should

93 further be tested since assay modifications could provide a better system for Pseudomonas spp. testing against M. incognita. Inconsistency across all assays presents the need to conduct preliminary trials to test environmental conditions, potting media, application frequency, application rate, and nematode quantification methods.

To test if Pseudomonas spp. application rate and M. incognita inoculum rate play a role in the effectiveness of Pseudomonas spp., we conducted greenhouse trials with three different Pseudomonas application rates and two different initial M. incognita inoculum rate. No significant differences in number of eggs/g of root were observed among the

Pseudomonas treated plants and non-treated inoculated controls. Greenhouse temperature fluctuated widely during the course of these experiments and could be a factor contributing to the variability and to the lack of efficacy observed. Several studies have shown that incubation temperatures of approximately 35°C have a negative effect on Pseudomonas fluorescens efficacy in vivo (Schmidt et al., 2004). Temperatures around 35°C also have a negative effect on the expression of Pseudomonas fluorescens biocontrol factors such as

DAPG and hydrogen cyanide in vitro (Shanahan et al., 1992; Humair et al., 2009).

Although in vitro assays in our studies showed inhibition of M. incognita hatching, the same results were not obtained under greenhouse conditions. Inconsistency between the results of in vitro assays and greenhouse assays have been observed in other studies. For instance, Subedi et al. (2019) found no correlation between in vitro inhibition of Ralstonia strains as measured by zones of inhibition and Pseudomonas spp. (OSU collection) efficacy in a tomato bioassay. Moreover, Al-Fattah et al. (2007) demonstrated that all Trichoderma spp. isolates tested were effective in causing M. incognita juvenile mortality in vitro,

94 however, when tested in vivo the results were not consistent. The soil drench assay did not provide results leading to the selection of Pseudomonas strains for further investigation.

Soil drench application of Pseudomonas spp. and other antagonistic bacteria is a prevalent method encountered in the literature. To develop a higher throughput method for the greenhouse testing of Pseudomonas spp. preliminary testing is needed to modify the current methodology. For instance, several studies evaluating Pseudomonas spp. for nematode control used sterilized sandy loam soil (Santhi and Sivakumar, 1995; Lax et al.,

2013; Siddiqui et al., 2000; Nam et al., 2018). In our studies, the use of sand/turface medium may have a considerable negative effect on the activity of Pseudomonas spp. This medium has substantially low moisture retention and a high infiltration rate, which can result in removal of the applied pseudomonads when plants are irrigated. Siddiqui and

Ehtesmahul-Haque (2000) reported that 50% and 75% moisture-holding capacity of soil enhanced P. aeruginosa activity against M. javanica in tomato. Using a factorial treatment design to evaluate moisture, Pseudomonas spp. application rates, and population densities of RKN could provide the necessary information for further testing. This assay may provide a fast and reliable method to determine the best parameters to test Pseudomonas spp. for antagonism against plant pathogens in planta.

Another critical component to consider is the nematode quantification methodology. In all of our assays, we extracted RKN eggs from the whole root system using a 0.5% sodium hypochlorite solution (100ml) and counted aliquots of the solution.

However, most studies found in the literature (Khan and Haque, 2011; Thiyagarajan and

Kuppusamy, 2014; Santhi and Sivakumar, 1987; Park et al., 2014) used a combination of

95 galls and egg mass counting combined with a soil sample (Baermann funnel-technique) to count juveniles. Using this method can reduce the inconsistency and variability encountered in most assays. Further studies could also evaluate the formulation and frequency of application because these elements could dramatically enhance the antagonistic activity of Pseudomonas spp. Previous researchers using a wettable powder formulation of P. chlororaphis showed RKN control at levels comparable to that of a standard commercial nematicide (Nam et al., 2018). The implementation of a proper positive control treatment as a reference should also be taken into account in future experiments. Going forward, these factors also need to be addressed to achieve a more efficient system to test Pseudomonas spp. against RKN.

In vitro preliminary studies show that multiple Pseudomonas strains have activity against all pathogens evaluated. The species that had the most activity across all in vitro assays was P. protegens. For instance, P. protegens 1B1 and P. protegens Darke had high antagonistic activity across all in vitro assays. Previous researchers (Lax et al., 2013) have shown that P. protegens can also suppress Nacobbus aberrans (false-root knot nematode) on tomato. Thus, these findings add to the practical value of further investigating

Pseudomonas spp. because it is effective against a variety of pathogens and could be potentially used as a broad-spectrum biocontrol. Future studies would provide necessary information about Pseudomonas spp. regarding efficacy under greenhouse and field conditions where interaction and competition with other microorganisms may occur.

Chapter 3. Tables and Figures

Table 3.1. Pseudomonas species and strains used in this study and source of origin of each strain.

Strain Pseudomonas species Source of origin 14B11 P. chlororaphis Missouri River 14D6 P. chlororaphis Mississippi River 48B8 P. chlororaphis Wisconsin Soil 48G9 P. chlororaphis Wisconsin Soil 1B1 P. protogens Mississippi River 1C5 P. protogens Mississippi River 1F2 P. protogens Mississippi River 12H11 P. protogens Missouri River 14B2 P. protogens Missouri River 15G2 P. protogens Missouri River 15G6 P. protogens Missouri River 15H3 P. protogens Missouri River 15H10 P. protogens Missouri River 38G2 P. protogens Wyoming Soil Clinton P. protogens Ohio Soil Darke P. protogens Ohio Soil Wayne P. protogens Ohio Soil 29G9 P. poae Herbarium 36C8 P. poae Wyoming Soil 88A6 P. rhodesiae Missouri Soil 90F12-1 P. rhodesiae Missouri Soil 36B7 P. brassicacearum Wyoming Soil 36D4 P. brassicacearum Wyoming Soil 93G8 P. brassicacearum Missouri Soil Wood 3 P. brassicacearum Ohio Soil 37D10 P. brassicacearum Wyoming Soil 48H11 P. brassicacearum Wisconsin Soil 38D7 P. brassicacearum Wyoming Soil 36C6 P. frederiksbergensis Wyoming Soil 37A10 P. frederiksbergensis Wyoming Soil 37A11 P. frederiksbergensis Wyoming Soil 38F7 P. frederiksbergensis Wyoming Soil 94G2 P. frederiksbergensis Missouri Soil 48C10 Pantoea agglomerans Wisconsin Soil Continued

Table 3.1 Continued

Strain Pseudomonas species Source of origin 2F9 P. fluorescens Missouri River 24D3 P. fluorescens Herbarium 28B5 P. fluorescens Herbarium 36B3 P. fluorescens Wyoming Soil 36F3 P. fluorescens Wyoming Soil 36G2 P. fluorescens Wyoming Soil 48D1 P. fluorescens Wisconsin Soil 48D5 P. fluorescens Wisconsin Soil 89F1 P. fluorescens Missouri Soil 2F9 P. fluorescens Missouri River 90F12-2 P. fluorescens Missouri Soil

Table 3.2. Pseudomonas spp. in vitro antagonistic activity against Pseudomonas syringae

pv. tomato Experiment 1 Experiment 2 Zone of Zone of Species Strain inhibition Species Strain inhibition (mm)x (mm)A 14B11 0 P. chlororaphis 48B8 0 P. chlororaphis 14D6 0 IC5 0 48G9 0 IF2 13.9±0.082 c 1B1 17.0±0.50y bz 12H11 0 15G2 13.5±0.07 c 14B2 0 P. protogens P. protogens 15H3 13.5±0.07 c 15G6 0 38G2 0 15H10 13.3±0.52 c Darke 30.4±0.20 a Clinton 0 29G9 0 Wayne 0 P. poae 36C8 0 36B7 0 88A6 0 P. 36D4 0 P. rhodesiae 90F12-1 0 brassicacearum Wood 3 0 93G8 0 48H11 0 P. 37D10 0 37A11 14.7±0.25 bc brassicacearum 38D7 0 P. 38F7 0 2F9 0 frederiksbergensis 94G2 0 24D3 0 36C6 0 36F3 0 28B5 0 P. fluorescens 36G2 0 36B3 0 P. fluorescens 89F1 0 48D1 0 Pantoea 48C10 0 48D5 0 agglomerans xMeans of zone of inhibition (represented in mm) after two days of incubation. Each treatment was represented by three plates, each containing two filters. Results of two independent experiments are shown.

y Standard error of mean zones of inhibition.

zMeans followed by the same letter do not differ significantly according to Fisher’s least significant difference at P < 0.05.

Control 48G9 90F12-1

B Control 15H3 48C10

RKN infested RKN Arabidopsis Juvenile In vitro assay roots s

Figure 3.1. (A) Pseudomonas spp. antagonistic activity against Colletotrichum coccodes

(CcOH19-01). The dual culture technique was used. Rate of inhibition was determined

after nine days. (B) Pseudomonas spp. antagonistic activity against Pseudomonas

syringae pv. tomato. Zones of inhibition were measured after 24 hours of co-culturing.

Number of hatched J2 were counted after five days of exposure to volatile organic

compounds of Pseudomonas strains. 48G9 = P. chlororaphis; 90F12-1 = P. rhodesiae;

15H3 = P. protogens; 48C10 = Pantoea agglomerans.

100

Control 38G2 15H3 B

Figure 3.2. (A) A greenhouse study to evaluate nematicidal effects of Pseudomonas spp.

against Meloidogyne incognita. Image represents all replicates of one experiment. (B)

Development of Meloidogyne incognita in tomato after Pseudomonas soil drench

application. Image includes non-treated inoculated control root and roots treated with P.

protegens 38G2 or P. protegens 15H3.

101

100

90 A A 80 A AB AB AB AB 70 AB 60 BC CD 50 DEF 40 EFG EFGH EFGH EFGH 30 GHIJ EFGHI FGHI

% Growth Inhibition HIJ 20 IJ IJ 10 0

1B1 14D6 24D3 89F1 38D7 36C8 88A6 36F3 93G837D10 14B11 48G9 36G2 15H348C10 29G9 15G2 38G2 Darke Wood 3 90F12-1

Figure 3.3. Pseudomonas spp. in vitro antagonistic activity against Colletotrichum coccodes. Fungal growth was measured after nine days of co-culture and percent inhibition was calculated using the formula (%)= A1-A2 ×100. Results of two A1 independent experiments are shown. Columns in red indicate 50% or more of growth inhibition. Vertical bars indicate standard error of the mean. Means with same letter are not significantly different (P < 0.05, least squares means option of the GLIMMIX procedure of SAS). Means of non-transformed data are presented for clarity.

102

100 90 A A A 80 A A 70 B B C 60 D 50 DE D DEF EF 40 FG GH GHI HIJ 30 HIJ HIJ IJ KJ 20 J

% Growth Inhibition % Growth 10 0

IF2 IC5 2F9 36D4 15G694G2 28B5 36B7 48D1 48D5 14B2 36B3 36C6 48B8 38F7 48H11 Wayne 37A10 12H11 15H1037A11 Clinton

Figure 3.4. Pseudomonas spp. in vitro antagonistic activity against Colletotrichum coccodes. Fungal growth was measured after nine days of co-culture and percent inhibition was calculated using the formula (%)= A1-A2 ×100. Results of two A1 independent experiments are shown. Bars in red indicate 50% or more of growth inhibition. Vertical bars indicate standard error of the mean. Means with same letter are not significantly different (P < 0.05, least squares means option of the GLIMMIX procedure of SAS). Means of non-transformed data are presented for clarity.

103

120 A 100 B 80 C

60 DE E 40

Number of Juveniles 20 GHIJKL GHIJ GH F FGH KL GHI IJKL GH GH GHI KGHIJ FG IJKL L JKL IJKL HIJKL 0

2F9 1B1 14D6 36C8 88A624D3 89F1 36F3 38D7 15G2Darke 93G8 14B1148G9 37D1038G236G2 29G9 48C1015H3 90F12-1 ControlControl A B

Figure 3.5. Meloidogyne incognita (RKN) egg hatching after exposure to Pseudomonas spp. volatile organic compounds. Results of two independent experiments are shown.

Each treatment was represented by three LB plates containing 30µl of a Pseudomonas strain that was inverted and placed on top of a plate containing 500 M. incognita eggs in a hatch chamber. Vertical bars indicate standard error of the mean. Means with same letter are not significantly different (P < 0.05, least squares means option of the GLIMMIX procedure of SAS).

104

20000

18000

16000

14000

12000

10000

8000 Eggs/Gram of Root 6000

4000

2000

0 Wood 3 15G2 48G9 89F1 24D3 Control A Control B

Figure 3.6. Development of Meloidogyne incognita (RKN) in tomato after Pseudomonas spp. soil drench application at a 1:100 dilution (108 CFU/ml). Number of eggs/g of root was calculated 45 days after inoculation. Control A is Luria-Bertani inoculated plants.

Control B is non-treated inoculated plants. Results of three independent experiments are shown. Each treatment was represented by ten plants. Vertical bars indicate standard error of the mean. ANOVA was used to test significant differences at P <0.05

(GLIMMIX procedure of SAS).

105

20000

18000

16000

14000

12000

10000

8000

Eggs/Gram of Root 6000

4000

2000

0 Clinton Wayne 36G2 38G2 15H3 Control A Control B

Figure 3.7. Development of Meloidogyne incognita (RKN) in tomato after Pseudomonas spp. soil drench application at a 1:100 dilution (108 CFU/ml). Number of eggs/g of root was calculated 45 days after inoculation. Control A is Luria-Bertani inoculated plants.

Control B is non-treated inoculated plants. Results of four independent experiments are shown. Each treatment was represented by ten plants. Vertical bars indicate standard error of the mean. ANOVA was used to test significant differences at P <0.05

(GLIMMIX procedure of SAS).

106

20000 18000 16000 14000 12000 10000 8000 Eggs/Gram of Root 6000 4000 2000 0 34A2 24D3 Darke IC5 37D10 Control A Control B

Figure 3.8. Development of Meloidogyne incognita (RKN) in tomato after Pseudomonas spp. soil drench application at a 1:100 dilution (108 CFU/ml). Number of eggs/g of root was calculated 45 days after inoculation. Control A is Luria-Bertani inoculated plants.

Control B is non-treated inoculated plants. Results of one independent experiment is shown. Each treatment was represented by ten plants. Vertical bars indicate standard error of the mean. ANOVA was used to test significant differences at P <0.05

(GLIMMIX procedure of SAS).

107

10000

9000

8000

7000

6000

5000

4000

Eggs/Gram of Root 3000

2000

1000

0 38G2 1:100 38G2 1:50 38G2 1:10 15H3 1:100 15H3 1:50 15H3 1:10 Control

Figure 3.9. Development of Meloidogyne incognita (RKN) in tomato after Pseudomonas spp. soil drench application. Three different dilutions (108 CFU/ml) of Pseudomonas protegens 38G2 and Pseudomonas protegens 15H3 were evaluated at an initial density of

1,000 M. incognita eggs. Results of one independent experiment is shown. Number of eggs/g of root was calculated 45 days after inoculation. Control is non-treated inoculated plants. Each treatment was represented by ten plants. Vertical bars indicate standard error of the mean. ANOVA was used to test significant differences at P <0.05 (GLIMMIX procedure of SAS).

108

10000

9000

8000

7000

6000

5000 Gram of Root \ 4000 Eggs 3000

2000

1000

0 38G2 1:100 38G2 1:50 38G2 1:10 15H3 1:100 15H3 1:50 15H3 1:10 Control

Figure 3.10. Development of Meloidogyne incognita (RKN) in tomato after

Pseudomonas spp. soil drench application. Three different dilutions (108 CFU/ml) of

Pseudomonas protegens 38G2 and Pseudomonas protegens 15H3 were evaluated at an initial density of inoculation of 2,000 M. incognita eggs. Results of one independent experiment is shown. Number of eggs/g of root was calculated 45 days after inoculation.

Control is non-treated inoculated plants. Each treatment was represented by ten plants.

Vertical bars indicate standard error of the mean. ANOVA was used to test significant differences at P <0.05 (GLIMMIX procedure of SAS).

109

10000

9000

8000

7000

6000

5000

4000

Eggs/Gram of Root 3000

2000

1000

0 88A6 1:100 88A6 1:50 88A6 1:10 48C10 1:100 48C10 1:50 48C10 1:10 Control

Figure 3.11. Development of Meloidogyne incognita (RKN) in tomato after

Pseudomonas spp. soil drench application. Three different dilutions (108 CFU/ml) of

Pseudomonas rhodesiae 88A6 and Pantoea agglomerans 48C10 were evaluated at an initial density of inoculation of 2,000 M. incognita eggs. Results of one independent experiment is shown. Number of eggs/g of root was calculated 45 days after inoculation.

Control is non-treated inoculated plants. Each treatment was represented by ten plants.

Vertical bars indicate standard error of the mean. ANOVA was used to test significant differences at P <0.05 (GLIMMIX procedure of SAS).

110

10000 A A 8000 AB 6000 AB ABC AB BC 4000 C Gram of Root \ 2000

Eggs 0 88A6 1:100 88A6 1:50 88A6 1:10 48C10 1:100 48C10 1:50 48C10 1:10 Control

20000 B A 18000 A AB 16000 ABC 14000 12000 C BC 10000 C 8000

Gram of Root 6000 \ 4000

Eggs 2000 0 88A6 1:100 88A6 1:50 88A6 1:10 48C10 1:100 48C10 1:50 48C10 1:10 Control

Figure 3.12. Development of Meloidogyne incognita (RKN) in tomato after

Pseudomonas spp. soil drench application. Three different dilutions (108 CFU/ml) of

Pseudomonas rhodesiae 88A6 and Pantoea agglomerans 48C10 were evaluated at an initial density of inoculation of 4,000 M. incognita eggs. Number of eggs/g of root was calculated 45 days after inoculation. Control is non-treated inoculated plants. Graph A represents one independent experiment. Graph B represents one independent experiment.

Data was not pooled together for analysis due to inconsistency between independent experiments in mean number of eggs/g of root on plants treated with Pseudomonas strains. Each treatment was represented by ten plants. Vertical bars indicate standard error of the mean. Means with same letter are not significantly different (P < 0.05, least squares means option of the GLIMMIX procedure of SAS).

111

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Chapter 4. Efficacy of commercial biocontrol products against Meloidogyne hapla

Introduction

Root-knot nematode (RKN; Meloidogyne spp.) is the most yield-limiting plant- parasitic nematode of tomato production. Though tomato crop loss data due to RKN specifically is not available, an average estimate of losses due to nematodes for all solanaceous crops in the US is 9.4% (Noling, 2014). RKNs have a broad host range, rapid reproduction rate, and are often detected in co-infections with other plant pathogens. In

Ohio, RKN is an emerging threat in tomato high tunnel production systems. The use of high tunnel systems increases the length of the growing season and creates warmer soil conditions that results in conditions that are conducive to the buildup of RKN populations and other soilborne pathogens. Hence, successful nematode control requires the implementation of integrated pest management strategies, including biocontrol, cultural practices, resistant cultivars, and mechanical and biological soil treatments.

Root-knot nematode management on enclosed production systems mostly relies on cultural practices and using resistant cultivars or rootstocks. On previous decades, one of the primary strategies for RKN control was the use of synthetic pesticides; however, environmental concerns have restricted the extensive use of numerous products, and some have been discontinued. Moreover, the increased demand for organically grown tomatoes and the move toward more sustainable agricultural production methods has led to the 118 demand for nematode-control products that are compatible with such demands. Thus, universities and agricultural companies are devoting more time and effort toward the study and development of biocontrol products for nematodes that meet organic standards. Several biocontrol products have been developed and commercially sold for nematode control however, independent verification of these products activities for use under high tunnel conditions are not currently available.

Products commercialized for nematode control include microbial antagonists and extracts produced from plants or seaweed. Fungi and bacteria are among the most studied organisms that have shown great potential as biological control agents for root-knot nematodes. Soil-inhabiting fungi such as Paecilomyces lilacinus, Myrothecium verrucaria,

Glomus spp., and Trichoderma spp. have a wide range of activity against root-knot nematode (Talavera et al., 2001; Siddiqui and Futai, 2009; Al-Fattah et al., 2007; Dong et al., 2015). Besides beneficial fungi, rhizobacteria such as Streptomyces lydicus and

Burkholderia spp. have shown great potential as biological control agents for RKN (Meyer et al., 2000; Bélair et al., 2011). Seaweed and plant extracts have also been examined as possible nematode-control agents. For instance, Quillaja Saponaria, Ascophyllum nodosum, and Thymus vulgaris have significantly reduced RKN infestation under different conditions (Oka et al., 2000; Giannakou, 2011; Radwan et al., 2012). More research is needed to understand their efficacy under different field conditions and their effect on different RKN species. In addition, more testing is necessary on commercial products that are not currently labeled for use to control nematodes but may have antagonistic activity toward root-knot nematodes that thrive in these enclosed production systems. 119

To the best of our knowledge, no studies have been reported in which efficacy of biocontrol products for control of M. hapla in tomatoes in the Midwest was evaluated. The present study was designed to examine the nematicidal potential of commercial bioproducts: Majestene® (Burkholderia sp. A396), Actinovate AG® (S. lydicus WYEC

108), DiTera® (M. verrucaria AARC-0255), Melocon® (P. lilacinus 251), MycoApply

Endomaxx® (mixture of Glomus intraradices, G. mosseae, G.aggregatum, and G. etunicatum), Rootshield® W.P. (Trichoderma harzianum KRL-AG2), Promax® (T. vulgaris), Monterey Nematode Control® (saponins of Q. saponaria) and Bio-Activate® (A. nodosum). Our first aim was to evaluate the nematicidal activity of these biocontrol products for the management of M. hapla on tomato plants under greenhouse conditions.

Our second aim was to examine the biocontrol products’ effects on tomato yield and evaluate their antagonistic activity for the control of M. hapla under high tunnel conditions.

Material and Methods

Nematode inocula. The RKN used in this study was Meloidogyne hapla isolate

HHT19 collected from Ohio (Highland County). Isolates were maintained and increased individually on tomato (‘Moneymaker’) in the greenhouse as described in chapter three.

For egg extraction, tomato roots were washed and free of adhering soil. Roots were cut into small pieces and placed in a beaker. A 0.5% sodium hypochlorite solution was then added for 4.5 minutes, followed by egg collection using a 500-mesh sieve. Sieve was rinsed for at least five minutes to eliminate bleach residues. Eggs were collected by pouring the eggs from the sieve into a beaker with H2O. The number of eggs was then counted under a Nikon

SMZ645 dissecting microscope in three 10µl droplets. Concentration for greenhouse 120 assays was adjusted to 15,000 eggs/2ml, and concentration for high tunnel assays was adjusted to 25,000 eggs/50ml.

Commercial biocontrol products and nematicide. The active ingredient, application rate, target pathogen(s), and product manufacturer are shown in Table 4.1. The experimental treatments, were the following: (1) non-inoculated, non-treated control; (2) non-treated control; (3) Majestene®; (4) Actinovate AG®; (5) DiTera®; (6) Melocon®; (7)

MycoApply Endomaxx®; (8) Rootshield® W.P.; (9) Promax®; (10) Monterey Nematode

Control®, and (11) Bio-Activate®. The carbamate pesticide oxamyl (12) was used as a positive control. Treatments 2-12 were inoculated with M. hapla. The application rate of each treatment was calculated by converting the highest dosage (rate/acre suggested by label) to the dosage needed for the area of the pot (0. 0.018m2) or microplot (0. 0.077m2).

Greenhouse experimental procedures. Greenhouse experiments were conducted in

Selby Hall, OSU Wooster Campus. Tomato ‘Moneymaker’ was used in all experiments.

Approximately 200 seeds were sown in Promix (Premier, Red Hill, PA) contained in two plastic 15cm plastic pots (Dillen Products, Inc., Middlefield, OH). Two weeks after seed germination, seedlings were washed and transferred to 15cm plastic pots containing a sand/

TurfaceTM substrate 1:1 (vol./vol.) (Figure 4.1). Pots were arranged in a completely randomized block design on five greenhouse benches. Each treatment had eight single plant replicates. Plants were maintained at 25 to 35°C under natural and supplemental lighting

(16 continuous hours of light per day, 1,000-watt high pressure sodium lamp, DigiLamp,

Allstate Garden Supply, Inc., Ontario, CA). Plants were irrigated overhead twice a day and

121 fertilized initially by mixing 5 grams of Osmocote® (14-14-14, Bloomington Brands LLC.,

Bloomington, IN) per liter of medium (Figure 4.1).

High tunnel experimental procedures. Experiments were done in a 30m by 9.1m high tunnel structure located at the Fry Farm of the Ohio Agricultural Research and

Development Center (OARDC), Wooster, OH. Microplots within the high tunnel consisted of 25cm by 30cm long (3cm width) polyvinyl chloride (PVC) pipes filled with topsoil from

Rock Shop in Wooster, Ohio (Figure 4.1). The high tunnel floor was covered with black ground cover (DeWitt©, Sikeston, MO) to maximize weed control within the structure.

PVC pipes were installed by digging holes in the soil with a 30cm auger drill and securing the pipes vertically within the holes using loose soil. There were five rows of 21 microplots placed 0.75m apart in total and each row 1.5m apart. Seeds of tomato (‘Red Deuce’,

Seedway, Hall, NY; resistant to Verticillium wilt, Fusarium wilt (races 1 and 2), and

Tobacco mosaic virus) were planted in a 50-cell square transplant tray with Promix in a greenhouse. Plants were watered daily and fertilized once weekly with a 20-20-20 fertilizer

(Peter’s Professional; ICL Fertilizers, Ltd., Beer Sheva, Israel). After six weeks, a single

10-15cm high tomato seedling was transplanted into each microplot. The experiment was established as a randomized block design with eight blocks. Dipel DF® (Valent

BioSciences LLC, Libertyville, Il) and Cease® (Bio Works, Inc., Victor, NY) were applied every 14 days beginning on May 24 to prevent pests and fungal diseases. Milstop® (Bio

Works, Inc., Victor, NY) was applied every 14 days beginning on May 31. Six-week-old plants were pruned once by removing the lower three to five suckers. Tomato staking was done once a week for one month when plants were three months old by tying twine around

122 a 1.4m pine stake (Yoder’s Produce Inc, Fredericksburg, OH) placed just outside of the microplot. Soil test analysis (Spectrum Analytics, Fayette County, Ohio) of topsoil was used as a guide for creating a fertilization program. Fertilizer (20-5-5, Miller®, Hanover,

PA) was applied once a week through the drip irrigation system with microtubes that provided water directly to each microplot. First 2-3 weeks after transplanting, fertilization was the equivalent of 340g of nitrogen/A/day. At week 4, fertilized was increased to 566g of nitrogen/A/day. Plants were irrigated for 2-3 hours 1-3 times per week, depending on weather conditions. High tunnel sidewalls were rolled up when temperatures exceeded approximately 10°C or down when temperatures were expected to be below approximately

10°C (Figure 4.1). Average maximum temperatures for 8-30 May, June, July, and 1-21

August were 22.8, 25.3, 29.5, and 28.3°C; average minimum temperatures were 10.6, 14.1,

17.9, and 16.4°C, respectively.

Product application and Meloidogyne hapla inoculation. One week after transplanting, treatments 2-12 were inoculated with M. hapla. For greenhouse assays, M. hapla was inoculated by pipetting the 15,000eggs/2ml into one hole made to a depth of 2-

4cm below the soil surface around the base of the plants. Products were applied in a 50ml aqueous solution around the base of the plants. For the high tunnel assay, M. hapla was inoculated by drenching 25,000eggs/50ml into one hole made 2.5-5cm below the soil surface around the base of the plants. The first treatment application was made on the same day, within four hours after inoculation. The second application of treatments was conducted one week later. The third and final application was made 30 days after M. hapla inoculation. Products were applied as a 100ml aqueous drench around the base of each 123 plant.

Yield and disease assessment. In the greenhouse assays, the experiments were terminated 45 days after M. hapla inoculation. The root system of each replicate plant was uprooted, washed, and cut into small pieces, and weighed. The number of eggs per root system was determined by 100ml of a 0.5% sodium hypochlorite solution was added to the complete root system of each plant for fifteen minutes while shaking on a Lab-Line

Thermal Rocker (6 speed). Six 10µl aliquots were taken for each sample, and eggs were counted using a Nikon SMZ645 dissecting microscope. For each replicate, the total number of eggs per 100ml 5% sodium hypochlorite solution per gram of roots was calculated.

In the high tunnel trial, fruit harvest started on August 1, 2019, 12 weeks after transplanting. Ripe fruits were picked weekly and were collectively weighed by plant.

Fruits were sorted and the number of marketable and unmarketable fruits were counted individually per plant. At the end of the 4-week harvesting period, all remaining green fruits were harvested and collectively weighed by plant. The root system from each tomato plant was uprooted, washed with running tap water, and weighed. The nematode damage was assessed by rating the root galling on the 0-10 scale of Bridge and Page (1980).

Statistical Analysis. The original galling rating scale (0-10, Bridge and Page, 1980) was modified to calculate the odds ratio according to a 1-4 scale: 1 = 0-4, 2= 4-6, 3=7, and

4 = 8-10. A cumulative logit proportional-odds model was used to assess treatment effects and calculate odds ratios using the GLIMMIX procedure of SAS statistical software (SAS

Institute, Cary, NC). For all count data (number of fruits, number of eggs/g of root) a

124 generalized linear mixed model was fitted assuming a Poisson distribution. Data expressed as percent were subjected to arcsine transformation to stabilize variance. However, all results and graphs are presented in the original data scale. All data were analyzed by analysis of variance using the GLIMMIX procedure of SAS software program and means were separated with least squares estimates of marginal means.

Results

Greenhouse evaluation. Plants treated with the positive control, oxamyl had the lowest number of eggs and was significantly different from all treated plants and the non- treated inoculated control plants. Non-treated-inoculated plants had similar number of egg/g of root as plants treated with Majestene®, Melocon®, Rootshield®, and Promax®.

Plants treated with Actinovate AG® and Bio-Activate® had significantly lower number of eggs/g of root of as compared to the non-treated-inoculated control plants (Figure 4.1).

However, plants treated with Monterey® and Ditera® had higher number of eggs/g of root than the non-treated-inoculated control plants.

High tunnel evaluation. Non-treated inoculated plants and non-inoculated plants did not significantly differ in mean yield/plant, mean number of fruits/plant, and percentage of marketable fruits (Table 4.2). Plants treated with oxamyl or MycoApply Endomaxx® had numerically higher mean yield/plant (18.5 lb/plant and 18.2 lb/plant, respectively) than non-treated, inoculated control plants (17.3 lb/plant) but the differences were not significant. Oxamyl treated plants did not significantly differ in number of fruits/plant or percentage of marketable fruits as compared to the non-treated, inoculated control plants. 125

In addition, there were no significant differences in mean yield/plant, mean number of fruits/plant, and percentage of marketable fruits among any of the treated plants and non- treated inoculated control plants. The same trends were observed when including the mean yield of green fruits (collected in the last harvest) to the analysis (data not shown). There were no significant differences in odds ratios among treated plants versus non-treated- inoculated plants (Table 4.3).

Discussion

In our greenhouse assays, oxamyl treated plants showed the highest reduction

(99%) in the number of eggs/g of root when compared to the non-treated inoculated control plants. Actinovate® and Bio-Activate® significantly reduced the number of eggs/g of root by 30% when compared to the non-treated inoculated control plants (Figure 4.1). Previous researchers (Bélair et al., 2011) have shown that the combined treatment of Actinovate® and chitin (1%) highly suppressed M. hapla infection in tomatoes. Actinovate®'s nematicidal activity in our assay may be due to the production of extracellular chitinases by Streptomyces lydicus, which cause premature hatching through the breakdown of chitin on the eggshells of M. hapla (McClure and Bird, 1976; Mercer et al., 1992). Bio-Activate® is labeled as a plant growth promoter; however, products with the same active ingredient

(Ascophyllum nodosum) have also been studied for RKN suppression in tomato (Whapman et al., 1994; Wu et al., 1998; Ngala, et al., 2015). Radwan et al. (2012) reported that

Algaefol® (a.i. A. nodosum) reduced M. incognita galling by 87% and significantly increased tomato shoot length. In our studies, an explanation of antagonistic activity of

Bio-Activate® against M. hapla may be due to betaines present in extracts of A. nodosum.

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Betaines and other active components of the seaweed extracts may have been absorbed by the plants and stimulated plant resistance which resulted in reduced levels of M. hapla damage (Wu et al., 1998).

The lack of activity for most of these products could be due to a for a myriad of reasons. Product application rates, environmental conditions, and the medium in which the plant is grown can play a role in the degree of suppression of RKN. For instance, soil drench application of Trichoderma harzianum at 10 ml/kg did not significantly reduced

(21.1%) the number of M. incognita juveniles in tomato as compared to the untreated control plants. Whereas T. harzianum at 50 ml/kg significantly decreased the number of juveniles (97.8%) as compared to the untreated control plants (Radwan et al. 2012).

Presumably, in our studies, by converting the acre application rate to the area of the pot, we may have underestimated the amount that is actually required for higher antagonistic effects of the biocontrol products tested. In addition, the number of applications may have affected the outcome of the experiment. Treatments were applied three times (1st application was done within four hours after M. hapla was inoculated, 2nd application was made a week after inoculation, and last application was made one month after inoculation).

Several product label recommendations suggested application every 7-14 days or similar intervals (DiTera®, Monterey®, Promax®, Actinovate®, and Majestene®). To the contrary, other products (Rootshield®, Majestene®, Mycoapply®, and Bio-Activate®) had no stated recommendations for re-application.

As previously discussed in chapter three, pot media and moisture-holding capacity could play a significant role in biocontrol activity (Siddiqui and Ehtesmahul-Haque, 2001).

127

There are studies were maintaining the soil moisture between 60% and 90% enhanced

Agrobacterium radiobacter activity against Globodera pallida infection in potato, albeit a

30% soil moisture did not significantly reduced G. pallida infection (Heckenberg and

Sikora, 1994). Moreover, Siddiqui and Ehtesmahul-Haque (2000) reported that P. aeruginosa activity against M. javanica in tomato was increased when soil was kept at 50% or 75% soil moisture, whereas a 25% soil moisture reduced bacterial efficacy.

Sand/TurfaceTM medium has low moisture holding capacity and high drainage rate; thus, the use of this medium may have contributed to the lack of antagonism of most of the biocontrol products tested.

Biocontrol treated plants in the high tunnel did not show any significant effects of

RKN galling severity as compared to the non-treated-inoculated plants; thus, no conclusion can be made on the efficacy of these treatments in RKN control. Interestingly, these findings differ from the greenhouse assays where the oxamyl treated plants resulted in significantly lower numbers of eggs than the other treated plants and the non-treated- inoculated control plants. Furthermore, treated and control plants did not significantly differ in the mean yield/plant, mean number of fruits/plant, and mean percentage of marketable fruits. The lack of nematode suppression by the biocontrol products and the standard oxamyl could be related to the use of topsoil in the microplot. Microbe population densities in the rhizosphere can affect the efficacy of disease suppression and plant growth promotion by biocontrol agents (Bull et al., 1991; Siddiqui and Ehtesmahul-Haque, 2000).

Many competing microbes may have been present in the topsoil; thus, decreasing the effect

128 of the treatments on yield and M. hapla suppression. Microbial agents in commercial products may not have properly colonized the rhizosphere of plants.

Moreover, the use of topsoil provides a diverse array of organic material that is a nutrient source for many microbes (Stirling, 1991). Consequently, the organic components of this medium may have reduced the activity of biocontrol agents. Another possible explanation is that the rhizosphere community may have also promoted plant growth and health (Berg, 2009), thereby increasing the areas for the nematodes to infect and diluting the overall concentration of the biocontrol products tested relative to the volume of the plant. Previous tomato microplot studies evaluating nematode infestation fumigated the microplot soil before conducting experiments (Barker et al., 1976; Di Vito et al., 1991;

Walters and Barker, 1994). One possible way to improve these studies is by autoclaving or fumigating the soil to reduce the effect of possible microbial competitors and plant growth- promoting microbes.

A possible explanation of the lack of efficacy of oxamyl treated plants under high tunnel conditions may be due to higher densities of initial inoculum in the high tunnel experiments (Pi of 25,000 eggs) as compared to greenhouse experiments (Pi of 15,000 eggs). Stephan and Trudgill (1983) reported that oxamyl applications at 3,000 and 4,000 ppm provided protection against M. hapla in tomato for up to 36 days. However, when oxamyl was applied at 1,000 and 2,000 ppm, it only provided partial protection. Therefore, in our studies, the dosage or frequency of oxamyl applications may have also affected the nematicidal activity against M. hapla under high tunnel conditions. Another common factor encountered in tomato microplot studies is the use of sandy loam soil or the existing soil

129 in the field (Walters and Barker, 1994; Koralis-Burelle and Rosskopf, 2011). A study by

Ownley et al. (2003) reported that high organic matter significantly reduced biocontrol activity of Pseudomonas fluorescens against take-all disease (Gaeumannomyces graminis var. tritici) in spring wheat. Thus, topsoil characteristics such as texture and organic matter may have negatively affected biocontrol and oxamyl activity. The use of sand/TurfaceTM medium in the greenhouse assays may have allowed faster absorption of oxamyl whereas the use of topsoil in the high tunnel assay may have reduced the movement and absorption through the roots. Further research is necessary to determine if media, application rate, and application frequency play a role in the efficacy of oxamyl and the biocontrol products tested.

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Chapter 4. Table and Figures

Table 4.1 List of commercial products used in high tunnel and greenhouse assays. Application Target Treatment Active Ingredient Company Ratex Pathogen Glomus intraradices, G. 0.0001gy MycoApply Valent BioSciences mosseae, G. 0.0002gz N/A Endomaxx® Corp., Libertyville, IL aggregatum, and G. etunicatum soilborne fungal 0.0340ml pathogens and Bio Huma Netics, Inc, Promax® Thymus vulgaris L 0.2086ml plant-parasitic Gilbert, AZ nematodes Trichoderma 0.0041g soilborne fungal BioWorks, Inc., Rootshield® harzianum Rifai 0.0173g pathogens Victor, NY strain KRL-AG2 Myrothecium 0.6350g plant-parasitic Valent BioSciences DiTera® verrucaria strain 2.7215g nematodes Corp., Libertyville, IL AARC-0255 Marrone Bio Burkholderia spp. 0.0341ml plant-parasitic Majestene® Innovations, Davis, strain A396 0.1447ml nematodes CA Bio- Ascophyllum 0.0032ml J.H. Biotech, Inc., N/A Activate® nodosum 0.0136ml Ventura, CA Streptomyces foliar and Actinovate 0.0015g Monsanto BioAg, St. lydicus strain soilborne fungal AG® 0.0065g Louis, MO WYEC 108 pathogens Monterey Lawn and Saponins of 0.0473ml plant-parasitic Monterey® Garden Products, Quillaja saponaria 0.1972ml nematodes Fresno, CA Paecilomyces 0.0082g plant-parasitic Certis USA, Melocon® lilacinus strain 251 0.0347g nematodes Columbia, MD 0.0102ml plant-parasitic DuPontÔ, Vydate® Oxamyl 0.0436ml nematodes Wilmington, DE x Highest rate suggested by label was used to calculate the amount applied per pot. y Rate applied per 15cm pot with 50ml of H2O in greenhouse assays. z Rate applied per microplot (25cm by 30cm PVC pipe) with 100ml of H2O in high tunnel assay.

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Table 4.2. Effects of commercial biocontrol products on yield parameters in soil infected with M. hapla in high tunnel tomato microplots.

Yield Number of % Marketable Treatmentx (kg/plant) fruits/plant fruitsy ® MycoApply Endomaxx 8.4 ± 0.14 33.0 ± 0.89 77.8 ± 0.88 Promax® 7.8 ± 0.17 33.0 ± 0.78 79.6 ± 0.81 ® Rootshield 8.1 ± 0.15 33.7 ± 0.61 79.7 ± 0.90 DiTera® 7.7 ± 0.14 33.1 ± 0.45 78.0 ± 1.13 Majestene® 6.9 ± 0.25 28.4 ± 1.01 81.1 ± 1.11 Bio-Activate® 8.2 ± 0.20 35.3 ± 0.63 79.8 ± 1.38

Actinovate AG® 6.3 ± 0.14 28.9 ± 0.87 78.1 ± 1.45 ® Monterey 7.7 ± 0.21 33.2 ± 0.91 80.8 ± 0.54 Melocon® 8.0 ± 0.25 35.7 ± 0.67 85.0 ± 0.73 Inoculated Control 7.8 ± 0.23 35.9 ± 0.48 69.6 ± 2.28 Non-inoculated Control 7.4 ± 0.17 32.4 ± 0.65 75.7 ± 1.79 Oxamyl 8.4 ± 0.14 36.1 ± 0.66 80.7 ± 0.47 P-valuez NS NS NS xResults of one independent experiment are shown; values are means of data from eight replicate plants + standard error of the mean. ANOVA was used to test significant differences. yMeans of non-transformed data are presented for clarity. zNS=non-significant differences.

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Table 4.3. Nematicidal activity of commercial biocontrol products against M. hapla in high tunnel tomato microplots.

x y Treatment Gall index P-value MycoApply Endomaxx® 2.9 ±0.35 0.5313 Promax® 3.1 ±0.35 0.9964 Rootshield® 3.5 ±0.53 0.7312

DiTera® 3.3 ±0.46 0.5313 ® 0.8708 Majestene 3.4 ±0.51 Bio-Activate® 3.0 ±0.0 0.5313 ® Actinovate AG 3.5 ±0.53 0.8708 Monterey® 3.4 ±0.51 0.5313 Melocon® 3.5 ±0.53 0.3609 Oxamyl 3.5 ±0.53 0.3103

Inoculated Control 3.5 ±0.53 - xResults of one independent experiment are shown; values are means of data from eight replicate plants + standard deviation. yThe galling index scale developed by Bridge and Page (year) was used to rate Meloidogyne hapla damage. Galling index was modified according to a 1-4 galling scale: 1 = 0-4, 2= 4-6, 3=7, and 4 = 8-10. zProportional odds ratio were used for the comparison of root knot-nematode gall ratings for non-treated inoculated plants compared to plants treated with commercial products. Only P-values of calculated odds ratio are shown due to no significant differences in plants treated with commercial products versus non-treated inoculated control plants.

133

A C ® Control Oxamyl Actinovate

Figure 4.1. Biocontrol assays for RKN management. (A) A high tunnel microplot system was used to evaluate the efficacy of commercial biocontrol products against Meloidogyne hapla. Overview of study with tomato plants. (B) A tomato microplot. (C) One block of a greenhouse study to evaluate the nematicidal activity of commercial biocontrol products against M. hapla. (D) Development of M. hapla in tomatoes from greenhouse (Left) negative control (non-treated), (middle) positive control (oxamyl), and (right) Actinovate

AG®.

134

A ABC AB

DC BDC D DC ED E E Eggs/Gram of Root

DiTera® Oxamyl Melocon® Promax® Majestene® Rootshield® Monterey® H2O control MycoApply® Bio-Activate® Actinovate AG®

Figure 4.2. Development of Meloidogyne incognita (RKN) in tomato after commercial biocontrol soil drench application in a greenhouse assay. The number of eggs per gram of root was determined 45 days after inoculation. The negative control was non-treated, inoculated plants (H2O control) and the positive control was plants treated with oxamyl.

Data were pooled from two independent experiments. Vertical bars indicate standard error of the mean. Means with same the letter(s) are not significantly different at P<0.05

(Least squares means option of the GLIMMIX procedure of SAS).

135

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