FACULTY OF SCIENCE UNIVERSITY OF COPENHAGEN

Early detection and quantification

of Ramularia beticola in sugar beets

using spore traps and QPCR and

the effect of early fungicide

treatment on Ramularia leaf spot

MSc Thesis Thies Marten Wieczorek

GCH350 December 2012

Supervisor (Sciences): Lisa Munk Supervisor (AU): Lise Nistrup Jørgensen Supervisor (AU): Annemarie Fejer Justesen Supervisor (extern): Anne Lisbet Hansen NBR Nordic Beet Research

Abstract

Ramularia leaf spot (RLS) is one of the most important foliar diseases in sugar beets in the temperate climates. It is caused by the ascomycete Ramularia beticola and thrives best at relatively high humidity (> 95 %) and temperatures between 17 and 20 °C. If not controlled, yield losses can amount to 15 ± 20 %. The disease is typically controlled using fungicides like epoxiconazole or epoxiconazole + pyraclostrobin. Preliminary field trials have indicated a more efficient control of fungal diseases and higher sugar yield if sugar beets are treated with fungicides before visual symptoms occur. For a better determination of the optimized timing of disease control, more advanced methods are needed. This MSc thesis was divided into two parts: 1) a molecular part with the goal to develop a QPCR-based method to detect and quantify R. beticola DNA recovered from plastic tape used for the purpose of spore trapping, and 2) an agronomical part evaluating the effect of early fungicide application on RLS as well as to investigate the fungus presence in the air above sugar beet fields in Denmark using Burkard-Hirst traps. Primers for the use in QPCR were designed based on the transcribed space region 2 (ITS 2) and tested on a number of R. beticola isolates and on isolates from relative fungi (M. graminicola, R. collo-cygni and C. beticola). Primer pair Rb2f/Rb4r was found to be highly specific. DNA form tape used in the spore traps was efficiently extracted using a commercially available kit. Using QPCR it was possible to detect low amounts of R. beticola DNA. Furthermore, it was demonstrated that the developed primers can be used for the detection of fungal biomass in living tissues. Two field experiments were conducted in order to elucidate the effect of early fungicide treatments. Results showed a tendency that treatments which took place 4 to 5 weeks prior to first visible symptoms had a positive effect on yield parameters and slowed down the development of the disease in the beginning of the leaf disease season. At the end of the season, however, all fungicides treatments performed equally well. Three Burkard spore traps were operated on the Danish island of Lolland from which air samples were collected on a daily basis throughout weeks 28 - 37. Based on the molecular method developed in 1) first spores in the air samples were detected 14 and 16 days prior to first visible symptoms on untreated plants, which is in accordance with the reported latency of 2 weeks of the fungus. The spore concentration in the air varied strongly. Three massive spore releases were observed, which were influenced by low mean temperatures and RH around 95 % one week before. It is expected that the results can lead to an IPM based monitoring/warning system where spraying only takes place when a real risk has been verified based on spore trapping or weather data.

I

Zusammenfassung Die Ramularia Blattfleckenkrankheit ist eine der wichtigsten Zuckerrübenkrankheiten in den gemäßigten Breiten. Sie wird hervorgerufen durch den Erreger Ramularia beticola, der am besten bei hoher Luftfeuchtigkeit (> 95 %) und Temperaturen zwischen 17 und 20 °C gedeiht. Zur Bekämpfung der Krankheit steht eine Reihe von Fungiziden zur Verfügung. Erfolgt keine Behandlung der Krankheit, können Ertragseinbußen von bis zum 15 ± 20 % erreichen werden. Einleitende Feldversuche haben gezeigt, dass frühzeitige Fungizidbehandlungen, d.h. vor dem Erscheinen erster sichtbarer Symptome, zu einer besseren Kontrolle und höherem Zuckerertrag führten. Um den idealen Behandlungszeitpunkt besser bestimmen zu können, bedarf es neuer Ansätze zur frühzeitigen Erkennung von Zuckerrübenkrankheiten. Diese Masterarbeit ist in zwei Teile gegliedert. Der erste Teil behandelt die Entwicklung einer auf QPCR basierender Methode, mit deren Hilfe R. beticola DNS detektiert und quantifiziert werden kann. Zusammen mit Burkard-Hirst Sporenfallen wird sich dieser Methode bedient, um die Fluktuation von R. beticola Sporen in der Luft zu folgen. Im zweiten Teil wird der Effekt von frühzeitigen Fungizidbehandlungen auf die Entwicklung der Ramularia Blattfleckenkrankheit und den Zuckerertrag getestet.

Die Primer (Rb2f/Rb4r) zur Anwendung von QPCR ZXUGHQ DXI %DVLV GHU ³WUDQVFULEHG VSDFH region 2´ ,76 HQWZLFNHOW'LHVHZLHVHQVLFKGXUFKHLQHKRKH6SH]LILWlWund Sensibilität aus. Es traten keine Kreuzreaktionen mit M. graminicola, R. collo-cygni und C. beticola auf. DNS wurde erfolgreich mittels eines kommerziellen erwerbbaren Kits extrahiert. Mit Hilfe der QPCR war es möglich niedrige Mengen von R. beticola DNS nachzuweisen. Zwei Feldversuche mit frühen Behandlungszeitpunkten wurden durchgeführt. Die Parzellen, die 4 bis 5 Wochen vor dem ersten Auftreten von Symptomen behandelt wurde, wiesen tendenziell die besten Ergebnisse hinsichtlich Erträge und Krankheitskontrolle auf. Drei Burkard-Sporenfallen wurden im Zeitraum vom KW 28-37 zur Sammlung von Luftproben eingesetzt. Mit Hilfe der Methode aus dem ersten Teil dieser Arbeit war es möglich R. beticola Sporen schon 14 bzw.16 Tage vor dem Erscheinen der ersten Symptome nachzuweisen. Die Sporenkonzentration variierte stark und wurde durch hohe mittlere Luftfeuchtigkeit und niedrigen mittlere Temperaturen eine Woche vor Masssporenabgabe beeinflusst. Mit Hilfe der QPCR war es über die Detektion von Sporen hinaus möglich die Entwicklung des Erregers in Zuckerrübenblätter zu verfolgen. Die Ergebnisse dieser Arbeiten könnten mit in die Entwicklung eines IPM - Warnsystems eingehen, mit dessen Hilfe Fungizidbehandlungen nur dann stattfinden, wenn ein erhöhtes Risiko für die Entwicklung von Blattkrankheiten nachgewiesen wurde.

II

Sammendrag

Ramularia (pletskimmel) er en alvorlig sygdom i sukkerroer i lande med tempereret klima. Årsagen til sygdommen er svampen Ramularia beticola. Ramularia kræver en høj luftfugtighed (> 95 %) og temperaturer mellem 17 og 20 °C. Forsøg har vist at svampebekæmpelse kan medføre udbytte stigninger op til 10 ± 20 %. I de nuværende anbefalinger angående fungicidbehandling (epoxiconazole eller epoxiconazole + pyraclostrobin) tilrådes at udføre første sprøjtning ved begyndende angreb. Imidlertid har indlendende markforsøg vist at der er potentiale for forøget sukkerudbytte og mere effektiv bekæmpelse af bladsvampe ved tidlig behandling, foretaget inden synlige symptomer forekommer. På nuværende tidspunkt findes ingen effektive metoder til at monitere, hvornår svampesporer befinder sig i marken, som et grundlag for fremtidigt at udvikle en risikovurdering for angreb og en ny bekæmpelsestærskel for hvornår der i givet fald kan behandles.

Formålet med specialet var 1) at udvikle en moniteringsmetode til detektion af forekomst af pletskimmelsporer baseret på den molekylær metode QPCR anvendt i forbindelse med Hirst- Burkard sporefangere til at detektere og kvantificere svampe DNA samt at følge forekomst af R. beticola sporer i luft ved hjælp af sporefangere og 2) at undersøge effekten af tidlig behandling med fungicider på udvikling af Ramularia og sukkerudbytte, samt at sammenholde resultaterne herfra med forekomst af sporer i luften detekteret i 1).

Til QPCR var grundlaget for primer designet svampens ribosomale DNA region "transcribed space region 2´ ,76 3ULPHUSDUUHW5EI5EUYLVWHVLJDWY UHPHJHWVSHFLILNWGDGHWEOHY testet med andre svampe (M. graminicola, R. collo-cygni and C. beticola). Det lykkedes at ekstrahere svampe DNA fra tape ved brug af et kommercielt kit og at detektere meget små DNA mængder af R. beticola DNA. I to markforsøg blev effekten af fungicidbehandlinger ved forskellige tidspunkter undersøgt. Selvom resultaterne ikke var signifikant forskellige, viste de en tendens til at behandlingerne påbegyndt 4 til 5 uger inden synlige begyndende symptomer af Ramularia havde den højeste udbyttestigning og en bedre bekæmpelseseffekt i starten af svampesæsonen. Det var tre Burkard sporefangere der blev sat i gang for at indsamle luftprøver på døgnbasis i uge 28 - 37. Ved hjælp af den tidligere udviklede QPCR metode var det muligt at identificere R. beticola sporer i luften allerede 14 til 16 dage inden synlige symptomer af bladsvampen forekom. I hele perioden varierede sporekoncentration meget. I alt blev det konstateret tre tidspunkter hvor massive frigivelser af sporer fandt sted. Sporefrigivelsen blev sammenholdt med samtidigt registreret relativ luftfugtighed og temperatur. Udover detektion af Ramularia-sporer fra sporefælderne viste metode sig også anvendelig til detektion på selve bederoebladene.

Det forventes at resultaterne kan bidrage til udvikling af et ny moniterings- / varslingssystem for i første omgang bekæmpelse af Ramularia og med deraf forbedret IPM model til bekæmpelse af sygdommen. Metoden forventes at kunne udvikles også for andre bladsvampe i bederoe

III

Résumé

La ramulariose est une maladie, provoquée par le champignon ascomycète Ramularia beticola affectant le feuillage de la betterave sucrière. Le développement de la ramulariose se produit à des températures relativement basses, comprises entre 17 et 20 °C ainsi qu´une humidité relative dépassant 95 %. Une perte de rendement remarquable, entre 15 et 20%, est observée en absence de traitements fongicides. De précédent essais ont démontré une augmentation du rendement sucrier moyen de par hectare ainsi qu´une meilleure gestion des maladies foliaires, lors d´une application préventive de fongicides précédent les premiers symptômes, cependant l´optimisation des applications fongicides au cours du cycle de croissance de la ramulariose nécessite encore des travaux de recherche plus approfondis.

Au cours de cette étude deux parties de recherche sont à distinguer. La première partie de cette étude a été consacrée au développement d´une méthode moléculaire axée sur l´utilisation de la QPCR pour la détection et quantificDWLRQGHO¶$'1GHR. beticola, extrait de UXEDQDGKpVLI/DGHX[LqPHSDUWLHV¶HVWD[pHVXUO¶pWXGHGHO¶HIIHWG¶XQHSXOYpULVDWLRQSUpFRFH GH IRQJLFLGH VXU O¶pYROXWLRQ GHV PDODGLHV HW OH UHQGHPHQW VXFULHU ¬ FHWWH ILQ GHV HVVDLV HQ champ ont été menés. En outre trois pièges des spores du type Burkard-Hirst ont été installés DILQGHFRQILUPHUODSUpVHQFHGHO¶DJHQWSDWKRJqQHSURYRTXDQWOD5DPXOLRVHHWGHVXLYUHVHV IOXFWXDWLRQVGDQVO¶DLU

/D GpWHFWLRQ G¶$'1 SURYHQDQW GH R. beticola, s´est effectuée avec le recours d´amorces en combinasion avec QPCR générées à l´intérieur des régions génétique « internal transcribed spacer ». Les amorces Rb2f/Rb4r se sont avérées très efficaces et spécifiques dans la détection G¶$'1 GH R. beticola, lequel ayant été extrait avec succès en utilisant un kit commercial. La technique moléculaire QPCR permet une détection fine et précise de très petites quantités G¶$'1/HVHVVDLVHQFKDPSRQWUpYpOpXQHDXJPHQWDWLRQGXUHQGHPHQWHWXQHUpGXFWLRQGHOD vitesse de développement de la maladie au cours la saison, lors d´une application de IRQJLFLGHVHQDPRQWGHO¶DSSDULWLRQGHVV\PSW{PHVGDQVFHFDVjVHPDLQHVDXSDUDYDQW Toutefois, à la fin de la saison de végétation aucune différence significative n´a été trouvée pour le rendement ni pour les niveaux de la maladie. Le piégeage de spores associé à la technique QPCR, ont permis la détection des spores de R. beticola 14 ± 16 jours avant O¶DSSDULWLRQ GHV SUHPLHUV V\PSW{PHV VXU SODQWHV QRQ-traitées à proximité des prédisposées ruban adhésifs, correspondant au temps de latence décrit et caractérisé pour ce champignon. La concentration de spores en air variait fortement. A la suite de trois épisodes climatique, lors desquels de basses températures combinées avec des humidités relatives d´environ 95% ont été observés, des concentrations de spores importantes ont été enregistrées. Les résultats obtenus au cours de cette étude aideront à développer des modèles IPM avec une pulvérisation de fongicides plus raisonnée basée sur une détection QPCR de présence ou d´absence de l´agent pathogène associé á des conditions climatiques favorisant sa croissance.

IV

Preface

This 45 ECTS-points MSc project was part of a 120 ECTS-SRLQWVPDVWHUSURJUDPµ$JULFXOWXUH¶ offered by the University of Copenhagen (Sciences). It was carried out as collaboration between the Department of Plant Biology (Sciences), the Department of Integrated Pest Management, Faculty of Agricultural Sciences (AU), Aarhus University, and NBR Nordic Beet Research, Holeby.

I would like to thank all my supervisors: associate professor Lisa Munk (Sciences), senior scientist Lise Nistrup Jørgensen (AU), senior scientist Annemarie Fejer Justesen (AU), and project manager Anne Lisbet Hansen (extern supervisor) for a great collaboration, inspiring conversations and support over the course of this thesis.

Special gratitude goes to Jens Nyholm Thomsen and NBR Nordic Beet Research for providing resources for the conduct of field trials and spore trapping. I also wish to thank the rest of NBR 1RUGLF%HHW5HVHDUFK¶VVWDIIIRUWKHLUKHOSFDUU\LQJRXWILHOGWULDOV

Further thanks go to the staff at Flakkebjerg research center especially to Jette Them Lilholt for the introduction to the laboratory facilities at Flakkebjerg Research Centre and kind help working with Ramularia beticola.

And at last thanks to my brother Till Wieczorek and Julian Heick for helping with corrections and layout.

Copenhagen Ø, November 27th, 2012

Thies Marten Wieczorek

GCH350

V

Contents

Part I

1 Introduction 1

1.1 Background 1 1.2 The Objectives 3 1.3 Method 4 1.4 Limitation 4

2 Ramularia leaf spot and the causal pathogen 5

2.1 The pathogen 5 2.1.1 Taxonomy 5 2.1.2 Morphology 5 2.2 Symptoms 6 2.3 Epidemiology and disease cycle 7 2.4 Non chemical control of RLS 10

3 Fungicide treatment in sugar beets 12

3.1 Fungicides 12 3.1.1 Triazoles 12 3.1.2 Strobilurins 12 3.2 Timing and purpose for fungicide application in sugar beets 13

4 Leaf disease monitoring in sugar beets 15

4.1 Disease monitoring 15 4.2 Leaf disease monitoring in Denmark 16

5 Spore trapping 18 6 QPCR 21

VI

Part II

7 Article 1 - A QPCR assay for the detection and quantification of sugar beet pathogen Ramularia beticola in leaf and air samples 24

7.1 Abstract 24

7.2 Introduction 25 7.3 Materials and Methods 26 7.3.1 Fungal isolates for primer design 26 7.3.2 ITS amplification and sequencing 26 7.3.3 Primer Design and primer specificity 27 7.3.4 QPCR 27 7.3.5 Amplification efficiency and spike test 27 7.3.6 DNA extraction from spore trap tape 28 7.3.7 DNA extraction from plant material 28 7.4 Results 29 7.4.1 Primer design and testing 29 7.4.2 Standard curve and spike test 30 7.4.3 Field trial 32 7.4.4 Leaf material 32 7.5 Discussion 33

8 Article 2 ± Does the early bird really catch the worm? ± Early detection of Ramularia beticola using QPCR and the effect of early control on in sugar beet 36

8.1 Abstract 36 8.2 Introduction 37 8.3 Materials and Methods 38 8.3.1 Field trials 38 8.3.2 Spore trapping 39 8.3.3 DNA extraction and QPCR 40 8.3.4 Statistical analysis 41 8.4 Results 41 8.4.1 Field trials 41 8.4.1.1 Disease severity 41 8.4.1.2 Effect of different spraying regimes of RLS 42 8.4.1.3 Yield 43 8.4.2 Spore trapping 45 8.4.3 Weather data 45 8.5 Discussion 48

VII

9 Additional experiments 52

9.1 Verification of Ramularia beticola isolates by sequence analysis of ITS regions 52 9.2 Validation of a primer optimization matrix to improve the QPCR performance 54 9.3 Primer design 55 9.4 Inoculation of plastic tape 56 9.5 Testing of an alternative DNA extraction protocol 58

10 Conclusion 61

11 Perspectives 63

12 References 65

Appendix 1: Spore trapping results 73

Appendix 2: Fungal isolates 77

Appendix 3: Disease assessment guide for RLS, rust and powdery mildew 77

Appendix 4: Field information incl. random plans 78

Appendix 5: Panoramic images 78

VIII

1 Introduction

1.1 Background

Sugar beet (Beta vulgaris) is a high value crop and predominately grown in the temperate regions for sugar production. In 2010 sugar beets were cultivated on a total area of 4,698,252 ha worldwide, producing 228,454 Mt of sugar. The main producers are France, the United States of America, Germany, the Russian Federation, and Ukraine. Even though the crop is grown worldwide, 70 % of the production takes place in Europe (FAOSTAT 2010).

Denmark has a great tradition for cropping sugar beets. Even thought the total area of sugar beets has declined from 48,500 to 39,200 ha after the Sugar Market Reform in 2006, sugar beet is still considered in some regions to be a valuable and important field crop in Danish crop rotations (Sukkerroedyrkere 2012). Sugar beet growing is concentrated on the southern islands i.e. Lolland, Falster, Møn, and the southern parts of Zealand. In 2011 3,333 Mt sugar were produced on 39,000 ha overall. Over the last two decades a continuous yield increase has been achieved. However the full potential is not exploited yet. In 2011 Nordic Sugar has ODXQFKHGDSURMHFWZLWKDQDPELWLRXVJRDOFDOOHG³the 20-20-20 initiative´RIVXJDUEHHW growers shall reach a sugar yield of 20 tons ha-1 by the year 2020 (Nordzucker 2011). Many factors play an important part in the accomplishment of this goal, such as improved panel of plant material, optimization of sowing and harvesting techniques, or the minimization of losses during harvest and storage. Another important aspect is the efficient control of leaf diseases during the growing season whicK GHVWUR\V WKH SODQW¶V leaf canopy, thereby reduces photosynthesis and consequently the sugar accumulation in the root. Ramularia leaf spot disease (RLS) caused by the fungus Ramularia beticola (R. beticola) is one of the major diseases in sugar beets in the temperate regions along with rust (Uromyces betae), powdery mildew (Erysiphe betae), and Cercospora leaf spot (Cercospora beticola)(Fautrey & Lambotte 1987). The fungus thrives best under cool mean temperature and at a relative humidity of approximately 95 % (Ruppel 1986). If untreated the disease causes premature defoliation that may end in destruction of the whole canopy. Consequently, root weight, sugar yield, and juice quality are highly negatively affected. Although other diseases may also be important this thesis will focus on the consequences from attack of R.beticola. Already in the 1970s Byford (1975) attached importance to RLS and late research found that yield losses may amount up to 25 % (Ahrens 1987; Byford 1975; Byford 1976). Similar results were documented in trials under Danish conditions ranging from 15 ± 20 % (Jørgensen 2003; Hansen 2008). After two major outbreaks in 1988 and in 1990 the occurrence of Ramularia leaf spot was low in Denmark (S. Nielsen 1991). More recent numbers from the Danish national leaf disease monitoring have shown a constant presence of

1 the disease in Denmark over the last decade (Wieczorek et al. 2011; Hansen & Torp-Thomsen 2007). This indicates that RLS has made its way to Denmark to stay and henceforth is to be considered a disease problem that farmers have to manage. RLS is also considered to be the predominating fungal disease in Finland and Sweden (Eronen 2008; Persson & Olsson 2008).

Today it is common practice to control fungal leaf diseases by applying fungicides. In the context of the principles of the Integrated Pest Management (IPM) effective damage-thresholds for fungal leaf diseases have been developed to determine the necessity of spraying (P. F. J. Wolf et al. 2001; P. F. J. Wolf & J. A. Verreet 2002). The effective damage-thresholds are made according to the date of onset of the diseases and the severity of the attack (in %). Depending on the date, attacks below 5 to 15 % are tolerated and do not release treatments with fungicides. As soon as the attacks are above the threshold, it is recommended to apply fungicides. However, recent studies suggest that already an early treatment may help to minimize the infection rate and thereby the actual infection and to augment yield (Hansen 2012b). In order to prevent infection, timing is crucial. For a preventative fungicide treatment, the agents should preferably be applied before the first symptoms can be seen. Yet one should be certain that the pathogen is present, in order not to waste money and not to harm the environment by applying fungicides unnecessarily.

A mean to keep track of pathogens and the infection pressure is to operate spore/pollen traps for the recovery of fungal spores. Several devices have been developed to serve this purpose, among others, the Hirst-Burkard spore trap (Hirst 1952; Giesecke et al. 2010). Previous studies have proven the applicability of spore traps for the detection of fungal spores in the air above sugar beet fields (Hestbjerg & Dissing 1995; J. Khan et al. 2004; J. Khan et al. 2009).

Air samples obtained from spore traps are commonly analyzed by spore counting. The emergence of new QPCR based tools allows a more advanced approach by detecting and quantifying DNA. QPCR in combination with spore traps might help to monitor pathogens and to optimize disease control (H. A. McCartney et al. 2003). To date, QPCR based methods have been developed for several plant pathogens. This include a very recent method for another important sugar beet pathogen Cercospora beticola (S. Lucas et al. 2012). However, no method has yet been published for a molecular detection and quantification of the R. beticola DNA.

2

1.2 The Objectives

The overall objectives of this MSc project are to elaborate a molecular technique for the detection of Ramularia beticola DNA, to test the performance of early fungicide application and its effect on sugar yield under Danish conditions, and to gain knowledge on under which climate conditions the fungus thrives best. In order to achieve this, the following questions were addressed:

x Is it possible to design primers suitable for QPCR for detection R. beticola DNA? This is done based on the ribosomal internal spacer (ITS) region of the fungus and by testing the primers specificity on different R. beticola isolates and isolates from closely related fungi.

x It is possible to extracted DNA from tape being used for the purpose of spore trapping? This will be answered by comparing two different DNA extraction methods on tape inoculated with fungal spores.

x Is it possible to combine spore trapping and QPCR in order to detect the first spores in the air above sugar beet field? This will be analyzed by operating three spore traps in sugar beet fields and employing the techniques developed beforehand.

x How effective is a control of Ramularia leaf spot applied before the appearance of visible symptoms? This will be determined by conducting two field trials where fungicides are applied at different timing.

x In how far is the presence of R. beticola spores influenced by temperatures and relative humidity? Data loggers will be placed in proximity to spore traps and it is tried to link the quantity of spores to weather data.

3

1.3 Method

This project involves laboratory and field work. Initially, the molecular techniques will be developed and tested under controlled conditions. As a first step molecular primers will be designed, tested and optimized. Then, different protocols for the extraction of DNA from Milenex tape will be tested. One of the protocols will be chosen to proceed with. The experimental work will take place at the Department of Integrated Pest Management, Faculty of Agricultural Sciences, Aarhus University (AU). As a second step two field trials will be conducted on the island of Lolland. At both sites Burkard spore traps are installed for air sampling. In addition, a third spore trap will be operated in a trial, which will be artificially inoculated with R. beticola. Also leaf samples will be retrieved from there. The field trials are kindly conducted by NBR Nordic Beet Research. DNA extraction, QPCR and data analyses will be performed continuously. The results will be presented in the form of two independent articles which build upon one another. While the first article will present the molecular part, the second article will focus on the agronomical point of view of this thesis. The entire project will be carried out in the period from April to December 2012.

1.4 Limitation

7KHWHUP³OHDIGLVHDVHV´LQVXJDUEHHWDVPHQWLRQHGDERYHUHIHUVWRDJURXSRIIRXUPDjor diseases whose occurrence and severity do not necessarily influence one another and may FRH[LVWHGRQWKHSODQW+HQFHWKHRYHUDOOFRQWULEXWLRQRI\LHOGORVVHVLQVXJDUEHHWGXHWR³OHDI GLVHDVHV´ FDQQRW EH GLIIHUHQWLDWHG 7R REWDLQ D SURIRXQG DQG FRPSOHWH XQGHUVWDQGLQJ RI WKH HIIHFWRI³OHDIGLVHDVHV´RQVugar beet, all four fungi need to be investigated. The inclusion of Cercospora beticola to the early detection of fungal diseases will be briefly mentioned. Relatively few studies have been conducted on powdery mildew and rust in sugar beet in terms of molecular analysis and will therefore be neglected in the presented study. Furthermore, the appearance and the severity of R. beticola in the field will be strongly influenced by the weather conditions in 2012. Results will therefore only illustrate one case rather than revealing the complete plant-environment-pathogen system.

4

2 Ramularia leaf spot and the causal pathogen

2.1 The pathogen

2.1.1 Taxonomy

The first documentary of Ramularia beticola as a disease dated back to the 1890s where it was described in France (Fautrey & Lambotte 1987). Around the same time the first occurrence of the disease was observed in Denmark and described as R. betae on sugar beet and on fodder beet (Beta vulgaris subsp. crassa) by Rostrup (Rostrup 1898). Even though the sexual reproductive stage (teleomorphs) has not been found until now, molecular characterization has proven that Ramularia beticola is the asexual reproductive stage (anamorphs) of Mycosphaerella (Crous et al. 2000; Crous et al. 2001). A list of the classification of Ramularia beticola is given in Table 2.1.

Table 2.1: Classification of Ramularia within the kingdom of fungi. Anamorph taxonomy based on morphological charactization and teleomorph taxonomy based on molecular characteristics (adapted from Thach et al. (Thach 2010)).

Anamorph Telemorph Division Deuteromycetes Ascomycota Sub-division Pezizomycotina Class Hyphomycetes Dothideomycetes Order Hyphomycetales Capnodiales Family Mucedinaceae Mycosphaerellaceae Genus Ramularia Mycosphaerella Species Ramularia beticola

2.1.2 Morphology

Conidia are produced by fascicled conidiophores. They are released as single or in branched chains. In literature conidia size varies from (6-)8-28(-30) x (1.5-)2-5(-6) µm (U. Braun 1998). They are cylindrical, ellipsoid-ovoid with rounded to pointy ends. Typically spores consisted of two cells, solemnly three to four ± celled spores are seen. Their appearance is hyaline and the surface can be smooth or rugged (Figure 2.1)(Hestbjerg & Dissing 1995). R. beticola can be cultured in vitro but grows slowly. The mycelium is hyaline-pink and grows equally centric from the from the whole structure (Thach 2010; Fautrey & Lambotte 1987). Mycelia form loose to dense stromata which are 10 ± 30 µm in diameter.

5

Figure 2.1: Conidia of Ramularia beticola observed with a scanning election micrograph (Scale bar = 5 µm) (left) and as schematic drawing (right) (U. Braun 1998; Hestbjerg & Dissing 1995).

2.2 Symptoms

R. beticola attacks preferably middle-aged to older leaves (Byford 1975; Hestbjerg & Dissing 1994). Initial symptoms of RLS are small chlorotic spots which after time become necrotic. First lesions often appear at the lower outer edge of the leaf (own observation). As leaf spots develop they turn light brown to brown and develop to circular to angular lesions, 2 ± 10 mm diameter in size (Ahrens 1989; Hestbjerg & Dissing 1994)(own observation). In the same year the color of leaves spots can vary from light to dark brown (own observation). As the disease progresses individual leaf spots coalesce causing wide areas of necrosis until the leaves collapse, die, and fall to the ground. In order replace the loss of photosynthetic active leaf material due to premature defoliation new leaves are produced from the heart using stored energy form the root (Figure 2.2). Consequently sugar content in the root is reduced, and thereby yield is lowered (Ruppel 1986).

6

Figure 2.2: Advanced attack of RLS (left). Individual leaf spots have started to coalesce. Regrowth of leaf material from the tuber heart after primary leaves have died and fallen to the ground (right). (Picture: T. M. Wieczorek)

Especially in the beginning symptoms of RLS can be easily mistaken for Cercospora leaf spot (CLS) (P. F. J. Wolf & J. A. Verreet 2002). If in doubt, a reliable method to distinguish RLS from CLS is to wait for sporulation. Whereas CLS produce stomata resembling black dots for the production of conidiophores and conidia at the center of the lesions, RLS produces a whitish sporulation mass. In addition, RLS appear to be more angular and larger in size.

2.3 Epidemiology and disease cycle

Ramularia leaf spot is a disease of the temperate regions and host-specific to species of the Beta family (Chenopodiaceae). It has been confirmed on sugar beet (Beta vulgaris spp. vulgaris var. altissima), red beet (Beta vulgaris spp. vulgaris var. conditiva), mangelwurzel (Beta vulgaris spp. vulgaris var. crassa), chard (Beta vulgaris spp. cicla) and sea beet (Beta vulgaris spp. maritima) (Fautrey & Lambotte 1987; Byford 1975; Rostrup 1898; U. Braun 1998).

Ramularia leaf spot is considered to be both a soil- and airborne disease (Ahrens 1987; S. Nielsen 1991). While surviving spores on plant debris are believed to be the primary inoculum, disease spread is the result of the secondary inoculum i.e conidia produced by the infection of the first inoculum under optimal climatic conditions and dispersed by wind (Hestbjerg & Dissing 1995). Therefore, conidia in the air represent potential risk of infections to occur.

7

Figure 2.3.1: Initial symptoms of RLS (left), moderate attack of RLS ± leaf spots have begun coalescing (center), severe attack of RLS (right). (Picture: T. M. Wieczorek)

Figure 2.3.2 illustrates the disease cycle of RLS. The inoculum of R. beticola survives on plant debris and as sclerotia (1). After sporulation spores are dispersed by water splashing and land on the lower leaves (2). Given favorable growing conditions, spores germinate and infiltrate the leaf through the stomata with hyphae. The reported latency of the fungus is 16 to 18 days at 17 °C (Ruppel 1986). Hestbjerg (1992) found the latent period to be 3 to 4 weeks under field conditions (Hestbjerg 1992). Conidiophores emerge through the stomata of the infected leaf and produce conidia (3). They are dispersed both by wind and by water splashing and start a new infection in the same season; hence the disease is polycyclic (4).

8

Figure 2.3.2: Disease cycle of sugar beet pathogen R. beticola. The fungus survives on plant debris (1). Under favorable conditions spores from debris are dispersed by water splashing infecting new plants (2). First visible symptoms occurred (3). After a while new infections sporulate and produce conidia, which are dispersed by wind (4). Leaves collapse and remain in the ground.

The fungus favors conditions with high relative humidity (> 95 %) and low temperatures form 17 to 20ƒC. 18 to 22 °C is the optimum temperature for sporulation observed in the field (Ahrens 1987). Development stops at temperature above 28 °C (Wenzel 1931). The ideal temperature for spore release is not described. Olsson and Persson found that the risk of infection increased significantly 14 days after a week of low mean temperatures. On the other hand high humidity did not show any significance (Olsson & Persson 2012). This is not in accordance with Ahrens (1989) who reported that mass spore production (accumulated) was followed after three periods with up to 7 days with humid conditions (Ahrens 1989). However, it is described that especially humid conditions in summer and early fall with optimum temperatures increase the risk of RLS infections and are perfect for further development of the disease. In order to be able to make a reliable prediction and forecast of RLS it is therefore

9 important that further research on favorable climate conditions of the fungus is conducted in such that weather data is correlated to disease onset and incidence severity.

2.4 None chemical control of RLS

Today the common European agricultural policy questions the increasing dependence of pesticides and supports the concept of integrated pest management (IPM) by establishing a framework for community action to achieve the sustainable use of pesticides (Directive 2009/128/EC). The new framework directive states that by 2014 all EU members must have implemented IPM, with the aim to reduce the impact and use of pesticides. The use of none chemical methods along with the use of thresholds and decision support systems are considered WR EH LPSRUWDQW HOHPHQWV LQ (8¶s IPM policy. Well-established guidelines for common practice can help to overcome difficulties in disease control and decrease the risks for building pesticide resistance. These common IPM practices are also employed for the prevention and control of RLS (Anon 2009; Gummert et al. 2011). These will be shortly discussed in the following.

As part of IPM in sugar beets the use of an optimal crop rotation system is recommended. Since spores of R. beticola survive primarily on plant residues, it should be avoided to continuously grow the same crops or, similarly, crops from the same family. By selecting a number of alternative species, which do not support the fungal growth, the risk for the pathogen surviving in the field and thereby future outbreaks are minimized. It is recommended to grow sugar beets only every three to four years in order to minimize infection pressures of pests. Another way to eliminate potentially infected plant material and to accelerate its degradation is the use of soil cultivation practices, such as tillage, deep plowing, incorporating or removal of plant debris. These procedures leave the fungus fewer opportunities for overwintering and thereby decrease the occurrence in the following years (Gummert et al. 2011).

Furthermore the ideal establishment of the crop helps to reduce infection pressure. The ideal seeding rate for sugar beets is approximately 80.000 plants per hectare. This allows a uniform and thus more efficient application of pesticides if needed. In addition, choosing the right sowing time will promote the establishment of healthy plants, rendering them less vulnerable to pests. However finding the right time can be difficult, since various parameters such as soil type, soil structure or field history need to be taken into account and ideal sowing time might differ from location to location. Even though it is not applicable to fungal leaf diseases but mentioning it is worthwhile for a more complete picture, seed treatment (e.g. with neonicotinoids) provides a fair protection against insect pests, e.g. green peach aphid (Myzus

10 persicae), pigmy mangold beetle (Atomaria linearis), or beet fly (Pegomyia betae), helping the plants overcome the critical, vulnerable phase of youth growth without being harmed (Gummert et al. 2011).

As a last but equally important precaution, IPM guidelines recommend choosing a cultivars that are adapted to local characteristics e.g. to sow a nematode-tolerant cultivar in case of a known infestation of nematodes. Regarding foliar diseases, great effort has been put forth in finding cultivars providing full or partial resistance. To date no sugar beet cultivar with complete resistance against R. beticola is available for farmers. However field experiments have demonstrated that cultivars show clear differences in susceptibility against R. beticola (Adams 1998). Each year NBR Nordic Beet Research tests a set of cultivars for tolerance against RLS. 5HVXOWVDUHSXEOLVKHGLQ1%5¶V annual report (Thomsen 2011). Especially in high risk areas, the control of RLS should include growing a cultivar that is less susceptible to the disease, lowering the dependency on frequent fungicide treatments.

11

3 Fungicide treatments in sugar beets

Besides primarily preventive cultural practices and precautions as mentioned above, fungicide application has become common practice to control fungal diseases. The two groups mainly used for disease control are strobilurins and azoles. Whereas both groups of actives are currently permitted in Denmark, only the use of strobilurins is allowed in Sweden.

3.1 Fungicides

3.1.1 Triazoles

A major milestone in chemical control was the introduction of the De-Methylation Inhibitors (DMIs, imidazoles and triazoles) in the late 1970s. DMIs mode of action works in the way that they bind to the 14Į-demethylase (CYP51) and thereby inhibit the C14 demethylation step within the ergosterol biosynthesis. Ergosterol is one of the main components of yeast and fungal cell membranes. The absence of ergosterol leads to alterations of the cell membranes and renders the fungus incapable to develop (Bean et al. 2009). As a consequence of intensive use over many years, the efficacy on several pathogens from DMIs has declined over the last couple of years. Reduced sensitivity and resistance has been well described for several pathogens and is related to mutation in the CYP51 target protein, enhanced active efflux or by overexpressing of the responsible genes (H.J. Cools & B. A. Fraaije 2011)

As representatives of this group, propiconazole and epoxiconazole are registered for the control of fungal diseases in sugar beets in Denmark. The products work systemically and provide both protective and curative control. Epoxiconazole products can be purchased under the trading names Opus® (BASF A/S), Maredo® (Makhteshim-Agan), and Rubric® (Cheminova A/S), all containing 125 g l-1 active ingredient. Propiconazole can be purchased under the tradename Bumper 25EC or Tilt 250EC (K. A. Nielsen et al. 2009).

3.1.2 Strobilurins

A second milestone was the introduction of strobilurin fungicides also known as 'Quinone outside inhibitors' (QoI). Strobilurins derive from fungi that produce the natural fungicidal ǃ- methoxyacrylates, e.g. Strobilurus tenacellus and Oudemansiella mucida (Anke et al. 1977; Beautement et al. 1991). Their mode of action differs from that of the DMIs. In short, strobilurins inhibit the Qo site of the cytochrome b complex in the mitochondria, blocking the electron transfer to cytochrome c in the cytochrome bc1 complex. As a result the ATP production in the mitochindria is inhibited. Having been introduced just in 1997, the first cases of resistance were already found one year later in wheat powdery mildew in Germany (Affourtitm et al. 2000). Later resistance developed in many more pathogens and in 2012, the

12 first case of resistance of C. beticola against strobilurins was reported by Kirk et al. (Kirk et al. 2012).

Deprived from the use of any triazoles, strobilurins are the only agent against fungal diseases in sugar beets to be used in Sweden. Pyraclostrobin is available under the registered trade name Comet® (BASF A/S) at a dose of 250 g l-1. In Denmark pyraclostrobin is market as Opera® (BASF A/S) - a mixture of 133 g l-1 pyraclostrobin with 50 g l-1 epoxiconazole (K. A. Nielsen et al. 2009)

3.2 Timing and purpose for fungicide application in sugar beets

For the control of fungal leaf diseases in sugar beets under Danish conditions, it is recommended to apply 0.25 to 0.5 l ha-1 Opera or Opus/Rubric/Maredo at low infection pressure (Hansen 2008). The dosage ought to be raised to 0.75-1 l ha-1 if the infection is already well established in the field. In field trials, all fungicides mentioned above have shown a very good efficacy to control R. beticola, C. beticola, rust and powdery mildew (K. A. Nielsen et al. 2009). However, propiconazole is not registered for the use on C. beticola. At a sole presence of powdery mildew, Opera has shown an higher effect (Hansen 2012a). In the last years a main focus has been set on predicting the onset of diseases and formulating mathematical models for forecasting (see chapter 4). Meanwhile, only few researches have been dedicated to investigate the effect of timing of fungicide application, especially early treatment. In 2011 NBR Nordic Beet Research performed field trials with the goal to determine whether fungicide treatment before the first visible symptoms had a positive effect on disease development and yield (Hansen 2012b). Results indicate that the highest yield was achieved, when fungicides were applied approximately 1 to 5 weeks prior the detection of any symptoms (Figure 3.1).

13

'Julietta' 'Cactus' 112 114 110 112 110 108 108 106 106 104 104 102 102 Relative sugar yield Relative sugar Relative sugar yield 100 100 98 98 Ubeh 27-30-33 28-31-34 29-32-35 30-33-36 Ubeh 27-30-33 28-31-34 29-32-35 30-33-36 1 2 3 4 5 1 2 3 4 5 Entry nr., weeks in which treatments took place Entry nr., weeks in which treatments took place Rel Yield Poly. (Rel Yield) Rel yield Poly. (Rel yield)

Figure 3.1: Relative sugar yield of two field experLPHQWV6XJDUEHHWV FXOWLYDUVµ-XOLHWWD¶ .:6 DQG µ&DFWXV¶ 0DULER6HHG ZHUHWUeated at different times with 0.25 l ha-1 Opera (Hansen 2012b). The arrows indicate when first symptoms of RLS were observed.

Besides the fact that plants treated with fungicides are less likely to develop attack of fungal pathogens, rendering the photosynthetic active parts of the plant intact, another explanation for a positive effect of fungicide treatment is described DV³WKHJUHHQLQJHIIHFW´(Estrobilurinas & Plantas 2003; Bartlett et al. 2002). This effect, notably not due to disease control, was found in several crops, among them wheat, winter rape seed and sugar beets (Spitzer et al. 2012; Ober et al. 2004). Taking this into account it is questionable if the IPM threshold approach is enough or if new concepts of plant protection ought to be developed, considering fungicides to be more than just a means to control diseases. In order to evaluate this question the background of disease monitoring according to IPM guidelines is briefly described in the following chapter.

14

4 Leaf disease monitoring in sugar beets

4.1 Disease monitoring

In the frame of the EU directive 2009/128/EG disease monitoring is listed as one point in the general Integrated Pest Management (IPM) program (Anon 2009). According to the IPM JXLGHOLQHV³SHVWGLVHDVHVDQGZHHGVVKDOOEHPRQLWRUHGZLWKDGHTXDWHPethods and tools to determine whether and when to apply direcWSHVWFRQWUROPHDVXUHV´. This implies the control of leaf diseases in any crops, including sugar beets. One method to decide whether or not control of a pest is needed is practicing disease monitoring in combination with so-called threshold values, i.e. which levels of attack of any kind of fungal leaf diseases are tolerated before control is needed. As soon as the determined threshold values are exceeded, treatment with fungicides is recommended in order not to risk high yield losses. On the other hand fungicides ought not to be applied if the threshold is not reach to avoid unnecessary spraying with fungicides, which potentially can harm the environment and have impact on human health as well as increasing the risk of building up resistance.

The development of epidemic relevant thresholds for leaf disease in sugar beets, which were orientated to the actual disease incidence situation started in Germany (J.-A. Verreet et al. 1996; P. F. J. Wolf et al. 2001; P. F. J. Wolf & J. A. Verreet 2002). Verreet et al. (1996) defined thresholds based on field data conducted in Bavaria for several succeeding years and implemented the model in practice. The method is based on the assessment of 100 leaf samples per field, which are plucked from the middle of plant while walking diagonally through the field. The leaves are scored for the presence of symptoms of any treatable fungal leaf disease and at the end per cent infected leaves are calculated. This value is than compared with the defined thresholds. From the June 1st on a first treatment is recommended, if the threshold of 5% is exceeded. If no first application of fungicides has taken place before August, 15th, the threshold value is raised to 45% infected leaves. A second application might be needed in this period if 40-50% infected leaves are found after August, 1st. No further application is needed after September, 1st (P. F. J. Wolf et al. 2001; P. F. J. Wolf & J. A. Verreet 2002).

Results obtained from monitoring are distributed online, as warning letters, and with the help of advisory services to update farmers on the current status of disease pressure. This method has found its way into advisory services throughout Europe and is conducted this way, besides in Germany, France, and Sweden (Richard-Molard 2012; Olsson & Persson 2012; Gummert et al. 2011).

15

A different and more advanced approach helping decision making and predicting the necessity of disease control is the implementation of a warning system based on a model predicting the development of leaf disease. In this area most research has been dedicated to CLS. This is due to the fact that this disease causes the highest yield losses in great sugar beet regions in Germany, the USA and France (Gallian 2000; Rossi & Battilani 1991; Weiland & Koch 2004). Already in the 1990s CERCOPRI - a computerized model for forecasting the primary infection of CLS based in weather data - was presented by Rossi and Battilani (Rossi & Battilani 1991). More recent examples for forecast models are CERBET 1-3 which have been developed consecutively and build upon one another (Racca et al. 2002; Racca & Jorg 2007; Racca & Jorg 2003). The latest model CERCBET 3, which is design for implementation as warning service, takes meteorological data (RH, temperature) into account and calculates the daily infection rate and the infection pressure index. The overall outcome is a prognosis at which day and for which date a severity threshold is reached. Until now no comparable models have been fully implemented for other major sugar beet diseases as RLS, beet rust or powdery mildew. Yet experiences made during the development of models like CERCOPRI and CERCBET 1-3 have been the basis for the creation of preliminary models for other plant pathogens (Thach 2009; Racca et al. 2010).

4.2 Leaf disease monitoring in Denmark

In Denmark, leaf monitoring is conducted as collaboration between NBR Nordic Beet Research, the advisory service DLSyd (Dansk Landbrug Sydhavsøerne Planteavlrådgivning), and Nordic Sugar Agricenter, DK. In accordance with the IPM sugar beet model developed by Verreet et al. (1996) disease severity is followed in the period starting in the last week of June finishing in the last week of September. Each year, 20 to 25 fields are chosen representing the growing area and a set of cultivars grown the most that year. In each field two times three spraying windows are marked (6 x 12 m) where 1) plants are left untreated, 2) plants are treated once, and 3) plants are treated twice if necessary. On a weekly base disease severity is evaluated on a scale form 0 (no occurrence) to 10 (death of plant) for each leaf disease separately i.e. rust, powdery mildew, CLS, and RLS (Wieczorek et al. 2011). In the critical period before any onset of any disease, fields are additionally searched for any first symptoms of leaf diseases by walking though the field diagonally. The decision of which fungicide to use and when is left to the farmers. Results from every location and recommendations are sent out weekly or can be retrieved online on Agricentrets website or on Landscentrets registeration net (www.landbrugsinfo.dk/regnet).

16

Figure 4.2: Development of RLS, rust, and powdery mildew in untreated spraying windows in the period from 2007 to 2012. Data for each year shown are mean values of a set of cultivars, representing those grown most in the corresponding year (Hansen n.d.). Mutual attacks of all three diseases on one plant at the end of the growing season (right bottom). (Picture: T. M. Wieczorek)

7KHSXUSRVHRIWKH³'DQLVK0RGHO´LVWRGHWHUPLQHWKHH[DFWWLPHZKHQILUVWV\PSWRPVDSSHDU Trials have shown, that if leaf diseases are treated at the onset of the disease, ½ or even a ¼ of the recommended dosage is sufficient to control the disease and to protect against new outbreaks (Hansen 2012a). To keep the infection pressure on a low level over time, it is important to follow the disease cycle in order to determine the right time for a second treatment. A second application is performed when new infections or new sporulation are observed ± depending on weather conditions, normally 2 to 3 weeks after the first application. By evaluating each disease separately, it is possible to compare disease occurrence over a period of several years (Figure 4.2). Moreover the susceptibility of different cultivars can be compared as well as the disease incidence in different areas.

17

5 Spore trapping

Spore and pollen trapping is a major part in the discipline of aerobiology. According to The $PHULFDQ+HULWDJHŠ6FLHQFH'LFWLRQDU\DHURELRORJ\LVGHVFULEHGDVWKH³VFLHQWLILFVWXG\RIWKH sources, dispersion, and effects of airborne biological materials, such as pollen, spores, and PLFURRUJDQLVPV´(The American Heritage Science Dictionary 2012). In many scientific fields of research as for example agronomy, allergology, or palaeoecology, spore trapping experiments contribute to the elucidation of different kinds of problems. The first experiment using spore trapping dates back to the 1910s. In 1918 the Swedish scientist Hesselman addressed the question of how to differentiate locally produced pollen from long distance traveled pollen. Hesselman designed the first trap intending to quantify the amount of pollen - a petri dish containing filter paper soaked in glycerin (Giesecke et al. 2010). Since the early days of pollen sampling, several different traps have emerged - here to PHQWLRQ7DXEHU¶VSROOHQWUDS7DXEHUGHVLJQHGWKLVW\SHRIWUDSDWWHPSWLQJWRILQGDQHZZD\ to analyze pollen deposition. The pollen trap is composed of a cylindrical container, 10 cm in ERWKKHLJKWDQGGLDPHWHUDQGDQ³DHURG\QDPLFDOO\VKDSHGFROODU´3ROOHQHQWHUVWKHFRQWDLQHU passively through a 5 cm orifice and land on a thin layer of glycerin on the bottom of the container. If needed, an aluminum roof can be attached for protection from rain (Tauber 1974; Levetin et al. 2000). The pollen is trapped passively, solely surrendered to gravity. Due to that IDFW7DXEHU¶VSROOHQWUDSEHORQJVLQWRWKHFDWHJRU\RI³PRGHUQJUDYLW\VDPSOHUV´ Nowadays, most instruments for studying air borne particulate matter are functioning as air samplers, which collect air samples actively. One of the most used representatives is the volumetric sampler known as Hirst sampler (Figure 5.1). The Hirst sampler consists of a vacuum pump which reduces pressure within a sampling chamber. Air is drawn in through a thin orifice into the sampling chamber at a recommended speed of 10 liter/min. In the sampling chamber a plastic tape coated with an adhesive agent is fixated onto a drum, driven by a 7-jewel clockwork movement. The drum has a fixed circumference which allows continuous sampling up to seven days (Giesecke et al. 2010; Hirst 1952). Today this design is offered by Burkard Manufacturer Ltd. with a few modifications, and is therefore also known as Burkard spore trap.

An example for the use of aerobiological data for medical purpose is the detection of grass and tree pollen that are known to cause allergies. Several studies have been conducted to follow the annual variations of pollen. Utilizing spore/pollen trapping it is possible to predict the onset of hay fever based on pollen counting (Hasnain et al. 2005; Winkler et al. 1991). Results obtained from pollen surveillance aid in the diagnosis and more efficient treatment of allergic diseases. Furthermore in combination with weather data, results can serve as a basis for the

18 formulation of mathematical models permitting forecasting of pollen and spore release (Jato 2004; Chamecki 2012). During spring and summer the Danish Asthma and Allergy Association collects data of six pollen types (alder, hazel, elm, birch, grasses, and mugwort) as well as of spores from Alternaria and Cladosporium (Astma-Allergi Danmark 2012). In another example, Brito et at. used a Burkard spore trap in a study correlating clinical symptoms of asthma with the abundance of Alternaria alternata spores, a risk factor for developing asthma (Brito et al. 2012). It was shown that higher levels of the allergen µAlt a 1¶ could be used for the forecast of respiratory symptoms in sensitive patients.

In plant pathology spore samplers are commonly utilized to determine the composition of fungal spores or pollen in the air, their distribution in the air, or their diurnal and seasonal pattern (M. Gonianakis et al. 2005; Desbois et al. 2006; Soldevilla et al. 1995). The number of fungal spores present in the air at certain times of day or over a defined period of time is often correlated to meteorological data in order to see if epidemiological pattern are present. Holb et al. (2004) used Burkard spore traps for the investigation of the dispersal of Venturia inequalis ascospores and disease gradient form a defined source of inoculums in apple orchards (Holb et al. 2004). As the only fungal disease in sugar beets, Burkard spore traps have also been successfully implemented for the detection of Cercospora beticola spores (J. Khan et al. 2004).

Figure 5.1: Burkard 7 Day Recording Volumetric Spore Sampler in sugar beet field. (Pictures: T. M. Wieczorek)

For the evaluation of air samples, spore counting by microscopy and culturing of samples on culture plates are probably the most widely used approaches. However, in the last couple of

19 decades various alternatives have been developed for the analysis of data obtained from spore traps such as immunoassays, molecular methods, flow cytometry or image analysis (Levetin 2004). Among them, real-time polymerase chain reaction (QPCR) based detection methods are considered to be the most promising techniques (S. L. Rogers et al. 2009; Calderon et al. 2002; Heuser & Zimmer 2002). This method can be used to forecast upcoming outbreaks of airborne diseases by keeping track of the presence of pathogen inoculums (J. S. West et al. 2008). As QPCR will play a major part in this thesis, a description is given in the following chapter.

20

6 QPCR

Real-time PCR, also called quantitative real-time PCR (QPCR) is a molecular technique which allows accurate amplifying and simultaneous quantifying of targeted DNA, RNA or mRNA molecules. QPCR uses a fluorescent reporter molecule, which during amplification of each cycle will be incorporated into the product making it possible to detect the amount of amplified DNA after each cycle. Consequently, methods to detect amplification products as a next step e.g. gel electrophoresis can be omitted (Bustin et al. 2005).

Two methods for detection of target DNA have been developed. Firstly, non-specific fluorescent dyes, which are incorporated into double-stranded DNA, e.g. SYBR Green. This method is simple and preferable, if primers utilized for the amplification of the target are specific enough; i.e. no occurrence of unwanted non-specific products that would influence the results, e.g. primer dimers or amplifications products from cross-reactions. Secondly, as an alternative, if the usage of a non-specific fluorescent dye is not feasible, a sequence-specific DNA probes (e.g. TaqMan probe or molecular beacon) can be developed. The TaqMan probe consists of ROLJRQXFOHRWLGHVDQGLVODEHOHGZLWKDIOXRUHVFHQWUHSRUWHULQWKH¶HQGDQGDTXHQFKHULQWKH ¶ HQG 7KH UHSRUter emits a signal when the probe is cleaved by the Taq polymerase and removed from the quencher. Molecular beacon has a hairpin structure which holds the reporter and quencher together but when the beacon hybridizes to the amplified product the quencher is removed from the reporter and fluorescence can be detected (Figure 6.2). Although the design and optimization of a DNA probe is time consuming and more expensive, it is necessary in some cases to add specificity to the assay. Furthermore it allows for multiplexing of several assays as you can label with different fluorescent dyes.

Figure 6.1: Model of a QPCR plot illustrating threshold, cycle threshold (Ct), baseline (where reporter florescent signal is present but under the limits of detection), and ȴRn (an increment of fluorescent signal at each time) (NCBI n.d.).

21

Figure 6.2: Comparison of modes of operation of TaqMan® and SYBR®-Green fluorescence dyes (Life Technologies 2012).

The outcome of a QPCR assay is a numeric value called threshold cycle (Ct). The Ct is described as intersection of an amplification curve and a threshold line and reveals the cycle number at which the fluorescence of a DNA product significantly differs from the background (Figure 6.1).

The lower the Ct value, i.e. the earlier a fluorescent signal is detected, the higher was the amount of the initial DNA concentration of the input target DNA. Based on the Ct, it is possible to calculate the actual amount of target DNA. To do so a standard curve is created from a dilution series prepared with known concentrations of the target DNA (Figure 6.2). Whenever a

QPCR assay is performed, the standard curve needs to be included. With the help of the Ct values obtained from the standard curve, defined amounts of DNA are correlated to Ct values. Hence, the exact amount of target DNA in any sample can be calculated.

22

Figure 6.2: Example for a standard curve generated form a ten-fold dilution series (A). Cycle threshold plotted against cycle number (B) (SABbiosciences 2009).

Today, QPCR is widely used in laboratories due to its simplicity, sensitivity, and its potential for high throughput. For many years, it has been applied routinely for the detection of human diseases or in food sciences (Karjalainen et al. 2012; Pavón et al. 2012; H. A. McCartney et al. 2003). Recently, more and more QPCR assays have been employed for the detection of plant pathogens, e.g. Mycosphaerella graminicola, Monilinia fructicola, or Botrytis squamosa (J. S. West et al. 2008; B A Fraaije et al. 2005; Luo et al. 2007; Carisse et al. 2009). Information obtained from QPCR based diagnostic methods can make important contributions to elucidate SDWKRJHQV¶ OLIH F\FOHV VXFK DV ILQGLQJ DOWHUQDWLYH KRVWV RU IROORZLQJ WKH HDUO\ VWDges of infection. In addition, it helps to understand the mechanisms of host resistance. Havis et al. presented a protocol with the goal to detect fungal pathogen Ramulaira collo-cygni in barley (Hordeum vulgare). They succeeded in correlating different DNA levels and disease symptoms (J. M. G. Taylor et al. 2010). In terms of sugar beets, recently a protocol was developed using QPCR to quantify C. beticola biomass in infected leaves (S. Lucas et al. 2012). To date this is the only published work on major fungal sugar beet disease including QPCR.

23

7 A QPCR assay for the detection and quantification of sugar beet pathogen Ramularia beticola in leaf and air samples

7.1 Abstract

Goals: The purpose of this study was to develop a QPCR method for the detection and quantification of Ramularia beticola DNA extracted from sugar beet leaves and from airborne spores caught on tape.

Methods and Results: The primers were designed based on the internal transcribed spacer region 2 (ITS 2) and showed a high specificity to Ramularia beticola. The QPCR assay was applied on DNA extracted from tape collected from a Burkard volumetric air sampler placed in a sugar beet field trial inoculated with R. beticola. In parallel sugar beet leaves harvested from the same field experiment were tested with the QPCR assay. The pathogen was successfully detected in air samples and in inoculated sugar beet leaves at concentrations as low as 2 pg per day and 0.1 pg/mg dry leaf matter, respectively.

Conclusion: It was possible to detect the presence of the fungal pathogen R. beticola in the air and on sugar beet leaves before the occurrence of visible symptoms and to follow the development during its latency until the first leaf spots were visible.

Significance and Impact of the Study: This is the first report of a molecular assay which allows screening for the presence of R. beticola in plant material and in air samples prior to the appearance of visible symptoms. An early detection of R. beticola in the crop may justify an early fungicide application for a more efficient control of leaf diseases in sugar beets.

Keywords: Ramularia leaf spot, early detection, real-time PCR, ITS, Beta vulgar

24

7.2 Introduction

Ramularia leaf spot (RLS) is one of the most important leaf diseases in sugar beets (Beta vulgaris) in northern European countries. It is caused by the ascomycete Ramularia beticola and thrives in cool, temperate climates at relatively high humidity and temperatures between 17 and 20°C (Asher & L.E. Hanson 2006; Hestbjerg & Dissing 1995). If not controlled, yield losses can amount to 15 ± 20 % (Hansen 2008; Jørgensen 2003). Over the last decades an increase of severe attacks of RLS has been reported in Denmark, Sweden, and Finland (Thach 2010; Eronen 2008; Persson & Olsson 2008). The disease is typically controlled using fungicides like epoxiconazole or epoxiconaozle + pyraclostrobin. In accordance with IPM guidelines, fungicides in Denmark are presently recommended to be applied if a severity threshold of 5 % is exceeded for the first application, normally on August 1st (Hansen 2012a; P. F. J. Wolf & J. A. Verreet 2002). The second application is performed when new infections are observed with thresholds of 5 - 10 %. Typically 25-50% of normal fungicide rate is recommended for each treatment (Hansen 2012a). Preliminary field trials have indicated a tendency for a more efficient control of fungal diseases and higher sugar yield if sugar beets are treated with fungicides before visual symptoms occur (Hansen 2012b). For a better determination of the optimized timing of disease control, more advanced methods are needed.

For many years spore/pollen traps have been used for the collection of air borne spores (Giesecke et al. 2010). Purposes for the operation of spore traps can be manifold e.g. to conduct epidemiological studies, for allergen sampling or for air inspection in airports (Giesecke et al. 2010; P. E. Taylor et al. 1999; Desbois et al. 2006). One of the most utilized spore traps is the Hirst-Burkard trap (Hirst 1952; Levetin et al. 2000; Hasnain et al. 2005). In terms of sugar beet pathogen, Hestbjerg and Dissing (1995) used this type of trap in the sugar beet field with the goal to reveal release patterns of R. beticola spores (Hestbjerg & Dissing 1995). Further, Burkard spore traps have been used for the detection of Cercospora beticola (J. Khan et al. 2009). The predominate method to evaluate air samples has been microscopy, which is rather time-consuming and needs trained eye for distinct identification of different air particles (Levetin 2004).

In the recent year a number of molecular assays have emerged, facilitating the analysis of air samples. One of the most widely use assays for the detection and quantification of DNA is quantitative real-time polymerase chain reaction (QPCR). QPCR collects continuously fluorescent signals from the amplified PCR product for a defined number of PCR cycles. Using this information, the PCR cycle number (Ct) at which the fluorescent signal exceeds a threshold can be obtained for each sample and this can be linked to known concentrations of target DNA in order to obtain the amount of target DNA in an unknown sample. QPCR assays have been

25 developed for a number of plant pathogens (Carisse et al. 2009; Luo et al. 2007; B. A. Fraaije et al. 2005), however for the detection of sugar beet pathogens only little research has been done and most work has been dedicated to C. beticola (Goodwin et al. 2001; Lartey et al. 2003; Weiland & Sundsbak 2000; S. Lucas et al. 2012).

The objectives of this study are 1) to develop primers for R. beticola DNA suitable for the application of QPCR and 2) to extract DNA from different media being exposed to R. beticola spores i.e. tape from spore sampler and sugar beet leaves. It is expected that an implementation of the findings can be useful within the frame of risk evaluation of RLS attacks.

7.3 Materials and methods

7.3.1 Fungal isolates for primer design

Ramularia beticola isolates were recovered from naturally infected leaves from sugar beet fields in Denmark. Spores and mycelia were transferred onto grass agar in 9 cm petri dishes incubated at 17 °C (12 h dark light/ 12 h UV light). After 21 days the petri dishes were transferred to white light (12 h per 24 h) at 17 °C. Prior to DNA extraction R. beticola samples were prepared from isolates 11-RR-02, 11-RR-04, 11-RR-05, 11-RR-06, 11-RR-07, 11-RR-08, 11-RR-09, and 11-RR-23 by scraping off 5 to 6 colonies from grass agar (GA) and transferring them directly into a mortar containing liquid nitrogen. After grinding, the material was transferred into 1.5 Eppendorf tubes and stored at ± 80°C until further use.

The fungal genomic DNA was extracted using the DNeasy® Plant Mini Kit (Qiagen GmbH, Hamburg, Germany) according to thHPDQXIDFWXUHU¶VSURWRFROVWHSWR7KHH[WUDFWHG'1$ was spectrophotometrically analyzed using NanoDrop (NanoDrop 1000 Spectrophotometer, Thermo Scientific, Waltham, MA, USA).

7.3.2 ITS amplification and sequencing

PCR amplification of R. beticola DNA was carried out within the internal transcribed spacer region (ITS). The PCR reaction was performed in a final volume of 25 µl. Each tube contained 11.3 µl autoclaved milliQ water, 5.0 µl 5x GoTaq PCR buffer (Promega, Fitchburg, WI, USA), 2.5 µl ITS4 primer, 2.5 µl ITS5 primer (both 10 µM), 1.5 µl MgCl2 (25 mM), 1.0 2.5 mM dNTPs, 0.2 µl GoTaq polymerase (Promega, Fitchburg, WI, USA) and 1.0 µl DNA template. In addition to the R. beticola isolates, DNA from a reference isolate as positive control and a negative control containing autoclaved milliQ water were included. The PCR was conducted in a thermal cycler (Applied Biosystems, 2720 Thermal Cycler version 2.09) according to the

26 following program: initial hot start at 94 °C for 2.5 minutes followed by 35 cycles of 15 seconds denaturation at 94 °C, 30 seconds annealing at 58°C, and 1 minute elongation at 72 ƒ&ILQDOHORQJDWLRQDWƒ&IRUPLQXWHVDQGWKHQLQFXEDWLRQDWƒ&IRU’A volume of five µl of PCR reaction was analyzed in a 1.5% agarose gel along with DNA size markers and stained with ethidium bromide. All nine PCR products were sent to Macrogen Europe (Utrecht, The Netherlands) for sequencing of both DNA strands with the primers ITS4 and ITS5

7.3.3 Primer Design and primer specificity

For primer design sequences of the internal transcribed regions (ITS2) of R. beticola (JF 30013.1 ± JF 30015.1) and Ramularia collo-cygni (AJ 536178-80), Mycosphaerella fragariae (AF 297235), M. areola (DQ 459073), M. graminicola (EU 019297), Cercospora beticola (AY 840527) were retrieved from the NCBI database. In addition, sequences from new isolates 11- RR-02, 11-RR-04, 11-RR-05, 11-RR-06, 11-RR-07, 11-RR-08, 11-RR-09, and 11-RR-23 were included. The sequences were aligned with help of CLC Genomics Workbench (CLC bio A/S, Aarhus, Denmark) and a set of primers was designed using the program Primer Express® (Applied Biosystems, Corporation, Carlsbad, CA, US). Specificity test of the primers were carried out by comparing sequences to the nucleotide sequence data base in GenBank using BLAST and by performing QPCR on isolates of R. beticola, C. beticola, R. collo-cygni, and M. graminicola. QPCR was run on samples containing 10 and 100 pg/µl, Moreover the primer concentrations were optimized using a 50/300/900 nM primer optimization matrix on target and non-target DNA to ensure specificity and minimize primer-dimer formation.

7.3.4 QPCR

All QPCR assays were carried out in a MicroAmp Optical 386-well reaction plate. Each reaction was performed in a total volume of 12.5 µl consisting of 6.250 µl Power SYBR® Green (Applied Biosystems Corporation, Carlsbad, CA, US), 0.375 µl Rb2f forward primer (10 µM), Rb4r 0.375 µl reverse primer (10 µM), 3.0 µl MilliQ water, and 2.5 µl template DNA. As a negative control, the template DNA was substituted with milliQ H2O. Duplicate reactions were set up for each sample. The QPCR was carried out in an AB applied Biosystems ViiA7 according to the following program: initial hot start at 95°C followed by 40 cycles of 15 seconds at 95°C and 60 seconds annealing and extension phase of 60 °C.

7.3.5 Amplification efficiency and spike test

A standard curve was created by plotting the logarithm of five-fold serial dilutions (5000, 1000, 200, 40, 8, 1.6, 0.32, and 0.064 pg) of R. beticola DNA against the according threshold

27 cycle (Ct) values. Using the standard curve, QPCR efficiency was calculated with the equation E = 10(-1/slope). A spike test was performed in order to confirm the ability of the primer set to identify and amplify R. beticola DNA in the presence of high concentrations of non target DNA. DNA was extracted from symptom free sugar beet leaves using the DNeasy® Plant Mini Kit 4LDJHQ*PE++DPEXUJ*HUPDQ\ DFFRUGLQJWRWKHPDQXIDFWXUHU¶VSURWRFRO7KHSODQW'1$ was diluted to concentrations of 0.5 and 10 ng/µl, respectively, and was used as diluent to prepare two fivefold dilution series of fungal DNA. A QPCR assay was carried out as described above with three replicates to show the reproducibility of the samples.

7.3.6 DNA extraction from spore trap tape

A Burkard spore trap was operated in a field trial inoculated with R.beticola from August 20th to September 2nd. The spore sampler was prepared for sampling by coating Melinex® tape with an adhesive (Vaseline + 10 parts toluene to 1 part paraffin wax) and the air flow was adjusted to 0.01 m3/min. The tape was collected every seven days and cut into pieces, each representing 24 h. Each piece of tape was further cut into 10 equally sized pieces under sterile conditions using a scalpel. These pieces were pooled and DNA was extracted using 3RZHU/\VHUŒ3RZHU6RLOŠ'1$ ,VRODWLRQ.LW 02%,2/DERUDWRULHV ,QF&DUOVEDG&$86$  The recovered DNA was stored at - 18°C until further use. A QPCR assay was carried out as described above.

7.3.7 DNA extraction from plant material

Total genomic DNA was extracted from inoculated sugar beet leaves. Leaf samples from the WZRFXOWLYDUVµ3DVWHXU¶ 6WUXEH6DDW*PE+ &R.*6|OOLQJHQ*HUPDQ\ DQGµ&DFWXV¶ 0DULER Seed, Holeby, Denmark) were recovered for a four-week period every third and fourth day. Sugar beet plants were inoculated twice. First inoculation took place on June 28th utilizing a spore solution with a spore density of 3.2 x104 spores/ml. Second inoculation (2.2 x 103 spores/ml) was applied on July 17th. Additional uninfected samples were taken three days prior to inoculation and served as control. One sample consisted of 25 punches (diameter 1 cm) which were recovered from five randomly chosen leaves. The samples were air-dried and subsequently stored at - 18°C. Before DNA extraction the leaf samples were powdered in a GenoGrinder in the presence of 10 steel balls (diameter 0.5 mm) at 1500 strokes per minute for three times 45 seconds. 20 mg of dry leaf material was used for DNA extraction using the '1HDV\Š3ODQW0LQL.LW 4LDJHQ*PE++DPEXUJ*HUPDQ\ DFFRUGLQJWRWKHPDQXIDFWXUHU¶V protocol step 1 to 11. The recovered DNA was stored at - 18°C until further use.

28

7.4 Results

7.4.1 Primer design and testing

A pair of forward and reverse primer was designed for a QPCR assay with Power SYBR® Green amplifying a 90 bp sequence in the ITS2 region. The sequence of the primers, their sizes and melting temperatures are shown in Table 7.4.1.

Table 7.4.1: Primer sequences for forward primers Rb2f and reverse primers Rb4r, including their lengths and melting temperatures (Tm °C).

Prime r Se que nce (5´ -> 3´) Le ngth Tm (°C) Rb2f CTTAAAGTCTCCGGCTGTCTGA 22 bp 58,0 Rb4r TTTGAAAGATTTAACGGCCTCAC 23 bp 57,1

The isolates 11-RR-02, 11-RR-04, 11-RR-05, 11-RR-06, 11-RR-07, 11-RR-08, 11-RR-09, and 11-RR-23 were confirmed to be R. beticola by sequence analysis and comparisons to previously sequenced R. beticola in GenBank.

These new and 13 already known R. beticola isolates (Thach 2010) as well as C. beticola, R. collo-cygni and M. graminicola were used in a specificity test of the primer pair Rb2f/Rb4r (Table 7.4.2). All R. beticola isolates were detected at both concentrations (10 and 100 pg/µl).

Ct values for R. beticola ranged from 20 to 23 for 10 pg/µl and 23 to 29 for 100 pg/µl. DNA of

C. beticola, R. collo-cygni and M. graminicola ZDVQRWDPSOLILHG ³XQGHWHUPLQHG´ &t values of above 35 are regarded as no amplification of the target DNA.

Table 7.4.2: Specificity test of primer pair Rb2f/Rb4r on DNA extracted of R. beticola, C. beticola, R. collo-cyngi, and M. graminicola isolates at concentrations of 10 pg/µl and 100 pg/µl. Amplification is

designated as the mean Ct YDOXHRIWZRUHDFWLRQV1RDPSOLILFDWLRQLVGHVLJQDWHGDV³XQGHWHUPLQHG´

Isolate Cycle threshold 10 pg/µl 100 pg/µl 11-RR-02 21 25 11-RR-04 20 23 11-RR-05 26 29 11-RR-06 21 24 R.beticola 11-RR-07 23 26 11-RR-08 22 25 11-RR-09 21 24 11-RR-23 20 23 C.beticola Cb1 Undetermined Undetermined RAMS-533 39 Undetermined R. RAMS-128 Undetermined collo-cygni RAMS-218 Undetermined M. FL.Stratego 37 38 graminicola FL.Breamer Undetermined

29

In terms of primer concentration, highest specificity was found when using a final concentration of 300 nM of primer Rbf2 and 300 nM primer Rbr4, respectively. Primer concentrations at 900 nM showed a tendency for primer dimer formation (data not shown).

7.4.2 Standard curve and spike test

For quantification of R. beticola DNA and for primer testing a standard curve was generated using a fivefold serial dilution (1000, 200, 40, 8, 1.6, 0.32, and 0.064 pg) of R. beticola isolate 11-RR-07. Each sample was run in triplicates.

For the standard curve the following equation was calculated: y = - 3.4172x + 29.53 (R2 = 0.9884). Variation among the triplicates was found to be extremely low (data not shown). No signal for the negative control was found in the reaction. An efficiency E of 96.1 % was calculated, based on a 100% efficiency of an optimal slope of a standard curve being ± 3.32. Figure 7.4.1 shows the amplification plot of the standards used for the generation of the standard curve as well as the standard curve plotting mean Ct values against the corresponding quantities.

A 40 B y = -3,4172x + 29,53 35 R² = 0,9884

30

25

20

15

10 Threshold cycles

5

0 -1,5 -1 -0,5 0 0,5 1 1,5 2 2,5 3 3,5 Log starting quantity (picograms)

Figure 7.4.1: (A) QPCR amplification plot of DNA samples from a fivefold dilution series of R. beticola.

Ct values are plotted against ȴRn. (B) Standard curve from R. beticola isolate 11-RR-07. Threshold cycle

values (Ct ) plotted against log starting quantity.

,QDVSLNHWHVWWKHSULPHUV¶DELOLW\WRGHWHFWGLIIHUHQWFRQFHQWUDWLRQVRIR. beticola DNA (1000 pg to 0.32 pg) in non target sugar beet DNA (10,000 pg and 500 pg) was determined. Each sample was analyzed in triplicates and the standard deviation of the replicates was found to be very low. Mean cycle threshold varied from 18 to 29 and 19 to 32 in the presence of 500 and 10,000 pg non target DNA, respectively (Table 7.4.3). The results show that the dynamic

30 range for quantification is between 0.32 and at least 1000 pg R. beticola DNA in a background of sugar beet DNA from leaves (Figure 7.4.2).

Table 7.4.3: DNA concentrations, Ct means, standard deviation (SD), and measured quantity from spike test analysis with two different concentrations of sugar beet DNA.

R. Sugar Sugar beticola be e t be e t C M e an C SD C M e an C SD DN A DN A t t DN A t t (pg) (pg) (pg) 1000 pg 18 0,04 19 0,04 200 pg 21 0,10 22 0,06 40 pg 500 23 0,16 10000 24 0,09 8 pg 25 0,19 27 0,14 1,6 pg 28 0,42 29 0,44 0,32 pg 29 0,49 32 1,55

30 y = -3,3852x + 28,483 28 R² = 0,9989

26

24

22

20

18 Threshold cycle y = -3,5597x + 29,721 16 R² = 0,9982

14

12

10 0 0,5 1 1,5 2 2,5 3 3,5 Log starting quantity (picograms)

0,5 ng/ul 10 ng/ul

Figure 7.4.2: A spike test with R. beticola DNA ranging from 1000 to 0.32 pg mixed with sugar beet

DNA in two concentrations (500 and 10,000 pg). Ct values are plotted against log starting quantity.

31

7.4.3 Field tape

R. beticola '1$ZDVVXFFHVVIXOO\H[WUDFWHGIURP0HOLQH[WDSHXVLQJ3RZHU/\VHUŒPower Soil® th th DNA Isolation Kit. Ct values ranged from 23 on August 25 and August 26 to 30 on August th 30 . Based on the Ct values daily amounts of R. beticola DNA was calculated varying from 90 to 9,840 pg (Table 7.4.4).

Total amount Ct Date DNA per day value (pg) 20.08.12 24 3792 21.08.12 28 327 22.08.12 24 4673 23.08.12 27 534 24.08.12 28 285 25.08.12 23 9840 26.08.12 23 8997 27.08.12 29 107 28.08.12 27 642 29.08.12 30 90 30.08.12 23 8559 31.08.12 26 931 01.09.12 28 377 02.09.12 26 861

Table 7.4.4: Total amount of R.beticola DNA (pg) per day extracted from tape used in Burkard spore trap. Samples were collected in a two-week period, when R. beticola was sporulating in the field.

7.4.4 Leaf material

DNA samples recovered from beet leaves inoculated with R. beticola IURPWKHFXOWLYDUVµ&DFWXV¶ DQGµ3DVWHXU¶ZHUHWHVWHGIRUWKHSUHVHQFHRIR. beticola DNA using QPCR as described above. Figure 3 shows the trend for both cultivars in a period of four weeks. On June 25th, two days th prior to inoculation, no fungal DNA was detected (Ct value of 40). On June 29 , two days after the first inoculation, Ct YDOXHV UHDFKHG  IRU µ&DFWXV¶ DQG  IRU µ3DVWHXU¶ FRQILUPLQJ WKH presence of the inoculum. These values corresponded to 16.2 and 10.4 pg fungal DNA per mg th dry leaf matter, respectively (Figure 7.4.3). On July 12 Ct values dropped to 24 for both cultivars indicating that a development of R. beticola had taken place. To ensure infection, a second inoculation was carried out on July 17th. First symptoms of RLS were observed on July th th 19 . On July 30 Ct reached a value of 16 for both cultivars indicating an increase in the

32 amount of R. beticola biomass. On July 30th, leaf spots started coalescing on older leaves on both cultivars (data not shown).

40 45000

35 40000 r e t 35000 t 30 a m )

t

First visible symptoms y C

30000 r (

25 d

d l First inoculation g o 25000 m h

s /

e 20 g r p h 20000 t

A e l

15 N c y Second inoculation 15000 D

l C a

10 g

10000 n u F 5 5000

0 0

25-06-12 29-06-12 02-07-12 05-07-12 09-07-12 12-07-12 16-07-12 19-07-12 23-07-12 26-07-12 30-07-12 Cactus 40 27 35 30 33 24 27 17 17 19 16 Pasteur 40 28 36 30 31 24 24 18 19 19 16 Quantity C 0 16,2 0,1 2,9 0,4 195,6 27,5 14790,8 15935,9 4986,5 39879,8 Quantity P 0 10,4 0,05 2,5 1,9 201,3 132,7 10593,5 5050,5 4033,6 32195,2

Figure 7.4.3: Development of R. beticola LQIHFWLRQLQVXJDUEHHWOHDYHVRILQRFXODWHGFXOWLYDUVµ&DFWXV¶

DQGµ3DVWHXU¶&t value and corresponding quantities of R. beticola DNA per mg leaf tissue (dry matter) over a four-week period. Leaf samples were collected every third to fourth day in the period from June 25th to July 30th.

7.5 Discussion

A tool for early detection of fungal diseases must be accurate, reliable, and reproductive at the same time. In the last decades spore trapping has been successfully used to detect airborne spores of various fungal pathogens, including R. beticola spores (Hestbjerg & Dissing 1995; Holb et al. 2004; J. Khan et al. 2004; Gillum & Levetin 2007). Visual spore assessment is the predominant method for confirmation of presence or absence of certain types of spores. This procedure is time consuming, requires a trained eye, and can be difficult if tapes are loaded with all kinds of air particles and spores to detect are small in size and resemble each other in shape (Levetin 2004). Therefore the development of a QPCR based assay that enables to

33 detect and quantify fungal DNA recovered from air samples is a good mean to fulfill the requirements mentioned above.

The method presented in this article allows detecting and quantifying R. beticola DNA both collected from tape obtained from spore traps and extracted directly from plant tissues. With these procedures the method becomes part of a series of developed assays using QPCR for the detection of pathogens in the ITS regions (J. M. G. Taylor et al. 2010; Heuser & Zimmer 2002; Luchi et al. 2005). To our knowledge it is the first time a QPCR assay is reported for the pathogen R. beticola and in combination with an extraction method from tape.

For an effective test, the developed primers need to have a high specificity towards the target DNA (Bates et al. 2001). This is especially the case if primers are used in combination with air samples, which might contain high amounts of non target DNA. In this study no cross reaction was found with other fungi from the Mycosphaerella and Cercospora geni. It was of special importance to test the reaction with C. beticola, another common sugar beet pathogen, which might be present in the air at the same time as R. beticola but favors different weather conditions (J. Khan et al. 2009). It was possible to quantify R. beticola DNA in a range from at least 1000 down to 0.064 pg. For the lowest concentration tested (0.064 pg) showed a mean

Ct value of 34.5 and a high standard deviation. Hence it is suggested that cycle threshold values of 35 and above should be handled with care. This is supported by the fact that with lower amounts of target DNA in combination with a higher concentration of primer solution Ct values of > 35 were achieved, indicating a low specificity ± presumably due to primer dimer formation (data not shown).

Spore trapping and QPCR combined can serve as an excellent tool to be used for accurately monitoring fungal pathogens in the air before the occurrence of visible symptoms on plants, thus early detection. In this study it was possible to extract DNA from a plastic tape used for the purpose of spore trapping. In a similar experiment Roger et al. (2009) did likewise recovering Sclerotinia sclerotiorum DNA from a wax-coated plastic tape used in a Hirst-Burkard trap (S. L. Rogers et al. 2009). In the present study we used a commercially available kit for extraction of DNA from tape and showed that it was possible to detect low amounts of R. beticola.

Furthermore, it was demonstrated that the developed primers can be used for the detection of fungal biomass in living tissues. This has previously been done for R. collo-cygni in spring barley and for C. beticola in beet leaves (S. Lucas et al. 2012; J. M. G. Taylor et al. 2010). Yet, results from the spike test indicated that sensitivity of the primers is influenced by the presence of sugar beet leaf extracts. QPCR that allows detecting pathogen at a very early

34 stage of infection might be of special interest for plant breeding, improving the screening process for resistance in cultivars (S. Lucas et al. 2012). Nevertheless, for the use of early detection in order to determine the right time for fungicide application, it might not be as practicable for two reasons. Firstly, in the initial phase it cannot be determined whether or not the spores have already germinated. In the events of rain or wind spores might be cleared away, that at this point spraying might not be necessary. And more important secondly, the collection of leaf samples that give information on a representative area might involve too many single samples and hence would be very time-consuming.

In the last two decades a claim for pesticide reduction in agriculture was made due to increasing environmental impacts, a decreasing acceptance of pesticide use on behalf of consumers, and the awareness that excessive pesticides use augments the risk of building-up resistance in major pests. Following decision makers have taken the initiative and elaborated frameworks with the help of which the impact of plant control should be minimized to an adequate degree. One well-accepted and widely used example is the guidelines of Integrated Pest Management (IPM). According to the IPM guidelines non-chemical methods are to be preferred and pesticides should only be applied as last mean. Moreover pesticide applications should be linked to tools for monitoring guaranteeing an optimal application on time (Anon 2009).

Main fungal diseases are spread by wind; it appears possible to estimate the infection pressure and give an early warning of upcoming epidemics by monitoring air samples for fungal spores (J. S. West et al. 2008). The practicability of collecting spores using spore traps has been successfully conducted for many diseases e.g. Venturia inaequalis (Holb et al. 2004). For several diseases QPCR assays have been developed which allow the detection and the quantification of the inoculums in the air (S. L. Rogers et al. 2009). At present results can be available within 24 hours from the of receipt of spore trapping samples to the test laboratory, however time for transportation of samples to the test laboratory has to be taken into consideration also.

It is expected that the results can lead to an IPM based treatment where spraying should only be recommended when a real risk has been verified based on spore trapping in combination with QPCR. Ramularia leaf spot is not the only disease that causes yield losses. In order to give a complete risk evaluation of fungal diseases in sugar beets, a multiplex QPCR assay should be developed that includes the detection of Cercospora leaf spot as well as of rust (Uromyces betae) and powdery mildew (Erysiphe betae). In the future this test might be available combined with spore trapping in a portable device which allows analyses in real-time and on- site.

35

8 Does the early bird really catch the worm? ± Early detection of Ramularia beticola using QPCR and the effect of early control on in sugar beet

8.1 Abstract

Ramularia leaf spot (RLS) caused by the ascomycete Ramularia beticola, is one of the most important leaf diseases in sugar beets (Beta vulgaris) in Northern European countries. Over the last decades an increase of severe attacks of Ramularia leaf spot has been reported in Denmark, Sweden, and Finland. In Denmark RLS is a typically controlled using fungicides like epoxiconazole or epoxiconaozle + pyraclostrobin. Preliminary field trials have indicated a more efficient control of fungal diseases and higher sugar yield if sugar beets are treated with fungicides before visual symptoms occur. A study was conducted with the goals (a) to determine the effect of early fungicide treatment on Ramularia leaf spot prior to the occurrence of visible symptoms, (b) to follow the presence of Ramularia beticola spores in the air above sugar beet canopy using spore traps and QPCR, and (c) to elucidate the influence of relative humidity and temperature on massive releases of Ramularia beticola spores. All treatments of fungicide performed equally well compared to the untreated control regarding yield and disease severity. However, results showed a tendency for higher yields and a slower development of RLS in treatments carried out 4-5 weeks before the occurrence of visual symptom. Three spore traps were operated in sugar beet fields at three different sites on the Danish island of Lolland from weeks 28 to 37. With the help of QPCR, R. beticola DNA was detected in the air samples 14 and 16 days prior to first visible symptoms on untreated plants, which is in accordance with the reported latency of 2 weeks of the fungus. Three major mass spore releases occurred during the observation period. All spore releases were favored by RH of 95 % and low mean temperature one week before the event took place. In conclusion, spore trapping combined with QPCR and metrological data could be powerful tool to predict the onset and development of RLS. The effect on early fungicide treatments needs further investigation. It is expected that the results can lead to an IPM based recommendation where spraying only takes place when a real risk has been verified based on spore trapping or weather data.

Keywords: Ramularia leaf spot, early fungicide application, IPM, real-time PCR, Beta vulgaris

36

8.2 Introduction

Ramularia leaf spot (RLS) is one of the most important leaf diseases in sugar beets (Beta vulgaris) in countries of the temperate climates (Asher & L.E. Hanson 2006). It is caused by the ascomycete Ramularia beticola and thrives in cool, temperate climate at temperatures between 17 and 20°C and relative humidity between 90-95 % (Fautrey & Lambotte 1987; Ahrens 1987; Thach 2010). The infection occurs under low temperatures and wet weather conditions. The first visible symptoms appear 14 to 18 days after infection in vitro. Spores are dispersed by wind and water splashing (Hestbjerg & Dissing 1994; Hestbjerg & Dissing 1995).

If not controlled, yield losses can amount to 10 ± 20 % (Jørgensen 2003; Hansen 2008). Over the last decades an increase of severe attacks of Ramularia leaf spot has been reported in Denmark, Sweden, and Finland (Eronen 2008; Thach 2010). The disease is typically controlled using fungicides like epoxiconazole or epoxiconaozle + pyraclostrobin. According to IPM guidelines, fungicides are presently recommended to be applied if a severity threshold of 5 % is exceeded (Gummert et al. 2011). The second application is performed when new infections are observed with threshold 5-10 %. In Denmark the first application is normally performed around August the 1st. Typically 25-50% of the normal fungicide rate is recommended for each treatment (Hansen 2012a). Though this practice has proven its applicability novel methods for disease assessment combined with precision crop protection can create new opportunities and the chance to minimize fungicide usage by optimizing the fungicide treatment (Zijlstra et al. 2011). One approach is to prevent severe outbreak of fungal diseases by applying fungicide as a certain spore quantity is detectable in the air ± which might also allow reducing rates of fungicides. Field trials in sugar beets have indicated a more efficient control of fungal diseases and higher sugar yield if sugar beets are treated with fungicides before visual symptoms occur (Hansen 2012b). For a better determination of the optimized timing of disease control, advanced molecular methods could make a big contribution. One of those methods is real-time quantitative PCR (QPCR), which allows detecting and simultaneously quantifying of target DNA. Its specificity, sensitivity and its potential for high through put use has led to the development of several QPCR protocols for the detection of fungal material in the last couple of years (Wunderle & Leclerque 2012; Heuser & Zimmer 2002; Bustin et al. 2005; J. M. G. Taylor et al. 2010). Recently QPCR based assays for the detection and quantification of DNA from sugar beet pathogen Cercospora beticola and Ramularia beticola have been developed (S. Lucas et al. 2012)(Wieczorek et al. (unpublished). Using this technique in combination with spore trapping and metrological data FRXOGKHOSERWKWRHOXFLGDWHIXQJL¶VFKDUDFWHULVWLFVHJLQWKHLr life cycles and to keep track of their dispersal (Levetin 2004). Moreover, findings could form a basis for future mathematical prediction models that evaluate the risk of disease onset and severity as already done for other diseases as Cercospora beticola (Racca & Jorg 2007).

37

The objectives of this paper are to (1) test the effect of early fungicide application on the development of Ramularia leaf spot, (2) to determine the first occurrence of Ramularia beticola spores in the air above sugar beet canopy using Burkard spore traps and QPCR, and (3) to elucidate the influence of relative humidity and temperature on massive releases of Ramularia beticola spores.

8.3 Materials and Methods

8.3.1 Field trials

Two field experiments were conducted on the Danish island of Lolland. The trials were sown at WKH ORFDWLRQV µ.QXWKHQERUJ¶ .1, Coordinates: N 54° 48.457¶ ( ° 30.092¶  DQG µ'DQ &KULVWLDQVHQ¶ '&, Coordinates: N 54° 48.689¶(° 33.218¶) on March 25th and March 28th, UHVSHFWLYHO\ 7KH FXOWLYDUV µ3DVWHXU¶ 6WUXEH 6DDW *PE+  &R .* 6|OOLQJHQ*HUPDQ\  DQG µ&DFWXV¶ 0DULER 6HHG $6 +ROHE\ 'HQPDUN  ZHUH VRZQ LQ a randomized complete block design in four replicates. Gross plot size was 27.0 m2, consisting of 6 rows. 0.25 l ha-1 Opera® - (50 g l-1 epoxiconazole + 133 g l-1 pyraclostrobin, BASF A/S) - were applied after the scheme shown in Table 8.3.1 with a 3 meter wide plot boom sprayer with 10 booms and a nozzle distance of 50 cm. The rate applied is equivalent to 1/6 of the standard dose. Occurrence of the diseases RLS, Cercospora leaf spot (CLS), rust (Uromyces betae), and powdery mildew (Erysiphe betae) were weekly assessed on whole plants in row 3 and 4 starting in week 27 (June 2nd). Last assessment took place in week 42 (October 15th). Severity was evaluated on a scale from 0 to 10, (0-1 = up to 20 leaf spots, 3 = leaf spot start coalescing on old leaves, 5 = older leaves start to wither due to RLS attack, 7 = middle aged leaves start to wither, 10 = all leaves are withered). Net plot size for disease assessment was 13.5 m2 in size and consisted of 4 rows. Rows 3 and 4 (net plot size 9.0 m2) were harvested approximately six weeks after the last fungicide application. Yield parameters amino-N (per 100 g sugar), root (t ha-1), and sugar (%, t ha-1, relative) were measured following the harvest.

2QDWKLUGVLWH 6.2 DSDQHORIVXJDUEHHWFXOWLYDUVDQGOLQHVLQFOXGLQJFXOWLYDUVµ&DFWXV¶ DQGµ3DVWHXU¶ZHUHVRZQDQGLQRFXODWHGWZLFHZLWKEXONHG R. beticola spore suspensions (first inoculation 3.2 x 104 spores/ml; second inoculation 2.2 x 103 spores/ml). No fungicides were applied at this location. General information on all three field trials is given in Table 8.3.2.

38

Table 8.3.1: Spraying scheme. Bold numbers indicate the week in which fungicides (0.25 l ha-1 Opera) were applied.

Treatment Spraying scheme - week number 1 27 28 29 30 31 32 33 34 35 36 2 27 28 29 30 31 32 33 34 35 36 3 27 28 29 30 31 32 33 34 35 36 4 27 28 29 30 31 32 33 34 35 36 5 27 28 29 30 31 32 33 34 35 36 6 27 28 29 30 31 32 33 34 35 36

Hobo® Data loggers (Onset Computer Cooperation, Cape Cod, MA, USA) were installed in direct adjacency of both fungicide trails DC and KN - one at each site. Changes of relative humidity (%) and temperature (°C) were recorded on an hourly basis during the time disease severity was assessed.

Table 8.3.2: Information overview on three sugar beet field trials in which Burkard spore traps were operated from weeks 28 to 37.

DC KN SKO Sowing (week) 12 13 12 Cultivar Cactus Pasteur Several Position of sampler oritice canopy level Fungicide application see table 1 see table 1 none Herbicide application common practice Fertilization common practice Infection natural natural artificial Assessments weekly weekly none Spore trapping yes yes yes Harvest (week) 42 42 not harvested

8.3.2 Spore tapping

Three Burkard® 7 day volumetric spore samplers (Burkard Manufacturing Co. Ltd., Rickmansworth, UK) were used to keep track of the presence of R. beticola spores in the air above sugar beet canopy in the period from weeks 28 to 37. Two spore traps were placed in direct proximity of the field trials DC and KN (Figure 8.3.1). The third spore trap was operated DW WKH LQRFXODWHG WULDO 6.2 7KH VSRUH WUDSV ZHUH LQVWDOOHG DW FDQRSLHV¶ KHLJKW DQG consequently raised 10 cm if canopy exceeded the orifice. The air flow was set on 0.01 m3/min. Melinex® tape used for spore trapping was prepared by coating with an adhesive agent (vaseline + 10 parts toluene to 1 par paraffin wax). The tapes were replaced once a

39 week, cut into pieces representing 24 h (in the following referred to as one day) and stored at 6°C until further use.

Figure 8.3.1: Burkard spore trap operated in a sugar beet field. (Picture: T. M. Wieczorek)

8.3.3 DNA extraction and QPCR

Each piece of tape representing one day was further cut into 10 equally sized pieces under VWHULOHFRQGLWLRQV7KHVHSLHFHVZHUHSRROHGDQG'1$ZDVH[WUDFWHGXVLQJ3RZHU/\VHUŒ3RZHU Soil® DNA Isolation Kit (MO BIO Laboratories, Inc. Carlsbad, CA, USA) as described in Wieczorek et al. (unpublished). DNA was stored at - 18°C until further use. A QPCR assay was performed in a MicroAmp Optical 386-well reaction plate. Each reaction made up a total volume of 12.5 µl consisting of 6.250 µl Power SYBR® Green (Life technologies corporation, Carlsbad, CA, USA), 0.375 µl forward primer Rbf2 (10 µM), 0.375 µl reverse primer Rbr4 (10 µM), 3.0 µl MilliQ water, and 2.5 µl template DNA. De-ionized water served as a negative FRQWURO7ZRDPSOLILFDWLRQVZHUHVHWXSIRUHDFKVDPSOHWRFRQILUPWKHUHVXOWV¶UHSURGXFLELOLW\ The QPCR was carried out in an AB applied Biosystems ViiA7 according to the following program: initial hot start at 95°C followed by 40 cycles of 15 seconds of 95°C and 60 seconds annealing phase of 60 °C. Florescence was measured during the annealing phase. During each reaction a standard curve based on a confirmed R. beticola isolate (11-RR-07) was included and served for the quantification of R. beticola DNA. The approximate number of R. beticola spores was calculated based on a genome of 30 Mb.

40

8.3.4 Statistical analysis

Data from field trials (harvest parameters and disease severity) were analyzed with a classical general linear model (Procedure GLM in SAS v 9.2) and results are presented with adjusted means (LSmeans; P = 0.05). Weather data and data from spore tapping were analyzed with Pearson Correlation Coefficients (N = 9, Prob > |r| under H0: Rho=0). Graphs were made in Excel 2010 based on untransformed data.

8.4 Results

8.4.1 Field trials

8.4.1.1 Disease severity

Disease severity of RLS, powdery mildew and rust was relatively low in 2012 compared to previous years. Only moderate attacks of RLS were found at the location KN (Figure 8.4.1 B). Attacks were less severe at DC. The first symptoms for RLS and rust occurred in week 31; the first symptoms of powdery mildew were observed in week 33. Cercospora leaf spot did not occur in this year.

A B C

Figure 8.4.1: Initial symptoms of RLS (A), moderate attack of RLS at KN ± leaf spots have begun coalescing (B), severe attack of RLS (C). (Picture: T. M. Wieczorek)

The development of leaf diseases in untreated plots at KN and DC over the entire evaluation period is shown in Figure 8.4.2. In the beginning disease levels were low - development of rust increased in week 34, attacks of powdery mildew and RLS increased notably in weeks 35 and 36, respectively. Thus the onset of leaf disease in sugar beets in Denmark 2012 was late. Late assessments in weeks 38, 41, and 42 indicate that increase in all three leaf diseases continued until sugar beets were harvested.

41

K N 10 D C 10 9 9 8 8 7 7

10) 6 10) 6 - - 5 5 4 4

Score (0 3 Score (0 3 2 2 1 1 0 0 27 28 29 30 31 32 33 34 35 36 37 38 41 42 27 28 29 30 31 32 33 34 35 36 37 38 41 42 Week Week

Ramularia Rust Powdery Mildew Ramularia Rust Powdery Mildew

Figure 8.4.2: Development of RLS, powdery mildew and rust in untreated plots at locations KN (left) and DC (right). Disease severity was assessed from week 27 to 42 on a scale from 0 (healthy plant) to 10 (dead plant).

8.4.1.2 Effect of different spraying regimes of RLS

Figure 8.4.3 illustrates the development of RLS at locations KN and DC. First symptoms appeared in week 31 and remained on a low level until week 35. In week 38, 15-20 days after the last application of fungicides, disease severity reached a score of 0.9 for untreated plots at both locations.

1 1 K N 0,9 D C 0,9 0,8 0,8 0,7 0,7 10) 0,6 10) 0,6 - - (0 (0 (0 0,5 0,5 0,4 0,4 Score Score 0,3 0,3 0,2 0,2 0,1 0,1 0 0 27 28 29 30 31 32 33 34 35 36 37 38 27 28 29 30 31 32 33 34 35 36 37 38 Week Week

Untreated Treatment 2 Treatment 3 Untreated Treatment 2 Treatment 3 Treatment 4 Treatment 5 Treatment 6 Treatment 4 Treatment 5 Treatment 6

Figure 8.4.3: Development of RLS in plots treated with different fungicide regimes at the locations KN (left) and DC (right). Disease severity was assessed from week 27 to 38 on a scale from 0 (healthy plant) to 10 (dead plant).

Treatments 2 to 6 showed significantly less symptoms of RLS compared to untreated plots. In average of two field trials treatments 2 ± 4 were significantly less infected compared to treatments 5 and 6 in week 39. Thus the best treatments were those where fungicides were

42 applied earlier. Occurrence of rust and powdery mildew in untreated plots were also significantly higher than in treated plot, however no differences were detected among the different treatments (Table 8.4.1). In week 42 significant differences were detected among treatments 2 ± 6 for neither disease.

Table 8.4.1: Disease severity of RLS, powdery mildew, and rust in weeks 39 and 42 in all treatments. Mean results from two field trials.

Powdery Powdery Entry Treatment Ramularia* Rust Ramularia Rust Mildew Mildew

Week 39 Week 42

1 Untreated 0,9 7,3 4,2 4,8 8,1 4,3 2 Week27 0,0 0,0 0,0 0,0 1,1 0,2 3 Week28 0,0 0,0 0,1 0,0 1,6 0,2 4 Week29 0,0 0,0 0,1 0,0 0,8 0,2 5 Week30 0,1 0,0 0,2 0,1 1,8 0,3 6 Week31 0,1 0,0 0,1 0,0 0,6 0,2 LSD1-7 0,1 0,0 1,2 1,5 1,5 1,1 * Disease severity was assessed on a scale from 0 (no symptoms) to 10 (dead plant).

8.4.1.3 Yield

43

112 111 110 109 108 107 106 KN 105 DC 104 avg 103 102 Relative Relative sugar yield (%) 101 100 a a a 99 UTC Week27 Week28 Week29 Week30 Week31 1 2 3 4 5 6

Figure 8.4.4: Relative sugar yield of two field trials including the average of both. Different letters represent significant differences at a 5 % level. Treatments are compared within the separate trials. (LSD-value KN = 4; LSD-value DC = 4; LSD-value avg = 1.4)

On average fungicide treatments yielded relatively 9 to 10 per cent more the untreated plots (= 100). Comparing treatments 2 ± 6 no significant differences were found in terms of relative sugar yield, sugar %, sugar t ha-1 or root t ha-1. Amounts of amino ± N (per 100 g sugar) varied from 53 in untreated sugar beets to 36 in sugar beets, which were treated starting from week 28 on. Thereby, amino ± N was significantly lower in sugar beets treated with fungicides. An overview of mean yield parameter results is given in Table 8.4.2.

Table 8.4.2: Mean results of yield parameters amino-N (per 100 g sugar), root (t ha-1), and sugar (%, t ha-1, relative) of two field trials where sugar beets were treated with fungicides at different times.

Entry Treatment Amino-N Root Sugar per 100 g sugar t/ha % t/ha relative 1 Untreated 53 89,1 18,07 16,10 100 2 Week27 38 97,2 18,22 17,71 110 3 Week28 36 96,3 18,15 17,49 109 4 Week29 41 97,6 18,09 17,67 110 5 Week30 37 96,6 18,11 17,51 109 6 Week31 37 96,2 18,16 17,48 109 LSD1-7 3 1,4 0,09 0,23 1 LSD2-7 3 ns ns ns ns

44

8.4.2 Spore trapping

It was possible to detect and quantify R. beticola DNA from tape. The first signals for the detection of R. beticola DNA were found on July 15th and July 17th at the sites KN and DC, respectively (Figure 8.4.5). In the two weeks before first visible symptoms varying amounts (pg) of fungal DNA were detected at all sites ranging from 4.9 to 142,9 pg per day. Assuming a genome equivalent of 16.5 spores pg-1 this amounts to 81 to 2358 spores. During the entire collecting period the quantity of conidia in the air varied from 81 to 25461 per day. First traces at the SKO were already detected on July 10th. Permanent presence of spores in the air was detectable from July 22nd on location SKO, from August 22nd on at KN, and from September 10th on at DC. Although the quantity of spores varied strongly from day to day and from location to location, several periods showed similar patterns. On July 21st no spores at all were observed on either site. And again no DNA was detected at either KN or DC on August 8th and August 9th as well as on August 21st. At the site of SKO where spore amounts were high at that point of the season, the number of spores declined also notably.

09-jul 10-jul 11-jul 12-jul 13-jul 14-jul 15-jul 16-jul 17-jul 18-jul 19-jul 20-jul 21-jul 22-jul 23-jul 24-jul 25-jul 26-jul 27-jul 28-jul 29-jul 30-jul

DC 4,9 9,7 18,9 18,9 72,9 KN 18,9 18,9 18,9 37,1 9,7 18,9 4,9 18,9 142,9 84,7

Signal No signal Date of first symptoms

Figure 8.4.5: Timeline from July 9th to July 30th indicating the detection for R. beticola DNA at locations DC and KN. In this period DNA quantity ranged from 4.9 to 142.9 pg per day. No DNA was detected before July 15th. First symptoms of RLS occurred on July 30th.

8.4.3 Weather data

During the entire period, the relative humidity and temperatures were recorded on an hourly basis, from which average daily values were calculated. The results indicate that the quantity of spores in the air increased after approximately a week of low mean temperature. Further spore release was promoted when RH dropped from 100 % to 95 % prior to the event. These values were then correlated to the quantity of spores of a 9 days period before massive spore release (here also referred to as µpeak¶ occurred.

45

Table 8.4.3: Correlations between the quantity of spores and average daily relative humidity (%) and temperature (°C) measured in a period of nine days prior to a mass spore release at locations DC and KN. Pearson Correlation Coefficients (N = 9, Prob > |r| under H0: Rho=0)

1. peak 2. peak 3. peak Corr Prob Corr Prob Corr Prob DC 0,50 ns (0,1748) 0,62 ns (0,0757) 0,23 ns (0,5514) RH (%) KN 0,16 ns (0,6852) 0,17 ns (0,6657) -0,17 ns (0,6712) DC -0,21 ns (0,5859) -0,40 ns (0,2798) 0,04 ns (0,9281) Temperature °C KN 0,28 ns (0,4714) -0,45 ns (0,2229) -0,3942 ns (0,2938)

A Pearson correlation was made in order to reveal putative connections between RH, temperature and spore quantity in the air. However, results show that none of the correlations obtained significance in these tests (Table 8.4.3). The development of spore quantities, RH and temperature for both sites is given in Figure 8.4.6. During the entire period temperatures measured at KN and DC were almost the same. However, the RH turned out to be more variable at the site DC. The concentration of spores in the air increased throughout the entire examination period. Three periods of massive conidial release were observed from week 28 (July 9th) to 37 (September 16th), each exceeding the previous one in intensity. The first concentration peak of spores took place between July 28th and July 29th, with 1202 and 2358 conidia per day at DC and KN respectively. On August 25th 2990 conidia at DC and 9097 conidia at KN were observed. The highest concentration peak, however, was reached at the end of the season on September 14th reaching 3536 and 25461 per day.

46

K N 30000 24,0

25000 22,0

20,0

20000 C) ° 18,0 15000 (quantity) 16,0

Spores 10000 Temperature( 14,0

5000 12,0

0 10,0 09.07.12 11.07.12 13.07.12 15.07.12 17.07.12 19.07.12 21.07.12 23.07.12 25.07.12 27.07.12 29.07.12 31.07.12 02.08.12 04.08.12 06.08.12 08.08.12 10.08.12 12.08.12 14.08.12 16.08.12 18.08.12 20.08.12 22.08.12 24.08.12 26.08.12 28.08.12 30.08.12 01.09.12 03.09.12 05.09.12 07.09.12 09.09.12 11.09.12 13.09.12 15.09.12

Spores Temp

30000 110,0

K N 25000 100,0

20000 90,0

15000 80,0 RH (%)

Spores (quantity) Spores 10000 70,0

5000 60,0

0 50,0 09.07.12 11.07.12 13.07.12 15.07.12 17.07.12 19.07.12 21.07.12 23.07.12 25.07.12 27.07.12 29.07.12 31.07.12 02.08.12 04.08.12 06.08.12 08.08.12 10.08.12 12.08.12 14.08.12 16.08.12 18.08.12 20.08.12 22.08.12 24.08.12 26.08.12 28.08.12 30.08.12 01.09.12 03.09.12 05.09.12 07.09.12 09.09.12 11.09.12 13.09.12 15.09.12

Spores RH

D C 4000 25,0 3500 20,0 3000 C) ° 2500 15,0 2000

1500 10,0 Spores(quantity) Temperature( 1000 5,0 500

0 0,0 09.07.12 11.07.12 13.07.12 15.07.12 17.07.12 19.07.12 21.07.12 23.07.12 25.07.12 27.07.12 29.07.12 31.07.12 02.08.12 04.08.12 06.08.12 08.08.12 10.08.12 12.08.12 14.08.12 16.08.12 18.08.12 20.08.12 22.08.12 24.08.12 26.08.12 28.08.12 30.08.12 01.09.12 03.09.12 05.09.12 07.09.12 09.09.12 11.09.12 13.09.12 15.09.12

Spores Temperature

4000 100,0

3500 95,0 90,0 D C 3000 85,0 2500 80,0 2000 75,0 RH (%) 1500 70,0 Spores (quantity) Spores 65,0 1000 60,0 500 55,0 0 50,0 09.07.12 11.07.12 13.07.12 15.07.12 17.07.12 19.07.12 21.07.12 23.07.12 25.07.12 27.07.12 29.07.12 31.07.12 02.08.12 04.08.12 06.08.12 08.08.12 10.08.12 12.08.12 14.08.12 16.08.12 18.08.12 20.08.12 22.08.12 24.08.12 26.08.12 28.08.12 30.08.12 01.09.12 03.09.12 05.09.12 07.09.12 09.09.12 11.09.12 13.09.12 15.09.12

Spores RH

47

Figure 8.4.6: Quantity of spores in combination with mean RH (%) and temperature (°C) from July 9th to September 17th at the sites KN (above) and DC (below). Black arrows indicate the time of mass spore releases.

8.5 Discussion

Although the yield increases were not significant, the results presented in this study indicate that an early treatment with fungicide has a positive effect on the sugar yield. In both trials the highest yield was found when the treatment started in week 27, approximately 5 weeks before the occurrence of initial symptoms. This is in accordance with results from a similar experiment performed by NBR Nordic Beet Research in 2011 (Hansen 2012b). Both cases show a tendency that a higher yield can be achieved if fungicides are applied at preventive or an early curative timing. With the help of QPCR, R. beticola DNA was detected in the air samples in week 29. This was 14 and 16 days prior to first visible symptoms on untreated plants, which corresponds to the reported latency of 2 weeks of the fungus (Ruppel 1986). These results indicate that the detection spores in the air and early application of fungicides can be successfully combined and lead to higher yield. Nevertheless it should be not neglected that use of some fungicides like the strobilurin fungicides provides a so-FDOOHG ³JUHHQLQJ HIIHFW´ The greening effect is well described and can partly be explained as an effect caused by the fact that healthier plants show an overall better performance by investing more energy into the build-up of photosynthetic active biomass than in withstanding fungal attacks (Bartlett et al. 2002). It should also be taken into account that fungicide treatments also provided very effective control of powdery mildew and rust and that the yield increases should be seen as a sum of the total disease control.

It was possible to extract and detect R. beticola DNA using a commercial available kit and QPCR as done in Wieczorek et al. (unpublished). In order to evaluate the results from QPCR assay, it is of advantage to be able to determine the actual quantity of spores. In a paper on Monilinia fructicola, Luo et al. (2007) performed a linear correlation with numbers of spores counted from parts of the tape with a compound microspore (Luo et al. 2007). The same method was used by Carisse et al. (2009) quantifying air borne inoculum of Botrytis squamosa on onions and Fraaije et al. on Mycosphaerella graminicola (B A Fraaije et al. 2005; Carisse et al. 2009). Due to the goal of early detection of R. beticola the entire tape was used for DNA extraction in our experiments and thus could not be used for microscopy. Therefore we chose to make a different approach by calculating the weight of one spore based on a genome size of 30 Mb, inspired by the genome size of R. collo-cygni. To date the exact genome size of R. beticola is unknown. Until this information is available spore counting in combination with

48

QPCR as performed in the studied mentioned will be the only way of quantifying the spore abundance in the air.

In 2012 the level of RLS attacks were relatively low compared to other years (Wieczorek et al. 2011). Also the occurrence of rust and powdery mildew were low and uniform. It is assumed that these two diseases had no influence in the development of RLS. Disease assessments at the end of the growing season indicate that all treatments had the same good effect controlling RLS compared to the untreated plants. Yet, no significance was found among treatments where fungicides were applied earlier (weeks 27-29) and treatments where fungicides were applied at a later date (weeks 30-31). For evaluation of disease scores interpretation of the significant levels should be done with caution since random variation in the statistical model are assumed to follow a normal distribution, which, however, often is not the case for such data. Nevertheless, it turned out that RLS attacks developed more slowly in treatments in which fungicide application was carried out in weeks 27, 28, and 29. In the case of an early onset of the disease and optimal infection condition at the same time, it can be assumed that an early fungicide application could be advantageous and help to reduce yield losses. The last assessments show an increase in disease severity exactly in the week of harvest, mainly due to optimal climate conditions. Taking a late but strong development of the disease into account, the results might have been clearer if the harvest of the trials was carried out later. It has been reported earlier that RLS thrives best in humid weather with low mean temperatures (Asher & L.E. Hanson 2006; Olsson & Persson 2012). Those conditions are often given in the end of summer and in the beginning of autumn. Considering the mild autumns and winters of the last years, RLS might become a major problem if the harvest period is further spread out. Therefore it should be further investigated which impact a late onset of the disease might have and if an early treatment helps minimizing disease severity later in the season. For future trials on fungal diseases, it might be a good idea to postpone harvest if the weather conditions allow it. Furthermore, in regard to a prolonged growing season due to the effects of climate change it might be necessary to reevaluate current spraying strategies. Whereas nowadays the permitted fungicide rate is divided into two to three applications, an extended growing season might require applying lower dosages but more often in order to hold disease levels down. Moreover, a further reduction of the overall permitted fungicide rate by the authorities might make it necessary to develop new spraying strategies. In these scenarios an early treatment of foliar diseases with an even lower dosage might be of interest. Here as well, further research is needed.

The correlations among weather data and the spores in the air demonstrated that massive spore releases were not significantly influenced by either temperature or relative humidity, although the correlation between massive spore release 2 at DC and RH is close of being

49 significant at a 5 % level. The results may be analyzed more thoroughly based on regression analyses. Hestbjerg and Dissing reported that 50 % of the variation in the concentration of conidia could be explained by the number of hours with a relative humidity above 95 % in the preceding 24 hours and including field data, time of day and vapor pressure (Hestbjerg & Dissing 1995).

Nevertheless results of this study show some tendencies that a mass spore release occurred with some days delay after relative humidity dropped from the maximum value (100 %) to values between 90 - 95 % and temperatures raised simultaneously after a period with low mean temperatures. This was observed at both sites for all three spore concentration peaks. Comparing the results of weather data measured at KN and DC, the temperatures followed evidently almost the exact same pattern. This is not the case for RH values. Even though at both sites a decline of RH was manifested prior to the presence of high amounts of spores in the air, RH measurements at DC appear to be more sensitive, indicating that the data logger installed at KN possibly did not work as thoroughly as the one at DC. In order to double-check if measurements are correct or not and as a future alternative to collect the correct data, RH values could be extracted from weather stations closest to the field site according to its zip code location. All correlations presented here are preliminary calculations. For a more comprehensive investigation, regression analyses will be performed at a later date. In any case, climate data must always be handled with care, since occurring interactions between all factors must not be neglected and in order to obtain a more profound understand various climate factors need to be taken into account e.g. precipitation, dew point, wind speed, and many more. By any means, the results of this study show that R. beticola conidial numbers can be linked to RH and temperatures and might make a contribution to the prediction of disease onset and to the evaluation disease severity. In combination with further research this knowledge could lead to the creation of a disease forecasting models to an optimized RLS management as done for CLS (Rossi & Battilani 1991).

When elucidating the development of a leaf disease like RLS other factors on top of weather data need to be included in the discussion in order to give a full picture of the disease risk. This should also include data on location, sowing date, previous crop and choice of cultivar. In previous NBR field trials both cuOWLYDUV µ&DFWXV¶ DQG µ3DVWHXU¶ KDYH VKRZQ WKH VDPH performance in terms of susceptibility towards leaf diseases (Thomsen 2011). Results from the KHUHSUHVHQWHGILHOGH[SHULPHQWVLQGLFDWHWKDWWKHFXOWLYDUµ3DVWHXU¶KDGDUHODWLYHEHWWHU\LHOG WKDQµ&DFWXV¶3DVWHXU¶VRYHUDOOUHODWLYH\LHOGZDVKLJKHUGHVSLWHRIPRUHVHYHUHDWWDFNVRIOHDI GLVHDVHFRPSDUHGWRµ&DFWXV¶7KLVIDFWFDQEHsimply explained by cultivar differences. Even though the locations KN and DC were situated in close vicinity to one another (5 km air line distance), the field characteristics differed. The field at KN was approximately 100 ha in size,

50 and thereby much bigger than the field at DC which counted approximately 20 ha. In addition DC was bordered by forest. It is assumed that this might had an impact on wind speed, thus on conidial spread. The spore trap at KN was placed in the middle of the field which turned out to be on a little hill. This might explain the higher levels of spore quantity in the air. Nevertheless, spore numbers show the same pattern at both sites. Despite of the differences in location and yield performance, both sites and cultivars showed the same tendency regarding conidial release pattern and yield.

It has been demonstrated that with the help of QPCR and spore trapping, the occurrence of R. beticola in the air can be detected and followed over a long period of time. For the future it can be assumed that with novel molecular technologies as QPCR, monitoring the dispersal of spores and thereby the prediction the risk of leaf disease can be optimized, to the extent that a portable device will help with decision making on-site and in real-time. After monitoring data can be analyzed to give recommendations to the farmer (Zijlstra et al. 2011). As shown in this study, it is possible to detect R. beticola already before the occurrence of visible symptoms i.e. in terms of RLS control these techniques are ready to be used. It is expected that the results can lead to a recommendation where spraying only takes place when a real risk has been verified based on spore trapping or weather data. The effect of an early fungicide treatment, however, still needs further investigation, also in regard to cost efficiency.

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9 Additional experiments

In the process of developing an optimized protocol for DNA extraction in combination with spore trapping, a number of tests was performed beforehand. These tests were not included in the two main articles as they only served for the acquisition of background knowledge. In this chapter a brief summary of these experiments is presented for the purpose of a more comprehensive overview.

9.1 Verification of Ramularia beticola isolates by sequence analysis of ITS regions

The purpose of this experiment was to verify R. beticola isolates obtained from naturally infested sugar beets from 2011.

Materials and methods

DNA purification

Genomic DNA from R. beticola isolates (11-RR-01, 11-RR-02, 11-RR-04, 11-RR-05, 11-RR-06, 11-RR-07, 11-RR-08, 11-RR-09, and 11-RR-23) was obtained for PCR and sequencing by scraping off 5 to 6 colonies from grass agar (GA) and transferring it directly into mortar and grinding it immediately. The fungal material and equipment were kept cool with liquid nitrogen in order to make a fine powder and to prevent degradation of nucleic acids. After grinding the material was transferred into 1.5 ml Eppendorf tubes and stored at ± 80 °C until further use. In addition to the nine putative R. beticola isolates from 2011, an already confirmed R. beticola isolate 10-RR-23 (CBS 113540) was included as control. The fungal DNA was extracted using the DNeasy® Plant Mini Kit (Qiagen GmbH, Hamburg, Germany) according to the PDQXIDFWXUHU¶V SURWRFRO VWHS  WR  ,Q VKRUW WR HDFK VDPSOH  —O $3 EXIIHU DQG  —O RNase were added and vortexed. Thereafter, the samples were incubated for 10 min at 65 °C. As a next step 130 µl AP2 buffer was added to each sample, then gently mixed and left for 5 °C min on ice. After centrifugation (5 min) at 14,000 rpm, the lysate was pipetted into a QIAshredder spin tube and again centrifuged at 14,000 rpm for 2 min. The obtained flow- through was thereafter mixed with 1.5 volumes of AP3 buffer. This mixture was then spun through a DNeasy Mini spin column and washed with the AW buffer to bind and rinse the DNA. As a last step, the DNA was recovered from the spin tube by adding 100 µl AE elution buffer and centrifugating at 8000 rpm. The total amount was 100 µl.

PCR amplification and sequencing

PCR amplification was carried out within the ITS (internal transcribed spacer) region 1 and 2 (Figure r). The PCR reaction was performed in a final volume of 25 µl. Each tube contained

52

11.3 µl autoclaved milliQ water, 5.0 µl 5x GoTaq PCR buffer (Promega), 2.5 µl ITS4 primer,

2.5 µl ITS5 primer (both 10 µM), 1.5 µl MgCl2 (25 mM), 1.0 2.5 mM dNTPs, 0.2 µl GoTaq polymerase (Promega) and 1.0 µl DNA template. In addition to the new R. beticola isolates, a positive control - CBS reference isolate 10-RR-23 - and a negative control containing autoclaved milliQ water were included.

ITS 5 ITS 4 18s rRNA ITS 1 5.8 S rRNA ITS2 28S rRNA

ITS 4 TCC TCC GCT TAT TGA TAT GC ITS 5 GGA AGT AAA AGT CGT AAC AAG C

Figure 9.1.1: Internal transcribed spacer region (ITS region, including the 5.8S gene) and sequences from primers ITS 4 and ITS 5. The arrows indicate the direction in which amplification is carried out (White et al. 1990).

The PCR was conducted in a thermal cycler (Applied Biosystems, 2720 Thermal Cycler version 2.09) according to the following program: initial hot start at 94 °C for 2.5 minutes followed by 35 cycles of 15 seconds denaturation at 94 °C, 30 seconds annealing at 58/59 °C, 1 minute elongation at 72 °CILQDOHORQJDWLRQDWƒ&IRUPLQXWHVDQGWKHQLQFXEDWLRQDWƒ&IRU’ 7KH UHDFWLRQ ZDV WHUPLQDWHG ZLWK  ƒ& IRU ’ A volume of 5 µl of the PCR reaction was analyzed in a 1.5% agarose gel along with DNA size markers and stained with ethidium bromide. All nine PCR products were sent to Macrogen Europe (Utrecht, The Netherlands) for sequencing of both DNA strands with the primers ITS 4 and ITS 5 (Figure 9.1.1).

Results & Conclusion

All 9 isolates were successfully amplified during PCR. All products were approximately 564 bp compared to the markers and reference isolate 10-RR-23. Isolates 11-RR-02 and isolate 11- RR-08 only showed a weak amplification. The result shows two bands for isolate 11-RR-01 ± one of the same size as R. beticola, another of approximately 800 bp. It is assumed that this isolate was contaminated (Figure 9.1.2).

53

Figure 9.1.2: Results of gelelectrophoresis of R. beticola isolates 11-RR-01, 11-RR-02, 11-RR-04, 11- RR-05, 11-RR-06, 11-RR-07, 11-RR-08, 11-RR-09, and 11-RR-23. All products are approximately 564 bp in size in comparison to the markers NJ+( OHIW DQGES'1$ ULJKW DVZHOODVWRWKHFRQWURO± a R. beticola reference isolate. No band is visible for the negative control.

9.2 Primer design

The process of primer design was already described in Article 1. In addition to the presented primer pair, another set of primers was designed. A complete overview of all primers is given in Table 9.2.1.

Table 9.2.1: Primer sequences for forward primers Rb1f and Rb2f and reverse primers Rb3r and Rb4r, including their lengths and melting temperatures (Tm °C).

Prime r Se que nce (5´ -> 3´) Le ngth Tm (°C) Rb1f GCCTTAAAGTCTCCGGCTGTCT 22 bp 62,1 Rb2f CTTAAAGTCTCCGGCTGTCTGA 22 bp 58,0 Rb3r TTAACGGCCTCACCGGTC 18 bp 58,2 Rb4r TTTGAAAGATTTAACGGCCTCAC 23 bp 57,1

All QPCR assays were conducted with primer pair Rb2f/Rb4r due to the better performance of this combination, regarding specificity and sensitivity. Especially combinations with Rb3r showed high tendency for primer-dimer formation (Table 9.2.2).

54

Figure 9.2.2: Specificity test of primer pairs Rb1f/Rb3r, Rb1f/Rb4r, Rb2f/Rb3r (concentration 300 Mn/300 Mn) on DNA extracted from isolates of R.beticola, C.beticola, R.collo-cyngi, and M.graminicola

isolates at concentrations of 10 pg/µl and 100 pg/µl. Amplification is designated as the mean Ct value of WZRUHDFWLRQV1RDPSOLILFDWLRQLVGHVLJQDWHGDV³XQGHWHUPLQHG´1W QRW tested.

Isolate Cycle threshold 10 pg/µl 100 pg/µl 10 pg/µl 100 pg/µl 10 pg/µl 100 pg/µl Rb1f/Rb3r Rb1f/Rb4r Rb2f/Rb3r R. beticola 'Pooled' 17 22 18 21 31 24 Ram331005 37 36 38 38 35 34 R. RAMS128 38 39 Undetermined Undeterminded 34 34 collo-cygni RamS218 37 37 38 Undeterminded 34 34 RamGøtt Undetermined 38 Undetermined 38 36 34 M. Fl. Stratego Undetermined 36 38 37 33 33 graminicola Fl Breamer 37 37 Undetermined Undeterminded 34 33 C. beticola Cb1 n.t. n.t. 35 33 Undetermined 35

9.3 Validation of a primer optimization matrix to improve the performance of QPCR

When designing primers, several tests need to be undertaken to ensure maximum specificity and yield. Furthermore, if primers are to be used in the presence of high amounts of non target DNA, a high sensitivity is desirable. Thus, in order to produce a well designed PCR protocol, many parameters need to be taken into consideration. One important step is the optimization of primer concentrations using a primer optimization matrix.

Materials and methods

QPCR was performed using the same protocol as described above. Solely the concentrations of the primers Rb2f and Rb4r were adjusted according to a primer optimization matrix (Figure 9.3.1). In all nine different combinations of primer combinations were tested: 50/50, 50/300, 50/900, 300/50, 300/300, 300/900, 900/50, 900/300, and 900/900 nM. Isolates tested were 11-RR-02 (Ramularia beticola), RAMS128 (Ramularia collo-cygni), and Cb1(Cercospora beticola) ± all were 10 x and 100 x diluted. H2O served as a control.

55

nM 50 300 900 50 1 2 3 300 4 5 6 900 7 8 9

Figure 9.3.1: Concentration matrix for primer optimization. In all nine combinations are tested: 50/50 (1), 50/300 (2), 50/900 (3), 300/50 (4), 300/300 (5), 300/900 (6), 900/50 (7), 900/300 (8), and 900/900 nM (9).

Results & Conclusion

Neither for R. collo-cygni nor for C. beticola isolates cross reactions were found at any concentration ± as seen in previous tests described in article 1. Concentration combinations 6, 8, and 9 showed a tendency for primer-dimer formation in the non-target reactions and in the water control. Accordingly Ct values for R. beticola were lower using these concentrations. Concentration combinations 2 and 3 worked well, yet small amounts of the amplification product were detected in the presence of low amounts of R. collo-cygni and C. beticola ± which could also be the result of the formation of primer dimers. Combinations 1, 4, 5, and 7 performed equally well. All non target samples were undetermined and Ct values for R. beticola were the same. It was decided to continue with combination 5 of Rbf2 and Rbr4 (Table 9.3.1).

Table 9.3.1: Primer optimization of primer pair Rb2f/Rbr4. Primer were test on R. beticola (11-RR-02), C. beticola (Cb 1), and R. collo-cygni (RAM S128) isolates in two concentrations (10x and 100x diluted). Nine primer combinations were tested: 50/50 (1), 50/300 (2), 50/900 (3), 300/50 (4), 300/300 (5), 300/900 (6), 900/50 (7), 900/300 (8), and 900/900 nM (9).

Concentration combination Isolate 1 2 3 4 5 6 7 8 9 11-RR-02 10x 21 19 19 20 19 19 20 17 17 11-RR-02 100x 24 22 22 23 23 22 23 21 21 Cb1 10x Undetermined Undetermined 39 Undetermined Undetermined 38 Undetermined 35 38 Cb1 100x Undetermined Undetermined Undetermined Undetermined Undetermined 38 Undetermined 37 39 R A MS128 10x Undetermined 39 39 Undetermined Undetermined 35 Undetermined 34 34 R A MS128 100x Undetermined Undetermined 36 Undetermined Undetermined 37 Undetermined 37 36 Negative control Undetermined Undetermined Undetermined Undetermined Undetermined 34 Undetermined 39 35

9.4 Inoculation of plastic tape

After it was proven, that the designed primers were specific enough to detect pure genomic R. beticola DNA, it was necessary to find a DNA-extraction method which would be optimal for extraction from a plastic tape which should be used for the future spore trappings in the field. 7KHREMHFWLYHRIWKLVH[SHULPHQWZDVWRWHVWWKH3RZHU/\VHUŒ3RZHU6RLOŠ'1$,VRODWLRQ.LW (MO BIO Laboratories, Inc. Carlsbad, USA) on tape inoculated with R. beticola spore suspensions. This kit is designed for extraction of microbial DNA from difficult sample types such as soil and environmental samples.

56

Materials and methods

Melinex® tape used for spore trapping in combination with Burkard® spore traps was prepared by coating with an adhesive (Vaseline + 10 parts toluene to 1 par paraffin wax). The tape was operated in a Burkard spore trap for seven days in a wheat field outside on the research station. After this period the tape was collected and a fivefold serial dilution of spore suspension starting at 1.33 x 1006 spores/ml were applied onto the tape by spraying with pressurized air (Figure 9.4.1). In order to reduce surface tension two drops of Tween®20 were added to each suspension. 0.5 ml of each suspension was applied.

Figure 9.4.1: Spore suspensions (left), plastic tape with spore suspension (middle), and pressurized air device to apply liquids (right).

After drying, the tape was divided vertically and then cut into pieces (Figure 9.4.2). DNA was H[WUDFWHGIURPDWRWDORIVDPSOHVXVLQJ3RZHU/\VHUŒ3RZHU6RLOŠ'1$,VRODWLRQ.LW 02 BIO Laboratories, Inc. Carlsbad, USA). Each sample was run in three replicates. The recovered DNA was stored at - 18°C until further use. A QPCR was conducted as described above

Day 1 Day 2 Day 3 Day 4 Day 5 Day 6 Day 7 1/1 x 1/5 x 1/25 x 1/125 x 1/625 x 1/3,125 x 1/15,625 x

Tape 1/1 x 1/5 x 1/25 x 1/125 x 1/625 x 1/3,125 x 1/15,625 x

Figure 9.4.2: A fivefold serial dilution of spore solution was prepared and sprayed on tape prepared for the use in a Burkard spore trap. The starting solution (1/1 x) has a spore density of 1.33E06 cfu/ml. Solutions were diluted by 1/5, 1/25, 1/125, 1/625, 1/3125, and 1/15625. The tape was retrieved from a spore trap that was run for 7 days (24 h) under field conditions in order to imitate field conditions.

57

Results & Conclusion

The QPCR assay revealed that it was possible to extract DNA using the Power Soil® DNA ,VRODWLRQ .LW IURP WDSH ZKLFK ZDV DVVXPHG WR FRQWDLQ ³HQYLURQPHQWDO LPSXULWLHV´ VLPLODU WR what could be expected in any spore sample from the field. Ct values ranged from 29 to 33.

The lowest Ct value was detected for the undiluted spore solution. The highest Ct value however was found for suspensions diluted by the factor 1/625, 1/3,125, and 1/15,625 (Table 9.4.1).

Table 9.4.1: Ct values for a fivefold serial dilution starting from dilution factor 1/1 x to 1/15,625 x. The positive control was R. beticola reference isolate 10-RR-23 (CBS 113540), the negative control autoclaved water.

Dilution factor Ct values 1/1 x 29 1/5 x 31 1/25 x 31 1/125 x 30 1/625 x 33 1/3,125 x 32 1/15,625 x 33

Negative Control Undetermined Positive Control 31

9.5 Testing of an alternative DNA extraction protocol

Little research has been done on the actual DNA extraction from plastic tape. In addition to the PowerSoil Kit a second method for DNA extraction kindly provided by Neil Havis, SAC was tested. This protocol was used with few modifications. A similar protocol was described by Williams et al. (2001) and has previously been used successfully with few modifications by Roger et al. (2009) on Sclerotinia sclerotiorum DNA from a wax-coated plastic tape using in a Hirst-Burkard trap (Williams et al. 2001; S. L. Rogers et al. 2009).

Materials and methods

Tape inoculated with a R. beticola spore suspension was cut into equally sized pieces and put into a 1.5 ml Eppendorf. 0.4 g sand and 440 Pl 2xTEN extraction buffer were added to each

58 tube in a fume hood. All samples were shaken in a grinder (2000 GENO/GRINDER.SPEX CertiPrep Inc. P®) for three times 30 seconds at 1500 strokes per minute. In between runs, the samples were allowed to settle for 2 minutes on ice. 1 µl/ml mercaptoethanol and 400 Pl 2% SDS were added to the tubes, then briefly vortexed and incubated for 30 minutes at 65°C. As a next step 800 Pl of phenol/chloroform/IAA (25:24:1) were added. After mixing, samples were centrifuged for 10 minutes at 13.000 rpm. In the meantime a new set of mircofuge tubes were prepared with 40 Pl of 0.5M Ammonium acetate, 600 Pl of isopropanol and 4 Pl of Glycoblue“ for DNA precipitation. After centrifugation, approximately 600 Pl supernatant was transferred to tube containing the mixture and mixed. The samples were then stored at -20qC overnight.

The next day after thawing, the samples were centrifuge at 13.000 rpm 20 minutes. The supernatant was discarded, without disturbing the blue DNA pellet. The blue DNA pellet was washed with 600 Pl 70% ethanol. After washing the tubes were allowed to dry for 15 minutes in the fume hood before resuspending the pellet in 50 Pl autoclaved distilled water. For complete resuspension the samples were put into a 50qC water bath for 10 minutes and afterwards gently pipetted. Subsequently, DNA amounts were measures with NanoDrop and a QPCR assay was performed as described above. The rest of the samples were stored at -20qC until further use.

Results & Conclusion

It was not possible to detect any DNA by NanoDrop. However, in two of the four samples, small blue pellets were visible on the side of the tube after centrifugation. This indicates that the actual DNA extraction worked (Figure 9.5.1).

Figure 9.5.1: R. beticola DNA sample obtained using an alternative protocol. At the tip of the arrow a small blue pellet of DNA is visible (right). On the left side a tube is shown during the extraction process - sand is seen at the bottom, dissolved plastic tape on the top (left).

59

Nevertheless, the QPCR assay did not detect any R. beticola DNA. In the same QPCR reaction, DNA samples from the same inoculated tape were run, which were obtained from DNA extraction using the PowerSoil kit as done in article 1. In that case R. beticola was detected (Table 9.5.1).

Table 9.5.1: Ct values for DNA samples extracted from tape. DNA was extracted using two different protocols: PowerSoil and an alternative one. The positive control was R. beticola reference isolate 10-RR- 23 (CBS 113540), the negative control autoclaved water.

Method Ct value 22 22 22 22

Power Soil Power 24 25 Undetermined Undetermined Undetermined

Alternative Protocol Undetermined Positive control 24 Negative control Undetermined

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10. Conclusion

The overall purpose of this thesis was to gain knowledge on the sugar beet disease Ramularia leaf spot and its causal pathogen Ramularia beticola. The effect of early fungicide application on RLS severity and on sugar yield under Danish conditions was analyzed, a molecular method based on QPCR was created to detect and to quantify R.beticola DNA in the air above sugar beet field and it was tried to elucidate to which degree climate conditions have an impact in WKHIXQJXV¶GHYHORSPHQW

Based on the results of this thesis the following conclusions can be drawn:

x Based on the internal transcribed spacer region two sets of primers were designed. Primer pair Rb2f/Rb4r was best in terms of specificity. It was of special importance that no cross reaction occurred when tested on sugar beet pathogen Cercospora beticola. The dynamic range for quantification is between 0.064 to at least 1000 pg R. beticola DNA. With the help of a standard curve it is possible to quantify the amounts of DNA in the samples.

x The developed QPCR protocol was successfully employed on DNA sample that were recovered from plastic tapes commonly used in combination with Burkard spore traps. The DNA was effectively extracted from the tape utilizing the commercially available extraction kit Powerlyzer Power Soil Kit, allowing a fast, easy, and standardized recovery of DNA samples. Furthermore the QPCR method was tested and approved on DNA samples from artificially infected sugar beet leaves.

x Using spore trapping and QPCR it was possible to detect R. beticola spores and to follow the fluctuation in quantity over at period of 9 weeks. Based on the results the presence of R. beticola spores prior to the appearance of first visible symptoms on untreated sugar beet plants in proximity to spore traps was confirmed. Thus, it was demonstrated that a putative risk of infection can be evaluated beforehand.

x Field trials were conducted in which the effect of an early application of fungicides was compared to application taking place after common practice under Danish conditions. In comparison to the untreated control, all treatments succeeded in holding disease levels low and yielded significantly more. Moreover, it was found that plots treated earlier showed a slight tendency for an increase in sugar yield overall and a better disease

61

control in the beginning of the leaf disease season. The treatments that showed at slight better performance were those, which were treated before the first spores of R. beticola were confirmed in the air. However, at the end of the growing season no difference was found among fungicide treatments which were treated earlier and those treated later. All different fungicide treatments were equally successful in minimizing fungal attacks. x It was shown that three intensive mass spore releases occurred during the observation period, each one exceeding the previous one in intensity. Relative humidity and temperature had an impact on the presence of R.beticola spores in the air. Weather records showed that mass spore releases were triggered by a decrease of relative humidity from a high level and by an increase of temperature after a period of mean low temperatures. Statistically no correlation was found between spore quantities in the air, temperatures, and relative humidity. Overall, this leads to the conclusion that results from spore trapping in combination with weather data can be used for a more accurate and local risk evaluation for the occurrence of leaf diseases and might serve as a basis for the creation of mathematical models, predicting the risk of disease onsets.

62

11 Perspectives

In this MSc thesis a QPCR assay is presented, with the help of which it is possible to detect and to quantify DNA of sugar beet pathogen Ramularia beticola in combination with spore trapping. Operating the spore traps and preparing DNA samples was straightforward. We chose a ³EDFNZDUGDSSURDFK´ILUVWFROOHFWLQJDOOWDSHVVDPSOHVRIWKHHQWLUHVHDVRQDQGWKHQH[WUDFWLQJ the DNA and performing QPCR all at once. However, because of the easy and fast handling, it is thought that it might be feasible to perform QPCR weekly to elucidate the actual risk potential of fungal air borne diseases. Even though the samples would be seven days delayed there should be enough time for decision making on fungicide treatment.

The main focus in this work was dedicated to R.beticola. R. beticola is just one disease of many. A next step could be to develop similar techniques for other major fungal diseases in sugar beet, notably powdery mildew, rust and Cercospora leaf spot. Primers suitable for QPCR are already available for C. beticola. Primers for rust and powdery mildew could be designed in the way as has been done in this study on R. beticola. If run together on air samples, a good overview can be given on the present infection pressure of leaf diseases. Furthermore it would be possible to combine all primers in an optimized multiplex QPCR assay for an even easier handling.

Yet, before launching into the development of a molecular method for leaf disease monitoring one simple question needs to be asked: What are the advantages and disadvantages of a QPCR based monitoring in comparison to other, already existing monitoring methods? The development and the performance of QPCR based monitoring are costly but in the long run it could pay off since weekly assessments of fields can be dropped. Furthermore additional information, such as spore abundance and the occurrence of mass spore release can be surveyed helping to elucidate the epidemiology of diseases. If correlated to weather data, the first onset of leaf disease could be predicted. Already existing monitoring methods, however, are based on the appearance of visible symptoms, when leaf diseases have already arrived in the field. And finally, even though the costs of innovative molecular methods such as QPCR are relatively high, it can be assumed that prices will decrease over time.

Not many field experiments have been conducted with early treatments for fungicides. The field experiments presented in this study have shown that there is a tendency for yield increase in early treated plots. In order to gain more knowledge further research is needed. Therefore, it would be interesting to continue this trial series to find out if the results can be repeated. In order to address several problems, several modifications could be taken into consideration to improve the field trials, such as: (1) the effect of fungicide application under

63 more severe attacks needs to be tested, (2) the harvest should be postponed or different timings of harvest should be introduced to show the effect of different treatments, especially with regard to R. beticola where disease onset has proven to be late, (3) the introduction of an entry in which treatment takes place according to result of spore trapping and weather forecast, (4) the use of different dosages of fungicides in order to see if early treatment can be effective at low dosages, and (5) a more encompassing collection of weather data, including temperature, relative humidity, dew point, and precipitations. Results of (4) might lead to a reconsideration of the currently used spraying strategy. Instead of carrying out two to three applications on high rates, an early application with a lower rate is performed in the beginning of the leaf disease season to keep disease levels low. If the infection pressure increases over time, the rate might be raised. If the infection pressure remains low, less or no further applications are needed, reducing the overall applied quantity of fungicides. Findings of (5) can be used to create a mathematical model for an even more accurate prediction of RLS and for comparison of epidemiology of other fungal disease. Overall, it is expected that the results can lead to an IPM based treatment where spraying should only be recommended when a real risk has been verified based on spore trapping in combination with climate factors.

Fungicide treatment is only one part in controlling leaf diseases. More resistant cultivars are needed in order to minimize infection pressure and to reduce fungicide applications. The use of QPCR in breeding programs might be an interesting supplement to visual disease assessment since non-disease related symptoms caused by senescence and environmental conditions can affect the accuracy of symptom assessments.

64

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Appendix 1: Spore trapping results

Lists 1 ± 3 below illustrate results of spore trapping at locations DC, KN, and SKO starting in week 28 throughout week 37. Ct values were given by Applied Bioinfomations. Quantities of Ramularia beticola (pg/day) DNA were calculated with a standard curve (y = - 3,4172x + 29.53). Spore quantity was estimated based on DNA quantity and assuming a 30 Mb genome size of Ramularia beticola.

1. DC

Week Date Ct Quantity Spores Week Date Ct Quantity Spores pg/day quantity/day pg/day quantity/day 09-jul 35 13-aug 35 10-jul 0 14-aug 31 27,4 452 11-jul 0 15-aug 33 7,0 115 12-jul 0 16-aug 37

Week 28 Week 13-jul 0 33 Week 17-aug 0 14-jul 35 18-aug 36 15-jul 37 19-aug 31 33,2 547 16-jul 36 20-aug 32 21,2 350 17-jul 34 4,9 81 21-aug 0 18-jul 0 22-aug 30 68,3 1127 19-jul 38 23-aug 0

Week 29 Week 20-jul 39 34 Week 24-aug 0 21-jul 0 25-aug 29 181,2 2990 22-jul 0 26-aug 29 142,6 2353 23-jul 0 27-aug 0 24-jul 36 28-aug 34 5,6 92 25-jul 37 29-aug 0 26-jul 33 9,7 159 30-aug 29 111,6 1842

Week 30 Week 27-jul 32 18,9 312 35 Week 31-aug 31 45,7 753 28-jul 32 18,9 312 01-sep 0 29-jul 30 72,9 1202 02-sep 33 9,1 150 30-jul 0 03-sep 0 31-jul 0 04-sep 33 8,1 133 01-aug 0 05-sep 30 57,0 941 02-aug 0 06-sep 32 15,7 259

Week 31 Week 03-aug 0 36 Week 07-sep 37 04-aug 0 08-sep 33 8,9 147 05-aug 0 09-sep 0 06-aug 0 10-sep 29 110,6 1825 07-aug 32 17,3 286 11-sep 30 71,7 1182 08-aug 40 12-sep 32 24,4 402 09-aug 0 13-sep 31 38,2 630

Week 32 Week 10-aug 32 18,6 307 37 Week 14-sep 28 214,3 3536 11-aug 38 15-sep 34 3,7 62 12-aug 34 3,6 59 16-sep 34 3,7 61

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2. KN

Week Date Ct Quantity Spores Week Date Ct Quantity Spores pg/day quantity/day pg/day quantity/day 09-jul 0 13-aug 32 20,0 329 10-jul 0 14-aug 31 32,5 536 11-jul 0 15-aug 29 149,8 2471 12-jul 0 16-aug 29 117,8 1943

Week 28 Week 13-jul 0 33 Week 17-aug 31 29,4 485 14-jul 0 18-aug 32 15,1 249 15-jul 32 18,9 311 19-aug 32 20,9 345 16-jul 32 18,9 312 20-aug 31 38,3 632 17-jul 32 18,9 312 21-aug 0 18-jul 31 37,1 613 22-aug 31 34,1 563 19-jul 0 23-aug 31 26,7 440

Week 29 Week 20-jul 33 9,7 159 34 Week 24-aug 31 31,2 515 21-jul 0 25-aug 27 551,3 9097 22-jul 32 18,9 312 26-aug 29 124,5 2055 23-jul 34 4,9 81 27-aug 31 39,6 654 24-jul 0 28-aug 33 10,1 166 25-jul 32 18,9 312 29-aug 33 11,9 196 26-jul 0 30-aug 27 472,8 7801

Week 30 Week 27-jul 0 35 Week 31-aug 31 44,7 738 28-jul 29 142,9 2358 01-sep 32 14,2 234 29-jul 0 02-sep 30 98,3 1622 30-jul 30 84,7 1397 03-sep 32 25,4 419 31-jul 0 04-sep 34 4,4 73 01-aug 0 05-sep 31 30,2 499 02-aug 34 6,1 101 06-sep 29 121,9 2012

Week 31 Week 03-aug 31 28,2 466 36 Week 07-sep 31 29,8 491 04-aug 33 11,2 184 08-sep 32 23,8 393 05-aug 33 8,2 135 09-sep 32 20,3 335 06-aug 32 20,0 330 10-sep 30 84,6 1396 07-aug 31 35,6 587 11-sep 28 346,0 5708 08-aug 0 12-sep 28 206,5 3407 09-aug 0 13-sep 28 344,6 5685

Week 32 Week 10-aug 0 37 Week 14-sep 25 1543,1 25461 11-aug 32 25,6 423 15-sep 33 11,5 190 12-aug 0 16-sep 31 46,3 764

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3. SKO

Week Date Ct Quantity Spores Week Date Ct Quantity Spores pg/day quantity/day pg/day quantity/day 09-jul 37 13-aug 26 781,1 12888 10-jul 30 90,3 1490 14-aug 27 623,0 10280 11-jul 39 15-aug 27 750,9 12390 12-jul 40 16-aug 22 13406,8 221213

Week 28 Week 13-jul 33 10,9 180 33 Week 17-aug 28 388,4 6408 14-jul 0 18-aug 26 813,8 13428 15-jul 0 19-aug 26 996,8 16448 16-jul 34 4,9 81 20-aug 24 3792,5 62576 17-jul 33 9,7 159 21-aug 28 326,7 5391 18-jul 22-aug 24 4673,5 77113 19-jul 31 37,1 613 23-aug 27 534,1 8813

Week 29 Week 20-jul 34 4,9 81 34 Week 24-aug 28 284,6 4695 21-jul 25-aug 23 9840,2 162364 22-jul 33 9,7 159 26-aug 23 8996,7 148446 23-jul 31 37,1 613 27-aug 29 106,9 1764 24-jul 30 72,9 1202 28-aug 27 642,4 10600 25-jul 29 142,9 2358 29-aug 30 89,7 1481 26-jul 29 142,9 2358 30-aug 23 8559,1 141226

Week 30 Week 27-jul 25 2116,6 34925 35 Week 31-aug 26 931,3 15366 28-jul 26 1079,0 17803 01-sep 28 377,3 6225 29-jul 28 280,4 4626 02-sep 26 860,7 14201 30-jul 31 37,1 611 03-sep 28 357,8 5904 31-jul 32 14,0 232 04-sep 27 403,4 6656 01-aug 29 121,8 2009 05-sep 27 740,6 12220 02-aug 27 411,5 6790 06-sep 28 304,7 5028

Week 31 Week 03-aug 28 306,2 5053 36 Week 07-sep 26 1378,0 22736 04-aug 24 4657,8 76853 08-sep 27 669,2 11041 05-aug 28 336,4 5551 09-sep 27 503,7 8311 06-aug 31 38,3 633 10-sep 26 1198,6 19776 07-aug 24 5036,5 83101 11-sep 24 4521,7 74608 08-aug 28 300,7 4962 12-sep 26 1155,0 19057 09-aug 27 750,9 12390 13-sep 27 689,8 11381

Week 32 Week 10-aug 27 712,2 11751 37 Week 14-sep 25 2804,3 46271 11-aug 26 1286,4 21226 15-sep 28 362,2 5976 12-aug 27 661,1 10908 16-sep 30 94,4 1557

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Appendix 2: Fungal isolates

Information (location, year and source) on isolates of Ramularia beticola, Cercospora beticola, and Ramularia collo-cyngi which were used during primer design and primer testing are listed above.

Year Isolate Country (locality) of Source origin

Ramularia beticola

10-RR-20 Sweden 2009 Andersson, Lennefors & Nihlgård 10-RR-21 Sweden 2009 Andersson, Lennefors & Nihlgård 10-RR-22 Switzerland 1967 CBS 10-RR-23 Sweden (Lund) 1985 CBS 09-RR-24 Denmark (Holeby) 2008 Anne Lisbet Hansen 10-RR-27 Denmark (Holeby) 2008 Anne Lisbet Hansen 10-RR-28 Denmark (Holeby) 2008 Anne Lisbet Hansen 10-RR-29 Denmark (Holeby) 2008 Anne Lisbet Hansen 10-RR-30 Sweden (Krisitanstad) 2009 Åsa Olsson 10-RR-31 Denmark (Nykøbing F) 2009 Tine Thach 10-RR-32 Denmark (Holeby) 2002 Anne Lisbet Hansen 10-RR-33 The Netherlands 2009 Elma Raaijmakers 10-RR-35 Denmark (Kastrup) 2009 Tine Thach

11-RR-01 Denmark (Sørbymagle) 2011 Thies Marten Wieczorek 11-RR-02 Denmark (Kastrup) 2011 Thies Marten Wieczorek 11-RR-04 Denmark (Nørre Alslev) 2011 Thies Marten Wieczorek 11-RR-05 Denmark (Nørre Alslev) 2011 Thies Marten Wieczorek 11-RR-06 Denmark (Krårup) 2011 Thies Marten Wieczorek 11-RR-07 Denmark (Maribo) 2011 Anne Lisbet Hansen 11-RR-08 Denmark (Rødby) 2011 Anne Lisbet Hansen 11-RR-09 Denmark (Holeby) 2011 Thies Marten Wieczorek 11-RR-23 Denmark (Nørre Alslev) 2011 Thies Marten Wieczorek

Cercospora beticola The Netherlands 10-CR-01 (Koningsbosch) 2000 Elma Raaijmakers

Ramulaira collo-cygni

RAM05-33-10 Switzerland RamS128 Denmark RAMS218 Denmark RAMGøtt Germany Fl. Stratego Denmark Fl. Breamer Denmark

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Appendix 3: Disease assessment guide for RLS, rust and powdery mildew

Assessment of leaf diseases

Ramularia leaf spot

0-1 20 spots or less on one plant 2 More than 20 spots on one plant 3 Spots start to coalesce on older leaves 4 Spots start to coalesce on middle leaves 5 Few older leaves wither because of RLS 6 Many older leaves wither because of RLS 7 Parts of middle-aged leaves wither 8 Some middle-aged leaves wither 9 All old leaves and some middle-aged leaved wither 10 Both old and middle-aged leaves wither

Rust

0,1 Few pustules Ca.100 1 pustules 2 First signs of sporulation 3 Beginning emergence of the yellow halos around pustules on older leaves 4 Beginning emergence of the yellow halos around pustules on older and younger leaves 5 Withering of few older leaves because of rust attack 6 Withering of many older leaves 7 Middle-aged leaves start withering 8 Withering of few middle-aged leaves because of rust attack 9 All old leaves and some middle-aged leaved wither 10 Both old and middle-aged leaves wither

Powdery mildew

0-1 0-10 % of the leaf surface attacked by powdery mildew 2 10-20 % of the leaf surface attacked by powdery mildew 3 20-30 % of the leaf surface attacked by powdery mildew 4 30-40 % of the leaf surface attacked by powdery mildew 5 40-50 % of the leaf surface attacked by powdery mildew 6 50-60 % of the leaf surface attacked by powdery mildew 7 60-70 % of the leaf surface attacked by powdery mildew 8 70-80 % of the leaf surface attacked by powdery mildew 9 80-90 % of the leaf surface attacked by powdery mildew 10 90-100 % of the leaf surface attacked by powdery mildew

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Appendix 4: Field information incl. random plans

Land Serie ForsøgsnrLokalitet GPS koordinater Forsøgsorganisation Country Series Trialno Site GPS coordinates Trial organisation DK 402 858 Knuthenborg, 4930 Maribo N 54° 48.457', E 11° 30.092' NBR/FFS 859 Dan Christiansen, 4990 Sakskøbing N 54° 48.689', E 11° 33.218' NBR/FFS

Random planer / Randoms plans:

858 KN (Pasteur) 6 2 4 1 3 5 IV

2 4 1 6 5 3 III 5 1 2 3 6 4 II 3 6 4 5 1 2 I 1 2 3 4 5 6

859 DC (Cactus RT, NT) 3 1 2 6 5 4 IV

2 6 5 1 4 3 III 3 1 4 6 2 5 II 1 6 3 5 4 2 I 1 2 3 4 5 6

Appendix 5: Panoramic images

Panoramic images of ORFDWLRQV.1 WRS DQG'& ERWWRP IURPWKHVSRUHWUDSV¶SHUVSHFWLYHV Pictures were taken on September 10th.

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