Preface

**Section 4 Advances in Molecular Plant Pathology 113**

Chapter 7 **Developing an Online Grapevine Trunk Disease**

**Diagnostic Aid 115**

Mizuho Nita

**VI** Contents

Plant pathology is the study of microorganisms that interfere with the normal metabolism of plants. It includes etiology, pathogenesis, epidemiology, and integrated pest management. Research in plant pathology has advanced from morphological and physiological analysis to more molecular studies involving interactions of gene products.

A plant is described as diseased when there are calculable deviations in physiology, structure, functions, and a deviation of its perceived economic value. Pathogens interfere with plants in many ways including obtaining nutrients, interfering with metabolic pathways, and through the production of metabolites, toxins, enzymes, etc. The diseased plant might slow down its water uptake, food translocation, mineral absorption, flowering, photosynthesis, fruiting, gaseous exchange, seed setting, food storage, respiration, and even its defense system.

This book combines the old approaches of plant-pathogen interactions with studies in mo‐ lecular and physiological changes in plants, which have been triggered by climate change, increase in pesticide resistance, pathogen mutations, invasive species, plant biotic and abio‐ tic adaptations, and plant breeding strategies. Recent advances in molecular biology and bi‐ oinformatics are enabling plant pathology to be studied using new technologies, such as RNAi, epigenetics, and nanotechnology. This book highlights modeling of plant diseases, quorum sensing, newly identified plant pathogens, counter defenses of plant viral patho‐ gens, pesticide resistance and diagnostic approaches of plant diseases.

Scientists have recently discovered interesting plant interactions between plants and micro‐ organisms in symbiotic relationships. These relationships involving endophytes and exo‐ phytes have enabled plants to survive and adapt in diverse environments.

> **Dr Josphert Ngui Kimatu, BSc., Mphil, PDGE, PhD** South Eastern Kenya University Kitui County, Kenya

**Section 1**

**Advances in Fungal Plant Pathology**

**Advances in Fungal Plant Pathology**

**Chapter 1**

**Provisional chapter**

**Modeling the Main Fungal Diseases of Winter Wheat:**

**Modeling the Main Fungal Diseases of Winter Wheat:** 

The first step in the formulation of disease management strategy for any cropping system is to identify the most important risk factors. This is facilitated by basic epidemiological studies of pathogen life cycles, and an understanding of the way in which weather and cropping factors affect the quantity of initial inoculum and the rate at which the epidemic develops. Weather conditions are important factors in the development of fungal diseases in winter wheat, and constitute the main inputs of the decision support systems used to forecast disease and thus determine the timing for efficacious fungicide application. Crop protection often relies on preventive fungicide applications. Considering the slim cost−revenue ratio for winter wheat and the negative environmental impacts of fungicide overuse, necessity for applying only sprays that are critical for disease control becomes paramount for a sustainable and environmentally friendly crop production. Thus, fungicides should only be applied at critical stages for disease development, and only after the pathogen has been correctly identified. This chapter provides an overview of different weather-based disease models developed for assessing the real-time risk of epidemic development of the major fungal diseases (Septoria leaf blotch, leaf rusts and Fusarium head blight) of winter wheat in Luxembourg.

**Keywords:** mechanistic model, stochastic model, integrated pest management

Plant disease epidemics involve changes in disease intensity in a host population over time and space. Acquiring comprehensive information on this process is necessary to understanding

> © 2016 The Author(s). Licensee InTech. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

© 2018 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use,

distribution, and reproduction in any medium, provided the original work is properly cited.

DOI: 10.5772/intechopen.75983

**Constraints and Possible Solutions**

**Constraints and Possible Solutions**

Additional information is available at the end of the chapter

Additional information is available at the end of the chapter

http://dx.doi.org/10.5772/intechopen.75983

Philippe Delfosse

Philippe Delfosse

**Abstract**

**1. Introduction**

Moussa El Jarroudi, Louis Kouadio, Bernard Tychon, Mustapha El Jarroudi, Jürgen Junk, Clive Bock and

Moussa El Jarroudi, Louis Kouadio, Bernard Tychon, Mustapha El Jarroudi, Jürgen Junk, Clive Bock and

#### **Modeling the Main Fungal Diseases of Winter Wheat: Constraints and Possible Solutions Modeling the Main Fungal Diseases of Winter Wheat: Constraints and Possible Solutions**

DOI: 10.5772/intechopen.75983

Moussa El Jarroudi, Louis Kouadio, Bernard Tychon, Mustapha El Jarroudi, Jürgen Junk, Clive Bock and Philippe Delfosse Moussa El Jarroudi, Louis Kouadio, Bernard Tychon, Mustapha El Jarroudi, Jürgen Junk, Clive Bock and Philippe Delfosse

Additional information is available at the end of the chapter Additional information is available at the end of the chapter

http://dx.doi.org/10.5772/intechopen.75983

#### **Abstract**

The first step in the formulation of disease management strategy for any cropping system is to identify the most important risk factors. This is facilitated by basic epidemiological studies of pathogen life cycles, and an understanding of the way in which weather and cropping factors affect the quantity of initial inoculum and the rate at which the epidemic develops. Weather conditions are important factors in the development of fungal diseases in winter wheat, and constitute the main inputs of the decision support systems used to forecast disease and thus determine the timing for efficacious fungicide application. Crop protection often relies on preventive fungicide applications. Considering the slim cost−revenue ratio for winter wheat and the negative environmental impacts of fungicide overuse, necessity for applying only sprays that are critical for disease control becomes paramount for a sustainable and environmentally friendly crop production. Thus, fungicides should only be applied at critical stages for disease development, and only after the pathogen has been correctly identified. This chapter provides an overview of different weather-based disease models developed for assessing the real-time risk of epidemic development of the major fungal diseases (Septoria leaf blotch, leaf rusts and Fusarium head blight) of winter wheat in Luxembourg.

**Keywords:** mechanistic model, stochastic model, integrated pest management

#### **1. Introduction**

Plant disease epidemics involve changes in disease intensity in a host population over time and space. Acquiring comprehensive information on this process is necessary to understanding

© 2016 The Author(s). Licensee InTech. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. © 2018 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

the factors that cause epidemics. However, even a complete set of data on disease intensity does not automatically lead to insights into the epidemic process. Furthermore, the information regarding risk of disease needs to be communicated stakeholders who can subsequently take management decisions to protect the crop when risk of an epidemic is deemed high. Various mathematical models are used to summarize the essential features of the data or measurements of interest regarding disease development. Models for biological or physical processes can be developed using several methods. Empirical models are developed to describe an observed process, phenomenon, or relationship between variables using established statistical principles, and do not use previously developed theory or concepts to establish the relationship between the response variable and predictor variables. In contrast, mechanistic models are developed based on a theory, hypothesis, or concept of how a phenomenon or process occurs. Data are later considered after the mechanistic model is developed and might be used to improve the theory on which the model is based.

disease suppression is justified. Aspects of this process differ from pathogen to pathogen, from crop to crop, and from location to location [4]. Goulds and Polly [7] and Binns et al. [8] draw a distinction between crop protection based on either curative or preventative action. Without necessarily wishing to adhere rigidly to this dichotomy, it is nevertheless clear that in some cases, sample data are the most important components of the information on which decision making is based. In others, data relating to the host and the environment often play a more important role, and the evidence on which a decision is made about the need for appropriate control action is therefore likely to be more wide ranging. The first step in the formulation of a disease management strategy for any cropping system is to identify the most important risk factors among those on the long list of possible candidates. This is facilitated by basic epidemiological studies of pathogen life cycles, and an understanding of the way in which weather and cropping factors affect the quantity of initial inoculum and the rate of the pathogen life cycle. To be able to identify risk factors, we need information both on the candidate risk factors

Modeling the Main Fungal Diseases of Winter Wheat: Constraints and Possible Solutions

http://dx.doi.org/10.5772/intechopen.75983

5

Jones [9] discussed a decision-making guideline based on impact on yield for fungicidal control of eyespot disease of winter wheat (*Triticum aestivum* L.). Treatment was considered to be worthwhile if ≥20% of tillers were diseased at growth stage (GS) 30–31. Accordingly, the recommendation was for a sample of tillers to be collected at the appropriate growth stage and a decision of whether to treat was made based on the percentage of tillers with symptoms of eyespot disease, in relation to the specified threshold. Decision making was based on a two-stage cluster sampling procedure, collecting a total of 50 tillers for the assessment [7]. The economic threshold is the level of risk exposure at which crop protection measures should be applied, in order to prevent the economic injury level from being reached. An economic threshold may be used to identify circumstances in which it becomes economically advantageous to apply crop protection measures. The economic threshold is a discrete choice threshold: the only options are to apply crop protection measures or to withhold them. However, the choice between these two options must be made before it is known for sure whether a crop will sustain economic loss resulting from reductions in the quantity and quality of yield. Thus, the economic threshold may be used as a basis for deciding whether or not crop protection measures are required, at a time when it is still possible to keep damage below the economic injury level. Weather-based systems, or weather-based systems combined with other disease or agronomic variables have been developed in various areas in Europe to determine whether fungicide sprays should be applied to prevent the risk of epidemics that might otherwise lead to yield loss. For example, Audsley et al. [10] developed a model in the UK based on weather, host resistance and inoculum pressure to project effects on green leaf area, which was coupled with effects on yield loss as a decision support system for Septoria leaf blotch,

In this chapter we specifically provide an overview of different weather-based disease models developed and used for assessing in real time the risk of epidemic development for the major fungal diseases (i.e., Septoria leaf blotch, powdery mildew, leaf rusts and Fusarium head

and on the definitive status of the crops in which they are studied.

**2.2. Basis of decision making**

powdery mildew, and yellow and brown rusts.

## **2. Challenges in predicting plant disease epidemic development**

In many of the models that are discussed in this chapter, diseased individuals are grouped in three categories. After infection of the host takes place, the infected individual first goes through a phase where the disease develops and "grows" in the individual, but the infected individual does not produce propagules or infectious units. The infected individual is in a latent state. After the latent period, the infected individual becomes an infectious individual, meaning that it now produces infectious units that have the potential to cause subsequent infections. "Disease forecasting," "disease prediction," and the development of "disease warning systems" are activities familiar to plant disease epidemiologists [1–6]. Having identified the factors that lead to epidemics, it is of great importance to use this information to provide a basis for the management of plant disease. The level of disease risk to which a crop is exposed may be influenced by many factors, some of these are beyond the control of growers, but some factors are integral components of crop production systems and can be managed to minimize that risk.

#### **2.1. Seasonality and the disease cycle**

Many cropping systems are cyclical or seasonal. With annual plants, the crop is planted and harvested at specific times each year. Planting a specific (or a few) genotype(s) results in an abrupt increase in population of susceptible individuals. While harvesting immediately decreases both the population of susceptible individuals and the population of latent, infectious individuals. In the period between harvest and planting, the pathogen has to survive either as propagules or on living or dead plant material left in the field, in the soil, or in other locations. Crops are exposed to a risk of infection from pathogens, the outcome of which is economic loss when the epidemic increases above a certain threshold, which results from reduction in both the quantity and quality of crop yield. In this chapter, we are interested in quantifying the risk of infection to which a crop is exposed as a basis for deciding whether intervention aimed at disease suppression is justified. Aspects of this process differ from pathogen to pathogen, from crop to crop, and from location to location [4]. Goulds and Polly [7] and Binns et al. [8] draw a distinction between crop protection based on either curative or preventative action. Without necessarily wishing to adhere rigidly to this dichotomy, it is nevertheless clear that in some cases, sample data are the most important components of the information on which decision making is based. In others, data relating to the host and the environment often play a more important role, and the evidence on which a decision is made about the need for appropriate control action is therefore likely to be more wide ranging. The first step in the formulation of a disease management strategy for any cropping system is to identify the most important risk factors among those on the long list of possible candidates. This is facilitated by basic epidemiological studies of pathogen life cycles, and an understanding of the way in which weather and cropping factors affect the quantity of initial inoculum and the rate of the pathogen life cycle. To be able to identify risk factors, we need information both on the candidate risk factors and on the definitive status of the crops in which they are studied.

#### **2.2. Basis of decision making**

the factors that cause epidemics. However, even a complete set of data on disease intensity does not automatically lead to insights into the epidemic process. Furthermore, the information regarding risk of disease needs to be communicated stakeholders who can subsequently take management decisions to protect the crop when risk of an epidemic is deemed high. Various mathematical models are used to summarize the essential features of the data or measurements of interest regarding disease development. Models for biological or physical processes can be developed using several methods. Empirical models are developed to describe an observed process, phenomenon, or relationship between variables using established statistical principles, and do not use previously developed theory or concepts to establish the relationship between the response variable and predictor variables. In contrast, mechanistic models are developed based on a theory, hypothesis, or concept of how a phenomenon or process occurs. Data are later considered after the mechanistic model is developed and might

be used to improve the theory on which the model is based.

minimize that risk.

4 Advances in Plant Pathology

**2.1. Seasonality and the disease cycle**

**2. Challenges in predicting plant disease epidemic development**

In many of the models that are discussed in this chapter, diseased individuals are grouped in three categories. After infection of the host takes place, the infected individual first goes through a phase where the disease develops and "grows" in the individual, but the infected individual does not produce propagules or infectious units. The infected individual is in a latent state. After the latent period, the infected individual becomes an infectious individual, meaning that it now produces infectious units that have the potential to cause subsequent infections. "Disease forecasting," "disease prediction," and the development of "disease warning systems" are activities familiar to plant disease epidemiologists [1–6]. Having identified the factors that lead to epidemics, it is of great importance to use this information to provide a basis for the management of plant disease. The level of disease risk to which a crop is exposed may be influenced by many factors, some of these are beyond the control of growers, but some factors are integral components of crop production systems and can be managed to

Many cropping systems are cyclical or seasonal. With annual plants, the crop is planted and harvested at specific times each year. Planting a specific (or a few) genotype(s) results in an abrupt increase in population of susceptible individuals. While harvesting immediately decreases both the population of susceptible individuals and the population of latent, infectious individuals. In the period between harvest and planting, the pathogen has to survive either as propagules or on living or dead plant material left in the field, in the soil, or in other locations. Crops are exposed to a risk of infection from pathogens, the outcome of which is economic loss when the epidemic increases above a certain threshold, which results from reduction in both the quantity and quality of crop yield. In this chapter, we are interested in quantifying the risk of infection to which a crop is exposed as a basis for deciding whether intervention aimed at Jones [9] discussed a decision-making guideline based on impact on yield for fungicidal control of eyespot disease of winter wheat (*Triticum aestivum* L.). Treatment was considered to be worthwhile if ≥20% of tillers were diseased at growth stage (GS) 30–31. Accordingly, the recommendation was for a sample of tillers to be collected at the appropriate growth stage and a decision of whether to treat was made based on the percentage of tillers with symptoms of eyespot disease, in relation to the specified threshold. Decision making was based on a two-stage cluster sampling procedure, collecting a total of 50 tillers for the assessment [7]. The economic threshold is the level of risk exposure at which crop protection measures should be applied, in order to prevent the economic injury level from being reached. An economic threshold may be used to identify circumstances in which it becomes economically advantageous to apply crop protection measures. The economic threshold is a discrete choice threshold: the only options are to apply crop protection measures or to withhold them. However, the choice between these two options must be made before it is known for sure whether a crop will sustain economic loss resulting from reductions in the quantity and quality of yield. Thus, the economic threshold may be used as a basis for deciding whether or not crop protection measures are required, at a time when it is still possible to keep damage below the economic injury level. Weather-based systems, or weather-based systems combined with other disease or agronomic variables have been developed in various areas in Europe to determine whether fungicide sprays should be applied to prevent the risk of epidemics that might otherwise lead to yield loss. For example, Audsley et al. [10] developed a model in the UK based on weather, host resistance and inoculum pressure to project effects on green leaf area, which was coupled with effects on yield loss as a decision support system for Septoria leaf blotch, powdery mildew, and yellow and brown rusts.

In this chapter we specifically provide an overview of different weather-based disease models developed and used for assessing in real time the risk of epidemic development for the major fungal diseases (i.e., Septoria leaf blotch, powdery mildew, leaf rusts and Fusarium head blight) of winter wheat in Luxembourg. A description of the models is provided along with the constraints associated with their use for in-season disease monitoring. The challenges faced using weather-based models in a changing climate are also discussed.

from infections arising between the emergences of leaf 2 (L2) and the flag leaf and roughly two latent periods before these leaves would naturally begin senescence. If the upper leaves are infected early in the cropping season, they are likely to suffer much more severe disease for two reasons: a) there is sufficient time for the pathogen to have more than one multiplication cycle on the leaves, with a longer time during which dissemination and infection may occur, resulting in premature loss of leaf area; b) these leaves are closer to the sources of the inoculum and extreme splashing events will no longer be necessary to disperse sufficient number of spore onto a susceptible tissue that is higher in the crop canopy. Furthermore, the structure of the wheat plants and the position of the source of the inoculum on specific leaves relative to each other are constantly changing and thus the risk of disease progression is dynamically complex and specific to each crop, cultivar and season [22]. In addition, the life of the upper leaves is considerably shortened by secondary infections resulting from the inoculum produced by primary lesions in the same leaf layer [19]. The detection of spores of *Z. tritici* during the season demonstrates the need for a predictive model [25, 26]. Both asexually produced pycnidiospores and sexually produced ascospores of *Z. tritici* are known to cause disease in wheat [22, 27], with ascospores being aerially dispersed over relatively long distances, and the pycnidiospores being primarily splash dispersed. Furthermore, the ascospores have an impact not only as primary inoculum in autumn and winter [27], but also as secondary inoculum at the end of spring and in summer. This airborne inoculum could help to colonize the upper leaves without the need for splash-dispersed pycnidiospores or could exacerbate the damage caused by splash-dispersed *Z. tritici* (**Photo 1**) due to the presence of

Modeling the Main Fungal Diseases of Winter Wheat: Constraints and Possible Solutions

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7

Due to the potential for yield loss from SLB, growers tend to spray fungicides several times during the winter wheat season to protect their crops. The development of resistance in

**Photo 1.** Symptoms of Septoria leaf blotch caused by *Zymoseptoria tritici* on leaf L3 of the cultivar Achat. The black dots in the tan lesions are the pycnidia that produce the splash dispersed pycnidiospores (photo taken on May 30, 2007 at

the additional ascospore inoculum [28].

Everlange, Luxembourg; photo credit: El Jarroudi M.).
