Graduate Studies, College of (Okanagan)
DSpace
UBCO
Williams, Katrina Anne
Master of Science - MSc
University of British Columbia
Post-harvest disease of apple fruit causes significant loss of fruit during and after storage with a considerable economic impact. Studying the factors that contribute to post-harvest disease and developing predictive models may help growers and packing house workers to make more informed decisions on disease forecasting and duration of storage for apples. Data for three major post-harvest pathogens, Penicillium expansum, Botrytis cinerea and Mucor piriformis, which were monitored and quantified in four orchards over three years during the growing season and then in storage, were available. Contrary to expectation, it was found that environmental data provided little to no explanation of the trends in inoculum detection. It was hypothesized that this lack of relationship between the amount of inoculum present and the environmental factors was due to the manner in which the data were collected. In contrast, it was found that a large proportion of the variation in storage disease outcomes (R2=0.506, p=0.000) could be predicted by the duration of storage, temperature and rainfall two weeks before storage, and the quantity of pathogen DNA detected on the plant tissue at harvest. In order to better understand the relationship between environmental factors and spore detection in the orchard, a Gaussian plume model was developed for describing spore dispersal. Model results had good qualitative agreement with the data that were collected in the field, suggesting that a high level of variability would be expected when only using one receptor location due to the dynamics of spore movement based on wind conditions. The model predicted that increasing the number of receptors, especially when they were evenly placed around the orchard, would decrease the variability of detection results. Based on the model outcomes, it was concluded that five receptors would give the most reasonable results for the least expenditure. This research develops the first predictive model for post-harvest apple disease outcomes in storage based on pre-storage factors, and gives new insight into the dispersal of fungal spores in an orchard setting, providing recommendations for improving future data collection and modeling work.
MODELING POST-HARVEST PATHOGENS IN APPLE FRUIT by KATRINA ANNE WILLIAMS B.Sc., Carleton University, 2010 A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF MASTER OF SCIENCE in THE COLLEGE OF GRADUATE STUDIES (Interdisciplinary Studies) THE UNIVERSITY OF BRITISH COLUMBIA (Okanagan) December 2012 © Katrina Anne Williams, 2012 Abstract Post-harvest disease of apple fruit causes significant loss of fruit during and after storage with a considerable economic impact. Studying the factors that contribute to post-harvest disease and developing predictive models may help growers and packing house workers to make more informed decisions on disease forecasting and duration of storage for apples. Data for three major post-harvest pathogens, Penicillium expansum, Botrytis cinerea and Mucor piriformis, which were moni- tored and quantified in four orchards over three years during the growing season and then in storage, were available. Contrary to expectation, it was found that environmental data provided little to no explanation of the trends in inoculum de- tection. It was hypothesized that this lack of relationship between the amount of inoculum present and the environmental factors was due to the manner in which the data were collected. In contrast, it was found that a large proportion of the variation in storage disease outcomes (R2=0.506, p=0.000) could be predicted by the duration of storage, temperature and rainfall two weeks before storage, and the quantity of pathogen DNA detected on the plant tissue at harvest. In order to better understand the relationship between environmental factors and spore detection in the orchard, a Gaussian plume model was developed for describing spore dispersal. Model results had good qualitative agreement with the data that were collected in the field, suggesting that a high level of variability would be expected when only using one receptor location due to the dynamics of spore movement based on wind conditions. The model predicted that increasing the number of receptors, especially when they were evenly placed around the orchard, would decrease the variability of detection results. Based on the model outcomes, ii it was concluded that five receptors would give the most reasonable results for the least expenditure. This research develops the first predictive model for post- harvest apple disease outcomes in storage based on pre-storage factors, and gives new insight into the dispersal of fungal spores in an orchard setting, providing recommendations for improving future data collection and modeling work. iii Contents Abstract . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ii Contents . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . iv List of Tables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . vii List of Figures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . viii Acknowledgements . . . . . . . . . . . . . . . . . . . . . . . . . . . . xi Dedication . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xiii 1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 1.1 Review of Relevant Literature . . . . . . . . . . . . . . . . . . . . 1 1.1.1 Background . . . . . . . . . . . . . . . . . . . . . . . . . . 1 1.1.2 Phyllosphere Microbial Dynamics . . . . . . . . . . . . . . 3 1.1.3 Post-Harvest Diseases of Pome Fruit . . . . . . . . . . . . 10 1.1.4 Pre-Harvest Factors Influencing Disease Outcomes . . . . . 12 1.1.5 Harvest Factors Influencing Disease Outcomes . . . . . . . 17 1.1.6 Post-Harvest Factors Influencing Disease Outcomes . . . . 18 1.1.7 Control Techniques . . . . . . . . . . . . . . . . . . . . . . 20 1.1.8 Mathematical Modeling . . . . . . . . . . . . . . . . . . . 24 1.2 Research Goals and Hypotheses . . . . . . . . . . . . . . . . . . . 36 1.2.1 Determine Relationships between Spore Detection and En- vironmental Conditions . . . . . . . . . . . . . . . . . . . . 36 1.2.2 Pre-Storage Model for Incoming Infection Risk . . . . . . . 37 iv 1.2.3 Combining the Pre-Storage and During-Storage Models . . 37 1.2.4 Circulation/Probability Modeling of Inoculum Contact . . 38 2 Data Collection and Experimental Set-up . . . . . . . . . . . . . 40 2.1 Synopsis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40 2.2 Field Experiments . . . . . . . . . . . . . . . . . . . . . . . . . . 40 2.2.1 Experimental Set-up . . . . . . . . . . . . . . . . . . . . . 40 2.2.2 Data Collection Techniques . . . . . . . . . . . . . . . . . 41 2.2.3 Descriptions of Orchards . . . . . . . . . . . . . . . . . . . 42 2.2.4 Data Collected by Year . . . . . . . . . . . . . . . . . . . . 44 2.2.5 Description of Data . . . . . . . . . . . . . . . . . . . . . . 45 2.3 Storage Experiments . . . . . . . . . . . . . . . . . . . . . . . . . 61 2.3.1 Experimental Set-up . . . . . . . . . . . . . . . . . . . . . 61 2.3.2 Data Collection Techniques . . . . . . . . . . . . . . . . . 61 2.3.3 Descriptions of Storage Techniques . . . . . . . . . . . . . 62 2.3.4 Data Collected by Year . . . . . . . . . . . . . . . . . . . . 63 2.3.5 Description of Data . . . . . . . . . . . . . . . . . . . . . . 64 3 Predicting Post-Harvest Disease Outcomes . . . . . . . . . . . . 69 3.1 Synopsis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 69 3.2 Data Analysis Techniques . . . . . . . . . . . . . . . . . . . . . . 69 3.3 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 69 3.3.1 Orchard Variables . . . . . . . . . . . . . . . . . . . . . . . 69 3.3.2 Storage Variables . . . . . . . . . . . . . . . . . . . . . . . 78 3.4 Comparison with Expectation . . . . . . . . . . . . . . . . . . . . 90 3.5 Pre-Storage Predictive Model . . . . . . . . . . . . . . . . . . . . 91 v 4 A Model for Dispersal of Fungal Spores in an Orchard . . . . . 94 4.1 Synopsis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 94 4.2 Model Choice . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 94 4.3 Structure of Simulated Orchard . . . . . . . . . . . . . . . . . . . 99 4.4 Sources of Spores . . . . . . . . . . . . . . . . . . . . . . . . . . . 102 4.5 Incorporating Wind Direction and Angle . . . . . . . . . . . . . . 104 4.6 Model Output . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 107 4.6.1 One Receptor . . . . . . . . . . . . . . . . . . . . . . . . . 107 4.6.2 Multiple Receptors . . . . . . . . . . . . . . . . . . . . . . 111 4.6.3 Location of Receptors . . . . . . . . . . . . . . . . . . . . 123 4.6.4 100 Receptor Profiles . . . . . . . . . . . . . . . . . . . . . 132 4.6.5 Varying Numbers of Sources . . . . . . . . . . . . . . . . . 136 4.7 Quantification of Results . . . . . . . . . . . . . . . . . . . . . . . 145 4.8 Comparison with Experimental Data . . . . . . . . . . . . . . . . 145 5 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 148 5.1 Analysis of Post-Harvest Disease Prediction Results . . . . . . . . 148 5.2 Analysis of Dispersal Modeling Results . . . . . . . . . . . . . . . 150 5.3 Overall Contributions of this Research . . . . . . . . . . . . . . . 151 5.4 Strengths and Limitations . . . . . . . . . . . . . . . . . . . . . . 152 5.5 Potential Applications and Future Research Directions . . . . . . 153 Bibliography . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 154 Appendix . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 163 Appendix A Glossary . . . . . . . . . . . . . . . . . . . . . . . . . . . . 163 vi List of Tables 1.1 Spotts and Gastagnoli’s (2010) relative risk model for pear fruit . 25 2.1 Orchard labels and locations. . . . . . . . . . . . . . . . . . . . . 43 2.2 Apple cultivars for studied orchards . . . . . . . . . . . . . . . . . 44 3.1 Average pathogen DNA on I-rods. . . . . . . . . . . . . . . . . . 71 3.2 Average pathogen DNA on plant tissue. . . . . . . . . . . . . . . . 71 3.3 Correlations of I-rod DNA and temperature. . . . . . . . . . . . . 72 3.4 Correlations of plant tissue DNA and temperature. . . . . . . . . 73 3.5 Correlations of I-rod DNA and rainfall. . . . . . . . . . . . . . . . 74 3.6 Correlations of plant tissue DNA and rainfall. . . . . . . . . . . . 75 3.7 Correlations of I-rod DNA and wind. . . . . . . . . . . . . . . . . 76 3.8 Correlations of plant tissue DNA and wind. . . . . . . . . . . . . 77 3.9 Correlations between disease incidence and timing of storage. . . . 79 3.10 Percentage of apples infected by year. . . . . . . . . . . . . . . . . 80 3.11 Percent of infected apples by orchard. . . . . . . . . . . . . . . . . 81 3.12 Percentage of apples infected by cultivar. . . . . . . . . . . . . . . 83 3.13 Percent of apples infected by if wounding occured . . . . . . . . . 83 3.14 Percentage of apples infected by storage type. . . . . . . . . . . . 85 3.15 Correlations of percentage infected with temperature. . . . . . . . 86 3.16 Average temperature by year. . . . . . . . . . . . . . . . . . . . . 87 3.17 Correlations of percentage infected with rainfall. . . . . . . . . . . 88 3.18 Correlations of percentage infected with pathogen DNA levels. . . 89 4.1 Empirical constants for stability classes. . . . . . . . . . . . . . . 97 vii List of Figures 1.1 Visual description of the Gaussian plume model solutions. . . . . 30 2.1 Map of the Okanagan Valley. . . . . . . . . . . . . . . . . . . . . . 41 2.2 Pathogen DNA in Orchard 1, 2007 . . . . . . . . . . . . . . . . . 47 2.3 Pathogen DNA in Orchard 2, 2007 . . . . . . . . . . . . . . . . . 48 2.4 Pathogen DNA in Orchard 3, 2007 . . . . . . . . . . . . . . . . . 49 2.5 Pathogen DNA in Orchard 4, 2007 . . . . . . . . . . . . . . . . . 50 2.6 Pathogen DNA in Orchard 1, 2008 . . . . . . . . . . . . . . . . . 51 2.7 Pathogen DNA in Orchard 2, 2008 . . . . . . . . . . . . . . . . . 52 2.8 Pathogen DNA in Orchard 3, 2008 . . . . . . . . . . . . . . . . . 53 2.9 Pathogen DNA in Orchard 4, 2008 . . . . . . . . . . . . . . . . . 54 2.10 Pathogen DNA in Orchard 1, 2010 . . . . . . . . . . . . . . . . . 55 2.11 Pathogen DNA in Orchard 2, 2010 . . . . . . . . . . . . . . . . . 56 2.12 Pathogen DNA in Orchard 5, 2010 . . . . . . . . . . . . . . . . . 57 2.13 Pathogen DNA in Orchard 6, 2010 . . . . . . . . . . . . . . . . . 58 2.14 2010 average temperature and rainfall data . . . . . . . . . . . . . 59 2.15 2010 wind speed and direction data . . . . . . . . . . . . . . . . . 60 2.16 2007 storage disease incidence. . . . . . . . . . . . . . . . . . . . . 66 2.17 2008 storage disease incidence. . . . . . . . . . . . . . . . . . . . . 67 2.18 2010 storage disease incidence. . . . . . . . . . . . . . . . . . . . . 68 3.1 Regression plot of percentage infected with year and duration of storage. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 80 3.2 Regression plot of percentage infected with orchard and duration of storage. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 82 viii 3.3 Regression plot of percentage infected with wounding and duration of storage. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 84 3.4 Regression plot of percentage infected with storage type and dura- tion of storage. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 85 3.5 Spotts and Gastagnoli’s (2010) relative risk model for pear fruit . 91 4.1 Vertical cross-section of plume with heights marked. . . . . . . . . 98 4.2 Contour plots for a single point source. . . . . . . . . . . . . . . . 99 4.3 Photograph of orchard. . . . . . . . . . . . . . . . . . . . . . . . . 100 4.4 Representative orchard layout for simulation. . . . . . . . . . . . . 102 4.5 Vertical cross-section with heights and tree. . . . . . . . . . . . . 103 4.6 Wind and source coordinate systems. . . . . . . . . . . . . . . . . 105 4.7 Contour plots for two point sources. . . . . . . . . . . . . . . . . . 106 4.8 Contour plots for two point sources with trees. . . . . . . . . . . . 107 4.9 Three trials for a single receptor with two sources. . . . . . . . . . 109 4.10 Average concentration for a single receptor. . . . . . . . . . . . . . 110 4.11 Average concentration for 100 receptors. . . . . . . . . . . . . . . 117 4.12 Normalized wind speed and average concentration. . . . . . . . . 118 4.13 Average detection for varying receptor numbers. . . . . . . . . . . 119 4.14 Range of detection for varying receptor numbers. . . . . . . . . . 120 4.15 Maximum values for increasing numbers of receptors. . . . . . . . 121 4.16 Percentage non-zero for varying receptor numbers. . . . . . . . . . 122 4.17 Locations of Receptors . . . . . . . . . . . . . . . . . . . . . . . . 124 4.18 Average detection for varying receptor locations. . . . . . . . . . . 128 4.19 Range of detection for varying receptor locations. . . . . . . . . . 129 4.20 Average maximum detection for varying receptor locations. . . . . 130 ix 4.21 Percentage non-zero detection for varying receptor locations. . . . 131 4.22 Day 1, trial 10 results. . . . . . . . . . . . . . . . . . . . . . . . . 134 4.23 Day 10, trial 20 results. . . . . . . . . . . . . . . . . . . . . . . . . 135 4.24 Highest maximum results. . . . . . . . . . . . . . . . . . . . . . . 135 4.25 Average maximum results. . . . . . . . . . . . . . . . . . . . . . . 136 4.26 Average spore detection varying location and numbers. . . . . . . 139 4.27 Range of spore detection varying location and numbers. . . . . . . 140 4.28 Highest maximum spore detection varying location and numbers. 141 4.29 Average maximum spore detection varying location and numbers. 142 4.30 Lowest maximum spore detection varying location and numbers. . 143 4.31 Percentage non-zero spore detection varying location and numbers. 144 x Acknowledgements I offer my enduring gratitude to the faculty, staff, and my fellow students at UBC Okanagan. I offer particular thanks to my two supervisors and their lab groups for all of their support and feedback over the course of my research. Dr. Rebecca Tyson and her mathematical biology lab group: Shaun Strohm, Meghan Dutot, Mazi Jalaal, Alex Blaessle, Andrea Hyde, Garrett Culos, Haley Dirksen, Alan Law, Lisa Kanary, Lisa Tessier, Franciane Azevedo, Nishat Tasnim, Chris Coutts, Haley Catton, Carly Rozins, Natasha Mandryk, and Miayan Yeremi, and Dr. Louise Nelson and her biology lab group: Molly Thurston, Daylin Mantyka, Tanja Voegel, Karen Fong, Kelly Berringer, Kersti Ojamaa, Chris Milner, Morgan Roosenmaallen, Haley Dirksen, Natashia Bose-Roberts, DeAnna Zanet, Kaela Loffler, Kyle MacPherson, Meghan Dutot, and Sara Malcolm. I thank my committee, Dr. Warren Hare, Dr. Sylvie Desjardins, and Dr. Sylvia Esterby for their support and input into my research. I also thank UBC Okanagan for the opportunity to work as a teaching assis- tant, both for the financial support and the chance to expand my education. As well, I was financially supported through Dr. Rebecca Tyson and her MITACS grant, and Dr. Louise Nelson and her AFMNet grant. I am also grateful to the wide range of people who, over the course of my degree, have asked me to explain my research to them, in passing, at social gatherings, at conferences, and beyond, for their curiosity and allowing me the opportunity to explain my work to those with a wide variety of backgrounds, and further investigate my own knowledge of my research. Special thanks are owed to my boyfriend, Dan, for his hours of support and xi attempts to understand the intricacies of my research, and always being a willing listener as I talked through whatever problem I was stuck on. As well, I offer special thanks to my siblings, Ariana for her time spent listening to presentations and learning more about apple fungus than she ever wanted to know, and David, for talking through my coding problems and setting me back on the right track. Most of all, I would like to thank my parents, Danita and Robert, for their love and support, for always believing in my ability to do anything I set my mind to, for their financial support of my education, and for making me the person I am today. xii Dedication To my parents xiii 1 Introduction A glossary of terms appears in Appendix 1 1.1 Review of Relevant Literature 1.1.1 Background Fungal disease leading to spoilage of stored apples (post-harvest disease) amounts to tens of millions of dollars of loss annually in producing countries (Spotts et al., 1999, Vinas et al., 1997). In British Columbia, apples are the largest tree fruit crop, with approximately 80% of this crop being stored for future use (Carew, 2000). Consequently, the economic impact of post-harvest disease is significant. For similar tree fruit (such as pears) it has been found that predictors of post harvest disease include the cultivar, rainfall levels, humidity, temperature during the growing season, inoculum levels close to harvest, wounding during harvest, orchard rating, and the application of fungicides (Spotts et al., 2009). The manner in which the fruit is handled has a significant effect on the likelihood of post-harvest disease, as many diseases attack the apple through wounds or damage in the skin (Spotts et al., 1999). After harvest, those apples that are not immediately sold or used in secondary production are packed into controlled atmosphere storage rooms. These storage rooms are sealed and maintained in a controlled environment that has increased CO2 levels and decreased O2 levels. As well, these rooms are maintained at near zero Celsius temperatures optimized for the variety of apple. Due to the difficulties associated with maintaining the accurately controlled temperature and atmospheric conditions, when the rooms are sealed they are not opened again until there is a demand for the apples. These 1 rooms preserve thousands of apples for a period of anywhere between a few weeks to nine months. There is currently no way to accurately predict if the apples will spoil before the room is opened. Economic losses due to post harvest disease could be reduced if the packing houses had some means to predict how long a given bin of apples could be stored before a certain percentage of fruit loss was likely to occur. This would allow them to make more informed decisions regarding the storage time for each bin of fruit. While orchard variables that have some relation to disease incidence have been identified (Spotts et al., 2009), current estimates of the extent and timing of disease are at best extremely qualitative. In this thesis, we will first present the data that were collected over a period of three years in the Okanagan Valley. These data include both values that were collected in the orchard during the growing season as well as the results of storage experiments. The first goal of our research is to analyze these data and to describe the trends and patterns that were found. Next, we will use these data to develop a statistical model that can be used to predict disease prevalence as a function of storage time and relevant orchard variables collected before storage. This regres- sion model will identify the orchard variables that are most predictive of disease occurrence throughout the storage period and provide a framework for verifica- tion with future studies. The eventual goal is a model that can be used to predict when apples should be removed from the packing rooms for a given level of disease incidence risk. Such a model would reduce losses incurred by packinghouses and orchardists with potentially significant economic benefits to the Okanagan and other fruit-growing regions. Statistical modeling will be the first approach that we use towards the goal of predicting disease outcomes through modeling. 2 The second approach that we will use will be to develop a mathematical model for spore dispersal and measurement within an orchard. It was found that the patterns of inoculum detection could not be understood through data analysis and statistical modeling. In order to better understand the system and to improve the capabilities of the model, our research will investigate one potential process leading to infection of these apples, specifically the transportation of spores in the orchard to the fruit surface. The goal of this portion of the research is to describe how inoculum transport to the fruit surface could be better measured, as well as predicted and potentially disrupted. This could be used to describe the effects of various management activities and provide a means to make decisions during the growing season. The model that has been selected for use in the orchard is a Gaussian Plume model, which will be used to further investigate the dynamics of the spread of pathogens within the orchard. In this chapter, we will review literature relevant to studying post-harvest disease and the intricacies of plant-pathogen-control dynamics in an orchard. As well, we will discuss literature surrounding both approaches that we will use later in this thesis, specifically, the statistical model and factors which are expected to contribute to disease outcomes, and the mathematical model, and surrounding literature in dispersion modeling. 1.1.2 Phyllosphere Microbial Dynamics The Phyllosphere As a habitat for microorganisms, plants can be divided into two very distinct environmental zones: the rhizosphere and the phyllosphere. The rhizosphere describes the underground region of the plant surrounding and af- fected by the roots. The roots of the plant are the main site of interaction between 3 plants and microorganisms, as the below ground environment is more sheltered, the temperature conditions are more stable, and nutrients and moisture are more available, making it much more conducive to the growth and survival of these microorganisms (Madigan et al., 2002). The phyllosphere, on the other hand, is defined as the above ground portion of the plant, and includes all of the aerial parts of the living plant such as the leaves, stems, buds, flowers, and/or fruit (Whipps et al., 2008). Above ground conditions are less habitable to microorganisms as they may have limited access to nutrients, experience large temperature fluctuations, are exposed to strong UV radiation, and are exposed to reduced moisture levels which change frequently. In addition, there is little protection from predation and environmental stress. These conditions mean that the environment of the phyl- losphere selects for species that have unusual traits that allow them to survive in this environment, many of which researchers are still exploring (Lindow and Brandl, 2003). The interface between the phyllosphere and the atmosphere is the largest biosphere-atmosphere interface in the world, and the global surface area of the phyllosphere is estimated at over 4 · 108 km2. A major feature of the phyllo- sphere interface is the cuticle of the plant, a waxy layer that covers the surface of all above ground portions of the plant (Beattie, 2002). It is in or on this layer that many microorganisms reside. Although the phyllosphere is not an en- vironment that is highly conducive to growth, it is nonetheless home to many microorganisms. The dominant species are bacteria, and in addition archaea, fil- amentous fungi, and yeasts are present (Whipps et al., 2008). Interactions in the phyllosphere influence the general growth and development of plants, and often specifically affect the parts of the plants that are used as food crops for human 4 consumption. By studying the dynamics of microorganisms in the phyllosphere, researchers can hope to improve our understanding of their roles, and influence the outcomes for plants, both by reducing the roles of harmful microorganisms and encouraging beneficial microorganisms. One of the major benefits to studying phyllosphere dynamics, other than preventing losses in food crops, is that some plant pathogens are also human pathogens and a greater understanding may allow us to prevent their spread (Lindow and Brandl, 2003). Recent research has been directed towards the area of biological control in the hopes that it will become an effective means of pathogen suppression. Microorganisms in the Phyllosphere There are three major classifications of microorganisms in the phyllosphere: the bacteria, the archaea, and the eukarya. Bacteria and archaea are defined as prokaryotes; organisms that do not have a nucleus or other membrane-enclosed organelles and usually have their DNA in a single circular molecule. Eukaryotes are organisms that have a “true” or closed nucleus and usually contain other organelles (Madigan et al., 2002). Yeasts and fil- amentous fungi are classified as eukaryotes. Each of these types of microorganisms has significant impact on the phyllosphere and interacts with its plant hosts and each other in a variety of ways. Bacteria are considered to be the most plentiful and diverse microorganisms in the phyllosphere, occurring in concentrations of 102 to 1012 cells/gram of leaf (Whipps et al., 2008). As Yang et al. (2001) reported, many phyllosphere species may still be unknown. Their study found that although all the species that could be identified via conventional culture techniques were well recorded and studied, culture independent methods demonstrated that the majority of species present had not previously been described as interacting in the 5 phyllosphere, and many of these species were previously undiscovered. This result influenced a great deal of work in the identification of phyllosphere microorgan- isms with culture independent methods and expanded our understanding of the dynamics in this environment (Rastogi et al., 2010, Redford et al., 2010, Saito et al., 2007). Four processes determine the populations of microbes on the leaf: immigration, emigration, growth, and death (Kinkel et al., 1989). Within the leaf environment, the growth and death rates of the microorganisms influence the population size, and are governed by factors such as nutrient and space availability. The envi- ronment acts as a source and sink of microorganisms as they are transported to the plant (immigrate), and leave the plant (emigrate) (Kinkel, 1997). There are many vectors by which microorganisms both immigrate and emigrate, including airborne dispersal, rain splash, water runoff, insect and animal transport, physical contact between the leaves, seeds, and movement from the soil and plant debris to the phyllosphere (Whipps et al., 2008). It has been suggested that immigration rates are often the limiting factor in the establishment of microbial communities, as the timing of arrival of different microorganisms has a large effect on their abil- ity to arrive on unoccupied space (Upper and Hirano, 2002). Although it has not been well studied, Whipps et al. (2008) also suggest that internal transmission of microorganisms from the roots to the aerial parts of the plant may be another means of dispersal that allows for the microorganisms to be protected from the external environment, and may be a significant means of transport for human pathogens. The focus of most research into the movement of microorganisms between plants has been on immigration, while research on emigration has been more lim- 6 ited. Future research into emigration could be significant in the field of biological control as it will affect the ability of the biological control agent (BCA) inoculated on one part of a plant to colonize new growth and other nearby plants (Levetin and Dorsey, 2006). Another field of research that may contribute to future un- derstanding of the spatial dynamics of microbe populations is in looking at the connections between within-leaf and extra-leaf processes, and how these relate to different spatial scales (Kinkel, 1997). This idea was clearly demonstrated by Kinkel et al. (2002) in looking at the implications of resource aggregation across different spatial scales. They found that resource aggregation alone had a sub- stantial effect on the within-leaf dynamics of microbial growth and death and the extra-leaf process of immigration, as well as a possible effect on the patterns of emigration. By looking at other leaf-scale processes across various spatial scales researchers may be able to better understand the dynamics of biological control and the interactions between microorganisms. To a bacterium or other microorganism, the entire surface of a leaf might be 108 or 109 times larger than the area in which the microbe lives its life cycle and from which it can take nutrients (Kinkel, 2002). From this microscopic perspective, the phyllosphere is a highly heterogeneous environment, with a great deal of variation in protection and nutrient availability. One of the key features of the leaves and other aerial habitats on the plant is their constant state of change. Although in many cases the leaf surface habitat is considered relatively static by researchers, in reality it is constantly growing and adapting to changing conditions. This growth results in a continuous supply of new plant cells for microorganisms to colonize, as well as changes in microclimate, nutrient availability, inhibitory compounds, and retention of water (Andrews and Harris, 2000). 7 The microorganisms that are found in the phyllosphere interact with the plant in many ways, and there is a continuum that stretches from strongly beneficial organisms, to those that have no effect, to those which are clearly detrimental to the plant (Andrews and Harris, 2000). In most cases, the majority of surface microorganisms have no detrimental effect on the plant, and these are known as epiphytes (Lindow and Brandl, 2003). Beneficial species may use mechanisms that increase the plant’s ability to defend against pathogens or influence nutrients and signals to promote the growth of the plant. Species that cause disease are known as pathogens, and the three main disease strategies are those of biotrophs, necrotrophs, and hemibiotrophs. Biotrophs are parasites of living hosts, using specialized infection structures and various strategies to remove nutrients from the plant. Necrotrophs take their nutrients from the dead cells of plants, either by killing the host, or by colonizing dead tissues. Hemibiotrophs act as biotrophs until they are well established, at which point they kill their hosts for nutrients (Divon and Fluhr, 2006). Knowing how a specific pathogen gains nutrients allows for a better under- standing of how biological controls can be used and adapted to control pathogen growth. For example, one of the most important nutrients for microorganisms is carbon. The most common forms of carbon exuded by the plant are glucose, fructose, and sucrose. It has been found that some of these sugars leach from the plant and are available to surface microorganisms. Sites of this leaching are highly localized, making the nutrient profile of the leaf extremely heterogeneous (Lindow and Brandl, 2003). Water availability is also a key factor for many microorgan- isms, and many pathogens are able to degrade the plant cuticle to enhance water availability. Even some epiphytes are capable of using small quantities of these 8 chemicals to access water resources in the phyllosphere without causing damage (Whipps et al., 2008). Other nutrients may be found in small quantities on the leaf surface, or the microorganisms may have developed strategies to synthesize these nutrients from the waste products of other microorganisms in the area. The heterogeneous nature of the leaf surface means that it is highly varied in its ability to support microbial life. The patterns of microbial populations have been most closely studied at the scale of individual leaves, although some work has been done on smaller scale within-leaf patterns (Kinkel et al., 2002). A high degree of spatial aggregation can occur on the leaf surface, and this clustering is caused by patterns of immigration, emigration, growth, death, nutrient availability, and leaf surface structures. Aggregation can facilitate the acquisition of nutrients by microorganisms and may also increase their ability to survive environmental stresses and predation (Kinkel et al., 2002). Zak (2002) found that aggregation of fungal species in the phyllosphere was not random, and was in fact an adaptive strategy to stressful environmental conditions. He suggests that growth limiting factors such as moisture, temperature, UV-B radiation, and leaf surface nutrient availability are the causal factors of aggregation, although different factors may be limiting in different situations. Aggregation can significantly impact the interactions between microorganisms. When resources are highly aggregated it increases the likelihood that a microor- ganism will be living in association with other microorganisms. As well, when the resources on the surface are highly aggregated there will be sites that are capable of supporting very high population densities of microorganisms, meaning that species that would normally be competing for the same nutrients are capable of sharing the environment. Resource aggregation also means that there are sites 9 where a weaker competitor can escape from a stronger competitor, allowing it to survive in the phyllosphere (Kinkel et al., 2002). Microorganisms may be detri- mental to each other, either because they are competing for the same resources, or because one or both have a direct means of inhibiting the other. There is also the possibility that they may not interact and can live in the same environment, or they may have synergistic interactions, where both species survive and grow better in the presence of the other. The focus of much of the microbe-microbe interaction has been in the field of biological control. Despite this research, many details of the interactions between microorganisms are still not understood (Andrews and Harris, 2000). 1.1.3 Post-Harvest Diseases of Pome Fruit Post-harvest diseases are characterized by latent infections of the fruit that are asymptomatic throughout the growing season and only develop on matured fruit after storage (Mantyka, 2010). Although there are many post-harvest diseases which affect pome fruit, a study done in British Columbia (Sholberg et al., 1996) found that the pathogens which had the greatest impact in packing houses located in Oliver, Kelowna, and Winfield were Penicillium species (spp.), Botrytis cinerea, and Mucor spp. This research also found that there was a positive relationship between the spore populations found in the packinghouses and the level of decay that was observed in the apples. Penicillium expansum Penicillium (P.) species accounted for 28.8% of the post-harvest pathogens detected in Okanagan orchards, and of these, Penicillium expansum was the most common (Sholberg and Haag, 1996). Penicillium expan- 10 sum is also known as apple blue mold, and produces the mycotoxin patulin, which can survive the pasteurization process and is therefore an important disease not only for fruit losses but also for health concerns. P. expansum can infect apples through wounds, the stem-end of the fruit, core rots, and through the lenticels (pores) of the fruit (Mantyka, 2010). P. expansum has been isolated in dump tank water in apple packing houses, as well as on the walls, floors, and in the wooden packing bins (Sanderson and Spotts, 1995). Botrytis cinerea Botrytis (B.) cinerea was isolated on 25.2% of diseased apples in Okanagan orchards (Sholberg and Haag, 1996). Also known as grey mold, B. cinerea initiates disease through wounds, stem punctures, or the stem or calyx portion of the fruit (Mantyka, 2010). In a study done examining the timing of B. cinerea infection in storage it was found that very little gray mold developed until between three and six months in storage (Lennox and Spotts, 2004). Botrytis cinerea spores can survive for long periods of time on organic matter, including on packing and storage bins, and have been found in the air and water inside packinghouses (Mantyka, 2010). Botrytis cinerea can colonize decaying plant tissue such as dead branches, fallen leaves, and decaying fruit, but can also live on weeds, grasses, and blackberry plants (Spotts et al, 2009). Mucor piriformis One study detected Mucor (M.) species on 21.5% of the diseased apples studied in Okanagan orchards (Sholberg and Haag, 1996). Prior to this study, Mucor spp. had not been considered to be a major apple pathogen. However, it has recently become a much more significant cause of decay, as it is not affected by most fungicides and the increased use of fungicides on other pathogens has reduced the competition for Mucor spp. M. piriformis infection 11 generally occurs through the stem end, the calyx, or puncture wounds (Michailides and Spotts, 1990). M. piriformis can grow and sporulate at 0 to 1 degree Celsius, which is the typical storage temperature range for pome fruit (Guo et al., 1999). However, M. piriformis cannot tolerate high temperatures (maximal temperature is 27 degrees Celsius) and therefore summer temperatures may limit the survival and growth of M. piriformis (Guo et al., 1999). Propagule numbers of M. pir- iformis increase during cool temperatures and high moisture levels (Michailides and Spotts, 1990). Mucor piriformis spores are generally found in the top 5cm of the soil in orchards, and higher spore concentrations occur in the vicinity of fallen infected fruit. (Dobson and Spotts, 1988). Spores can also germinate, grow, and release inoculum on leaves, fallen fruit, and some species of weeds (Michailides and Spotts, 1990). However, M. piriformis does not generally survive on the surface of pome fruit, and one study did not find propagules in the air or by washing harvested fruit (Michailides and Spotts, 1990). M. piriformis generally enters packinghouses via soil that adheres to picking bins and is then able to colonize fruit at maturity and at the lower temperatures found in the packing house (Guo et al., 1999). 1.1.4 Pre-Harvest Factors Influencing Disease Outcomes Although disease generally occurs while apples are in storage, the disease levels are strongly influenced by a number of pre-harvest factors. Some of these factors can be influenced over the course of the growing season to reduce the eventual disease outcomes. Other factors may not be controllable, but must be recognized for their impact on incoming infection levels and susceptibility of the fruit. 12 Cultivar The most fundamental influencing factor when considering disease outcome on apples are the characteristics of the apples themselves. The cultivar of apple determines how susceptible it is to any given disease, as well as how it responds to environmental conditions. These in turn affect how the apple is handled and how it is stored. An examination of the decay resistance, the force required to break the epidermis, and the degree of sinus opening for twelve apple cultivars in combination with four different pathogens found that there was significant variation between cultivars (Spotts et al., 1999). This research introduced a measure of disease outcome termed “Infection Severity” (IS), which is the value obtained by calculating the percentage of wounds infected multiplied by the lesion diameter of these wounds. Spotts et al. (1999) also examined some of the characteristics of apples that cause them to be more susceptible to post-harvest pathogens. Most post-harvest pathogens require a wound in the skin of the apple, or some other opening, before they can colonize the apple and cause infection. They may also enter through the stem, the calyx end and sinus of the apple, or the lenticels in the skin. Spotts et al. (1999) found that the apple cultivars studied differed significantly in the force required to break the epidermis, and the degree of sinus opening. This suggests that there are a variety of factors that must be considered in order to determine the susceptibility of an apple cultivar to any given disease, and the extent to which it is at risk from damage. Another study by Amiri and Bompeix (2005) found that cultivars that had a greater number of open lenticels, and also those apples with a large diameter, were the most susceptible to P. expansum. Sholberg and Haag (1996) also found that there were significant differences in infection levels for different cultivars for each pathogen studied. For example, B. cinerea 13 was isolated from 48.1% of diseased Spartan apples, but was isolated from 0.0% of diseased Jonagold apples, suggesting that Jonagold apples are resistant to B. cinerea. On the other hand, Mucor spp. were isolated from 47.6% of Jonagolds, and only 16.7% of Spartans. It may be possible to influence disease outcomes by using more resistant cul- tivars, and by breeding apples for increased resistance to disease. However, the planting of apple cultivars is primarily governed by market demand, and due to the timescale involved in replanting orchards, switching cultivars for disease resistance is not a reasonable means of controlling disease levels. Instead, it is important to consider the susceptibility of a given cultivar when determining how long it can be safely stored. Environmental Conditions As with apple cultivar, the environmental condi- tions during apple growth have a significant impact on eventual disease outcome, but are not a factor that growers or packinghouses change to reduce disease in- cidence. Many models to predict fungal disease presence and infection outcomes rely primarily or solely on environmental conditions during the growing season. These conditions include temperature, rainfall, and humidity (Nita et al., 2008, Xu, 1999, Xu et al., 2000). Spotts et al. (2009) found that rainfall in the two weeks before harvest increased the risk of post-harvest decay by B. cinerea. Sim- ilar results have been found for M. piriformis (Michailides and Spotts, 1990). Rainfall can have a number of different effects on disease presence. Rainfall can wash spores off fruit, but can also transfer spores from leaves and other surfaces to fruit due to splash effects. On balance, the extra moisture seems to result in increased disease presence based on the results quoted here. Each pathogen has 14 a range of temperatures at which it can survive, and an optimal temperature at which it can grow and reproduce most efficiently. Thus, it is expected that envi- ronmental conditions will have a significant effect on the incidence of post-harvest disease as there will be an increased spore presence in the orchard during favorable conditions, and more latent infections of the fruit would result. Age of Orchard The age of the orchard is another factor that cannot be con- trolled practically by the growers, but that has an impact on eventual disease outcome. Newly planted apple trees do not produce fruit for the first few years of their lives as they become established, and it is costly to pull down old trees and replant. However, many orchardists have recently made the switch from full canopy orchards to high-density orchards. This affects the distance between trees, the scale of the canopy, and the degree of protection that is provided to fruit. In addition, older orchards are more likely to have trees with dead or dying limbs, and to have greater build up of pathogen inoculum in the surrounding leaf litter and plants (Spotts et al., 2009). Maintenance Practices A factor that growers and packinghouse managers may be able to control through good maintenance practices is the inoculum levels present while apples are growing. Propagules of these post-harvest decay fungi can be found in the soil, plant litter, in the air, and on decaying fruit (Lennox et al., 2003). Spotts et al. (2009) found that orchard management rating was the most important factor in their model for predicting disease risk, where high ratings indicated less likelihood of post-harvest disease. The youth of the orchard, the degree of maintenance, good pest, disease, and weed control practices, and beneficial horticultural practices increased the orchard management rating. Fac- 15 tors such as dead and diseased branches, inefficient irrigation, and lack of mowing and removal of weeds and dropped fruit counted against the orchard management rating. The orchard management factor explained 49.2% of the predicted disease risk. Unlike factors discussed in previous sections, it is relatively easy to influence how often mowing occurs, or the irrigation system that is used, as well as the other maintenance factors. As all of the post-harvest pathogens can grow and in- oculate fruit during the growing period, orchardists could potentially reduce the levels of latent infection by removing fallen fruit, weeds, and other decaying plant tissues from the surrounding area. It is expected that this relationship could be quantified, as there would be a relationship between the amount of time or money put into maintenance and the disease outcomes for the orchard. Inoculum Levels Spotts (1985b) found that the fungal conidia of B. cinerea, P. expansum, and M. piriformis had half lives of approximately 14.4, 6.1, and 4.2 days, respectively, under orchard conditions. This suggests that the last month to two weeks of the growing season will be the most important time for observation and control of inoculum levels. It was also found that at lower temperatures these half-lives increased significantly, suggesting that fungal spores could endure longer in the packing house, and have a greater chance of causing infection. Lennox et al. (2003) studied the sources and population sizes of B. cinerea and Penicillium spp. in pear orchards, packing houses, and pre-sale storage, to determine if there was a relationship between detected population sizes and the incidence of post-harvest decay. It was found that in many cases the levels of these species were undetectable. It was also found that there were significant positive correlations between the incidence of decay and population measurements 16 of fungal pathogens on the fruit surface. 1.1.5 Harvest Factors Influencing Disease Outcomes Maturity of Fruit Spotts (1985a) found that the resistance of pear fruit to decay by fungal post-harvest diseases decreased with increasing fruit maturity. As these pome fruit grew they became increasingly susceptible to infection. It is expected that the same patterns would be observed in apples. This suggests that if fruit are picked when they are not fully mature they will be expected to have a greater degree of resistance to post harvest disease. Michailides and Spotts (1990) found that over-mature fruit was especially susceptible to decay by M. piriformis. Thus, the timing of harvest should be such that the fruit is ripe enough for sale, but not over-matured such that it is at an increased disease risk level. Harvest Methods and Amount of Watering Spotts and Cervantes (2001) found that B. cinerea and P. expansum conidia were capable of colonizing wounds even when present at very low concentrations (<10 conidia per cm2 of surface). In another study apples were surveyed for wounds, and it was found that the incidence of wounding at harvest varied from 12% to 36% (Amiri and Bompeix, 2005). In the same study it was found that the disease incidence due to P. expansum when inoculated at harvest was constantly higher than the incidence of wounding. These studies suggest that when any inoculum of P. expansum is present at harvest the incidence of wounding will strongly influence the disease outcome. As wounding is such an important factor in disease outcomes, the care with which the fruit is handled is important. During harvesting, it was found that the 17 average incidence of wounding in pear fruit was 4.3% when workers harvesting the fruit were paid by the hour, and 13.9% when workers harvesting the fruit were paid by the number of bins harvested (Spotts et al., 1998). This effect may also include fruit which has fallen to the orchard floor being placed in the bins when fruit is harvested by the bin. It is suggested that even brief contact with the orchard soil can be enough to inoculate fruit and they can then spread disease to the other fruit in the bin (Michailides and Spotts, 1986). Conditions at Harvest Spotts and Cervantes (2001) also showed that fruit that were wet when they came into contact with spores and were subsequently wounded were more likely to develop infection. This suggests that rainfall during or just before harvest may increase the eventual disease severity. Michailides and Spotts (1986) also suggest that harvesting during wet soil conditions should be avoided to reduce fungal disease incidence. 1.1.6 Post-Harvest Factors Influencing Disease Outcomes Once apples reach the packinghouse there are still many places where they can become inoculated with pathogen spores. These include, but are not limited to, other apples, wash water, contaminated bins and walls, defrost water used in cooling units, and sorting machinery. Wounding may also occur during sorting and packing, increasing the incidence of disease. Research by Amiri and Bom- peix (2005) showed that contamination of apples by Penicillium spp. occurred mainly in storage, and that in the pre-harvest period there were very low levels of inoculum. 18 Sanitation It was found in a study by Teixido et al. (1999) which examined the microbial population dynamics on Golden Delicious apples throughout the growing season that levels of P. expansum and B. cinerea were very low prior to harvest. This suggests that the majority of inoculation of apple fruits occurs after harvest, from the bins, the packing equipment, or from spores in the air or water at the packinghouse. Thus, one of the best ways to prevent infection spread is to maintain good sanitation practices at the packing house, and treat the wash water and defrost water regularly. It was found by Sholberg and Haag (1996) that defrost water used in cooling units at all nine sites studied was contaminated with fungal propagules. By treating this water to reduce or prevent the likelihood of fungal inoculum being repeatedly spread over apples in storage, it is possible that disease risk could be greatly reduced. All of the post-harvest pathogens considered can survive on wooden fruit bins, and it is thus suggested that these should be thoroughly washed or steam-cleaned and covered internally to prevent spread to fruit (Michailides and Spotts, 1990). Storage Type When apples are stored they are typically placed into either cold air storage, or controlled atmosphere storage. Cold air storage simply con- sists of regular air storage with temperatures reduced to somewhere between 0 and 5 degrees Celsius, depending on the cultivar. Although many post-harvest diseases can still grow and reproduce at these temperatures, their growth rates are significantly reduced. The other type of storage, controlled atmosphere storage, maintains the fruit at similar temperatures, while reducing oxygen concentrations and increasing carbon dioxide concentrations. This serves both to slow the ripen- ing of fruit and to substantially reduce the growth of pathogens (Mantyka, 2010). 19 For example, M. piriformis decay levels were found to be decreased in low oxygen atmosphere storage, although M. piriformis can grow at as low as 0 degrees Cel- sius. Lowering the humidity in storage was also an effective way to reduce Mucor rot (Michailides and Spotts, 1990). Duration of Storage Spotts (1985a) observed that detached pear fruits showed decreasing resistance to post-harvest disease over time. This suggests that the longer pome fruits remain in storage, the more likely they are to become infected. It also suggests that the rate of spread of infections should increase with duration of storage. Thus, the longer pome fruits are stored, the higher the disease risk levels become. 1.1.7 Control Techniques Fungicides Fungicides are the most common method used to control the de- velopment of post-harvest disease. Although fungicides are effective, they carry with them a number of problems. Fungicides generally have a significant environ- mental impact, and so they are slowly being phased out. Fungicides also carry a significant cost, and therefore economic interests also come into play. Another significant reason for the move away from fungicides is the fact that within a few years of a fungicide being released for use on the market, resistant strains of many pathogen species can be isolated. Sholberg and Haag (1996) were able to isolate resistant species in many of the packinghouses they studied, with as high as 44.4% of diseased apples having resistant species identified on them. In addition, although fungicides can be used against P. expansum and B. cinerea, M. piriformis is highly resistant to the fungicides typically used against post-harvest 20 diseases (Michailides and Spotts, 1990). It has also been suggested that the use of fungicide may decrease the populations of non-pathogenic microorganisms that would normally compete with the pathogenic microorganisms for resources and naturally control their populations (Teixido et al, 1999). With the growing or- ganic movement, as well as increasing concern for the environment, a “natural” solution would be considered best. Biological Control Biological control is the process of using organisms to con- trol pests, either by introducing the organisms into the environment of the pest, or, if it is already present, enhancing its ability to multiply or become more ef- fective in controlling the pest. Biological control in agriculture has become the focus of a great deal of recent research for its potential to be used to control plant pathogens without the use of expensive and environmentally damaging fungicides. Although there have been successes in the development of biocontrol strategies, there is still a great deal not known about the mechanisms by which biocontrols operate and how best to optimize our biocontrol strategies. The main mechanisms of biological control that have been considered in the literature are mycoparasitism, competition, antibiosis, and induced resistance. Biological control by mycoparasitic organisms occurs when the biological control agent (BCA) acts as a parasite on the pathogen, killing it and then using it for nutrients. BCAs which use competition as their mechanism have some limiting factor, generally nutrients or space, which they compete with the pathogen for, hopefully excluding it from attaining one or several of the factors which it requires to survive. The antibiosis mechanism is when the BCA produces some compound that is damaging to the pathogen. Induced resistance is when the BCA stimu- 21 lates the plant’s natural defense mechanisms, which allows the plant to fend off attack by the pathogenic organism (Jeger et al., 2008). In the past ten years there have been great advances in understanding the molecular basis for mechanisms of biological control, leading to new developments in the types of biological control agents being researched and developed (Andrews and Harris, 2000). In order to better understand the processes of biocontrol, attempts have been made in the literature to model the phyllosphere and the behavior of pathogens and biocon- trol agents (Jeger et al., 2008, Xu et al., 2010a). Future research in modeling, especially where it can be verified by experimental data, may result in a much greater understanding of biological control. One of the most important features of the phyllosphere dynamics for biolog- ical control is the concept of resource aggregation. A high degree of aggregation allows for the pathogen to escape competition and find sites where it can sur- vive. Consequently, competition-based biological control is unlikely to be 100% effective under highly aggregated conditions (Kinkel et al., 2002). Johnson (2010) considers the concept of pathogen refuge in detail in his recent work, consider- ing not only the refuge given by aggregation of resources, but also looking at temporal, spatial, structural, chemical, probabilistic, behavioral, taxonomic, and evolved strategic antagonist-free refuges. He argues that understanding the meth- ods that pathogens employ to attain refuges will allow us to develop biological control strategies that can eliminate these refuges. With respect to biological control, the most significant type of refuge is physical, allowing the pathogen to physically escape from its antagonist by using some physical structure of the phyl- losphere environment such as resource aggregation. Johnson (2010) also discusses how the most successful models of disease and biocontrol are those that have 22 taken pathogen refuge into account. He suggests that with any singular biocon- trol there will always be some proportion of the pathogen that will be unaffected, as pathogens have evolved the ability to compete for antagonist-free space. He suggests that in order to have effective biological control, mixtures of antagonists that cannot affect different refuges and have complementary mechanisms and life strategies should be used. Xu et al. (2010a) also considered the use of different biological control strategies, and found differences in effectiveness of combinations of strategies. As was suggested by Johnson (2010) strategies that define different refuges such as competition and antibiosis are the most successful when used in combination. Pathogen refuge in the phyllosphere may also lead to an inability for biological control to be 100% effective. Stockwell et al. (2002) consider the example of bio- logical control of fire blight in pome fruit. It had previously been found that the biological controls were successful in suppressing disease, but did not eliminate it. Success ranged from about 30 to 60% suppression. This research examined the influences of the physical environment and indigenous microflora, as well as using combinations of chemical suppression techniques in combination with biological control. They found that although the biocontrol was able to grow with indige- nous microflora, the timing of application in relation to weather conditions was necessary to achieve good suppression. They also emphasize the need to consider the mechanisms that are used by biological controls in order to understand the conditions that will result in reliable suppression. Although there is a great deal of research into the behavior and interaction of microorganisms, it is still difficult to predict the degree of success of a biological control agent when used in the field. There are many elements that affect the 23 abilities of BCAs, including their interactions with other microorganisms. In the paper by Xu et al. (2010a) they discuss a model for the interaction between one pathogen and one BCA under a variety of conditions. This model successfully predicted the relative abilities of different strategies to be successful based on their application time in comparison with the pathogen, the amount of coverage of the BCA, the mortality rate of the BCA, as well as predicting the success when mechanisms were used in combination in one BCA. Current research includes studying the effects of using multiple biological control agents against a pathogen (Xu et al., 2010b). Due to its impact on human food sources and economy, the diseases of food crops are the most well studied type of phyllosphere pathogens. There have been many models developed in an attempt to understand the dynamics of plant pathogens, with the hope of leading to a greater ability to predict and control plant disease (De Wolf and Isard, 2007). With biological control being a grow- ing field, the focus of research has also turned to evaluating the effectiveness of non-pathogenic microorganisms against various plant pathogens. Etebarian et al. (2004) found that Pseudomonas fluorescens strains were able to significantly re- duce the severity of apple blue mold decay post-harvest. It was also found that these strains were effective against gray mold (Mikani et al., 2007). 1.1.8 Mathematical Modeling Modeling Post-Harvest Disease Outcomes in the Orchard The only model which is currently available to predict post-harvest disease in pome fruit based on pre-storage factors was published by Spotts et al. (2009) in the article At-harvest prediction of grey mould risk in pear fruit in long-term cold storage. 24 Table 1.1 shows the model as developed to give relative risk ratings for pears by year. This model was successful in predicting the relative disease risk within the year, and was able to explain 97.3% of the variation using measures of fungal DNA on the fruit surface, the application (or lack there of) of a fungicide prior to harvest, rainfall in the two weeks before harvest, and the orchard management rat- ing. For this model, the Spearman correlation coefficient relating predicted grey mould risk level and actual incidence was 0.882 (P≤0.001). Spotts et al. (2009) found that orchard rating (age of orchard, maintenance practices, etc) explained the largest portion of the variation. Table 1.1: Spotts and Gastagnoli’s (2010) relative risk model for pear fruit: Adapted from: Relative risk model for pear fruit from Robert A. Spotts and Steve Gastagnoli, Predicting and managing gray mold rot of pear in Oregon, Oregon State University Extension Service, January 2010. Gray Mold Preharvest Preharvest Orchard Condition DNA Fungicide Rain Good Average Poor Low Yes No Low Low Moderate Low Yes Yes Low Moderate High Low No No Low Moderate High Low No Yes Moderate High Extreme High Yes No Low Moderate High High Yes Yes Moderate High Extreme High No No Moderate High Extreme High No Yes High Extreme Extreme The categories used in the relative risk models were in some cases very subjec- tive. The cut off value used to determine if there were low or high levels of fungal DNA on the fruit surface was 0.50 pg cm-2, and no variation was explored outside of the low and high classification. Although the orchard management rating was 25 found to be the most predictive variable, this rating was done in a subjective manner by rating the orchards as being 1, 2, or 3, where most typical orchards were 2, well maintained orchards were 1, and poorly maintained orchards were 3. To apply this orchard rating system in the field would require an impartial third party to rate all of the orchards that might be considered with the model. The result of these subjective categories is that, although the predictions within the year were relatively accurate, between years there was not a consistent level of risk that was considered to be “low,” “moderate,” or “high.” The actual percentage of fruit that developed grey mould after six months when rated as “low” predicted disease risk varied from 0.1% to 2.1%, when rated as “moderate” ranged from 0.6% to 5.9%, and when rated as “high” ranged from 3.0% to 8.7% over the three years studied. Thus, although this model would provide a relative disease risk level for grey mould within the year that could allow for decisions to be made about which bins to store long-term, it does not allow for an estimation of disease outcome, or for cost assessments to be done on factors such as pre-harvest fungicide. This is an area which our research addresses and some of these aspects are quantified. Modeling Post-Harvest Disease Outcomes in Storage In a related re- search project, a model for the progress of disease on apple fruits in storage over time is being developed (M. Dutot, L. Nelson & R.C. Tyson, "Predicting post- harvest disease of apples caused by major fungal pathogens", in progress). This research involves a storage simulation that can encompass factors such as the susceptibility of the apple cultivar to the pathogen under consideration, and the initial infection levels of apples coming into storage. This model can consider varying storage intervals, and predict the percentage of apples that will become 26 infected at each time and under different conditions. Our research aims to comple- ment this related research by addressing factors that influence disease outcomes that occur before storage. Modeling Spore Dispersal in the Orchard Previous research has attempted to describe the dispersal of spores and pollen from a variety of plants (McCartney, 1994). There are many reasons for this interest, including the spread of disease, crop fertilization, and the spread of genetically modified organisms. Many models consider the effect of air currents and wind in dispersing spores over both short and long distances. In addition to air currents, rain splash is a major agent of dispersal for pathogen spores (Hirano et al., 1996, Fitt et al., 1989). Rain droplets are often capable of dislodging spores where wind is not. This also suggests that overhead irrigation could be a mechanism of spore dispersal. The architecture of the plants also has an impact on rain splash dispersal (Schoeny et al., 2008), meaning that it is likely there is a difference in the degree of rain splash between older full canopy orchards and newer high density orchards. A model developed by Rodriguz-Rajo et al. (2010) used aerobiological methods to model B. cinerea spore concentrations. This model used the growth stages of the plant, as well as weather conditions and previous spore concentrations to forecast future spore concentrations with good results. This research demonstrates how the growth stage of the plant can affect the spore counts. Another method that has been considered is the use of Brownian motion to account for the movement of spores. This method was used by Klein et al. (2003) to describe the dispersal of corn pollen. This method is quite complex, and includes the stochasticity involved in the movement of tiny particles in air currents. A model by Aylor and Flesch 27 (2001) also incorporated stochasticity by using a Lagrangian stochastic simulation model to estimate spore release rates and spread above and inside plant canopies. Although a multitude of models has been proposed, the “Gaussian Plume” method (as discussed below) is cited as the simplest approach to modeling the dispersal of particles by wind (McCartney, 1994). In the article Ability of the Gaussian plume model to predict and describe spore dispersal over a potato crop (Spijkerboer et al., 2002) the authors describe field experiments done to validate the use of the Gaussian plume model as a tool to describe and predict the at- mospheric dispersal of biological particles. The biological particles of interest are such things as fungal spores or plant pollen, and the authors are particularly in- terested in dispersal over long distances. An article by Skelsey et al. (2008) both develops and validates a quasi-Gaussian plume model to describe the dispersal of spores in three dimensions. The quasi-Gaussian model describes a more realistic representation of vertical diffusion, and the results of this model compared well with the experimental data. Atmospheric dispersion modeling research attempts to mathematically de- scribe the movement of particles in the atmosphere. Dispersion refers to both the processes of diffusion due to turbulent eddy motion in the atmosphere and advection due to wind currents. The Gaussian plume model is valued because it is relatively simple, has an analytical solution, and requires minimal experimental data. However, it does not model the plant canopy well, and was developed to describe the movement of gases. There are four main factors to consider in describing atmospheric dispersion. These are diffusion and advection, as mentioned, the source of particles and any sinks in the system such as deposition due to encountering a barrier or falling due 28 to gravity. In atmospheric systems, diffusion is due to turbulent eddy motion, which is a diffusion process where substances are mixed in the atmosphere due to eddy currents. As eddy motion is influenced by weather conditions, but is essentially a random process, it can be difficult to model. One way to describe this randomness is with probability distributions, such as the Gaussian distribution. The Gaussian Plume Model The objectives of Spijkerboer et al. (2002) were to evaluate the Gaussian plume model for describing spore dispersal at a distance of up to 100 meters from a source, and to quantify some of the model parameters, such as the escape fraction. The Gaussian plume model is used to describe the transport of a single contaminant from a point source. The derivation (Stockie, 2010) is based on the law of conservation of mass, and results in the solution c(r, y, z) = Q 4πur exp  −y 2 4r  exp  −(z −H) 2 4r  + exp  −(z +H) 2 4r  (1.1) where r is a function of downwind distance x, Q is the spore release rate (spores per second), u is the horizontal wind speed, and H is the height of release. Figure 1.1 provides a visual description of the model solutions. It shows the Gaussian concentration profiles, and the source in alignment with the wind direction. Spijkerboer et al. (2002) make some modifications to the Gaussian plume solution using empirical results. They allow for absorption of the particles at the ground by including the parameter R, which describes the fraction of the plume reflected at the earth’s surface (where 1 is complete reflection and 0 is complete absorption). The model is also modified to depend on the standard deviations of 29 Figure 1.1: Visual description of the Gaussian plume model solutions: Note the Gaussian concentration profiles in the horizontal and vertical directions, as well as the alignment of the x axis with the wind direction (Adapted from Stokie, 2010). the height and width of the plume, σz and σy respectively. These are empirical functions that are described by σz = K(z0)ax b, (1.2) σy = K(z0)10 pxq, (1.3) where a, b, p, and q are dimensionless empirical constants and K(z0) is calculated as K(z0) = (10z0) 0.53x−0.22 , (1.4) 30 where z0 is the roughness length which is characteristic of a particular type of surface cover. Spijkerboer et al. (2002) used a formula specific for potato crops from Legg et al. (1981) to calculate z0 for the crop height, which they measured at 0.7 meters. With these empirical modifications, the Gaussian plume solution becomes c(x, y, z) = Q 2πu exp  − y22σ2y  σy 1 σz  exp  −(H − z) 2 2σ2z  +R · exp  −(H + z) 2 2σ2z  . (1.5) The effect of weather conditions is empirically classified into six classes, A through F, based on the amount of mixing that occurs under each condition. The constants a, b, p, and q are determined from the stability class. Class A describes the most unstable atmosphere, which would see more mixing and plumes that are wide and high. Class F describes the most stable atmosphere, where the least mixing would occur and plumes would be low and narrow. Spijkerboer et al. (2002) found that changing the parameters that correspond with these stability classes, when all other parameters are held constant, led to ground level contours that showed wide plumes that did not continue long at ground level for unstable conditions, and narrow plumes that were long at ground level for stable conditions. The experiment performed to validate this model consisted of releasing spores of the fern Lycopodium clavatum near the top of the canopy of a potato field, as described in Spijkerboer et al. (2002). Before each trial the fraction of cloud cover was estimated, and wind speed data were obtained from a weather station nearby. There were 35 trials performed. Some trials had to be rejected and the only trials considered in the analysis were those where the peak of the plume and some measurements on either side were recorded. 31 The predictive power of this model was found to be moderate in relation to the experimentally collected data. Comparing the predicted concentrations with the measured concentrations resulted in a moderate correlation (R = 0.4). Although the results are not highly accurate, Spijkerboer et al. (2002) suggest that the Gaussian plume model could be well used as a predictor of the “worst case scenario” for any given conditions, which would be useful in most situations, especially where it was being used to predict disease risk. The model also was used to determine parameter values that would be ac- curate for the biological system in question for future modeling attempts. The most important of these was the escape fraction, the proportion of spores that are carried out of the plant canopy, which was found to be approximately 64% and depended on the stability class. The experiment found that the results for biological particles were as good as those for pollutant gasses. They argue that be- cause the plumes that the model predicted were both wider and higher than those determined experimentally, gravitational settling was unlikely to be to blame for model inaccuracies. They conclude that the Gaussian plume model is well suited for use in describing the motion of biological particles, and can be easily applied to disease prediction systems. Other modeling attempts have included stochasticity. Aylor (1989) used a Markov-chain random-flight simulation model in which the trajectory of a parcel of air carrying spores was simulated. The simulated trajectory was comprised of a series of short line-of-flight segments, each representing the motion of the parcel of air over a short period of time. This model was quite complex, and it was only possible to obtain numerical solutions. As input for the model a number of measurements were required, including the stem and leaf area values for each 32 layer the canopy was separated into for modeling, the concentrations of spores in the air at a number of locations, the deposition flux, the sedimentation rate, the horizontal wind speeds, and the wind direction. From this model they obtained reasonable agreement with the data. The predicted escape fraction from this model was up to 50% depending on the environmental conditions. Another model developed by Donald Aylor at a later date (2005) also included stochasticity. This Lagrangian stochastic simulation model describes the location of individual pollen grains in two dimensions in a vertically inhomogeneous turbu- lent wind field. Again this model has only numerical solutions. The model must be parameterized for specific crops, and is experimentally demanding, requiring the measurement of the leaf area of the crop, the number of pollen grains released per plant per day, aerial concentrations, flux measurements, wind speed and direc- tion, air temperature, relative humidity, and solar irradiance. This model found a good prediction of pollen fluxes within the plant canopy (r2 = 0.73). Other models have been developed that do not incorporate stochasticity. A model developed by Legg and Powell (1979) used a two dimensional numerical model based on a K-type model that relied on finite differences to predict spore concentrations. It required the measurement of the initial spore concentration profile, the wind speed profile, the profiles of the foliage area density (measure- ments of the stems, leaves, and heads done separately), and measurements of the coefficients of turbulent diffusion (K) for the three dimensions. This model involved extensive descriptions of different shapes of plant parts such as the dif- ferences between the flat leaves and cylindrical stems in regard to deposition of spores. Legg and Powell (1979) used two different types of spores, E. graminis and Lycopodium. The agreement between the experimentally collected data and 33 the predictions of the model were quite good for the concentrations of Lycopodium spores, but were poor for E. graminis spores, consistently over predicting. The model developed by De Jong et al. (1991) also used turbulent diffusion equations to model movement of spores within a forest. The forest was separated into multiple layers, including the sources at the ground level and the canopy of the trees. Again, this required area indices for the needles and branches, the area of the forest floor, release rates, spore densities, and wind speed. This model also involved the use of the turbulence coefficients (K) used by Spijkerboer et al. (2002) as shown in Equation (1.4). The agreement between this model and the experimental results was moderate. They suggest that their model could be used as input to the Gaussian plume model to be used in risk analysis. The most recent model, building on the results of Spijkerboer et al. (2002), was developed by Skelsey et al. (2008) and describes the use of a quasi-Gaussian plume model to improve on the results gained from the Gaussian plume model (Equation (1.5)). They use data from Spijkerboer et al. (2002) to validate their model and compare it with the Gaussian plume model for the transport of botanical spores. Their work demonstrates that the Gaussian plume model can be improved by using Gaussian profiles for horizontal dispersal. Further improvement can be achieved by modeling the vertical profile with an exponential form. Skelsey et al. (2008) present evidence that botanical particles have been seen to disperse with a vertical exponential form. Instead of the stability classes used by Spijkerboer et al. they use surface-layer similarity theory to calculate the Monin-Obukhov length, giving a continuous measure of stability. This hybrid model showed improved performance in comparison with the Gaussian plume model. Spijkerboer et al. (2002) found that the Gaussian plume model, which is quite 34 accurate for describing the movement of pollutant gases, could also be used to describe the motion of biological particles. The Gaussian plume model provides a relatively simple analytical means to describe this transport. Although the predictions of the model did not see high correlation with the experimental data, it was found that for worst-case scenario prediction purposes it could provide a good estimation of the risk of contact. In ecological situations the requirements are often for a simple model that can be easily and cost effectively employed in order to predict disease risks or other negative results. The other models described required far more demanding experimental data collection, as well as far more complex numerical simulations. The major drawback of the Gaussian plume model is that it is of limited use within the plant canopy, especially where deposition is of interest, rather than the distance of spread. For particle movement within the canopy therefore, it may be necessary to combine the Gaussian plume model with elements of other models. Modeling Spore Dispersal in Storage As well as an interest in modeling spore dispersal outdoors, there has been a great deal of interest in particle dispersal in indoor environments (Lacey and West, 2006). Modeling of indoor particles has been used to measure air quality, and to determine flows of bacteria, fungal spores, and human diseases, among many other things. In agriculture, the movement of pathogens within greenhouses is of interest (Boulard et al., 2008, Boulard et al., 2010), as well as the movement of spores during storage. In research done by Sholberg and Haag, (1996) it was found that many of the major post-harvest pathogens of apple crops could be found in the defrost cooling water used in storage rooms. This indicated that uninfected apples can encounter inoculum 35 after being stored, and that treatment of defrost cooling water is important. 1.2 Research Goals and Hypotheses 1.2.1 Determine Relationships between Spore Detection and Environ- mental Conditions Previous research suggests that there is a relationship between the environmental conditions in the orchard and the ability of fungal inoculum to be transported to, and attach to, any foliar part of the plant. For the spores to cause an infection in the apple, they must reside on the fruit surface until such time as they are able to come into contact with a susceptible part of the mature fruit. These susceptible parts include wounds, lenticels, and the stem or calyx end of the fruit. As environmental conditions influence the growth of the fungus and its ability to produce spores, its ability to disperse from one location to another, and the ability of the inoculum to reside on susceptible tissue until an infection can be established, one would expect that environmental conditions should have a significant impact on the detection of spores on and around the fruit. One goal of this research was to determine the relationship between spore de- tection in the orchard and the environmental conditions. Based on previous work presented above, it is expected that warmer temperatures will lead to increased growth of the pathogen, and that rainfall events will provide moisture and also lead to increased growth. From the Gaussian plume modeling literature it is also expected that there will be a significant relationship between spore detection and wind conditions. 36 1.2.2 Pre-Storage Model for Incoming Infection Risk This research also aimed to make predictions of disease outcomes for the apple crops based on the growing season data collected. The concept for this research comes from the model proposed by Spotts et al. (2009), which was developed for predicting relative disease risks to allow for decision making. The model by Spotts et al. (2009) is a highly qualitative model with very subjective categories, and as such is not ideal for use in industry. Based on Spotts et al.’s model, the data were analyzed and wherever possible more quantitative predictive factors were identified. The goal of our modeling work was to give a prediction of disease risk based on pre-harvest and harvest factors that will allow for decisions to be made about which fruit is the most suited for long term storage and which should be sold as soon as possible or sent on to secondary production. It is expected that factors such as environmental conditions, apple cultivar, and orchard conditions will yield relationships that indicate trends and will give quantifiable impacts on eventual disease outcomes. However, it is also expected that much of the variation will not be captured by the data, as the data are limited in their descriptions of orchard management factors and fruit treatments after harvest. It is hoped that this model will serve as a starting point for future studies. 1.2.3 Combining the Pre-Storage and During-Storage Models In order to be able to predict eventual disease levels in apple fruit in storage, one must consider both pre-storage and post-storage factors. Research being completed in a related project into predicting post-harvest disease in storage based on initial infection level (M. Dutot, L. Nelson & R.C. Tyson, in progress) could 37 be combined with a pre-storage predictive model, such as the one our research aimed to develop, to give a more robust picture of disease outcomes. This parallel research considers a storage simulation that models the progress of disease in an apple bin in controlled-atmosphere storage over various storage intervals. Our goal in working with Dutot et al.’s model is to be able to link factors that predict incoming disease from pre-harvest and harvest factors to a measure of initial infection. This measure could then be used as an input parameter in the storage model. 1.2.4 Circulation/Probability Modeling of Inoculum Contact After preliminary analysis of the data it was observed that the detection of spores in the air and on apple tissues was an uncommon event. This agrees with previous work (Spotts et al., 2009), showing that the surface DNA measurement was not very significant in predicting eventual disease outcome. This finding is counter- intuitive based on the importance of the orchard condition measures, which are directly tied to the ability of the fungus to grow and spread. The data suggest that this may be an interesting modeling problem. There were two main questions that we aimed to answer by modeling this system. The first question is if the variability and unpredictability of the data were caused not by the environmental or orchard conditions, but instead by the sampling techniques. If so, our model would aim to provide a better understanding of how sampling should be completed to give more representative results. The second question is if the variation is not solely the result of the sampling methods, then what other factors explain it, and what will this suggest for future studies. Modeling spore dispersal in the orchard is expected to result in a greater un- 38 derstanding of the effects of environmental conditions during the growing season, and allow us to make recommendations toward better decision making in future sampling and experiments. 39 2 Data Collection and Experimental Set-up 2.1 Synopsis In this chapter we discuss the data used for this study, how they were collected, and factors that may affect the results. We include descriptions of the data and some basic descriptive statistics. 2.2 Field Experiments 2.2.1 Experimental Set-up The data were collected between 2007 and 2010 by researchers at UBC Okana- gan and the Pacific Agri-Food Research Centre (PARC) in Summerland. In the Okanagan, for each year there were four orchards used in the experiments, two at PARC, and two in Kelowna. The orchards from which data were collected at PARC were the same all four years. The orchards used in Kelowna changed between 2008 and 2010. Figure 2.1 shows the area where these studies were done. Kelowna is situated on the East side of Okanagan Lake, and the four orchards in Kelowna used for collecting data were medium to high density commercial orchards. Summerland is further South and is on the West side of the lake, and the two orchards used were research orchards planted at high density. In the Okanagan Valley there are mountain ranges running on either side of the lake from North to South. 40 Figure 2.1: Map of the Okanagan Valley (source: maps.google.com). 2.2.2 Data Collection Techniques For the three years under consideration in this study, the data available include measures of I-rod samplers (pathogen inoculum DNA sampled in the air), tissue samples (pathogen inoculum DNA on the surface of sampled areas of apples), daily temperatures, rainfall data and/or leaf wetness data, and wind speed and direction measurements. Although leaf wetness and temperature data were col- lected directly in the orchards in 2007 and 2008, there were some cases where there were large unexplained gaps in the recorded data. Therefore, it was necessary to use historical Environment Canada weather data from nearby weather stations to describe weather conditions for all of the orchards in this study. Although it is expected that these weather values are not as accurate, as they were not collected on site, they are preferred to the on-site measurements for consistency across all 41 of the values used. Thus, each year, the same weather conditions were used in the model for the two Summerland fields and the same weather conditions were used in the model for the two Kelowna fields. Detection of fungal pathogens was done as described in Mantyka (2010) using DNA macroarray technology. It is expected that this technique yields concentra- tion values for pathogen DNA on the samples taken, and is expected be highly accurate. Samples were taken both for DNA concentration in the air and DNA concentration on the apple tissue surfaces. To detect fungal spores in the air, I- rod samplers were used. I-rods are clear polystyrene rods coated in silicone grease that are placed within a spore trap that rotates, causing the rods to extend down- wards and collect airborne particles (Mantyka, 2010). To detect fungal spores on the apple tissue surfaces, individual apples were picked, and then a portion of the apple skin was removed and sampled for concentration. 2.2.3 Descriptions of Orchards Location Table 2.1 describes the six orchards that were studied during 2007- 2010. The same orchards were used in Summerland during all three years of the study, however, it was necessary to change the studied commercial orchards in Kelowna between 2008 and 2010. Orchard Rating and Maintenance Since Spotts et al. (2009) found that orchard maintenance was a major factor in disease outcomes, it is likely an im- portant variable in our study as well. Unfortunately, as discussed in Spotts et 42 Table 2.1: Orchard labels and locations: These locations are where data was taken for the three years of this study. Name Location Coordinates 2007 2008 2010 Orchard 1 Summerland (49° 33’ 58.49” N; 119° 38’ 41.93” W) X X X Orchard 2 Summerland (49° 33’ 54.81” N; 119° 38’ 56.73” W) X X X Orchard 3 Kelowna (49° 50’ 41.45” N; 119° 24’ 54.24” W) X X Orchard 4 Kelowna (49° 50’ 58.78” N; 119° 23’ 32.79” W) X X Orchard 5 Kelowna (49° 51’ 27.72” N; 119° 23’ 59.82” W) X Orchard 6 Kelowna (49° 49’ 55.72” N; 119° 27’ 56.45” W) X al. (2009) it is difficult to quantify how well an orchard has been maintained. It is hoped that for future studies, records that describe factors such as the age of the trees, the number of person-hours spent on orchard maintenance, and the dates of mowing could be used to more quantitatively describe the orchard rating. Although spray records were available for two of the orchards, this was not a large enough sample from which to draw conclusions, and thus we did not include these data in our analysis. The two research orchards, Orchards 1 & 2, were assumed to be well main- tained according to conventional agricultural practice. Orchards 3 & 4 were se- lected for the 2007 and 2008 field work because they were considered to be “dirty” orchards, that is, poorly maintained relative to the first two orchards in the study. Preliminary analysis found that these “dirty” orchards did not have significantly higher disease outcomes, as will be discussed in Chapter 3, and thus for 2010 “dirty” orchards were not selected. In fact, Orchards 5 & 6 were considered to be well maintained commercial orchards, although it was noted that Orchard 5 seemed significantly better cared for than Orchard 6 in a visit in 2011. 43 Cultivars Table 2.2 shows the cultivar of apple grown in each orchard during the period of study. Table 2.2: Apple cultivars for studied orchards: Here (S) designates an orchard in Summerland and (K) designates an orchard in Kelowna. Orchard Name Cultivar Orchard 1 (S) Jonagold Orchard 2 (S) Gala Orchard 3 (K) Gala Orchard 4 (K) Red Delicious Orchard 5 (K) Gala Orchard 6 (K) Ambrosia Irrigation Orchards 1, 2, 5, and 6 were visited in 2011. All of these orchards used drip irrigation or micro sprinklers close to the base of the tree for watering. In Orchard 1 both overhead and drip irrigation were used, while in Orchard 2 only drip irrigation was used. Although it was considered to be possible that irrigation techniques were a factor in disease outcomes, as water splash is a known method of fungal spore dispersal, there was not enough variation in irrigation techniques for it to be possible to analyze for this sample size. 2.2.4 Data Collected by Year 2007 In 2007 data were collected from Orchards 1 through 4. Tissue samples were taken throughout the growing season in all of the orchards, and I-rods were taken in all orchards except Orchard 4. In all years the time interval for sampling was irregular (See Figures 2.2 through 2.13). 44 2008 In 2008 data were collected from Orchards 1 through 4. Tissue samples and I-rods were taken throughout the growing season for all orchards. 2010 In 2010 data were collected from Orchards 1, 2, 5 and 6. Again, tissue samples and I-rods were taken for all orchards. 2.2.5 Description of Data In addition to the pathogen DNA detection, weather data were also collected. In 2007 and 2008 on site detectors were used to record daily temperatures and leaf wetness readings. However, the data sets for the growing season were incomplete, and not available for all the orchards, and for this reason it was decided to access historical weather data from Environment Canada (http://www.weatheroffice.gc .ca/canada_e.html). Using the weather stations in Kelowna and Summerland, the daily maximum temperatures, minimum temperatures, and average temperatures (all in degrees Celsius) were recorded. As well, the rainfall (mm), maximum wind speed (km/h) and direction of max gust (degrees) were collected. Figures 2.2 through 2.13 show the pathogen DNA collected by orchard for each of the years of the study. These graphs show the nonuniform sampling intervals that occurred in all years, as well as showing that pathogen DNA detection was overall a rare event. The figures show many zeros, with a few sudden peaks in concentration values. Figures 2.14 and 2.15 give a representative sample of the weather data used, showing the data collected for 2010 for Kelowna. Similar patterns were seen in the data for other years and locations, so these graphs are not shown. As expected, Figure 2.14 shows a gradual increase in temperature into the summer months, and then a gradual decrease, with day to day variability. It also shows 45 that rainfall events in the Okanagan are uncommon, and that even on the days where rainfall occurred the rainfall never exceeded 10mm over a 24 hour period. Figure 2.15 shows that there was a great deal of variability in both the wind speed and direction. As discussed previously, historical Environment Canada data were used for these weather results. The wind speed and direction of the maximum gust were used, and where these data were not available the maximum wind speed recorded and the average angle for that day were used. Figure 2.15 shows that the wind angle was most often close to 180º (South), which is likely due to bias introduced by averaging the angles for some data values. It is not known how much of this is due to the averaging of the angles, and how much is due to there being a dominant wind direction in the Okanagan Valley, which would be expected based on the geography. 46 Figure 2.2: Pathogen DNA in Orchard 1, 2007: Detected pathogen DNA in the air and on the apple tissue for the three species, B. cinerea, M. piriformis, and P. expansum for Orchard 1 in 2007. 47 Figure 2.3: Pathogen DNA in Orchard 2, 2007: Detected pathogen DNA in the air and on the apple tissue for the three species, B. cinerea, M. piriformis, and P. expansum for Orchard 2 in 2007. 48 Figure 2.4: Pathogen DNA in Orchard 3, 2007: Detected pathogen DNA in the air and on the apple tissue for the three species, B. cinerea, M. piriformis, and P. expansum for Orchard 3 in 2007. 49 Figure 2.5: Pathogen DNA in Orchard 4, 2007: Detected pathogen DNA on the apple tissue for the three species, B. cinerea, M. piriformis, and P. expansum for Orchard 4 in 2007. 50 Figure 2.6: Pathogen DNA in Orchard 1, 2008: Detected pathogen DNA in the air and on the apple tissue for the three species, B. cinerea, M. piriformis, and P. expansum for Orchard 1 in 2008. 51 Figure 2.7: Pathogen DNA in Orchard 2, 2008: Detected pathogen DNA in the air and on the apple tissue for the three species, B. cinerea, M. piriformis, and P. expansum for Orchard 2 in 2008. 52 Figure 2.8: Pathogen DNA in Orchard 3, 2008: Detected pathogen DNA in the air and on the apple tissue for the three species, B. cinerea, M. piriformis, and P. expansum for Orchard 3 in 2008. 53 Figure 2.9: Pathogen DNA in Orchard 4, 2008: Detected pathogen DNA in the air and on the apple tissue for the three species, B. cinerea, M. piriformis, and P. expansum for Orchard 4 in 2008. 54 Figure 2.10: Pathogen DNA in Orchard 1, 2010: Detected pathogen DNA in the air and on the apple tissue for the three species, B. cinerea, M. piriformis, and P. expansum for Orchard 1 in 2010. 55 Figure 2.11: Pathogen DNA in Orchard 2, 2010: Detected pathogen DNA in the air and on the apple tissue for the three species, B. cinerea, M. piriformis, and P. expansum for Orchard 2 in 2010. 56 Figure 2.12: Pathogen DNA in Orchard 5, 2010: Detected pathogen DNA in the air and on the apple tissue for the three species, B. cinerea, M. piriformis, and P. expansum for Orchard 5 in 2010. 57 Figure 2.13: Pathogen DNA in Orchard 6, 2010: Detected pathogen DNA in the air and on the apple tissue for the three species, B. cinerea, M. piriformis, and P. expansum for Orchard 6 in 2010. 58 Figure 2.14: 2010 average temperature and rainfall data: Taken from historical Environment Canada data for Kelowna.59 Figure 2.15: 2010 wind speed and direction data: Taken from historical Environment Canada data for Kelowna. Wind direction is measured in degrees from North.60 2.3 Storage Experiments 2.3.1 Experimental Set-up The storage data were collected primarily to test the efficacy of biological control agents as described in Mantyka (2010). This meant that much of the data avail- able had to be omitted, as the biological control agents were present, or because apples had been artificially inoculated. For our research, the natural disease inci- dence (NDI) values are of interest, as they best represent the disease that would have occurred in storage had these apples been destined for commercial use. The NDI was determined by taking a number of apples from each field, artificially wounding each apple three times using a 3-mm diameter nail embedded in a cork, so that the wounds were a standard size, and then storing the apples without inoculation with any of the pathogens in question. Although artificially wounding the apples is likely to increase the incidence of disease significantly, without inten- tional wounding, it is possible that none of the apples in the storage experiment would have developed any disease. Since these pathogens require an entry wound or other decay of tissue to infect the apple, we suggest that the experiment with wounded apples reflects the potential disease load on the apple that would be seen in longer term storage. Artificial wounding of the fruit allowed results for disease incidence to be gathered within the timeframe of the study. 2.3.2 Data Collection Techniques In order to measure disease incidence two values were taken. One was the percent of apples infected, and the other was the “infection severity” value, calculated as Infection Severity(IS) = Infection(%)×Average Lesion Diameter(mm)/100(%) 61 Apples were stored for between one and eight months, and during this time the wounds on the apples were monitored, both for disease presence, and lesion diameter on diseased apples. In some cases the apple was considered to be com- pletely rotten, and in this case the lesion size was considered to be the same as the average apple diameter for that group of apples. As apples had been previously sorted and graded by size, the apples sampled in each group were of very similar sizes, and this is considered a reasonable assumption. 2.3.3 Descriptions of Storage Techniques One Degree The primary method used to store apples to measure disease progress was one degree storage, which simply involved storing the apples un- der low temperature refrigeration conditions. This technique was valuable as it was easy to sample the apples at regular time intervals, and disease progression was relatively quick under one degree storage, so outcome differences could be observed. As discussed in Chapter 1, this storage technique slows the ripening of apples and the progression of disease, but many pathogens can still grow under these conditions. Controlled Atmosphere Controlled Atmosphere (CA) storage is a more ad- vanced storage technique which is commonly used in industry to store apples for long durations, and therefore was of interest to this research as it reflects the actual conditions that the apples are likely to experience. In CA storage temperatures are lowered to near zero and are optimized for individual cultivars to enhance their longevity in storage. As well, the balance of CO2 and O2 is altered from that of normal air. The CO2 levels are increased from about 0.03% to 0.4%-4.5% 62 and the O2 levels are decreased from about 20%-21% to 1%-3% to slow ripening of fruit and progress of disease (Mantyka, 2010). Although this storage technique has many advantages, the major limitation for this study, as well as in industry, is that it is not possible to sample stored apples until they are removed from storage for sale, due to the costs involved in allowing people into the storage rooms. This meant that for those apples which were stored in CA storage, measurements could only be taken at the beginning and end of the storage period. Combination In some cases, apples had to be removed from CA storage before there had been a significant amount of disease progress. In these cases it was decided to transfer the apples to one-degree storage and to continue measuring these apples for disease at regular intervals. 2.3.4 Data Collected by Year In some years, apples were treated with 1-Methylcyclopropene (1-MCP) which is a chemical that is used to slow apple ripening, and it was expected that it would also allow apples to resist disease, since disease incidence increases as apples ripen. For the purposes of our study we were not specifically interested in the effects of 1-MCP, but it is important to note where it was used. 2007 In 2007, storage outcomes were only available for Orchards 1 and 2; there were no storage data collected for Orchards 3 and 4. Orchards 1 and 2 apples were placed in one-degree storage, and sampled after 3 months and after 6 months. Both orchards had replicates that were treated with 1-MCP and were not treated with 1-MCP. All replicates were artificially wounded. 63 2008 In 2008, storage outcomes were available for all orchards (Orchards 1-4). Some apples from each orchard were placed into CA storage and sampled after 4.5 months. In these trials, one set of replicates was artificially wounded and one was not. As well, some apples from each orchard were placed into one-degree storage, and were sampled after 1, 2, 4, and 6 months. Again, half of these apples were artificially wounded and half were not. Apples were not treated with 1-MCP this year. 2010 In 2010, all apples were artificially wounded. For Orchard 1, apples were placed into one-degree storage and measured every month from 1 to 6 months in storage. For Orchard 2, some apples were placed in one-degree storage and were also sampled monthly between 1 and 6 months. As well, some apples from Orchard 2 were placed into CA storage, where they were sampled after 2 months. After this time they were placed in one-degree storage, and were sampled monthly from 3.5 to 6.5 months from the start of CA storage. For Orchard 3, apples were also placed in CA storage for 2 months, when they were sampled, and then one-degree storage where they were measured every month following from 3.5 months to 6.5 months. For Orchard 4, apples were placed in CA storage, and were not sampled until 8 months later. This was the only disease outcome value for Orchard 4. 2.3.5 Description of Data Figures 2.16-2.18 show the disease incidence outcomes for the storage trials com- bined across the different storage techniques. It was expected that the variation in disease outcomes would increase with storage duration, as we know that in many cases apples stored for long periods of time may come out of storage disease-free, 64 or may be completely destroyed. We also expected that the overall disease sever- ity would be greater when fruit has been stored longer, as we know that fruit is more susceptible to disease the longer it is stored. Combining these two trends, we expected to see a somewhat triangular pattern. The patterns in storage out- comes seem to reflect this in most cases, although there is a notable exception in the 2010 data at eight months, where the disease levels are lower and show less variation. As mentioned previously, these are the data for Orchard 4, which was in CA storage up until eight months, which is likely why it shows a different pattern. 65 Figure 2.16: 2007 storage disease incidence: The blue diamonds represent the percentage of apples infected and the red squares represent the infection severity that was calculated (IS = % infected × lesion diameter / 100%). These values combine all storage techniques. 66 Figure 2.17: 2008 storage disease incidence: The blue diamonds represent the percentage of apples infected and the red squares represent the infection severity that was calculated (IS = % infected × lesion diameter / 100%). These values combine all storage techniques. 67 Figure 2.18: 2010 storage disease incidence: The blue diamonds represent the percentage of apples infected and the red squares represent the infection severity that was calculated (IS = % infected × lesion diameter / 100%). These values combine all storage techniques. 68 3 Predicting Post-Harvest Disease Outcomes 3.1 Synopsis In this chapter we outline the data analysis performed with the data collected in the orchards as well as in storage. We describe our data analysis techniques, and discuss our results in comparison with expectations. Further, we will outline a preliminary pre-storage predictive model based on the results of the analysis. 3.2 Data Analysis Techniques Data analysis was done using JMP (Version 9.0.2., 2010 SAS Institute Inc) and SPSS Statistics (Version 20., 2011 IBM Corporation) using various built-in sta- tistical functions. Both quantitative and qualitative techniques were employed to draw conclusions about patterns in the data and predict results. 3.3 Results 3.3.1 Orchard Variables In order to predict disease outbreaks due to fungal spores in the orchard it was of interest to know what factors increased the growth and spread of inoculum within the orchard during the growing season. Based on previous research, it was expected that there would be a relationship between environmental conditions and spore spread. The data available include measures of pathogen DNA in the air and on the plant tissue surface during the growing season, which it is hoped will be representative of the inoculum level in the orchard. As shown in the detection graphs in Chapter 2 (Figures 2.2-2.13), the spore detection was highly variable 69 for both measures, and had many zeros. Expectations Based on previous research, the initial hypothesis was that more spores would be seen in Orchards 3 and 4 than in the other orchards, as these were considered to be the “dirty” orchards in the experiments. It was also expected that spore levels would increase with increasing temperatures and rainfall rates, and that higher wind speeds would dislodge more spores and also increase spore counts at the sensors. Creating a model incorporating these factors was expected to give a prediction of inoculum levels throughout the growing season. Orchard Tables 3.1 and 3.2 show the mean detection level by orchard. Perform- ing a statistical analysis of variance on the means over the growing season resulted in measures of significant difference, here represented by the superscripted letters associated with each mean. Means not connected by the same letter are consid- ered to be significantly different from each other. It was expected that the “well maintained” orchards would see fewer spores as there would be fewer sources of inoculum. However, these trends were not seen in the data. In Table 3.1 it can be seen that Orchard 4 had significantly higher levels of spores detected on the I-rods than any of the other orchards, and this was one of the “dirty” orchards. However, none of the other orchards show marked differences and there is no clear trend between “clean” and “dirty” orchards. There were no differences across all the orchards in pathogen DNA detection on the plant tissue, as seen in Table 3.2. 70 Table 3.1: Average pathogen DNA on I-rods: Average levels of pathogen DNA detection on I-rods by field over the entire growing season, where SD stands for the Standard Deviation, showing the amount of variability that was seen in the detection. Superscript letters on average detection values indicate significant differences. Where values have no common letters there is a significant difference between the percentage infected in those orchards. Thus, in the table above, Orchards 2, 3, 5, and 6 are not significantly different from each other, as they all have a superscript b, Orchards 1, 3, 5, and are not significantly different as they all have a superscript c. However, Orchard 1 is significantly different from 2 and 4 as they have no letters in common, and 2 is different from 1 and 4. Orchard 4 has no common letters with any other orchards and was significantly different from all the others. Orchard Number Average Detection (ng) Number of Samples SD of Detection Orchard 1 0.09c 255 0.421 Orchard 2 0.33b 261 1.025 Orchard 3 0.26b,c 222 0.962 Orchard 4 0.62a 138 1.352 Orchard 5 0.18b,c 108 1.031 Orchard 6 0.03b,c 96 0.227 Table 3.2: Average pathogen DNA on plant tissue: Average levels of pathogen DNA detection on plant tissue by field over the entire growing season. See Table 3.1 for an explanation of superscript letters. Orchard Number Average Detection (ng) Number of Samples SD of Detection Orchard 1 0.11a 81 0.418 Orchard 2 0.17a 75 0.529 Orchard 3 0.21a 204 0.830 Orchard 4 0.36a 207 1.281 Orchard 5 0.28a 57 0.648 Orchard 6 0.18a 66 0.552 Temperature It is known that the optimal growing temperature for Botrytis cinerea is reported to be somewhere within the range of 20ºC and 25ºC (Nair and Allen, 1992), although sporulation has been reported to be optimal at 15ºC 71 (ONeill et al., 1997). Another study found that infection of fruit by Botrytis cinerea was not significantly affected by temperature differences within the range of 10ºC to 25ºC (Xu et al., 2009). The optimum in vitro temperature for Mucor piriformis has been measured at 20ºC, and growth occurs within a range of -1ºC to 25ºC (Spotts and Cervantes, 1994). For Penicillium species it has been found that the highest growth rates occurred in vivo at temperatures between 10ºC and 25ºC, and growth generally stopped beyond 30ºC (Lahlali et al., 2005, Baert et al., 2007). Although temperatures in the summer in the Okanagan valley can be over 25ºC, a positive relationship is expected with temperature over the growing season, as the majority of days had average temperatures well below this value. Tables 3.3 and 3.4 show the correlation between all pathogen species and temperature variables. Note that there is a strong correlation between all tem- perature variables, which is expected. The values of interest are those in the first row/column, showing the correlation between the I-rod detection in the orchard and the various temperature values. There was no significant correlation found between the I-rods and any of these values, and the correlations show no regular trends. Table 3.3: Correlations of I-rod DNA and temperature: Correlation matrix of pathogen DNA detected on I-rods with temperature variables. I-Rods (ng) Maximum Minimum Average N = 1080 Temperature Temperature Temperature Pearson Correlation 0.012 -0.033 -0.012 Sig (2-tailed) 0.697 0.272 0.701 Temperature Temperature of Day Before 3 Days Before Pearson Correlation 0.007 0.013 Sig (2-tailed) 0.829 0.659 72 With the spore detection on plant tissue, it was found that all of the cor- relations were positive, and the most significant relationship was between spore detection on the plant tissue and the temperature three days before the sample was taken. Although there were correlations with all of the temperature vari- ables, they are highly correlated with each other, as mentioned, so only the most significant variable is used in setting up the model. Table 3.4: Correlations of plant tissue DNA and temperature: Correlation matrix of pathogen DNA detected on plant tissue surface with temperature variables. * Correlation is significant at the 0.05 level (2-tailed). ** Correlation is significant at the 0.01 level (2-tailed). Plant Tissue (ng) Maximum Minimum Average N = 690 Temperature Temperature Temperature Pearson Correlation 0.078* 0.082* 0.091* Sig (2-tailed) 0.041 0.031 0.017 Temperature Temperature of Day Before 3 Days Before Pearson Correlation 0.084* 0.123** Sig (2-tailed) 0.028 0.001 As the growths of the three individual pathogens might have different rela- tionships with temperature, each pathogen was also considered separately. For Botrytis cinerea there were no significant relationships between temperature and I-rod detection, and there was an R2 value of 0.02 with a significance value of 0.0105 for the relationship between plant tissue surface DNA detection and the temperature three days before. For Mucor piriformis there was a significant re- lationship between I-rods and the daily maximum temperature (R2= 0.01, sig = 0.0499) and for plant tissue surface the most significant relations were maximum temperature on the day of sampling (R2= 0.03, sig = 0.0054) and the average temperature the day before (R2= 0.03, sig = 0.0057). For Penicillium expansum 73 there were again no significant relationships for I-rod detection, and the most sig- nificant relationship for plant surface DNA detection was the average temperature on the day of the reading (R2= 0.04, sig = 0.0015). The general trends seen in the data did not suggest that there was a significant cut off in spore detection past a certain temperature. Rainfall It is known that the fungal pathogens under consideration are more likely to grow in warm, moist environments. The expectation is that when tem- peratures are warm, and rainfall occurs, pathogen growth will increase, and spore detection will increase at some time period later. It is also possible that spore detection may increase during rain events, as fall-out due to rain and rain splash from infected tissue would both increase. In order to consider these patterns, rainfall levels were considered on the day of sampling, as well as the day before sampling, and three days before sampling. As shown in Table 3.5, there were no significant correlations between pathogen DNA detection on the I-rods and rainfall measurements. Table 3.5: Correlations of I-rod DNA and rainfall: Correlation matrix between I-rod DNA detection and rainfall variables. I-Rods (ng) Rainfall of Day Rainfall 3 Days N = 1080 Rainfall (mm) Before (mm) Before (mm) Pearson Correlation -0.016 -0.029 -0.032 Sig (2-tailed) 0.609 0.346 0.289 In contrast to Table 3.5, Table 3.6 shows that there was a significant correlation between pathogen DNA detection on the plant tissue surface and rainfall events the day before samples were taken. However, in all cases rainfall events had a negative correlation, which was unexpected. This suggests that rainfall may 74 cause spores to be washed off the surface of the plant tissue, or that wash out of spores decreases the number of spores that reach the surface of the apple. In either case, the correlation between detection and rainfall was not very strong. Table 3.6: Correlations of plant tissue DNA and rainfall: Correlation matrix between plant tissue DNA detection and rainfall variables. * Correlation is sig- nificant at the 0.05 level (2-tailed). Plant Tissue (ng) Rainfall of Day Rainfall 3 Days N = 690 Rainfall (mm) Before (mm) Before (mm) Pearson Correlation -0.019 -0.079* -0.001 Sig (2-tailed) 0.611 0.038 0.989 In order to consider the hypothesis that warm, moist environments are most likely to be favorable to fungal growth, multiple regression models were run be- tween plant tissue pathogen DNA detection and temperature and rainfall vari- ables. As temperature three days before the sample was taken was found to be highly significant for spore detection, the first model considered the effects of temperature and rainfall three days before sampling together. This model had an R2 value of 0.015 and a significance value of 0.005, however, rainfall three days before was still not highly significant even when considered in association with temperature (sig = 0.650). However, it is interesting to note that the coefficient for rainfall in this model was positive. The second model considered used tem- perature three days before sampling with rainfall one day before sampling. This model had an R2 value of 0.020 and a significance value of 0.001, and in this case rainfall the day before, when associated with temperature three days before, was significant (sig = 0.070) if not highly so. In this model the coefficient for rainfall was again negative. 75 Wind Speed and Direction There are a number of possible sources of spores in the orchards, including neighbouring orchards, weeds and ground cover crops, decaying leaf and other plant tissue, and fallen fruit. Spores from most potential sources would require transport, either by rain or wind, to reach the I-rod detectors and fruit surfaces. For this reason, wind speed and direction were also considered. The data available from Environment Canada included the wind speed during the maximum gust detected for the day, as well as the direction of this maximum gust. It was initially hypothesized that higher wind speeds would be more likely to transport spores from their source to the detection location. Table 3.7 shows that the correlation between pathogen DNA detection on the I-rods and the wind speed was not significant, and the correlation with the wind direction was. However, the correlation is quite small. A correlation with the wind direction suggests that spore detection may have depended on whether the wind was carrying spores towards the I-rod detector or not, and may not have as much to do with the biology of the pathogen and the actual levels of spores in the orchard as originally assumed. Table 3.7: Correlations of I-rod DNA and wind: Correlation matrix showing relationships between I-rod pathogen DNA detection and wind variables. * Cor- relation is significant at the 0.05 level (2-tailed). I-Rods (ng) Wind Direction at Wind Speed at N = 1080 Maximum Gust (Degrees) Maximum Gust (km/hr) Pearson Correlation -0.077* -0.050 Sig (2-tailed) 0.012 0.103 76 Table 3.8: Correlations of plant tissue DNA and wind: Correlation matrix showing relationships between plant tissue pathogen DNA detection and wind variables. * Correlation is significant at the 0.05 level (2-tailed). Plant Tissue (ng) Wind Direction at Wind Speed at N = 690 Maximum Gust (Degrees) Maximum Gust (km/hr) Pearson Correlation -0.009 0.094* Sig (2-tailed) 0.822 0.014 Table 3.8 shows that the correlations for pathogen DNA detection on the fruit surface had the opposite trends from the I-rod detectors. There is a significant correlation with the wind speed, which is positive but not very strong. However, there is no correlation between the wind direction. This could potentially be caused by the fact that apples were taken from different locations throughout the growing season for sampling, while the I-rod detector was in the same location throughout the growing season. Regression Model A regression model was built using a stepwise linear re- gression procedure for both the pathogen DNA detected on the I-rods and for the DNA detected on the plant tissue using the variables that were found to be the most significant. For detection on the I-rods, the only variable that was significant was the direction of the wind at its maximum gust. This regression model had an R2 value of 0.006 and a significance value of 0.012. As it was possible to explain less than 1% of the variation in the outcomes for detection on the I-rods and the significance value was not very high, this model is not considered to be useful for prediction. Using the stepwise procedure on the variables that are associated with pathogen DNA detection on the plant tissue it was found that the average daily temperature three days before the reading was taken, the wind speed during the maximum wind gust, and the rainfall the day before the reading was taken were 77 all significant predictive factors, and when used in a multiple regression model it was found that R2 = 0.023 and the significance value is 0.001. Again, it was found that it was only possible to explain 2% of the variation in pathogen DNA detection using all of the growing season variables collected. After performing linear regression on these variables, there was no reason to believe that extending to other types of regression would provide any more information. 3.3.2 Storage Variables In order to improve upon existing research and to predict disease outcomes in stor- age, multiple variables summarizing both storage conditions and growing season data were analyzed to try and create a predictive model that would be quantitative and easy to use. Expectations It is expected that the primary factor in disease measurements will be how long the apples have been stored. It is known that the pathogens under consideration only cause disease in fully ripened fruit. The susceptibility of the apples to these diseases increases with the duration of storage. It is also expected that greater disease incidence will be seen on apples that came from orchards considered to be “dirty,” as this was the major predictive factor in the model created by Spotts et al. (2009). In addition, it is expected that there will be differences between cultivars of apples, as they are known to have different susceptibilities to the considered diseases, and it is expected that increased spore presence at harvest should lead to increased disease incidence in storage. Duration of Storage As was seen in Chapter 2 (Figures 2.16-2.18), both the percentage of apples infected and the infection severity increased with increased 78 duration of storage. As well, the variability in infection severity was seen to increase over the duration of storage. Correlations of percentage infected with infection severity, the number of months in storage, and the year gave the results shown in Table 3.9. These values describe a very strong positive and significant correlation between the duration of storage and the percent of apples infected. It can also be seen in Table 3.9 that the percent infected and the infection severity are strongly correlated with each other. There was also found to be a strong positive correlation between the duration of storage and the infection severity. As any disease presence in commercial storage means that apples can no longer be sold, the percentage infected is of greater interest than the infection severity. It is expected that the percentage infected may be artificially high as many of the trials involved artificially wounding the apples. However, this can be considered as a worst case estimate. As percentage infected and infection severity are so highly correlated, it is unlikely that different results would occur during analysis of one over the other regardless of which is used, and we will primarily consider percentage infected for analysis in the remainder of this work. Table 3.9: Correlations between disease incidence and timing of storage: * Cor- relation is significant at the 0.05 level (2-tailed). ** Correlation is significant at the 0.01 level (2-tailed). Percent Infected Infection Number of N = 72 Severity Year Months Pearson Correlation 0.667** 0.174 0.419** Sig (2-tailed) 0.000 0.144 0.000 Year Although the year is not itself a potential predictive factor, it is of interest to compare differences in disease outcomes by year to consider general trends. Differences in disease outcomes by year may suggest weather effects or other con- 79 ditions that will be further analyzed. Table 3.10 shows the average percentage of infected apples by year. It is interesting to note that the percentage of apples infected in 2008 was significantly lower than in 2007 or 2010. Figure 3.1 shows the same trends when the number of months in storage is accounted for. Table 3.10: Percent of apples infected by year: Mean percentage of apples infected by year. Superscript letters show significant differences. Where letters are differ- ent there is a significant difference between the percentage infected. See Table 3.1 for an explanation of superscript letters. Year Average Percent Infected Number of Samples SD of Percent Infected 2007 83.88a 8 6.198 2008 20.77b 40 29.373 2010 57.25a 24 30.506 Figure 3.1: Regression plot of percentage infected with year and duration of stor- age: Regression plot of percentage infected by number of months in storage, divided by the effect of year. 80 Orchard It was expected that there would be greater disease incidence in Or- chards 3 and 4, as they were considered to be the most poorly maintained, or “dirtiest” orchards. However, as can be seen in Table 3.11, the percentage of in- fected apples was actually the lowest for these orchards. Even when controlling for the number of months in storage (Figure 3.2) it can be seen that Orchards 3 and 4 experienced very low percentages infected. In speaking with the biologists in- volved in the data collection (D. Mantyka, personal communication) it was found that the apples collected from the “dirty” orchards were substantially smaller than those collected from the other orchards, and the hypothesis made was that these orchards tended to pick their apples before they were completely ripe, which led to lower quality apples which had a higher resistance to disease and thus these apples could be stored longer. As there were not sufficient data on elements such as ripeness of apples at harvest, maintenance records, and fungal sprays to draw significant conclusions about orchard rating for all of the orchards in this study, it is not possible to draw firm conclusions about the effects of the “dirty” or “clean” orchards in this research. However, the fact that the storage data for Orchards 3 and 4 were taken in 2008 may explain the trends seen in Figure 3.2. Table 3.11: Percent of infected apples by orchard: Percentage of infected apples across all storage trials by orchard. See Table 3.1 for an explanation of superscript letters. Orchard Number Average % Infected Number of Samples SD of % Infected Orchard 1 42.48a,b 21 33.456 Orchard 2 51.25a 25 38.440 Orchard 3 8.90b 10 9.422 Orchard 4 15.17b 10 22.858 Orchard 5 83.20a 5 14.184 Orchard 6 46.00a,b 1 n/a 81 Figure 3.2: Regression plot of percentage infected with orchard and duration of storage: Regression plot of percentage infected by month, divided by effect of orchard number. Here, the lines represent the average percentage infected for each orchard based upon trends caused by the number of months in storage. Since the regression used both factors simultaneously, the lines are all parallel to each other. Cultivar of Apple It is known that different cultivars of apples have different susceptibilities to the post-harvest pathogens under consideration in this study. However, as seen in Table 3.12, the variation within cultivars during this study was so high that there was no statistically significant difference between the cul- tivars. Controlling for number of months in storage showed similar trends in susceptibilities. Thus, while cultivar of apple may be an important factor for future consideration, it is not a significant variable in this study. 82 Table 3.12: Percentage of apples infected by cultivar: See Table 3.1 for an expla- nation of superscript letters. Average Percentage Number of SD of Percentage Cultivar Infected Samples Infected Ambrosia 46.00a 1 n/a Gala 44.65a 40 38.686 Jonagold 42.48a 21 33.456 Red Delicious 15.17a 10 36.124 Wounding In many of the trials in this study apples were artificially wounded. This was hypothesized to lead to higher disease incidence than would normally be seen in storage. Table 3.13 shows that there is a significant difference between the percentage of apples infected when apples are artificially wounded versus when they are not. Figure 3.3 shows that these trends are consistent when the number of months in storage are taken into account. As mentioned previously, this suggests that any model developed from these data is more likely to over predict than under predict disease outcomes in storage, leading to a “worst-case” model for disease in storage. Table 3.13: Percentage of apples infected by if wounding occurred: See Table 3.1 for an explanation of superscript letters. Artificially Average Percent Number of SD of Percentage Wounded Infected Samples Infected Yes 53.60a 20 5.558 No 4.43b 52 33.475 83 Figure 3.3: Regression plot of percentage infected with wounding and duration of storage: Regression plot showing trends in percentage infected based on if apples were artificially wounded or not taking into account the number of months in storage. Storage Type There were three different storage types used in this research. Apples were put into controlled atmosphere storage, one-degree storage, or con- trolled atmosphere storage and then one degree storage. It was expected that ap- ples in controlled atmosphere storage would see the lowest disease levels, followed by apples in combined controlled atmosphere followed by one degree storage, and then apples in one-degree storage. These trends were expected as controlled at- mosphere storage should substantially decrease disease levels in comparison with one degree storage, which is part of why controlled atmosphere storage is used commercially. Table 3.14 shows that these trends were not followed as expected, and disease outcomes in controlled atmosphere and one-degree storage were not significantly different, while the percentage infected for the storage combination was much higher. Figure 3.4 shows these trends when the number of months in storage is 84 accounted for. This result suggests that there may have been some other factor that is not accounted for that came into effect in the combination storage trials, such as an increased risk of infection during the transition between the controlled atmosphere and one degree storage locations. Table 3.14: Percentage of apples infected by storage type: See Table 3.1 for an explanation of superscript letters. Average Percentage Number of SD of Percentage Storage Type Infected Samples Infected One-degree (1C) 35.38a 52 35.167 Controlled Atmosphere (CA) 26.50a 12 23.941 Combination (CA+1C) 89.75b 8 5.064 Figure 3.4: Regression plot of percentage infected with storage type and duration of storage: Percentage of apples infected by storage type accounting for number of months in storage. 85 Temperature Table 3.15 shows the correlations between the percentage of ap- ples infected and a number of temperature variables. It was found in previous studies that the last 50 days of the growing season (Mantyka, 2010) and the last two weeks of the growing season (Spotts et al., 2009) were important periods for relating environmental variables with disease outcomes, which is why these pe- riods were considered. As seen in Table 3.15, the strongest correlation between temperature and the percentage of apples infected was obtained using the aver- age temperature during the last fourteen days before harvest. This correlation is highly significant, and strongly positive. As would be expected, all tempera- ture variables are highly correlated with each other, so a warmer growing season would suggest a higher disease incidence across all variables. This trend may explain some of the variability due to year that was seen previously; this is sup- ported by Table 3.16 which shows that the average temperature was significantly different in 2008 during the final two weeks before harvest. Table 3.15: Correlations of percentage infected with temperature: Correlation matrix for percentage of apples infected and temperature variables. * Correlation is significant at the 0.05 level (2-tailed). ** Correlation is significant at the 0.01 level (2-tailed). Percent Infected Average Temperature Average Temperature N = 72 During Growing Season During the Last Fifty Days Pearson Correlation 0.241* 0.327** Sig (2-tailed) 0.042 0.005 Average Temperature Temperature on the During the Last Fourteen Days Harvest Day Pearson Correlation 0.409** 0.348** Sig (2-tailed) 0.000 0.003 86 Table 3.16: Average temperature by year: Average temperature during the final two weeks before harvest for each year of the research. For clarification, in 2007 the standard deviation of the average temperature was 0 due to the all the orchards experiencing the same average temperature during these final two weeks. See Table 3.1 for an explanation of superscript letters. Average Temperature Number of SD Average Year Last Fourteen Days Samples Temperature 2007 14.7a 8 0 2008 11.8b 40 3.5 2010 14.8a 24 0.9 Rainfall Table 3.17 shows the correlations between the percentage of apples in- fected in storage and the cumulative rainfall before harvest. As with temperature, it was found that the rainfall during the final two weeks before harvest was the most significantly predictive of the percentage infected, and was again strongly positive. The average rainfall over the entire growing season was also considered, but was not significant. It is interesting to note that the correlation with rainfall is in this case strongly positive, while when correlated with pathogen DNA detec- tion the result was weakly negative. There are two likely explanations for this. The first is that high levels of rainfall affect the fruit in such a way that it is more susceptible to disease in storage. The second is that rainfall increases the growth of the pathogen in the field, resulting in a higher inoculum load on the fruit be- fore it is put into storage. This second possibility would support the hypothesis that the results for DNA detection were influenced by the sampling method, as a negative relationship would not occur if this second explanation were true and data analysis displayed trends accurately. Spore DNA Further to the idea of sampling methods influencing the pathogen DNA detection results, it was found that there was no correlation between the 87 Table 3.17: Correlations of percentage infected with rainfall: Relationship between percentage of apples infected and the cumulative rainfall over the timeframes of interest. * Correlation is significant at the 0.05 level (2-tailed). ** Correlation is significant at the 0.01 level (2-tailed). Percent Infected Total Rainfall Rainfall During the N = 72 (Cumulative) Last Fifty Days (Cumul.) Pearson Correlation 0.253* -0.195 Sig (2-tailed) 0.032 0.100 Rainfall During the Rainfall on Last Fourteen Days (Cumul.) Harvest Day Pearson Correlation 0.575** 0.165 Sig (2-tailed) 0.000 0.167 spore count in the air (I-rods) and the percentage of apples infected in storage (Table 3.18). There was, however, a significant relationship between the average pathogen DNA detected on the plant tissue and the percentage infected, both during the last two weeks before harvest, and on the date of harvest. Based on what was found for correlations between weather conditions and spore detection on the apple tissue, it is expected that there will be some interaction between these variables. This will be explored further in the next section. Regression Model Considering all of the above, a regression model was built using the number of months the apples were stored, the average temperature during the last two weeks before harvest, the cumulative rainfall during this time, and the amount of pathogen DNA detected on the plant tissue at harvest. All of these variables were significant when considered individually. This resulted in a model with an R2 value of 0.506 and a significance value of 0.000. However, when the other variables, which were all highly significant, were taken into account, the pathogen DNA levels at harvest had a significance value of 0.285, and were 88 Table 3.18: Correlations of percentage infected with pathogen DNA levels: Cor- relations between pathogen DNA detection and percentage of apples infected over the timeframes of interest. * Correlation is significant at the 0.05 level (2-tailed). ** Correlation is significant at the 0.01 level (2-tailed). Percent Infected Average Spore Ave. Spore Count During N = 72 Count (I-Rods) the Last 50 Days (I-Rods) Pearson Correlation -0.224 -0.174 Sig (2-tailed) 0.059 0.151 Ave. Spore Count During Spore Count on the Last 14 Days (I-Rods) Harvest Day (I-Rods) Pearson Correlation -0.162 0.030 Sig (2-tailed) 0.179 0.804 Average Spore Ave. Spore Count During Count (Tissue) the Last 50 Days (Tissue) Pearson Correlation -0.181 -0.096 Sig (2-tailed) 0.128 0.421 Ave. Spore Count During Spore Count on the Last 14 Days (Tissue) Harvest Day (Tissue) Pearson Correlation 0.320** 0.433** Sig (2-tailed) 0.006 0.000 therefore not significant in this model. Removing this variable resulted in a change in the R2 value to 0.498 and no change in the significance value for the model. Thus, in this case, the pathogen DNA detection at harvest explained 0.8% of the variation and was not significant when other factors were taken into account. Using a step-wise regression procedure to select the best model resulted in an improvement in R2 to 0.558 and maintained the significance of 0.000. In this case, the variables selected were the average rainfall during the last two weeks before harvest, the number of months in storage, the cumulative rainfall over the last 50 days, the pathogen DNA detected in the air at harvest, and the cumulative rainfall over the entire growing season. However, it is likely that rainfall over 89 these different periods will be correlated in many cases, and we chose to select the model that considers temperature and rainfall individually as it is more likely to avoid correlation problems in the future. 3.4 Comparison with Expectation In section 3.3 we looked at two sets of data. In the first, we considered pathogen DNA detection on surfaces during the growing season. In the second, we consid- ered the percentage of apples that became infected after they were stored. It was expected, based on knowledge of the pathogen biology and the collection of growing season variables, the levels of pathogen DNA detection in the orchard during the growing season could be explained, at least to some degree. However, it was found that only 2% at most of the variation in growing season DNA detection could be explained with all the variables available. As noted in Chapter 2, the data had very few points where detection actually occurred, and saw a lot of variation in values. It is therefore hypothesized that the variation in detection data may have less to do with orchard variables such as temperature and rainfall and more to do with sampling methods. Thus, in Chapter 4, a model is developed to try and understand these patterns in more detail. It was also found that around 50% of the variation in the percentage of apples that ended up being infected could be explained by variables collected before storage. This value is higher than was expected given that the sample size is fairly small (N=72) and there is very little incorporated into the model to describe orchard management practices. All of the coefficients for the model were positive, which agreed with the expectations based on the biology of the pathogens. 90 3.5 Pre-Storage Predictive Model Figure 3.5 (also seen in Chapter 1 and repeated for ease of reading) shows the pre-storage predictive model developed by Spotts et al. in 2009. This model was developed for Botrytis cinerea on pears, but as apples and pears are closely related and as we are not aware of any models developed for apples, we consider Spotts et al.’s (2009) model to be the best existing model for pome fruit in general, and use it as our starting point for the development of our model. Spotts et al. (2009) found that rainfall during the two weeks before harvest increased the risk of post harvest decay. In our research, the same results were found. Further, our work found that when all other variables in the model are held constant, increasing the cumulative rainfall during the last two weeks before harvest by one millimeter increased the percentage of apples infected by 2.08%. Figure 3.5: Spotts and Gastagnoli’s (2010) relative risk model for pear fruit: Rela- tive risk model for pear fruit adapted from Robert A. Spotts and Steve Gastagnoli, Predicting and managing gray mold rot of pear in Oregon, Oregon State University Extension Service, January 2010. Reproduced from Chapter 1 for convenience. Gray Mold Preharvest Preharvest Orchard Condition DNA Fungicide Rain Good Average Poor Low Yes No Low Low Moderate Low Yes Yes Low Moderate High Low No No Low Moderate High Low No Yes Moderate High Extreme High Yes No Low Moderate High High Yes Yes Moderate High Extreme High No No Moderate High Extreme High No Yes High Extreme Extreme Spotts et al. (2009) also found that high levels of Botrytis cinerea DNA on 91 the fruit surface at harvest increased the risk of post-harvest decay. However, they found that this measure only contributed 3% to the prediction accuracy of their model. In our model the pathogen DNA detected at harvest on the fruit surface only contributed 0.8% to the predictive ability of the model, and was not found to be highly significant. Our model suggests that for each nanogram of pathogen DNA detected on the fruit surface there will be an increase in percentage of infected apples by 26.16%. This value may seem high, but pathogen DNA detection of greater than one nanogram was unusual in this study. As well, the significance value associated with this coefficient suggests that it may not be entirely reliable. The other coefficients in the model for our research were the number of months in storage, where when all other variables were held constant, an increase by one month in storage resulted in an increase in the percentage of infected apples by 5.56%, and the average temperature during the last two weeks before harvest, where an average increase of one degree Celsius resulted in an increase in the percentage of infected apples of 3.14%. The most significant factors in the model developed by Spotts et al. (2009) were the orchard condition and the application of a pre-harvest fungicide. We performed some analysis of orchard condition, but as concluded above, the number of samples was too small and the potential for variation was too large to be able to draw conclusions about the effect of orchard condition. Although spray records were available for some of the orchards we used in this study, there were not enough available to analyze and make predictions based on pre-harvest fungicide. The resulting model is linear in all variables, and uses the amount of pathogen DNA detected on the fruit tissue before storage (ng) (P), the number of months 92 in storage (M), the cumulative rainfall in the last two weeks before harvest (R), and the average temperature during the last two weeks before harvest (T) in combination to explain approximately 50% of the variation in percentage of apples infected (given as a percentage as opposed to a decimal) (I) given by I = 26.16P + 5.56M + 2.08R + 3.14T − 43.29 the duration of storage combined with the amount of inoculum on the apples and the condition of the apples when harvested predict the amount of infection which will occur. Based on the model we developed, it is expected that it would be possible to explain about 50% of the variation in the percentage of apples that became infected over the course of being stored. Using this model would help to predict when apples should be removed from storage before they reach a certain per- centage of predicted disease. It is likely that the predictions of this model will over-estimate the percentage of apples that would become diseased in commercial storage, and could be considered a “worst-case” prediction. 93 4 A Model for Dispersal of Fungal Spores in an Orchard 4.1 Synopsis In the previous chapter we discuss our findings from analysis of the data and the construction of predictive regression models. We discuss the difficulty of predict- ing fungal pathogen DNA in the air or on the fruit surface using environmental conditions in the orchard. In this chapter we discuss the creation of a model for dispersal of spores in an orchard that we can use to form a better understanding of how sampling techniques and environmental conditions may have affected the data. This model will be used to make recommendations for sampling methods to be used in future work. 4.2 Model Choice A Gaussian plume model was selected to describe the dispersal of spores within the orchard. There are many models available that have been used to describe dispersion of airborne particles. The Gaussian plume model was selected because it is relatively simple and the existing data could be readily used. However, even for this simple model there were insufficient data to estimate many of the parameters, and approximate values that gave reasonable results were used. As the goal of this modeling is not to predict spore deposition through the growing season, but rather to gain a better understanding of the dynamics in the orchard and make recommendations for future sampling, these values are considered to be a first approximation that could be improved upon with future experimental 94 work. As discussed in Chapter 1, the Gaussian plume model solution for a point source is given by c(r, y, z) = Q 4πur exp  −y 2 4r  exp  −(z −H) 2 4r  + exp  −(z +H) 2 4r  (4.1) where r = 1 u ˆ x 0 K(ξ)dξ (4.2) and c is the concentration of the contaminant, in this case the fungal spores, Q is the release rate of spores (in spores/second), u is the wind speed (in m/s), and H is the height of the point source (m). The assumptions that were used to derive this model are as follows 1. The contaminant is emitted at a constant rate from a single point source which is at a constant height. 2. The wind velocity is constant and aligned with the positive x-axis. 3. The solution is steady state and the wind velocity and other parameters are independent of time, and the time scale is long. 4. The eddy diffusivities, K, are functions of the downwind distance x only, and the diffusion is isotropic. 5. The wind velocity is sufficiently large that the diffusion in the x-direction can be neglected. 95 6. Variations in topography are negligible. 7. The contaminant does not penetrate the ground, that is, the ground is a perfectly reflecting boundary. Assumption seven will clearly not work for the orchard simulations, as the parti- cles of interest are fungal spores that are deposited on the ground, due to gravity, and on the plant tissue that they encounter, due to both gravity and impaction. Following the work of Spijkerboer et al. (2002), empirical modifications are made to the derived model to account for deposition of spores (a modification of as- sumption seven) and to improve the plume shapes under varying environmental conditions. The model now becomes C(x, y, z) = Q 2πu exp  − y22σ2y  σy 1 σz  exp  −(H − z) 2 2σ2z  +R · exp  −(H + z) 2 2σ2z  . (4.3) The quantity R is the fraction of spores that are reflected when they reach the ground, where 1 is complete reflection, as in (4.1) and 0 is complete absorption. σz and σy are the standard deviations of the height and width of the Gaussian plume, and are given by the empirical functions σz =K(z0, x)ax b σy =K(z0, x)10 pxq. (4.4) Here, a, b, p, and q are dimensionless empirical constants that describe the effect of weather conditions, determined using the percentage of cloud cover and the wind speed, on the amount of mixing. Weather conditions are divided into six 96 classes, A through F, each characterized by different values for a, b, p, and q, as shown in Table 2.1. Class A describes the most unstable atmosphere, where there will be more mixing and plumes that are wider and higher. Class F describes the most stable atmosphere, where there will be less mixing and plumes that are lower and narrower. We calculate K(z0, x), which is a function of downwind distance, as K(z0, x) = (10z0) 0.53x−0.22 , (4.5) where z0 is the roughness length, which is a characteristic of the surface cover. Table 4.1: Empirical constants for stability classes: Empirical constants for sta- bility classes A through F. Reproduced from Spijkerboer et al., 2002. Stability Class a b p q A 0.28 0.9 -0.27819 0.865 B 0.23 0.85 -0.43063 0.866 C 0.22 0.8 -0.67985 0.897 D 0.2 0.76 -0.89279 0.905 E 0.15 0.73 -1.00877 0.902 F 0.12 0.67 -1.18709 0.902 97 Figure 4.1: Vertical cross-section of plume with heights marked: Visual schematic of a vertical cross-section of a Gaussian plume (adapted from Stockie, 2010) with three approximate heights marked. These three heights are represented by the yellow lines, and are considered “ground-level”, “mid-plume,” and “top of plume” (0 meters, 2.5 meters, 5 meters). The colours in the profile are a visual cue showing that the concentration increases from bottom to top and are intended as a guide for the eye. This basic model for one spore was plotted using Matlab (Version 7.8.0.529, The MathWorks Inc, 2009). Figure 4.1 shows the three heights at which contours were taken during modeling, which will be labeled as “ground-level,” “mid-plume,” and “top of plume.” These three heights were set at 0 meters, 2.5 meters, and 5 meters above the ground, respectively. The contour plots in the following figures were plotted at these z values. The results of this model for a single point source located at ground level 98 (a) Ground-level (b) Mid-plume (c) Top of plume Figure 4.2: Contour plots for a single point source: Contour plots based on model output for a single point source located at (1, 0, 0). are shown in Figure 4.2. The concentration profiles show good agreement with expected results as seen in previous research. 4.3 Structure of Simulated Orchard One major limitation of this model is that the plant canopy is quite significant in apple orchards. Figure 4.3 shows a photograph of one of the experimental orchards, where it can be seen that the foliage of the trees is quite substantial and extends well above the ground. In most of the spore dispersal experiments 99 modeled with Gaussian plume models, the plant canopy is low and considered to be essentially the ground plane (Spijkerboer et al., 2002). In the case of apple trees, the canopy extends well above the spore emission point, and so the effect of the canopy on results may be significant. However, modeling the flow of turbu- lence around trees within an orchard is a very complex problem, and modeling an entire orchard accurately would be very demanding (Endalew et al., 2011). For this modeling effort, only two basic effects of the canopy are included, and in a very simplified manner. First, the canopy is considered to increase the deposition rate at the location of the canopy (effective only at the ground plane), and sec- ond, the canopy is represented by a change in roughness length value, increasing turbulence. As there is no simple way to model the plant canopy more accurately, this approximation yields a simplistic starting point for modeling of the spore dispersal detection problem. Figure 4.3: Photograph of orchard: Photograph taken by author of one of the orchards used in this research showing the layout of trees in rows with ground cover in between rows. Figure 4.4 shows the orchard layout used for simulations. The trees occur in 100 the green stripes and the open cover crop area is in the white stripes. The trees were four meters tall in all simulations, consistent with the high-density orchards used in the data collection (Chapter 2). Spijkerboer et al. (2002) use a formula specific for potato crops taken from Legg et al. (1981) to find roughness length value (z0) from the crop height. They obtained z0 = 0.029 m. Although there were no studies found which calculated roughness lengths for apple orchards, it is known that roughness length is related to the height of the surface elements, and that for trees the value of z0 should be somewhere between 0.5 and 1.0 (the approximate values for bushes and forest respectively) (Wieringa, 1980). For our implementation, z0 was set to z0N = 0.5 and z0T = 0.6 in the absence and presence of trees, respectively. It is unclear what value of z0 should be used between the rows. Although z0N is higher than for an open grass field, using a lower value for z0N caused numerical difficulties. We hypothesize that the space between trees has more turbulence than an open grass area because the neighbouring trees would disturb the airflow. Without the ability to measure turbulence in the orchard directly, we took this value as a first approximation to the turbulence within the orchard. The parameter R in Equation 4.3 describes the fraction of spores that are absorbed at the boundary with the ground. In essence, this coefficient modifies the zero flux boundary condition by adding a negative flux term at the z = 0 boundary. Spijkerboer et al. (2002) assume an R of 1 (complete reflection) for their model over potato crops. As deposition is of interest to our research, we did not consider an R value of 1 to be a reasonable assumption. Since we did not know what the appropriate R value for spore dispersal in an orchard would be, we set our coefficient to values that seemed to give reasonable results. For R values that were close to 0 (complete absorption) there was no detection of spores at our receptor 101 Figure 4.4: Representative orchard layout for simulation: Representative orchard layout as used in simulations. Here, the green rows represent the locations of the trees and the white rows represent the open areas between the trees. The red dot shows the approximate location of the receptor when one receptor was placed in the orchard. This corresponds to estimations of I-rod detector location in the orchard during experiments. or in the contour plots generated. In order to get reasonable results, R was set to 0.99 where there were no trees, and 0.9 where there were trees. Again, this is considered a first approximation, and would need to be experimentally modified. However, as will be discussed in the following section, using these values for the different regions of the orchard gave reasonable results while not differing too far from previous research. 4.4 Sources of Spores One of the major limitations of our modeling work is that it is not known where the sources of spores in the experimental orchard were located, nor is there a measure of the spore release rates. There are many potential sources of spores in an apple orchard, including, but not limited to, leaf litter, ground cover crops, 102 Figure 4.5: Vertical cross-section with heights and tree: Vertical cross-section of a Gaussian plume (adapted from Stockie, 2010) with “ground-level”, “mid-plume,” and “top of plume” represented. The tree in the image shows the height of the trees in the simulation and their relationship with these three heights. See Figure 4.1 for more detail. neighboring orchards, and fallen or culled fruit. It is of interest to know how much of the variation in detection outcomes may be due to sampling methods and how much is due to natural variation within the system. As it is not known where the spores came from in the experimental orchards, there were initially two schemes considered, one where the ground cover between the apple trees was a line source of spores, and another where fallen or culled apples that had become infected were considered as point sources of spores. It was decided that based on the experimental data (see Chapter 2) line sources of spores in every row of the orchard would result in spores being ubiquitous throughout the orchard, and would not lead to the high numbers of zeros and sharp spikes in spore levels that were seen in the data. It would also be more likely that spore detection patterns could be predicted based upon environmental conditions (see Chapter 3) if spores were ubiquitous when present and depended only on the life cycle of the pathogen. By this reasoning it was decided that spores 103 would be modeled as dispersed from point sources. In addition, for the size of the model orchard and based on the amount of disease observed on leaf litter and fruit debris commonly found in orchards, two point sources of spores is reasonable (M. Thurston, personal communication). Late in this thesis (Section 4.6.5) we investigate the effect of increasing the number of sources. Thus, in the model, in each trial two point sources of spores were placed randomly at ground level. The contributions of these spores to the concentrations at any point are simply the sum of the two individual concentrations due to each point source (Briant et al., 2011). 4.5 Incorporating Wind Direction and Angle Assumption 2 above states that the wind must be aligned with the positive x-axis for the model to hold. In some experimental designs, the receptors are moved in order to deal with this assumption, such as in the research by Spijkerboer et al. (2002). In this case however, the receptor location was stationary, and the wind speed and direction varied. Thus, it was necessary to modify our model to account for changes in wind direction. This was achieved by incorporating a rotation of the plane into the model as was done by Briant et al. (2011). Figure 4.6 shows two coordinate systems, one which is the coordinate system of the source and receptor locations, and one which rotates with the wind in order to maintain its x-axis in the direction of the wind. It is possible to transform between these two coordinate systems using the rotation matrix  x y  =  cos θ − sin θ sin θ cos θ   x y  (4.6) 104 which gives xwind = xsource cos θ − ysource sin θ ywind = xsource sin θ + ysource cos θ. (4.7) In (4.7) θ is the angle of rotation of the wind coordinates from the source coordi- nates as shown in Figure 4.6. Substituting (4.7) back into the equations for the point source (4.3) results in a model that can account for wind direction as well as wind speed. Figure 4.6: Wind and source coordinate systems: Representation of wind and source coordinate systems. The wind is blowing in the −→x wind direction. The source is the wide line segment along −→y source. Adapted from Briant et al, 2011. Again, the contributions from the two point sources are additive. Figure 4.7 below shows the results at three plume heights for two receptors, one at (2, 0, 0) and the other at (7, 2, 0), where the wind has now been rotated to a 45º angle to the left of the previous contour plots shown in Figure 4.2. With the addition of the deposition parameter to approximate the effect of 105 (a) Ground-level (b) Mid-plume (c) Top of plume Figure 4.7: Contour plots for two point sources: Contour plots based on model output for two point sources located at (2, 0, 0) and (7, 2, 0). orchard trees on the plume we arrive at the contours shown in Figure 4.8. The sources are in the same location, and the wind is at the same angle as previously in Figure 4.7. Note that the green lines show the boundaries between the two regions, where trees do and do not occur. In comparison with Figure 4.8 there are fewer spores in the regions where trees occur, which is expected, as deposition rates are higher. 106 (a) Ground-level (b) Mid-plume (c) Top of plume Figure 4.8: Contour plots for two point sources with trees: Contour plots based on model output for two point sources located at (2, 0, 0) and (7, 2, 0) with effects of the orchard incorporated. Green lines show tree boundaries. 4.6 Model Output 4.6.1 One Receptor Our model was run using the wind data collected for June 2010. For each sim- ulation, spore concentration was measured at the receptor location on each day given the wind speed and direction for that day. Source locations were selected randomly from a uniform distribution of x and y values over the entire orchard at 107 the start of each trial, and sources remained in this location for the full 30 days. As wind speed and angle data were the same for each trial, and the same number of spores were released from each source (Q = 360000 spores/release period), the difference in the concentration values at the receptor between trials is only due to the location of the spore release. As there were no data on the stability of the atmosphere at the time, and the Okanagan is relatively stable, stability class F (the most stable) was used for all trials. Figure 4.9 shows three trials of the model, where the only difference between the sub-figures is the location of the sources. Figure 4.10 shows the output for twenty trials with one receptor over thirty days. This graph shows the average detection of spores from this one receptor over twenty trials. It can be seen that even over twenty trials there remains a great deal of variability in the average detection levels due to the different locations of the spores, and the different wind directions and angles by day. Thus, even for a constant release rate of spores in an orchard, taking one sample daily is expected to lead to a great deal of variability in results, which is reinforced by the images in Figure 4.9. This reflects what was seen in the experimental data, where there was a great deal of variability in measurements of spores during the growing season. 108 Figure 4.9: Three trials for a single receptor with two sources: Three trials of model (as described in Sections 4.2 through 4.5) for a single receptor in an orchard with two sources. Concentration detected is plotted on the y-axis, and day of detection on the x-axis. Note y-axis scale variation between the three trials despite the release of 360000 spores from two sources each day. Also note wide variation in the number of days when spores were detected. 109 !" #!" $!" %!" &!" '!" (!" )!" *!" +!" !# ,- ./ " !$ ,- ./ " !% ,- ./ " !& ,- ./ " !' ,- ./ " !( ,- ./ " !) ,- ./ " !* ,- ./ " !+ ,- ./ " #! ,- ./ " ## ,- ./ " #$ ,- ./ " #% ,- ./ " #& ,- ./ " #' ,- ./ " #( ,- ./ " #) ,- ./ " #* ,- ./ " #+ ,- ./ " $! ,- ./ " $# ,- ./ " $$ ,- ./ " $% ,- ./ " $& ,- ./ " $' ,- ./ " $( ,- ./ " $) ,- ./ " $* ,- ./ " $+ ,- ./ " %! ,- ./ " !" #$ %# &' () "# *" +*, -" '% .* /(&%* 01%'(2%*!"#$%#&'()"#*+'"3*4#%*5%$%-&"'* Figure 4.10: Average concentration for a single receptor: Average concentration over twenty trials for one receptor using the wind data from the month of June, 2010. 110 4.6.2 Multiple Receptors We suspect that much of the variability in the spore detection data was due to the way in which the sampling was done, specifically that only one reading was done at each time interval. In order to explore this possibility, the model was run with various numbers of receptors. The results are shown in Figures 4.11 through 4.16 (pages 130 through 135). Figure 4.11 shows the results for the average concentration readings, as in Figure 4.10, but in this case instead of a single receptor, 20 evenly spaced receptors were used in each of the five tree rows, resulting in 100 receptors. Surprisingly, there is still a good deal of variation in the results. Much of this variation is due to the changing wind conditions and wind angle. In the Gaussian plume model, the wind speed comes into the model as an inverse term, so the detection of spores and the wind speed will have an inverse relationship if everything else is held constant. Low wind speed explains the large peak in spore detection on June 12th. However, the wind angle and position of the spores must also have a role to play in the variation in the results, as the inverse relationship between wind speed and spore detection is not strictly followed. A normalized (values divided by the maximum value) graph comparing the average concentration from these 100 receptors over the 20 trials and the wind speed at each day (Figure 4.12) shows that while there is a clear general trend, other factors must also be included. It is not surprising, given that the sources are randomly placed during each trial, that there would be variability in the results, however, it was expected that this variability would decrease as receptors were added. Figure 4.13 shows the average detection value for each number of receptors, averaged over all the receptors and over the twenty trials. Error bars show the 111 standard error on these values, expressing the variation in average detection that was seen from the model. Considering the one receptor case, the parallel to what was done in the experiments (Chapter 2), it can be seen that the variation is quite high. As well, zero is within the standard error of the mean, which suggests that there were many trials with zero or near zero average detection values (as suggested by Figure 4.9). The variation decreases substantially as more receptors are added, and with two receptors the variation is already greatly decreased. However, there was not a clear trend toward decreasing variation after two receptors, the average detection value seems to level out somewhere between 60 and 80 spores after three or four receptors are used, but the variation remains high. Increasing receptor numbers to 25 through 100 there is reduced variation and the values have leveled out quite a bit. However, considering feedback from the orchardist perspective (M. Thurston, personal communication), for an orchard the size of the one being used in this model, receptor numbers greater than ten are unrealistic, and greater than five may even be too many. It appears that five receptors perform reasonably well, giving a spore detection value that is reasonably similar to that obtained from higher receptor numbers. Variation in the average detection levels over multiple receptors is expected, as the majority of the time not all of these receptors will be located within the plume of spores emitted by the sources. Thus, the variation seen in Figure 4.14 is also expected. In Figure 4.14, the range was found by taking the minimum detection value over each trial (over all receptors), and the maximum detection value over each trial, and subtracting these to get a range. This gave a range value for each trial, which was then averaged over the twenty trials done for each simulation to give the graph shown in Figure 4.14. Again, it is clear that one 112 receptor does not perform well, the range of values often had an average near zero. Again, the points level out to a range of somewhere between about 150 and 200 spores, with the ranges for higher numbers of receptors looking more stable. However, in this case it appears that using ten receptors is comparable to using 100 receptors. Within the realistic constraints that no more than five receptors be used, it appears that using four or five receptors results in a reasonably small spore detection range. The results are difficult to quantify, and we discuss this in Section 4.7. In order to consider the results without including the variation that arises due to some of the receptors generally not falling within the plume, the maximum detection values were plotted. Figure 4.15 shows the results of running trials considering these maxima. In Figure 4.15 the first line (shown in blue) represents the maximum overall detection value, referred to as the “highest maximum.” These values were found by taking the maximum value over all receptors each day, then taking the maximum over all days, and then considering the maximum of these values over the twenty trials. It is the highest value that was recorded by any of the receptors over all twenty trials. The “lowest maximum” was found in a similar manner, namely, the maximum was taken across all receptors each day, and then the minimum of these values over all days was taken, and then the minimum of these values over all twenty trials was considered. This value is the smallest of all of the maximums recorded. The third line shows the “average maximum,” which was computed by using the maximum value across all receptors each day, and then taking the average over the thirty days and the twenty trials. It is expected that as you increase receptor numbers, you increase the probability that the randomly generated source location will occur close to a receptor, and 113 thus that the maximum detection level would increase. The hope was again that as receptor numbers increased this maximum detection value would level out, which would suggest that adding additional receptors past that point did not significantly change the probability of being close to a source. However, as can be seen in Figure 4.15, this did not occur. As the receptor numbers increase the maximum values also increase. It is possible that the values may level out at some significantly higher level of receptors, but as mentioned previously, even using receptor numbers in the 25-100 range would be unreasonable for experimental work. The other trend which can be taken from this graph is that on average the variability of the maximums seems to be increasing as receptor numbers increase, that is, the lowest maximum and the highest maximum appear to on average get further away from the average maximum in either direction. Comparing the results for the receptor numbers that have been previously mentioned to perform well, it can be seen that the changes seem to level out around four or five receptors. Thus far it has been determined that there is a great deal of variation in the concentration values detected at any receptors placed in the orchard. The goal of this modeling work is to try and understand the trends that were seen in the experimental data, to make hypotheses about why so little of the variation in spore detection levels could be explained by the orchard data, and to make recommendations to improve future studies. Considering Figure 4.16, it is clear that there remains a great deal of variation, even at high receptor numbers. Figure 4.16 shows the average percentage of receptors that had non-zero values over each of the thirty days of each trial and the twenty trials that were performed for each number of receptors. The average percentage of non-zero receptors seems to level out around 65% of the receptors, suggesting that with two sources of spores, on 114 average two thirds of the receptors will detect spores on any given day. Again, one receptor sees a huge amount of variation, where none of the receptors having non- zero values is well within one standard deviation of the mean; in some of the trials with only one receptor all of the receptors had completely zero readings. From approximately five receptors onwards the variation and the average percentage do not change significantly. It is interesting to note that the range that is within one standard deviation of the mean is approximately from 40% to 90% of the receptors having non-zero readings. It was also found that when considering the lowest percentage of receptors having non-zero readings and the highest percentage of receptors having non-zero readings, that in all cases there were some days where all of the receptors had a non-zero reading (100% non-zero detection), and some days where all of the receptors only detected zero spores (0% non-zero detection). Again, this suggests that there is a great deal of variation in the system. It also suggests that in order to have a good chance of detecting spores, should they be present in the orchard, it should be assumed that at any given time only 40% (or fewer) of the receptors will have a non-zero reading. This means that the minimum number of receptors that would be expected to have at least one with non-zero detection 90% of the time would be three receptors. From Figure 4.16, it appears that at five receptors in the percent non-zero detection leveled off. If we consider the 40% non-zero detection as a conservative estimate (the lower bound within one standard deviation) this suggests that if five receptors are used, the probability of all of them having a non-zero detection should spores be present (at the levels used in this modeling work) should be approximately 7.8% (.605 = 0.078, given a 60% chance of all zero detection for each receptor). Thus, it seems reasonable to suggest that using one receptor could result in a non-zero 115 value only rarely over the growing season, as seen in the experimental data, even when spores are present. Using five receptors will significantly improve results for detection of spores should they be present in the orchard, and will also yield results that allow for better prediction of spore growth and dispersal based on environmental factors. 116 !" #!" $!" %!" &!" '!!" '#!" '$!" '%!" '&!" #!!" !' () *+ " !# () *+ " !, () *+ " !$ () *+ " !- () *+ " !% () *+ " !. () *+ " !& () *+ " !/ () *+ " '! () *+ " '' () *+ " '# () *+ " ', () *+ " '$ () *+ " '- () *+ " '% () *+ " '. () *+ " '& () *+ " '/ () *+ " #! () *+ " #' () *+ " ## () *+ " #, () *+ " #$ () *+ " #- () *+ " #% () *+ " #. () *+ " #& () *+ " #/ () *+ " ,! () *+ " !" #$ %# &' () "# *" +*, -" '% .* /(&%* 01%'(2%*!"#$%#&'()"#*+'"3*455*6%$%-&"'.* Figure 4.11: Average concentration for 100 receptors: Average concentration over twenty trials for 100 receptors using the wind data from the month of June, 2010. 117 Figure 4.12: Normalized wind speed and average concentration: Normalized plot of wind speed data and average concentration over twenty trials for 100 receptors. Note that although there is a roughly inverse relationship between wind speed and concentration the pattern is not strictly followed.118 !"#$ !%#$ #$ %#$ "#$ &#$ '#$ (##$ (%#$ ()##$ %)##$ *)##$ ")##$ +)##$ &)##$ ,)##$ ')##$ -)##$ (#)##$ %+)##$ +#)##$ ,+)##$ (##)##$ ! "# $% &# '( #) #* + ,- '. %/ 0# ' 1023#$',4'5#*#6),$7' !"#$%&#'(#)#*+,-'.%/0#7'38'1023#$',4'5#*#6),$7' Figure 4.13: Average detection for varying receptor numbers: Average detection values over twenty trials for increasing numbers of receptors. Error bars show the standard error on each value. 119 Figure 4.14: Range of detection for varying receptor numbers: Range of detection values for increasing numbers of receptors. Error bars show the standard error on the average range over twenty trials. Range was found by taking the minimum detection value over each trial (over all receptors), and the maximum detection value over each trial, and subtracting these to get a range, which was then averaged over the twenty trials for each number of receptors. Note the varying scale on the x-axis in order to show the variation. 120 Figure 4.15: Maximum values for increasing numbers of receptors. 121 !"#$% "% "#$% "#&% "#'% "#(% "#)% "#*% "#+% "#,% "#-% $% $#$% $% &% '% (% )% *% +% ,% -% $"% &)% )"% +)% $""% !" #$ "% &' (" )* +% ,- "# +) *./0"#)+1)2"$"3&+#4) !"#$"%&'(")+1)2"$"3&+#4)56&7)*+%,-"#+)8'9."4) Figure 4.16: Percentage non-zero for varying receptor numbers: Percentage of receptors that had non-zero spore detection values over the twenty trials conducted for each number of receptors. Error bars show the standard error on the percentage of receptors with non-zero values.122 4.6.3 Location of Receptors The previous results (Sections 4.6.1 and 4.6.2) were obtained using evenly spaced receptors placed between or in the orchard rows. It was suggested that the more important consideration may not be the number of receptors, but rather, the locations of these receptors (Stockie, personal communication). This idea was tested through simulation experiments where the location was varied and the number of receptors was held constant. Based on the results of our model in Sections 4.6.1 and 4.6.2, five receptors were chosen as a reasonable number to consider within the orchard, both because of the high quality results across all the data analysis, and because this number was considered to be reasonable for practical use in an orchard with dimensions similar to that used in the modeling. These five receptors were placed in nine different configurations, as shown in Figure 4.17. It was expected that the sets of receptor coordinates H, I, B and D would perform the most poorly, as they were not well distributed throughout the orchard. Sets H and I were intentionally chosen to cluster the receptors together, and it was expected that this would give highly variable results. It was also expected that coordinate sets with receptors close to the center (such as F or G) would see more representative results (Stockie, personal communication). Figures numbered 4.18 through 4.21 show the results of model simulations using these seven coordinate sets. 123 (a) Coordinates A (b) Coordinates B (c) Coordinates C (d) Coordinates D (e) Coordinates E (f) Coordinates F (g) Coordinates G (h) Coordinates H (i) Coordinates I Figure 4.17: Locations of Receptors Figure 4.18 shows the average detection levels over the five receptors during the thirty days of each trial, and then over twenty trials. As before, error bars show the standard error on this average detection value. Coordinate sets B, H, and I, saw large variation in detection values, and had zero average detection as a fairly likely outcome. As mentioned in the previous sections (4.6.1 and 4.6.2, where 124 coordinate set A was used for the trials) having all five receptors detect zero spores for this level of release (which was constant over all trials) should be reasonably improbable. Having an average detection of zero or near zero over thirty days and twenty trials with coordinate set B, where receptors are not spaced evenly suggests that the location of receptors within the orchard can have a substantial effect on the detection outcomes. For coordinate sets H and I, where the receptors were clustered in the top left and bottom right of the orchard respectively, there is a noticeable difference in the average detection levels, but both coordinate sets have large variation in results. It is known that there is a dominant wind direction in the Okanagan, and these data suggest that results can be affected not only by placement of receptors, but also by how the placement of those receptors relate to the dominant wind direction. This suggests that dominant wind direction may be important information for growers to have about their orchards. The results for the other coordinate sets do not distinguish as clearly between each other, average detection was higher for coordinate sets A and F, and variation was smaller for A, D, and G. However, none of these trends suggest that one coordinate set performed significantly better than all of the others. The range of detection values was also calculated for each configuration in the same manner as discussed in section 4.6.2 and is shown in Figure 4.19. Again, the variation over the different days and different trials is much higher for coordinate sets B, H, and I than for any of the others. The variation was also quite high for coordinate set E, which was expected to perform reasonably well as receptors were placed across the orchard. Again, there is not a clear coordinate set that outperforms the others in terms of variation. The variation seen for coordinate sets H and I for the ranges suggests that these arrangements would be very unreliable, 125 as would be expected. The range values for these coordinate sets was also quite high. One probable explanation for this is that having receptors clustered fairly closely together results in a lower chance of detecting spores. However, should one of the sources be randomly placed near the location of this receptor cluster, the probability of having a high detection value is increased. Figure 4.20 shows the average maximum for the different location sets. In this case, the variation is quite high for all receptor locations, although H and I are noticeably higher, and the average maximum value error bars included the value zero for receptor sets B, D, and I. As these were the coordinate sets that were expected to perform poorly and not give reliable detection results in comparison with the others, it is not surprising that their average maximum value set included the value zero for some source locations. As mentioned for the previous figure, the high maximum values and extreme variation for coordinate sets H and I are likely due to there being a trial during which the spore source was close to the cluster of receptors. Figures 4.18-4.21 demonstrate that coordinate sets B, H, and I are the least reliable for detecting spore presence in the orchard. Figure 4.21 shows that co- ordinate set D is also highly unreliable for spore detection. The percentage of receptors that are detecting spores was determined to be about 65% for receptor numbers of five and upwards, and for five receptors the lower bound of the stan- dard error was at approximately 40% non-zero detection. Coordinate set D has a lower bound on detection of approximately 17%, and an upper bound of over 100% (implying that a value of 100% is fairly likely to occur), which suggests that for receptor set D results could vary essentially between all zero detection and all positive readings with high probability and would therefore be very unreliable. 126 The same is seen for coordinate sets H and I. Coordinate set B again saw poor performance, although not as extreme as the others, while the other coordinate sets were fairly close to the 65% average non-zero and the 40% lower bound. Figures 4.18-4.21 have demonstrated that the location of the receptors does have a significant effect on the results. Coordinate sets B and D, which were not evenly spaced throughout the orchard, and did not have receptors near the centre of the field, consistently performed unreliably in terms of spore detection. Coordinate sets H and I, which were unrealistically clustered in one corner of the orchard consistently saw the highest variation, and would be likely to over- predict if a source was near the cluster, and otherwise to severely under-predict. Coordinate set C did not have a receptor in the centre, but had two receptors splitting centre, and still performed well. The other coordinate sets, which all contained receptors close to the centre of the field and were reasonably evenly spaced, performed fairly similarly. Thus, when placing receptors in orchards for future experiments it is suggested that at least one receptor should be placed near the centre of the field, and other receptors should be placed evenly through the remainder of the field. In the presence of a dominant wind direction, that should be taken into account. 127 Figure 4.18: Average detection for varying receptor locations: Average detection values over twenty trials of thirty days each for five receptors in nine receptor arrangements. Error bars are only an indication of the variability in the data. Since the data are not normal the error bounds on the average detection values in some cases fall below zero.128 Figure 4.19: Range of detection for varying receptor locations: Range of detection values over twenty trials of thirty days each for five receptors in nine receptor arrangements. See Figure 4.18 for a discussion on the error bars. 129 Figure 4.20: Average maximum detection for varying receptor locations: Average maximum detection values over twenty trials of thirty days each for five receptors in nine receptor arrangements. See Figure 4.18 for a discussion on the error bars.130 Figure 4.21: Percentage non-zero detection for varying receptor locations: Percentage of non-zero detection values over twenty trials of thirty days each for five receptors in nine receptor arrangements. Error bars are only an indication of the variability in the data. The maximum value in all cases was 100 percent non-zero detection, and the minimum was 0 percent non-zero detection. Error bars are provided in order to give a visual cue of the variability in the data. See Figure 4.18 for a discussion on the error bars. 131 4.6.4 100 Receptor Profiles Matlab was used to produce profiles for trials done with 100 receptors in order to confirm the model was behaving as expected. Twenty receptors were evenly spaced down the five rows of trees in the orchard, and no receptors were placed in the open areas between the trees. Although there would be far more than 100 apples in an orchard this size, this was considered to roughly represent the patterns of spore dispersal that apples would receive, as they could be considered as multiple receptors within the orchard. Figures 4.22 and 4.23 show the spatial pattern of spore detection for the wind data for June 1st, 2010, during the first trial and the twentieth trial, respectively. Very few spores were detected on this particular day in the first trial, and in fact, very few spores were detected throughout trial one. As suggested by Figure 4.22, this is likely due to the fact that both sources had been randomly placed near the bottom left corner of the orchard, and due to the dominant wind direction, on most days the majority of the spores were blown out of the orchard, and did not reach the receptors. Based on the results in Figure 4.23, the randomly generated source placement for trial twenty saw many more spores reaching the receptors. The sources would have been located near the maximum points seen in the figures about one third and two thirds of the way up the right side of the orchard. The wind on this day was blowing approximately towards the upper left, as can be seen from the two profiles. Note that the dark blue in between the rows does not imply that there were no spores in these locations, but rather that there were no receptors placed in the open areas between the rows of trees, and thus no receptor values are available for these areas. Figure 4.24 shows the overall maximum value that was detected at each recep- tor location during the twenty trials of thirty days each. These highest maximum 132 values would have occurred when the source(s) of spores were close to the recep- tor, and the wind speed was low with a direction favorable to the spores reaching the receptor but not being blown past it. The greatest of these highest maximum values occurred in the centre near the top of the orchard. Figure 4.25 shows the average of the maximum results, that is, the maximum value over the thirty days of the trial was taken for each receptor location, and then these maximums were averaged over the twenty trials. This figure suggests that there is a dominant wind direction in the orchard, as the average maximums are much higher on the left of this figure than they are on the right, which was also reflected in Figure 4.24. Considering the weather data (Figure 2.15), the majority of the wind direction angles were between 150° and 200°, which agrees with what is seen in these maxi- mum figures. This confirms the patterns that were expected due to this dominant wind direction. It is also possible that the randomly generated locations for the sources of spores caused this pattern, but this seems less likely. 133 Figure 4.22: Day 1, trial 10 results: Day one, trial one results for 100 receptors evenly spaced throughout the rows of trees within the orchard. Scale measures the number of spores detected in that location. Axes are the x and y locations in the orchard. Note the rows corresponding to the locations of the trees as shown in Figure 4.4. Comparing to Figure 4.4, the rows in between trees show zero values because there are no receptors. 134 Figure 4.23: Day 10, trial 20 results: Day ten, trial twenty results for 100 receptors evenly spaced throughout the rows of trees within the orchard. Scale measures the number of spores detected in that location. Axes are the x and y locations in the orchard. Note the rows corresponding to the locations of the trees as shown in Figure 4.4. Figure 4.24: Highest maximum results: Overall maximum results for 100 receptors evenly spaced throughout the rows of trees within the orchard over thirty days and twenty trials. Scale measures the highest maximum number of spores detected in that location. Axes are the x and y locations in the orchard, rows corresponding to the locations of the trees as shown in Figure 4.4. 135 Figure 4.25: Average maximum results: Average maximum results for 100 recep- tors evenly spaced throughout the rows of trees within the orchard over thirty days and twenty trials. Scale measures the average maximum number of spores detected in that location. Axes are the x and y locations in the orchard, rows corresponding to the locations of the trees as shown in Figure 4.4. 4.6.5 Varying Numbers of Sources Thus far, results have been discussed for different numbers and locations of recep- tors for two sources with randomly generated locations. In the following section, the results for different numbers of sources are considered. Simulations were run for one to four sources of spores, all with a release rate of 360,000 spores / day, as before. The location of each of these sources was randomly generated at the start of each trial. The results for one to five receptors, evenly spaced throughout the orchard, are discussed. Figure 4.26 shows the average spore detection over the thirty days and twenty trials for each combination of numbers of receptors and numbers of sources. As would be expected, the average detection values in general increase with increas- 136 ing numbers of sources. As well, on average the difference between the detection values for increasing numbers of sources seems to increase as more receptors are considered, which suggests that the variability is so high for small receptor num- bers that it would be difficult to distinguish between fields with higher inoculum levels. The most striking example of this is for two receptors, where the values for two sources and the values for three sources are approximately equal. Figure 4.27 shows similar trends, with the range of detection values increasing as sources are added. In this case again there is some overlap between the values for two and three sources, which continues all the way up to five receptors. Figure 4.28 shows the overall maximum detection values over the thirty days and twenty trials. Again, the maximum detection increases on average as the number of sources increase, although in some cases we see higher detection levels with fewer receptors. It is expected that the probability of a receptor being close to a source of spores will increase as both the number of sources and the number of receptors increase, however, there is still a chance of a source being close to a receptor for lower numbers, resulting in a high absolute maximum. From the results, when there was one source and one receptor, in 0.5% of cases there was a detection of over 400 spores. For four sources and five receptors, in 31% of cases there was a detection of over 400 spores. In Figure 4.29, which shows the average maximums, the trends are much more defined, which is expected, as the averaging of the maximums should account for some of the variability that arises due to a source being very close to a receptor regardless of the numbers involved. The average maximums seem to level out somewhat around four or five receptors, and for one or two receptors the ability to distinguish between numbers of sources would be limited. Figure 4.30 shows the lowest maximum taken from each of the 137 twenty maximums over the thirty days of the trial. Again, there is not a clear distinction for smaller receptor numbers, and for one receptor the lowest maximum is essentially zero for all numbers of sources. The results for four sources are quite a bit higher than those for one, two, or three sources, which suggests that the likelihood of a source being close to a receptor was fairly high for four sources and four or five receptors. The information that is most of interest is the number of receptors that would be needed to get a reasonable picture of the inoculum in the orchard, expecting that some of the receptors would fall outside of the plume of spores. Figure 4.31 shows the percentage of the receptors that had non-zero detection for each combi- nation of numbers of sources and numbers of receptors. As mentioned previously, for two sources it was found that the percentage of non-zero receptors leveled out at about 65%. Here, for one source the value seems to be around 50%, for three it seems to be around 75%, and for four around 80%. Surprisingly, even for one receptor four sources have close to an 80% non-zero detection rate, which suggests that when the inoculum load is high enough, even one receptor will generally fall within the plumes. With more trials it might be possible to quantify this trend, as discussed in Section 4.7. 138 Figure 4.26: Average spore detection varying location and numbers: Graph of average spore detection results for varying source numbers (1-4) and varying receptor numbers (1-5). 139 Figure 4.27: Range of spore detection varying location and numbers: Graph of range of spore detection results for varying source numbers (1-4) and varying receptor numbers (1-5). 140 Figure 4.28: Highest maximum spore detection varying location and numbers: Graph of overall maximum spore detection results for varying source numbers (1-4) and varying receptor numbers (1-5). 141 Figure 4.29: Average maximum spore detection varying location and numbers: Graph of average maximum spore detection results for varying source numbers (1-4) and varying receptor numbers (1-5). 142 Figure 4.30: Lowest maximum spore detection varying location and numbers: Graph of lowest maximum spore detection results for varying source numbers (1-4) and varying receptor numbers (1-5). 143 Figure 4.31: Percentage non-zero spore detection varying location and numbers: Graph of percentage non-zero spore detection results for varying source numbers (1-4) and varying receptor numbers (1-5). 144 4.7 Quantification of Results Due to the time constraints on this research and the computational cost of running experiments, twenty repetitions of the thirty days were performed for each tested scenario in order to average results. Twenty trials was selected as ten trials was not found to be high enough to account for the randomness of the inputs. Using twenty trials increased the computational cost, however, it was still found that there was a high amount of variability remaining due to randomized location of sources in each trial. We feel that an increase to one hundred or more trials might allow for quantification of results in an accurate manner, but was beyond the scope of this research. Additional simulations could also be performed controlling for location and wind direction in such a manner as to quantify the relationship between the distance of the sources and the receptors that results in detection, as well as the probabilities of detection over some threshold. As the qualitative behavior seen agreed with expectations from the data, we consider this a first approximation, and leave quantification of results for future research. 4.8 Comparison with Experimental Data The main conclusions that can be drawn from the modeling work that has been discussed are that there is a great deal of variability in results within the orchard and that one receptor is highly unlikely to capture the true spore levels reaching the apples. The results obtained in a twelve meter by twelve meter orchard from simulations with a single receptor were very similar to the experimental data that were collected, both for the iRod DNA detection in the air, and the apple tissue DNA detection on the fruit surface. This suggests that the large number of zeros 145 in the the data were due not necessarily because there were very few spores in the orchard, but rather because the plume was not reaching the receptor location. This suggests that more receptors should be used for future studies, and that more apples should be sampled at each time. This also suggests that the inability of the data collected for the environmental conditions to explain the variation in the spore detection may be due to the small number of samples and the single receptor location, as opposed to no relationship existing between spore release and environmental conditions. If future studies were to use more receptors, it is possible that correlations with environmental conditions could be determined. One of the limitations of this modeling work is that the actual source of spores in the orchard is unknown. However, as there was a qualitative agreement between the experimental results and the modeling results, it is suggested that the results of the modeling could be used as a place to start for future work. In particular it appears that five receptors should give a much more reliable result for spore detection, and that these receptors must be placed evenly through the orchard, preferably with one close to the centre of the field. Another limitation when comparing with the experimental data, is that the threshold for spore detection using the experimental techniques is unknown. It was assumed during modeling that if there was any spore presence at the receptor it could be counted as non- zero detection. However, spore presence according to Equation 4.3 was often very low. The question is whether using ten spores at the detection point, or perhaps 100 spores at the detection point, would be a more realistic cut-off for considering the percentage of non-zero detection. Further experimental work would need to be done to determine what threshold would be reasonable for modeling. A third limitation, which will be discussed in the following section, is the incorporation 146 of the apple trees themselves which is currently highly unrealistic. The apple tree canopies certainly have strong effects on air flow in the orchard, with consequences for plume shape and deposition patterns. Overall, this model has explained experimental data that taken alone suggest that there were low spore numbers in the orchard and no relationship between environmental data and spore detection. Based on the results from our model, we conclude that a high occurrence of zeros in the experimental data and the inability to predict spore detection levels using environmental data were due to the high degree of variability within the system, and the fact that only a single receptor was used. It is possible that not only would the ability to predict spore dispersal with environmental data increase with increased receptor numbers, but also that the relationship between spore detection in the orchard and the storage disease outcomes might become more significant and predictive. Orchardists might be able to use these measurements to time sprays and determine when to harvest crops, and packing houses might be able to use these data to make better decisions on storage durations for different apple crops. 147 5 Conclusion 5.1 Analysis of Post-Harvest Disease Prediction Results Our research examined data that were collected over a period of three years and which included environmental variables, growing season measures of spores found in the air and on the fruit surface, and disease outcomes in storage. We examined a number of pre-storage factors, both to determine if there was a relationship between the presence of pathogen inoculum and the environmental conditions, and to determine if disease outcomes during storage could be predicted using the pre-storage factors that were available. In our analysis of the data we looked for relationships between environmental variables and the levels of pathogen inoculum that were found in the air and on the fruit surface. We expected that the pathogen lifecycle would be governed mainly by temperature and moisture, and therefore that there would be a relationship between these variables and the amount of inoculum present. After considering all of the available variables, we found that the environmental data provided little to no explanation of the trends in inoculum detection. We developed a regression model for detection levels in the air, where 0.6% of the variation could be explained by the direction of the maximum wind gust. As well, we developed a regression model for detection levels on the fruit tissue, where 2.3% of the variation could be explained using the average temperature three days before spore measurements, the rainfall the day before the measurement, and the wind speed during the maximum wind gust on the day of measurement. Prior to our analysis of these data, we had anticipated that the amount of variation in spore detection that we would be able to explain would be much higher. 148 Using regression procedures, we found that approximately 56% of the varia- tion in disease outcomes in storage for the apples in these experiments could be explained by the average rainfall during the last two weeks before harvest, the number of months in storage, the cumulative rainfall over the last fifty days, the pathogen DNA detected in the air at harvest, and the cumulative rainfall over the entire growing season. However, since we expected that rainfall during these periods will be highly correlated in many cases, we derive a model which explained 51% of the variation using the number of months the apples were stored, the aver- age temperature during the last two weeks before harvest, the cumulative rainfall during this time, and the amount of pathogen DNA detected on the plant tissue at harvest for application to future experiments. The high percentage of variation that was explained by the regression model for disease outcomes in storage suggests that pre-storage factors collected during the growing season can be used to make predictions of disease outcomes in storage, which we expected based on findings in previous literature. On the other hand, although we expected that the amount of inoculum present would have a large effect on disease outcomes, and that inoculum detection levels could be predicted from environmental variables, neither of these was found to be the case. Based upon the trends in the data and our results, we hypothesized that the lack of relationship seen could be influenced by the dynamics in the orchard and the sampling methods. In order to examine this hypothesis we created a model for the system. 149 5.2 Analysis of Dispersal Modeling Results A Gaussian plume model was selected for describing spore dispersal within the orchard due to the previous applications to agricultural systems. It was found in previous research to be reasonably accurate, and required relatively minimal data collection, allowing for many of the inputs to be taken from our experimental data. Many approximations were still required in order to make the model usable, especially in modifications made to apply the model to the orchard situation. Model results had good qualitative agreement with the data, and quantitative agreement was not expected due to the nature of the approximations. The model output suggests that the inability of the data to predict inoculum levels and the high variability in the data are due to the dynamics of spore movement within the orchard and the manner in which the experimental data were collected. The model results confirmed that using one receptor within the orchard should result in a high level of variability, and presented similar trends to those seen in the data. Trials using more receptors confirmed that variability decreased significantly with an increase in the number of receptors. It was determined that based on both the feasibility of real world application of these results and the trends taken from the model that five receptors within the representative orchard gave the most reasonable results for the least amount of effort. It was also found that these five receptors should be spread throughout the orchard, and that at least one receptor should be placed near the centre of the orchard for best results. The model also suggests that should there be a dominant wind direction in the orchard, receptors should be placed in such a manner as to intercept spores being transported in this dominant direction. 150 5.3 Overall Contributions of this Research The only model that is currently available in the literature to describe the in- coming disease risk for post-harvest decay of pome fruit based on pre-storage factors is for pear fruit and only considers Botrytis cinerea (Spotts et al., 2009). In this thesis, we have focused on apple fruit, and considered the three major pathogens which cause decay in packing houses in the Okanagan valley, Penicil- lium expansum, Botrytis cinerea, and Mucor piriformus. We used experimental data collected within the region to develop a model for disease outcomes in stor- age. We found that approximately 50% of the variation in disease outcomes for apple fruit during storage could be predicted by pre-storage factors. As most of this variation was explained by factors that could be measured at little or no cost to the industry, it could be used to make decisions about how long to store apples in the packing house in the future. After analyzing the data and creating statistical models to explore the pre- dictive ability of the data and the trends in the orchards that were considered in this study, we modified a mathematical model in order to examine the trends of the dispersion of spores in the orchard. We then developed a simulation using environmental data. This was a unique application of the Gaussian plume model, as most modeling work has been done for ground cover crops. As well, the use of the model to determine optimal receptor configurations resulted in greater un- derstanding of the dynamics of the system and allowed for recommendations to be made that should result in improved data collection in future work. However, just as the data used in this research were limited by cost constraints, the results from our modeling suggest that cost to industry will continue to be a barrier to applying the results of this model. Until more cost effective methods for spore 151 detection are developed, our modeling work suggests that it would be more effec- tive to sample from multiple locations in the orchard during the last two weeks before harvest to determine inoculum levels prior to storage. At this time, having five receptor locations in every orchard for the entire growing season would be too expensive to be feasible. It is clear, however, that using more than one receptor will improve results. 5.4 Strengths and Limitations One of the greatest strengths of this research is that it provides a solid basis for future experiments and further modeling. Throughout the research the modeling results, both for the statistical and the mathematical models, had qualitative agreement with our understanding of the system dynamics, and these results were readily explained by the biology of the pathogen and the dynamics within the orchard. This work makes general recommendations for future experiments, and provides an explanation of the orchard dynamics that was not previously available, but is relatively simple and easy to apply. This work was limited by the large number of approximations that had to be made in the modeling, and there were not sufficient data available to verify that the model holds for situations other than the ones which were used to develop both models. The modeling results in both cases should not be taken as an absolute quantitative description of what will happen in any given orchard, but rather as a qualitative descriptor of general trends which can be used to make informed decisions for future work. 152 5.5 Potential Applications and Future Research Directions There are many areas in which further research could be done. The statistical model developed gives a general basis for decision making in apple packing houses, showing which factors are more likely to result in greater disease levels. In the future, this model could be verified and improved with additional data, and the addition of margins of error and greater knowledge of orchard factors such as how ripe apples were at harvest could allow for significantly more confidence in deciding how long apples can safely be stored before some risk level for disease is reached. The mathematical model could be applied to real orchard layouts and by including more data could be improved to provide optimal receptor numbers and locations for future experiments. As well, this model could be extended to allow for identification of possible inoculum sources, with the goal of permanently reducing the disease levels in the orchard. Elements of many other researchers’ work could be incorporated into our model to improve its results. Combining with the storage model would allow researchers to track the progress of disease throughout the apple’s lifecycle, and potentially discover points in the process where disease levels can be significantly decreased with small cost to the industry. 153 Bibliography Amiri, A., and Bompeix, G. 2005. Diversity and population dynamics of Peni- cillium spp. on apples in pre- and post-harvest environments: consequences for decay development. Plant Pathology. 54: 74-81. Andrews, J.H., and Harris, R.F. 2000. The ecology and biogeography of microor- ganisms of plant surfaces. Annual Review of Phytopathology. 38(1): 145-180. Aylor, D.E. 1982. Modeling spore dispersal in a barley crop. Agricultural Meteo- rology. 26: 215-219. Aylor, D.E. 1989. Aerial spore dispersal close to a focus of disease. Agricultural and Forest Meteorology. 47: 109-122. Aylor, D.E. 2005. Quantifying maize pollen movement in a maize canopy. Agri- cultural and Forest Meteorology. 131: 247-256. Aylor, D.E., and Ferrandino, F.J. 1989. Dispersion of spores released from an elevated line source within a wheat canopy. Boundary Layer Meteorology. 46: 251-273. Aylor, D.E., and Flesch, T.K. 2001. Estimating spore release rates Using a La- grangian stochastic simulation model. Journal of Applied Meteorology. 40: 1196- 1208. Baert, K., Valero, A., De Meulenaer, B., Samapundo, S., Ahmed, M.M., Bo, L., Debevere, J., and Devlieghere, F. 2007. Modeling the effect of temperature on the growth rate and lag phase of Penicillium expansum in apples. International Journal of Food Microbiology. 118: 139-150. Barrett, S.R.H., and Britter, R.E. 2009. Algorithms and analytical solutions for rapidly approximating long-term dispersion from line and area sources. Atmo- spheric Environment. 43: 3249-3258. Beattie, G.A. 2002. Leaf surface waxes and the process of leaf colonization by microorganisms. In Phyllosphere Microbiology, eds. Lindow, S.E, Hecht-Poinar, E.I., and Elliott, V.J., American Phytopathological Society, St. Paul, M.N. 3-26. Benson, P.E. 1992. A review of the development and application of the CALINE3 and 4 Models. Atmospheric Environment. 26(3): 379-390. 154 Briant, R., Korsakissok, I., and Seigneur, C. 2011. An improved line source model for air pollutant dispersion from roadway traffic. Atmospheric Environment. 45: 4099-4107. Briggs, G.A., 1973. Diffusion estimation for small emissions report. NOAA. n. 79. Oak Ridge. TN (USA). Carew, R., 2000. A hedonic analysis of apple prices and product quality charac- teristics in British Columbia, Canadian Journal of Agricultural Economics. 48(1): 241-257. Calder, K.L. 1973. On estimating air pollution concentrations from a highway in an oblique wind. Atmospheric Environment. 7: 863-868. Davis, C.N. 1966. Deposition from moving aerosols. Aerosol Science. C.N. Davis, Ed., Academic Press, 393-446. De Jong, M.D., Wagenmakers, P.S., Goudriaan, J. 1991. Modelling the escape of Chondrostereum purpureum spores from a larch forest with biological control of Prunus serotina. Netherlands Journal of Plant Pathology. 97: 55-61. De Wolf, E.D., and Isard, S.A. 2007. Disease cycle approach to plant disease prediction. Annual Review of Phytopathology. 45(1): 203-220. Divon, H.H., and Fluhr, R. 2007. Nutrition acquisition strategies during fungal infection of plants. FEMS Microbiology Letters. 266(1): 65-74. Dobson, R.L., and Spotts, R.A. 1988. Distribution of sporangiospores of Mucor piriformis in pear orchard soils. Plant Disease. 72: 702-705. Dutot, M., Nelson, L., and Tyson, R.C. In progress. Predicting post-harvest disease of apples caused by major fungal pathogens. Endalew, A.M., Debaer, C., Rutten, N., Vercammen, J., Delele, M.A., Ramon, H., Nicolai, B.M., and Verboven, P. 2011. Modelling the effect of tree foliage on sprayer airflow in orchards. Boundary Layer Meteorology. 138(1): 139-162. Esplin, G.J. 1995. Approximate explicit solution to the general line source prob- lem. Atmospheric Environment. 29(12): 1459-1463. 155 Etebarian, H.R., Sholberg, P.L., Eastwell, K.C., and Sayler, R.J. 2005. Biological control of apple blue mold with Pseudomonas fluorescens. Canadian Journal of Microbiology. 51(7): 591-598. Faulkner, W.E., Powell, J.J., Lange, J.M., Shaw, B.W., Lacey, R.E., Parnell, C.B. 2007. Comparison of dispersion models for ammonia emissions from a ground-level area source. Transactions of the Asabe. 50(6): 2189-2197. Fitt, B.D.L., McCartney, H.A., and Walklate, P.J. 1989. The role of rain in dispersal of pathogen inoculum. Annual Review of Phytopathology. 27: 241-270. Guo, L.Y., Michailides, T.J., and Morgan, D.P. 1999. Survival ofMucor piriformis in soil of apple orchards in California. Plant Disease. 83(2): 189-193. Hirano, S.S., Baker, L.S., Upper, C.D. 1996. Raindrop momentum triggers growth of leaf-associated populations of Pseudomonas syringae on field-grown snap bean plants. Applied and Environmental Microbiology. 62(7): 2560-2566. Hirst, J.M., Stedman, O.J. 1962. The epidemiology of apple scab (Venturia in- aequalis) II. Observations on the liberation of ascospores. Annals of Applied Biology. 50(3): 525-550. Jeger, M.J., Jeffries, P., Elad, Y., and Xu, X.M. 2008. A generic theoretical model for biological control of foliar plant diseases. Journal of Theoretical Biology. 256(2): 201-214. Johnson, K.B. 2010. Pathogen refuge: A key to understanding biological control. Annual Review of Phytopathology. 48(1): 141-160. Kinkel, L.L., Newton, M.R., and Leonard, K.J. 2002. Resource aggregation in the phyllosphere: Implications for microbial dynamics across spatial scales. In Phyllosphere Microbiology, eds. Lindow, S.E, Hecht-Poinar, E.I., and Elliott, V.J.. American Phytopathological Society, St. Paul, M.N. 317-340. Kinkel, L.L. 1997. Microbial population dynamics on leaves. Annual Review of Phytopathology. 35: 327-347. Kinkel, L.L., Andrews, J.H., and Nordheim, E.V. 1989. Fungal immigration dy- namics and community development on apple leaves. Microbial Ecology. 18(1): 45-58. 156 Klein, E.K., Lavigne, C., Foueillassar, X., and Gouyon, P.H. 2003. Corn pollen dispersal: quasi-mechanistic models and field experiments. Ecological Mono- graphs. 73(1):131-150. Lahlali, R., Serrhini, M.N., and Jiiakli, M.H. 2005. Studying and modelling the combined effect of temperature and water activity on the growth rate of P. ex- pansum. International Journal of Food Microbiology. 103: 315-322. Legg, B.J. 1983. Movement of plant pathogens in the crop canopy. Philos. Trans. Roy. Soc. London. 302B: 559-574. Legg, B.J., and Powell, F.A. 1979. Spore dispersal in a barley crop: a mathemat- ical model. Agricultural Meteorology. 20: 47-67. Legg, B.J., Long, I.F., and Zemroch, P.J. 1981. Aerodynamic properties of field bean and potato crops. Agricultural Meteorology. 23(1): 21-43. Lennox, C.L., Spotts, R.A., and Cervantes, L.A. 2003. Populations of Botrytis cinerea and Penicillium spp. on pear fruit, and in orchards and packinghouses, and their relationship to post-harvest decay, Plant Disease. 87(6): 639-644. Lennox, C.L., and Spotts, R.A. 2004. Timing of preharvest infection of pear fruit by Botrytis cinerea and the relationship to post-harvest decay. Plant Disease. 88: 468-473. Levetin, E., and Dorsey, K. 2006. Contribution of leaf surface fungi to the air spora. Aerobiologia. 22(1): 3-12. Lindow, S.E., and Brandl, M.T. 2003. Microbiology of the phyllosphere. Applied and Environmental Microbiology. 69(4): 1875-1883. Madigan, M.T., Martinko, J.M., and Parker, J. 2002. Brock Biology of Microor- ganisms. Pearson Education, Inc., Upper Saddle River, N.J. Mantyka, D.L. 2010. Detection, quantification and biological control of post- harvest pathogens of pome fruit. Master of Science Thesis UBC Okanagan. May, K.R., Clifford, R. 1967. The impaction of aerosol particles on cylinders, spheres, ribbons, and discs. Ann. Occup. Hyg.10: 83-95. 157 McCartney, H.A. 1994. Dispersal of spores and pollen from crops. Grana. 33: 76-80. McCartney, H.A., and Aylor, D.E. 1987. Relative contributions of sedimentation and impaction to deposition of particles in a crop canopy. Agricultural Forest Meteorology. 40: 343-358. Mendenhall, W., Beaver, R.J., Beaver, B.M., and Ahmed, S.E. 2009. Introduction to Probability and Statistics: Preliminary Canadian Edition. Nelson Education Ltd., Toronto, Canada. Michailides, T.J., and Spotts, R.A. 1986. Factors affecting dispersal of Mucor piriformis in pear orchards and into the packinghouse. Plant Disease. 70(11): 1060-1063. Michailides, T.J., and Spotts, R.A. 1990. Postharvest diseases of pome and stone fruits caused by Mucor piriformis in the Pacific Northwest and California. Plant Disease. 74(8):537-543. Mikani, A., Etebarian, H.R., Sholberg, P.L., O’Gorman, D.T., Stokes, S., and Alizadeh, A. 2008. Biological control of apple gray mold caused by Botrytis mali with Pseudomonas fluorescens strains. Postharvest Biology and Technol- ogy. 48(1): 107-112. Moore, D.S. 2010. The Basic Practice of Statistics, Fifth Edition. W.H. Freeman and Company, New York, NY, USA. Nair, N.G., and Allen, R.N. 1992. Infection of grape flowers and berries by Botryris cinerea as a function of time and temperature. Mycological Research. 97(8): 1012-1014. Nita, M.E., Ellis, M.A., and Madden, L.V. 2008. Variation in disease incidence of Phomopsis cane and leaf spot of grape in commercial vineyards in Ohio. Plant Disease. 92: 1053-1061. ONeill, T.M., Shtienberg, D., and Elad, Y. 1997. Effect of some host and micro- climate factors on infection of tomato stems by Botrytis cinerea. Plant Disease. 81(1): 36-40. Pasquill, F. 1961. The estimation of the dispersion of windborne material. Mete- orology Magazine. 90(1063): 33-49. 158 Rastogi, G., Tech, J.J., Coaker, G.L., and Leveau, J.H.J. 2010. A PCR-based toolbox for the culture-independent quantification of total bacterial abundances in plant environments. Journal of Microbiological Methods. 83(2): 127-132. Redford, A.J., Bowers, R.M., Knight, R., Linhart, Y., and Fierer, N. 2010. The ecology of the phyllosphere: geographic and phylogenetic variability in the distri- bution of bacteria on tree leaves. Environmental Microbiology. 12(11): 2885-2893. Rodriguez-Rajo, F.J., Jato, V., Fernandez-Gonzalez, M., and Aira, M.J. 2010. The use of aerobiological methods for forcasting Botrytis spore concentrations in a vineyard. Grana. 49:56-65. Ross, L.J., Johnson, B., Kim, K.D., Hsu, J. Prediction of methyl bromide flux from area sources using the ISCST. Journal of Environmental Quality. 25(4): 885-891. Saito, A., Ikeda, S., Ezura, H., and Minamisawa, K. 2007. Microbial community analysis of the phytosphere using culture-independent methodologies. Microbes and Environments. 22(2): 93-105. Sanderson, P.G., and Spotts, R.A. 1995. Postharvest decay of winter pear and apple fruit caused by species of Penicillium. Phytopathology. 85: 103-110. Sawford, B.L., Guest, F.M. 1991. Lagrangian statistical simulation of the turbu- lent motion of heavy particles. Boundary Layer Meteorology. 54: 147-166. Schoeny, A., Menat, J., Darsonval, A., Rouault, F., Jumel, S., and Tivoli, B. 2008. Effect of pea canopy architecture on splash dispersal of Mycosphaerella pinodes conidia. Plant Pathology. 57: 1073-1085. Sholberg, P.L., and Haag, P.D. 1996. Incidence of post-harvest pathogens of stored apples in British Columbia, Canadian Journal of Plant Pathology. 18: 81-85. Skelsey, P., Holtslag, A.A.M., van der Werf, W. 2008. Development and vali- dation of a quasi-Gaussian plume model for the transport of botanical spores. Agricultural and Forest Meteorology. 148: 1383-1394. Spijkerboer, H.P., Beniers, J.E., Jaspers, D., Schouten, H.J., Goudriaan, J., Rab- binge, R., van der Werf, W. 2002. Ability of the Gaussian plume model to predict and describe spore dispersal over a potato crop. Ecological Modelling. 155: 1-18. 159 Spotts, R.A. 1985a. Effect of preharvest pear fruit maturity on decay resistance, Plant Disease. 69(5): 388-390. Spotts, R.A. 1985b. Environmental factors affecting conidial survival of five pear decay fungi, Plant Disease. 69(5): 391-392. Spotts, R.A., and Cervantes, L.A. 1993. Effects of soil moisture, temperature and nutrient availability on the population dynamics ofMucor piriformis. Mycological Research. 98(3): 342-346. Spotts, R.A., Sanderson, P.G., Lennox, C.L., Sugar, D., and Cervantes, L.A. 1998. Wounding, wound healing and staining of mature pear fruit. Postharvest Biology and Technology. 13:27-36. Spotts, R.A., Cervantes, L.A., Mielke, E.A., 1999. Variability in post harvest decay among apple cultivars, Plant Disease. 83(11): 1051–1054. Spotts, R.A., and Cervantes, L.A. 2001. Disease incidence-inoculum dose rela- tionships for Botrytis cinerea and Penicillium expansum and decay of pear fruit using dry, airborne conidia, Plant Disease. 85(7): 755-759. Spotts, R.A., Serdani, M., Wallis, K.M., Walter, M., Harris-Virgin, T., Spotts, K., Sugar, D., Xiao, C.L., Qu, A., 2009. At-harvest prediction of grey mould risk in pear fruit in long-term cold storage. Crop Protection. 28: 414–420. Stockie, J.M. 2010. The mathematics of atmospheric dispersion modelling. At- mospheric Environment. 44(8): 1097-1107. Stockwell, V.O., Johnson, K.B., and Loper, J.E. 2002. Biological control of fire blight: Understanding interactions among introduced and indigenous microbial communities. In Phyllosphere Microbiology, eds. Lindow, S.E, Hecht-Poinar, E.I., and Elliott, V.J. American Phytopathological Society, St. Paul, M.N. 225-236. Teixido, N., Usall, J., Magan, N., and Vinas, I. 1999. Microbial population dy- namics on Golden Delicious apples from bud to harvest and effect of fungicide applications. Annals Applied Biology. 134: 109-116. The Canadian Oxford Dictionary. 1998. ed. Katherine Barber. Oxford University Press. 160 Thomson, D.J. 1987. Criteria for the selection of stochastic models of particle trajectories in turbulent flows. Journal of Fluid Mechanics. 180: 529-556. Thurston, M. P.Ag. Field Service. BC Tree Fruits. Okanagan Tree Fruit Coop- erative. Upper, C.D., and Hirano, S.S. 2002. Revisiting the roles of immigration and growth in the development of populations of Pseudomonas syringae in the phyllo- sphere. In Phyllosphere Microbiology, eds. Lindow, S.E, Hecht-Poinar, E.I., and Elliott, V.J. American Phytopathological Society, St. Paul, M.N. 69-79. Venkatram, A., and Horst, T.W. 2006. Approximating dispersion from a finite line source. Atmospheric Environment. 40: 2401-2408. Vinas, I., Usall, J., Teixido, N., Sanchis, V., 1998. Biological control of major post-harvest pathogens on apple with Candida sake, International Journal of Food Microbiology. 40: 9–16. Whipps, J.M., Hand, P., Pink, D., and Bending, G.D. 2008. Phyllosphere micro- biology with special reference to diversity and plant genotype. Journal of Applied Microbiology. 105(6): 1744-1755. Wilson, J.D. 2000. Trajectory models for heavy particles in atmospheric turbu- lence: comparison with observation. Journal of Applied Meteorology. 39: 1894- 1912. Wieringa, J., 1980. Representativeness of wind observations at airports. Bulletin American Meteorological Society. 61: 962-971. Xu, X.M. 1999. Modelling and forecasting epidemics of apple powdery mildew (Podosphaera leucotricha). Plant Pathology. 48: 462-471. Xu, X.M., Harris, D.C., and Berrie, A.M. 2000. Modeling infection of strawberry flowers by Botrytis cinerea using field data. Phytopathology. 90: 1367-1374. Xu, X.M., Robinson, J.D., and Berrie, A.M. 2008. Infection of blackcurrant flow- ers and fruits by Botrytis cinerea in relation to weather conditions and fruit age. Crop protection. 28(5): 408-413. Xu, X.M., Salama, N., Jeffries, P., and Jeger, M.J. 2010a. Numerical studies of biocontrol efficacies of foliar plant pathogens in relation to the characteristics of a biocontrol agent. Phytopathology. 100(8): 814-821. 161 Xu, X.M., Robinson, J., Jeger, M., and Jeffries, P. 2010b. Using combinations of biocontrol agents to control Botrytis cinerea on strawberry leaves under fluctuat- ing temperatures. Biocontrol Science and Technology. 20(4): 359-373. Yang, C.H., Crowley, D.E., Borneman, J., and Keen, N.T. 2001. Microbial phyl- losphere populations are more complex than previously realized. Proceedings of the National Academy of Sciences of the United States. 98(7): 3889-3894. Zak, J.C. 2002. Implications of a leaf surface habitat for fungal community struc- ture and function. In Phyllosphere Microbiology, eds. Lindow, S.E, Hecht-Poinar, E.I., and Elliott, V.J. American Phytopathological Society, St. Paul, M.N. 299- 315. 162 Appendix Appendix A Glossary Biological Terms Calyx: The sepals collectively, forming the protective layer of a flower in bud (Oxford Canadian Dictionary, 1998). On an apple, the calyx end, also referred to as the blossom end, is the end opposite the stem where the flower had been before the apple developed. Leads to an opening referred to as the sinus. Conidium (plural Conidia): A spore produced asexually by various fungi (Oxford Canadian Dictionary, 1988). Cultivar: A plant variety produced by cultivation. (Oxford Canadian Dic- tionary, 1998) Inoculum: Any substance used for inoculation, that is, to introduce an in- fective agent into an organism. (Oxford Canadian Dictionary, 1998) Lenticels (of an Apple): Raised pores in plant tissue that allow gas ex- change between the atmosphere and the internal tissues. (Oxford Canadian Dic- tionary, 1998) Mycotoxin: Any toxic substance produced by a fungus. (Oxford Canadian Dictionary, 1998) Spp: The standard short form for species in reference to microorganisms. For example, Penicillium spp. means the group of Penicillium species generally. 163 Statistical Terms Coefficient of Determination (R2, R2 value): The coefficient of determi- nation describes the proportion of the total variation in the relationship between variables that is explained by the linear regression of the response variables on the predictor variable (Mendenhall et al., 2009). It is calculated as the square of the Pearson product moment sample coefficient of correlation, as given below. It can take values anywhere between 0 and 1, where a value of 0 signifies that the percent reduction in the total variation obtained by using the regression instead of the sample mean to predict the response variable is 0%, and 1 signifies that by using the regression 100% of the variation is explained. Linear Regression: A method of modeling which generally uses the least squares approach to minimize error over a line fitted to the data which describes a linear relationship between two or more variables. For a linear regression between two variables, the slope of the best fit line can be found as b = R sy sx where b is the slope, R is the Pearson Correlation, and sy and sx are the variation in the dependent and independent variables respectively. The intercept is found by a = ȳ − bx̄ where a is the intercept, ȳ is the mean of the data for the dependent variable, and x̄ is the mean of the data for the independent variable. 164 Pearson Product Moment Sample Coefficient of Correlation (Pear- son Correlation, R): The Pearson product moment sample coefficient of corre- lation (often shortened to the correlation coefficient) is a measure of the strength of the linear relationship between two variables. It may take a value ranging from +1 to -1, where +1 describes a perfect positive linear relationship, and -1 describes a perfect negative linear relationship. 0 describes no relationship between the two variables. The correlation coefficient is calculated as R = sxy sxsy = Sxy SxxSyy the variances and covariance are calculated as sxy = Sxy n− 1 s2x = Sxx n− 1 s2y = Syy n− 1 the correlation coefficient is widely used as a measure of the strength of the linear dependence between two variables (Mendenhall et al., 2009) Significance Value (Sig, P-value): Often referred to as P, the significance value gives the probability of the sample or data being observed under some null hypothesis. The calculation of the P-value depends on the type of statistical test being performed. The null hypothesis is set to be something which the researcher is trying to disprove, for example, that there is no relationship between variables. Thus, a low P-value gives strong evidence against this null hypothesis, saying 165 there is a very low probability that this data would be observed if there was no relationship present. In general, P-values of less than 0.05 are considered to give reasonably strong evidence of a relationship, and these results can be said to be statistically significant at the 0.05 level. Spearman Correlation Coefficient: Statistical correlation measures the di- rection and strength of the linear relationship between two quantitative variables. (Moore, 2010) The Spearman rank correlation coefficient is a non-parametric mea- sure of correlation, where 1 or -1 would indicate perfect negative or positive cor- relation respectively and 0 indicates no correlation. It is calculated as rs = Sxy SxxSyy where xi and yi represent the ranks of the ith pair of observations and Sxy =  (xi − x̄)(yi − ȳ) =  xiyi − (  xi)(  yi) n Sxx =  (xi − x̄)2 =  x2i − (  xi)2 n Syy =  (yi − ȳ)2 =  y2i − (  yi)2 n . Equations as given in Mendenhall et al. (2009). Standard Deviation (SD, s): The standard deviation (s) is the square root of the variance of a set of observations, and is a measure of spread from the centre of a data set (Moore, 2010). The variance is given by s2 = 1 n− 1  (xi − x̄)2. 166 Step-wise Regression: A method of determining the best model where there are multiple predictive variables. It is an iterative process, where at each step, variables with a p-value smaller than some selected significance level are added to a model, and those which have too low a p-value are removed from the model. Modeling Terms Aerobiology: The study of airborne microorganisms, pollen, spores, etc., especially as agents of infection. (Oxford Canadian Dictionary, 1998) Brownian Motion: The erratic random movement of microscopic particles in a liquid, gas, etc., as a result of continuous bombardment from molecules of the surrounding medium (Oxford Canadian Dictionary, 1998). Brownian motion is often modeled as a random walk in continuous time and space. When applied to the dispersal of biological particles, these particles are considered to undergo a random walk with drift, or overall directionality, due to both wind and gravity. Gaussian: AGaussian distribution is also referred to as a normal distribution, where the shape of the distribution is symmetric, and governed by the mean and the standard deviation of the variable. Thus, a Gaussian Plume is a plume shape which is governed by the Gaussian distribution and described using this symmetric shape. K-type Model: A model which describes diffusion using diffusion coefficients governed by turbulent eddy diffusivities which are generally functions of position. These turbulent eddy diffusivities have been empirically determined for a number of situations, and are fairly commonly used in diffusion modeling. 167 Lagrangian Stochastic Simulation Model: Lagrangian is in opposition to Eulerian modeling. In fluid mechanics, a Eulerian model considers a location and describes the flow of fluid passing this location. It averages particle distributions to provide a description of the flow. On the other hand, a Lagrangian model fol- lows an individual particle or fluid parcel as it moves through both space and time to provide a description of the flow. Stochastic is defined as something determined by a random distribution or pattern of probabilities, so that its behaviour may be analyzed statistically but not predicted precisely; a process which is characterized by a sequence of random variables (Oxford Canadian Dictionary, 1998). A La- grangian stochastic simulation model used in spore dispersal models this dispersal by tracing the trajectory of individual spores through the environment, where they undergo random (stochastic) processes. For example, deposition on plant surfaces would be determined by a random variable providing some probability that the spore is deposited when it encounters the plant surface. Monin-Obukhov Length: The height at which the turbulence experienced is generated more by buoyancy than by the wind shear. It measures the relative importances of mechanical and thermal forces based on environmental conditions and is used to determine the wind speed profiles in the surface layer of the atmo- sphere. Quasi-Gaussian Model: A Gaussian model where some of the constraints have been relaxed or altered in order to modify the results. 168
Thesis/Dissertation
eng
Interdisciplinary Studies
Vancouver : University of British Columbia Library
Attribution-NonCommercial-NoDerivs 3.0 Unported
Graduate
Modeling post-harvest pathogens in apple fruit.
Text