Browsing by Author "Sparks, Adam H."

Now showing 1 - 16 of 16
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    Adapting disease forecasting models to coarser scales: Global potato late blight prediction
    Sparks, Adam H.; Forbes, Gregory Allan; Garrett, Karen A. (2009)
    Many predictive models of plant disease rely upon fine-scale weather data collected in hourly increments, or finer. This data requirement is a major constraint when applying disease prediction models in areas of the world where hourly weather data are unreliable or unavailable. In response to the need to apply predictive models where only coarse resolution weather data are available, we developed a framework to adapt an existing potato late blight forecast model, SimCast. We envision this type of coarser resolution model being useful in long term decision making rather than for within growing season. For long term modeling we may be satisfied with being able to estimate the magnitude of upward and downward trends.
  • Anticipating and responding to biological complexity in the effects of climate change on agriculture
    Garrett, Karen A.; Forbes, Gregory Allan; Pande, S.; Savary, S.; Sparks, Adam H.; Valdivia, Corinne; Vera Cruz, C.; Willocquet, L. (IOP Publishing, 2009)
    The effects of climate change on biological systems are complex. This is particularly apparent for multispecies systems such as plant diseases and plant-herbivore interactions where climate can affect each species individually as well as influencing the interactions between species. This article was presented in Copenhagen as part of the conference on Climate Change: Global Risks, Challenges and Decisions, held 10-12 March 2009.
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    Beyond Yield: Plant disease in the context of ecosystem services
    Cheatham, M. R.; Rouse, M. N.; Esker, Paul D.; Ignacio, S.; Pradel, W.; Raymundo, R.; Sparks, Adam H.; Forbes, Gregory Allan; Gordon, T. R.; Garrett, Karen A. (2009)
    The ecosystem services concept provides a means to define successful disease management more broadly, beyond short-term crop yield evaluations. Plant disease can affect ecosystem services directly, such as through removal of plants providing services, or indirectly through the effects of disease management activities, including pesticide applications, tillage, and other methods of plant removal. Increased plant biodiversity may reduce disease risk if susceptible host tissue becomes less common, or may increase risk if additional plant species are important in completing
  • Complexity in climate-change impacts: An analytical framework for effects mediated by plant disease
    Garrett, Karen A.; Forbes, Gregory Allan; Savary, S.; Skelsey, P.; Sparks, Adam H. (Plant Pathology, 2011)
    This article describes a framework of analysis that was developed to gauge the complexity of climate change effects on ecosystem services. Specifically, these researchers examine how climate change affects plant diseases because of the important implications plant disease can have for food production. For example, drought stress, sometimes caused by an increase in global temperature and extreme weather events, can cause either an increase in susceptibility to diseases or induce resistant reactions. These reactions to climate change are very complex and important for analyzing the risk of disease for plants in all geographic areas. This research framework is intended to be used as a tool for other researchers to examine specific components in an ecosystem in order to further understand the effects of climate change.
  • Disease risk mapping with metamodels for coarse resolution predictors: Global potato late blight risk now and under future climate conditions
    Sparks, Adam H. (Manhattan, KS: Kansas State University, 2009)
    Late blight of potato, caused by Phytophthora infestans, is a pernicious disease of potatoes worldwide. This disease causes yield losses as a result of foliar and tuber damage. Many models exist to predict late blight risk for control purposes with-in season but rely upon fine-scale weather data collected in hourly, or finer, increments. This is a major constraint when working with disease prediction models for areas of the world where hourly weather data is not available or is unreliable. Weather or climate summary datasets are often available as monthly summaries. These provide a partial solution to this problem with global data at large time-steps (e.g., monthly). Difficulties arise when attempting to use these forms of data in small temporal scale models. My first objective was to develop new approaches for application of disease forecast models to coarser resolution weather data sets. I created metamodels based on daily and monthly weather values which adapt an existing potato late blight model for use with these coarser forms of data using generalized additive models. The daily and monthly weather metamodels have R-squared values of 0.62 and 0.78 respectively. These new models were used to map global late blight risk under current and climate change scenarios, and resistant and susceptible varieties. Changes in global disease risk for locations where wild potato species are indigenous, and disease risk for countries where chronic malnutrition is a problem were evaluated. Under the climate change scenario selected for use, A1B, future global late blight severity decreases. The risk patterns do not show major changes; areas of high risk remain high relative to areas of low risk with rather slight increases or decreases relative to previous years. Areas of higher wild potato species richness experience slightly increased blight risk, while areas of lower species richness experience a slight decline in risk.
  • Ecology and epidemiology in R: Disease forecasting and validation
    Esker, Paul D.; Sparks, Adam H.; Campbell, L.; Guo, Z.; Rouse, M. N.; Silwal, S. D.; Tolos, S.; Van Allen, B.; Garrett, Karen A. (St. Paul, MN: The American Phytopathological Society, 2008)
    This online training module shows how to approach plant disease forecasting and validation using the free R programming environment. It gives an overview of the background of plant disease forecasting, the current ways plant disease forecasting is currently being implemented, and teaches students of the limitations of this method. This module also teaches students techniques to apply, modify and interpret the output from disease forecasting.
  • Ecology and epidemiology in R: Disease progress over time
    Sparks, Adam H.; Esker, Paul D.; Bates, M.; Dall'Acqua, W.; Guo, Z.; Segovia, V.; Silwal, S. D.; Tolos, S.; Garrett, Karen A. (St. Paul, MN: The American Phytopathological Society, 2008)
    This online training module shows how to analyze disease progress over time using the free R programming environment. Students reading this module will be able to assess and interpret disease progress, and apply modeling methods to analyze the data.
  • Ecology and epidemiology in R: Modeling dispersal gradients
    Esker, Paul D.; Sparks, Adam H.; Bates, M.; Dall'Acqua, W.; Frank, E. E.; Huebel, L.; Segovia, V.; Garrett, Karen A. (The American Phytopathological Society, 2007)
    This training module shows how to model and analyze pathogen dispersal using the free R programming environment.
  • Ecology and epidemiology in R: Spatial pattern analysis
    Sparks, Adam H.; Esker, Paul D.; Antony, G.; Campbell, L.; Frank, E. E.; Huebel, L.; Rouse, M. N.; Van Allen, B.; Garrett, Karen A. (St. Paul, MN: The American Phytopathological Society, 2008)
    This online training module shows how to analyze disease spatial patterns using the free R programming environment. Students reading this module will learn the types of spatial patterns, and be able to differentiate between them so they can apply their knowledge to different spatial analysis methods.
  • An introduction to the R programming environment
    Garrett, Karen A.; Esker, Paul D.; Sparks, Adam H. (The American Phytopathological Society, 2007)
    This training module provides an introduction to how to use the free R programming environment.
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    A metamodeling framework for extending the application domain of process‐based ecological models
    Sparks, Adam H.; Forbes, Gregory Allan; Hijmans, R. J.; Garrett, Karen A. (2011-08)
    Process‐based ecological models used to assess organisms' responses to environmental conditions often need input data at a high temporal resolution, e.g., hourly or daily weather data. Such input data may not be available at a high spatial resolution for large areas, limiting opportunities to use such models. Here we present a metamodeling framework to develop reduced form ecological models that use lower resolution input data than the original process models. We used generalized additive models to create metamodels for an existing model that uses hourly data to predict risk of potato late blight, caused by the plant pathogen Phytophthora infestans. The metamodels used daily or monthly weather data, and their predictions maintained the key features of the original model. This approach can be applied to other complex models, allowing them to be used more widely.
  • Plant pathogens as indicators of climate change
    Garrett, Karen A.; Nita, Mizuho; De Wolf, E. D.; Gomez, L.; Sparks, Adam H. (New York: Elsevier Press, 2009)
    We have reviewed how changes in the pattern of plant disease can be analyzed for evaluation of evidence for climate change impacts.
  • Plant pathology in the context of ecosystem services
    Rouse, M. N.; Cheatham, M. R.; Esker, Paul D.; Cardenas, S. I.; Pradel, W.; Raymundo, R.; Sparks, Adam H.; Forbes, Gregory Allan; Gordon, T. R.; Garrett, Karen A. (St. Paul, MN: The American Phytopathological Society, 2007)
    Ecosystem services are processes by which the environment supplies resources that benefit humans. Evaluations of interactions between humans and the environment, such as the Millennium Ecosystem Assessment, are increasingly using the ecosystem services framework. We develop a conceptual model for plant disease within the context of ecosystem services. For example, greater plant biodiversity may provide the service of reducing disease risk in agricultural and natural systems; rice variety mixtures have been successful for managing rice blast. When tillage or the removal of other plant species is motivated by plant disease management, plant disease indirectly results in the reduction of the ecosystem services provided by plants and plant debris, such as regulating soil erosion and provisioning wildlife habitat. Disease may extirpate plant species provisioning food; the loss of chestnut trees due to chestnut blight has reduced the mass produced in Eastern North American forests. Plant pathologists can contribute to evaluation of ecosystem services by clarifying the role of plant disease and to maintenance of ecosystem services by developing disease management methods that optimize for multiple services.
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    Regional predictions of potato late blight risk in a GIS incorporating disease resistance profiles, climate change, and risk neighborhoods
    Sparks, Adam H.; Raymundo, R.; Simon, R.; Forbes, Gregory Allan; Garrett, Karen A. (2008)
    This poster prioritizes efforts to manage late blight and measuring their impact now and under future climate scenarios demands a national and global perspective. We used a model in Geographic Information Systems (GIS) to create late blight severity predictions under current and future climate conditions.
  • Regional predictions of potato late blight risk in a GIS incorporating disease resistance profiles, climate change, and risk neighborhoods
    Sparks, Adam H.; Raymundo, R.; Simon, R.; Forbes, Gregory Allan; Garrett, Karen A. (2008)
    This abstract proposes a study to investigate models of forecasting plant pest and disease risk in potatoes due to climate change. The objective is to predict how climate change will affect production across different regions of the world, including Peru and Uganda.
  • Teaching modules for epidemiological concepts in the R programming environment
    Sparks, Adam H.; Ester, P.; Garrett, Karen A. (St. Paul, MN: The American Phytopathological Society, 2007)
    This abstract regarding the development of new teaching modules for techniques to evaluate plant disease pathology was prepared for presentation at the APS - SON Joint Meeting, San Diego, CA, 28 July - 1 August 2007.