Description

Abstract: 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 objectives 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.