תפריט נגישות
ניגודיות עדינהניגודיות גבוהה
מונוכרום
הדגשת קישורים
חסימת אנימציה
פונט קריא
סגור
איפוס הגדרות נגישות
להורדת מודול נגישות חינם תפריט נגישותניגודיות עדינהניגודיות גבוההמונוכרוםהדגשת קישוריםחסימת אנימציהפונט קריאסגוראיפוס הגדרות נגישותלהורדת מודול נגישות חינםB. Firester
D. Shtienberg
L. Blank
Potato late blight caused the Irish Potato Famine and still causes billions of dollars of annual crop damage. Tracking and predicting its spread remains problematic. Growers overspray potato fields with fungicide as a precaution, irrespective of the prevalence or actual risk of the disease. This study created a new weather‐based mathematical model for the spread of late blight at a regional scale using empirical data, and validated it using a novel approach for presence‐only data. The model was tested using a contrapositive ‘proof’ by comparing predicted to actual weather patterns to examine its accuracy. The model was then used to create risk maps showing the likelihood of future outbreaks in the region. Such risk maps can help growers optimize late blight suppression and fungicide use by alerting them to the most probable day at which they are at the most risk, using real‐time weather data from the previous few days. These risk maps would be updated daily to account for conditions needed for sporulation. Overall, this work offers a methodology to understand and model a disease's spread in time and space.
B. Firester
D. Shtienberg
L. Blank
Potato late blight caused the Irish Potato Famine and still causes billions of dollars of annual crop damage. Tracking and predicting its spread remains problematic. Growers overspray potato fields with fungicide as a precaution, irrespective of the prevalence or actual risk of the disease. This study created a new weather‐based mathematical model for the spread of late blight at a regional scale using empirical data, and validated it using a novel approach for presence‐only data. The model was tested using a contrapositive ‘proof’ by comparing predicted to actual weather patterns to examine its accuracy. The model was then used to create risk maps showing the likelihood of future outbreaks in the region. Such risk maps can help growers optimize late blight suppression and fungicide use by alerting them to the most probable day at which they are at the most risk, using real‐time weather data from the previous few days. These risk maps would be updated daily to account for conditions needed for sporulation. Overall, this work offers a methodology to understand and model a disease's spread in time and space.
