Epsilon Archive for Student Projects

Abstract

Decision support systems can be used to monitor disease development of apple scab. Decision support systems require weather data to function, which in Sweden is currently provided by conventional weather stations. Conventional weather stations supply reliable weather data if correctly installed and maintained but are costly and require continuous error-checking. Virtual weather data is becoming an increasingly popular option, where the data is calculated based on a combination of observations from local weather stations and weather radar and satellites. In this study, virtual weather data was compared with physical weather stations for apple scab monitoring, to evaluate the suitability of virtual data as a replacement for conventional weather stations. This was done by evaluating differences in predicted apple scab infections using the apple scab model in the decision support system RIMpro for the 2019 and 2020 seasons. Virtual weather data lacks the leaf wetness parameter, which had to be calculated based on other weather parameters. Thus, the use of leaf wetness calculations as an alternative to leaf wetness sensors was investigated.
The study showed that virtual weather data correctly predicted the number and severity of infections, similar to conventional weather stations, with some margin of error especially for low category infections. This indicates that virtual weather data could be a suitable replacement for physical weather stations. With respect to the discrepancies seen in infection severity, few were due to a difference in leaf wetness, demonstrating that leaf wetness calculations may indeed be a suitable option for replacing leaf wetness sensors. This study was carried out after the 2020 apple scab season ended; thus, some crucial aspects were not accounted for, such as checking for errors from the weather stations during the seasons studied. Before implementation of virtual data can take place in Sweden, the virtual data should be evaluated during the growing season.