Impact of climate change in relation to ascochyta blight on field pea in Western Australia

摘要

Using a weather-based model, the G2 Blackspot Manager, the impact of climate change was studied in relation to a major disease of field pea, ascochyta blight, in three different field pea growing locations of Western Australia: Esperance, Lake Grace and Merredin representing high, medium and low rainfall zones. The model was run with weather data for two 30-year periods: the period centering on 1990 (termed as "current climate") and another centering on 2050 (termed as "future climate"). The model outputs were summarised as the epidemic-initiating ascospore-load that crops would be exposed to, disease severity, and yield loss in relation to nine times of sowing within the current sowing-window of field pea crops in Western Australia. Results show a decreased pressure of ascospore-load across the sowing-window in all three locations because of changed summer conditions (more rainy days in conjunction with higher temperatures) under future climate, which could be translated as lower disease severity compared to the current climate. The relationship between disease severity and time of sowing showed a significant decrease (P a parts per thousand currency signaEuro parts per thousand 0.05) in the intercept of the regression lines for future climate compared to current climate in all three locations, but there was no significant difference between the slopes of the regression lines. This indicated a decreased initial disease pressure for future climate compared to the current. When the impact was assessed in terms of yield loss, results in Lake Grace, in contrast to Esperance and Merredin, showed insignificant difference between current and future climates. This was a consequence of the projected dry-finishing conditions of cropping seasons, the number of which could increase two-fold in the projected future climate. It is concluded that any decrease in ascochyta blight severity as a result of climate change would most likely be location-specific.