Survival potential of Phytophthora infestans sporangia in relation to environmental factors and late blight occurrence

• Published in: Journal of plant protection research 2016-01, Vol.56 (1), p.73-81
• Main Authors: Modesto, Olanya Ocen, Anwar, Mohd, He, Zhongqi, Larkin, Robert Philip, Honeycutt, Charles Wayne
• Format: Article
• Language: English
• Subjects: climatic variables; Cox hazard model; Phytophthora infestans; potato; sporangia; survival; tomato
• ISSN: 1899-007X; 1427-4345; 1899-007X
• DOI: 10.1515/jppr-2016-0011

Potato is an important crop globally and late blight (Phytophthora infestans) often results in severe crop loss. The cost for late blight control can be in excess of $210 million in the United States. We utilised a non-parametric density distribution analysis of local temperature (T) and relative humidity (RH), from 2005 to 2009, to assess and validate sporangia survival potential using survival model and late blight risks during the potato cropping season at Presque Isle, in the northern part of the state of Maine, USA. Modelbased analyses showed that ambient temperatures of 3−30°C and RH values of 45−100% were conducive for sporangia survival. Disease outbreaks and risk periods coincided with a high sporangia survival probability (15−35%). Due to the omission of solar radiation (SR) in the computation of survival potential in previous research, we applied a Cox proportional model to estimate the probability of sporangia survival [i.e. hazard at a specific time H(t)] as a function of baseline hazard (H ) and the influencing parameters. The model is: H(t) = H (t) × exp(0.067ET + 0.138T + 0.083RH + 0.001SR) where ET is exposure time. The survival model indicated that RH (β = 0.083) and T (β = 0.138) were significant (p < 0.05) factors in sporangia survival in comparison to SR (β = 0.001). The hazard ratio, indicative of sporangia survival risk, varied with the predictors. For the unit increase of T, sporangia survival hazard increased by 1.148 times. The Cox model and sporangia hazard probabilities can be used for short-term disease forecasts based on the risk period most conducive for pathogen survival and targeted fungicide applications for optimum late blight management.