A Forecast Model for the First Occurrence of Phytophthora Blight on Chili Pepper after Overwintering

Abstract

An infection risk model for Phytophthora blight on chili pepper was developed to estimate the first date of disease occurrence in the field. The model consisted of three parts including estimation of zoosporangium formation, soil water content, and amount of active inoculum in soil. Daily weather data on air temperature, relative humidity and rainfall, and the soil texture data of local areas were used to estimate infection risk level that was quantified as the accumulated amount of active inoculum during the prior three days. Based on the analysis on 190 sets of weather and disease data, it was found that the threshold infection risk of 224 could be an appropriate criterion for determining the primary infection date. The 95% confidence interval for the difference between the estimated date of primary infection and the observed date of first disease occurrence was 8 ± 3 days. In the model validation tests, the observed dates of first disease occurrence were within the 95% confidence intervals of the estimated dates in the five out of six cases. The sensitivity analyses suggested that the model was more responsive to temperature and soil texture than relative humidity, rainfall, and transplanting date. The infection risk model could be implemented in practice to control Phytophthora blight in chili pepper fields.

Antagonistic interaction between Trichoderma asperellum and Phytophthora capsici in vitro

“…The pathogen causes not only root and crown rot of pepper, but also aerial blight of the leaves, fruit, and stems. The survivals of sporangia, zoospores, and hyphae of P. capsici are short lived in fields, but its thick-walled oospores are durable and play a key role in the infectious life cycle, surviving under unfavorable conditions for long periods of time and serving as a dispersal propagule when established in the soil (McDonald and Linde, 2002;Do et al, 2012). Removing viable oospores from the field is therefore of great importance in the control of Phytophthora blight.…”

Application of Numerical Weather Prediction Data to Estimate Infection Risk of Bacterial Grain Rot of Rice in Korea

“…weather data to identify conditions for infection Magarey et al, 2005) or to simulate one or more processes in disease cycle (De Wolf and Isard, 2007;Do et al, 2012;González-Domínguez et al, 2014;Olatinwo and Hoogenboom, 2014;Park et al, 1997). By using observed weather data from automated weather stations (AWS) at near real-time basis, the models are able to determine if weather conditions favorable for pathogen infection have occurred in the immediate past period.…”

Field Validation of PBcast in Timing Fungicide Sprays to Control Phytophthora Blight of Chili Pepper

“…Daily precipitation and soil water content of the experimental field which were estimated by sub-models of PBcast(Do et al, 2012) based on observed ambient temperature, precipitation and soil texture data in 2012 and 2013. Soil texture data were obtained from the National Institute of Agricultural Sciences, Rural Development Administration of Korea.…”

“…Daily mean ambient temperature and soil temperature monitored by an automated weather station installed at the chili pepper field of Chungcheongbuk-do Agricultural Research and Extension Services during the growing seasons of 2012 and 2013. Daily mean soil temperature were estimated were estimated by a sub-model of PBcast(Do et al, 2012) based on observed ambient temperature.…”