Winter Survival of the Perennial Ryegrass Pathogen Magnaporthe oryzae in North Central Indiana

Philip F Harmon1, Richard Latin1

  • 1Department of Botany and Plant Pathology, Purdue University, West Lafayette, IN 47907.

Plant Disease
|February 24, 2019
PubMed

Insights

Winter survival of Magnaporthe oryzae in perennial ryegrass residue is poor in Indiana. Ambient conditions significantly reduce conidia production, limiting disease development and informing risk assessments.

Area of Science:

  • Plant Pathology
  • Mycology
  • Agricultural Science

Background:

  • Sporadic incidence of Magnaporthe oryzae (M. oryzae) disease in north central Indiana prompted investigation.
  • Understanding the overwintering survival of M. oryzae is crucial for disease management.

Purpose of the Study:

  • To investigate the winter survival of M. oryzae in perennial ryegrass residue.
  • To assess the conidia production potential of M. oryzae from infested residue under various conditions.

Main Methods:

  • Perennial ryegrass residue infested with M. oryzae was exposed to ambient winter conditions and controlled treatments.
  • Conidia production was quantified over time.
  • Airborne M. oryzae conidia were monitored using volumetric air samplers.

Main Results:

  • Initial conidia production was high (approx. 50,000 conidia/g dry weight), but ambient winter conditions reduced this to <60 conidia/g by spring.
  • Drying residue before storage reduced conidia production potential.
  • Airborne conidia peaked in September, coinciding with typical outbreak periods, but disease was minimal in later years.

Conclusions:

  • Poor overwintering survival of M. oryzae and low viable inoculum populations limit disease development in north central Indiana.
  • Disease risk assessments should incorporate estimates of viable M. oryzae inoculum.

Related Concept Videos

The Central Dogma01:25

The Central Dogma

Overview
139.5K
Survival Curves01:18

Survival Curves

Survival curves are graphical representations that depict the survival experience of a population over time, offering an intuitive way to track the proportion of individuals who remain event-free at each time point. These curves are widely used in fields such as medicine, public health, and reliability engineering to visualize and compare survival probabilities across different groups or conditions.
The Kaplan-Meier estimator is the most common method for constructing survival curves. This...
698
Survival Tree01:19

Survival Tree

Survival trees are a non-parametric method used in survival analysis to model the relationship between a set of covariates and the time until an event of interest occurs, often referred to as the "time-to-event" or "survival time." This method is particularly useful when dealing with censored data, where the event has not occurred for some individuals by the end of the study period, or when the exact time of the event is unknown.
 Building a Survival Tree
Constructing a...
418
Measures of Central Tendency02:16

Measures of Central Tendency

The "center" of a data set is also a way of describing location. The two most widely used measures of the "center" of the data are the mean (average) and the median. The words "mean" and "average" are often used interchangeably. The substitution of one word for the other is common practice. The technical term is "arithmetic mean" and "average" is technically a center location. However, in practice among non-statisticians,...
21.1K
Introduction To Survival Analysis01:18

Introduction To Survival Analysis

Survival analysis is a statistical method used to study time-to-event data, where the "event" might represent outcomes like death, disease relapse, system failure, or recovery. A unique feature of survival data is censoring, which occurs when the event of interest has not been observed for some individuals during the study period. This requires specialized techniques to handle incomplete data effectively.
The primary goal of survival analysis is to estimate survival time—the time...
771
Comparing the Survival Analysis of Two or More Groups01:20

Comparing the Survival Analysis of Two or More Groups

Survival analysis is a cornerstone of medical research, used to evaluate the time until an event of interest occurs, such as death, disease recurrence, or recovery. Unlike standard statistical methods, survival analysis is particularly adept at handling censored data—instances where the event has not occurred for some participants by the end of the study or remains unobserved. To address these unique challenges, specialized techniques like the Kaplan-Meier estimator, log-rank test, and...
583