Abstract
Aim:
Weather
parameters play a critical role in the survival and propagation of the causal
pathogen Ascochyta rabiei. In the absence of vertical host resistance,
the present study was designed to explore the weather-disease
interrelationship for effective disease management.
Methodology:
Field
experiments were conducted with three chickpea cultivars and three sowing
times for two years. The relationship between the percent severity index
(PSI) at weekly interval and observed weather data was explored using
multivariate statistical techniques.
Results:
The
correlation networks revealed that temperature and relative humidity
parameters were negatively and positively associated with the blight PSI,
respectively. Humid thermal ratio (HTR) significantly correlated with PSI
among all three chickpea cultivars. The linear regression of PSI with weather
variables exhibited a significant negative slope (β) with all thermal
variables whereas a strong positive β was displayed with relative humidity
and HTR. The Best subset multiple regression models further affirmed that PSI
in three chickpea cultivars can be strongly predicted with models constituted
with temperature, humidity and HTR. Principal component analysis showed that
PC1 and PC2 explained 88.2% variability with weather variables affecting the
PSI. The clustering of thermal and humidity condition parameters around the
first two PCs explained their pivotal role in disease severity progress.
Interpretation:
The
multivariate analysis revealed that air temperature, relative humidity and
HTR had significant influence on periodic Ascochyta blight severity in
chickpea crop. Present exploration will be vital in developing more targeted
disease prediction and management strategies to secure chickpea production.
Key
words: Ascochyta
blight, Best subset regression, Chickpea, Correlation network, Weather
variables
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