Rice Blast Forecasting Using Interval Valued Data at Coimbatore, India

Abstract

Aims: The persistence of rice blast, caused by the fungus Magnaporthe oryzae, continues to pose a significant threat to rice production worldwide, impacting both yields and food security. The primary goal of this study is to apply interval-valued independent weather data to accurately model the dependent variable of percentage disease incidence.
 Study Design: In this paper, we present a detailed study on forecasting rice blast outbreaks through the application of Average method, Center method and Min Max method using interval valued weather data and percentage disease incidence.
 Place and Duration of Study: The blast disease data include percent disease incidence (PDI) collected at the Paddy Breeding Station (PBS), Tamil Nadu Agricultural University, Coimbatore, from 2018 to 2021.And Weather variables includes the following: Maximum Temperature, Minimum Temperature, Relative humidity (morning), Relative humidity (evening) from 2018 to 2021.
 Methodology: The available interval weather parameter data and disease incidence data are utilized to fit a regression model, specifically employing simple linear regression and multiple linear regression, in the R version 4.3.0.
 Results: Upon analyzing various methods, it is evident that the variables of Minimum temperature exhibit a significant relationship with a high level of significance, indicating a significance level at
 P 0.001.
 Conclusion: Minimum temperature shows more contribution in disease incidence followed by relative humidity at evening.