Indian Journal of Entomology
Open Access
SCOPUSWeb of Science
  • Year: 2026
  • Volume: 88
  • Issue: 2

Population Dynamics of Yellow Stem Borer Scirpophaga incertulas Walker

  • Author:
  • Abishek Krishnan1,*, M Sajitha Vijayan1, K Karthikeyan2, Dayana David1, Chitra Parayil1
  • Total Page Count: 5
  • Page Number: 420 to 424

1College of Agriculture, Vellanikkara, Kerala Agricultural University, KAU, P O, Thrissur, 680656, Kerala, India

2Regional Agricultural Research Station, Pattambi, Mele Pattambi P.O., Palakkad, 679306, Kerala, India

*Email: abhishekkrishnan0@gmail.com (corresponding author):

Online published on 13 March, 2026.

Abstract

Population dynamics of the rice yellow stem borer (Scirpophaga incertulas Walker) was evaluated at the Regional Agricultural Research Station, Pattambi, Kerala, for 27 years (1997 to 2023) using regression models. The weekly distribution data showed two prominent time periods of peak incidence- first during the (10th-16th) standard meteorological week (SMW) and 38th-48th SMW. There was a significant negative correlation of incidence during peak incidence i.e. 12th SMW with maximum temperature (TMAX), minimum temperature (TMIN), morning relative humidity (RH I), and evening relative humidity (RH II). During 43rd SMW incidence revealed a negative association with TMIN, RH I and RH II. Multiple linear regression model of incidence at 12th peak week with weather variables TMAX, TMIN, RH I and SSH revealed a R2 value of 0.83. Composite regression indicated a significant interaction effect of temperature and relative humidity for the peak periods of 10th -16th and 38th-48th SMW’s; these models yielded higher model accuracy than multiple linear regression models. The distribution of incidence for the 12th and 43rd peak week i.e. low, medium and high was studied using multinomial logistic regression models, which had an accuracy of 92 and 72%, respectively.

Keywords

Scirpophaga incertulas correlation, Weather variables, Peak period, Incidence, Composite regression, Joint effects, Interaction, Weighted variable, Logistic regression, Log odds, Model accuracy