Open AccessInfluence of prevailing weather parameters on population dynamics of spotted stem borer, Chilo partellus (Swinhoe) and its natural enemies on maize in Haryana
Author(s) -
Gaurav Singh,
Maha Singh Jaglan,
Tarun Verma,
Sucheta Khokhar
Publication year - 2021
Publication title -
journal of agrometeorology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.295
H-Index - 11
eISSN - 2583-2980
pISSN - 0972-1665
DOI - 10.54386/jam.v22i3.191
Subject(s) - chilo , trichogramma , population , infestation , biology , pyralidae , kharif crop , pest analysis , larva , sunshine duration , relative humidity , agronomy , toxicology , crop , horticulture , parasitoid , ecology , geography , demography , sociology , meteorology
The experiment was conducted at CCS Haryana Agricultural University Regional Research Station, Karnal to ascertain the influence of prevailing meteorological parameters on population dynamics of Chilo partellus and its natural enemies on maize during Kharif, 2017. Maximum oviposition (0.75 egg masses per plant) was recorded during 28th standard meteorological week (SMW) whereas larval population was at peak during 31st SMW (3.8 larvae per plant). Cumulative (47.5%) and fresh plant infestation (11.5%) were maximum during 34th and 28th SMW, respectively. Maximum egg parasitisation (6.53%) by Trichogramma sp. and larval parasitisation (31.64%) by Cotesia flavipes was recorded during 28th and 33rd SMW, respectively. Changes in pest population were correlated and regressed with weather parameters. Egg and larval populations of C. partellus and parasitisation by Trichogramma sp. exhibited significant positive correlation with average minimum temperature whereas C. flavipes exhibited significant negative correlation with average maximum temperature (r = -0.741) and highly significant positive correlation with evening relative humidity (r = 0.695). Plant infestation and dead heart formation were significantly correlated with average minimum temperature and non-significantly correlated with all other weather parameters. The multiple linear regression analysis explained the variability due to various weather parameters. This information can be utilised while formulating integrated management tactics against this pest.