Open AccessPredicting the severity of Spodoptera litura on groundnut in relation to climatic variability using ordinal logistic model
Author(s) -
Girish Kumar Jha,
Gajab Singh,
S. Vennila,
M. S. Rao,
H. Panwar,
M. S. Hegde
Publication year - 2016
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.v18i2.951
Subject(s) - spodoptera litura , morning , relative humidity , pest analysis , kharif crop , evening , logistic regression , sunshine duration , mathematics , maximum temperature , statistics , toxicology , zoology , biology , geography , ecology , lepidoptera genitalia , horticulture , field experiment , atmospheric sciences , meteorology , botany , physics , astronomy , geology
In this paper, an ordinal logistic regression model was developed for predicting the severity of tobacco caterpillar, Spodoptera litura (Fabricius) on groundnut using the pest dynamics vis a vis climatic data of twenty five years (1990-2014) pertaining to Kharif (26 to 44 standard meteorological weeks (SMW)) season of Dharwad (Karnataka). Trend analysis of climatic data using Mann-Kendall nonparametric test showed that mean and minimum temperatures, and rainfall to be increasing while morning and evening relative humidity and their mean to be decreasing over time. The weekly male moth catches of S. litura (nos./trap/week) during maximum severity period (34 SMW) was modeled with climatic variables lagged by two weeks. The developed model indicated that the maximum temperature and morning relative humidity prior to two weeks contributed significantly to the occurrence of high level of pest attack. Results suggested that for each degree increase in maximum temperature during 32 SMW, the odds of being high pest attack (as opposed to lower or medium) increased by a multiple of 8.6 as compared to the odds of being high or medium (as opposed to low) increasing by 6.4 times for each per cent rise in the morning relative humidity.