Predicting the severity of Spodoptera litura on groundnut in relation to climatic variability using ordinal logistic model

Abstract

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 non parametric 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.

Keywords

• Trend analysis
• Mann-Kendall test
• Ordinal logistic model
• proportional odds model
• Spodoptera litura