 Shannon information theory a useful tool for detecting significant abiotic factors influencing the population dynamics of Helicoverpa armigera (Hübner) on cotton cropM. Pratheepa, Abraham Verghese and H. Bheemanna Ecological Modelling, 2016, vol. 337, issue C, 25-28 Abstract: Helicoverpa armigera is a major pest on cotton (Gossypium spp.) and India ranks second in world production of cotton. This pest is highly adapted to different environments and abundance of this pest is due to both abiotic factors and hosts. In this study, the data mining technique based on Shannon information theory has been used for finding the significant factors that affect H. armigera incidence. This has been discussed in detail. The crop stage of cotton, season and abiotic factors like maximum temperature, minimum temperature, morning relative humidity, evening relative humidity, rainfall, number of rainy days in a week, have been considered for the analysis. The results of Shannon information theory showed that among all the factors, crop stage played a major role followed by number of rainy days in a week and relative humidity for the pest incidence and agreed well with correlation analysis. Keywords: Helicoverpa armigera; Abiotic; Cotton; Shannon information theory; Data mining; Crop stage (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:eee:ecomod:v:337:y:2016:i:c:p:25-28 DOI: 10.1016/j.ecolmodel.2016.06.003 Access Statistics for this article Ecological Modelling is currently edited by Brian D. Fath More articles in Ecological Modelling from Elsevier |
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