Forecasting of exotic bondars nesting whitefly, Paraleyrodes bondari Peracchi (Hemiptera: Aleyrodidae) by prediction models

Forecasting of exotic bondars nesting whitefly, Paraleyrodes bondari Peracchi (Hemiptera: Aleyrodidae) by prediction models | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Forecasting of exotic bondars nesting whitefly, Paraleyrodes bondari Peracchi (Hemiptera: Aleyrodidae) by prediction models E Sankarganesh, Kusal Roy, N Elakkiya, T Gowthaman This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4328009/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 4 You are reading this latest preprint version Abstract Whiteflies, as polyphagous pests, hold global significance due to their substantial economic impact. In recent times, the exotic bondar’s nesting whitefly (BNW), Paraleyrodes bondari Peracchi (Hemiptera: Aleyrodidae) Martin, has profoundly disrupted the horticultural ecosystem in India. This disruption has garnered significant attention of agricultural communities. The severity of its influence has been exacerbated by shifting of weather patterns and climatic conditions. To address these challenges, a comprehensive study was initiated in a coconut plantation. The primary objectives of this study were to unravel the factors that exert influence on the behavior of the bondar’s nesting whitefly and furthermore, to develop predictive models aimed at forecasting its incidence. The correlation studies conducted between biotic and abiotic factors and the population of BNW revealed noteworthy findings. Notably, maximum temperature (with a correlation coefficient i.e. r = 0.28*), bright sunshine hours (r = 0.3*), and canopy temperature (r = 0.28*) exhibited a significant positive influence on the whitefly population build up. These results underscore the crucial role that climatic conditions play in driving the dynamics of BNW population. The prediction of BNW outbreaks based on prevailing weather conditions and the timely implementation of appropriate control measures for effective pest management are of paramount importance. Among the prediction models assessed, artificial neural network (ANN) models emerged as the most promising. The model demonstrated superior performance and were identified as the optimal choice for accurately predicting BNW incidence. Weather. Bondar’s nesting whitefly. Prediction models. Artificial neural network Full Text Cite Share Download PDF Status: Under Review Version 1 posted Reviewers agreed at journal 08 May, 2025 Reviewers invited by journal 07 May, 2025 Editor assigned by journal 29 Apr, 2024 First submitted to journal 28 Apr, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4328009","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":453256547,"identity":"749785ca-a934-45c3-9456-5420dfdc1360","order_by":0,"name":"E Sankarganesh","email":"","orcid":"","institution":"Bihar Agricultural University","correspondingAuthor":false,"prefix":"","firstName":"E","middleName":"","lastName":"Sankarganesh","suffix":""},{"id":453256548,"identity":"17763a41-418f-4f69-a003-bdf57227f1af","order_by":1,"name":"Kusal Roy","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA3klEQVRIiWNgGAWjYJACxgYgwcfeA2EwHAAjIrSw8ZwhWYtEDkILXmDe3mPAOLOtTp5N8u3BjzPbGOT4biQwHi7Ao0XmzBkDxo1thw3bpPOSJTe2MRhL3khgODwDjxYJiRwDxodtBxjbpHPMGB+cYUjcANLCQ1hLnX2b5BmwlnritGxsY05sk+AxY9xQwZBgQFALz7GCgzPOHU5u4wH6ZUaFhOHMMw8b8Gthb974sKeszraf/ezBjz0GNvJ8x5MPf8anhYGBw+AAshEM0HjCB9gfEFAwCkbBKBgFIx4AAAb8TkdcaGnTAAAAAElFTkSuQmCC","orcid":"https://orcid.org/0000-0002-5179-2373","institution":"Bidhan Chandra Krishi Viswa Vidyalaya Faculty of Agriculture","correspondingAuthor":true,"prefix":"","firstName":"Kusal","middleName":"","lastName":"Roy","suffix":""},{"id":453256549,"identity":"f5a3faa7-866d-4f38-8068-0e2dc414bcb8","order_by":2,"name":"N Elakkiya","email":"","orcid":"","institution":"ICAR - Central Institute of Brackishwater Aquaculture","correspondingAuthor":false,"prefix":"","firstName":"N","middleName":"","lastName":"Elakkiya","suffix":""},{"id":453256550,"identity":"943685bb-7f53-49b4-83d0-e03039a93fdd","order_by":3,"name":"T Gowthaman","email":"","orcid":"","institution":"National Centre for Disease Informatics and Research","correspondingAuthor":false,"prefix":"","firstName":"T","middleName":"","lastName":"Gowthaman","suffix":""}],"badges":[],"createdAt":"2024-04-26 07:41:06","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4328009/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4328009/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":82479958,"identity":"b496bc15-714e-4d3b-8f30-af586d565485","added_by":"auto","created_at":"2025-05-12 03:21:54","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":609840,"visible":true,"origin":"","legend":"","description":"","filename":"BNWManuscriptText.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4328009/v1_covered_076c7045-ce84-4ade-99e6-65e508d466b5.pdf"}],"financialInterests":"","formattedTitle":"Forecasting of exotic bondars nesting whitefly, Paraleyrodes bondari Peracchi (Hemiptera: Aleyrodidae) by prediction models","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"international-journal-of-tropical-insect-science","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"jtis","sideBox":"Learn more about [International Journal of Tropical Insect Science](http://link.springer.com/journal/42690)","snPcode":"42690","submissionUrl":"https://www.editorialmanager.com/jtis/default2.aspx","title":"International Journal of Tropical Insect Science","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Weather. Bondar’s nesting whitefly. Prediction models. Artificial neural network","lastPublishedDoi":"10.21203/rs.3.rs-4328009/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4328009/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eWhiteflies, as polyphagous pests, hold global significance due to their substantial economic impact. In recent times, the exotic bondar\u0026rsquo;s nesting whitefly (BNW), \u003cem\u003eParaleyrodes bondari\u003c/em\u003e Peracchi (Hemiptera: Aleyrodidae) Martin, has profoundly disrupted the horticultural ecosystem in India. This disruption has garnered significant attention of agricultural communities. The severity of its influence has been exacerbated by shifting of weather patterns and climatic conditions. To address these challenges, a comprehensive study was initiated in a coconut plantation. The primary objectives of this study were to unravel the factors that exert influence on the behavior of the bondar\u0026rsquo;s nesting whitefly and furthermore, to develop predictive models aimed at forecasting its incidence. The correlation studies conducted between biotic and abiotic factors and the population of BNW revealed noteworthy findings. Notably, maximum temperature (with a correlation coefficient i.e. r\u0026thinsp;=\u0026thinsp;0.28*), bright sunshine hours (r\u0026thinsp;=\u0026thinsp;0.3*), and canopy temperature (r\u0026thinsp;=\u0026thinsp;0.28*) exhibited a significant positive influence on the whitefly population build up. These results underscore the crucial role that climatic conditions play in driving the dynamics of BNW population. The prediction of BNW outbreaks based on prevailing weather conditions and the timely implementation of appropriate control measures for effective pest management are of paramount importance. Among the prediction models assessed, artificial neural network (ANN) models emerged as the most promising. 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