[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-126367-en":3,"doc-seo-126367-105":31,"detail-sidebar-cat-0-en-105":93},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":28,"seo_description":14,"update_tm":29,"read_time":30},126367,962085564549,"Genevieve","https://ap-avatar.wpscdn.com/davatar_085a072bc5b1113ac321206ff7593b45",8,"Research & Report","Temporal dynamics of sapota pest damage and Phytophthora disease: insights from time series and machine learning models","Temporal variability in sapota (Manilkara zapota) pest damage and Phytophthora disease is examined to clarify how climate shapes long-term biotic stress patterns. A decade-long dataset from major sapota-growing districts in Maharashtra (2014–2022) is analyzed using statistical and machine learning time-series methods including ARIMA, SARIMA, and VAR, supplemented by Random Forest feature-importance evaluation and correlation analyses. Forecasting performance and driver relationships are quantified, revealing distinct roles of rainfall, temperature extremes, humidity, and minimum temperature in shaping bud borer, seed borer, and disease severity.","TYPE Original Research PUBLISHED 23 September 2025 DOI 10.3389/fpls.2025.1659709  \nOPEN ACCESS  \nEDITED BY  \nXiao Ming Zhang,  \nYunnan Agricultural University, China  \nREVIEWED BY  \nBalaji Bn,  \nNational Bureau of Agricultural Insects Resources, India  \nIvan Malashin,  \nBauman Moscow State Technical University, Russia  \n*CORRESPONDENCE  \nMeenakshi Malik  \n [minaxi.2007@gmail.com](minaxi.2007@gmail.com)[ ](minaxi.2007@gmail.com)Niranjan Singh  \n [attri.ns@gmail.com](attri.ns@gmail.com)[ ](attri.ns@gmail.com)Amoghavarsha Chittaragi  \n [amoghchittaragi@gmail.com](amoghchittaragi@gmail.com)  \nRECEIVED 07 July 2025  \nACCEPTED 14 August 2025  \nPUBLISHED 23 September 2025  \nCITATION  \nMalik M, Singh N, Chittaragi A, D R, Patil B and Manisha BL (2025) Temporal dynamics of sapota pest damage and Phytophthora disease: insights from time series and machine learning models.  \nFront. Plant Sci. 16:1659709 .  \ndoi: 10.3389/fpls.2025.1659709  \nCOPYRIGHT  \n© 2025 Malik, Singh, Chittaragi, D, Patil and Manisha. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.  \nTemporal dynamics of sapota pest damage and Phytophthora disease: insights from time series and machine learning models  \nMeenakshi Malik 1*, Niranjan Singh 2*, Amoghavarsha Chittaragi 3*, Raghavendra D 4, Balanagouda Patil 5 and Bachu Lakshmi Manisha 4  \n1Agricultural Statistics, Indian Council of Agricultural Research (ICAR)-National Research Institute for Integrated Pest Management, New Delhi, India, 2Computer Applications, ICAR-National Research Institute for Integrated Pest Management, New Delhi, India, 3 ICAR-KVK, Chintamani, University of Agricultural Sciences, GKVK, Bangalore, India, 4 Entomology, ICAR-National Research Institute for Integrated Pest Management, New Delhi, India, 5 Plant Pathology, Keladi Shivappa Nayaka University of Agricultural and Horticultural Sciences, Iruvakki, Sagar, India  \nIntroduction: Sapota (Manilkara zapota L.) is a major tropical fruit crop prone to damage by bud borer (Anarsia achrasella), seed borer (Trymalitis margarias), and fruit rot caused by Phytophthora species. Climatic variability strongly influences these biotic stresses, yet long-term temporal patterns remain poorly quantified. Methods: A decade-long dataset (2014–2022) from 21 major sapota-growing districts of Maharashtra, India, was analyzed to study pest and disease dynamics. Statistical and machine learning approaches, including ARIMA, SARIMA, and VAR time-series models, along with Random Forest feature importance analysis, were applied to quantify climatic influences and forecast severity trends. Correlation analyses were used to assess weather–pest/disease associations.  \nResults: Trend analysis revealed fluctuating bud and seed borer damage, while Phytophthora disease severity remained relatively stable. Bud borer incidence was positively correlated with rainfall (r = 0.69), seed borer with maximum temperature (r =0 .47), and Phytophthora with minimum temperature (r = 0 .64) . The ARIMA model provided accurate forecasts for bud borer (MSE = 8 . 03) and Phytophthora (MSE = 0.20), while the VAR model performed best for seed borer (MSE = 17 .96) . Random Forest analysis identified minimum temperature as the most critical driver of bud borer and Phytophthora severity, whereas relative humidity was most influential for seed borer.  \nDiscussion: The integration of statistical and machine learning models provides robust insights into sapota pest and disease epidemiology under climatic variability. These findings highlight the importance of temperature, humidity, and rainfall in shaping pest–pathogen ","cbCainpxGEDXLf3E","https://ap.wps.com/l/cbCainpxGEDXLf3E","pdf",1879777,7,1,14,"English","en",105,"# Introduction\n# Methods\n# Results\n# Discussion\n# Keywords","[{\"question\":\"What sapota pests and disease are studied in the paper?\",\"answer\":\"The study focuses on bud borer and seed borer damage and Phytophthora-caused fruit rot disease affecting sapota cultivation.\"},{\"question\":\"Which data and locations are used for the analysis?\",\"answer\":\"A decade-long dataset from 21 major sapota-growing districts of Maharashtra, India, covering 2014–2022, is used to quantify temporal dynamics.\"},{\"question\":\"How do ARIMA, SARIMA, and VAR models contribute to the findings?\",\"answer\":\"These time-series models are used to forecast severity trends for bud borer, seed borer, and Phytophthora, with model performance assessed by mean squared error.\"}]","Temporal dynamics of 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sapota pests and disease are studied in the paper?","Question",{"text":77,"@type":78},"The study focuses on bud borer and seed borer damage and Phytophthora-caused fruit rot disease affecting sapota cultivation.","Answer",{"name":80,"@type":75,"acceptedAnswer":81},"Which data and locations are used for the analysis?",{"text":82,"@type":78},"A decade-long dataset from 21 major sapota-growing districts of Maharashtra, India, covering 2014–2022, is used to quantify temporal dynamics.",{"name":84,"@type":75,"acceptedAnswer":85},"How do ARIMA, SARIMA, and VAR models contribute to the findings?",{"text":86,"@type":78},"These time-series models are used to forecast severity trends for bud borer, seed borer, and Phytophthora, with model performance assessed by mean squared 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