{"title":"作物产量预测文献的文献计量分析:以往研究成果的启示和未来研究的展望。","authors":"Seyed Erfan Momenpour, Saeed Bazgeer, Masoumeh Moghbel","doi":"10.1007/s00484-024-02628-2","DOIUrl":null,"url":null,"abstract":"<p><p>This research presents a bibliometric analysis of articles predicting crop yield using machine learning methods. While several systematic review articles exist, a comprehensive bibliometric analysis illustrating the knowledge structure and research trends, along with collaboration networks among authors, institutions, and countries in this field, has not been conducted. The study focused on 826 articles published over a 32-year period (1992 to 2023) and revealed a significant increase in publications, particularly in recent years. Zhang Zhao from China authored the majority of articles, while the highest number of citations was associated with articles by Zhu Yan and Lobell. Leading countries in article publications are the USA, China, India, Germany, Australia, and Canada, showing strong interconnections in citing each other's research. The Chinese Academy of Sciences and the US Department of Agriculture are the institutions with the highest number of articles and citations in this domain. The journals Agricultural and Forest Meteorology and Remote Sensing are recognized as top ranking journals in this field (Q1). Based on co-occurrence analysis, three main thematic domains were identified: weather and crop yield prediction, plant growth simulation models, and crop yield prediction using remote sensing data. The research suggests a focus on variables such as disease, pests, insects, and soil salinity when predicting yield. Additionally, greater attention should be given to discussions on food security and crop yield, especially in developing countries.</p>","PeriodicalId":588,"journal":{"name":"International Journal of Biometeorology","volume":null,"pages":null},"PeriodicalIF":3.0000,"publicationDate":"2024-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"A bibliometric analysis of the literature on crop yield prediction: insights from previous findings and prospects for future research.\",\"authors\":\"Seyed Erfan Momenpour, Saeed Bazgeer, Masoumeh Moghbel\",\"doi\":\"10.1007/s00484-024-02628-2\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>This research presents a bibliometric analysis of articles predicting crop yield using machine learning methods. While several systematic review articles exist, a comprehensive bibliometric analysis illustrating the knowledge structure and research trends, along with collaboration networks among authors, institutions, and countries in this field, has not been conducted. The study focused on 826 articles published over a 32-year period (1992 to 2023) and revealed a significant increase in publications, particularly in recent years. Zhang Zhao from China authored the majority of articles, while the highest number of citations was associated with articles by Zhu Yan and Lobell. Leading countries in article publications are the USA, China, India, Germany, Australia, and Canada, showing strong interconnections in citing each other's research. The Chinese Academy of Sciences and the US Department of Agriculture are the institutions with the highest number of articles and citations in this domain. The journals Agricultural and Forest Meteorology and Remote Sensing are recognized as top ranking journals in this field (Q1). Based on co-occurrence analysis, three main thematic domains were identified: weather and crop yield prediction, plant growth simulation models, and crop yield prediction using remote sensing data. The research suggests a focus on variables such as disease, pests, insects, and soil salinity when predicting yield. Additionally, greater attention should be given to discussions on food security and crop yield, especially in developing countries.</p>\",\"PeriodicalId\":588,\"journal\":{\"name\":\"International Journal of Biometeorology\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":3.0000,\"publicationDate\":\"2024-05-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"International Journal of Biometeorology\",\"FirstCategoryId\":\"89\",\"ListUrlMain\":\"https://doi.org/10.1007/s00484-024-02628-2\",\"RegionNum\":3,\"RegionCategory\":\"地球科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"2024/2/1 0:00:00\",\"PubModel\":\"Epub\",\"JCR\":\"Q2\",\"JCRName\":\"BIOPHYSICS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Journal of Biometeorology","FirstCategoryId":"89","ListUrlMain":"https://doi.org/10.1007/s00484-024-02628-2","RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2024/2/1 0:00:00","PubModel":"Epub","JCR":"Q2","JCRName":"BIOPHYSICS","Score":null,"Total":0}
A bibliometric analysis of the literature on crop yield prediction: insights from previous findings and prospects for future research.
This research presents a bibliometric analysis of articles predicting crop yield using machine learning methods. While several systematic review articles exist, a comprehensive bibliometric analysis illustrating the knowledge structure and research trends, along with collaboration networks among authors, institutions, and countries in this field, has not been conducted. The study focused on 826 articles published over a 32-year period (1992 to 2023) and revealed a significant increase in publications, particularly in recent years. Zhang Zhao from China authored the majority of articles, while the highest number of citations was associated with articles by Zhu Yan and Lobell. Leading countries in article publications are the USA, China, India, Germany, Australia, and Canada, showing strong interconnections in citing each other's research. The Chinese Academy of Sciences and the US Department of Agriculture are the institutions with the highest number of articles and citations in this domain. The journals Agricultural and Forest Meteorology and Remote Sensing are recognized as top ranking journals in this field (Q1). Based on co-occurrence analysis, three main thematic domains were identified: weather and crop yield prediction, plant growth simulation models, and crop yield prediction using remote sensing data. The research suggests a focus on variables such as disease, pests, insects, and soil salinity when predicting yield. Additionally, greater attention should be given to discussions on food security and crop yield, especially in developing countries.
期刊介绍:
The Journal publishes original research papers, review articles and short communications on studies examining the interactions between living organisms and factors of the natural and artificial atmospheric environment.
Living organisms extend from single cell organisms, to plants and animals, including humans. The atmospheric environment includes climate and weather, electromagnetic radiation, and chemical and biological pollutants. The journal embraces basic and applied research and practical aspects such as living conditions, agriculture, forestry, and health.
The journal is published for the International Society of Biometeorology, and most membership categories include a subscription to the Journal.