Shuangjin Wang , Puxuan Wang , Richard Cebula , Maggie Foley , Chen Liang
{"title":"Scientometric analysis of development and opportunities for research in digital agriculture innovation management","authors":"Shuangjin Wang , Puxuan Wang , Richard Cebula , Maggie Foley , Chen Liang","doi":"10.1016/j.envsoft.2025.106392","DOIUrl":null,"url":null,"abstract":"<div><div>Digital agriculture has transformed the landscape of agricultural technology innovation and has led to increased attention towards managing innovation in this domain. This study seeks to provide a comprehensive understanding of digital agriculture innovation management by proposing a new retrieval strategy and constructing a dataset of 1878 research papers from the WoS-SSCI core collection spanning the years 2000 through 2023. The research employs scientific methods and tools to analyze the overall development, collaboration networks, frontier hotspots, and contribution paths in the Chinese context, as well as future opportunities for research in digital agriculture innovation management. The study reveals that digital agriculture innovation management research has experienced accelorated growth since 2020 and is expected to undergo further changes in the near future. The keywords extracted from the WoS-SSCI core collection and CNKI (China National Knowledge Infrastructure) core database exhibit the characteristics of Zipf's Law, indicating certain terms are more frequently used than others. The analysis identifies 44 frontier hotspots in digital agriculture innovation management research within the WoS-SSCI, with topics such as “precision agriculture”, “remote sensing”, and “food security” displaying notable prominence in different sub-disciplines due to their high centrality and density. This scientometric analysis not only provides strategic guidance and methodological inspiration for theoretical research and disciplinary development in digital agriculture innovation management but also offers practical recommendations for implementing digital agriculture strategies and promoting rural development. The findings of this study lay a solid foundation for future research in digital agriculture innovation management and emphasize the potential for further advancements in this field.</div></div>","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"188 ","pages":"Article 106392"},"PeriodicalIF":4.8000,"publicationDate":"2025-02-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Environmental Modelling & Software","FirstCategoryId":"93","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S1364815225000763","RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS","Score":null,"Total":0}
引用次数: 0
Abstract
Digital agriculture has transformed the landscape of agricultural technology innovation and has led to increased attention towards managing innovation in this domain. This study seeks to provide a comprehensive understanding of digital agriculture innovation management by proposing a new retrieval strategy and constructing a dataset of 1878 research papers from the WoS-SSCI core collection spanning the years 2000 through 2023. The research employs scientific methods and tools to analyze the overall development, collaboration networks, frontier hotspots, and contribution paths in the Chinese context, as well as future opportunities for research in digital agriculture innovation management. The study reveals that digital agriculture innovation management research has experienced accelorated growth since 2020 and is expected to undergo further changes in the near future. The keywords extracted from the WoS-SSCI core collection and CNKI (China National Knowledge Infrastructure) core database exhibit the characteristics of Zipf's Law, indicating certain terms are more frequently used than others. The analysis identifies 44 frontier hotspots in digital agriculture innovation management research within the WoS-SSCI, with topics such as “precision agriculture”, “remote sensing”, and “food security” displaying notable prominence in different sub-disciplines due to their high centrality and density. This scientometric analysis not only provides strategic guidance and methodological inspiration for theoretical research and disciplinary development in digital agriculture innovation management but also offers practical recommendations for implementing digital agriculture strategies and promoting rural development. The findings of this study lay a solid foundation for future research in digital agriculture innovation management and emphasize the potential for further advancements in this field.
期刊介绍:
Environmental Modelling & Software publishes contributions, in the form of research articles, reviews and short communications, on recent advances in environmental modelling and/or software. The aim is to improve our capacity to represent, understand, predict or manage the behaviour of environmental systems at all practical scales, and to communicate those improvements to a wide scientific and professional audience.