数字农业革命：文献计量分析
The Digital Agricultural Revolution: a Bibliometric Analysis Literature Review
摘要：
数字技术在农业中的应用可以改善传统做法，以适应气候变化，减少温室气体（GHG）排放，并促进粮食安全的可持续集约化。一些作者认为，我们正在经历一场数字农业革命（DAR），这将促进可持续农业。这项研究旨在寻找正在进行的DAR过程的证据，并澄清其根源，它意味着什么，以及它的发展方向。我们调查了科学文献与文献计量分析工具，以产生一个客观的和可重复的文献综述。我们通过查询2012-2019年的Web of Science数据库检索了4995篇文章，并分析了获得的数据集以回答三个具体的研究问题：i）DAR相关术语的频谱是什么？（2）哪些是关键文章，哪些是最有影响力的期刊、机构和国家？iii）主要的研究方向和新兴课题是什么？通过对作者在出版物上报告的关键词进行分组，我们确定了五个主要的研究方向：气候智能型农业（CSA）、特定地点管理（SSM）、遥感（RS）、物联网（IoT）和人工智能（AI）。为了对每个主题进行广泛的概述，我们分析了相关的综述文章，并在这里介绍了主要成就和正在面临的挑战。最后，我们展示了过去三年（2017年，2018年，2019年）的热门话题。
英文摘要：
The application of digital technologies in agriculture can improve traditional practices to adapt to climate change, reduce Greenhouse Gases (GHG) emissions, and promote a sustainable intensification for food security. Some authors argued that we are experiencing a Digital Agricultural Revolution (DAR) that will boost sustainable farming. This study aims to find evidence of the ongoing DAR process and clarify its roots, what it means, and where it is heading. We investigated the scientific literature with bibliometric analysis tools to produce an objective and reproducible literature review. We retrieved 4995 articles by querying the Web of Science database in the timespan 2012-2019, and we analyzed the obtained dataset to answer three specific research questions: i) what is the spectrum of the DAR-related terminology?; ii) what are the key articles and the most influential journals, institutions, and countries?; iii) what are the main research streams and the emerging topics? By grouping the authors' keywords reported on publications, we identified five main research streams: Climate-Smart Agriculture (CSA), Site-Specific Management (SSM), Remote Sensing (RS), Internet of Things (IoT), and Artificial Intelligence (AI). To provide a broad overview of each of these topics, we analyzed relevant review articles, and we present here the main achievements and the ongoing challenges. Finally, we showed the trending topics of the last three years (2017, 2018, 2019).
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
