Please use this identifier to cite or link to this item: https://repository.rsif-paset.org/xmlui/handle/123456789/354
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dc.contributor.authorNikiema, Théodore-
dc.contributor.authorEzin, Eugène C.-
dc.contributor.authorChogou, Sylvain Kpenavoun-
dc.date.accessioned2024-02-22T13:23:01Z-
dc.date.available2024-02-22T13:23:01Z-
dc.date.issued2023-11-04-
dc.identifier.urihttps://repository.rsif-paset.org/xmlui/handle/123456789/354-
dc.descriptionJournal articleen_US
dc.description.abstractAs a sustainable and holistic approach to agriculture, agroecology has received considerable attention in recent years because of its potential to address the environmental, economic, and social challenges of agricultural systems. In order to identify key trends, influential authors, impactful journals, and emerging research themes surrounding the agroecological adoption topic, we performed a bibliometric analysis based on metadata from documents dealing with the topic and methods employed for its assessment over the period of January 1990 to July 2023, extracted from the Web of Science database. Based on the metadata of more than 1280 articles, our analyses show that the “identification of agroecology adoption determinants” and the “development of knowledge for food sovereignty” are among the trendiest research topics, while farming systems management and biodiversity issues are basic themes. The issue of analyzing agricultural productivity is more often linked to the impact of climate change. Also, we find that machine learning methods are not yet widely used to model the process of farm agroecological adoption. The contribution of African countries to the topic remains marginal in terms of documents produced, despite the predominance of traditional agriculture on the continent.en_US
dc.publisherSustainabilityen_US
dc.subjectagroecology; transition; metadata; farms; bibliometric method; machine learning; African countriesen_US
dc.titleBibliometric Analysis of the State of Research on Agroecology Adoption and Methods Used for Its Assessmenten_US
dc.typeArticleen_US
Appears in Collections:ICTs including Big Data and Artificial Intelligence



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