Crop diversification for soil carbon sequestration and greenhouse gas mitigation: Research trends, mechanisms, and future directions

Abstract

Agricultural intensification over the 20th century has increased crop yields but has also led to substantial environmental impacts, including greenhouse gas (GHG) emissions and declining soil quality. Crop diversification is increasingly viewed as a sustainable strategy to improve soil health and mitigate GHG emissions. This study presents a bibliometric analysis of global research trends on crop diversification for soil carbon (C) sequestration and GHG mitigation from 1979 to 2024. A total of 9036 articles were analyzed, revealing a substantial increase in research output since 2010, marking a key turning point in the field. Key contributing nations, including the United States and China have driven this surge in interest. Co-occurrence and keyword analyses revealed four major research clusters: soil health and sustainable tillage systems, agricultural management practices and cropping systems, analytical methods and assessment, GHG emissions and mitigation strategies. Long-term field experiments and models were identified as critical methodologies in studying soil C dynamics and GHG emissions. We also summarized the underlying mechanisms by which diversified cropping enhances soil carbon sequestration while mitigating greenhouse gas emissions. This review highlights the growing importance of crop diversification for sustainable agriculture and provides future research directions in soil management and GHG mitigation.

Graphical abstract

Keywords

crop diversification / sustainable agriculture / soil carbon sequestration / GHG emission / bibliometrics

Highlight

	● Research on crop diversity and soil C sequestration has exponentially grown since 2010.
	● The United States and China dominated the related field.
	● Diversification integrated management enhances soil carbon, though the benefits manifest long-term.
	● Still researching gaps in long-term experiments and predictive models.

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