Tracing the contribution of cattle farms to methane emissions through bibliometric analyses

doi:10.1016/j.jia.2024.05.003

Journal of Integrative Agriculture

2025, Vol. 24

Issue (4): 1220-1233 DOI: 10.1016/j.jia.2024.05.003

Section 1: Livestock production systems

Advanced Online Publication | Current Issue | Archive | Adv Search

Tracing the contribution of cattle farms to methane emissions through bibliometric analyses

Shakoor Abdul^1*, Zaib Gul^2,3*, Ming Xu^4,5,1#

¹College of Geography and Environmental Science, Henan University, Kaifeng 475004, China

²Institute of Epigenetics and Epigenomics, College of Animal Science and Technology, Yangzhou University, Yangzhou 225009, China

³College of Veterinary Medicine, Yangzhou University, Yangzhou 225009, China

⁴Guangdong-Hong Kong Joint Laboratory for Carbon Neutrality, Jiangmen Laboratory of Carbon Science and Technology, Jiangmen 529199, China

⁵BNU-HKUST Laboratory for Green Innovation, Advanced Institute of Natural Sciences, Beijing Normal University at Zhuhai, Zhuhai 519087, China

Highlights
● Methane emissions contribute substantially to climate change. The current study aids in identifying research trends and interests.
● Contributions of cattle farms to methane emissions were studied through bibliometric tools to decipher its role in climate change. Leading countries, institutions, prolific authors, keywords, collaborative networks, and prolific journals were identified.
● We suggest reducing methane emissions in cattle farms by changing feed quantity, quality, and gut microflora alterations. Our findings have implications for highlighting methane emissions in cattle farms via a bibliometric method.

Abstract
References

Download: PDF in ScienceDirect
Export: BibTeX | EndNote (RIS)

Abstract

Methane contributes to global warming, and livestock is one of the sources of methane production. However, methane emission studies using bibliometric tools in livestock are lacking. Given the negative impact of climate change on the ecosystem and the rise in methane emissions, it is essential to conduct a bibliometrics study to provide an overview and research trends. We used the Bibliometrix package and VOSviewer to decipher bibliometric indices for methane emissions in cattle farms (MECF). Current dataset were collected from the Web of Science (Core Collection) database, and 8,998 publications were analyzed. The most co-occurring keywords scientists preferred were methane (1,528), greenhouse gas (443), methane emissions (440), and cattle (369). Methane was the most frequently used keyword in the published scientific literature. Thematic evolution of research themes and trend results highlighted carbon dioxide, methane, dairy cattle, cattle, and risk factors during 1999–2017. Chinese Academy of Sciences ranked on top with 485 publications, followed by Agriculture & Agri-Food Canada, University of Colorado, National Oceanic and Atmospheric Administration, and Aarhus University. Chinese Academy of Sciences was also the most cited organization, followed by the University of Colorado, Agriculture & Agri-Food Canada, National Oceanic and Atmospheric Administration, and United States Geological Survey. Source analysis showed that the Science of the Total Environment was cited with the highest total link strength. Science of the Total Environment ranked first in source core 1 with 290 citation frequencies, followed by Journal of Dairy Science with 223 citation frequencies. Currently, no bibliometric study has been conducted on MECF, and to fill this knowledge gap, we carried out this study to highlight methane emissions in cattle farms, aiming at a climate change perspective. In this regard, we focused on the research productivity of countries authors, journals and institutions, co-occurrence of keywords, evolution of research trends, and collaborative networking. Based on relevance degree of centrality, methane emissions and greenhouse gases appeared as basic themes, cattle, and dairy cattle appeared as emerging/declining themes, whereas, methane, greenhouse gas and nitrous oxide appeared to fall amongst basic and motor themes. On the other hand, beef cattle, rumen and dairy cow seem to be between motor and niche themes, and risk factors lie in niche themes. The present bibliometric analysis provides research progress on methane emissions in cattle farms. Current findings may provide a framework for understanding research trends and themes in MECF research.

Keywords: methane emission cattle farms climate change greenhouse gases networking bibliometrics

Received: 30 October 2023 Accepted: 30 March 2024

Fund:

This study was supported by the Special Fund for Science and Technology Innovation Strategy of Guangdong Province, China (2022660500250009604).

About author: # Correspondence Ming Xu, E-mail: 91122020071@bnu.edu.cn * These authors contributed equally to this study.

	Service
	E-mail this article
	Add to citation manager
	E-mail Alert
	RSS
	Articles by authors

Cite this article:

Shakoor Abdul, Zaib Gul, Ming Xu. 2025. Tracing the contribution of cattle farms to methane emissions through bibliometric analyses. Journal of Integrative Agriculture, 24(4): 1220-1233.

Aleixandre-Tudo J L, Castello-Cogollos L, Aleixandre J L, Aleixandre-Benavent R. 2021. Trends in funding research and international collaboration on greenhouse gas emissions: A bibliometric approach. Environmental Science and Pollution Research, 28, 32330–32346.

Ali N I M, Aiyub K, Lam K C, Abas A. 2022. A bibliometric review on the inter-connection between climate change and rice farming. Environmental Science and Pollution Research, 29, 30892–30907.

Andreo-Martinez P, Oliva J, Gimenez-Castillo J J, Motas M, Quesada-Medina J, Camara M A. 2020. Science production of pesticide residues in honey research: A descriptive bibliometric study. Environmental Toxicology and Pharmacology, 79, 103413.

Aria M, Cuccurullo C. 2017. Bibliometrix: An R-tool for comprehensive science mapping analysis. Journal of Informetrics, 11, 959–975.

Aria M, Misuraca M, Spano M. 2020. Mapping the evolution of social research and data science on 30 years of social indicators research. Social Indicators Research, 149, 803–831.

Baceninaite D, Dzermeikaite R, Antanaitis R. 2022. Global warming and dairy cattle: How to control and reduce methane emission. Animals, 12, 2687.

Black J L, Davison T M, Box I. 2021. Methane emissions from ruminants in australia: Mitigation potential and applicability of mitigation strategies. Animals, 11, 951.

Bogdan V, Popa D N, Belenesi M, Rus L, Scorte C M. 2023. Gender diversity and business performance nexus: A synoptic panorama based on bibliometric network analysis. Sustainability, 15, 1801.

Bornmann L, Mutz R, Daniel H D. 2008. Are there better indices for evaluation purposes than the h index? A comparison of nine different variants of the h index using data from biomedicine. Journal of the American Society for Information Science and Technology, 59, 830–837.

Brookes B C. 1969. Bradford’s law and the bibliography of science. Nature, 224, 953–956.

Callon M, Courtial J P, Laville F. 1991. Co-word analysis as a tool for describing the network of interactions between basic and technological research: The case of polymer chemsitry. Scientometrics, 22, 155–205.

Coderoni S, Dell’Unto D, Cortignani R. 2024. Curbing methane emissions from Italian cattle farms. An agroeconomic modelling simulation of alternative policy tools. Journal of Environmental Management, 351, 119880.

Crippa M, Solazzo E, Guizzardi D, Monforti-Ferrario F, Tubiello F N, Leip A. 2021. Food systems are responsible for a third of global anthropogenic GHG emissions. Nature Food, 2, 198–209.

van Eck N J, Waltman L. 2010. Software survey: VOSviewer, a computer program for bibliometric mapping. Scientometrics, 84, 523–538.

Egghe L.2006.Theory and practices of the g-index. Scientometrics, 69, 131–152.

Fan J, Liu G, Xia Z, Cai S. 2022. A bibliometric analysis of climate change risk perception: Hot spots, trends and improvements. Frontiers in Environmental Science, 10, 917469.

Fraumann G, Mutz R. 2021. The h-index. In: Ball R, ed., Handbook Bibliometrics. De Gruyter, Germany. pp. 169–178.

Gerber P J, Steinfeld H, Henderson B, Mottet A, Opio C, Dijkman J, Falcucci A, Tempio G. 2013. Tackling Climate Change Through Livestock – A Global Assessment of Emissions and Mitigation Opportunities. Food and Agriculture Organization of the United Nations (FAO). Rome, Italy.

Gong S, Shi Y. 2021. Evaluation of comprehensive monthly-gridded methane emissions from natural and anthropogenic sources in China. Science of the Total Environment, 784, 147116.

Guler A T, Waaijer C J, Palmblad M. 2016. Scientific workflows for bibliometrics. Scientometrics, 107, 385–398.

Haunschild R, Bornmann L, Marx W. 2016. Climate change research in view of bibliometrics. PLoS ONE, 11, e0160393.

Hirsch J E. 2005. An index to quantify an individual’s scientific research output. Proceedings of the National Academy of Sciences of the United States of America, 102, 16569–16572.

Hood W W, Wilson C S. 2001. The literature of bibliometrics, scientometrics, and informetrics. Scientometrics, 52, 291–314.

Hou Y, Wang Q. 2021. A bibliometric study about energy, environment, and climate change. Environmental Science and Pollution Research, 28, 34187–34199.

Johnson K A, Johnson D E. 1995. Methane emissions from cattle. Journal of Animal Science, 73, 2483–2492.

Kamada T, Kawai S. 1989. An algorithm for drawing general undirected graphs. Information Processing Letters, 31, 7–15.

Kamalanathan S, Houlahan K, Miglior F, Chud T C S, Seymour D J, Hailemariam D, Plastow G, de Oliveira H R, Baes C F, Schenkel F S. 2023. Genetic analysis of methane emission traits in holstein dairy cattle. Animals, 13, 1308.

Liu S, Proudman J, Mitloehner F M. 2021. Rethinking methane from animal agriculture. Commonwealth Agricultural Bureaux International Agriculture and Bioscience, 2, 22.

Lotka A J. 1926. The frequency distribution of scientific productivity. Journal of the Washington Academy of Sciences, 16, 317–323.

Le Mer J, Roger P. 2001. Production, oxidation, emission and consumption of methane by soils: A review. European Journal of Soil Biology, 37, 25–50.

Milojevic S, Leydesdorff L. 2013. Information metrics (iMetrics): A research specialty with a socio-cognitive identity? Scientometrics, 95, 141–157.

Peng S, Piao S, Bousquet P, Ciais P, Li B, Lin X, Tao S, Wang Z, Zhang Y, Zhou F. 2016. Inventory of anthropogenic methane emissions in mainland China from 1980 to 2010. Atmospheric Chemistry and Physics, 16, 14545–14562.

Reisinger A, Clark H, Cowie A L, Emmet-Booth J, Gonzalez Fischer C, Herrero M, Howden M, Leahy S. 2021. How necessary and feasible are reductions of methane emissions from livestock to support stringent temperature goals? Philosophical Transactions of the Royal Society (A: Mathematical, Physical and Engineering Sciences), 379, 20200452.

Rosentreter J A, Borges A V, Deemer B R, Holgerson M A, Liu S, Song C, Melack J, Raymond P A, Duarte C M, Allen G H, Olefeldt D, Poulter B, Battin T I, Eyre B D. 2021. Half of global methane emissions come from highly variable aquatic ecosystem sources. Nature Geoscience, 14, 225–230.

Shaw J T, Shah A, Yong H, Allen G. 2021. Methods for quantifying methane emissions using unmanned aerial vehicles: A review. Philosophical Transactions of the Royal Society (A: Mathematical, Physical and Engineering Sciences), 379, 20200450.

Smith P, Reay D, Smith J. 2021. Agricultural methane emissions and the potential formitigation. Philosophical Transactions of the Royal Society (A: Mathematical, Physical and Engineering Sciences), 379, 20200451.

Sossa C L G, Sanogo S, Naab J B, Sintondji L C. 2022. Trends and research features on greenhouse gas emissions from rice production: Review based on bibliometric analysis. Environmental Science and Pollution Research, 29,73828–73841.

Tee T P, Goh Y M, Zainudin M H M, Candyrine S C L, Sommart K, Kongphitee K, Sumamal W, Phaowphaisal I, Namsilee R, Angthong W, Sunato S, Keaokliang O, Maeda K, Thu N V, Trung T T, Dong N T K, Purnomoadi A, Kurihara M, Jayanegara A, Higuchi K. et al. 2022. Enteric methane emission models for diverse beef cattle feeding systems in South-east Asia: A meta-analysis. Animal Feed Science and Technology, 294, 115474.

USEPA (United States Environmental Protection Agency-EPA). 2019. Global Non-CO2 greenhouse gas emission projections & mitigation potential: 2015–2050. [2023-07-13]. https://www.epa.gov/global-mitigation-non-co2-greenhouse-gases/global-non-co2-greenhouse-gas-emission-projections

Waltman L, van Eck N J. 2012. A new methodology for constructing a publication-level classification system of science. Journal of the American Society for Information Science and Technology, 63, 2378–2392.

Waltman L, Van Eck N J. 2013. A smart local moving algorithm for large-scale modularity-based community detection. The European Physical Journal B, 86, 1–14.

Waltman L, van Eck N J, Noyons E C M. 2010. A unified approach to mapping and clustering of bibliometric networks. Journal of Informetrics, 4, 629–635.

Wang L H, Wang Q, Zhang, X, Cai W, Sun X. 2013. A bibliometric analysis of anaerobic digestion for methane research during the period 1994–2011. Journal of Material Cycles and Waste Management, 15, 1–8.

Wang S, Hohler J, Ang F, Oude Lansink A. 2023. Dutch dairy farmers’ adoption of climate mitigation measures – The role of socio-psychological and socio-demographical factors. Journal of Cleaner Production, 427, 139187.

Xu J, Xiao P. 2022. A bibliometric analysis on the effects of land use change on ecosystem Services: Current status, progress, and future directions. Sustainability, 14, 3079.

Yang H. 2013. Livestock development in China: Animal production, consumption and genetic resources. Journal of Animal Breeding and Genetics, 130, 249–251.

Zaib G, Cui H, Hu X. 2022. Global network mapping research landscape and trends of the endogenous retroviruses: A look through bibliometric analysis. Rendiconti Lincei Scienze Fisiche e Naturali, 33, 663–672.

Zaib G, Hu X, Cui H. 2023. Global Maps of avian leukosis viruses: Research trends and themes based on networking. Veterinary Sciences, 10, 16.

Zhang C, Xu T, Feng H, Chen S. 2019. Greenhouse gas emissions from landfills: A review and bibliometric analysis. Sustainability, 11, 2282.

Zhang L, Tian H, Shi H, Pan S, Chang J, Dangal S R S, Qin X, Wang S, Tubiello F N, Canadell J G, Jackson R B. 2022. A 130-year global inventory of methane emissions from livestock: Trends, patterns, and drivers. Global Change Biology, 28, 5142–5158.

Zhang X, Estoque R C, Xie H, Murayama Y, Ranagalage M. 2019. Bibliometric analysis of highly cited articles on ecosystem services. PLoS ONE 14, e0210707.

Zhang X, Li F, Li X. 2021. Bibliometric analysis of ecological compensation and its application in land resources. Landscape and Ecological Engineering, 17, 527–540.

[1]	Yunlong Liu, Mi Zhou, Qiyu Diao, Tao Ma, Yan Tu. Seaweed as a feed additive to mitigate enteric methane emissions in ruminants: Opportunities and challenges[J]. >Journal of Integrative Agriculture, 2025, 24(4): 1327-1341.
[2]	Hairen Shi, Pei Guo, Jieyan Zhou, Zhen Wang, Meiyue He, Liyuan Shi, Xiaojuan Huang, Penghui Guo, Zhaoxia Guo, Yuwen Zhang, Fujiang Hou. Effects of stocking rate on growth performance, energy and nitrogen utilization, methane emission, and grazing behavior in Tan sheep grazed on typical steppe[J]. >Journal of Integrative Agriculture, 2025, 24(4): 1234-1245.
[3]	Gang Fu, Guangyu Zhang, Huakun Zhou. Effects of long-term experimental warming on phyllosphere epiphytic bacterial and fungal communities of four alpine plants[J]. >Journal of Integrative Agriculture, 2025, 24(3): 799-814.
[4]	DONG Li-feng, JIA Peng, LI Bin-chang, WANG Bei, YANG Chun-lei, LIU Zhi-hao, DIAO Qi-yu. Quantification and prediction of enteric methane emissions from Chinese lactating Holstein dairy cows fed diets with different dietary neutral detergent fiber/non-fibrous carbohydrate (NDF/NFC) ratios[J]. >Journal of Integrative Agriculture, 2022, 21(3): 797-811.
[5]	ZHANG Hao, SUN Ling-wei, WANG Zi-yu, MA Tie-wei, DENG Ming-tian, WANG Feng, ZHANG Yan-li. Energy and protein requirements for maintenance of Hu sheep during pregnancy[J]. >Journal of Integrative Agriculture, 2018, 17(01): 173-183.
[6]	JI Shou-kun, JIANG Cheng-gang, LI Rui, DIAO Qi-yu, TU Yan, ZHANG Nai-feng, SI Bing-wen. Growth performance and rumen microorganism differ between segregated weaning lambs and grazing lambs[J]. >Journal of Integrative Agriculture, 2016, 15(4): 872-878.

No Suggested Reading articles found!

Viewed

Full text

Abstract

Cited

Shared

Discussed

Cite this article:

About JIA

Editorial board

For authors