The databases selected as the main sources of data for this study were the WOS Core Collection Science Citation Index Expanded (SCI Expanded), Social Sciences Citation Index (SSCI), and Emerging Sources Citation Index (ESCI). Using an advanced search formula with the following criteria animal production methane or carbon dioxide climate change a thorough search pertaining to research on how animal production affects climate change was carried out. A total of 6558 WOS documents were found with this search; the data was current as of 5 December 2023.

Research design, data collecting, data analysis, data visualization, and interpretation are the five steps of the process (Zupic and Cater, 2015; Secinaro et al., 2020). It was developed three particular research questions during the research design stage, focusing on three areas: the overall patterns, the published paper status, and the future research trend of spatial accessibility. We decided to make spatial accessibility the main emphasis of our study. The Web of Science databases SSCI, E-SCI, and SCI Expanded were chosen as the research data sources. A literature search yielded 6,558 items in total, which paved the way for the data gathering phase. To ensure the dependability of scientific communication, encourage important research topics, and ensure the accuracy of scientific results, a document type filter was implemented on WOS (Secinaro et al., 2020). Articles, review articles, and data papers were all included in this filter. Additionally, because the research unit is based on years, it used biblioshiny’s filter tool to choose papers published between 1990 and 2023 for the study’s timeframe in order to preserve consistency. As we moved on to the data analysis phase, R software was used to perform explanatory bibliometric analysis, which produced a large matrix that included every document. Afterwards, VOSviewer and Biblioshiny were used in the data visualization phase to create science maps, nation collaboration networks, and charts. The study’s last phase, interpretation, involves extrapolating significant findings and conclusions from the total data gathered during the investigation. Science mapping, a method that makes use of bibliometric instruments to identify patterns in scientific research, was applied in Bibliometrix analysis (Aria and Cuccurullo, 2017). It adds quantitative accuracy to the subjective evaluation of the literature and offers convincing proof of theoretically defined categories in articles (Velasco-Muñoz et al., 2018). Keywords are frequently used in academic publications to categorize their main research questions and topics. These keywords offer a succinct synopsis of the paper’s main ideas and are useful instruments for defining both the paper’s content and the underlying research question. A criterion of at least five occurrences of each term was set for inclusion in the word cloud analysis in order to produce an accurate depiction of the most commonly occurring keywords (Arnaboldi et al., 2021). Trend topics present a set of themes arranged according to their density on the y-axis and centrality on the x-axis in a two-dimensional space (Cobo et al., 2011). Bradford’s law is used in the study to characterize how article titles are distributed in a specific area. According to Singh et al. (2016), there are declining returns when the body of literature is published completely, supporting their claim that the law operates on the basis of centric productivity zones.

The Walktrap algorithm is used to generate the co-occurrence network, which is based on the conceptual structure themes and trends. The normalization approach employed is association. Words that occur together in a document have a network relationship. The various themes in our search field and the related problems are understood through the use of the conceptual structure. It also emphasizes how research has changed throughout time (Pons and Latapy, 2005).

Collaboration network analysis reveals the research partners of the authors for these ideas. The stronger and more intense the bibliometric association between two authors or two studies, the larger the label size and the thicker the line. The number of connections in the study increases with node size (Tang et al., 2021).

Figure 1 illustrates the distribution of publications by year on the impact of animal production on climate change in the Web of Science. It is noteworthy that throughout the last 34 years (1990–2023), the publications have shown an irregular growing tendency. It can be seen that, with a peak of publications in 2022 (947), more than half of the chosen articles have been published in the last 6 years (2018–2023, 3,643 out of 6,558 papers). This demonstrates how scientists are paying more and more attention to how animal production affects climate change.

In the field, directions and hotspots can be found using keyword analysis. The top ten keywords in the field “effect of animal production on climate change” are listed in Table 2. The most frequently occurring term was “carbon dioxide,” with 3,251 occurrences. This was followed by “animals,” 2060 occurrences, “methane,” 1,555 occurrences, “climate change,” 850 occurrences of “cattle,” 779 occurrences of “carbon,” 770 occurrences of “ecosystem,” 729 occurrences of “rumen,” 705, occurrences of “animal feed,” and 678 occurrences of “diet.” In the study of keywords, the cattle type has the most frequency among animal types.

Livestock productivity is highly dependent on the surroundings of the animals. Because the environment provides the necessary inputs (water, solar radiation, and temperature) for plant and animal growth, agricultural production is impacted by climate change. The viability of cattle production systems, ecosystems, and the existence of many species are all seriously threatened by climate change (Moss et al., 2000).

The most often used terms in the articles are shown in Figure 2. The word clouds approach was used to identify the most popular keywords. Word clouds, a type of data mining technique, are known to display the most frequently used terms in a passage or text. The word in the center, as is well known, displays the most often used word related to that topic. The word is used less frequently as the word size gets smaller and gets farther from the center. The most often searched terms are, according to the findings, carbon dioxide (f=3,251), animals (f=2060), methane (f=1,555), climate change (f=1,373), and cattle (f=850). Furthermore, Figure 3 displays the terms that were most often used in the abstracts of the papers that were examined.

Using Web of Science, the ten writers with the most articles have been obtained. Wang Y had the top ranking with 107 articles, trailed by Liu Y at 89, Wang X at 66, Zhang Y at 63, Wanapat M at 60, Wang J at 57, Zhang X at 56, and Dijkstra J at 53 (Figure 4).

R software analysis was used to determine the author’s influence and the date the article was published. Since 2001, Dijkstra J has been concentrating on this area, and his publications will be released until 2022. According to Figure 5, he is the author with the greatest experience in this field. Since Wang Y is the most prominent author on a publication about the impact of animal production on climate change, he has made the most contribution in this field. He has dedicated a significant amount of time (2011–2023) to this field of study (Figure 5).

A graphic that uses Lotka’s Law to illustrate authors’ productivity in the context of the research field under study is shown in Figure 6. The figure displays the standard Lotka’s Law distribution, which is defined as having more authors with less publications and fewer authors with more publications. This is to be expected. The differences in the productivity of research across writers in the discipline are reflected in this long-tailed distribution pattern. There could be a number of consequences from the significant percentage of authors who have only a few articles.

The top ten academic institutions and organizational connections that provide the greatest contributions to the study of how animal agriculture affects climate change are shown in Table 3. The universities with the most scientific contributions in the area of how animal production affects climate change are Pennsylvania State University in University Park, United States; it is followed by Western Sydney University in Penrith, Australia, and Khon Kaen University in Khon Kaen, Thailand. Universities from all around the world are expressing interest in conducting research in this field.

Figure 7 displays the findings from the analysis of the affiliations the writers are a part of. The number of documents reviewed determines how the number of publications changed throughout the course of the analysis. The top five affiliations in terms of publications are displayed.

Examining the data in Figure 7, it is evident that the five universities that publish the most on the topic have published more often, particularly in recent years. As seen by the growth in recent years, “The Pennsylvania State University” is the most prolific institution, followed by “Hawkesbury Institute for The Environment, Western Sydney University, Penrith".

The top ten publications that publish on the subject of how animal production affects climate change are shown in Table 4 and Figure 8. The Bibliometrix package (4.3.1) in the R programming language was used to collect the data that are provided in. The quantity of papers published determines the importance. The Science of the Total Environment (f=370) and Global Change Biology (f=310) are placed second and third, respectively, after Environmental Science and Pollution Research International (f=504).

Journals can be classified into multiple zones based on the quantity of articles they include, as per Bradford’s law. According to Bradford’s law of scattering, the primary sources of information for knowledge management studies are the following: Animal: An International Journal of Animal Bioscience, Animals: An Open Access Journal From MDPI, Proceedings of The National Academy of Sciences of The United States of America, The Science of The Total Environment, Global Change Biology, Journal of Dairy Science, Plos One, Journal of Animal Science, Journal of Environmental Management, and Environmental Science and Pollution Research International (Figure 9).

Figure 10 shows an extract from Biblioshiny that shows the evolution of hot subjects and offers important insights into the latest research activities (or direction) between 2017 and 2020. “Quality of life,” “economic development,” “renewable energy,” “technology,” “China,” “greenhouse gases,” and “environmental pollution” were the identified trending subjects based on the gathered material. According to this analysis, a significant portion of climate change researchers continue to concentrate primarily on finding effective ways to modify carbon dioxide. While the focus of the articles published between 2002 and 2004 was on climate change, including policy making and ionophores, the focus of the current publications has switched to keywords like “renewable energy” and “qualify of life” in tandem with the global concerns associated with climate change. In recent years, research has frequently included terms related to nutrition, digestion, animals, methane, carbon dioxide, and climate change.

A thematic map highlighting commonly occurring keywords in animal production affects climate change research that were determined from author keywords is presented in Figure 11. This study has found two clusters, with the top three sentences representing each cluster.

Basic themes, located in the lower-right quadrant, have a significant centrality but low density, indicating their significance as foundational study topics and essential to the development of the discipline. “Animals, methane, and cattle” are among the subjects covered in this quadrant.

Niche themes, or the upper-left quadrant, are characterized by low centrality and high density. Topics that fall into this quadrant include “carbon dioxide, climate change, and carbon,” all of which are extremely detailed, underrepresented, and changing quickly.

Figure 12 s co-occurrence network illustrates how the influence of animal production on research on climate change have changed over time. The size of each network node corresponds to the degree of the term or study topic it represents. It is evident from the first cluster that a few keywords, including “carbon dioxide,” “climate change,” “carbon,” “hydrogen-ion concentration,” and “nitrogen,” were concentrated around this term. The “animals” component of the second cluster includes the following topics: “fermentation,” “methane,” “cattle,” “female,” “rumen,” and “animal feed”. Especially, because it serves as a network link, “animals” is crucial to this cluster.

Figure 13 shows the co-authorship network for research on the implications of livestock production on climate change. The author network’s eight separate clusters show how well various research groups collaborate with one another. The connections between the orange, green, purple, blue, and red clusters imply that the authors in these groups collaborate often. Within their network, some authors Wang Y and Wang J, for example, have published publications more regularly. Dijkstra J, Kebreab E, Weisbjerg MR, and Hristov AN; Wang M, Beauchemin KA, and Wang R; Morgavi DP and Martin C; Lee SS, Patra AK, and Yu Z are among the other active writers in their collaborative structure. In general, the analysis of collaborative networks sheds light on how animal production affects climate change.

China, the United States, and Australia had the most publications among the nations with the highest levels of scientific output in related subjects, as shown in Figure 14 and Table 5.

The degree of publication activity in a given region is shown by the intensity of the blue hue on the nation map; a darker shade of blue indicates a higher number of published papers. On the globe, the red connecting lines represent collaborations between nations; larger lines denote more frequent collaboration. Table 5 lists the country/region collaborations according to the frequency of collaboration, using the Bibliometrix program (version 4.3.1). With 184 relationships, China and the United States exhibited the highest amount of collaboration, followed by the United States and Australia with 143 partnerships and China and Australia with 103 partnerships, according to the results.

Sankey diagrams, or three-field plots, are used in Figure 14 to illustrate the relationships among research themes, affiliations, and publishing sources (Riehmann et al., 2005). The figure efficiently displays the pertinent scientific subjects that affiliations publish on for each publication. The three main areas of research in this field are methane, rumen fermentation, and climate change. Researchers have focused a great deal of emphasis on these subjects because they are essential to the advancement of earth science understanding. The most eminent academic institutions involved in this field of study are the University of California, Western Sydney University, Aarhus University, Nanjing Agriculture University, and Khon Kaen University. These universities have significantly advanced the field, and peers and business experts value the research they provide. The analysis concluded that the best journals to publish research in this field are Nature, The New Phytologist, and Global Change Biology. These esteemed journals are renowned for disseminating top-notch research that expands our understanding of earth science. This data can help researchers choose which journals will best serve their interests and improve the exposure and significance of their work. A thorough overview of the distribution of study topics across various universities and the journals they choose to publish in is given by the plot in Figure 15, which offers insightful information about the trends and patterns of this field’s research. The results have the potential to guide future investigations and joint ventures, resulting in field progress and the creation of long-term remedies for environmental problems.