Bibliometric analysis of management practices in US corn (1990–2020)

Abstract

Bibliometric analysis explores large volume of scientific data, revealing trends and insights in a specific research field. Consistently, a bibliometric analysis of 30 years (1990–2020) was performed within the US corn (Zea mays L.) production using the Scopus database and VOSviewer. Search query was performed within the article title, abstract, and keywords indicative of management practices in corn. Exclusion criterion based on subject area and journals generated a total of 7468 publications. The data analysis revealed contributions from 7327 authors and 47 organizations documented in 69 journals. The top five organizations leading the investigation were United States Department of Agriculture – Agricultural Research Service, Iowa State University, University of Nebraska, University of Illinois, and Purdue University. The most prolific authors were Dr. Rattan Lal (Ohio State University, Columbus, OH), Dr. Douglas L. Karlen (USDA-ARS, Ames, IA), Dr. Kenneth G. Cassman (University of Nebraska, Lincoln, NE), Dr. Lajpat Rai Ahuja (USDA-ARS, Ft. Collins, CO), and Dr. John Walsh Doran (USDA-ARS, Lincoln, NE). Journals with most publications were Agronomy Journal; Soil Science Society of America Journal; Soil and Tillage Research; Crop Science; and Agriculture, Ecosystems & Environment. Furthermore, author keywords differed from queried keywords, and no-till, nitrogen, cover crop, soybean, irrigation, phosphorus, conservation tillage, yield, and water quality were most prominent. Moreover, there was an evident shift in keywords and an observed trend between 1998 and 2020. Overall, these findings allow researchers to explore network maps via the hyperlinks present in papers, identifying research gaps and advancing original studies to bridge gaps in the literature.

Abbreviations
• APSIM
• agricultural production systems simulator
• Av. Cit
• average citation
• Cit.
• total number of citations
• Cl.
• cluster number
• E
• environment
• G
• genotypes
• h-index
• Hirsch's index
• M
• management
• NIFA
• National Institute of Food and Agriculture
• NSF
• National Science Foundation
• SJR
• SCImago journal rank
• SNIP
• source normalized impact per paper
• TLS
• total link strength
• US DOE
• United States Department of Energy
• USAID
• United States Agency for International Development
• USDA-ARS
• United States Department of Agriculture – Agricultural Research Service
• WoS
• Web of Science

INTRODUCTION

Corn (Zea mays L.) is a major staple food all around the world providing carbohydrates, protein, and other nutrients for humans (Ai & Jane, 2016; Taylor & Tanumihardjo, 2010). Corn is cultivated on a large scale contributing approximately $94 billion to the US economy (Saavoss et al., 2021). Corn is used for food, feed, ceramics, paints, and ethanol production (Jiao et al., 2022). There has been continuous improvement in genetic breeding and technological advancement for increasing corn production (Cabrera-Ponce et al., 2019; Mosheim & Schimmelpfennig, 2021; Yadava et al., 2017). However, climate change affects corn production (Prasad et al., 2018; Sharma et al., 2022). Moreover, corn yield stagnation has been noted in Africa and Asia, particularly in India and China (Ray et al., 2012). Furthermore, southern US states such as Mississippi have also shown yield stagnation with a yield gap varying between 2 and 5.6 Mg ha⁻¹ in the last decade (Dhillon et al., 2022). Moreover, a recent study noted an increasing yield gap in maize over 56% of area globally (Gerber et al., 2024). The observed yield gap is worrisome considering population growth and ecosystem maintenance (Power, 2010). It is evident that the interaction between environment (E), genetics (G), and management (M) significantly influences corn production (Assefa et al., 2016). Moreover, with minimal control on E, G is managed for long-term effects, and M is used to control short-term effects (M. Cooper et al., 2021). So, it is imperative to follow the progression and identify key management practices to counter short-term effects for filling the existing yield gap and pursuing research efforts to increase corn production sustainably.

Bibliometric analysis is considered a valuable tool to identify the research gap based on network mapping of keywords, authors, and organizations (de Oliveira et al., 2019). Bibliometric analysis is a scientific computer-assisted review methodology to identify core research or authors, as well as their relationship, based on publications in a given domain or field from large databases such as Scopus, Google Scholar, ProQuest, Pubmed, Dimensions, or Web of Science (WoS) (I. Cooper, 2015; Donthu et al., 2021). The bibliometric analysis is gaining popularity in recent years on diverse topics to recognize the research gap (Donthu et al., 2021; Ellegaard & Wallin, 2015). Several bibliometric studies on topics such as digital agriculture (Sott et al., 2021), corn (Yuan & Sun, 2020), agronomy (Cañas-Guerrero et al., 2013), soil nutrients (Pan et al., 2021), corn–soybean [Glycine max (L.) Merr.] intercropping (Feng et al., 2022) have been conducted. However, the bibliometric analysis specifically on management practices targeted in US corn production is still lacking. Moreover, the exploration of past corn agronomics research will shed light on nuances and lead to future research directions in areas that lacked adequate research.

Therefore, the first objective of the study was to determine top organizations, authors, articles, and journals working collaboratively toward improving corn production via optimizing the management practices for corn production in the United States. The second objective was to identify research trends of corn agronomics in past 30 years and possible future research avenues to fill the existing corn yield gap while maintaining production quality.

Core Ideas

• There has been a consistent increase in corn research in last 30 years.
• United States Department of Agriculture – Agricultural Research Service (USDA-ARS) was the top funding sponsor and leading investigator.
• Queried keywords differed from author keywords, and a trend was observed in their use.
• The top journal with most publications on corn production was Agronomy Journal.

MATERIALS AND METHODS Data acquisition

Elsevier's Scopus provides access to abstract and citation databases of peer-reviewed literature and is an important tool for bibliometric analysis (Singh et al., 2021; Vieira & Gomes, 2009). The documents were searched within article title, abstract, and keywords as “corn” OR “maize” OR “Zea mays” in search documents in Scopus. Subsequently, another search field was added, and keywords related specifically to management practices in corn production such as “planting density” OR “planting date” OR “row configuration” OR “planting depth” OR “crop rotation” OR “soil fertility” OR “nutrient” OR “hybrid” OR “irrigation” OR “harvest practices” OR “tillage” OR “weed control” OR “disease control” were searched, accumulating 42,073 documents. The above keywords were selected to reflect and focus only on the management practices adopted in corn production. Documents were limited to time period between 1990 and 2020 (30 years), country (USA), document type (article), subject area (agricultural and biological sciences), language (English), and source type (journal), which resulted in 8686 documents. The implemented limit excluded 2072 conference papers, 984 reviews, and 823 book chapters. Journals in source titles such as Journal of Dairy Science, Poultry Science, Journal of Applied Poultry Research, Journal of Animal Science, Journal of Economic Entomology, Environmental Entomology, Asian Australasian Journal of Animal Sciences, Animal Feed Science and Technology, and Florida Entomologist were excluded. The excluded journals mainly focus on aspects of animal science, entomology, and related fields, and as such were deemed out of study focus, which reduced the final documents to 7468 documents. The 7468 US-based research publications (documents) from 1990 to 2020 were downloaded in .csv format on March 3, 2023, from the Scopus database. The protocols for Scopus search and data filtering are shown in Table 1. However, it should be noted that the selection of time span for bibliometric analysis is an important decision and should be based on research questions and objectives (Öztürk et al., 2024). As such considering objectives of our study, and changes in corn agronomy over last decades (Erenstein et al., 2022), we limited our analysis to a 30-year period to provide a comprehensive overview of the trends and changes.

TABLE 1 Specific protocol followed to download corn management practices papers from Scopus.

Time period	1990–2020 (30 years)
Search field	Article title, abstracts, keywords
Keywords	“corn” OR “maize” OR “Zea mays”
Search field added (keywords related to management practices)	“Planting density” OR “planting date” OR “row configuration” OR “planting depth” OR “crop rotation” OR “soil fertility” OR “nutrient” OR “hybrid” OR “irrigation” OR “harvest practices” OR “tillage” OR “weed control” OR “disease control”
Limited to	Country (USA), document type (article), subject area (agricultural and biological sciences), language (English), source type (journal)
Excluded journals	Journal of Dairy Science, Poultry Science, Journal of Applied Poultry Research, Journal of Animal Science, Journal of Economic Entomology, Environmental Entomology, Asian Australasian Journal of Animal Sciences, Animal Feed Science and Technology, Florida Entomologist

Analysis

The analyses of documents by year, funding sponsor, and affiliation were analyzed in Scopus. Documents by funding sponsor and affiliation were updated, where the United States Department of Agriculture – Agricultural Research Services (USDA-ARS) and United States Department of Agriculture (USDA) were combined under USDA-ARS.

Scopus was also used to determine Hirsch's index (h-index) of different authors to identify their research impact. Hirsch's index is defined as “A scientist has index h if h of his/her papers have at least h citations each and the rest have fewer than h citations each” (Burrell, 2007; Hirsch, 2005). Journals with the highest citations on CiteScore, source normalized impact per paper (SNIP), and SCImago journal rank (SJR) values were also downloaded from Scopus. Different journal names were entered under the title in Scopus to get the CiteScore. The CiteScore is calculated by “dividing the number of citations in one year to documents published in three previous years” (Fang, 2021). The citation metric, SJR, measures the frequency of citation in other journals within 3 previous years compared to 2 years in calculating the journal impact factor (Choudhri et al., 2015).

Network map visualization

Bibliometric analyses were performed on 7468 documents using a software tool, VOSviewer version 1.6.19 (van Eck & Waltman, 2010). Visualizations in VOSviewer are distance-based, where relatedness of the nodes is depicted by distance between two nodes, enabling bigger network visualization (van Eck & Waltman, 2014). Several network, overlay, and density maps were created based on co-authorship, co-occurrence, bibliographic coupling, and co-citation, as shown in Table 2. The created datafile was selected in VOSviewer to generate a map based on bibliographic data from selected data type, as shown in Figure 1. Data source was chosen as “read data from bibliographic database file,” and a different analysis was conducted as indicated in Table 2. Fractional counting was selected for constructing bibliometric networks in all the analyses to avoid overestimation of highly cited publications and articles with a large reference list such as review articles (van Eck & Waltman, 2014). The total strength of the relatedness was calculated, and documents/keywords with the greatest total link strength were selected. Thesaurus file was created for keyword occurrences where corn, Zea mays, Zea mays L., and Zea were replaced with Maize. Other files for sources and organizations were also created for analysis. The method of association was selected as association strength from the analysis tab. The scale of visualization was selected by changing weights as links, total link strength, documents, citations, or normalized citations based on the analysis. To ensure reader's understanding and enhance clarity of results, detailed explanations of all relevant terms and procedures are provided in Section 3.

TABLE 2 Types and units of analysis in the VOSviewer software.

Types of analysis (5)	Unit of analysis (19)
Co-authorship	Authors
	✓ Organizations
	Countries
Co-occurrence	All keywords
	✓ Author keywords
	Index keywords
Citation	Documents
	Sources
	Authors
	Organizations
	Countries
Bibliographic coupling	✓ Documents
	✓ Sources
	Authors
	Organizations
	Countries
Co-citation	Cited references
	Cited sources
	✓ Cited authors

Note: The types of analysis selected are bolded. The check marks in the unit of analysis represents the units used for bibliometric analysis.

FIGURE 1. Flowchart to create bibliometric network maps in VOSviewer.

RESULTS AND DISCUSSION Publication trend and funding sponsors

Overall, there has been a linear increase in the number of publications on corn from 1990, and these numbers quadrupled from 95 to 444 publications in last 30 years (Figure 2A). Although the trend has a positive slope, a few years showed lower number of publications such as in years 2001, 2008, and 2010 and higher number of publications such as in 1999 and 2017 (Figure 2A). However, it is hard to speculate the specific reasons behind this fluctuation as bibliometric analysis does not reveal these intricacies. The USDA-ARS (418), National Science Foundation (270), National Institute of Food and Agriculture (254), US Department of Energy (89), and US Agency for International Development (71) were top five funding sponsors in past 30 years (Figure 2B). Overall, the total publications were higher when universities were combined compared to the USDA-ARS. However, individually based on the affiliation data presented in Figure 2C, the USDA-ARS (2046 publications) holds the top position with ∼3.5 times higher publications than Iowa State University, which ranks second with 592 publications. University of Nebraska holds the third place with 588 publications, closely following Iowa State University.

FIGURE 2. Publication trend in the past 30 years from 1990 to 2020. Numbers on top of the bars represent the total number of publications per year (A); publication trend based on funding sponsors in the past 30 years from 1990 to 2020 from Scopus. Numbers on top of the bars represent the total number of publications sponsored by each organization (B); publication trend based on author affiliation in the past 30 years from 1990 to 2020. Numbers in stacked bars represent the total number of publications contributed by each institute, as shown in the legend (C). NIFA, National Institute of Food and Agriculture; NSF, National Science Foundation; USAID, United States Agency for International Development; USDA-ARS, United States Department of Agriculture – Agricultural Research Service; US DOE, United States Department of Energy.

Bibliographic coupling–document analysis

Bibliographic coupling is a similarity measure, and it occurs when two studies refer to a third common study (Kessler, 1963). In other words, two documents or research papers are bibliographically coupled when both of them cite the same articles in their study (Tan, 2022). The links in bibliographic coupling refer to the number of these connections between documents, and link strength is numerical value associated with each bibliographic coupling. Moreover, the coupling strength increases as they share more citations to other documents. Bibliographic coupling performs better than co-citation and citation for finding the relationships between documents, authors, and journals (Boyack & Klavans, 2010; Ma et al., 2022). Moreover, bibliographic coupling is useful in finding related research and authors. The minimum number of citations of a document was chosen as five, and 6462 documents met the threshold out of 7468. The total strength of the bibliographic coupling links with other documents was calculated, and the greatest total link strength was selected in 1000 documents. The largest set of connected items consisted of 987 items, 17 clusters, 15,625 links, and 8919 total link strength (Figure 3). Bibliographic coupling revealed the top 10 publications as shown in Table 3. Top 10 cited papers were mostly on management practices focused on improving the soil quality, crop yield, carbon sequestration, no-till practices, cover cropping, and ecosystem services. The citations of top 10 published papers ranged from 304 to 1648. The top five papers were published in Soil Science Society of America (1648), Agriculture, Ecosystems & Environment (569), Crop Science (511), Field Crops Research (440), and Agriculture & Forest Meteorology (418). Specifically, the top 10 cited papers can be categorized in three groups: (a) soil and carbon sequestration, (b) crop yield and farming techniques, and (c) soil quality and microbial activity. Two meta-analyses were among the top-cited papers, and all these papers were published between 1994 and 2015.

FIGURE 3. Network map of bibliographic coupling–document analysis. Each node in the network map represents the reference of a publication. The larger the node size, the document is more bibliographically coupled. The connecting lines represent the coupling between different documents (https://tinyurl.com/2gyjnp2c).

TABLE 3 Top 10 research publications based on bibliographic coupling–document analysis.

Rank	Title	Journal	Cit.	TLS	Cl.	Link	Reference
1	Soil organic carbon sequestration rates by tillage and crop rotation	Soil Science Society of America Journal	1648	48	1	91	(West & Post, 2002)
2	replacing bare fallows with cover crops in fertilizer-intensive cropping systems: A meta-analysis of crop yield and N dynamics	Agriculture, Ecosystems & Environment	569	41	1	90	(Tonitto et al., 2006)
3	Can changes in canopy and/or root system architecture explain historical maize yield trends in the U.S. Corn Belt?	Crop Science	511	27	2	72	(Hammer et al., 2009)
4	When does no-till yield more? A global meta-analysis	Field Crops Research	440	17	1	43	(Pittelkow et al., 2015)
5	Annual carbon dioxide exchange in irrigated and rainfed maize-based agroecosystems	Agricultural and Forest Meteorology	418	26	3	64	(Verma et al., 2005)
6	Determining soil quality indicators by factor analysis	Soil and Tillage Research	372	17	1	50	(Shukla et al., 2006)
7	Bacillus thuringiensis (Bt) toxin released from root exudates and biomass of Bt corn has no apparent effect on earthworms, nematodes, protozoa, bacteria, and fungi in soil	Soil Biology and Biochemistry	362	29	6	4	(Saxena & Stotzky, 2001)
8	A framework for evaluating ecosystem services provided by cover crops in agroecosystems	Agricultural Systems	348	17	1	49	(Schipanski et al., 2014)
9	Crop residue effects on soil quality following 10-years of no-till corn	Soil and Tillage Research	309	0	0	0	(Karlen et al., 1994)
10	Soil microbial biomass carbon and nitrogen as affected by cropping systems	Biology and Fertility of Soils	304	15	8	7	(Moore et al., 2000)

Abbreviations: Cit., total number of citations; Cl., cluster number; TLS, total link strength.

Bibliographic coupling–source analysis

The bibliographic coupling–source analysis helps in identifying the most active journals in the past 30 years. The selected minimum number of documents of a source and minimum number of citations of a source was chosen to be 20. Sixty-nine sources met the threshold out of 577 journals. For each of the 69 journals, the total strength of bibliographic coupling links with other journals was calculated, and journals with the greatest total link strength were selected. The visualization weight was changed from documents to citations in VOSviewer. The minimum strength was changed to 20, and maximum lines were changed from 1000 to 200 to visualize the networks distinctly. There were three clusters, 1560 links, and 17,442 total link strength. The clusters were distinguished by three different colors as red, green, and blue (Figure 4).

FIGURE 4. Bibliographic coupling–source analysis. Each node in the network map represents a journal. The connecting lines represent the coupling between different journals. Journals close to each other are closely related, and the thicker connecting line indicates a higher frequency of bibliographic coupling between two journals (https://tinyurl.com/2emgvl8z).

Red cluster was composed of journals such as Agronomy Journal that publishes novel research in “agriculture, natural resources, soil science, crop science, agroclimatology, agronomic modeling, production agriculture, and instrumentation studies”; Soil and Tillage Research that explores “the physical, chemical and biological changes in the soil caused by tillage, and field traffic”; and Soil Science Society of America Journal that publishes “basic and applied soil research in soil chemistry, soil physics, soil pedology, and hydrology in agricultural, forest, wetlands, and urban settings.”

Green cluster comprised journals such as Crop Science that publishes “significant scientific advances in crop sciences”; Weed Technology publishes articles “focused on understanding how weeds are managed”; Weed Science that publishes “fundamental research in herbicide resistance, weed control tools, chemistry and genetics”; and Pest Management Science is “an international journal of research and development in crop protection and pest control.”

Blue cluster included journals such as Agricultural Water Management that publishes papers related to “science, economics, and policy of agricultural water management”, and Field Crop Research publishes articles on “original experimental and modeling research, meta-analysis, original technologies at crop, field, farm, and landscape levels.”

The analysis also provided top journals listed in Table 4, where the number of citations ranged from 1297 to 25,748. The number of articles ranged from 80 (Agricultural Systems and Journal of Agricultural and Food Chemistry) to 675 (Agronomy Journal). Therefore, the top five journals with most publications on corn production were Agronomy Journal; Soil Science Society of America Journal; Soil and Tillage Research; Crop Science; and Agriculture Ecosystems, and Environment. The CiteScore calculation from Scopus showed top 10 journals, ranging from 3.2 (Agronomy Journal) to 11 (Soil Biology and Biochemistry), as shown in Table 5. CiteScore is the average number of citations per article over a 3-year period that measures the impact of journals. Agronomy Journal published 675 research papers, two times more than Soil Science Society of America Journal publishing 331 papers. However, both journals had lower CitesCore of 3.2 and 3.8, respectively. Therefore, researchers do have a choice while selecting the journal for publication, whether they seek to reach more people or have more impact.

TABLE 4 Top 20 journals that published studies related to management practices in US corn in past 30 years (1990–2020).

Rank	Journal	Article	Citation	Av. Cit.	TLS
1	Agronomy Journal	675	25,748	38.14	5431.64
2	Soil Science Society of America Journal	331	18,160	54.86	3209.49
3	Soil and Tillage Research	305	5624	18.43	2048.91
4	Crop Science	284	10,677	37.59	2253.76
5	Agriculture, Ecosystems & Environment	237	14,450	60.97	1732.22
6	Agricultural Water Management	201	6974	34.69	1294.81
7	Field Crops Research	192	8958	46.65	1733.77
8	Weed Science	162	10,120	62.46	1373.61
9	Plant and Soil	160	2270	17	741.08
10	Soil Biology and Biochemistry	154	8483	55.08	518.05
11	Weed Technology	137	2708	19.77	1165.54
12	Theoretical and Applied Genetics	134	3394	25.33	423.87
13	Agricultural and Forest Meteorology	114	3702	32.47	468.95
14	Biomass and Bioenergy	113	6969	61.67	130.83
15	Biology and Fertility of soils	103	3412	33.13	250.22
16	Plant Physiology	101	1656	16.40	247.84
17	Plant Cell	97	1297	13.37	241.44
18	Transactions of the American Society of Agricultural Engineers	86	3485	40.52	161.46
19	Agricultural Systems	80	5411	67.64	595.59
20	Journal of Agricultural and Food Chemistry	80	1669	20.86	304.93

Abbreviations: Av. Cit., average citation (calculated by dividing citations from articles); TLS, total link strength.

TABLE 5 Top 10 journals based on highest CiteScore.

Source title	CiteScore	2017–2020 citations	2017–2020 documents	Percentage of cited	SNIP	SJR	Publisher
Soil Biology and Biochemistry	11	13,810	1256	85	2.016	2.91	Elsevier
Field Crops Research	9.5	9423	988	88	2.379	1.951	Elsevier
Agriculture, Ecosystems & Environment	9	11,909	1325	87	1.896	1.844	Elsevier
Agricultural and Forest Meteorology	8.9	12,437	1390	87	1.776	1.837	Elsevier
Soil and Tillage Research	8.6	8063	943	88	2.182	1.708	Elsevier
Agricultural Water Management	7.6	12,433	1626	87	2.027	1.493	Elsevier
Weed Science	4.1	1254	308	78	1.353	0.914	Cambridge University Press
Crop Science	3.8	4128	1089	75	1.132	0.76	Wiley-Blackwell
Soil Science Society of America Journal	3.8	2408	636	71	0.994	0.836	Wiley-Blackwell
Agronomy Journal	3.2	4137	1295	68	1.179	0.752	Wiley-Blackwell

Note: CiteScore was calculated by dividing number of citations on 2017–2020 by the number of documents in the years 2017–2020.

Abbreviations: SJR, scientific journal rankings; SNIP, source-normalized impact per paper.

Co-authorship–organization analysis

Co-authorship is the collaboration between different authors to disseminate knowledge (Newman, 2004). Co-authorship–organization analysis determines the organizations that collaborated for research and publications. The maximum number of organizations co-authored per document was chosen as 25. The minimum number of documents and citations of an organization was chosen to be five. Scopus data on organizations were not harmonized since organization names were not in a consistent format. Therefore, a thesaurus file was created by entering only the university's name in place of the whole address. The organization from other countries was not included in the analysis since the objective was to determine top organization in the United States. Pioneer Hi-Bred International was renamed to Dupont Pioneer in the thesaurus file. After uploading the thesaurus file, 45 organizations met the threshold out of 15,970 organizations. Figure 5 shows the largest set of connected items consisting of 33 organizations, eight clusters, 90 links, and 145 total links strength since 12 organizations in the network were not connected. The top 10 organizations that frequently collaborated were Iowa State University, University of Nebraska, University of Illinois, Purdue University, Michigan State University, University of Wisconsin, Kansas State University, North Carolina State University, University of Minnesota, and Cornell University, as shown in Table 6. The ranking is based on total documents that were produced through collaboration. As shown in Figure 5, Iowa State University, being the top organization, collaborated with 15 organizations. Top eight organizations with which Iowa State University collaborated in the past 30 years were University of Nebraska, University of Wisconsin, Michigan State University, Mississippi State University, Purdue University, the USDA-ARS, Iowa, Southern Illinois University, and North Carolina State University. Similarly, University of Nebraska had collaboration with Purdue University, Kansas State, Cornell University, Michigan State, University of Illinois, North Carolina State, and University of Missouri.

FIGURE 5. Network map of co-authorship-organization. Each node represents organization conducting research related to corn and management practices. The connecting link represents co-authorship (https://tinyurl.com/2cnnd4ch).

TABLE 6 The top 10 organizations based on co-authorship–organization analysis.

Rank	Organization	Documents	Cit.	TLS	Av. Cit.
1	Iowa State University	239	9447	46	39.53
2	University of Nebraska	145	4205	35	29
3	University of Illinois	115	3744	19	32.56
4	Michigan State University	83	2781	21	33.5
5	Purdue University	83	2603	22	31.36
6	University of Wisconsin	64	2691	17	42.05
7	Kansas State University	59	1768	14	29.97
8	University of Minnesota	50	2464	10	49.28
9	Cornell University	48	3818	6	79.54
10	North Carolina State University	46	1990	12	43.26

Abbreviations: Av. Cit., average citation (calculated by dividing citations by documents); Cit., total number of citations; TLS, total link strength.

Co-citation-cited authors analysis

Co-citation is the citation of two different documents by one document or when two documents are cited together in the same publication (Small, 1973). The two different research articles are referred as co-cited when they both appear in a common research article. Co-citation is a measure revealing how impactful the author is in a scientific community (White & Griffith, 1981). According to the analysis, there have been 7327 authors with a minimum of 20 citations out of 206,339 authors studying corn in the past 30 years. For each of the 7327 authors, the total strength of co-citation links with other authors was calculated in VOSviewer. The 1000 authors with the greatest total link strength were selected for creating the network map. There were six clusters of several authors where cluster one is denoted by red color, cluster two is denoted by green color, cluster three is denoted by blue color, cluster four is denoted by yellow color, cluster five is denoted by purple color, and cluster six is denoted by light blue color, as shown in Figure 6. Based on the different clusters, authors in the same cluster are co-cited, and they are correlated with each other in the same cluster. Top 10 authors are shown in Table 7, and their citations ranged from 790 to 2228 with the h-index ranging from 48 to 129. The h-index of each author was retrieved from Scopus as shown in Table 7. The top 10 most prolific authors were Dr. Rattan Lal (Ohio State University), Dr. Douglas L. Karlen (USDA-ARS, IA), Dr. Kenneth G. Cassman (University of Nebraska), Dr. Lajpat Rai Ahuja (USDA-ARS, CO), Dr. John Walsh Doran (USDA-ARS, NE), Dr. Keith Paustian (Colorado State University), Dr. Liwang Ma (USDA-ARS, CO), Dr. Terry A. Howell (USDA-ARS, TX), Dr. Gerrit Hoogenboom (University of Florida), and Dr. Cynthia A. Cambardella (USDA-ARS, IA). Most of the top authors were affiliated with the USDA-ARS. Furthermore, it should be noted that almost all the top-cited authors are either retired, near retirement, or late-career professionals.

FIGURE 6. Network map of co-citation-cited author. Each node represents an author. Nodes closer to each other represent co-citation between other authors. Nodes far from each other indicate less frequent co-citation (http://tinyurl.com/23vnllhs).

TABLE 7 Top 10 authors based on co-citation-cited authors analysis.

Rank	Author	Organization	Citation	TLS	h-index
1	Dr. Rattan Lal	Ohio State University, Columbus, OH	2228	2005.05	129
2	Dr. Douglas L. Karlen	USDA-ARS, Ames, IA	1500	1435.68	66
3	Dr. Kenneth G. Cassman	University of Nebraska, Lincoln, NE	1012	956.05	82
4	Dr. Lajpat Rai Ahuja	USDA-ARS, Ft. Collins, CO	935	850.98	53
5	Dr. John Walsh Doran	USDA-ARS, Lincoln, NE	868	837.01	56
6	Dr. Keith Paustian	Colorado State University, Ft. Collins, CO	846	823.23	80
7	Dr. Liwang Ma	USDA-ARS, Ft. Collins, CO	831	772.35	45
8	Dr. Terry A. Howell	USDA-ARS, Bushland, TX	823	746.77	57
9	Dr. Gerrit Hoogenboom	University of Florida, Gainesville, FL	811	750.32	66
10	Dr. Cynthia A. Cambardella	USDA-ARS, Ames, IA	790	775.12	48

Note: h-index, Hirsch's index; TLS, total link strength.

Author keywords co-occurrence analysis

Author keywords are the terminologies mentioned in the keywords section by author to summarize the content of scientific research articles (Lu et al., 2020). Author keywords might not involve the terms used in the title and abstracts (Gbur & Trumbo, 1995). Author keywords co-occurrence analysis is the existence of different words occurring concurrently in a different set of research articles. Keyword co-occurrence analysis is used to identify the thematic clusters and the relationship between different research fields (González et al., 2018). Visualization of the keyword co-occurrence analysis provides directions for future and interdisciplinary research (Radhakrishnan et al., 2017). In VOSviewer, 780 out of 11,866 keywords met the threshold of a minimum five times of keyword occurrence in a research article. The network mapping resulted in 15 clusters, 10,553 connecting links, and 5866 total link strength. The number of clusters was reduced from 15 to four by changing the clustering resolution from 1 to 0.50 in the analysis section of the VOSviewer software. The network map of author keyword co-occurrence with four clusters is shown in Figure 7. Evidently, the word “maize” was the biggest node. Node size represents the number of publications in which the keyword appeared. The larger the size of a node, higher the frequency of that keyword in several publications. The distance and linkages between the keywords determine the co-occurrence of different keywords in the same articles. Smaller distance between two keywords indicates that the keywords appeared together frequently. If the distance between two keywords is large, the frequency of keyword co-occurrence is lower.

FIGURE 7. Network map of author keyword co-occurrence. Each node represents a keyword term. Node size indicates the number of articles that authors listed in the publication. The connecting lines indicate the co-occurrence between different keywords. The closer the keywords, they appear together more frequently in the publication (https://tinyurl.com/2glg8c3m).

The top 10 keywords based on the number of occurrences were maize (1294), no-till (245), nitrogen (212), crop rotation (201), cover crop (179), tillage (177), soybean (139), irrigation (122), and phosphorus (116). The four different clusters in a network map represent four different corn research areas. The cluster of keywords with the same color represents the co-occurrence of those keywords together in a publication. The four different clusters focused on scientific theme are listed below.

Red cluster (309 items) represents studies that include keywords such as biochar, carbon sequestration, crop rotation, conservation tillage, leaching, root exudates, biofuel, greenhouse gases, run-off, irrigation, modeling, water quality, crop yield, no-till, legumes, leaching, bioenergy, agroforestry, cover crop, best management practices, and so forth.

Green cluster (240 items) represents biotechnology and breeding studies with keywords such as Aspergillus flavus, Aspergillus parasiticus, Bacillus thuringiensis, Fusarium, Mycorhizzae, planting breeding, plant resistance, hybrids, transgenic plants, corn earworm, mycotoxin, insect resistance management, forage, quantitative trait locus, transgenic maize, stomatal conductance, biological control, planting date, and so forth. Maize was shown in green cluster.

Blue cluster (146 items) represents weed control, herbicide resistance studies with keywords such as glufosinate, glyphosate, mulch, resistance management, integrated weed management, dicamba, weed, plant height, kernel weight, competition, plant population, nicosulufron, atrazine, mulch, population dynamics, allelopathy, absorption, cytochrome p450, weed interference, root-knot nematode, and so forth.

Yellow cluster (85 items) represents crop modeling studies with keywords such as Landsat, normalized difference vegetation index, remote sensing, transpiration, nutrient use efficiency, evapotranspiration, yield potential, simulation model, deficit irrigation, phenology, decision support system, bioeconomic model, vegetation indices, water use efficiency, nitrogen use efficiency, yield, irrigation, decision support system for agrotechnology transfer, root zone water quality model, precision agriculture, and so forth.

Some keywords are not linked to any other keywords such as herbicide efficacy; perennial weed control, weed species such as Panicum dichotomiflorum (PANDI), Digitaria sanguinalis L. (DIGSA), Xanthium strumarium L. (XANST), Setaria glauca L. (SETLU), Ipomoea hederacea L. (IPOHE), and Ipomoea lacunose (IPOLA); and application timing in blue cluster. Semi-arid and Bowen ratio— the ratio between sensible heat flux and latent-heat flux, used to calculate evapotranspiration (Bowen, 1926)— is not linked to any other keywords in yellow cluster. Nitrogen movement, microbial communities, agricultural intensification, Hairy vetch (Vicia villosa), nitrogen fixation, and microbial ecology in red cluster and genetic gain, silage, protease, and dry matter in green cluster are not linked to another keyword.

The overlay visualization shows the keyword co-occurrence based on different years between 1990 and 2020 (Figure 8). The dark purple nodes show the keywords more prevalent in the years 1991–1998. The light purple nodes are prevalent in the years 2000–2005. The orange nodes are present in the years 2008–2013, and yellow nodes are prevalent in the years 2014–2020, as shown in Figure 8. The close analysis of keyword occurrence helps in identifying the past and present status of scientific studies. On close analysis of overlay visualization (Figure 8), it is evident that between 1990 and2000, keywords such as plant resistance, tissue culture, plant growth, alternative agriculture, and nitrogen fixation were more frequently studied. Between 2000 and 2010, keywords such as integrated weed management, conservation tillage, transgenic maize, hybrids, Bacillus thruingiensis, Aspergillus flavus, and mycotoxin were studied. In 2012–2015, evapotranspiration, water use efficiency, irrigation, crop simulation, remote sensing, and best management practice terms were extensively used in research studies. During 2016–2020, keywords such as yield gap, ecosystem services, agricultural production systems simulator (APSIM), soil microbial community, microbiome, and food security were of strong interest to the scientific community. The above analysis can help researchers find research gaps to work collaboratively in finding solutions to improving corn production. The nodes that are not connected to other nodes can be used to combine areas for advancement in research ideas.

FIGURE 8. Overlay map of author keyword co-occurrence. Different colors represent time in which research was performed between 1990 and 2020.

To visualize keywords in depth, the minimum number of occurrences of a keyword was changed from 5 to 15, resulting in 199 keywords (Figure 9). The years in analysis dropped from 1990–2020 to 1998–2020. The resolution of clustering was chosen as 1, which delivered nine clusters. The minimum strength of connecting lines was changed from 0 to 0.5, which decreased the number of connecting links from 3315 to around 200 links for better visualization. Label was changed from circles to frames, and the color was changed to plasma from pre-defined colors. The keywords were exported to visualize the total link strength to determine the shift in usage of keywords over the period of 5 years (Table 8). The period of 1998–2003 focused on studying different weeds and reduce weed competition. During 2003–2008, research shifted to the implementation of management practices such as crop rotation, tillage, integrated weed management, Bt crop, Aspergillus flavus, run-off, and improving soil quality. Research in the years 2009–2014 shifted from tillage to no-till, incorporation of cover crop, climate change, soil organic carbon, and modeling. Recent research during 2015–2020 emphasized on conservation agriculture, soil health, biochar, APSIM, and food security. Keywords related to nutrients such as nitrogen, phosphorus, and nutrient cycling spanned from year 2003 to 2020.

FIGURE 9. Overlay map of author keyword co-occurrence. Different colors represent time in which research was performed between 1998 and 2020. Node size indicates the number of articles that authors listed in the publication. The connecting lines indicate the co-occurrence between different keywords. The closer the keywords, they appear together more frequently in the publication. The hyperlink showing network map (https://tinyurl.com/27pjno9t).

TABLE 8 List of keywords in different year ranges acquired from overlay map of keyword co-occurrence analysis.

1998–2003			2003–2008			2009–2014			2015–2020
Keyword	cl.	TLS	Keyword	cl.	TLS	Keyword	cl.	TLS	Keyword	cl.	TLS
Abuth	4	24	Maize	3	860	No till	1	210	Conservation agriculture	6	34
Cheal	4	24	Crop rotation	1	165	Nitrogen	7	200	Biochar	1	32
Amare	4	16	Tillage	1	161	Cover crop	1	166	APSIM	2	17
Earthworm	1	15	Conservation tillage	4	97	Soybean	9	137	Water productivity	2	17
Nematode	1	15	Water quality	7	89	Irrigation	2	104	Soil health	1	17
Setfa	4	15	Soil organic matter	1	81	Phosphorus	7	103	Nutrient cycling	1	15
Nicrosulfuron	4	12	Soil quality	1	62	Evapotranspiration	2	93	Food security	6	13
			Cropping system	1	59	Yield	2	90	Growth performance	3	8
			Run-off	7	53	Soil organic carbon	1	81
			Integrated weed management	4	41	Wheat	3	77
			Crop residue	1	39	Climate	6	64
			Bt	5	39	Manure	1	63
			Resistance	5	38	Water use efficiency	2	55
			Sustainable agriculture	1	35	Grain yield	2	49
			N fertilizer	1	34	Carbon sequestration	1	48
			Atrazine	4	33	Fertility	6	46
			Aflatoxin	5	33	Biofuel	8	41
			Conventional tillage	6	31	Cotton	9	41
			Economic analysis	4	30	Modeling	2	39
			Aspergillus flavus	5	29	Agriculture	5	39

Abbreviations: APSIM, agricultural production systems simulator; cl., cluster number; TLS, total link strength.

Strengths and limitations of bibliometric analysis

The above bibliometric analysis can be reproduced and tweaked as per specific research preferences. The links provided with network maps can help researchers analyze the results accordingly. Moreover, the years until 2020 were included even though analysis was started in 2023 to avoid the underestimation of newly published work (Hou et al., 2022).

However, it is important to note that bibliometric analysis has its limitations, which should be considered in the study (Donthu et al., 2021). Specifically, this analysis focuses solely on citations and does not consider the quality of an article, and citations may not fully reflect the usefulness of research to society. Research articles can be extensively cited for negative reasons, such as for refuting claims or highlighting flaws to further develop the methodology, conceptual knowhow, and generic understanding of the research domain under question. Moreover, self-citation or disproportionate citations may distort bibliometric measures (Swanson et al., 2016). Furthermore, famous authors or papers tend to be overly cited due to their visibility, and that could significantly skew the impact of specific works (Blakeman, 2018).

In addition, the selection of document type plays an important role since method papers, review papers, and meta-analyses often receive more citations than original research papers (Janssens & Gwinn, 2015). The other limitation is the usage of only Scopus database for analysis. There might be differences in databases such as WoS, Google Scholar, ProQuest, and Dimensions (Hou et al., 2022). Other database such as WoS, google scholar, and Dimensions can be used for bibliometric analysis following the similar methodology discussed in the study. Consequently, developed papers can be compared for further robust analysis.

CONCLUSIONS

The current bibliometric study results indicated that the top 10 papers by citations between 1990 and 2020 were focused on topics of biochar, crop-rotation, nitrogen, tillage, and C-sequestration. However, management practice keywords such as planting depth, row configuration, harvest practices, and so forth did not appear in the network maps in the analysis. Henceforth, this could be inferred in several ways such as these keywords are infrequently used by authors, these management practices result in nonresponsive data, nonresponsive data are seldom published, scholarly attention to these topics is limited due to low funding (funding often drives higher volume of publications), or the area represents an under-researched focus. Moreover, these results present an opportunity to researchers to further analyze the network maps using the hyperlinks provided in the paper and advance research to fill the knowledge gap in the literature.

AUTHOR CONTRIBUTIONS

Namita Sinha: Formal analysis; methodology; visualization; writing—original draft. Jagmandeep Singh Dhillon: Conceptualization; funding acquisition; project administration; resources; supervision; writing—review and editing.

ACKNOWLEDGMENTS

This publication is a contribution of the Mississippi Agricultural and Forestry Experiment Station. We would like to thank Dr. Ramandeep Kumar Sharma for his comments and suggestions on the initial draft.

CONFLICT OF INTEREST STATEMENT

The authors declare no conflicts of interest.