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Review

Bibliometric Network Analysis of Crop Yield Gap Research over the Past Three Decades

by
Yimin Hu
1,
Shuqi Yang
1,
Xin Qian
2,
Zongxin Li
2,
Yuchuan Fan
3,
Kiril Manevski
4,5,
Yuanquan Chen
1,* and
Wangsheng Gao
1
1
College of Agronomy and Biotechnology, China Agricultural University, Beijing 100193, China
2
Crop Research Institute, Shandong Academy of Agricultural Sciences, Jinan 250100, China
3
Soil, Water, and Ecosystem Sciences Department, University of Florida, Gainesville, FL 32611, USA
4
Department of Agroecology, Aarhus University, Blichers Allé 50, 8830 Tjele, Denmark
5
Sino-Danish College, University of Chinese Academy of Sciences, Eastern Yanqihu Campus, Beijing 101200, China
*
Author to whom correspondence should be addressed.
Agriculture 2023, 13(11), 2105; https://doi.org/10.3390/agriculture13112105
Submission received: 30 September 2023 / Revised: 26 October 2023 / Accepted: 30 October 2023 / Published: 6 November 2023
(This article belongs to the Section Crop Production)
Browse Figures
Review Reports Versions Notes

Abstract

:
Achieving global food security requires an understanding of the current status and the future trends in the yield gap for cropping systems worldwide. The aim of this study was to scientifically understand the existing yield gap research, recognize the knowledge base and influential articles, and uncover key research themes and how these have evolved over the past three decades. Bibliometric methods were used to analyze articles related to the yield gap available in the largest scientific database, the Web of Science. A total of 6049 relevant articles published from 1993 to 2023 were numerically analyzed for patterns, trends, and clusters. The findings identified a few primary authors of widely cited publications. Geographically, the United States and China were the two major contributors to the publication pool, with articles from China mostly affiliated with the Chinese Academy of Sciences and China Agricultural University, while the United States had a more scattered distribution of affiliated institutions. The research on yield gaps primarily focused on biological factors, such as the effects of crop varieties, agronomic management, climate change, and soil conditions, with a limited exploration of social and economic factors. Within the auspices of the current food issues worldwide, this study provides a thorough view of the progress and key topics in crop yield gap research, contributing to the existing body of knowledge and providing guidance for researchers, policymakers, and stakeholders involved in agricultural productivity enhancement and sustainable food production. Amid the increasing trend in hunger worldwide over the past decade, we thus concluded that, by establishing appropriate benchmarks, re-prioritizing research needs, and focusing on transforming natural resources rather than inputs, the crop yield gap approach can be useful in terms of the clear inclusion of local contexts and socioeconomic constraints.

1. Introduction

Population growth and economic development both contribute to the constant demand for ample, available, and nutritional food worldwide [1]. In order to provide for the 9 billion people that will populate the world by 2050, food production will need to increase by 60 % [2]. Moreover, new evidence has continuously shown that human hunger has globally increased for more than a decade, reaching nearly 10 % in 2022, that is one in every nine people [3]. This poses key questions regarding the scope, the organization, and effect effect of the past agricultural policies on one hand, and methods and decisions pertaining to food security, on the other, along with associated agriculture research priorities regarding yield gaps, food systems, and human health [3].
The Green Revolution that occurred from the 1960s to the 1990s resulted in a significant yield increase in staple crops, such as wheat, maize, and rice, especially in the large developing countries, such as India, Brazil, and China, embracing the Green Revolution advances in terms of farm mechanization and made significant public-sector investments in irrigation, fertilization, and crop breeding [4]. In these countries, yields of the most common staples, such as wheat, rice, potato, and cassava, increased between 36 and 208 % [4]. One of the most important discoveries in the 21st century, resulting in an increase in grain yield by nearly 10%, was the “knocking out” of the KRN2 and OsKRN2 genes in maize and rice, respectively, through bio-breeding techniques [5]. There have also been studies using bioengineering techniques such as molecular breeding, transgenic technology [6], or increasing photosynthetic efficiency under fluctuating light, which resulted in more than a 30% increase in in the yield of soybean- one of the most important protein crops [7]. More recent studies have shown that the continuation of the Green Revolution efforts is improving yield gaps, with implications pertaining to crop production and food supply worldwide. For instance, the potential yields for wheat, maize, and rice worldwide are 7.7, 10.4, and 8.5 t ha, respectively; however, their corresponding actual yields are only 4.1, 5.5, and 4.0 t ha [8]. The yields of these major crops and others could be increased by 50 to 70% worldwide [9,10,11]. Currently, closing the crop yield gap is the only viable approach to increase crop production [12].
The concept of yield gap was likely first proposed by De Datta [13], and depicts the difference between actual and potential (maximal) yield at experimental stations. Multiple factors contribute to the yield gap worldwide, including weather variability and climate change [14], crop varieties [15,16], edaphic factors, and associated water and nutrient availability [17,18,19], pests and diseases [20,21], and other agronomic management factors, such as planting density and crop sequence over time [22,23,24], along with the combined effects of genetics, the environment, and management (G × E × M) [25]. To date, numerous studies have been conducted on crop yield differentials. Model-based studies have estimated large yield gaps and a global genetic yield potential of wheat ranging from 30% to 70% [26], similar to global rice yields under the influence of climate change [27]. Regionally, in large crop production areas such as Pakistan, Iran, and China, factors pertaining to yield gaps include nitrogen fertilizer input, irrigation, and sowing date, for instance crop variety for canola [28], appropriate cultivation management for sugar beet [29], genotypes and management measures of wheat [30], and sowing advances and nitrogen fertilizer for rice [31]. Moreover, yield gaps also depend on the characteristics and the socioeconomic conditions in which farmers and the agricultural chains operate, information that is not typically available or considered in most yield gap studies [32]. These and many other studies published in the past several decades have comprised large pool suitable for bibliometric methods to comprehensively analyze research dynamics (research streams and perspectives over time), identify hotspots (e.g., countries, authors, journals), and predict new research directions in the field of crop yield gaps. Previous bibliometric analyses have focused on agricultural systems and included the effects of climate change on global agricultural systems [33], the role of sustainable agricultural practices [34], and methods for precision agriculture [35]. To the best of the authors’ knowledge, a structured bibliometric and network evaluation of yield gap studies has not been conducted thus far, but is particularly important and timely amid the global nexus of current crises involving increased hunger and disrupted food production, effects on environment and biodiversity, and global change.
This study analyzes the yield gaps for wheat and maize through a bibliometric analysis of the literature published over the past 30 years. It explores research trends, including descriptions of the current state of yield gap research, and identifies new research directions necessary to bridge the gap between field and high-yield production for farmers.

2. Methodology

2.1. Data Sources

The crop yield data for this study were obtained from the Web of ScienceTM Core Collection Science Citation Index Expanded database. The search was limited to the time frame of May 1993 to May 2023, covering a period 30 years. The global advancement in agricultural science and technology, combined with changes in agricultural markets, have resulted in a steady rise in food production in many parts of the world; however, disparities in production between and within regions also become increasingly pronounced. We concentrated on maize and wheat because they are the two most essential and widely consumed food crops globally. We used the precise keywords for wheat and maize yield gaps, influential factors, closing the yield gap, and cereal crop yield. The search was, TS = (wheat yield gap or maize yield gap or wheat yield influencing factors or maize yield influencing factors or closing yield gap or cereal crop yield gap or cereal crop yield influencing factors). The search produced a total of 6049 relevant documents, including theses, dissertation articles, and reviews. Approximately 4% of the documents were removed after manual filtering as these were either duplicate or irrelevant articles. The remaining 5840 were exported as plain text files, which included both full records and cited references, for further analysis (Table 1).

2.2. Statistical Analysis

There are several tools and algorithms available for bibliometrics and scientometrics to identify a relevant and contemporary research issue, such as the yield gap. CiteSpace isa Java application that is freely accessible, andaims to analyze and visually represent trends and patterns in the scientific literature in a delineated knowledge domain [36]. The focus of this application is to find a key point in the development of an area or a domain, especially the turning point of knowledge and the key point of using vocabulary. As a result, CiteSpace has built networks of co-citation, coupling, scientific collaboration, and co-word analysis. More details on its structure and case studies can be found elsewhere [37,38,39,40]. We used CiteSpace (5.7.R2 version) alongside descriptive statistics and cluster analysis for literature keywords, authors, countries and institutions, journals, and their network relationships. The CiteSpace software as a scientific knowledge-mapping tool combines metrology data and information visualization. It takes the field of knowledge as its research object and realizes the visual expression of knowledge in multiple stages and dynamics through the interpretation of images and the analysis of genealogy. Such approach can demonstrate the development process of a field or subject from a macro perspective via testing and organizing the characteristics of publications, identifying research hotspots, building a knowledge base, and predicting future directions [41].
The workflow is shown in Figure 1. We used Original 2021 (OriginLab Corporation, Northampton, MA, USA) to create the final visualization, in which the number of connections around a node shows its centrality; nodes with an intermediary centrality greater than 0.1 are identified as critical nodes.

3. Results

3.1. Publications and Citation Dynamics

The number of publications in time reflects the development and the rate of publishing in the field. From 1993 to 2023, there were 201 annual publications in the “yield gap” area (Figure 2). However, in the first two decades, from 1993 to 2012, only 27.2% (1645 articles) of the total number of publications were published. The number of publications increased exponentially from 2013 to 2023, when 440 articles or 72.8% (4404 articles) of the total number of publications were published each year for 10 years. The number of publications in 2022 was almost 8 times higher than in 2003, indicating a significant increase in interest in research to understand, quantify, and close the crop yield gap.
Figure 3A lists the most frequently cited journals and the top 10 journals in the “output gap” publications, based on a co-citation network analysis of journals. Over a 30-year period, these most cited journals accounted for15% of all citations, while the other 85% were relatively evenly distributed among the other journals. These findings indicate a field-scale and a mostly agrotechnological focus of the yield gap studies cited in journals such as Field Crops Research and Agronomy Journal, as well as an absence of journals aiming to resolve technoeconomic and social constraints on the yield gap. Many studies have also been conducted globally or on a large scale, often published in the journals preferring such spatial domains such as Science or Nature. Out of the top authors cited (Figure 3B), the first was from a public university in the United States, and the second was a member of the United Nations’ Food and Agriculture Organization (FAO), who was followed by an author from a public university in the Netherlands, with only 10 citations fewer than the previous. The widespread citation of articles authored by so few (five) scientists or organizations also indicates a fairly close and less diverse research environment in this field of yield gap, while the yield gap affects significantly many different disciplines and therefore authors. Figure 3C shows the global author network based on the number of publications. Connections between nodes represent collaboration relationships, and bold lines indicate a higher frequency of co-occurrence. The size of the node reflects the author’s publication volume, and the number of connections around each node represents its centrality. Nodes with a central value of more than 0.1 are considered critical. We observed four “teams” which the authors in the research field can be grouped into, for instance, Shao kun Li from the Chinese Academy of Agricultural Sciences, Grassini Patricio from the University of Nebraska—Lincoln, and Fusuo Zhang and Xiaoguang Yang, both from China Agricultural University.

3.2. Analysis of Geographical Distribution and Institutions

Figure 4 shows the co-occurrence network of countries in the field of research, visualizing the relationships between countries based on their publication collaboration. Node size indicates the number of publications from each country, while thickness of the links reflects the level of cooperation among countries. The outer color of the node represents their betweenness centrality, with higher centrality nodes displaying a purple outer ring. According to the data, the United States had the most publications in the field (1327 articles), followed by China (1112 articles), Germany (468 articles), Australia (375 articles), and the United Kingdom (258 articles). The results show that globally, the United States, China, and Germany are the three countries with the highest paper production in the field of yield gap research. Among them, China and Germany have more cooperation in issuing papers in this field compared to the United States. Figure 5 shows that the Chinese Academy of Sciences ranked first globally with 254 publications, followed by China Agricultural University (205 articles), the Chinese Academy of Agricultural Sciences and Wageningen University (155 and 112 articles, respectively), and the University of Chinese Academy of Sciences (95 articles). This shows the forefront of research institutions in mainland China in terms of publication volume. Another interesting observation was, a rather large inequality in the distribution of publications between Chinese and American research institutions.

3.3. Analysis of Research Hotspots and Research Frontier Forecasts

By examining and analyzing keywords, researchers can gain insight into research hotspots and predict future research frontiers. The co-occurrence network and the burst analysis to develop neuronal networks revealed “wheat”, “maize”, “crop growth”, “field management”, “climate change”, “nitrogen fertilizer input”, “water”, “model analysis”, “factors affecting yield”, “yield gap”, and “use efficiency” as the most frequent keywords (Figure 6). All keywords were grouped into ten major clusters (Figure 7, right). The first cluster focused on transcription factors, suggesting that the research concentration was placed on crop breeding to improve crop yield and close the yield gap. The second cluster focused on the use of remote sensing to estimate crop yield. Group Ⅲ (#2 food security), Group Ⅳ (#3 nitrogen), Group Ⅶ (#6 pattern), and Group Ⅹ (#9 global warming potential) were associated with food security, nitrogen fertilizer input, modeling, and climate change, respectively, while Group Ⅴ (#4 mycorrhizal dependency) and Group Ⅷ (#7 bacteria) were related to microbial research.
The burst analysis showed the dynamic change of core keywords in recent years, especially in the last ten years. As seen from the results on Figure 7, starting in 2016, the core keywords have gradually shifted toward photosynthesis, genetic breeding, large-scale remote sensing, microbial communities, soil organic carbon, and biocontrol research, all of which influence crop yield.
The keywords timeline (Figure 8) visualizes the evolution of research hotspots in each cluster over time, Group I (#0 transcription factor) currently focuses on drought-resistant varieties’ development, Group II (#1 remote sensing) revolves around combining remote sensing and crop models to estimate crop yield, Group III (#2 food security) emphasizes on the diversification of food types, Group VI (#5 meta-analysis) suggests that big data analytics methods are often used to study the field as well, and Group IX (#8 damage) concentrates on pest control. Researchers can analyze the timeline of keywords to understand the changes in research hotspots within clusters over time and predict future research directions in the field.

4. Discussion

4.1. Retrospective View of the Yield Gap Research

The results of this study indicate that the number of research papers on yield gap analysis has been steadily increasing over the past 30 years, with a marked acceleration from 2012 to 2022 (Figure 2). In April 2014, the International Yield Gap Assessment Workshop was held in Wuhan, China, where researchers presented their findings [42,43,44,45,46,47]. For example, “Yield gap analysis with local to global relevance—A review” [48] is the most cited paper in the field over the past 30 years (Table 2), and the journal Field Crops Research ranked first in terms of published output in the field (Figure 3). In 2013, the journal Field Crops Research also launched a Special Issue on yield gap analysis, providing a comprehensive overview of research methods and findings. Therefore, 2013 can be considered a peak year for yield gap analysis in crop production research.
The most cited journals and researchers primarily focused on crop physiology, agroecology, and experimental and modeling studies conducted in both controlled environments and field conditions, consistent with the research on yield gap analysis. Someone [54] provide scientific definitions of concepts such as yield gaps and yield potential, and offer detailed explanations on the factors influencing yield gaps and their quantification, making them frequently cited by researchers in the field. On the other hand, the authors with the most published papers in the past 30 years (Figure 5) were sixth in the list of highly cited papers (Table 2), mainly in agriculture, environmental science, crop physiology, and ecology.
In the field of yield gap analysis in crop production, Chinese authors rank high in terms of published yields (Figure 5), thanks to projects such as Project 973, Matching and Regulation of High-Yielding and High-Efficiency Crop Populations with Key Ecological Factors, which focused on smallholder farmers [55], intensive agricultural cropping systems [56], fertilizer use efficiency rates [55,56,57,58], technology implementation rates [50], and environmental benefits [59,60,61,62]. These findings indicate the essential role of local context and national funding in shaping the research amount and quality in the area of yield gap analysis.
Upon analyzing the countries and institutions contributing to the area of yield gap research, the United States ranks first in terms of publication output globally, while the distribution of research institutions’ publications appears to be relatively balanced. China ranked the second in terms of publication output globally, with research institutions Chinese Academy of Sciences, China Agricultural University, and Chinese Academy of Agricultural Sciences holding an absolute advantage. The reasons for this trend may be attributed to the relatively late start in publishing the first articles in the field in 2004, resulting in a lower overall publishing output than in the United States. At the same time, the research theme of the study does not explicitly focus on closing crop yield gaps in the domestic regions or in other regions of the world.

4.2. Current Status of the Yield Gap Research

According to the keywords clustering analysis, crop genetic breeding ranked first. It is well known that crop breeding has been a major aspect of the Green Revolution over the past 20 years, and genetic modification still appears to continue to be at the frontline of food security globally [62]. It is estimated that the global average yield gap due to varietal differences is between 30% and 70%, with an average of 51% [63]. The design and utilization of new and advanced breeding techniques to cultivate optimal crop varieties (e.g., CRISPR-Cas9) is an effective approach to enhance crop yield potential and close the yield gap. There are several management practices considered sustainable and environmentally friendly such as crop rotation and conservation tillage [64]. In addition, breeding new varieties adapted under sustainable management conditions (e.g., organic farming, reduced tillage) represents an effective way to close the crop yield gap [65].
The application of remote sensing technology in yield gap studies ranked as the second clustering label. Against traditional destructive methods involving harvest and field counting on small areas, remote sensing technology utilizes the spectral reflection characteristics of crop canopies and soil surfaces related to crop yield [66]. Researchers combine remote sensing technology with crop models to estimate crop yield potential in yield gap studies [67]. Food security is also a prominent research direction in this field (Figure 7 and Figure 8). Climate change and biocontrol are also hotspots in this domain of research. The latest assessment report of the Intergovernmental Panel on Climate Change (IPCC) highlights that climate change caused by human activities, including extreme weather events, is already a threat to global food security [68]. High temperature stress at different stages of crop development affects crop yield potential, mainly through drought or heat stress, primarily reducing grain yield by decreasing the number of grains per plant [69]. Pests and diseases cause annual agricultural production losses of 20% to 40% worldwide [70]. The chemical characteristics of traditional pesticides can lead to biotoxicity, environmental pollution, and other ecological and health issues [71], making biocontrol an environmentally friendly plant conservation method with a low-carbon footprint. Nitrogen fertilizer and mycorrhizal dependency are also research hotspots. The results showed that under optimal nitrogen input, maize grain yield significantly increases by 30% [72]. Increasing nitrogen fertilizer use efficiency is a principle of intensive agricultural production and a guarantee for increasing crop yields [73]. Arbuscular mycorrhizal fungi (AMF) can establish mutually beneficial symbiotic relationships with approximately 80% of terrestrial plants [74]. They usually colonize the roots of plants and form mycelia that reach into the soil, enhancing the absorption of soil moisture and nutrients by plants, thus increasing crop yield [7].
In summary, the focus and direction of research in the area of yield gap primarily revolve around biological factors, climate factors, varieties, management measures, and soil factors that influence crop yield. These factors are generally classified as direct factors. In actual production, social factors, economic development, farmer endowments, and agricultural policies indirectly affect crop yield through farmers’ management practices [75,76,77,78,79]. Africa is the sole continent lacking research of plausible direct and especially indirect factors affecting crop yield gap, thus being the only region in the world where increased agricultural production causes a decline in per capita food production [80]. Therefore, future research in the area of the yield gap shouldconsider socioeconomic factors. As shown in Figure 9, the relationship among the research hotspots in the field of farmland, farmers, and governments have been classified and analyzed. Overall, closing the yield gap and increasing crop yield require a system thinking approach. Integrating technology, farmers, and government/new technology extension institutions is crucial. Furthermore, based on the research findings, future research in the field should focus on regional empirical studies in addition to large-scale modeling studies, allowing for the validation and verification of modeling results at the field level.

4.3. Future Research Trends for Yield Gap Based on the Bibliometric Analyses

The concept of crop yield differentials has been articulated and quantified differently by agronomists and agricultural economists, who commonly use field trials, crop growth models, etc., to estimate the impacts of different agronomic practices on crop yields. The main limitation of this theory of the production ecology theory derived from this study of crop yield differentials is that it does not consider the effects of socioeconomic factors such as the behavior habits of agronomic operators, their economic objectives, and their own endowments on crop yield plausible supply chains and associated market [81]. Agricultural production at the field level is the level of aggregation where the psychosociology, agroeconomics, and agroecology disciplines interact most profoundly [82]. On the contrary, economists emphasize factors such as prices, markets, and efficiency without considering the ecophysiological characteristics of agricultural regionalism [81]. In recent years, Silva et al. have proposed a comprehensive yield gap analysis framework that combines agroecology theory with agroeconomics and divides the crop yield gap into three components: efficiency yield gap, resource yield gap, and technology yield gap. The new analytical framework uses a stochastic frontier analysis approach in combination with a crop modeling approach to objectively and comprehensively address the natural and socioeconomic factors affecting crop production. This new integrated analytical framework will be an important method for analyzing the causes and quantifying crop yield differentials in the future [83].

5. Conclusions

This is the first bibliometric analysis of yield gap studies to numerically and visually quantify the current status and the trends in the field over the past 30 years. The results indicate that the United States and the Netherlands are among the few countries whose authors are most frequently cited in this field, in journals such as Field Crops Research and Agronomy Journal. The United States is the country with the highest number of publications, and the Chinese Academy of Sciences is the most productive research institution. Research topics in the field indicated that over the past 30 years, studies on the global crop yield gap primarily focused on crop varieties, large-scale remote sensing technology, crop growth models, as well as nitrogen fertilizer input, soil microbiota, and biocontrol. However, a lack of extensive research on technology levels, socioeconomic conditions, and policy regulations was also identified. In the future, scholars and researchers, as well as funding agencies should consider the impact of socioeconomic factors, in addition to biological and environmental factors, on crop yield and on closing the yield gap. Furthermore, strengthening empirical studies at the local level is recommended to build upon existing research. Lastly, new algorithms such as machine learning could be tested to quantify yield gaps and to focus more on addressing yield gaps on a small field scale.

Author Contributions

Conceptualization, W.G. and Y.C.; methodology, Z.L.; software, Y.H.; formal analysis, X.Q.; writing—original draft preparation, Y.H.; writing—review and editing, K.M. and Y.F.; visualization, Y.H. and S.Y.; supervision, Y.C. and W.G.; funding acquisition, Y.C. All authors have read and agreed to the published version of the manuscript.

Funding

This study was funded by the National Key Research and Development Program of China (grant number 2022YFD2300901).

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

Not applicable.

Acknowledgments

The authors also greatly appreciate the valuable and insightful comments by the Editor and anonymous reviewers.

Conflicts of Interest

The authors declare no conflict of interest.

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Figure 1. Flowchart of the bibliometric study consisting of data the collection stage and bibliometric analysis.
Figure 1. Flowchart of the bibliometric study consisting of data the collection stage and bibliometric analysis.
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Figure 2. Number of papers published globally in the field of crop yield gap research over the past three decades.
Figure 2. Number of papers published globally in the field of crop yield gap research over the past three decades.
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Figure 3. (A) Co-citation analysis of journals, with left panel showing the most cited journals, and right showing the top 10 most cited journals, and (B) authors; left panel shows the first authors of the most cited journals, and right panel shows the top 10 authors. (C) Co-occurrence analysis of authors; left panel shows co-occurrence author network, and right panel shows ranking of the top 10 authors in terms of number of publications.
Figure 3. (A) Co-citation analysis of journals, with left panel showing the most cited journals, and right showing the top 10 most cited journals, and (B) authors; left panel shows the first authors of the most cited journals, and right panel shows the top 10 authors. (C) Co-occurrence analysis of authors; left panel shows co-occurrence author network, and right panel shows ranking of the top 10 authors in terms of number of publications.
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Figure 4. View of national and regional cooperation networks in the area of crop yield gap research and the top 10 countries with the largest number of publications.
Figure 4. View of national and regional cooperation networks in the area of crop yield gap research and the top 10 countries with the largest number of publications.
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Figure 5. View of the cooperative network of universities or institutions in the field of crop yield gap research and the top 10 institutions with the largest number of publications.
Figure 5. View of the cooperative network of universities or institutions in the field of crop yield gap research and the top 10 institutions with the largest number of publications.
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Figure 6. Co-occurrence analysis of keywords: left panel shows co-occurrence keywords network, and right panel shows ranking of the top 20 keywords.
Figure 6. Co-occurrence analysis of keywords: left panel shows co-occurrence keywords network, and right panel shows ranking of the top 20 keywords.
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Figure 7. Clustering, emergence, and time evolution analysis of keywords, with the left panel showing clusters of keywords, and the right panel showing the top 20 most clicked keywords from 2013 to 2023.
Figure 7. Clustering, emergence, and time evolution analysis of keywords, with the left panel showing clusters of keywords, and the right panel showing the top 20 most clicked keywords from 2013 to 2023.
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Figure 8. Evolution of keyword clustering results over time.
Figure 8. Evolution of keyword clustering results over time.
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Figure 9. Research directions and future themes in the field of crop yield gap.
Figure 9. Research directions and future themes in the field of crop yield gap.
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Table 1. Summary of data source and parameters used to construct the search model for the bibliometric analysis.
Table 1. Summary of data source and parameters used to construct the search model for the bibliometric analysis.
ItemsComment
Data sourceWeb of Science Core Collection
Searching periodMay 1993 to May 2023
Searching keywords“Wheat and maize yield gaps”, “influencing fac-tors”, “closing the yield gap”, “cereal crop yield”
Research areasCrop science, crop yield
Document typeArticles, dissertation and review papers
Document languageEnglish, Chinese
Sample records6049
Filtering criteriaLiterature with incomplete analytical elements and irrelevance to the topic
Final sample records5840
Table 2. Top 10 highly cited papers in research areas.
Table 2. Top 10 highly cited papers in research areas.
RankTitle of PaperJournalTimes of CitationPublished YearReference
1Yield gap analysis with local to global relevance—A reviewFIELD CROP RES1792013(Van Ittersum and Cassman et al., 2013) [48]
2Closing yield gaps through nutrient and water managementNATURE972012(Mueller and Gerber et al., 2012) [42]
3Can sub-Saharan Africa feed itself?PNAS712016(Van Ittersum and Van Bussel et al., 2016) [49]
4Closing yield gaps in China by empowering smallholder farmersNATURE532016(Zhang and Cao et al., 2016) [50]
5When yield gaps are poverty traps: The paradigm of ecological intensification in African smallholder agricultureFIELD CROP RES532013(Tittonell and Giller, 2013) [44]
6How good is good enough? Data requirements for reliable crop yield simulations and yield gap analysisFIELD CROP RES522015(Grassini and van Bussel et al., 2015) [51]
7Estimating crop yield potential at regional to national scalesFIELD CROP RES502013(Van Wart and Kersebaum et al., 2013) [46]
8Solutions for a cultivated planetNATURE452011(Foley and Ramankutty et al., 2011) [52]
9Rising temperatures reduce global wheat productionNAT CLIM CHANGE442015(Asseng and Ewert et al., 2015) [53]
10Crop Yield Gaps: Their Importance, Magnitudes, and CausesANNU REV ENV RESOUR442009(Lobell and Cassman et al., 2009) [54]
Note: Statistical literature was searched on Web of ScienceTM Core Collection Science Citation Index Expanded database.
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Hu, Y.; Yang, S.; Qian, X.; Li, Z.; Fan, Y.; Manevski, K.; Chen, Y.; Gao, W. Bibliometric Network Analysis of Crop Yield Gap Research over the Past Three Decades. Agriculture 2023, 13, 2105. https://doi.org/10.3390/agriculture13112105

AMA Style

Hu Y, Yang S, Qian X, Li Z, Fan Y, Manevski K, Chen Y, Gao W. Bibliometric Network Analysis of Crop Yield Gap Research over the Past Three Decades. Agriculture. 2023; 13(11):2105. https://doi.org/10.3390/agriculture13112105

Chicago/Turabian Style

Hu, Yimin, Shuqi Yang, Xin Qian, Zongxin Li, Yuchuan Fan, Kiril Manevski, Yuanquan Chen, and Wangsheng Gao. 2023. "Bibliometric Network Analysis of Crop Yield Gap Research over the Past Three Decades" Agriculture 13, no. 11: 2105. https://doi.org/10.3390/agriculture13112105

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