Research Trends in Evaluation of Crop Water Use Efficiency in China: A Bibliometric Analysis

Abstract

Water scarcity has become a significant constraint to agricultural development in China. In this study, we employed bibliometric methods to systematically review the current research on crop water use efficiency (WUE) and the development trends in the North China Plain (NCP) and Northwest China (NWC). We analyzed 1569 articles (NCP = 788; NWC = 781) from the Web of Science Core Collection (1995–2025) using visualization tools such as CiteSpace and VOSviewer to investigate annual numbers of publications, leading scholars and research institutions, and then to map keyword co-occurrence and co-citation structures. Our results showed that keyword clustering exhibited high structural quality (NCP: Q = 0.7345, S = 0.8634; NWC: Q = 0.758, S = 0.8912), supporting reliable thematic interpretation. The bibliometric analysis indicates a steady growth in annual publications since 1995, with the Chinese Academy of Sciences and China Agricultural University as leading contributors. From 1995 to 2005, studies centered on irrigation, yield and field-scale WUE, emphasizing the optimization of irrigation strategies and crop productivity. During 2006–2015, the thematic focus has broadened to encompass nitrogen use efficiency, crop quality and eco-environmental performance, thereby moving toward integrated evaluation frameworks that capture ecological synergies. Since 2016, the literature now emphasizes system integration, regional adaptability, climate-response mechanisms and the ecological co-benefits of agricultural practices. Future studies are expected to incorporate indicators such as crop quality, water footprint and carbon isotope indicators to support the sustainable development of agricultural water use. This study offers insights and recommendations for developing a comprehensive crop WUE evaluation framework in China, which will support sustainable agricultural water management and the realization of national “dual carbon” targets.

1. Introduction

Water security is foundational to food security, and a pronounced spatiotemporal mismatch of water resources, compounded by ongoing degradation of aquatic ecosystems, has widened the supply–demand gap, elevating water security to an urgent national priority in China [1]. In 2023, China’s total water consumption reached 590.65 billion m³, with agricultural water use accounting for approximately 62.2%. The average irrigation water use per hectare of arable land was 347 m³, while the irrigation water use coefficient was only about 0.576, significantly lower than those observed in water-efficient countries such as Israel and Australia [2]. Improving agricultural crop water use efficiency (WUE) has become an urgent priority [3]. Crop water use encompasses the total water required by plants throughout their entire growth cycle, including transpiration through stomata, evaporation from soil and plant surfaces and water input through irrigation and effective precipitation [4]. Efficient crop water use depends not only on irrigation management but also on the crop’s genetic potential for drought tolerance and its underlying physiological and ecological regulatory mechanisms. By minimizing non-productive transpiration, it is possible to maximize yield under water-limited conditions. Integrating drought-resilient genetic traits with hormone-regulated signaling pathways, such as those mediated by abscisic acid and implementing precision irrigation strategies across spatial and temporal scales, can significantly minimize non-productive water losses. These strategies optimize plant physiological responses, improve the allocation of photosynthates to harvestable organs and support the coordinated achievement of high yield, superior quality and improved WUE [5]. Strengthening research on WUE mechanisms and advancing efficient water resource utilization are therefore critical to water-saving agriculture and long-term sustainability.

The North China Plain (NCP) comprises 13% of China’s land area yet produces 23% of national grain output, serving as the primary base for winter wheat and summer maize and thus a cornerstone of food security [6]. The region exhibits marked interannual precipitation variability: mean annual rainfall is 520 mm, with 65–75% concentrated in summer, driving recurrent drought–flood alternation. About 60% of groundwater withdrawals are used for agricultural irrigation, and decades of over-extraction have created the world’s largest groundwater-depression cone [7]. Furthermore, nitrogen use efficiency remains low, at only 25–30%, due to excessive and inefficient fertilization practices, which exacerbate non-point source pollution and nitrogen leaching [8]. The Northwest of China (NWC), which covers nearly one-third of the country, contributes 12% of China’s grain output and is a key production region for wheat, maize, cotton and fruit [9]. More than 70% of NWC receives <400 mm of precipitation annually, while potential evaporation exceeds precipitation by a factor of 8–10. Glacial meltwater supplies over 40% of the region’s water resources, but rising temperatures have caused a 27.2% decline in glacier area since the 1950s, exacerbating water scarcity [10]. In addition, widespread wind erosion has also led to land desertification, soil salinization and oasis shrinkage, creating a “desert advancing, people retreating” scenario that seriously threatens local livelihoods [11]. Evaluating crop WUE in the NCP and NWC is thus essential for identifying crop-specific water use patterns, improving water resource management and addressing critical ecological challenges, thereby supporting national food security and sustainable agricultural development [4,12].

Extensive research has examined agricultural water use at watershed, irrigation-district and field-scale evapotranspiration levels. However, existing research on crop WUE has primarily focused on limited aspects, lacking a comprehensive understanding of overall crop WUE. Wang et al. [13] evaluated estimation methods for reference crop evapotranspiration at varying scales and emphasized model–scale compatibility in improving irrigation accuracy. Feng et al. [14] synthesized the application and performance of water-saving irrigation technologies in China and Europe, highlighting their effects on yield and water productivity. Traditional narrative reviews are useful for summarizing existing research, but they depend heavily on subjective interpretation and qualitative analysis. Although traditional review methods can efficiently summarize research findings, they have several limitations, including subjectivity, text-heavy content and insufficient visualization [15]. Additionally, current crop WUE assessments predominantly rely on field experiments and simulation models, which are often constrained by incomplete indicator systems, limited methodological diversity and uncertainties in parameterization, thereby impeding the development of a comprehensive and widely applicable evaluation framework [16]. Compared with narrative reviews, bibliometric analysis is data-driven and uses text mining, network mapping and statistical modeling to quantify citation patterns, collaboration networks and thematic evolution, providing an objective and systematic overview of the field [17]. A structured synthesis of existing studies further identifies trends and gaps, tracks field dynamics, reduces subjectivity and strengthens analytical rigor.

This study focuses on the evaluation of crop WUE in the NCP and NWC and employs bibliometric methods in combination with visualization analysis tools such as CiteSpace and VOSviewer to conduct an in-depth analysis of the literature retrieved from the Web of Science database. This study aims to investigate the current state of research on crop WUE in China by analyzing annual publication trends, key scholars and their affiliated institutions. Additionally, keyword clustering analysis and burst detection are further applied to explore research hotspots and frontiers in this field.

2. Data Sources and Methodology

2.1. Data Sources

To investigate the research evolution of crop WUE evaluation, this study used the Web of Science Core collection as the primary data source. As one of the most comprehensive and authoritative citation databases worldwide [18], it provides extensive coverage of global research output. The search was limited to articles (Document Type = Article) published between 1995 and 2025. Building on this corpus, we implemented region-specific search strategies for the NCP and NWC as detailed below.

Region-specific queries were formulated as follows. For the NCP, the following advanced query was used: Topic Search (TS) = (“Water Evaluation” OR “Water Management” OR “Water Use Efficiency” OR “WUE” OR “Water Footprint Assessment For Crops” OR “Evaluation Indicators” OR “Evaluation Metrics” OR “Evaluation System” OR “Evaluation Framework” OR “Assessment Criteria”) AND TS = (“NCP” OR “North China” OR “North China region”) AND Document Type (DT) = Article AND Publication Year (PY) = 1995–2025, yielding a total of 788 records. For the NWC, the query was: TS = (“Water Evaluation” OR “Water Management” OR “Water Use Efficiency” OR “WUE” OR “Water Footprint Assessment For Crops” OR “Evaluation Indicators” OR “Evaluation Metrics” OR “Evaluation System” OR “Evaluation Framework” OR “Assessment Criteria”) AND TS = (“Northwest China” OR “Northwest China Plain”) AND DT = Article AND PY = 1995–2025, resulting in 781 records.

Prior to analysis, a rigorous data pre-processing procedure was implemented. A combination of keyword filtering and manual screening was applied to eliminate irrelevant records. First, the literature unrelated to the evaluation of crop WUE was excluded through a two-step process involving automated keyword filtering and manual review. For publications with ambiguous thematic relevance, titles and abstracts were carefully examined to determine inclusion based on thematic alignment. Second, duplicate entries were initially identified and removed based on the title, author and publication year. A further de-duplication step was conducted during the analysis process to ensure the accuracy of network construction and statistical outputs.

2.2. Methodology

This study employed bibliometric approaches to conduct a quantitative analysis and visual mapping of the literature related to crop WUE evaluation. Bibliometrics, which combines citation-based analysis with visualization techniques, offers a powerful approach for mapping the development, structure and evolution of scientific knowledge [19]. Two widely used visualization tools were adopted: CiteSpace (6.3.R1) Basic and VOSviewer (1.6.20). CiteSpace, developed by Chen [20], is particularly effective for constructing burst detection maps and generating time-sliced co-citation networks, thereby facilitating the identification of research frontiers and the evolution of thematic trends. Key settings were as follows: time span 1995–2025 aligned with our retrieval window; 1-year slicing to capture annual shifts; pruning with Pathfinder and Pruning sliced networks to remove redundant links and highlight core clusters; and burst detection using the top 50 nodes per slice. VOSviewer, a tool created by Nees Jan van Eck and Ludo Waltman, is designed for bibliometric and knowledge domain visualization and structure analysis [21]. It enables in-depth examination of inter-topic linkages through high-frequency keyword co-occurrence and clustering techniques. We adopted cosine similarity as the association measure and retained keywords with occurrence greater than 10 to balance representation of dominant topics with inclusiveness of emerging subthemes.

In terms of analytical methods, this study integrates co-citation analysis, co-word analysis and cluster analysis. The co-citation analysis investigates the frequency with which influential documents are cited together, revealing the underlying knowledge linkages and the structural evolution of the research domain [22]. The co-word analysis evaluates the strength of association between keywords based on their co-occurrence within publications, generating visual networks that help delineate the intellectual landscape of the field [23]. Cluster analysis organizes similar items into cohesive groups based on quantitative characteristics, facilitating thematic classification [24]. The citation frequency of a publication is used to characterize its true contribution. By synthesizing these analytical techniques, this study analyses the development trends, research hotspots and academic cooperation relationships in the field of crop WUE evaluation in the NCP and NWC, with the goal of informing future research directions and supporting strategic academic planning.

3. Results and Discussion

3.1. Analysis of Publication and Collaboration Networks

3.1.1. Trends in Publication Output

The quantity of papers can reflect the heat of research to some extent [25]. Research on crop WUE in both the NCP and NWC remained in an exploratory phase from 1995 to 2005, with fewer than 10 publications per year in either region (Figure 1a,b). Between 2006 and 2015, research activity increased substantially, with average annual publications rising by more than threefold compared to the previous decade. Since 2015, publication output has continued to grow, reaching 91 and 92 articles in 2023 for the NCP and NWC, respectively, indicating a sustained and steady growth in scholarly attention. Over the full study period (1995–2025), the average annual publication count was 26.3 for the NCP and 26.0 for the NWC, reflecting a comparable overall research intensity. However, the standard deviation and coefficient of variation were higher in the NWC (29.9 and 115.0%) than in the NCP (26.5 and 100.8%), suggesting greater interannual fluctuations in the former. Despite this, an F-test revealed no statistically significant difference in annual publication variance between the two regions (F = 1.28, p = 0.514), implying that the observed variation likely stems from temporal shifts in research focus and policy incentives rather than fundamental structural differences. In recent years, the NWC has exhibited rapid growth in research output, albeit with greater variability, while the NCP has followed a more stable and consistent trajectory. The NCP maintained a leading position during the early years, whereas the NWC has accelerated significantly over the past decade, surpassing the NCP in annual publication volume and reflecting a growing national emphasis on agricultural water management in arid western regions.

3.1.2. Institutional Collaboration Network Characteristics

As shown in Figure 2a, the size of the circle indicates the number of posts. The institutional collaboration network in the NCP is anchored by the Chinese Academy of Sciences (CAS), which exhibits the highest total link strength (TLS = 295), underscoring its central role in regional scientific cooperation. China Agricultural University (CAU) (TLS = 202) and the University of Chinese Academy of Sciences (TLS = 116) rank second and third, respectively, forming a closely connected research cluster with CAS. In terms of publication output, CAS contributed 248 articles (31.5% of the total), followed by CAU and the Chinese Academy of Agricultural Sciences, with 159 and 83 publications, respectively. Collectively, these three institutions accounted for over 63% of the total publications in the NCP, highlighting their dominant position in the region’s research landscape. A similar pattern is observed in the NWC (Figure 2b), where CAS also occupies a central position (TLS = 217), followed by Northwest A&F University (TLS = 187) and CAU (TLS = 154). Notably, Northwest A&F University leads in publication volume with 171 articles (22.2% of the total), ahead of CAS (155 articles) and CAU (145 articles). The collaboration network in this region exhibits a more decentralized structure, with multiple institutions actively engaged in crop WUE research. The two regions display distinct organizational patterns in research collaboration. The differences in the cooperation models between the two regions are as follows: the NCP has concentrated cultivated land and complete agricultural infrastructure, making it more suitable for conducting systematic basic research. The NWC is characterized by drought, scarce rainfall and fragile ecology. It is necessary to focus on regional issues, strengthen the implementation and promotion of technologies and reflect the adaptive evolution path of efficient water use research under different natural conditions and policy orientations.

3.1.3. Productive Authors and Collaboration Networks

The NCP and NWC have formed well-defined author collaboration network in the field of crop WUE research, with core authors exhibiting a high degree of concentration in both publications output and collaboration intensity (Figure 3a,b). The author collaboration network in the NCP is centered around Xiying Zhang (34 papers, TLS = 90), who has formed a stable collaborative group with Hongyong Sun (27 papers, TLS = 83) and Suying Chen (25 papers, TLS = 78). This network exhibits a multi-nodal collaborative structure centered on Xiying Zhang, with peripheral authors such as Bingzhi Li and Liandong Wang also maintaining active connections, resulting in a relatively dispersed collaboration pattern. In contrast, the NWC is centered around Shaozhong Kang (41 papers, TLS = 144), who has collaborated closely with Taisheng Du (37 papers, TLS = 120) and Sien Li (35 papers, TLS = 109) to form a highly interconnected author cluster. Additional author groups led by Ping Guo, Hao Feng, and others result in a more cohesive network structure with stronger collaborative intensity. Overall, the NCP cooperation network has multiple cores and covers a wide range of institutions, while the NWC is dominated by leading regional universities, characterized by a more compact network and higher cooperation intensity. These differences are reflected in terms of research organization and capacity building mechanisms. However, cooperation among different authors still primarily centers around core authors, with limited cross-collaboration between core teams, primarily constrained by factors such as institutional affiliation, geographical distance and team distribution. To address these challenges, future efforts should focus on establishing interregional and interinstitutional partnerships to promote cooperation among core teams, strengthen resource sharing and method integration.

3.1.4. Citation Analysis

Among the top 20 most-cited studies on crop WUE evaluation in the NCP (Table 1), The paper by Sun et al. [26], published in Agricultural Water Management, has received the highest total number of citations. It systematically analyzed WUE dynamics of the wheat–maize rotation system under different irrigation regimes. Si et al. [27] ranked highest in total citations per year, investigated the combined effects of nitrogen application and irrigation strategies on drip-irrigated winter wheat in the NCP, and identified optimal treatments that enhanced both yield and resource use efficiency. Fang et al. [28] used regional datasets and model simulations to evaluate long-term trends in water availability, crop yield, evapotranspiration and WUE in the NCP, and proposed integrated agronomic strategies including irrigation scheduling, fertilization management and cropping system adjustment to improve water productivity under increasing water scarcity. Zhang et al. [29] investigated the effects of conservation tillage and irrigation scheduling on crop yield and WUE under various water conditions, identifying synergistic benefits of reduced tillage and optimized water inputs. Building on this, Zhang et al. [30] further explored coordinated strategies integrating drip irrigation, mulching and deficit irrigation, demonstrating their combined effectiveness in improving both water productivity and soil moisture retention in arid regions. Collectively, these influential works have shaped a multidimensional research framework that integrates irrigation management, nitrogen regulation, crop modeling and climate adaptation. These studies advanced crop WUE evaluation in the NCP by integrating field experiments, water and nitrogen management, cultivar improvement and regional modeling to optimize water productivity under diverse conditions.

Among the top 20 most-cited publications on crop WUE evaluation in the NWC (Table 1), the study by Kang et al. [31], published in Agricultural Water Management, recorded the highest total citations and total citations per year. This work provided a comprehensive assessment of agricultural irrigation water productivity trends and drivers across China over the past six decades, offering strategic insights for future improvement. Du et al. [32] demonstrated that mild to moderate deficit irrigation, when applied during non-critical growth stages, can significantly improve WUE while maintaining stable grain yields in water-scarce regions of the NWC. Zhao et al. [33] conducted field experiments to systematically evaluate the yield-increasing and water-saving effects of full plastic film mulching combined with ridge-furrow planting in semi-arid rainfed agroecosystems. Li et al. [34] investigated ridge and furrow mulching tillage in rainfed areas of the Loess Plateau and found that it stabilized surface temperature, reduced soil evaporation and improved maize yield. Wang et al. [35] conducted a multi-site assessment across semi-arid regions of China and confirmed that plastic film mulching consistently improved maize yield and WUE under dryland conditions. Cheng et al. [36] applied an integrated modeling approach using hydrological, ecological and socio-economic data to assess the trade-offs between agricultural production and ecosystem stability under different water allocation scenarios in the Heihe River Basin. They emphasized the integration of multiple strategies, such as optimized irrigation, mulching, rainwater utilization, remote sensing and mechanistic modeling, to achieve stable yields and improved WUE. These approaches support the transition toward adaptive, environmentally friendly and high-efficiency water management systems in the NWC. While the NCP research emphasizes long-term monitoring, modeling and input optimization under intensive cropping systems, the NWC studies focus more on adaptive agronomic strategies for coping with water scarcity in dryland environments. The integration of these region-specific insights provides a robust foundation for developing scalable, climate-adaptive and resource-efficient water management frameworks across China’s major agricultural zones.

Table 1. The 20 most-cited publications and references in the field of crop water use efficiency WUE evaluation in the North China Plain and Northwest China.

Ranking	NCP				NWC
	Author	DOI	Citations	TC/Y	Author	DOI	Citations	TC/Y
1	[26]	10.1016/j.agwat.2006.04.008	372	19.58	[31]	10.1016/j.agwat.2016.05.007	587	73.38
2	[37]	10.1016/j.agwat.2009.06.004	292	19.47	[36]	10.1093/nsr/nwu017	493	44.82
3	[28]	10.1016/j.agwat.2010.01.008	263	17.53	[34]	10.1016/j.agwat.2012.10.001	367	30.58
4	[38]	10.1007/s00271-002-0059-x	230	10.45	[33]	10.1016/j.fcr.2014.02.013	313	28.45
5	[39]	10.1016/j.agwat.2015.12.026	202	22.44	[32]	10.1093/jxb/erv034	286	28.60
6	[27]	10.1016/j.agwat.2020.106002	200	40.00	[40]	10.1016/bs.agron.2018.08.003	241	34.43
7	[41]	10.2134/agronj2004.0194	199	9.95	[42]	10.1016/j.fcr.2018.02.002	211	30.14
8	[43]	10.1016/j.agwat.2008.11.012	198	13.20	[44]	10.1016/j.agwat.2013.09.015	203	18.45
9	[45]	10.1016/j.fcr.2018.02.011	192	27.43	[46]	10.1093/nsr/nwaa146	178	35.60
10	[47]	10.1016/j.fcr.2016.07.009	186	20.67	[48]	10.1016/j.agwat.2010.03.008	175	11.67
11	[49]	10.1016/j.agwat.2009.02.012	177	11.80	[50]	10.1016/j.agwat.2015.07.019	171	17.10
12	[51]	10.1016/j.agwat.2020.106238	176	35.20	[35]	10.1016/j.agrformet.2016.01.142	152	16.89
13	[29]	10.1016/j.agwat.2011.02.003	169	12.07	[52]	10.1016/j.ecolind.2016.06.022	148	16.44
14	[53]	10.1016/j.agwat.2019.03.011	146	24.33	[54]	10.1016/j.fcr.2017.05.026	146	18.25
15	[55]	10.1016/j.agwat.2009.06.003	142	9.47	[56]	10.1016/j.agwat.2019.105986	146	29.20
16	[57]	10.1016/j.eja.2005.06.001	142	7.47	[58]	10.1016/j.fcr.2019.01.002	139	23.17
17	[59]	10.1016/j.scitotenv.2018.06.157	138	19.71	[60]	10.1016/j.still.2017.04.008	137	17.12
18	[30]	10.1016/j.agwat.2016.05.004	137	17.12	[61]	10.1016/j.agwat.2016.05.029	133	16.63
19	[62]	10.1016/j.scitotenv.2017.10.284	115	16.43	[63]	10.1016/j.agwat.2016.05.031	132	16.50
20	[64]	10.1016/j.jhydrol.2015.01.010	111	11.10	[65]	10.1016/j.agwat.2016.08.019	121	15.12

Table 1. The 20 most-cited publications and references in the field of crop water use efficiency WUE evaluation in the North China Plain and Northwest China.

Ranking	NCP				NWC
	Author	DOI	Citations	TC/Y	Author	DOI	Citations	TC/Y
1	[26]	10.1016/j.agwat.2006.04.008	372	19.58	[31]	10.1016/j.agwat.2016.05.007	587	73.38
2	[37]	10.1016/j.agwat.2009.06.004	292	19.47	[36]	10.1093/nsr/nwu017	493	44.82
3	[28]	10.1016/j.agwat.2010.01.008	263	17.53	[34]	10.1016/j.agwat.2012.10.001	367	30.58
4	[38]	10.1007/s00271-002-0059-x	230	10.45	[33]	10.1016/j.fcr.2014.02.013	313	28.45
5	[39]	10.1016/j.agwat.2015.12.026	202	22.44	[32]	10.1093/jxb/erv034	286	28.60
6	[27]	10.1016/j.agwat.2020.106002	200	40.00	[40]	10.1016/bs.agron.2018.08.003	241	34.43
7	[41]	10.2134/agronj2004.0194	199	9.95	[42]	10.1016/j.fcr.2018.02.002	211	30.14
8	[43]	10.1016/j.agwat.2008.11.012	198	13.20	[44]	10.1016/j.agwat.2013.09.015	203	18.45
9	[45]	10.1016/j.fcr.2018.02.011	192	27.43	[46]	10.1093/nsr/nwaa146	178	35.60
10	[47]	10.1016/j.fcr.2016.07.009	186	20.67	[48]	10.1016/j.agwat.2010.03.008	175	11.67
11	[49]	10.1016/j.agwat.2009.02.012	177	11.80	[50]	10.1016/j.agwat.2015.07.019	171	17.10
12	[51]	10.1016/j.agwat.2020.106238	176	35.20	[35]	10.1016/j.agrformet.2016.01.142	152	16.89
13	[29]	10.1016/j.agwat.2011.02.003	169	12.07	[52]	10.1016/j.ecolind.2016.06.022	148	16.44
14	[53]	10.1016/j.agwat.2019.03.011	146	24.33	[54]	10.1016/j.fcr.2017.05.026	146	18.25
15	[55]	10.1016/j.agwat.2009.06.003	142	9.47	[56]	10.1016/j.agwat.2019.105986	146	29.20
16	[57]	10.1016/j.eja.2005.06.001	142	7.47	[58]	10.1016/j.fcr.2019.01.002	139	23.17
17	[59]	10.1016/j.scitotenv.2018.06.157	138	19.71	[60]	10.1016/j.still.2017.04.008	137	17.12
18	[30]	10.1016/j.agwat.2016.05.004	137	17.12	[61]	10.1016/j.agwat.2016.05.029	133	16.63
19	[62]	10.1016/j.scitotenv.2017.10.284	115	16.43	[63]	10.1016/j.agwat.2016.05.031	132	16.50
20	[64]	10.1016/j.jhydrol.2015.01.010	111	11.10	[65]	10.1016/j.agwat.2016.08.019	121	15.12

3.2. Keyword Co-Occurrence and Cluster Analysis

In response to increasing demands for agricultural water conservation, nitrogen mitigation and green low-carbon transformation in agriculture, the evaluation system for crop WUE needs to evolve from a single-efficiency focus to a system-oriented, synergistic optimization framework [66]. Keyword co-occurrence analysis reveals three main thematic clusters in the NCP (Figure 4a). The blue cluster, centered on “WUE”, “Soil”, “Winter Wheat” and “Nitrogen” reflects field-scale studies on soil–water–fertilizer interactions and crop physiological responses, aiming to improve both water and nitrogen efficiency. The red cluster, dominated by “Evapotranspiration”, “Irrigation”, “Model” and “Temperature” focuses on simulating water fluxes, optimizing irrigation regimes and assessing climate impacts, thus forming a core logic of “simulate-control-optimize”. The green cluster, marked by “Yield”, “Growth” and “Nitrogen” highlights agronomic practices such as ridge-furrow tillage and their effects on yield improvement. In the NWC, the co-occurrence network reveals a similarly three-pronged thematic structure, reflecting both shared concerns and region-specific research priorities (Figure 4b). The red cluster emphasizes “Climate Change”, “Evapotranspiration” and “Irrigation” indicating a focus on climate-induced water redistribution and sustainability at the watershed level through model-based, multi-scale approaches. The blue cluster, featuring “WUE”, “Winter Wheat”, “Yield” and “Nitrogen” parallels the NCP but places greater emphasis on drought adaptation and resilient crop management. The green cluster, with terms like “Grain Yield”, “Supplemental Irrigation”, “Conservation Tillage” and “Root” underscores strategies for coping with water scarcity through supplemental irrigation, soil conservation and root system optimization.

The divergence of regional research paths is evident from the keyword co-occurrence network, in which the NCP and NWC differ in their specific emphases within the overarching theme of crop WUE. Research in the NCP primarily focuses on system modeling and climate responsiveness, emphasizing production-oriented strategies such as “efficient irrigation”, “water-nitrogen coupling” and “wheat-maize rotation”. In contrast, the NWC prioritizes agronomic optimization and ecological adaptation, featuring keywords like “drought stress”, “evapotranspiration” and “carbon isotope discrimination”. These differences stem not only from variations in natural resource availability and climate conditions but also from differentiated management objectives. The NCP emphasizes food security and resource use efficiency [67], whereas the NWC focuses on ecological stability and conservation [68]. Recent studies have increasingly addressed improved water and nutrient use efficiency [69], enhanced carbon monitoring [70], spatiotemporal soil quality assessment, tillage system optimization [71] and agroecological system evaluation. In practice, efforts have focused on improving WUE through refined irrigation technologies (e.g., drip, micro, precision irrigation) and the integration of water–fertilizer strategies to achieve high yields under water-limited conditions. The synergistic role of water-saving technologies in stabilizing agricultural systems is also receiving growing attention, pointing toward more sustainable and resilient farming practices. Keyword co-occurrence and cluster analysis further indicate that both regions are converging toward a multidimensional WUE research paradigm that integrates resource management with crop physiological responses. This trend lays the foundation for the development of a regionally tailored and systematic frameworks for the evaluation and advancement of water-saving agriculture.

3.3. Analysis of Thematic Evolution Trends

The keyword clustering timelines clearly illustrate the thematic evolution of crop WUE research in both regions [72]. The quality of the cluster maps is high and reliable for interpretation. For the NCP (Figure 5a), the modularity is Q = 0.7345 and the weighted mean silhouette is S = 0.8634. For the NWC (Figure 5b), Q = 0.758 and S = 0.8912. Since values of Q > 0.5 and S close to 1 indicate well separated and internally coherent clusters, both maps exhibit high cluster quality. In the NCP, the evolution of research themes is characterized by a clear stage-by-stage progression. From 1995 to 2005, studies focused primarily on keywords such as “winter wheat”, “climate change” and “crop yield”, reflecting early concerns over agricultural water risks in major wheat-growing areas under climatic change. Between 2006 and 2015, the emphasis shifted toward “drip irrigation” and “nitrogen use efficiency”, highlighting integrated water–nutrient management strategies. Since 2016, the focus has expanded to system-level themes such as “agricultural irrigation management” and “groundwater recharge system”, indicating growing interest in multi-scale governance and sustainable transformation. Overall, the research theme evolution in the NCP follows a “resource recognition–technology coupling–system coordination” pathway, emphasizing process-level optimization and mechanism development for agricultural water management. A similarly structured, stagewise evolution of research themes is also evident in the NWC, reflecting a comparable progression in adapting research focus. From 1997 to 2005, keywords such as ‘fruit yield’ and ‘arid region’ dominated, with research primarily addressing drought-induced productivity constraints. During 2006–2015, attention turned to adaptive practices like ‘saline water irrigation’ and ‘plastic mulch,’ reflecting efforts to enhance resilience under water resource scarcity. Since 2016, the emergence of terms such as “carbon isotope discrimination” and “WUE” signals a shift toward deeper exploration of crop physiological mechanisms and water uptake efficiency. This evolution reflects a transition from “stress identification” to “adaptive regulation” and ultimately to “mechanistic enhancement”, underscoring a systematic approach to improving WUE under arid and semi-arid conditions.

Although existing frameworks have increasingly incorporated dimensions such as nitrogen use efficiency, crop quality and environmental considerations, they still have limitations in adequately capturing ecological representativeness. Existing studies show that metrics related to ecological environment quality, water footprint and carbon footprint have yet to be systematically incorporated. This absence limits the development of comprehensive frameworks for high-efficiency water use. We observe this constraint in both regions. At the methodological level, research in the NCP has increasingly employed crop models such as APSIM to simulate the long-term effects of cropping systems and irrigation regimes on WUE over multi-decadal periods [73]. However, these approaches are primarily model-driven. Furthermore, multi-criteria methods (e.g., analytic hierarchy process, entropy weighting) have been used to develop assessment frameworks. For instance, some studies focus heavily on engineering control and water quotas, often with limited attention to ecological and socio-economic dimensions [74]. Research on comprehensive indicator systems and methodological integration also remains limited in the NWC. Studies here have innovated by developing regional evaluation scales based on long-term WUE and water productivity data (e.g., in the Heihe River Basin), and by applying a water footprint framework to assess sustainability [75,76]. While these approaches (like the decision-support model for drip-irrigated systems) demonstrate progress, a unified framework that integrates ecological, climatic and socioeconomic dimensions remains largely absent across both regions [77].

Future research should prioritize the integration of multi-source datasets and the enhancement of simulation platforms. This includes coupling models such as APSIM, SWAT and DSSAT with remote sensing technologies and deep learning algorithms to enable cross-scale dynamic simulation and improve decision-making capabilities. The central research frontier lies in establishing a multi-dimensional coordination framework. This framework must integrate productivity, environmental quality and ecological sustainability to achieve synergies across water-saving, nitrogen reduction and emissions mitigation [78]. Developing regionally adaptable and policy-relevant integrated evaluation systems, potentially incorporating life cycle assessment approaches, is vital for constructing multi-objective coupled frameworks that simultaneously assess water footprint, carbon footprint, and ecological impacts [79].

3.4. Keyword Burst Analysis

Using the burst detection algorithm embedded in CiteSpace, this study identified and analyzed the 21 keywords with the highest burst strength to reveal temporal shifts in research focus. In the NCP (

Table 2. Highlighted keywords in crop water use efficiency evaluation studies in the North China Plain.

Note: The colors are software generated to distinguish time spans: red denotes the prominent period of keyword research focus, and light blue indicates the extended timeline of relevance.

), early bursts included “evaporation” (2001–2012), “environment” (2003–2008), “yield” (2005–2012) and “groundwater” (2006–2011). These terms reflect early research interests in crop water demand, evaporative losses and groundwater dynamics, with a strong emphasis on coupling water balance and crop yield [80,81]. The prolonged bursts of “balance” (2006–2015) and “deficit” (2007–2017) indicate a subsequent focus on irrigation optimization and the mitigation of groundwater overexploitation [26,82]. After 2008, keywords such as “carbon dioxide” (2008–2013), “photosynthesis” (2009–2010) and “simulation” (2010–2014) emerged, indicating increased attention to crop physiological responses under elevated CO₂ conditions and the application of simulation models such as DSSAT and APSIM to analyze crop growth and water dynamics [83,84]. In the following years, terms like “resources” (2011–2015), “maize” (2013–2016) and “conservation tillage” (2013–2016) gained prominence, highlighting a shift toward water-saving practices for major crops and integrated soil–water management. Studies on protective tillage and surface hydrothermal regulation were also intensified [85,86]. From 2017 onward, bursts in “dry matter” (2017–2019), “durum wheat” (2017–2019) and “impact” (2019–2020) reflect a gradual transition from yield-centered evaluations to research focused on crop traits, variety adaptability and the effectiveness of water use strategies [87,88]. More recently, terms such as “system” (2020–2025), “nitrate” (2021–2023) and “sustainability” (2022–2023) have indicated a growing focus on integrated water–nitrogen management and sustainable production approaches under increasing ecological constraints [89,90]. The latest bursts, including “supplemental”, “precipitation” and “quality” (2022–2025), suggest increasing research interest in precision irrigation strategies and quality-oriented agronomic management in response to precipitation variability and changing water availability [91,92].

In the NWC (

Table 3. Highlighted keywords in crop water use efficiency evaluation studies in the Northwest China.

Note: The colors are software generated to distinguish time spans: red denotes the prominent period of keyword research focus, and light blue indicates the extended timeline of relevance.

), early burst keywords such as “yield” (2006–2012), “spring wheat” (2007–2018) and “water use” (2010–2016) indicate a focus on crop productivity and water-saving irrigation in spring wheat systems, with particular attention to improving irrigation efficiency [93,94]. Around 2015, the appearance of “Heihe River Basin” (2015–2018), “resources” (2015–2019) and “basin” (2015–2019) marked a shift to basin-scale water management and ecological water reallocation, with the Heihe River Basin serving as a typical case in arid inland regions [95,96]. Since 2016, burst terms such as “crop yield”, “crop production” and “NWC” have pointed to a growing emphasis on regional-scale agricultural productivity and strategic planning for arid-zone development [97,98]. The emergence of “ridge-furrow” (2017–2020), “moisture” (2017–2019) and “optimization” (2018–2020) reflects intensified interest in rainwater harvesting, soil moisture conservation and water use efficiency under drought conditions [99,100]. The burst of “groundwater” (2019–2020) highlights increasing attention to the sustainability of groundwater resources under climate pressure. Since 2020, keywords such as “Zea mays L.” (2020–2022), “drought” (2021–2023) and “quality” (2022–2025) have indicated a continued shift toward crop stress resilience and quality improvement [101,102]. Notably, the concurrent emergence of “accumulation”, “maize yield”, “cotton” and “water” (2022–2025) underscores an evolving research trend toward multi-objective optimization of dry matter accumulation, yield stability and WUE in both food and cash crop systems across arid regions [103,104].

3.5. Thematic Content Analysis

The evaluation of crop WUE research in the NCP and NWC identified nine and ten major thematic clusters, respectively (Table 4), and the specific research frontiers within these clusters are further analyzed through representative literature analysis. In the NCP, Cluster #0: “NCP” focuses on region-specific agricultural challenges, including water resource utilization, crop productivity, soil management and climate change impacts. Representative studies address integrated production systems and water management strategies tailored to regional conditions [105,106,107]. Cluster #1: “Drip Irrigation” highlights the design and optimization of drip irrigation systems, a key technology for improving WUE in water-limited environments. Related research mainly explores its effects on crop growth, water use and yield performance [108,109,110]. Cluster #3: “Winter Wheat” centers on water-saving agronomic practices for Triticum aestivum, the region’s dominant cereal crop. Studies emphasize irrigation scheduling and cultivation techniques to enhance WUE [111,112,113]. Cluster #4: “Nitrogen Use Efficiency” addresses integrated water–fertilizer management, aiming to synergize water and nutrient use while mitigating environmental risks [114,115,116]. Cluster #7: “Agricultural Irrigation Management” explores system-level innovations in irrigation scheduling under water-scarce conditions, with research focusing on strategies to ensure stable water supply and improve irrigation performance [117,118,119].

In the NWC, Cluster #0: “Crop Coefficient” investigates the variability of crop coefficients under diverse water stress levels, soil types and climatic conditions. Emphasis is mainly placed on developing regionally adapted estimation methods suitable for arid and semi-arid zones [31,120,121]. Cluster #2: “WUE” examines water and nutrient management strategies to enhance WUE in arid farming systems. Studies report positive impacts of optimized irrigation and adaptive tillage practices [122,123,124]. Cluster #3: “Transpiration” focuses on physiological processes of transpiration and their interaction with environmental factors, root development and water availability, highlighting their role in yield formation and WUE under drought conditions [122,123,124]. Cluster #5: “NWC” addresses region-specific challenges in water conservation and eco-environmental sustainability. Research emphasizes water governance, eco-agricultural development and sustainability assessments under climate stress [31,125,126]. Cluster #9: “Carbon Isotope Discrimination” employs isotopic techniques to evaluate photosynthetic efficiency and WUE. Carbon isotope discrimination is generally used as a physiological indicator to quantify crop responses to drought and water-saving practices [127,128,129].

The distinct thematic clusters identified in the NCP and NWC reflect differences in regional research priorities. Studies in the NCP emphasize agronomic optimization, irrigation scheduling and integrated nutrient management under intensive cropping systems, whereas research in the NWC focuses more on eco-environmental adaptation, crop physiological responses and water resource sustainability in arid regions. These differences underscore how contrasting agroecological conditions drive the formation of region-specific research agendas in crop WUE evaluation.

Table 4. Representative references for thematic clusters in crop water use efficiency evaluation studies in the North China Plain and Northwest China.

Cluster	NCP		NWC
	Keyword	Representative Cited References	Keyword	Representative Cited References
#0	NCP	[41,59,84,105,106,107,130]	Crop Coefficient	[31,120,121,131,132,133,134]
#1	Drip Irrigation	[73,82,108,109,110,135,136];	Grain Yield	[69,137,138]
#2	Climate Change	[122,139,140,141,142,143,144]	Water Use Efficiency	[31,99,122,123,124,136,145]
#3	Winter Wheat	[59,110,111,112,113,139,146,147,148]	Transpiration	[122,149,150,151]
#4	Nitrogen Use Efficiency	[59,106,111,114,115,116,146,147,152]	Soil Temperature	[153,154,155,156,157]
#5	Eddy Covariance	[106,112,125,142,158,159]	NWC	[31,125,126,160,161]
#6	Tillage Practices	[73,146,147,162]	Environmental Flows	[162,163,164,165]
#7	Agricultural Irrigation Management	[73,117,118,119,129,139,166,167]	Arid Region	[168,169,170,171,172]
#8	Water ManagementSoil	[167,170,173,174,175]	Water Balance	[137,170,176,177,178]
#9	NA	NA	Carbon Isotope Discrimination	[122,127,128,129,179]

Note: NA = Not Available. The keyword or representative references cited were not found for this cluster in the corresponding region.

Table 4. Representative references for thematic clusters in crop water use efficiency evaluation studies in the North China Plain and Northwest China.

Cluster	NCP		NWC
	Keyword	Representative Cited References	Keyword	Representative Cited References
#0	NCP	[41,59,84,105,106,107,130]	Crop Coefficient	[31,120,121,131,132,133,134]
#1	Drip Irrigation	[73,82,108,109,110,135,136];	Grain Yield	[69,137,138]
#2	Climate Change	[122,139,140,141,142,143,144]	Water Use Efficiency	[31,99,122,123,124,136,145]
#3	Winter Wheat	[59,110,111,112,113,139,146,147,148]	Transpiration	[122,149,150,151]
#4	Nitrogen Use Efficiency	[59,106,111,114,115,116,146,147,152]	Soil Temperature	[153,154,155,156,157]
#5	Eddy Covariance	[106,112,125,142,158,159]	NWC	[31,125,126,160,161]
#6	Tillage Practices	[73,146,147,162]	Environmental Flows	[162,163,164,165]
#7	Agricultural Irrigation Management	[73,117,118,119,129,139,166,167]	Arid Region	[168,169,170,171,172]
#8	Water ManagementSoil	[167,170,173,174,175]	Water Balance	[137,170,176,177,178]
#9	NA	NA	Carbon Isotope Discrimination	[122,127,128,129,179]

Note: NA = Not Available. The keyword or representative references cited were not found for this cluster in the corresponding region.

4. Conclusions

This paper provides a comprehensive and quantitative analysis of crop WUE research in the NCP and NWC using bibliometric methods. The number of publications on WUE evaluation has steadily increased since 1995, with a marked acceleration after 2010, indicating a sustained rise in academic interest and denser collaboration networks in the Chinese Academy of Sciences and China Agricultural University. From 1995 to 2010, the research scope was mainly dominated by field-scale, technology-centric topics (irrigation scheduling, evapotranspiration, drip systems, yield). Since 2011, the focus has shifted toward system-level and multi-indicator assessments that link WUE with nitrogen use efficiency, transpiration physiology, carbon-isotope discrimination and multi-scale evaluation. Keyword timelines and clustering metrics corroborate this shift and its interpretability (NCP: Q = 0.7345, S = 0.8634; NWC: Q = 0.758, S = 0.8912), indicating well-separated and internally coherent topic groups. Considering the diversity of agroecological conditions across the NCP and NWC, future research should explore multi-scale and multi-dimensional assessment systems that link crop-level practices with regional sustainability goals. Furthermore, enhancing coordination between water-saving technologies, evaluation indicators and climate response mechanisms will be critical for advancing high-efficiency agricultural water use across different regions.