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Article

Structural Changes to China’s Agricultural Business Entities System Under the Perspective of Competitive Evolution

by
Shenghao Zhu
,
Guanyi Yin
*,
Qingzhi Sun
,
Zhan Zhang
,
Guanghao Li
and
Liangfei Gao
College of Geography and Environment, Shandong Normal University, Jinan 250358, China
*
Author to whom correspondence should be addressed.
Sustainability 2025, 17(7), 3024; https://doi.org/10.3390/su17073024
Submission received: 25 February 2025 / Revised: 26 March 2025 / Accepted: 26 March 2025 / Published: 28 March 2025
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Abstract

:
With the development of new agricultural business entities in China, a complex competitive evolutionary dynamic has emerged among diversified agricultural business entities (abbreviated as ABEs), including farmers (traditional ABEs), cooperatives, agricultural enterprises, and family farms (new ABEs). Based on the Lotka–Volterra model, the dominance index, the Shannon–Wiener index of ecological theories, and the geo-detector, this study examines the spatiotemporal evolution and driving factors of ABEs’ structural changes across 286 Chinese cities from 2012 to 2021. Key findings include: (1) Farmers maintain absolute numerical dominance, but their relative advantage has declined. (2) The Shannon–Wiener index of diversified ABEs has increased significantly, indicating that differences between ABEs decreased, which means a trend toward structural homogenization. High Shannon–Wiener index values were observed in the Northeast Plain, Xinjiang, Hebei, Gansu, and Shanxi, while low values were concentrated in Yunnan, Guizhou, and the Guangdong-Guangxi region. Both areas experienced a shrinking trend. (3) Agricultural production factors such as multiple cropping indexes and theindustrial structure strongly explained the structural changes to ABEs, while the explanatory power of socio-economic factors can be enhanced after the interaction with agricultural production factors. (4) The relationship between farmers and new ABEs has shifted from a symbiotic relationship favoring farmers to a symbiotic relationship favoring new ABEs, with a significant spatial heterogenous layout among 286 cities. This study proposes a three-stage differentiation framework for ABEs: a simple structure dominated by traditional farmers, a competitive evolutionary dynamic among diversified ABEs, and a modernized structure led by new agricultural business entities. Based on these stages, this paper provides targeted recommendations for building a high-quality ABE system and advancing agricultural modernization.

1. Introduction

China is a typical example of a “large country with small-scale farming,” where the per capita arable land area is only 0.09 hectares, accounting for merely 40% of the global average [1,2,3]. This has caused a phenomenon of multiple ABEs. Specifically, to address the production inefficiencies caused by fragmented land tenure under the household con-tract system, China has recently focused on fostering new agricultural business entities to promote scaled agricultural operations. These new agricultural business entities (abbreviated as NABEs), comprising family farms, enterprises, cooperatives, and large-scale agricultural households, leverage the advantages of concentrated land management, mechanization, and the provision of socialized services [4,5,6]. NABEs have the potential to overcome the traditional smallholder challenges of land fragmentation, mechanization constraints, and low market integration.
The rise of NABEs has transformed China’s traditional family-based agricultural model [7,8,9], creating a diversified landscape of multiple coexisting ABEs. However, as urbanization accelerates, rural labor forces are increasingly migrating to cities for non-agricultural employment, leading to a gradual shift in income and residential patterns towards urban areas [10,11,12]. This trend may result in an implicit decline in the household labor force available to traditional smallholder farmers, who historically dominated in terms of numerical strength [13,14,15]. In contrast, the growth and survival of NABEs are differentiated by fierce competition for land, subsidies, the market, etc. Some NABEs have emerged as enduring operators and resource holders, while others have failed to the competition and eventually exited the system.
Moreover, China’s varied agricultural geography induces significant spatial differentiation in the structural evolution of ABEs. Liu Bin observed that farmers’ cooperatives in Hunan exhibit notable spatial agglomeration, with the overall spatial distribution characterized by a “single core, one circle, and multiple points” [16]. Hang Li examined the family farms in the middle reaches of the Yangtze River urban agglomeration, noting a shift from a single-core agglomeration in 2013 to a multi-core agglomeration by 2021, with a higher density in the southeastern region compared to the northwest [17]. Wei Wei’s analysis further revealed a clear clustering trend in the spatial distribution of new agricultural business entities, with their aggregation scale increasing progressively over time, particularly in the Middle and Lower Yangtze Plain and Sichuan Basin regions [18]. Consequently, understanding the dynamic changes and spatiotemporal variations in the structure of diverse ABEs, as well as the underlying mechanisms driving these transformations, is critical. Addressing these issues will enhance our understanding of China’s agricultural modernization pathways during this transformative period.
Currently, academic researches on the relationship among ABEs primarily focus on the interactive dynamics between NABEs and farmers. Some research examines the driving effect of NABEs on farmers and their collaborative development, with an emphasis on green production, efficiency improvement, cultural integration, and income enhancement. Specifically, NABEs can improve the productivity of traditional farmers through the sharing of modern agricultural technologies and the provision of comprehensive socialized services [9,19,20]. NABEs can also increase farmers’ income through the mechanisms of employment and profit-sharing [7,21]. Moreover, rural endogenous NABEs can achieve cultural integration by maintaining localized social relationships and cultural values [22]. Conversely, some have argued that NABEs may form a monopolistic operation and squeeze the interests of farmers [23,24,25]. In addition, because of the limitation of agricultural resources, competition between NABEs and farmers, different NABEs, and multiple other entities may exist as a hidden phenomenon [26,27]. As a result, relevant studies have indicated both “coordinated development among different ABEs” and “competitive relationships among different ABEs”, and no consensus has been reached so far, though many studies are limited to theoretical extrapolation because of data accessibility [28,29,30].
As a result, the real relationship between different ABEs is still unanswered; cooperation or competition among ABEs is debated, especially between NABEs and traditional farmers. While related studies have contributed valuable perspectives on the relationships among various ABEs, there are still several gaps that require further exploration: First, most research has examined the radiating effects of NABEs on farmers in terms of production efficiency, green production, and income, but there is a lack of in-depth exploration of the internal structural evolution of the diverse system of ABEs. As a result, the complex competitive and evolutionary relationships between NABEs and farmers, as well as the competition among NABEs, remain insufficiently analyzed. Second, there is a lack of exploration into the driving mechanisms of the evolution of diversified ABEs or an investigation into the formation paths of regional differences. Third, limited by data accessibility, most research is restricted to regional scales and lacks concrete quantification and abstraction of long-term and large-scale patterns.
To answer the evolution of the internal structural relationship among ABEs and its mechanism from the perspective of a long time and space span, this paper used the dominance index, Shannon–Wiener index, and Lotka–Volterra model to reflect the competition evolution process of ABEs in mainland China during 2012–2021. Additionally, geographical detectors were used to explore the factors influencing the structural composition of ABEs. This study can help to scientifically assess the evolutionary process of ABEs in an modernizing agricultural country and propose development strategies for the collaborative and symbiotic growth of ABEs.

2. Theoretical Analysis and Hypothesis

The theory of ecological niches is a fundamental principle of community ecology, which studies the position, function, and role of species within a community as well as the competitive relationships between species [31,32]. An ecological niche refers to the position a species occupies within a natural ecosystem and its relationship with other related species. It can also be described as the relative position and function of a population within a specific ecosystem, resulting from its interaction with the environment [33,34]. Specifically, in a community, different organisms occupy varying amounts of resources such as land, water, sunlight, and food, which leads to distinct positions and functions, thus occupying different levels of ecological niches. The shared resources required by species lead to “niche overlap”, and as a result, organisms engage in competition for limited resources, causing further differentiation of niches among populations [32,35,36].
Similarly, in this paper, the agricultural business entities system is abstracted as a “community” (Figure 1). Different ABEs occupy different ecological niches based on their access to resources such as arable land, agricultural labor, markets, and government subsidies. Within the “community”, the resources required by various ABEs are often limited, leading to a competition for resources or a cooperation of resource sharing, which causes the differentiation of niches. This differentiation may take three distinct forms: First, NABEs may gradually occupy the ecological niches previously held by traditional farmers because of NABEs’ advantages for accessing larger land, markets, and information, or they may form a cooperation with traditional farmers and share the same niche with them. Second, due to NABEs’ shared advantages—such as larger scale, higher market integration, and broader social services [8,37,38]—NABEs will gradually face a limitation of resources along with their development. As a result, niche shifts may occur among different NABEs. Third, in the competition for limited resources, successful ABEs may expand in scale or seek external cooperation, thus resulting in a change in their own niches. Therefore, Hypothesis 1 is proposed.
Hypothesis 1 (H1):
Through resource competition and cooperation, the ecological niches of different ABEs will undergo significant differentiation, with a decrease in the absolute dominance of traditional farmers and an increase in the dominance of NABEs.
With the improvement of policy support, the development environment of NABEs is getting better, thus forming the driving force for their development and growth. In contrast, due to the dual issues of urbanization and low returns from small-scale agricultural production, traditional farmers are moving out of rural areas to seek livelihoods in non-agricultural industries, resulting in a decrease in the number of traditional farmers. The reverse development of these two groups will lead to a shrinking of scale difference between NABEs and farmers, resulting in a “convergence” of their numbers. Secondly, ecological niches not only change between farmers and NABEs but also differentiate among different NABEs and may also change within a single ABE along with its development. As a result, the structure of the ABEs system will become increasingly complex along with diversified changes in different ABEs. Based on this, Hypothesis 2 is proposed.
Hypothesis 2 (H2):
With the differential development of NABEs and traditional farmers, the structure of the diversified agricultural business entities system will gradually evolve to become more complex, but the gap between new and old entities may weaken.
In regions with higher agricultural development endowments, there is often a combination of larger land resources, more advanced market configurations, and higher technical levels. These regions are thus better equipped to provide natural resources, financial resources, labor, and technology to facilitate the efficient flow and full utilization of these factors across different ABEs. This allows for the optimization and development of different ABE types and their spatial layouts. The advantage of agricultural development in these regions often stems from unique natural conditions, which, compared to socio-economic factors, are more prone to path dependence and lock in. Consequently, these factors exert a more significant influence on the evolution of the agricultural business entities system. Based on this, Hypothesis 3 is proposed.
Hypothesis 3 (H3):
Agricultural endowments have a more significant impact on the evolution of the diversified ABEs system than socio-economic factors.

3. Research Data and Methods

3.1. Research Data

The number of different types of NABEs was acquired in bulk from the Qichacha database platform (https://www.qcc.com/) (accessed on 25 June 2024). After setting the industry scope filter to “Agriculture”, we set the keyword filters to include “family farms”, “farmers’ specialized cooperatives”, and “agricultural enterprises”. The data covered the period from 2000 to 2021, with the number of newly registered NABEs counted annually and accumulated year by year. The total number of various NABEs for each year was derived, and comparisons were made between 2012 and 2021. The analysis was conducted at the city level.
This study used the rural labor force as a representation of traditional farmers. The number of traditional farmers and geographical-detector-related indicators were sourced from the statistical yearbooks and related statistical bulletins of each city, with missing data being supplemented using linear interpolation.

3.2. Methods

Indicators used to analyze population diversity, quantitative dominance, and competitive evolution levels usually include the Simpson, Shannon Wiener, evenness, dominance, predator–prey, Logistic, and Lotka–Volterra models [39,40,41]. Specifically, compared with the Simpson index, the Shannon–Wiener index is more adaptable to the analysis of different agents with large differences in quantity and scale and can better calculate the diversification degree of a complex agricultural business entities system [42]. Secondly, compared with the evenness index, the dominance index can more directly reflect the influence of the leading subject in the study of competition relations and is more suitable for analyzing the advantages of a certain type of agricultural business entity in the ABE system [43]. Compared with the Logistic model, which is widely used to study the internal structural changes of a single population, and the predator–prey model that can only describe unidirectional resource flow, the Lotka–Volterra model can describe the interaction between multiple populations, such as predation, competition, cooperation, and other dynamic processes, while also reflecting the bidirectional resource flow relationship [44]. Geographical detectors can identify driving factors of spatial differentiation without the need for predefined models. They are capable of uncovering nonlinear relationships and factor interactions. Compared to other spatial regression models, they offer a more objective approach for analyzing the spatial distribution of ABEs [45]. Therefore, this paper finally chooses to use the Shannon–Wiener index, dominance index, Lotka–Volterra model, and geographical detectors to depict the structural evolution and competitive evolution of China’s agricultural business entities system.

3.2.1. Analysis of the Dominance and Shannon–Wiener Indexes

This study used the dominance index to capture the structural characteristics of diversified ABEs [39,46,47]. Specifically, dominance measures the proportion of each ABE type relative to the total number of ABEs, revealing shifts in the structure of the agricultural system. Equation (1) showed the calculation of the Dominance index:
p i = x i ∑ i = 1 n x i
p i represents the dominance of the i -th type of ABE, x i denotes the quantity of the i -th type, and n indicates the total number of ABE types. The ABEs with the highest dominance are considered the “dominant species” within the system, followed by the subdominant species [48,49,50,51].
In addition, this study used the Shannon–Wiener Index(Equation (2)) to analyze the structural changes in the diversity of ABEs [42,52,53].
X S h a n n o n = − ∑ i = 1 s P i ln ⁡ P i
X S h a n n o n represents the Shannon–Wiener index, P i denotes the proportion of the i -th type of ABEs, and s represents the total number of entity types.

3.2.2. Geographical Detector

This study adopted the geographical detector approach to explore the driving factors behind the changes in the diversified ABE system. The geographical detector is a statistical method that identifies spatial heterogeneity in geographical phenomena and uncovers the underlying driving factors [54,55]. It is capable of detecting single-factor influences as well as interactions between multiple factors. In this study, potential influencing factors were selected from the economic, social, and technological domains (Table 1). The factor detection and interaction detection methods were applied to identify the driving factors behind the Shannon–Wiener index, with the q value used to quantify the explanatory strength of each factor.

3.2.3. Lotka–Volterra Model

The Lotka–Volterra model was employed to study the interaction between farmers and NABEs. This classic ecological model, originally used to analyze predator–prey relationships, has also been widely applied in the study of competitive and symbiotic relationships between different entities [56,57,58]. Furthermore, the model has been introduced into various fields such as economics and physics to analyze the competitive and cooperative dynamics among different entities. Though it has limitations such as simplifying assumptions of external factors, the Lotka–Volterra model is still effective for revealing the interaction of social and economic entities because of the succinct observation of the evolution of individual numbers [57,59].
The competition and cooperation model used in this study for farmers and NABEs was as follows(Equations (3) and (4)):
d x 2 d t = r 2 x 2 1 − β K 1 x 1 − κ 2 K 2 x 2
d x 1 d t = r 1 x 1 1 − κ 1 K 1 x 1 − α K 2 x 2
Here, x 1 and x 2 represent the number of farmers and NABEs, respectively; t denotes time; r 1 and r 2 are the natural growth rates of farmers and NABEs, respectively; κ 1 and κ 2 are the self-regulation coefficients for farmers and NABEs, respectively; α / K 2 represents the competitive and cooperative interaction coefficient from NABEs to farmers; and β / K 1 represents the competitive and cooperative interaction coefficient from farmers to NABEs. When α / K 2 > 0, it indicates that the suppressive effect outweighs the promoting effect, demonstrating a competitive relationship; when α / K 2 < 0, it implies that the promoting effect outweighs the suppressive effect, signifying a cooperative relationship. Similarly, β / K 1 > 0 indicates a competitive relationship, while β / K 1   < 0 signifies a cooperative relationship. K 1 and K 2 represent the environmental carrying capacities of farmers and NABEs, respectively. In the model, parameters κ 1 , κ 2 , K 1 , and K 2 are estimated by using time series data for the number of ABEs based on the least squares method. This allowed for the determination of the best-fit values, enabling the model to most accurately describe population dynamics. This model was calculated by MATLAB 2020a. Based on the values of α / K 2 and β / K 1 , the type of competition and cooperation relationship between farmers and NABEs was determined. The types of these relationships are presented in Table 2.

3.3. Research Area

This study analyzed 286 cities across China, classifying them into nine major agricultural regions based on the national agricultural zoning standards for a comparative study (Figure 2). Due to data accessibility constraints, the research excluded certain cities from the Qinghai-Tibet Plateau as well as cities from provinces such as Sichuan, Xinjiang, and Inner Mongolia.

4. Results Analysis

4.1. The Decline of the Traditional Farmers’ Relative Advantage

The results of the dominance analysis (Figure 3a) showed that farmers remained the dominant group in both 2012 and 2021, but their dominance significantly decreased over the period. In contrast, the dominance of three types of NABEs grew rapidly, with a clear bidirectional trend emerging between NABEs and farmers, which indicated NABEs gradually showed advantages in the competition of agricultural resources and induced the opposite differentiation of ecological niches for NABEs and farmers, thus confirming Hypothesis 1. This reverse development of new and traditional agricultural business entities has also been confirmed by other scholars [60,61]. Among these NABEs, cooperatives consistently ranked as the subdominant species, while agricultural enterprises and family farms exhibited a reverse trend in the later stages. Family farms surpassed agricultural enterprises in terms of numerical advantage in 2013, suggesting that niche shifts also occurred due to competition among NABEs. This phenomenon indicates that the structure of diversified NABEs is becoming complex because of different changes in ecological niches among different entities, validating the complexity of the ABE system in Hypothesis 2.
Furthermore, the composition of diversified NABEs showed significant regional differences (Figure 3b). For instance, with respect to NABEs, cooperatives were dominant in the Northeast Plain Region, Huang-Huai-Hai Plain Region, Middle and Lower Yangtze River Plain Region, Loess Plateau Region, and Northern Arid and Semi-Arid Region. Family farms were particularly dominant in the Northeast Plain Region, Middle and Lower Yangtze River Plain Region, and Huang-Huai-Hai Plain Region. Agricultural enterprises exhibited the highest dominance in the Northeast Plain Region, Huang-Huai-Hai Plain Region, and Middle and Lower Yangtze River Plain Region. On the whole, it is evident that various types of NABEs showed strong dominance in the Northeast Plain Region, Huang-Huai-Hai Plain Region, and Middle and Lower Yangtze River Plain Region. This dominance is likely related to favorable geographical conditions such as flat terrain; optimal configurations of temperature, light, and water; as well as supportive policies for agricultural production in these primary agricultural regions, which has been supported by related studies [16,17].

4.2. The Shrinking Gap in the Number of “New” and “Old” Agricultural Business Entities

The results of the Shannon–Wiener index analysis showed that the average index value across cities increased from 0.008 in 2012 to 0.05 in 2021 (Figure 4). This suggests that the reduction in the number of farmers, who had a stronger numerical advantage, and the continuous increase in NABEs reduced the differences between new and old entities, which confirms that the large gap between NABEs and traditional farmers have weakened, as stated in Hypothesis 2. It is worth noting that the growth rate of the Shannon–Wiener index decreased rapidly in 2014–2019, which indicates that the weakening trend in the gap between NABEs and traditional farmers is slowing with time.
In terms of spatial patterns, the Shannon–Wiener index showed a distinct north–south division, with a “step-like” decrease from north to south. High-value areas of the Shannon–Wiener index were concentrated in the Northeast Plain, Northwestern Xinjiang, and Western Inner Mongolia, indicating that the difference in the number of NABEs and farmers was smaller in these regions. Low-value areas were concentrated in the Southern Yunnan-Guizhou Plateau and the Guangxi and Guangdong regions. Farmers’ absolute numerical advantage over NABEs was higher compared to other regions, which is the primary reason for the lower Shannon index in these areas. Moreover, previous study also emphasized that the distribution of different ABEs is spatially heterogenous, which may be the reason for the different layout of the Shannon–Wiener index [62,63].
In terms of value added, regions with a high increase in the Shannon–Wiener index (Figure 5c) largely overlapped with the high-value areas (Figure 5a) but with a reduced range. This suggests that the most pronounced homogenization in the number of ABEs occurred in regions where the initial numerical gap between NABEs and farmers was smaller.
Using the geographical detector to analyze the factors influencing the Shannon–Wiener index (Table 3), the results indicated the following order of explanatory power for 2012: industrial structure > multiple cropping index > urbanization > farmers’ income > effective irrigation rate. In 2021, the explanatory power order shifted to multiple cropping index > industrial structure > mechanization > effective irrigation rate > farmers’ income > agricultural development level > educational level. All explanatory factors showed a positive direction of influence. Moreover, agricultural production variables showed a higher q value than socio-economic variables, which confirmed the assumption of Hypothesis 3, indicating that the agricultural development level has a greater effect on the diversity of the ABE system than socio-economic development.
The reasons for this are as follows: (1) The explanatory power of farmers’ income increased. An increase in farmers’ income helps expand the scale of operations, encouraging the formation of cooperatives and family farms. Additionally, increased income may stem from non-agricultural sources, leading to a reduction in the number of farming households and stimulating the formation of new agricultural business entities, thus altering the homogeneity and complexity of the agricultural business entities’ structure. (2) The explanatory power of the agricultural development level increased from nonsignificant to significant. Improved agricultural development effectively promotes technological advancement, reduces labor demand, and facilitates the expansion of NABEs, leading to changes in the homogeneity and complexity of the agricultural business entities’ structure. (3) The explanatory power of industrial structure decreased, and urbanization became nonsignificant. During the urbanization process, the regional industrial structure shifted toward secondary and tertiary industries, providing more non-agricultural employment opportunities. This attracted more farmers to leave agriculture, while NABEs increased to address labor shortages, resulting in changes to the agricultural business entities’ structure. As urbanization and industrial transformation stabilize, the rate of change in farmers and NABEs slows, causing the explanatory power to gradually decrease. (4) Mechanization became significant, and the explanatory power of the effective irrigation rate increased. The improvement in mechanization and irrigation rates significantly enhances agricultural productivity, laying the foundation for large-scale operations, which benefits the development of NABEs and leads to changes in the structure of the agricultural business entities system. (5) The explanatory power of the multiple cropping index decreased. A higher multiple cropping index indicates greater land utilization intensity, with higher inputs and yields. This allows new entities with advanced machinery and capital to leverage income advantages, influencing the structure of diversified ABEs. Since the intensity of crop rotation on a certain scale of land has an upper limit, there is also an upper limit to the improvement of the multiple cropping index. Therefore, the improvement of the multiple cropping index for ABEs’ diversity gradually began to decline.
Note: * indicates significance at the 0.1 level; ** indicates significance at the 0.05 level; *** indicates significance at the 0.01 level.
The results of factor interaction detection indicated that the interactions between factors exhibited both dual-factor enhancement and nonlinear enhancement, suggesting that the Shannon–Wiener index across cities was influenced by the combined effect of multiple factors (Figure 6). In 2012, the explanatory power of the interaction between industrial structure and agricultural development level, mechanization, multiple cropping index, farmers’ income, and effective irrigation rate was enhanced. Similarly, urbanization, when interacting with the multiple cropping index and industrial structure, also showed a significant increase in explanatory power. Furthermore, the interaction between the multiple cropping index, farmers’ income, and urban–rural income gap led to a stronger effect (q > 0.3). In 2021, the explanatory power of factor interactions decreased slightly, with only the interaction between urbanization, the multiple cropping index, effective irrigation rate, educational level, and industrial structure achieving an explanatory power above 0.3. Notably, although the explanatory power of socio-economic factors like urbanization and industrial structure was relatively weaker than agricultural factors, when socio-economic factors interacted with agricultural production factors, the explanatory power of both increased significantly. This suggests that a favorable integration of socio-economic and agricultural production factors is essential to exert a significant influence on the process of increasing NABEs and decreasing traditional farmers. This provided further insights for Hypothesis 3, i.e., policy makes need to consider the interaction of the socio-economic and agricultural endowment environments to keep a reasonable structure of different agricultural business entities.

4.3. The Evolution of Competitive Relationships Among ABEs

From a national perspective, the competitive relationship between farmers and NABEs has evolved from a symbiotic relationship favorable to farmers to a symbiotic relationship favorable to NABEs (Table 4). Specifically, during the first two stages (2012–2015 and 2015–2018), the competitive relationship among diverse ABEs was advantageous to farmers. This can be attributed to the fact that NABEs were in the early stages of development, i.e., by adopting modern agricultural technologies and a market-driven network system, these NABEs gradually established a cooperative mechanism that benefited farmers. This included practices such as allowing farmers to participate in land transfer and shareholder cooperatives, which resulted in a progressively positive impact on farmers ( | α / K 2 | increased from 0.07 to 0.2). During this process, NABEs, being relatively new-born entities, experienced slower growth and a smaller scale, while the growth of farmers was driven by stable intergenerational population reproduction, thus reflecting a symbiotic relationship that was favorable to farmers. In the third stage (2019–2021), the positive impact of NABEs on farmers shifted to a weak competitive suppression ( | α / K 2 | = 0.01). This change may have been due to the large-scale monopolistic operations of new capital, which, through the flow of key resources such as land, markets, and government subsidies, positioned these NABEs as the primary beneficiaries. As a result, the Matthew effect emerged, squeezing the profit margins of farmers, thus leading to a gradual shift in the competitive relationship in favor of NABEs.
Additionally, the types of competitive relationships between farmers and NABEs exhibited significant spatiotemporal differentiation. From 2012 to 2015, cities with a symbiotic relationship favorable to farmers were the most prevalent (139 cities), followed by those with a competitive suppression relationship (128 cities), and much fewer cities with a symbiotic relationship favorable to NABEs (19 cities). It is worth mentioning that cities with a cooperative symbiotic relationship were nonexistent. From 2016 to 2019, cities with a competitive suppression relationship sharply decreased (57 cities), while cities with a symbiotic relationship favorable to farmers and symbiotic relationship favorable to NABEs increased (159 and 61 cities, respectively), and cities with a cooperative symbiotic relationship emerged (9 cities). During 2019–2021, the number of cities with a symbiotic relationship favorable to NABEs surpassed those with a symbiotic relationship favorable to farmers (114 versus 100 cities), while cities with a competitive suppression relationship saw a significant reduction (only 43 cities), and the last number of cities had a cooperative symbiotic relationship (only 29 cities).
Spatial distribution of cities with a competitive suppression relationship between farmers and NABEs experienced a large change (Figure 7 and Table 5). In the first stage, cities with a competitive suppression relationship were widely located, accounting for the most area of mainland China except rare northeastern and eastern cities. By the final stage, cities with a symbiotic relationship favorable to NABEs were primarily concentrated in the Northeast Plain Region, Northern Huang-Huai-Hai Plain Region, Northern Middle and Lower Yangtze Plain Region, Eastern Loess Plateau Region, and the Sichuan Basin and surrounding areas. Cities with a symbiotic relationship favorable to farmers were mostly found in the Southern Huang-Huai-Hai Plain Region, Southern Middle and Lower Yangtze Plain Region, Western Loess Plateau Region, and Northern Arid and Semi-arid Region. Cities with a competitive suppression relationship among ABEs were concentrated in South China Region and the Yunnan-Guizhou Plateau Region. Furthermore, internal differentiation within various agricultural regions was evident
The transformation of competitive relationships among ABEs exhibited distinct regional heterogeneity across stages (Figure 8). During Stage 1 to Stage 2, three trends emerged: (1) 100 cities maintained symbiotic relationships favoring farmers, concentrated in the Northern Arid and Semi-Arid Region, Western Huang-Huai-Hai Plain, Southern Middle-Lower Yangtze Plain, and Yunnan-Guizhou Plateau; (2) 62 cities shifted from competitive suppression to farmer-favoring symbiosis, clustered in the Northern Arid and Semi-Arid Region and Northern Middle-Lower Yangtze Plain; (3) 57 cities sustained competitive suppression, primarily in Eastern South China, Yunnan-Guizhou Plateau, and Western Loess Plateau.
During Stage 2 to Stage 3, four patterns were observed: (1) 91 cities retained farmer-favoring symbiosis, dominant in the Southern Middle-Lower Yangtze Plain, Southern Huang-Huai-Hai Plain, Northern Arid and Semi-Arid Region, and Yunnan-Guizhou Plateau; (2) 71 cities transitioned from favoring farmers to NABE-favoring symbiosis, concentrated in the Northeast Plain and Huang-Huai-Hai Plain; (3) 43 cities continued competitive suppression, mainly in South China and Yunnan-Guizhou Plateau; (4) 42 cities maintained NABE-favoring symbiosis, localized in the Northern Huang-Huai-Hai Plain and Northern Middle-Lower Yangtze Plain.
Nationally, competitive relationships shifted from farmer-favoring to NABE-favoring dynamics. However, a city-level analysis revealed the coexistence of competitive suppression, asymmetric symbiosis (favoring one entity), and limited cooperative symbiosis. While reduced competitive suppression and increased asymmetric symbiosis indicated a transition from “suppression” to “one-sided dominance,” critical concerns persisted: the absence of balanced cooperative symbiosis risked marginalizing traditional farmers’ rights in benefit distribution and decision making within agricultural systems, potentially exacerbating structural power imbalances among stakeholders.

5. Discussion

With the rapid development of China’s economy and the advancement of agricultural modernization, NABEs have flourished, driven by advantages in capital, technology, markets, and large-scale production. Traditional farmers, on the other hand, have increasingly migrated to urban areas, gradually withdrawing from agricultural production. However, due to China’s long-standing urban–rural dual structure, which has resulted in a large and substantial rural population, traditional farmers still hold an important position in agricultural production. As a result, the coexistence of new agricultural business entities and traditional farmers will remain a long-term reality.
This study reveals that the relationship among different ABEs has evolved from being favorable to farmers to being more advantageous to NABEs, yet a cooperative symbiotic situation among multiple ABEs has yet to emerge. Moreover, a competitive relationship among diverse ABEs has also shown significant regional differentiation. This result provides important insights for policy design. It suggests that agricultural production in China can be divided into three stages: a simple structure dominated by traditional farmers, the competitive evolutionary stage of agricultural business entities, and a modernized agriculture stage dominated by NABEs (Figure 9).
Stage 1: Simple Structure Dominated by Traditional Farmers. This stage was observed in regions such as the Yunnan-Guizhou Plateau Region, South China Region, and Western Loess Plateau Region. In these areas, NABEs are still in their nascent stages, characterized by small scales, a lack of operational experience, and slow development. Agricultural production remains dominated by traditional small-scale family farming. Other scholars have suggested similar results and emphasized that the topographic limitation of these areas is the reason for the slow development of NABEs and rural poverty [64,65]. At this stage, it is essential to cultivate the soil for the development of NABEs by improving the property rights system, implementing subsidy policies, and creating other external incentives to promote their formation. Efforts should be made to diversify income sources for traditional farmers and improve social security mechanisms. Additionally, processes for land resource exchange between traditional farmers and NABEs should be accelerated, encouraging the formation of appropriately scaled operations considering local geographical conditions and other factors. In such a scenario, it is conducive to respecting the limitations of objective conditions such as terrain and economic development, address the policy objective of consolidating poverty alleviation in these regions, and give priority to protecting the livelihood of farmers.
Stage 2: Competitive Evolution of ABEs. In this stage, the number of NABEs increases, while the number of traditional farmers declines rapidly. There is a symbiotic relationship between farmers and NABEs that is favorable to farmers or NABEs. The competition among different ABEs intensifies with a reduction in the availability of transferable land resources, and the simultaneous increase in NABEs deepens the competition among them. This results in the first half of this stage favoring farmers and the latter half favoring NABEs. From a regional perspective, regions such as the Southern Middle and Lower Yangtze River Plain Region, Southern Huang-Huai-Hai Plain Region, and Northern Arid and Semi-Arid Regions belong to the first half of this stage, while areas like the Northeast Plain Region, Northern Huang-Huai-Hai Plain Region, Northern Middle and Lower Yangtze River Plain Region, Eastern Loess Plateau Region, and Sichuan Basin and surrounding areas have entered the latter half of this stage. Relevant studies have proposed similar opinions that the rapid decrease in traditional farmers and increase in newly born agricultural entities are usually located in these main agricultural production regions in China [66,67]. Therefore, it is important to address the fragmentation of property rights in densely populated areas with limited land resources, particularly in the Middle and Lower Yangtze River Plain Region, by facilitating land transfer among ABEs. Additionally, leveraging the geographical advantages of regions such as the Northeast Plain Region and Sichuan Basin and surrounding areas, as well as the sparsely populated nature of the Loess Plateau Region and Northern Arid and Semi-Arid Regions, will help improve market mechanisms and policy frameworks, supporting the sustainable development of existing NABEs. Moreover, attention should be given to multi-dimensional cooperation between traditional farmers and NABEs in areas such as land leasing, employment, and agricultural social services, transitioning competitive relationships into cooperative ones. This cooperation will help stabilize the relationship between NABEs and traditional farmers during the transitional period, laying the foundation for entering the stage of full agricultural modernization. The scenario of this stage is exactly what we are facing, and the successful passing of this stage will help us more quickly achieve the policy goal of agricultural modernization.
Stage 3: Agricultural Modernization Dominated by NABEs. In this stage, NABEs replace most traditional farmers and become the dominant force in agricultural production. Through large-scale land consolidation and management, they realize highly centralized and modernized agricultural production. At this stage, traditional farmers either seek opportunities to transform into medium-scale family farms or other NABEs, or they continue their existence through deepened cooperative relationships with NABEs. Farmers who exit agriculture undergo structural changes in non-agricultural livelihoods and gradually stabilize through generational accumulation. Resource allocation and sharing among ABEs reach a relatively rational level, with mutually beneficial and win–win relationships formed both between NABEs and traditional farmers as well as among different NABEs. Related research has proposed the similar suggestion that “NABEs will become the new farmers of China in the future” [29,30]. In this stage, it is crucial to establish a long-term security mechanism for the development of NABEs, ensuring their cooperation with existing farmers, consolidating the achievements of agricultural business entity evolution, and building a high-quality agricultural production and management team. Strengthening the agricultural social services system is also essential to ensure the continuous and stable improvement in the quality of ABE systems. In this scenario, a strong team of agricultural modernization and efficient land management can be generated, and a multi-stakeholder win–win path can be laid for complete and thorough “revitalization” of rural areas.

6. Conclusions

This study aimed to reveal the spatiotemporal patterns, dynamic differences, and influencing mechanisms of the competitive evolution of multiple ABEs. Using multi-source data and an ecological niche theory perspective, we analyzed the evolutionary relationships of four distinct ABEs in China—farmers, family farms, cooperatives, and agricultural enterprises—from 2012 to 2021. The conclusions are as follows:
The dominance analysis confirmed the trend of increased NABEs and the reduction in traditional farmers. However, spatial differences existed. Among NABEs, cooperatives showed dominance in the Northeast Plain Region, Huang-Huai-Hai Plain Region, Middle and Lower Yangtze River Region, the Loess Plateau Region, and the Arid and Semi-Arid Region. Family farms and agricultural enterprises were dominant in the Northeast Plain Region, the Middle and Lower Yangtze River Plain Region, and the Huang-Huai-Hai Plain Region.
The national Shannon–Wiener index increased, signaling a trend toward a more homogeneous structure in the number of ABEs. The spatial distribution of the Shannon–Wiener index exhibited a “staircase” pattern, decreasing from north to south. Higher values were found in Northeast and Northwest China, while lower values were concentrated in the Yunnan-Guizhou Plateau Region and Guangdong. Intermediate values were found in the Middle and Lower Yangtze River Plain Region and the Sichuan Basin and surrounding areas. This suggests that the evolution of the diversified ABE system is becoming more complex, both in terms of quantity structure and spatial differentiation.
The factors influencing the structural evolution of the diversified ABEs included a multiple cropping index, industrial structure, and mechanization. Moreover, the interaction between socio-economic and agricultural production factors showed an enhanced explanatory power.
The relationship among different ABEs shifted from a symbiotic relationship favorable to farmers to a symbiotic relationship favorable to NABEs. However, the emergence of a cooperative symbiotic relationship remained extremely rare and slow. Across different cities, various types of relationships coexisted and showed a significant spatial heterogeneity. Nevertheless, the overall trend toward a more favorable cooperative symbiotic dynamic among diverse ABEs remained weak, raising concerns about the potential deterioration of farmers’ interests.
Therefore, this study proposes a three-stage framework for the evolution of agricultural business entities systems based on the relationships between different ABEs: (1) a simple structure dominated by traditional farmers, (2) a complex structure characterized by the competition and evolution of multiple ABEs, and (3) an agricultural modernization structure dominated by NABEs. Targeted development strategies should be formulated for each stage, considering regional endowments and specific characteristics, to achieve the comprehensive development of modern agricultural production and the high-quality growth of ABE systems.
Finally, this paper still has limitations. Although prefecture-level data can depict macro trends, agricultural production and management activities may have stronger heterogeneity at the county and township levels. For example, there may be different agricultural development models between the plain area and the mountain area within the same city. Therefore, the use of coarse-grained data may mask the micro-competitive relationships between agricultural business entities. In addition, this paper still has some limitations in the single perspective on the number competition between ABEs, the competition for market share, and capital throughput and other perspectives that were not analyzed due to data accessibility. In the future, more field investigation of ABEs can help to answer these questions more deeply.

Author Contributions

Conceptualization, S.Z.; Methodology, G.Y.; Software, S.Z.; Formal analysis, S.Z. and G.Y.; Resources, G.Y., S.Z., Q.S., Z.Z., G.L. and L.G.; Investigation, G.Y., S.Z., Q.S., Z.Z., G.L. and L.G.; Data curation, S.Z, Q.S. and Z.Z.; Writing—original draft preparation, S.Z.; Writing—review and editing, G.Y.; Visualization, S.Z.; Validation, S.Z. and G.L.; Supervision, G.Y.; Project administration, G.Y.; Funding acquisition, G.Y. All authors have read and agreed to the published version of the manuscript.

Funding

This paper was funded by the National Natural Science Foundation of China (Project No. 42171253); the Youth Innovation Team of Shandong Universities, China—“The Youth Innovation Science and Technology Support Program” (Project No. 2021RW034); the Shandong Social Science Planning Fund Program (Project No. 21CCXJ15); and the Shandong Province philosophy and social science “111” leading talent cultivation project (hosted by the corresponding author).

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

The data presented in this study are available on request from the corresponding author (accurately indicate status).

Acknowledgments

The authors extend great gratitude to the anonymous reviewers and editors for their helpful review and critical comments.

Conflicts of Interest

The authors declare no conflicts of interest.

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Figure 1. Theoretical framework diagram.
Figure 1. Theoretical framework diagram.
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Figure 2. Research area.
Figure 2. Research area.
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Figure 3. Changes in the degree of dominance of different ABEs in different regions during 2012–2021((a) The dominance of ABEs of the whole study area from 2012–2021, (b) The dominance of ABEs in different agricultural regions in 2021).
Figure 3. Changes in the degree of dominance of different ABEs in different regions during 2012–2021((a) The dominance of ABEs of the whole study area from 2012–2021, (b) The dominance of ABEs in different agricultural regions in 2021).
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Figure 4. Shannon–Wiener index and its rate of growth.
Figure 4. Shannon–Wiener index and its rate of growth.
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Figure 5. Spatial distribution of the Shannon–Wiener index.
Figure 5. Spatial distribution of the Shannon–Wiener index.
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Figure 6. Factor interaction probes for the Shannon–Wiener index in 2012 and 2021. ((a). 2012 (b). 2021).
Figure 6. Factor interaction probes for the Shannon–Wiener index in 2012 and 2021. ((a). 2012 (b). 2021).
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Figure 7. The spatial distribution of relationship types between farmers and NABEs.
Figure 7. The spatial distribution of relationship types between farmers and NABEs.
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Figure 8. Transition in competitive relationship types among ABEs in different stages.
Figure 8. Transition in competitive relationship types among ABEs in different stages.
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Figure 9. Path of construction of agricultural business entities system adapted to the characteristics of stage differentiation.
Figure 9. Path of construction of agricultural business entities system adapted to the characteristics of stage differentiation.
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Table 1. Selection of geo-detector indicators.
Table 1. Selection of geo-detector indicators.
Objective LayerIndicator LayerUnitIndicator Description
EconomicGDP X1100 million yuan Gross regional product
Agricultural development level X310,000 yuan Proportion of agricultural output
Farmers’ income X6yuanPer capita disposable income of rural residents
SocialUrbanization X2%Urbanization rate of the resident population
Educational level X8%Proportion of students enrolled in higher education
Urban–rural income gap X10%Income gap between urban and rural populations
TechnologicalEffective irrigation ratio X7%Irrigated area per cultivated land area
Mechanization X4(kW/km2)Total agricultural machinery power per cultivated land area
Industrial structure X9%Proportion of secondary and tertiary industry outputs
Multiple cropping index X5%Cropped area per cultivated land area
Table 2. Types of competitive relationships between farmers and NABEs.
Table 2. Types of competitive relationships between farmers and NABEs.
Value CombinationRelationship TypeRelationship Characteristics
α K 2 > 0   a n d   β / K 1 > 0 Competitive suppression Mutual suppression
α K 2 < 0   a n d   β / K 1 > 0 Symbiosis favoring farmersThe NABEs have a promoting effect on farmers, while farmers have a suppressing effect on the NABEs
α K 2 > 0   a n d   β / K 1 < 0 Symbiosis favoring NABEsThe farmers have a promoting effect on NABEs, while NABEs exert a suppressing effect on the farmers
α K 2 < 0   a n d   β / K 1 < 0 Cooperative symbiosisFarmers and NABEs promote each other and develop together
Table 3. Single-factor detection of the Shannon–Wiener index.
Table 3. Single-factor detection of the Shannon–Wiener index.
YearGDP (X1)Urbanization (X2)Agricultural Development Level (X3)Mechanization (X4)Multiple Cropping Index (X5)Farmers’ Income (X6)Effective Irrigation Ratio (X7)Education Level (X8)Industrial Structure (X9)Urban–Rural Income Gap (X10)
20120.0030.113 ***0.0590.0720.204 ***0.078 **0.060 **0.0230.250 ***0.063
20210.0160.0740.071 **0.132 **0.186 ***0.087 **0.100 *0.0470.143 **0.032
Table 4. Competitive relationships between farmers and NABEs.
Table 4. Competitive relationships between farmers and NABEs.
α / K 2 β / K 1 Relationship Type
2012–2015−0.070.38Symbiosis favoring farmers
2015–2018−0.200.34Symbiosis favoring farmers
2018–20210.01−3.37Symbiosis favoring NABEs
Table 5. Number of cities with different competitive relationships within agricultural regions in 2021.
Table 5. Number of cities with different competitive relationships within agricultural regions in 2021.
Competitive SuppressionSymbiosis Favoring FarmersSymbiosis Favoring NABEsCooperative Symbiosis
Northeast Plain Region002110
Huang-Huai-Hai Plain Region217235
Middle and Lower Yangtze River Plain Region831319
South China Region161210
Yunnan-Guizhou Plateau Region141330
Sichuan Basin and Surrounding Areas02133
Northern Arid and Semi-Arid Regions020130
Loess Plateau Region3682
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MDPI and ACS Style

Zhu, S.; Yin, G.; Sun, Q.; Zhang, Z.; Li, G.; Gao, L. Structural Changes to China’s Agricultural Business Entities System Under the Perspective of Competitive Evolution. Sustainability 2025, 17, 3024. https://doi.org/10.3390/su17073024

AMA Style

Zhu S, Yin G, Sun Q, Zhang Z, Li G, Gao L. Structural Changes to China’s Agricultural Business Entities System Under the Perspective of Competitive Evolution. Sustainability. 2025; 17(7):3024. https://doi.org/10.3390/su17073024

Chicago/Turabian Style

Zhu, Shenghao, Guanyi Yin, Qingzhi Sun, Zhan Zhang, Guanghao Li, and Liangfei Gao. 2025. "Structural Changes to China’s Agricultural Business Entities System Under the Perspective of Competitive Evolution" Sustainability 17, no. 7: 3024. https://doi.org/10.3390/su17073024

APA Style

Zhu, S., Yin, G., Sun, Q., Zhang, Z., Li, G., & Gao, L. (2025). Structural Changes to China’s Agricultural Business Entities System Under the Perspective of Competitive Evolution. Sustainability, 17(7), 3024. https://doi.org/10.3390/su17073024

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