The Impact Mechanism of Land Scale on Farmers’ Participation in New Agricultural Business Entities

Abstract

Facing the widespread cooperation among different agribusiness entities in China, this study explores the impact mechanism of land scale on farmers’ cooperation with new agricultural business entities (abbreviated as NABEs), including family farms, cooperatives, and agribusinesses. The effects of income within the cooperation mechanism are further analyzed. Based on survey data from 1558 farmers in 10 provinces, applying binary Logit regression and mediation effect models, the study finds the following: (1) The current land area, past growth of land, and future willingness to expand land all positively affect farmers’ cooperation with new agricultural business entities; (2) An inverted U-shaped relationship exists between land size and the proportion of farmers joining new agricultural business entities. The probabilities of joining family farms, cooperatives, and agribusinesses peak at land sizes of 2.65, 6.82, and 7.04 acres, respectively; (3) The current income situation has an intermediary effect on the cooperation between farmers and family farms, while the future income expectation has an intermediary effect on the cooperation between farmers and cooperatives and agribusinesses; (4) The effect of land scale on cooperation is more significant for farmers of village officials or agricultural organization members, full-time farmers, and those with green production and modern sales. This study proposes a development growth curve of farmers, which can be divided into “self-development–cooperation–transformation” stages, and gives solutions for each stage, to facilitate moderate-scale operations and long-term cooperation among various entities in the context of market reforms and social division of labor.

1. Introduction

In China, the household contract responsibility system has led to a special situation where small-scale farmers manage fragmented farmland. To further enhance the efficiency of farmland utilization, the government is committed to fostering new types of agricultural business entities, including family farms, cooperatives, and agribusinesses, and further promoting the cooperation of these new entities and farmers. The reform process of China’s agricultural modernization has given rise to a new situation of cooperative development between traditional farmers and NABEs [1]. Under the framework of the family contract responsibility system, the operation mode of Chinese farmers is facing the reality of small scale, weak technical level, poor risk-resistant ability, aging labor force, and feminization, which objectively causes farmers to be at the bottom of the modern agricultural industrial system, and it is difficult to keep up with the pace of change of the modernization of agriculture only by self-accumulation [2]. Therefore, relying on the market competitiveness and large-scale production advantages of NABEs, adopting cooperation and interest linkage to unite farmers to improve the modern agricultural production structure system, and enhance the status of farmers in the agricultural production system has become a general trend [3,4]. In order to realize the effective convergence of the development of “farmers + NABE”, in 2017, the report of the 19th CPC National Congress clearly put forward the implementation of the rural revitalization strategy throughout the country, and explicitly cultivate NABE, improve the agricultural socialized service system, and realize the convergence of farmers and modern agricultural development, which points out the direction of the development of modern agriculture in China [5,6]. This pointed out the direction for China’s modern agricultural development. 2021 At the 32nd meeting of the Standing Committee of the 13th National People’s Congress, the Ministry of Agriculture and Rural Affairs (MARD) made the “Report of The State Council on Accelerating the Construction of a New Type of Agricultural Operation System and Promoting the Linkage between Farmers and Modern Agriculture”, which further elucidated the results and problems related to the development of farmers and the construction of a new type of agricultural business system, and emphasized that it should continue to focus on the development of farmers and strengthen the ability of family farms and farmers’ professional cooperatives to link up with and bring farmers [7]. Specifically, supportive policies are implemented through different terms. In terms of taxation, the “Notice of the State Taxation Administration of China No. 2 of 2010” and the “Enterprise Income Tax Law Implementation Regulations” stipulate that agricultural enterprises adopting the “company + farmer” model can enjoy income tax reduction. In terms of subsidies, the Ministry of Finance’s “Notice on Supporting the Cultivation of New-Type Agricultural Business Entities” clearly proposes to implement differentiated subsidies for different agricultural business entities, especially the entities that have completed the task of assisting farmers. The Central Document No. 1 for 2020–2025 points out that agricultural consortia should be cultivated, guiding family farms, cooperatives, agribusinesses, and other entities to closely cooperate with farmers through methods such as guaranteed dividends, equity participation and shareholding, and service-driven approaches, to improve the income-sharing mechanism between farmers and NABEs. It can be seen that the production structure system of “farmers + NABE” has become an effective carrier and an important force for rural revitalization and the modernization of agricultural production [8].

The cooperative development between NABEs and farmers can leverage the complementary advantages of both sides. On one hand, by utilizing the socialized services provided by these new entities, farmers can free up young, capable laborers at home, which supports the intensification, mechanization, and modernization of agriculture [9,10]. On the other hand, the livelihood security function of arable land is maximized. Through the cooperation model of “farmers + NABE”, the allocation of resources related to arable land and other production factors is optimized. This model allows farmers the flexibility to either leave home for work or return to agricultural activities, ensuring that the land remains a critical safety net for their livelihoods [11]. Within the framework of the household contract responsibility system, the “farmers + NABE” production structure can more effectively achieve multiple objectives: protecting the fundamental rights of farmers, addressing the fragmentation of arable land, and promoting the modernization of agricultural production. This contributes to advancing agricultural modernization in a way that is centered around the people [12].

However, the “farmers + NABE” cooperation model is often influenced by the individual decision-making of farmers, which is shaped by a variety of factors that lead to significant differentiation [13]. Among these factors, the scale of family-managed land plays a crucial role, exerting a dual influence on farmers’ decision-making. On one hand, farmers with larger landholdings tend to have greater enthusiasm and willingness to engage in agricultural operations. However, their limitations in resources, labor allocation, and the adoption of new technologies reduce their resilience to business risks. As a result, cooperating with NABEs to address shortcomings in cost, sales, and technology may become a key factor motivating these farmers to join such entities. Therefore, partnering with NABEs to compensate for disadvantages in cost, sales, and technology could serve as a major driving force for farmers to engage with these entities [14,15,16]. On the other hand, when large-scale farmers collaborate with family farms, cooperatives, or agribusinesses, the costs associated with contracting and execution are often higher. Additionally, some NABEs hold significant influence over the establishment of the benefit distribution mechanism within the cooperative model. This can lead to the reduction of farmers’ rights and interests, becoming a major deterrent for them to join family farms, cooperatives, or agribusinesses [17,18]. As a result, whether the scale of land management plays a significant role and what impact it has within the decision-making process of the synergistic development between farmers and NABE remains a critical issue that warrants further empirical investigation.

Specifically, Qu et al. have demonstrated that the heterogeneity of land scale significantly impacts the divergence between farmers’ willingness and actual behavior in purchasing socialized agricultural services, revealing a threshold effect of land scale on farmers’ cooperation decisions [19]. In Indonesia, Sukayat et al. have found that the levels of activity and involvement in farmers’ groups significantly influence sustainable agricultural practices [20]. In Poland, Wieliczko et al. have shown that the area of farmland and gross value added have a particularly significant positive impact on savings, and these factors are also considered to affect sustainable development [21]. In China, Wu et al. have indicated that appropriate land scale management significantly increases the income of family farms, especially those participating in cooperatives [22]. In Mongolia, Ahado et al. have demonstrated that cooperation, particularly through cooperative organizations, can significantly enhance the productivity and efficiency of smallholder farmers [23]. In Ghana, research has pointed out that farm size is a significant factor influencing farmers’ participation in contract farming, with larger farms being more likely to engage in such arrangements [24].

Studies have shown that the land scale is a key factor in agricultural cooperation models. However, there are some limitations of existing research, which include the lack of a systematic theoretical analysis of land scale management in the “farmers + New Agricultural Business Entities (NABE)” cooperation model; the absence of clearly defined appropriate land scale thresholds for different cooperation models; and the failure to explore how farmers’ income levels influence their cooperation decisions with NABEs. These issues are the focus of this study. This study addresses these gaps by using empirical data to analyze the impact mechanisms of land scale management in the “farmers + NABE” cooperation model, setting appropriate land scale thresholds for different cooperation models, and providing actionable insights for policymakers. This study addresses several key issues: (1) the influence mechanism of land scale management on the occurrence of cooperation between farmers and family farms, cooperatives, and agribusinesses; (2) the definition of a reasonable land scale threshold under different cooperative models; and (3) the role of income levels in farmers’ decision-making regarding cooperation with NABEs. Using farm household research data, this paper conducts empirical analysis, and the findings will contribute to advancing the cooperative business model of diversified agricultural business entities, while helping to construct a path for agricultural modernization that aligns with the coexistence of diverse agricultural business entities in China.

2. Theory and Hypothesis

2.1. The Mechanism of Land Scale on the Cooperation Probability Between Farmers and NABEs

Under the guidance of land transfer policies, the land operation scale of farmers is expanding. However, this expansion is not unlimited; it has a certain cooperation threshold. When the land operation scale is small, the probability of cooperation between farmers and NABEs increases as the land operation scale expands. The reason is that small-scale farmers have limited resources, technology, and sales conditions. According to the transaction cost theory proposed by Oliver Williamson, cooperation with NABEs can help them reduce transaction costs in areas such as technology acquisition, market information search, and sales channel expansion, thereby sharing the production dividends [25].

When the land operation scale reaches the cooperation threshold, the probability of cooperation between farmers and NABEs peaks. At this point, the cost–benefit structure becomes more favorable, and the allocation of production factors for farmers is more rational. They are thus more inclined to choose a cooperation model that is more conducive to long-term development.

When the land operation scale exceeds the cooperation threshold range, farmers already have sufficient resources and capabilities. Choosing to cooperate with NABEs may be restricted by their management systems, institutional norms, and profit distribution. As a result, they may opt for independent operation or establish their own cooperatives or agricultural enterprises, thereby reducing the probability of cooperation with NABEs. Moreover, as the operation scale continues to expand, the risks and operational costs faced by farmers also increase. Blindly expanding the scale may lead to losses and decision-making mistakes, dampening the production enthusiasm of farmers and thus reducing the probability of their cooperation with NABEs.

Based on this, this study proposes the following hypothesis:

H1. 

Affected by the law of marginal decline, the probability of cooperation between farmers and NABEs will show an inverted U-shaped curve with the increase in land scale.

2.2. Mediating Mechanism of Income Status and Expectation

The current income situation and future income expectations of farmers may play a significant intermediary role in their decision-making regarding cooperation with NABEs. For farmers with smaller land scales, the current income level directly influences their willingness to cooperate, particularly the decision to join a family farm. Because family farms are typically based on family operations, a farmer’s satisfaction with their current income can significantly affect whether they choose to join. For farmers with larger land holdings, future income expectations are a key consideration when engaging with cooperatives or agribusinesses. Cooperative and agribusiness models often involve more complex profit-sharing and long-term contracts, and farmers tend to join only if they anticipate a significant increase in future income. According to Prospect Theory, as proposed by Daniel Kahneman and Amos Tversky, farmers tend to evaluate potential gains and losses relative to a reference point [26]. For small-scale farmers, this reference point is their current income level, while for large-scale farmers, it is their expected future income. Therefore, income status and future expectations may play distinct mediating roles in the influence of land scale on farmers’ cooperative decision-making.

Based on this, the following hypothesis is proposed in this study:

H2. 

Income plays an intermediary role in the influence mechanism of land scale on the cooperation between farmers and NABEs, and shows heterogeneity for different NABEs.

2.3. Heterogeneity of Farmers’ Duties, Livelihood Structure, Green Production, and Sales Channels

Farmers’ attribute differences show heterogeneity in the impact of land scale on their cooperation with NABEs. Specifically, for farmers whose households are headed by village officials or members of agricultural economic organizations, the land scale has a more significant effect on their willingness to cooperate due to their stronger resource acquisition ability and information advantages. Full-time farmers, who are more dependent on land, see the expansion of land scale as directly promoting their cooperation with NABEs. Additionally, farmers with higher levels of green production or modern sales channels have more advantages in production technology and market docking, and land scale has a more prominent positive impact on their cooperation decisions.

These heterogeneity characteristics regulate the relationship between land scale and cooperation probability by influencing farmers’ resource endowment, risk appetite, and production efficiency. David Ricardo’s Comparative Advantage Theory suggests that each individual or group has relative advantages in certain aspects of production [27]. Farmers with stronger resource acquisition ability and information advantages, such as those whose households are headed by village officials or members of agricultural economic organizations, can leverage these advantages to enhance cooperation with NABEs. Similarly, full-time farmers and those with advanced green production practices or modern sales channels can benefit from their unique comparative advantages, leading to different cooperation preferences and behaviors with NABEs. Moreover, the sociology of agriculture theory indicates that the social structure and relationships within the agricultural sector significantly shape farmers’ behaviors and decisions [28]. For example, social networks and community ties among farmers can influence their willingness to adopt new practices or engage in cooperative arrangements. Farmers who are more integrated into these social structures are more likely to cooperate with NABEs due to the social support and shared norms that encourage collective action.

Based on this, the following hypothesis is proposed in this study:

H3. 

The differentiation among farmers in terms of duties, livelihood structure, green production, sales channels, etc., will lead to the heterogeneity of the cooperation between different farmers and NABEs.

3. Data Sources and Methodology

3.1. Data Sources

The data used in this study were obtained from the China Rural Revitalization Survey conducted in 2020 by the Rural Development Institute of the Chinese Academy of Social Sciences (http://rdi.cssn.cn/ggl/202409/t20240920_5778769.shtml (accessed on 12 February 2025)). The survey covers various aspects of agricultural production, rural development, farmers’ livelihoods, and social well-being. It was designed based on economic development levels, spatial distribution, and agricultural and rural development, utilizing an equidistant random sampling method. A total of 300 villages across 10 provinces—Guangdong, Zhejiang, Shandong, Anhui, Henan, Guizhou, Sichuan, Shaanxi, Ningxia Hui Autonomous Region, and Heilongjiang—were included in the survey. Although the dataset does not cover all continents, it includes the Northeast, South China, North China, Central China, and Western China regions. The agricultural production types and geographical elements in these regions have distinct characteristics, thus, they are representative. The data were carefully cleaned to remove anomalies and missing observations, ensuring their reliability and validity. The reliability test yielded a Cronbach’s alpha value of 0.886, confirming the data’s reliability. The final dataset consists of observations from 1558 farm households.

3.2. Variables Selection

(1) Explanatory variables. The CRRS database includes data on whether farmers join family farms, cooperatives, or agribusinesses, which are used to characterize the cooperation decisions between farmers and NABEs, respectively.

(2) Core explanatory variables. Land scale is the core explanatory variable, with three indicators from the CRRS database—“existing land management area”, “past increase in area”, and “future management willingness”—employed to depict the current status, past expansion rate, and future trends in land scale management. These indicators collectively represent the comprehensive potential of land-scale management [29,30].

(3) Intermediary variables. Five variables were selected to verify the mediating mechanism of the current status and expectations of farm household income, including the following: whether the household is classified as a poor household with documented cards, the total amount of state agricultural-related subsidies, wage income/working income, satisfaction with past household income, and expectations of future income levels [31,32].

(4) Control variables. To eliminate the potential influence of omitted variables on the model results, 22 indicators from four areas—household characteristics, production and business behaviors, farm development status, and financial and fiscal status—are controlled for. The inclusion of these control variables will allow for a more accurate assessment of the impact of land size on farmers’ decisions to join family farms, cooperatives, or agribusinesses [33,34,35,36] (

Table 1. Correlation variable declaration.

Types of Variables	Variable Names		Definition
Explanatory variable	Whether farmers join the family farm		1 = Yes; 0 = No
	Whether the farmer is a member of a cooperative		1 = Yes; 0 = No
	Whether or not the farmer is enrolled in an agribusiness		1 = Yes; 0 = No
Core explanatory variables	Arable land area currently operating (hm2)		Existing land management area
	Arable land area increased in the last year (hm2)		The increased area in the last year
	Arable land area the farmer wants to operate in the future		1 = want to reduce the farmland; 2 = unchanged; 3 = want to expand
Mediator variable	Poverty definition		Whether it is a registered poverty household: 1 = yes; 0 = No
	Total state agricultural subsidies (CNY)		Total annual income of national agricultural-related subsidies
	income satisfaction		Satisfaction with the current income level of the family: 1 = very satisfied; 2 = relatively satisfied; 3 = general; 4 = relatively dissatisfied; 5 = very dissatisfied
	Proportion of wage income (%)		Salary income/total annual income
	Future income expectations		Expected changes in income levels next year: 1 = to reduce more; 2 = decreased; 3 = almost; 4 = increased; 5 = increase more.
Control variable	Personal characteristics of the head of the household	Sexuality	Sex of head of household: 1 = male; 0 = female
		Age (years)	Age of the head of the household
		Education degree	The education level of the head of the household: 1 = not attending school; 2 = primary school; 3 = junior high school; 4 = high school/technical secondary school/vocational high school; 5 = university; 6 = graduate student
		Position in the village	Position in the village: 1 = ordinary villagers; 2 = village secretary/village director; 3 = Members of the cooperative or economic organization
		Employment status	The current employment status of the head of household: 1 = full-time farmers; 2 = concurrent business; 3 = Non-agricultural employment
	Green production and the environment	Straw handling methods	1 = discarded on roadsides, ditches; 2 = recycled and disposed of as fertilizer/cultivation substrate/fuel
		Packaging disposal methods	1 = discard, bury on site/burn; 2 = recycle to fixed point/agricultural market
		Changes in pesticide use	Change in pesticide use: 1 = increase; 2 = no change; 3 = decrease
		Change in fertilizer application	Change in fertilizer application:1 = increase; 2 = no change;
		Water accessibility	Access to water: 1 = yes; 0 = no
		Categorize garbage	Whether domestic waste is separated for disposal: 1 = yes; 0 = no
	Agricultural production and business practices	Types of crops grown	Number of crop types planted in the year
		Shifting cultivation	Whether or not rotational plowing of arable land is carried out: 1 = yes; 0 = no
		Fallow	Whether to fallow arable land: 1 = yes; 0 = no
		Online trading	Whether the business has products traded over the internet: 1 = yes; 0 = no
		Order or contract sales	Whether the product acquisition process is by order or contract: 1 = yes; 0 = no
		E-commerce training and guidance services	Received e-commerce training and mentoring services: 1 = yes; 0 = no
		Total input cost (CNY)	Total cost of inputs to agricultural production in the year
		Total production (kg)	Total crop production for the year

).

3.3. Model Construction

Since the explained variable “whether farmers join family farms/cooperatives/agribusinesses” is a binary selection variable, the binary Probit model is selected to build the econometric model:

$$P\left(Y_i=1\mid X_i\right)=\mathsf{\Phi }\left({\mathrm{X}}_{\mathrm{i}}\right)=\alpha +{\beta }_i{T}_i+{\gamma }_i{M}_i+{\delta }_i{X}_i+{\mu }_i$$

(1)

where: P(Y_i = 1|X_i) represents the probability that a sample farmer chooses to join a family farm/cooperative/agribusiness; Φ(.) denotes the cumulative distribution function of the standard normal distribution; T_i is the land size variable; M_i is the current status and future expectations of income variable; X_i is the control variable; β_i, γ_i, and δ_i are the coefficients to be estimated; α is the constant term; and μi is the random error term, which was calculated by constructing a binary Logit model using State15.0.

For farmers, the size of land holdings leads to differences in income levels, and income often affects family livelihood capital, thereby influencing their decision-making in farming operations. For instance, farmers with larger land holdings tend to have higher agricultural incomes, and their willingness to cooperate with new entities may be higher as a result. Therefore, income might serve as an important mediating variable between land size and farmers’ decisions. Therefore, this study incorporates the income variable into the intermediary effect model to analyze the mechanism of “farmers+ NABE” cooperation. Following the mediation effect test proposed by Zhonglin Wen et al. [37], three regression models were established: the independent variable to the dependent variable, the independent variable to the mediator variable, and both the independent and mediator variables to the dependent variable. The specific measurement models are as follows:

$$Y_i=V_1+{\alpha }_i{L}_i+b_{1i}{X}_{1i}+{\epsilon }_{1i},$$

(2)

$$I_i=V_2+c_i{L}_i+b_{2i}{X}_{2i}+{\epsilon }_{2i}$$

(3)

$$Y_i=V_3+d_i{L}_i+e_i{I}_i+b_{3i}{X}_{3i}+{\epsilon }_{3i}$$

(4)

where I refers to the current status and expectations of income; specifically, whether a household is considered “poverty household” by the government can reflect the current income level of the household. Agricultural subsidies can increase the agricultural income of households and reduce financial risks. The proportion of wage income can reflect the degree of farmers’ reliance on agriculture for their income. Satisfaction with current income reflects the subjective perceptions of the households on the current income level, while expectations for future income can reflect the enthusiasm of the households in judging their income. These indicators can all reflect the subjective and objective differences in income from different dimensions. Therefore, this paper selects the variables of “poverty household” recognition, agricultural subsidies, the proportion of wage income, income satisfaction, and expectations for future income as the mediating variables to be included in the model separately.

Y represents the probability that a sample farmer chooses to join a family farm/cooperative/agribusiness; L is the land size; I represents the current income situation and expectations. X denotes the control variable; α is the total effect of land size on farmers’ decisions to join family farms/cooperatives/agribusinesses; c is the effect of land size on farmers’ incomes; e is the effect of income on farmers’ decisions to join family farms/cooperatives/agribusinesses; d is the direct effect of land size on farmers’ decisions after controlling for the effect of other variables; b_1i, b_2i, and b_3i are the coefficients of the control variables in each model; V₁, V₂, and V₃ are the constant terms for each model; and ε₁, ε₂, and ε₃ are the random disturbance terms.

4. Analysis of Results

4.1. Impact of Land Size on “Farmers + New Agricultural Business Entities”

Model (1) tests the relationship between land operation area and farmers’ joining family farms, cooperatives, and agribusinesses (

Table 2. Impact of land size on smallholder farmers’ participation in family farms, cooperatives, and agribusinesses.

	Model (1)			Models (2)
Variant	Family Farm	Cooperatives	Agribusinesses	Family Farm	Cooperatives	Agribusinesses
Operating land area	2.544 **	2.819 **	2.394 **	---	---	---
Square footage of operating area	−0.079 **	−0.034 *	−0.028 **	---	---	---
Past trend of increase in operating space	---	---	---	1.291 ***	1.696 ***	1.368 ***
Willingness to increase business space in the future	---	---	---	0.969 ***	1.355 ***	0.958 ***
A constant (math.)	1.227	−0.923	0.845	0.733	−1.144	0.733
Control variable	Controlled			Controlled
Observed value	1558			1558

Note: ***, **, and * indicate significance at the 1%, 5%, and 10% levels, respectively.

). The results show that the regression coefficients (β₁) of land operation area and farmers’ joining family farms, cooperatives, and agribusinesses are all greater than 0 and significant at the 5% level, and the regression coefficients of the quadratic terms of land management area (β₂) are all less than 0 and significant, indicating that there is some kind of curvilinear relationship between the two, but it still cannot indicate that the curve shape is inverted U-shaped. Two other conditions are needed to further test the relationship between the inverted U-shaped curve: (i) The slope at the two endpoints of the inverted “U” curve is obviously steep, i.e., the slope is positive when the land management area is taken as the minimum value, and negative when it is taken as the maximum value, and according to the descriptive statistics brought into the calculation, it can be seen that, among all the farmers who have joined family farms, cooperatives, and agribusinesses, the one with the smallest area corresponds to the slope of the slope, and the one with the smallest area corresponds to the slope of the slope. The slope values corresponding to the farmers with the smallest area are 2.465, 2.765, and 2.377, and the slope values corresponding to the farmers with the largest area are −22.262, −10.781, and −10.413, which satisfy the condition; (ii) It requires that the inflection point takes the value in the range of the values of the sample data. The value of the correlation coefficient derived from the model (1) is brought into the calculation to obtain the inflection points of the curve of farmers joining family farms, cooperatives, and agribusinesses, 2.65, 6.82, and 7.04, respectively, which belong to the actual range of the operating area of the sample arable land of the farmers to satisfy the condition. Therefore, there exists an inverted “U” curve relationship between land operation scale and farmers’ joining family farms, cooperatives, and agribusinesses, and the probability of farmers joining family farms, cooperatives, and agribusinesses increases and then decreases with the expansion of land scale, with the inflection points appearing at 16.1, 41.4, and 42.75 acres, respectively. In addition, every one-unit increase in the scale of land management of farmers will lead to an increase in the probability of farmers joining family farms, cooperatives, and agribusinesses by 2.544, 2.819, and 2.394 times, respectively. The possible reason is that as the land scale expands, the production costs and economic risks of farmers increase accordingly. During this process, some farmers choose to cooperate with the new entities in order to share the economic benefits or bear the risks together. As a result, the probability of their cooperation increases. However, when the land scale reaches a certain threshold, farmers are no longer traditional farmers but professional large-scale farmers. They already have the advantages of scale economy. At this point, if they continue to cooperate with the new entities, they may be restricted in terms of benefit distribution, compliance with the organizational management regulations of large companies, etc. At this time, the probability of their cooperation may decrease instead [38]. As a result, Hypothesis 1 is confirmed.

Model (2) tested the effects of the past increasing trends of land scale and the future increasing willingness of farmers to join family farms, cooperatives, and agribusinesses. The results show that the regression coefficients of the increasing trend of land scale and the willingness to expand land scale in the future are significant and greater than 0, indicating that every 1-unit acceleration of the past increasing trend of land scale will increase the probability of farmers joining family farms, cooperatives, and agribusinesses by 1.291 times, 1.696 times, and 1.368 times, respectively. For every 1 unit of expansion in the future increase in land size, the probability of farmers joining family farms, cooperatives, and agribusinesses will be increased by 0.969, 1.355, and 0.958 times, respectively. Among them, the coefficient of the past increasing trend of the operation scale is high, indicating that the farmers who already have the behavior of opening up land and expanding land are more inclined to join NABEs, the possible reason is that, on one hand, the increase of the operation scale may make the farmers affected by the scale benefit to a certain extent, and the development of the operation situation is better, and they are more in a position and willingness to associate with the higher level of the agricultural organization; on the other hand, some of the farmers, despite the expansion of their scale of operation, may not have kept up with the level of agricultural infrastructure, production technology, labor force, and management, and therefore choose to seek help from family farms, cooperatives, and agribusinesses.

The results from the control variables indicate that, with respect to farmers’ characteristics, the average education level of the household head has a significant positive effect on farmers’ participation in family farms, cooperatives, and agribusinesses, while age exerts a negative effect. Furthermore, the higher the farmer’s position within the village, the more likely they are to engage with higher-level agricultural organizations. Regarding green production and the living environment, the coefficients of green production variables, such as the reduction of chemical fertilizers and pesticides, the implementation of straw recycling, and the recycling of pesticide packaging, are both high and statistically significant. Notably, the coefficient for farmers joining family farms is higher than that for those joining cooperatives and agribusinesses. In terms of agricultural production and business behavior, indicators of modernization in farmers’ operations, such as engaging in online sales and order-based contract transactions, show a stronger positive correlation with farmers joining cooperatives, in comparison to family farms and agribusinesses. These findings suggest that, while adhering to the law of land scale, it is important to consider the individual differences among farmers in terms of education, age, job position, production methods, marketing strategies, and other factors (Figure 1).

4.2. Analysis of Mediating Effects

The improvement of income is a very important factor to consider, but its effect is indirect, because the most important basis for farmers to make decisions is the production factor (land) they own. Therefore, the income variable is included in the intermediary effect in this study, and the mechanisms of cooperation between them and new subjects are deeply analyzed. Farmers’ poverty identification, agricultural subsidies, income satisfaction, and future income expectations were used as mediating variables to specifically test the mediating effects of current income status and future expectations on the influence of land size on farmers’ cooperative decisions (

Table 3. Results of mediating effects of current and future expectations of farm household income.

Variant	Joining a Family Farm	Access to Cooperatives	Joining an Agribusiness
Operating land area	0.578 ***	0.561 **	0.502 ***
Past trend of increase in operating space	0.398 **	0.302 **	0.201 **
Business area future business intention	0.203 *	0.326 **	0.302 **
Poverty determination	−0.029 ***	---	---
Agricultural subsidies	0.035 ***	---	---
Income satisfaction	---	0.034***	0.030 ***
Future revenue expectations	---	0.031 **	0.045 ***
Sample size	1558	1558	1558
R2	0.036	0.013	0.025
F-value	F(5,1552) = 11.588, p = 0.000	F(5,1552) = 4.122, p = 0.001	F(6,1545) = 6.489, p = 0.000

Note: ***, **, and * indicate significance at the 1%, 5%, and 10% levels, respectively.

).

Poverty identification and agricultural subsidies have a mediating effect on the decision of farmers to join family farms, indicating that the current situation of farmers’ lives and production will play a more significant mediating role in their decision to join family farms, which are based on the basic model of family management. Under the state’s policy system of support for family farms, some farmers with large land sizes may choose to transform and upgrade their farms or join family farms in order to obtain state agricultural subsidies.

In the context of farmers joining cooperatives and agribusinesses, income satisfaction and future income expectations play a significant intermediary role. This suggests that farmers’ expectations about their future livelihoods are crucial in their decision to join cooperatives and agribusinesses, which are more organized and commodity-based agricultural entities. The reason behind this is that farmers tend to exercise caution when joining new agricultural organizations that have more mature production chains, stricter organizational systems, and tighter contractual regulations. To maximize the retention of their economic interests, they will only choose to join cooperatives and agribusinesses when they hold long-term positive expectations regarding future business income.

Our results propose that current income plays a mediating variable in the cooperation of farmers + family farms, but the expectation of income plays a mediating variable in the cooperation of farmers + cooperation/enterprises, which confirms the heterogeneity in Hypothesis 2. With the increase in income level, farmers may have positive expectations for the output brought by land management input, and then try to cooperate with family farms. The reason is that the threshold of cooperation with family farms is relatively low, and most farms are set up by local families and villagers. However, with the increase of enthusiasm for future income expectations, some farmers with entrepreneurial enthusiasm and higher risk tolerance may choose to cooperate with larger new entities, such as large cooperatives and leading agribusinesses, to enjoy the operating dividends brought by the operation mode of large enterprises.

4.3. Model Testing

• Endogeneity test: The size of the farm household is influenced by the personal subjective factors of the household head, which may lead to endogeneity in the farmers’ decision to join family farms, cooperatives, or agribusinesses. Therefore, an endogeneity test is necessary. Although there is a relationship between the farmers’ transferred land area, current business area, and future business intentions, the transferred land area does not directly affect their decision to join family farms, cooperatives, or agribusinesses. As a result, the transferred land area is chosen as an instrumental variable to test for endogeneity using a two-stage regression analysis. The results show that the p-value from the three Wu–Hausman tests is not significant, indicating that there is no endogeneity among the selected endogenous variables (

  Table 4. Endogeneity test results.

  Landholding	Wu–Hausman Test
  	Statistic	p-Value
  Size of land under operation	0.581	0.562
  Trend toward increased scale of operations	1.539	0.222
  Willingness to increase the scale of operations	0.669	0.102

  ).

2.

Robustness test: In order to test the robustness of the regression analysis, this paper chooses the number of operating plots and the largest piece of land area as the proxy variables for land operating area, and the ideal operating area of farmers as the proxy variable for the willingness to increase the land scale in the future, and then carries out the Logit analysis again. The results show that after variable substitution, land size is still significantly and positively correlated with the probability of farmers joining family farms, cooperatives, and agribusinesses (

Table 5. Robustness test results.

Variant	(1)			(2)			(3)
	Family Farm	Cooperatives	Agribusinesses	Family Farm	Cooperatives	Agribusinesses	Family Farm	Cooperatives	Agribusinesses
Number of plots	0.621 **	0.424 **	0.658 **				0.642 ***	0.541 ***	0.712 **
Maximum plot size	1.251 **	1.561 **	1.051 **				1.322 **	1.388 **	1.854 **
Ideal Business Area				0.961 **	1.083 ***	1.101 ***	1.547 ***	1.013 **	1.032 ***
control variable	Controlled			Controlled			Controlled

Note: ***, ** indicate significance at the 1%, 5% levels, respectively.

), which is basically consistent with the results in the previous paper and proves that the estimation results above are robust.

4.4. Heterogeneity Analysis

Since the current land operation area, its past growth trend, and the willingness to expand it in the future are used as the core explanatory variables, only the linear effect of land size on farmers’ decisions to join NABEs is considered. This approach may overlook the marginal effects of various family attributes and production characteristics. To address this, the paper empirically tests the heterogeneity of land size’s impact on farmers’ decisions to join family farms, cooperatives, and agribusinesses, using respondents’ job titles, employment types, green production levels, and product sales channels as grouping variables. Furthermore, to better reflect the complex relationships among the variables, we also constructed the interactivity between land scale and key household attribute dummy variables to conduct in-depth tests on their heterogeneity (see Supplementary Materials).

From the perspective of intra-village job heterogeneity, the influence of land size on farmers’ decisions to join NABEs has a stronger facilitating effect on village officials and members of agricultural economic organizations, while its impact is less significant on ordinary villagers (

Table 6. Heterogeneity analysis of positions within the village.

	Ordinary Villager			Village Official			Members of Agricultural Economic Organizations
Classifier for principles, items, clauses, tasks, research projects, etc.	family farms	cooperatives	agribusinesses	family farms	cooperatives	agribusinesses	family farms	cooperatives	agribusinesses
Operating land area	0.327 *	0.802	0.708 *	0.802 *	0.908 ***	0.811 **	1.006 *	1.114 ***	1.005 **
Increasing trend in operating space	0.302	0.312	0	0.326 *	0.647 **	0.631 **	0.604 **	0.815 **	0.766 **
Willingness to increase business space	0.502	0.585	0.503 *	0.636 **	0.523 **	0.604 *	0.536 **	0.556 **	0.665 ***
Log(Sigma)	−1.906 **	−0.906 **	−0.890 **	−1.412 **	−0.765 **	−1.148 **	−1.524 **	−0.810 **	−1.021 **
Sample size	1322	1322	1322	108	108	108	128	128	128

Note: ***, **, and * indicate significance at the 1%, 5%, and 10% levels, respectively.

). This is likely because village officials and agricultural economic organization members possess a certain degree of resource advantages and foresight, which enhances their ability to access, comprehend, and apply information. Within the group of agricultural economic organization members, land size has the most significant impact on farmers’ decisions to join cooperatives. This may be due to the cooperatives’ strong adaptability to rural environments, organizational power, high levels of trust among farmers, and the close-knit social networks within these cooperatives.

In terms of the heterogeneity of farm households’ livelihood structures (

Table 7. Heterogeneity analysis of farmers’ livelihoods.

	Full-Time Farming			Part-Time Agricultural Worker
Classifier for principles, items, clauses, tasks, research projects, etc.	family farm	cooperatives	agribusinesses	family farm	cooperatives	agribusinesses
Operating land area	0.875 **	1.102 **	1.208 ***	0.932 **	0.704 **	0.716 *
Increasing trend in operating space	0.453 *	0.504 **	0.502 **	0.412 **	0.309	0.332 **
Willingness to increase business space	0.451 **	0.618 ***	0.526 **	0.609 *	0.515 **	0.553 *
Control variable	Controlled			Controlled
Sample size	946	946	946	422	422	422

Note: ***, **, and * indicate significance at the 1%, 5%, and 10% levels, respectively.

), land size plays a more significant role in facilitating the participation of full-time farmers in NABEs. This is likely because full-time farming households are more reliant on arable land for their livelihoods. The larger their land size, the higher their input costs, which increases the demand for improved land productivity. This, in turn, motivates them to join new agricultural organizations to reduce costs and mitigate risks. In contrast, farming households engaged in both farming and non-farming activities typically prioritize deploying young and strong laborers to the non-farming sector. The “left-behind” elderly and female farmers tend to focus on maintaining their own livelihoods rather than agricultural production. As a result, their enthusiasm for farming and reliance on cultivated land diminishes, making it difficult for them to invest significant time and resources into land cultivation. This lack of capacity and willingness to engage with higher-level agricultural organizations impedes the development of cooperative efforts.

When selecting cooperative partners, full-time farmers are more likely to join cooperatives and agribusinesses, while part-time farmers tend to prefer joining family farms. This is likely because cooperatives and agribusinesses, with their focus on commercial agricultural production and operations, have higher entry requirements for cooperation. Full-time farmers, who invest more energy into their agricultural activities, are therefore a priority group for these entities. In contrast, family farms, which are primarily engaged in agricultural production and management, are more accessible for cooperation. Additionally, family farms are often formed through blood ties, kinship, and emotional bonds, lacking the strict hierarchical management systems found in larger organizations. As a result, the cooperation threshold is lower, and the requirement for farmers to work full-time in agriculture is reduced, making it easier for part-time farmers to establish cooperative relationships with family farms.

In terms of the heterogeneity of green production level (

Table 8. Heterogeneity analysis of the level of green production in agriculture.

	Low-Level Group			Intermediate Group			High-Level Group
Classifier for principles, items, clauses, tasks, research projects, etc.	family farm	cooperatives	agribusinesses	family farm	cooperatives	agribusinesses	family farm	cooperatives	agribusinesses
Operating land area	−0.301	0.601 *	0.613	1.202 **	0.921 **	0.805 **	1.761 ***	1.014 ***	0.910 ***
Increasing trend in operating space	−0.001	0.306 *	0.481	0.501 **	0.642 *	0.501 **	0.714 ***	0.701 ***	0.681 ***
Willingness to increase business space	0.425	0.442 **	0.217	0.662 **	0.524 *	0.454 **	0.681 ***	0.633 ***	0.651 ***
Log(Sigma)	−1.903 **	−0.885 **	−0.888 **	−1.721 **	−0.861 **	−0.922 **	−1.842 **	−0.905 **	−0.931 **
Sample size	481	481	481	601	601	601	476	476	476

Note: ***, **, and * indicate significance at the 1%, 5%, and 10% levels, respectively.

), the green production level of farm households was measured by the subjective–objective combination of empowerment methods with four green production behaviors, namely, returning straw to the field, pesticide packaging recycling, protective farming practices, and reduction of pesticide and fertilizer use, and the green production level of farm households was classified into the low-level group, the medium-level group, and the high-level group. The results show that for the group of farmers with the highest level of green production, the effect of land size on their joining family farms, cooperatives, and agribusinesses are all significantly positive and strongest at the 1% level, and for the farmers with the lowest level of green production in agriculture, the effect of land size on the cooperative decision-making of farmers is only significant in joining cooperatives. The research data show that 82.8% of the farmers with high levels of green production are already members of NABEs, while most of the farmers with low levels of green production are still ordinary farmers, indicating that it is easier for NABEs and the farmers cooperating with them to form a production community, which has a driving effect on the green production behaviors of the farmers through the sharing of resources and information, which makes this part of the farmers enjoy the benefits of increased production benefits and further increase the number of farmers in the case of a larger land scale. The benefits of improved production efficiency further increase the probability of joining NABEs, while ordinary farmers in the absence of external forces to drive the environment, the lack of technology, information, and resources is difficult to make it difficult to improve the level of green production in the short term, and often fall into a disadvantage in the cooperation with the NABE.

In terms of the heterogeneity of product sales channels, among the group of farmers engaging in network sales and order contract sales, the land scale has a more significant role in promoting farmers’ joining NABEs (

Table 9. Heterogeneity analysis of sales channels.

	Online Trading			Order Contract Sales			Traditional Market Transactions
Term (in a mathematical formula)	family farm	cooperatives	agribusinesses	family farm	cooperatives	agribusinesses	family farm	cooperatives	agribusinesses
Operating land area	0.812 **	0.913 *	1.104 ***	0.662 **	1.344 ***	1.616 **	0.751 *	0.652 **	0.757 ***
Increasing trend in operating space	0.311 **	0.427 *	0.437 *	0.411 **	0.524 **	0.482 *	0.402 *	0.412 **	0.321 *
Willingness to increase business space	0.334	0.529 *	0.676 **	0.457 *	0.331 **	0.513 *	0.305 *	0.313 *	0.208 *
Log(Sigma)	−1.199 **	−0.744 **	−0.800 **	−1.090 **	−0.793 **	−0.768 **	−1.911 **	−0.896 **	−0.930 **
Sample size	56	56	56	44	44	44	1464	1464	1464

Note: ***, **, and * indicate significance at the 1%, 5%, and 10% levels, respectively.

). The reason may be that farmers engaged in order and network sales have more advanced production and management concepts, and a larger land operation scale will prompt them to expand cooperation opportunities outward, pursuing higher profits and operational efficiency with the model of farmer + NABE. To sum up, the effect of land scale on cooperation is more significant for households of village officials or agricultural organization members, full-time farmers, and those with green production and modern sales, which confirms the heterogeneity in Hypothesis 3.

4.5. Diagnosis of Growth Curves and Decision-Making of Multiple Actors Based on Land Size

To further explore optimal decision-making for farmers with different land sizes, this paper examines relevant studies on the appropriate land size for farmers based on various objectives (Table S2 in Supplemental Material) [39,40,41,42,43,44,45,46,47,48,49,50,51,52]. The results show that significant differences exist in the optimal land size for farmers due to regional variations in resource endowment, crop systems, and the differentiation of objective function settings. As a result, it is challenging to provide a clear diagnosis of optimal decision-making for farmers based solely on land size. Moreover, quantifying the appropriate land size using statistical data does not account for the errors caused by micro-level differences between farmers. Therefore, it is essential to summarize the curves representing the optimal land scale for farmers and to identify specific development pathways and appropriate scale intervals for farmers, depending on different land sizes and objectives.

This paper points out that with the increase in land scale, the probability of cooperation between farmers and NABEs changes accordingly. Therefore, under the rigid resource endowment constraints of tense human–land relations, to resolve the conflict between dispersed and small-scale family operations and modern agricultural development, we should devote ourselves to the effective transformation of small-scale agricultural operations to the transformation of moderate-scale operations, and organically combine with agricultural organizing and socialization of agricultural services on the basis of family operation. Specifically, with the expansion of land scale, farmers should follow their own advantages and market environment to adjust in time, forming a three-stage dynamic growth curve of self-development–cooperative development–transformation and development (Figure 2):

(1)

When the land scale is small, small farmers generally have the ability to manage the existing land due to the influence of the advantages of intensive farming, but in the face of the production disadvantages of decentralized operation and backward technology, at this time, there is the potential that the farmers should pay more attention to the improvement of their own productivity and the initial linkage with family farms. With the increase in land scale, the probability of farmers seeking external cooperation increases. Since the threshold of the family farm’s family operation mode is relatively low, cooperation between farmers and family farms may become the first step. According to the results of this paper, the probability of cooperation between farmers and family farms may reach a peak when the land scale reaches 2.65 acres.

(2)

When the land scale increases further, it is difficult to meet the production needs by solely relying on family cooperative management and self-development, and the weakness of family farm management begins to appear, including the lack of marketization of products, availability of large subsidies, or other policy advantages. These prompts some of the farmers with a long-term willingness for farming to choose to cooperate with larger cooperatives or agribusinesses, to import advanced varieties, production materials, and financial resources, and to broaden the market and sales channels, to improve the degree of specialization, scale, and organization of agricultural production. However, with the expansion of land scale, cooperation with large cooperatives or agribusinesses may restrict their independent decision-making or benefit distribution. According to the results of this paper, when the land scale reaches 6.82 acres, the probability of cooperation between farmers and large cooperatives or agribusinesses reaches the maximum, and when the land scale exceeds this threshold, the probability of cooperation between farmers and large cooperatives or agribusinesses reaches the maximum. Cooperation with large cooperatives or agribusinesses may no longer be the best option for farmers, and then enter the third stage of the curve.

(3)

Because of the complexity of the benefit linkage mechanism in the cooperative model, small farmers face the risk losing their voice; therefore, as the size of the land continues to increase, farmers with advantages in labor, capital and resource endowment and a strong vision for future development should be further transformed and upgraded with the support of the policy, e.g., registering as a family. After registering as family farms, they can develop jointly with cooperatives and agribusinesses to realize large-scale operations and enhance market competitiveness; groups undertaking the status of village officials or members of economic organizations can organize the establishment of cooperatives through self-organization, absorb other members to join, and realize specialized division of labor. The establishment of agribusinesses, to achieve full docking with the market, improves the upstream and downstream industrial chain, strictly controls the quality of products, relies on the standardization and organization of their own business mode, and ultimately achieves the long-term goal of the transformation of multiple entities to agricultural modern production entities. (Figure 2).

5. Policy Implications

Therefore, the following recommendations are proposed for enhancing collaboration between farmers, family farms, cooperatives, and agribusinesses:

(1)

Establish differentiated development pathways for small farmers of varying sizes. For small farmers whose land size falls within a moderate range suitable for collaboration and who demonstrate a long-term, stable commitment to farming, they should be encouraged to transition into higher-level agricultural organizations, becoming key participants or organizers in family farms, cooperatives, and agribusinesses. This transition would allow them to benefit comprehensively from both internal organizational structures and external preferential policies, achieving win–win or even multi-win outcomes. To facilitate this transition, practical measures should be implemented, such as providing training programs on modern agricultural techniques and cooperative management, offering financial incentives like grants and low-interest loans, and ensuring legal support to protect their rights during the transition. For small farmers whose land area has not yet reached an optimal scale, efforts should focus on improving their business performance through socialized services such as technology and marketing [53,54], helping them meet the profit threshold necessary for cooperative operations. Once profit growth is achieved, differentiated development strategies should be tailored to the preferences of small farmers. For instance, farmers inclined to expand should be encouraged to join family farms, cooperatives, and agribusinesses to optimize the linkage and distribution of benefits, stimulate the intrinsic development momentum of NABEs, and achieve economies of scale. On the other hand, farmers with a more conservative approach to management should be supported in securing a long-term profit mechanism, improving operational efficiency, and enhancing risk resilience through more standardized management practices [55,56,57].

(2)

Maximize the mediating role of both current and anticipated farmer income. First, adjust the agricultural subsidy system. While maintaining the current universal direct subsidies, seed subsidies, and comprehensive agricultural subsidies, the incremental portion should be directed more toward small farmers’ groups. In promoting significant reforms such as agricultural price and income insurance, rural finance, and disaster insurance, special attention should be given to supporting disadvantaged farmers, offering sufficient encouragement and protection. Additionally, willing farmers should be guided to join family farms, thereby continuously improving the performance of agricultural subsidy programs [58]. Second, enhance farmers’ expectations of future income and improve the agricultural income guarantee system. The key to agricultural operations lies in people, and the enthusiasm of farmers to engage in food production and their willingness to cooperate depend largely on economic returns [59,60]. Therefore, the government should strengthen policies aimed at benefiting agriculture, such as stabilizing food prices, preventing the devaluation of grain that harms farmers, expanding agricultural insurance coverage, and boosting farmers’ expectations of agricultural income. These measures will encourage farmers to voluntarily participate in cooperative management. Finally, optimize the linkage and benefit distribution mechanisms between NABE and small farmers. This should be achieved by improving both external conditions, such as relevant laws, regulations, and policy documents, and internal conditions, such as management capabilities and technical levels of the responsible bodies. This integrated approach will allow small farmers to be incorporated into the development system of the entire agricultural industry chain, enabling them to share the value gains generated by the development of the agricultural industry chain.

(3)

Emphasize the heterogeneity of resource endowments and guide small farmers toward effective growth. First, different types of small farmers exhibit varying relationships with agricultural interests and differ in their practices. From the perspective of the quality of agricultural operations, attention should be given to full-time farmers and those engaged in advanced agricultural production and management, such as village officials or members of cooperative organizations. Second, there are significant differences in agricultural resource endowments across regions [61,62]. Therefore, it is necessary to adopt region-specific development paths that allow for various cooperation models and articulations. Strategies should be formulated that take into account local conditions, breaking geographical and resource constraints to improve resource utilization efficiency. For example, in remote mountainous areas with underdeveloped transportation and information, the “Farmers + NABE” business model should be promoted. By focusing on remote areas with well-developed transportation and information, contract farming can be utilized to ensure timely and effective communication of order information to farmers responsible for production and processing. This approach will enable farmers to access market information in a timely manner, master industry standards, and enhance their negotiating power in price-setting discussions, preventing the stagnation of agricultural product sales. Finally, new forms of agricultural business cooperation should be actively explored. To avoid falling into the “Farmers + NABE” path lock-in, diversification through models such as “Farmers + Family Farms + Cooperatives/Agribusinesses” should be promoted. This approach can leverage the demonstration and leadership role of NABE, utilizing talents, technology, and capital to assist farmers in accessing market information and innovating agricultural cooperation models.

6. Conclusions

Based on research data from 1558 farmers across 10 provinces in China, along with binary Logit regression and mediation effect analysis, this paper explores the influence mechanism of land size on farmers’ decision-making regarding cooperation with family farms, cooperatives, and agribusinesses. The findings are as follows:

(1)

The land size of small farmers is a key factor influencing their cooperation with family farms, cooperatives, and agribusinesses. The current land area of smallholders, past trends of land size increase, and their future willingness to expand land all significantly and positively contribute to their willingness to cooperate with family farms, cooperatives, and agribusinesses.

(2)

The relationship between the land operation area and farmers’ participation in NABE exhibits an inverted U-shaped curve. The probability of farmers cooperating with family farms, cooperatives, and agribusinesses is maximized when the land size of farmers is 2.65 acres, 6.82 acres, and 7.04 acres, respectively.

(3)

The current income status of farmers mediates the effect of land size on their participation in family farms, while farmers’ future income expectations play a mediating role in the mechanism influencing their participation in cooperatives and agribusinesses.

(4)

The positive impact of land size on farmers’ involvement in NABEs is more pronounced among farmers who serve as village officials or members of agricultural economic organizations, work full-time in agriculture, engage in green production practices, and have access to modern marketing channels.

(5)

In the process of farmer development, the cooperative model is the most effective way to overcome the inherent constraints of the existing system and factor endowments, thereby promoting moderate-scale operations. This approach not only capitalizes on the economies of scale in land use, expanding the boundaries of agricultural production and operation, and simplifying the conditions for economies of scale, but also effectively addresses the conflict between decentralized, small-scale family operations and the interface with modern agriculture.

Based on the quantitative analysis of the cooperation between farmers and NABEs, the dynamic growth curve proposed in this paper is conducive to improving agricultural production efficiency through the cooperation and transformation of various entities, and to improving the resilience of rural livelihoods through the establishment of a benefit-sharing mechanism between NABEs and farmers. In addition, the transformation and cooperative development of farmers to the NABE is conducive to the improvement of scientific and technological production, such as the mechanization to reduce carbon emissions, and the improvement of production efficiency is also conducive to meeting the strategic needs of China’s food security.

However, it is important to acknowledge the limitations of the study’s generalizability. Due to the limitation of the dataset, this paper only covers the data of 300 villages in 10 provinces, and lacks extensive discussion on more regions of the country. In future studies, field investigations and data collection of relevant cases covering more regions and different planting systems, including conducting multi-region surveys or meta-analyses, will be more conducive to interpreting the results of this paper and enhancing the generalizability of the findings.