Organizational Support, Knowledge Distance, and the Agricultural Ecological Efficiency of Smallholders: Comparing Government and Market Drivers

Abstract

The support of external organizational forces is essential for the promotion of agricultural ecological efficiency to improve agricultural green development and boost China’s prosperity in agriculture. To identify the different impact of different organizational support on improving farmers’ agricultural ecological efficiency and investigate the mechanism by which organizational support affects agricultural ecological efficiency, this study explores the internal logic of farmers’ promotion of agricultural ecological efficiency and empirically examines the impact of organizational support and knowledge distance on agricultural ecological efficiency using 1011 household-level survey data from Henan province in China. The study shows the following: (1) Enhancing organizational support can significantly promote agricultural ecological efficiency, and the effect of organizational support in descending order is agricultural material distributors, peasant cooperatives, village committees, agricultural technology service centers, and agricultural associations. (2) Knowledge distance partially mediates the influence of organizational support on agricultural ecological efficiency. The mediating mechanism of spatial distance is the most significant, followed by content distance and cognitive distance. (3) Market-oriented organizations play a stronger role in incremental support, and government organizations play a stronger role in radical support. This study adds considerable value to the empirical literature and provides precise guidance for improving agricultural ecological efficiency.

1. Introduction

Agricultural green development remains a pivotal global concern, integrating sustainable practices, environmental conservation, and resource efficiency to address ecological balance, climate change mitigation, and food security. Agricultural ecological efficiency is an effective indicator for measuring the efficient utilization of agricultural resources and the coordinated ecological development. The promotion of agricultural ecological efficiency can not only reduce carbon emissions but also achieve carbon sequestration, which is an inherent requirement and effective support for the high-quality development of agriculture and the construction of agricultural power [1]. Agricultural ecological efficiency has obvious positive externalities [2], but farmers, as basic participants, do not have enough motivation to reduce agricultural inputs to reduce undesirable outputs because of their characteristics of risk avoidance, long-term behavioral habits, and the long-time interval between unscientific production behavior and environmental problems in agricultural production [3]. Therefore, the public goods attribute of agricultural ecological efficiency determines that external forces must intervene to improve agricultural green practices rather than relying only on the conscious adjustment of farmers themselves.

Rural primary-level governance is an important environmental factor that affects farmers’ behavior. In rural environments with insufficient information, organizations that provide support to farmers mainly include government organizations dominated by village committees, agricultural technology service centers, and agricultural associations, as well as market-oriented organizations dominated by peasant cooperatives and agricultural material distributors. Various primary-level governance organizations provide effective knowledge support for farmers by transmitting ecological production knowledge to farmers, which not only improves farmers’ environmental knowledge and cognition but also reduces their production risks and uncertainties, thereby affecting their production decisions. The existing literature has analyzed the impact of different primary-level organizations on farmers’ production decisions. For instance, agricultural extension agents in Ghana can better support smallholder farmers in navigating and addressing the effects of climate change on food production [4]. Joining a cooperative group is an effective approach to enhance farmers’ willingness to engage in green agricultural production [5]. The program of farmer field schools in Beijing significantly increases the adoption rate of environment-friendly irrigation and fertilization technologies [6]. However, the above research has only explored the support role of a single organization for farmers, ignoring the fact that farmers are supported by multiple organizations at the same time. In addition, there is obvious heterogeneity in the ways and degrees of influence of different organizational supports on farmers. Then, what is the impact of different organizational supports on improving farmers’ agricultural ecological efficiency, and what are the differences in the roles between government organizations and market-oriented organizations?

Furthermore, farmers’ behavior is the result of their production decisions in which farmers continuously adopt effective information and knowledge to eliminate their uncertainty [7]. The exchange of knowledge, technology, and information is the foundation for farmers to optimize their production decisions and improve their agricultural ecological efficiency [8]. In the decision-making process, farmers receive knowledge from different content, times, sources, and channels. The influence and transmission path of knowledge of different natures on farmers’ production decision-making are not the same, and whether knowledge can be adopted by farmers is the key to influencing their decision-making [9]. In particular, a large number of decentralized small-scale farmers coexist with new agricultural operators in China, leading to significant heterogeneity among farmers [10]. Even for the same knowledge, the acceptance and adoption by different farmers are not the same. In the process of agricultural production decision-making, the differences in the understanding of knowledge among farmers and the differences in knowledge adoption by different farmers are rooted in knowledge distance. Close knowledge is more easily accepted and absorbed by farmers, which has different impacts on their farming methods and degree of utilization of production materials [11]. Then, will organizational support narrow knowledge distance and optimize farmers’ production decisions, thereby improving agricultural ecological efficiency?

To examine the different effects of various organizational support types on enhancing farmers’ agricultural ecological efficiency and explore the mediating role of knowledge distance mechanism through which organizational support influences ecological efficiency, we use 1011 household-level survey data from Henan province in China to examine the effects of organizational support on agricultural ecological efficiency and explore the mediating role of knowledge distance. We find that enhancing organizational support can significantly promote agricultural ecological efficiency, and the role of market-oriented organizations is greater than that of government organizations. The effect of organizational support, in descending order, is agricultural material distributors, peasant cooperatives, village committees, agricultural technology service centers, and agricultural associations. Furthermore, knowledge distance plays a partial mediating role in the influence of organizational support on agricultural ecological efficiency in descending order of spatial distance, content distance, and cognitive distance. In addition, market-oriented organizations play a stronger role in incremental technological support, and government organizations play a stronger role in radical technological support.

Possible contributions to the existing research lie in three aspects. First, in terms of mechanism analysis, rooted in the production decision-making process of farmers, we reveal knowledge distance as a novel mediating pathway through which organizational support enhances agricultural ecological efficiency, thereby extending current theoretical interpretations of rural sustainability mechanisms. Second, we divide organizational support into incremental support and radical support based on its content and find that market-oriented organizations play a stronger role in incremental support, while the role of government organizations is more effective in radical support. This accurately identifies the differences between government organizations and market-oriented organizations in supporting farmers to improve agricultural ecological efficiency, providing more precise guidance for promoting green development in agriculture. Third, regarding indicator measurement, we adopt net carbon sink as an environmental variable to measure agricultural ecological efficiency, which not only comprehensively considers the input and output factors of agricultural production but also aligns with the goal of carbon peaking and carbon neutrality.

The remainder of this paper is structured as follows. In the second section, we construct the theoretical framework of the effect of organizational support on agricultural ecological efficiency. The “Methodology and data” section describes the nature of data, the econometric methods, and variables. The econometric results and discussion are presented in the “Results” and “Discussion” sections, and the “Conclusion” section concludes with the policy implications.

2. Theoretical Analysis

2.1. The Impact of Organizational Support on Agricultural Ecological Efficiency

Compared to complete information decision-making, bounded rational farmers are constrained by internal cognition and external knowledge, and their decision results may not be optimal [12]. Therefore, the promotion of agricultural ecological efficiency requires organizational support beyond farmers. Increasing external organizational support can reduce knowledge distance in the process of information transmission, and the narrowing of knowledge distance has a positive impact on farmers’ behavior and brings their decision results closer to the optimal state.

First, organizational support improves farmers’ production techniques. The support provided by organizations includes government subsidies, agricultural machinery services, high-quality seeds, and production management experience, which are beneficial for farmers to master advanced planting methods and techniques, helping them complete agricultural production with high quality and efficiency. Second, organizational support achieves economies of scale. Organizational support expands farmers’ social relationship networks and achieves low-cost land transfer, thereby realizing economies of scale. Meanwhile, organizational support enables knowledge spillover of production management experience to small-scale farmers, resulting in crop convergence and contiguous planting.

Based on the above analysis, we propose

Hypothesis 1: 

Enhancing organizational support can promote agricultural ecological efficiency.

2.2. The Mechanism of Organizational Support Affecting Agricultural Ecological Efficiency

In the process of agricultural production decision-making, knowledge distance is manifested as the differences in the information adoption of farmers, reflecting the relationship between production knowledge that improves the agricultural environment (such as soil testing and formula fertilization, biological fertilizers, etc.) and farmers’ production decisions. Knowledge distance can be examined from three dimensions: cognitive distance, spatial distance, and content distance [13]. Cognitive distance refers to the degree of deviation between knowledge content and farmers’ prior cognition [14]. Spatial distance refers to the distance between the location where knowledge is acquired and the location where the corresponding agricultural production process is implemented. Content distance refers to the operability or enforceability of knowledge.

First, organizational support promotes agricultural ecological efficiency by narrowing cognitive distance. The ecological information advocated by the government and market-oriented organizations can enhance farmers’ ecological awareness, improve their understanding of ecological production behavior, and narrow the gap between farmers’ basic knowledge and ecological production knowledge. Farmers with strong ecological awareness are more likely to accept environmental, policy, and technical information related to ecological production behavior, which enhances their attitude and cognition towards ecological production [15]. Narrowing the cognitive distance can promote farmers’ green transformation by reducing the uncertainty of their income and technological application in the decision-making process [16].

Second, organizational support promotes agricultural ecological efficiency by narrowing spatial distance. On the one hand, delivering agricultural materials to households or fields by market-oriented organizations is a widely adopted agricultural material distribution method in rural China, which shortens the space interval between farmers receiving advice and application, making it easier for farmers to decide the consumption of agricultural materials based on their own experience and the advice of agricultural distributors, resulting in more reasonable application decisions. On the other hand, productive services are generally adopted by farmers during land preparation and sowing, which convey more reasonable information on agricultural material application and optimize farmers’ agricultural material application behavior [17]. In addition, the field technical guidance provided by agricultural service centers or agricultural technicians effectively reduces spatial distance. Narrowing the spatial distance reduces the transaction cost of adopting information knowledge by compressing the knowledge dissemination chain, which guides farmers to transform their production methods and ultimately optimizes their production decisions.

Third, organizational support promotes agricultural ecological efficiency by narrowing content distance. The narrowing of content distance enhances the usability of technical knowledge through the transmission of technical information [18] and changes the factor allocation ability to improve allocation efficiency. On the one hand, the narrowing of content distance means that the stronger the usability of technical knowledge, the greater the possibility of farmers adopting new technologies [19]. The adoption of new technologies often means changes and optimizations in the allocation of agricultural production factors, reflecting the improvement in farmers’ ability to allocate factors. On the other hand, a shorter knowledge distance means that farmers can obtain more policy, market, and production information. By weakening information asymmetry, farmers can enhance their ability to allocate factors and optimize the factor allocation status of agricultural production [20]. Specifically, a shorter knowledge distance helps farmers quickly grasp the technical key points of ecological production behavior and enhance their ability to apply new technologies [7].

Based on the above analysis, we propose

Hypothesis 2: 

Organizational support improves agricultural ecological efficiency by narrowing cognitive, spatial, and content distances.

2.3. Differences in the Impact of Organizations Support on Agricultural Ecological Efficiency

From the perspective of the technical attributes of organizational support, the support provided by organizations in the production process of farmers can be divided into two categories. One is incremental technical knowledge, such as the optimization of farming methods, bio-fertilizers, and biopesticides. These supports, with their characteristics of high usability, high commercialization level, and short return cycle, are easy to operate and well coupled with local natural resources and other conditions. Meanwhile, market-oriented organizations are motivated to actively provide timely and effective information and tool services to farmers to maintain customers and profits. Therefore, in terms of incremental technical knowledge support, the support role of market-oriented organizations is more evident. The other is radical technological knowledge, such as new crop seeds and integrated water and fertilizer technologies. These supports, with the character of high risk or large infrastructure investment, mainly rely on government promotion. At the same time, during the promotion process, government organizations will provide multiple and systematic technical training, such as field guidance, thus resulting in a good application effect. Therefore, in terms of radical technical knowledge support, the support role of government organizations is more obvious.

Based on the above analysis, we propose

Hypothesis 3: 

Market-oriented organizations play a stronger role in incremental support, and government organizations play a stronger role in radical support.

Finally, we construct the theoretical framework shown in Figure 1.

3. Methodology and Data

3.1. Data Sources

The data employed in this article are sourced from a questionnaire survey of primary-level farmers in Henan Province. The selection of research areas is mainly based on two reasons: First, Henan Province is an important grain planting area in China. Second, Henan is located in the north–south boundary zone, where there are significant differences in natural and social conditions among different regions. There are typical crops from the north such as wheat and corn, as well as typical crops from the south such as rice, which can ensure the diversity of variable data in the sample.

The combination of stratified random sampling and typical sampling methods was used to determine the samples. Specifically, 18 counties were selected, including Huaxian county, Qingfeng county, and Nanle county in the northern region, Gushi county, Huaibin county, and Shangcheng county in the southern region, Sui county, Zhecheng county, Yucheng county, Huaiyang district, and Luyi county in the eastern region, Wolong district, Wancheng district, and Xixia county in the western region, and Biyang county, Runan county, and Shangcai county in the central region. A total of 1–3 townships in each county, 1–3 villages in each township, and about 20 farmers in each village were randomly selected. The geographical locations of the study areas are shown in Figure 2. In the process of selecting farmers within the village, to ensure the heterogeneity of the sample, each village’s farmers include large-scale farmers, general farmers, and village cadres. The questionnaire covered the characteristics of household heads, family conditions, and agricultural production and management situations. “One-on-one” interviews were conducted with household heads, and the questionnaires were filled out by the investigators to avoid the bias of farmers directly filling out the questionnaire and ensure a high efficiency of the questionnaire. Finally, a total of 1039 planting households were surveyed, 1011 valid samples were obtained after excluding a few questionnaires with inaccurate information, and the effective rate of the questionnaires was 97.31%.

3.2. Model Setting

3.2.1. Quantile Regression

To improve the accuracy of model estimation, avoid the influence of extreme values on estimation results, and test the different impact of organizational support on heterogeneous agricultural ecological efficiency, we employ quantile regression [21] to examine the effect of organizational support on agricultural ecological efficiency. The quantile regression method can estimate regression coefficients at different quantiles of the explained variable and has the advantage of relaxing distribution assumptions, being suitable for models with heteroscedasticity and more robust estimation results [22]. We construct the following quantile regression model:

$$Q_{\theta }\left(Y_i\left|X_i,Z_i\right.\right)={\beta }_{0\theta }+{\beta }_{i\theta }{X}_i+{\displaystyle \sum {\gamma }_{i\theta }{Z}_i+{\epsilon }_i}$$

(1)

where $Q_{\theta }\left(Y_i\left|X_i,Z_i\right.\right)$ represents the conditional quantile of $Y_i$ for a given quantile point $\theta$ and the explanatory variable $X_i$; $Y_i$ denotes the green transformation of farmer i; $X_i$ refers to organizational support; $Z_i$ represents control variables, including individual, family, and village characteristics of farmers; ${\beta }_{i\theta }$ is the estimation coefficient of the explanatory variable; ${\epsilon }_i$ is a stochastic error term.

3.2.2. Recursive Model for Mediating Effect Test

To explore the pathways of organizational support affecting agricultural green transformation, based on the test method of Hayes [23], the recursive model for the mediating effect test is set as follows.

$$M_i={\beta }_0+{\beta }_1{X}_i+{\displaystyle \sum _j{\lambda }_j{Z}_{ij}}+{\xi }_i$$

(2)

$$Y_i={\gamma }_0+{\gamma }_1{X}_i+{\gamma }_2{M}_i+{\displaystyle \sum _j{\lambda }_j{Z}_{ij}}+{\xi }_i$$

(3)

where $M_{it}$ represents the mediating variables, ${\beta }_0,{\gamma }_0$ are the intercept terms, and ${\beta }_1,{\gamma }_1,{\gamma }_2,{\lambda }_j$ are the estimation coefficients of corresponding variables. The first equation is used to test the effect of the independent variable on the mediating variables, and the second equation is used to test the effect of the independent variable on the dependent variable after introducing mediating variables. If the regression coefficient on organizational support decreases or becomes insignificant, it indicates that the impact of organizational support on agricultural green transformation comes partly or entirely through the pathway of the mediating variable.

3.3. Variable Selection

3.3.1. Dependent Variable: Agricultural Ecological Efficiency

Given that agricultural production faces a series of interference factors such as weather and natural disasters, we adopt the multi-output stochastic frontier analysis method, which is based on the output-oriented distance function, to obtain agricultural ecological efficiency [24]. Agriculture has a relatively weak ability to adjust input factors such as land and capital, and the adjustment speed is relatively slow, so the specific form of agricultural production function is suitable to employ Cobb Douglas production function [25,26,27]. The range of values for agricultural ecological efficiency is 0–1. A value close to 0 indicates low agricultural ecological efficiency, while a value close to 1 indicates high agricultural ecological efficiency.

In the calculation of agricultural ecological efficiency, referring to the existing literature [28,29,30], the output variable is the output value of crops planted by farmers, and the input variables are land, labor, seeds, pesticides, fertilizers, and other capital inputs. The environmental variable is net carbon sink, which is obtained by subtracting carbon emissions from carbon sink. The calculation formula and coefficients for carbon sink and carbon emissions are based on the research of Zhu et al. [31].

The estimation of agricultural ecological efficiency and calculation of net carbon sink are shown in the Supplementary Materials.

3.3.2. Independent Variable: Organizational Support

We employ the number of organizations that farmers receive support from to characterize organizational support.

3.3.3. Mediating Variables: Knowledge Distance

Based on the section theoretical analysis, cognitive distance, spatial distance, and content distance are selected to measure knowledge distance to study the influencing mechanism of organizational support to agricultural ecological efficiency.

Cognitive distance refers to the degree of deviation between knowledge content and farmers’ prior cognition [32]. We employ the reciprocal of the sum of ecological production behaviors adopted by farmers in the past three years to measure cognitive distance, and the range of cognitive distance values is 0–1. A value close to 0 indicates a closer distance, while a value close to 1 indicates a farther distance. Spatial distance refers to the distance between the location where knowledge is acquired and the location where the corresponding agricultural production process is implemented, and we use the space interval between farmers acquiring and using knowledge to represent spatial distance. Content distance refers to the operability or enforceability of knowledge, and we use the degree of executable knowledge perceived by farmers to represent content distance. The range of spatial distance and content distance is 1–5, with a value close to 1 indicating a closer distance and a value close to 5 indicating a farther distance.

3.3.4. Control Variables

To reduce estimation bias, factors that may affect organizational support and agricultural ecological efficiency at the individual, household, and village levels are controlled in our models. The control variables at the individual level include gender, age, health status, education level, and risk preference. The control variables at the household level include household size, household structure, social capital, agricultural insurance, income structure, land area, number of land plots, and land quality. The control variables at the village level include agricultural material sales points within the village, e-commerce platforms within the village, environmental policy advocacy, the fairness and impartiality of village officials, and village regulations and agreements. The explanation of controlled variables are shown in the Supplementary Materials.

The definitions and descriptive statistics of the variables are shown in

Table 1. Variable definition and descriptive statistics.

Variables	Definitions and Assignments	Mean	S.D.	Min.	Max.
Dependent variable
Agricultural ecological efficiency	Agricultural ecological efficiency (the distance between farmers’ actual production status and production forefront)	0.9399	0.0274	0.6630	0.9849
Independent variable
Organizational support	The sum of the number of organizations obtained by farmers	3.44	2.2697	0.0000	14.0000
Mediating variables
Cognitive distance	The reciprocal of the sum of ecological production behaviors adopted by farmers in the past three years	0.5882	0.2334	0.2500	1.0000
Spatial distance	The space interval between farmers acquiring and using knowledge	2.5470	0.9241	1.0000	5.0000
Content distance	The specific degree of executable knowledge perceived by farmers	2.9159	1.1663	1.0000	5.0000
Control variables
Gender	Gender of household head (1 = female; 2 = male)	1.1187	0.3236	1.0000	2.0000
Age	Age of household head	54.4867	5.6019	39.0000	74.0000
Health condition	Health condition of household head (1 = very bad; 2 = bad; 3 = normal; 4 = good; 5 = very good)	4.2295	0.5347	1.0000	5.0000
Education	Level of education (1 = 6 years and below; 2 = 6–9 years; 3 = 9–12 years; 4 = 13 years and above)	2.0317	0.2537	1.0000	3.0000
Risk attitude	Acceptance level of planting new varieties (1 = very reluctant; 2 = more reluctant; 3 = normal; 4 = more willing; 5 = very willing)	2.1573	1.0206	1.0000	5.0000
Household size	Total household population	3.4570	0.8916	1.0000	7.0000
Aging	Individuals aged 65 and above/total household population	0.1666	0.2378	0.0000	1.0000
Part-time	part-time individuals/total household population	0.5666	0.2108	0.0000	1.0000
Social capital	The number of village cadres and party members among the household population	0.4174	0.7445	0.0000	2.0000
Agricultural insurance	Whether the household pay agricultural insurance? (1 = Yes, 0 = No)	0.9535	0.2106	0.0000	1.0000
Income structure	Agricultural income/total income	0.4587	0.4046	0.0000	8.3059
Land scale	The actual cultivated land area of the family	26.3398	32.3508	2.0000	200.0000
Land parcels	The actual number of land parcels cultivated by the household	6.9130	6.6040	1.0000	34.0000
Land quality	1 = very bad; 2 = bad; 3 = normal; 4 = good; 5 = very good	4.0010	0.1297	2.0000	5.0000
Agricultural material distributors	Whether there are agricultural material distributors in the village? (1 = Yes, 0 = No)	0.4837	0.5000	0.0000	1.0000
E-commerce	Whether there are E-commerce service organizations in the village? (1 = Yes, 0 = No)	0.7962	0.4030	0.0000	1.0000
Policy advocacy	Whether the village committee publicize environmental policies? (1 = Yes, 0 = No)	0.9990	0.0315	0.0000	1.0000
Fairness	Whether village cadres are fair and just in their work? (1 = Yes, 0 = No)	0.9980	0.0445	0.0000	1.0000
Regulations	Whether the village has village regulations? (1 = Yes, 0 = No)	0.7676	0.4226	0.0000	1.0000

.

4. Results

In this section, empirical econometric results will be presented and explained. In the benchmark regression section, the basic impact of organizational support on agricultural ecological efficiency and the roles of each specific organization will be presented. In the endogeneity test section, instrumental variable and two-stage residual inclusion will be employed to validate the robustness of the basic regression results. In robust analysis section, we will test the robustness by replacing explanatory variables, changing the measurement method of the dependent variable, modifying sample data, fixing county effects, and replacing the regression model. In the mechanism analysis section, we will examine the mediating role of cognitive distance, spatial distance, and content distance in the impact of organizational support on agricultural ecological efficiency. In the further analysis section, organizational support will be divided into radical technological knowledge support in the southern and incremental technological knowledge support in the central and northern regions to examine the differences in the role of different organizational supports.

4.1. Benchmark Regression

4.1.1. Organizational Support and Agricultural Ecological Efficiency

Figure 3 illustrates the marginal contributions and trends of organizational support to agricultural ecological efficiency across all quantiles. The horizontal axis represents quantiles, the vertical axis represents quantile regression coefficients of corresponding variables, i.e., marginal contribution rates to agricultural ecological efficiency, the dashed line represents OLS regression coefficients and 5% confidence intervals, and the solid line and shadow represent quantile regression coefficients and their 5% confidence intervals.

Firstly, across all quantiles of agricultural ecological efficiency, the regression coefficient of organizational support is significant at 1% level, indicating that the enhancement of organizational support can improve the agricultural ecological efficiency of farmers and verifying Hypothesis 1. Secondly, as the quantile increases, the coefficient reaches its maximum at the 20% percentile and then gradually decreases. This indicates that with the improvement of agricultural ecological efficiency, the promoting effect of organizational support shows a downward trend. Thirdly, there are significant differences in the influence coefficients at different quantiles, with a coefficient of 0.0031 at the 90% quantile, which is less than half of the coefficient at the 20% quantile. The reason for this phenomenon is that, with the support of various organizations, farmers often have a wide social network and maintain close relationships with various organizations. The rich numbers of organizations can provide effective information, technology, and tool support for farmers, thereby promoting agricultural ecological efficiency. At the same time, considering that there is a maximum limit to agricultural ecological efficiency, which means that agricultural ecological efficiency cannot continue to improve indefinitely, and the effect of diverse types of organizations on promoting agricultural ecological efficiency cannot be maintained indefinitely, the impact coefficient gradually decreases.

4.1.2. Differences in the Roles of Different Organizations

We employ the OLS model to measure the effect of different organizations on farmers’ production decision. The regression results of the impact of different organizations on agricultural ecological efficiency are shown in

Table 2. Regression results of different organizations on agricultural ecological efficiency.

		(1)	(2)	(3)	(4)	(5)
Government organizations	Village committees	0.0152 ***
		(0.0024)
	Agricultural technology service centers		0.0135 ***
			(0.0025)
	Agricultural associations			0.0098 ***
				(0.0019)
Market-oriented organizations	Peasant cooperatives				0.0167 ***
					(0.0025)
	Agricultural material distributors					0.0387 ***
						(0.0034)
Constant		1.0047 ***	0.9858 ***	1.0025 ***	1.0313 ***	0.9735 ***
		(0.0339)	(0.0345)	(0.0342)	(0.0339)	(0.0327)
Control variables		Yes	Yes	Yes	Yes	Yes
R2		0.3055	0.2996	0.2965	0.3091	0.3624
N		1011	1011	1011	1011	1011

Note: The values in parentheses are standard error. *** represents significant levels at 1% statistical level.

. To visually demonstrate the roles of different organizations, we further illustrated the differences in regression coefficients using Figure 4.

Firstly, the effect of market-oriented organizations is greater than that of government organizations. Secondly, the effect of agricultural material distributors in market-oriented organizations is greater than that of peasant cooperatives. The possible reason is that agricultural material distributors are the main way for farmers to obtain chemical agricultural materials, which are key variables affecting the agricultural environment [33]. Although some peasant cooperatives also provide chemical agricultural material services, their main focus is on providing information and technical services. Thirdly, among government and public institutions, the effect of agricultural associations is the smallest. The reason may be that the agricultural associations around farmers are not green agriculture associations, which cannot provide technical support to farmers to enhance agricultural ecological efficiency.

4.2. Endogeneity Test

Despite the positive relationship between organizational support and agricultural ecological efficiency, biases might remain because of the endogenous problems caused by variable omission and reverse causality. The feasible approach to solving endogeneity issues is to introduce instrumental variables that satisfy both correlation and exogeneity [17], and a common approach to seeking instrumental variables is to select the mean of the explanatory variable at the upper level as the instrumental variable. We adopt the average level of organizational support obtained by farmers in their village as an instrumental variable because the average level of organizational support is exogenous to a specific farmer in the village and does not directly affect his agricultural ecological efficiency. However, it is positively correlated with the organizational support obtained by the farmer through demonstration effects and neighborhood effects, meeting the requirement of being highly correlated with the explanatory variable and not correlated with the error term.

We employ instrumental variable two-stage residual inclusion (2SRI) for robustness analysis as the regression coefficients may be non-linear when the dependent variable is non-normally distributed or restricted [34]. The regression results are exhibited in

Table 3. Endogeneity test results.

	(1)
Organizational support	0.0036 ***
	(0.0006)
Residual error in the first stage	−0.0112 ***
	(0.0018)
Control variables	Yes
Adjusted R2	0.3549
N	1011
Coefficient of IV in the first stage	0.6078 ***
	(0.0572)
Value F in the first stage	109.39

Note: The values in parentheses are standard error. *** represents significant levels at 1% statistical level.

.

Firstly, the significant residual coefficient value in the first stage shows that the exogenous assumption of organizational support should be rejected, indicating the existence of endogeneity issues. Therefore, using instrumental variable two-stage residual inclusion for estimation is an effective approach. Secondly, the coefficient of the instrumental variable in the first stage is significantly positive at the 1% level, indicating a strong correlation between the instrumental variable and organizational support. Meanwhile, the value F in the first stage greater than the empirical reference value of 10, indicating that there is no weak instrumental variable problem. Thirdly, consistent with expectations, the coefficient is significantly positively correlated at the level of 1%, indicating that the farmer exhibits a high degree of green transformation when the average level of organizational support in the village is high. Overall, the results obtained by instrumental variable two-stage residual inclusion are in good agreement with the benchmark regression results, further confirming the robustness of the benchmark regression results and the validity of Hypothesis 1.

4.3. Robust Analysis

To further verify the robustness of the effect of organizational support on improving agricultural ecological efficiency, we test the robustness by replacing the explanatory variable, changing the measurement method of the dependent variable, changing sample data, fixing county effects, and replacing regression models.

Specifically, we adopt the average communication frequency between farmers and different organizations to characterize organizational support, and the regression results are shown in column (1) of

Table 4. Robust analysis results.

	(1)	(2)	(3)	(4)	(5)
Organizational support	0.0088 ***	0.0036 ***	0.0031 ***	0.0031 ***	0.0047 ***
	(0.0014)	(0.0008)	(0.0004)	(0.0005)	(0.0005)
Constant	0.9882 ***	0.9638 ***	0.9290 ***	0.9521 ***	0.9547 ***
	(0.0342)	(0.0471)	(0.0220)	(0.0300)	(0.0337)
Control variables	Yes	Yes	Yes	Yes	Yes
R2	0.3062	0.2383	0.3162	0.5103	−0.0916
N	1011	1011	910	1011	1011

Note: The values in parentheses are standard error. *** represents significant levels at 1% statistical level. Pseudo R² in column (5).

. We recalculate agricultural ecological efficiency using the transcendental logarithmic production function, and the regression results are shown in column (2). Considering the possibility of outliers in the sample, we remove 5% of the data before and after the agricultural ecological efficiency value, and the estimated results of the processed sample are shown in column (3). To mitigate the estimation bias caused by missing variables at the village level and above, we further control the fixed effects at the county level, and the regression results are shown in column (4). In addition, considering that the value of agricultural ecological efficiency is limited to the range of 0 to 1, we use the Tobit model for estimation, and the regression results are shown in column (5). All the above results show that the regression coefficient is still positive and significant at the 1% level, indicating that organizational support has a positive impact on agricultural ecological efficiency; that is, the benchmark regression results are robust, and Hypothesis 1 is valid.

4.4. Mechanism Analysis

Table 5. Mechanism test.

	Cognitive Distance	Spatial Distance	Content Distance	Agricultural Ecological Efficiency
	(1)	(2)	(3)	(4)	(5)	(6)	(7)
Organizational support	−0.0250 ***	−0.1309 ***	−0.1133 ***	0.0047 ***	0.0040 ***	0.0025 ***	0.0034 ***
	(0.0051)	(0.0131)	(0.0146)	(0.0005)	(0.0005)	(0.0005)	(0.0005)
Cognitive distance					−0.0277 ***
					(0.0033)
Spatial distance						−0.0170 ***
						(0.0012)
Content distance							−0.0113 ***
							(0.0011)
Constant	0.4326	0.5683	0.1174	0.9547 ***	0.9667 ***	0.9644 ***	0.9560 ***
	(0.3186)	(0.8190)	(0.9171)	(0.0341)	(0.0330)	(0.0311)	(0.0325)
Control variables	Yes	Yes	Yes	Yes	Yes	Yes	Yes
R2	0.1903	0.6587	0.7314	0.3291	0.3740	0.4418	0.3909
N	1011	1011	1011	1011	1011	1011	1011

Note: The values in parentheses are standard error. *** represents significant levels at 1% statistical level.

presents the impact of organizational support on agricultural ecological efficiency through cognitive distance, spatial distance, and content distance. Firstly, as shown in column (4), the richness of organizational support can significantly promote agricultural ecological efficiency. Secondly, as shown in columns (1), (2), and (3), the richness of organizational support can significantly narrow knowledge distance. Thirdly, as shown in columns (5), (6), and (7), after including cognitive distance, spatial distance, and content distance in the regression analysis of the impact of organizational support on agricultural ecological efficiency, although the coefficients of organizational support did not change significantly, their values decreased to varying degrees. This indicates that cognitive distance, spatial distance, and content distance play a partial transmission role in the process of organizational support affecting agricultural ecological efficiency, thus verifying Hypothesis 2. Specifically, after incorporating spatial distance into the analysis of the impact of organizational support on agricultural ecological efficiency, the regression coefficient of organizational support decreased the most, indicating that the mediating mechanism of spatial distance is the most significant, followed by content distance and cognitive distance.

4.5. Further Analysis

For the southern study area, the cultivation of regenerated rice, which refers to the rice that is harvested twice in a crop, is being vigorously promoted. This is a typical new planting mode that is completely different from traditional planting, and it belongs to a radical technology knowledge support. Meanwhile, the planting in the central and northern regions is mainly traditional varieties, and the organizational support received by farmers is mainly incremental technology knowledge support.

Table 6. Further analysis.

	South Area	North and Middle Area
Government organizational support	0.0107 ***	0.0084 ***
	(0.0013)	(0.0014)
Market-oriented organizational support	0.0124 ***	0.0143 ***
	(0.0022)	(0.0020)
Constant	0.9173 ***	0.9591 ***
	(0.0238)	(0.0335)
Control variables	Yes	Yes
R2	0.7220	0.4199
N	200	811

Note: The values in parentheses are standard error. *** represents significant levels at 1% statistical level.

reflects the impact of organizational support on agricultural ecological efficiency in the southern, central, and northern regions. Firstly, the coefficient of government organization support in the southern region is higher than that in the central and northern regions, indicating that the role of government organization support is more significant in the southern region. Secondly, the coefficient of market-oriented organization support in the southern region is lower than that in the central and northern regions, indicating that the role of market-oriented organization support is more significant in the central and northern regions, thus confirming Hypothesis 3. The possible reason is that the regenerated rice planted in the southern region belongs to a relatively new variety. On the one hand, due to risk avoidance considerations, farmers have doubts about the feasibility and effectiveness of regenerated rice. As the main body trusted by farmers, primary-level governments play a more prominent role. On the other hand, farmers have limited knowledge and experience in the cultivation of regenerated rice, and agricultural technology stations within government organizations can provide more targeted guidance for farmers. In addition, the cultivation and management techniques of regenerated rice may still be in the stage of continuous exploration; hence, there might not be enough market organizations that have mastered this technology, resulting in relatively limited support from market-oriented organizations.

5. Discussion

5.1. The Role of Organizational Support in Agricultural Ecological Efficiency

For economic entities, whether to engage in an activity depends on both their internal driving forces and external environmental forces. The agricultural environment is a typical public good, and, as a fundamental participant, even completely rational farmers do not have enough motivation to reduce agricultural inputs and minimize undesirable outputs for the sake of maximizing their interests, let alone farmers who are not completely rational. That is, farmers do not have enough motivation to promote agricultural ecological efficiency practices [35]. Therefore, external organizational support is essential for agricultural ecological efficiency. In terms of expansion, for activities with public goods attributes and externalities, external organizational support is generally required [36]. For instance, Blackstock et al. explore the claim that policy instruments are necessary to influence the mix of public goods provided by Scottish agricultural and forested areas [37].

On the impact of organizational support on agricultural ecological efficiency, our viewpoint is consistent with existing research. In the field of agricultural production, farmers’ organizations, such as associations, cooperatives, and women’s groups, provide services that are widely viewed as contributing to income and productivity for small-scale producers [38]. Antwi-Agyei et al. identify that the organizational support provided by agricultural extension agents in Ghana can better support smallholder farmers in navigating and addressing the effects of climate change on food production [4]. Research has also found that the organizational support provided by cooperative groups has a significant impact on farmers’ willingness to engage in agricultural green production to promote sustainable development of the agricultural economy [5]. In the study of tomato and cucumber farm households in Beijing of China, organizational training support provided by farmer field schools significantly increases the adoption rate of environment friendly irrigation and fertilization technologies [39].

Compared with previous studies, our research identified the magnitude of the impact of different organizational support on farmers’ agricultural ecological efficiency. As stated in the Results section (Section 4.1), the order of the effectiveness of organizational support is agricultural material distributors, peasant cooperatives, village committees, agricultural technology service centers, and agricultural associations.

5.2. Differences in the Role of Organizational Support and Interactive Perspectives

The theory of social embeddedness emphasizes that human economic behavior is always influenced by the surrounding social environment [40]. The production behavior of farmers has a dual nature of economic and social behavior and is inevitably embedded in the external environment outside of the farmers themselves. From the perspective of organizations in the external environment, the support provided by the government and social organizations mainly includes policy promotion, planting subsidies, technical training, and the establishment of demonstration zones. A sound policy support system can weaken the risk expectations of farmers suffering losses when using new production technologies or production materials and effectively encourage farmers to adopt production technologies and production materials that can enhance agricultural ecological efficiency. The support provided by agricultural technology promotion stations and market-oriented organizations mainly includes production technology and production material service support. The availability of technical services and production materials is one of the prerequisites for meeting the specialized production needs of farmers and enhancing agricultural ecological efficiency. The increase in the types and quantities of organizational support entities means the enrichment of technology promotion paths and the enhancement of neighborhood demonstration roles, which strengthens the advantages of farmers in technology screening and cost optimization, thereby affecting their production decisions and behaviors.

The support content provided by different organizations has their own emphasis, and their support content is integrated with each other, jointly providing effective support for farmers to enhance their cognitive level, weaken risk expectations, and improve the possibility of adopting new production technologies and production materials. In the study on the protective tillage behavior of farmers in black soil, Fei et al. found that policy tools can enhance the promotion effect of interest perception on farmers’ protective tillage behavior in black soil through their regulatory role while weakening the inhibitory effect of risk perception on farmers’ protective tillage behavior in black soil, significantly increasing the positive impact of value perception on farmers’ decision-making in protective tillage behavior in black soil [41]. For incremental technologies with higher commercialization, usefulness, ease of use, and applicability, training conducted by market-oriented organizations had a stronger promotion effect on technology adoption. For radical technologies with low usefulness, ease of use, and applicability, training provided by public welfare organizations had a stronger promoting effect on technology adoption. Meanwhile, the formation of farmers’ production decisions is a process in which farmers continuously absorb effective information and knowledge to eliminate their uncertainty.

5.3. Knowledge Distance and Agricultural Production Decision-Making

For micro-entities in agricultural production, the knowledge distance between farmers and farmers, as well as between farmers and external organizations, objectively exists. The knowledge base of farmers, such as social networks, education level, and production and operation scale, determines the distance of knowledge. For farmers, assessing remote knowledge and making production decisions based on it is a challenge as remote knowledge only represents potential future benefits. Uncertainty is a key factor affecting farmers’ adoption of remote information. There is a long interval between farmers’ adoption of ecological production technologies or production materials and crop harvest time. There is a certain degree of uncertainty as to whether the adoption of ecological production technologies or production materials will affect crop yield, which can lead farmers to avoid remote knowledge. In addition, processing remote knowledge is usually more costly than processing familiar knowledge, so farmers are more likely to completely avoid remote knowledge.

The agricultural ecological efficiency often requires the intervention and support of external new knowledge. When the knowledge distance is close, the similarity of the knowledge received by farmers is higher, the resistance to absorption is smaller, and the possibility of implementing adoption behavior is greater. When the knowledge distance is large, the correlation between new knowledge and farmers’ existing knowledge is not strong, and farmers’ understanding ability of knowledge decreases, making knowledge transfer more difficult. Meanwhile, a larger knowledge distance also increases farmers’ adoption costs, thus increasing the possibility of farmers exhibiting empirical fixed behavioral habits.

5.4. The Necessity and Adaptability of This Research in Global Agriculture

The agricultural pattern dominated by small-scale farmers has significant international commonalities [42]. Especially in Sub-Saharan Africa and Southeast Asia, the proportion of small-scale farmers operating is relatively high. For example, Malawi’s economy is heavily dependent on agriculture, with small-scale farmers accounting for over 80% of the population [43]. Small-scale farmers may face three challenges in agricultural green development: Firstly, the highly fragmented production resources lead to an increase in the cost of adopting green technologies, such as in soil testing and formula fertilization techniques, where the unit area promotion cost of small farmers is higher than that of large-scale farms [44]. Secondly, the knowledge iteration required for green development may sharply conflict with the traditional experience system of small-scale farmers, such as the cognitive bias of farmers towards biological pesticides [45]. Thirdly, the decentralized operation model exacerbates market information asymmetry, leading to a vicious cycle of “high input low premium” for small-scale farmers. In these contexts, we construct a three-level intervention model of “policy guidance, knowledge transmission, behavior transformation” and systematically reveal the pivotal role of organizations such as government, agricultural technology extension stations, and cooperatives in solving the above difficulties. This organizational empowerment is not only reflected in the integration of production factors but, more importantly, by narrowing the knowledge distance and transforming ecological agriculture technology into actionable localized solutions, thereby substantially enhancing the green development capabilities of small-scale farmers.

This similarity of small-scale farming operations makes our research conclusions highly constructive for many countries and regions around the world. Firstly, organizational support can weaken knowledge barriers and promote green agricultural development. In the study of tomato production in Ethiopia, improving extension services and expanding education in rural regions can help improve agricultural competitiveness and production efficiency. Organizational support and useful knowledge might help farmers offset high production costs, improve farm revenue, promote economic development, and use the right combination of input mix to achieve ecological efficiency improvement [46]. Secondly, organizational support can enhance the market bargaining power of small-scale farmers in terms of factor prices and product prices. Significant performance has been observed in the Peruvian Coffee Small Farmers Alliance, where farmers have gradually formed a high-quality production system through the assistance of the government and international organizations, such as joining cooperatives to obtain organic certification, fair trade, and rainforest alliance certification, promoting the recognition and influence of Peruvian coffee in the international market. Peruvian small farmers have gradually gained recognition and influence of Peruvian coffee in the international market by joining cooperatives to obtain certifications such as organic certification and fair trade [47].

6. Conclusions

Based on the public goods attributes of the rural environment and the production decision-making process of farmers, we constructed a theoretical framework for organizational support to influence agricultural ecological efficiency through knowledge distance and empirically analyzed the impact of organizational support on agricultural ecological efficiency and the mechanism path role of knowledge distance using 1011 survey data from main grain growers in Henan Province. The specific research conclusions include the following aspects:

Firstly, enhancing organizational support can improve agricultural ecological efficiency. The role of market-oriented organizations is greater than that of government organizations. In market-oriented organizations, the role of agricultural material distributors is greater than that of peasant cooperatives. In government institutions, the role of agricultural associations is the smallest. Secondly, knowledge distance plays a partial mechanistic role in the impact of organizational support on agricultural ecological efficiency. The mechanism path of spatial distance is the most prominent, followed by content distance and cognitive distance. Thirdly, market-oriented organizations play a stronger role in incremental support, and government organizations play a stronger role in radical support.

The above conclusion applies to the sustainable development practices of global small-scale agriculture. Based on the conclusion of this study, the following policy recommendations can be extended:

In terms of organizational support, the rural governance system and rural ecological governance structure need to be continuously improved. Firstly, policy attention should be paid to enriching the type and quantity of organizations, forming a comprehensive rural ecological governance system jointly built by governments and enterprises and fully leveraging the role of various organizations in promoting agricultural ecological efficiency. Secondly, leveraging the diverse roles of various organizations should be focused. For incremental technological support, market-oriented organizations should play a major role, while for radical technological support, government organizations should play a greater role.

In terms of knowledge distance, narrowing the various dimensions of knowledge distance needs to be strengthened. Firstly, it is valuable to enhance the dissemination of ecological concepts and knowledge such as green agricultural production technology and biological fertilizers to improve farmers’ awareness and cognitive level of green production. Secondly, it is necessary to pay attention to the timeliness and short distance of ecological knowledge dissemination to ensure that farmers adopt green agricultural production technologies and production materials without any supply barriers. Thirdly, it is important to value both the usefulness and accessibility of knowledge to make farmers not only aware that ecological production can bring benefits to them and society but also adopt and implement ecological production knowledge easily.

There are also some potential limitations in this paper. First, in terms of the research scope, our research is limited to agricultural ecological efficiency of main grain growers. Although planting is the most important agricultural sector, agriculture also includes other sectors such as forestry, and each sector is organically connected, forming a circular ecosystem. Future research can further expand the scope of research to include agriculture, forestry, animal husbandry, and fisheries. Second, in terms of the research data, our research data come from cross-sectional data in Henan Province. Although it includes as many control variables as possible, the unobservable heterogeneity of farmers themselves and regional culture may lead to bias. In addition, although Henan Province is an important grain-producing area, it cannot take into account the special characteristics of crop growth, farmers’ production decisions, and agricultural ecological efficiency decisions in other regions. Therefore, in future research, the research area can be expanded to other provinces, and the research time can be extended to multiple surveys to form panel data. Third, in terms of the research method, random controlled experiments can be used to more accurately identify policy effects to obtain cleaner data and enhance the external validity of research.