Income Effects and Mechanisms of Farmers’ Participation in Agricultural Industry Organizations: A Case Study of the Kiwi Fruit Industry

Abstract

Eliminating all forms of poverty is a core component of the United Nations’ Sustainable Development Goals. At the household level, poverty and income inequality significantly threaten farmers’ sustainable development and food security. Based on a sample of 1234 kiwi farmers from the Shaanxi and Sichuan provinces in China, this paper empirically examines the impact of participation in agricultural industry organizations (AIOs) on household income and income inequality, as well as the underlying mechanisms. The results indicate the following: (1) Participation in AIOs increased farmers’ average household income by approximately 19,570 yuan while simultaneously reducing the income inequality index by an average of 4.1%. (2) Participation increases household income and mitigates income inequality through three mechanisms: promoting agricultural production, enhancing sales premiums, and improving human capital. (3) After addressing endogeneity concerns, farmers participating in leading agribusiness enterprises experienced an additional average income increase of 21,700 yuan compared to those participating in agricultural cooperatives. Therefore, it is recommended to optimize the farmer–enterprise linkage mechanisms within agricultural industry organizations, enhance technical training programs, and strengthen production–marketing integration and market connection systems, aiming to achieve both increased farmer income and improved income distribution.

1. Introduction

Smallholder farmers are a crucial component of global agricultural production, contributing to most of the world’s food supply. However, due to limited land resources, outdated production technologies, and restricted market access, the income levels of smallholders are generally low, and income disparities are significant [1,2,3,4,5]. According to a report by the United Nations Food and Agriculture Organization, there are approximately 500 million smallholder farmers globally, accounting for about 80% of the agricultural population. These farmers not only face external risks such as natural disasters and market fluctuations but also structural challenges like inequitable resource distribution and insufficient social security systems [6,7,8,9]. The first and second goals of the United Nations Sustainable Development Goals (SDGs)—“No Poverty” and “Zero Hunger”—are closely related to improving smallholder incomes and reducing income inequality. Addressing global poverty and hunger, the core aims of the SDGs, hinges on boosting the income and reducing the poverty of smallholder farmers. The SDGs state that by 2030, the global population living in poverty should be reduced by at least 50%. One pathway to achieving this target is enhancing smallholder productivity, increasing their income sources, and facilitating their fair participation in markets. Currently, income inequality among smallholders not only undermines their ability to escape poverty but also exacerbates rural poverty [10,11]. Data from the World Bank indicates that in developing countries, income and consumption disparities among rural populations have increased over the past few decades. This income inequality in rural areas has intensified poverty, particularly among resource-scarce smallholders who find it difficult to escape poverty through agricultural production alone [10]. Reducing income inequality can provide the poorest farmers with more resources and opportunities, thereby helping reduce poverty [12,13]. Moreover, reducing income inequality can enable more farmers to access social security and welfare benefits, further alleviating poverty and enhancing the sustainable development capacity of farmers [14,15]. This is of significant strategic importance and urgency in achieving the SDGs.

As a typical developing country, China has implemented a series of policies and measures aimed at increasing smallholder incomes and reducing rural income inequality. For example, the Chinese government has promoted a targeted poverty alleviation policy to enhance the production capacity and living standards of smallholders in impoverished areas. According to data from China’s National Bureau of Statistics, from 2013 to 2020, China’s rural poor population decreased by 98.99 million under the current poverty line, with targeted poverty alleviation playing a critical role in this achievement. The core of targeted poverty alleviation lies in implementing differentiated policies for specific impoverished populations, such as promoting industrial development, education, and financial support to enhance the production capacity and human capital of smallholders, helping them increase income and escape poverty [16,17,18,19,20]. Similar measures have been adopted in other developing countries. For instance, the Indian government has promoted the National Rural Livelihoods Mission (NRLM), which supports smallholders in diversifying their operations, improving technical skills, and increasing household income [21,22]. Brazil’s National Program for Strengthening Family Farming supports smallholder agricultural production by providing credit, technical training, and market information [23,24,25]. The common thread in these measures is government intervention to improve smallholder production capacity and market participation, thereby alleviating income inequality.

While China and other developing countries have made progress in raising smallholder incomes, existing policies and measures still face several shortcomings in implementation. For example, although targeted poverty alleviation has performed well in reducing poverty, its large-scale population coverage and standardized execution have meant that some smallholders have not fully benefited. Especially in resource-poor rural areas, the dissemination and effectiveness of poverty alleviation measures vary, leaving some smallholders with unstable incomes [26,27,28]. Similar issues are observed in other developing countries. In India, for example, while the NRLM provides some financial support to smallholders, many smallholders lack sufficient credit records and financial knowledge, resulting in the low utilization of credit resources and limited real benefits [29,30,31]. Additionally, restrictions on market participation and inadequate infrastructure present bottlenecks to income improvement. The root cause often lies in policy designs that emphasize production technology improvement while overlooking the need for diversified production and market participation, limiting the competitiveness of smallholders in markets.

Agricultural industry organizations (AIOs) have shown unique potential in this context, addressing gaps in current measures. With the continued modernization of agriculture in China, a range of AIOs led by agribusiness companies and agricultural cooperatives have emerged. These organizations are flexible and decentralized, meeting the diverse needs of smallholders, and complementing existing policies. Previous studies have shown that AIOs can optimize resource allocation through collective action. This is particularly evident in resource-scarce and disaster-prone areas, where the collective resource-sharing mechanisms within organizations help compensate for the resource constraints faced by individual farmers [32]. By centralizing the procurement of agricultural inputs and managing technology extension, these organizations help farmers in remote areas access the same resource support as farmers in more central regions. This mechanism not only improves resource use efficiency but also ensures more equitable resource distribution, reducing disparities in policy implementation. Another major flaw in current policies is the lack of financial support and poor credit accessibility. AIOs can address this by establishing internal credit cooperatives or partnering with financial institutions to offer more flexible and farmer-friendly financial services [33]. Through mutual lending mechanisms within the organization, they alleviate farmers’ credit constraints, enabling them to access the necessary funds for agricultural production and technological upgrades. Additionally, collective guarantees and risk-sharing mechanisms within AIOs reduce the financial risks for institutions providing credit to smallholders, increasing farmers’ access to financial services [34,35]. This collective credit mechanism effectively addresses the issue of individual smallholders struggling to access credit, offering greater flexibility and adaptability compared to government financial support. AIOs can also provide more effective technical support and information-sharing mechanisms [36,37,38,39]. Unlike government’s top–down support, AIOs operate through cooperation among members, offering smallholders timely updates on technology and production guidance. Through contract farming arrangements, they help farmers better manage market risks [40], thereby increasing household income, reducing income inequality, and promoting sustainable development [41]. The existing literature has widely acknowledged the vital role of AIOs in optimizing resource allocation, improving financial services, and providing technical support, offering a solid foundation for understanding how AIOs contribute to income growth among farmers. However, several research gaps remain. First, there is a lack of systematic analysis on how AIOs influence household income structures and income inequality through micro-level mechanisms. Second, most existing studies focus primarily on income growth, while the role of AIOs in mitigating income inequality has received insufficient attention. Third, current research fails to adequately distinguish the heterogeneous effects of different types of AIOs on both income enhancement and income inequality reduction among farmers.

To better capture the impact of AIOs on farmers’ income, this study focuses on kiwi fruit growers as the research sample. The choice is based on several considerations. First, the kiwi industry is characterized by high added value and a long value chain. Second, most kiwi growers are small- and medium-scale farmers located in mountainous and hilly areas, who commonly face multiple challenges, including resource constraints, technological bottlenecks, and market fluctuations. Against this backdrop, AIOs play a crucial role in enhancing farmers’ bargaining power, facilitating technology adoption, and mitigating operational risks, thereby helping farmers stabilize and improve their household income. These characteristics make kiwi growers a highly representative group for analyzing the mechanisms through which AIOs promote income growth and improve income distribution among smallholder farmers.

Based on this, the study aims to empirically investigate how smallholder farmers improve their household income and alleviate income inequality through participation in agricultural industry organization (PAIO). It further explores the underlying mechanisms by which AIOs help farmers gain better market access, improve production efficiency, and increase non-agricultural income. Moreover, this paper examines the heterogeneous effects of different types of AIOs on farmers’ income growth and inequality reduction. By thoroughly analyzing the characteristics of the kiwi industry and the challenges faced by smallholder farmers, this research offers valuable policy implications, highlighting the potential of AIOs as key rural development organizations in enhancing farmers’ income and reducing rural poverty. These findings not only provide empirical support for the sustainable development of the kiwi industry but also offer insights applicable to other high-value agricultural sectors.

2. Theoretical Analysis and Research Hypothesis

New Institutional Economics posits that organizational structures can help individual farmers better cope with market uncertainties and resource constraints by reducing transaction costs, enhancing cooperation efficiency, and providing a platform for collective action [42,43]. In traditional agricultural markets, kiwi farmers often face issues of information asymmetry and high transaction costs due to a lack of information and weak bargaining power [44]. New Institutional Economics highlights information asymmetry and high transaction costs as key factors limiting market efficiency [45,46]. AIOs can reduce bargaining costs between farmers and market actors through collective action [47,48]. Additionally, by standardizing contracts and transaction rules, AIOs reduce the uncertainty of contract enforcement and lower default risks, thereby effectively increasing farmers’ incomes [49,50,51].

AIOs also play a vital role in mitigating income inequality. According to property rights theory and public choice theory within New Institutional Economics, low-income farmers often struggle to increase their incomes individually due to a lack of market resources and information [52,53]. This offers a reasonable explanation for the persistent income challenges faced by smallholder farmers and provides a theoretical foundation for potential solutions. By PAIO, low-income farmers can leverage collective resources to access more development opportunities, particularly in entering high-value markets, sharing advanced technologies, and expanding financing channels. Through collective action, industry organizations reduce the monopolistic advantages of higher-income farmers and broaden production opportunities for lower-income farmers, thereby narrowing income disparities. Additionally, risk-sharing mechanisms, such as agricultural insurance and quality standards, reduce operational risks for low-income farmers, stabilizing their incomes and further alleviating income inequality. Therefore, this study proposes the following hypothesis:

Hypothesis 1 (H1).

PAIO increases household income and reduces income inequality among farmers.

2.1. The Production Promotion Mechanism

AIOs significantly enhance kiwi farmers’ access to capital- and technology-intensive production factors by providing public goods and shared services. Kiwi cultivation relies heavily on advanced production technologies such as intelligent drip irrigation systems, green pest control, and precision soil improvement. These technologies require substantial upfront investment and are often inaccessible to individual farmers due to high entry barriers. For example, in Meixian County, Shaanxi Province, the cost of independently installing an efficient water-saving irrigation system can reach tens of thousands of yuan, far beyond the financial capacity of ordinary smallholder farmers. However, by joining AIOs, farmers can share irrigation facilities collectively procured and constructed by the organization at a lower cost. They also receive regular technical guidance from agricultural experts hired by the organization, effectively overcoming constraints related to capital and technology and achieving economies of scale. According to the economies of scale theory in New Institutional Economics, farmers can effectively spread fixed costs and reduce marginal costs through organized collective action, thereby improving overall production efficiency [54,55]. In addition, many AIOs have established mutual financial aid or loan guarantee mechanisms. For instance, some cooperatives collaborate with local rural credit cooperatives to provide members with low-interest production loans. This arrangement enables low-income farmers, who typically lack collateral and struggle to access credit, to secure the necessary funds for facility upgrades or production expansion. This collective credit mechanism not only reduces the financial risks faced by lending institutions but also substantially alleviates farmers’ financing constraints. More importantly, with the support of precision agriculture technologies promoted by AIOs, farmers can significantly increase their yields while improving fruit quality. Previously, low-income farmers, constrained by limited capital and technology, often relied on traditional, extensive farming practices, resulting in lower fruit quality and weak market bargaining power. Through the technology-sharing and standardized management systems facilitated by AIOs, these farmers can now produce high-quality kiwi fruit that meets premium market standards. Furthermore, they benefit from collective branding and price premiums while reducing transaction costs, ultimately narrowing the income gap between themselves and higher-income farmers. Therefore, this study proposes the following hypothesis:

Hypothesis 2 (H2).

PAIO increases household income and reduces income inequality by promoting production.

2.2. The Sales Premium Mechanism

AIOs significantly improve kiwi farmers’ market access and resource allocation efficiency by optimizing resource distribution. For fragmented smallholder farmers, independently entering high-end markets often involves prohibitively high transaction costs and stringent market entry requirements. It is typically difficult for individual farmers to establish stable trading relationships with large supermarkets or export channels, and they generally lack the brand recognition necessary to command premium prices. However, by PAIO, kiwi farmers can leverage collective brands and well-established market networks to effectively overcome market barriers and access high-value markets, thereby securing higher sales prices [56,57]. Meanwhile, AIOs integrate multiple stages of the value chain, including production, processing, storage, logistics, and sales, achieving the coordinated optimization of the supply chain. This significantly reduces internal friction costs in farmers’ transactions. Such vertical integration combined with horizontal cooperation not only enhances transaction efficiency but also strengthens the bargaining power of organizational members [58]. In practice, this means that farmers no longer need to invest considerable time and effort in searching for buyers, negotiating prices, or arranging transportation. Instead, the organization centrally manages market connections, handles cold-chain logistics, and negotiates prices on behalf of its members, allowing farmers to focus on improving product quality. This collective market access mechanism is particularly crucial for low-income farmers. In the past, these farmers—lacking brands, standardized production, and stable marketing channels—were heavily reliant on traditional wholesale markets or intermediaries, leading to persistently low incomes. By lowering transaction costs and providing reliable market access, AIOs create opportunities for smallholder farmers to compete on more equal footing with large-scale producers. Consequently, this not only significantly increases household income but also narrows the income gap between low- and high-income farmers, thereby contributing to the reduction in income inequality. Therefore, this study proposes the following hypothesis:

Hypothesis 3 (H3).

PAIO increases household income and reduces income inequality by improving sales premiums.

2.3. The Human Capital Enhancement Mechanism

By PAIO, kiwi farmers are able to effectively enhance their human capital and expand non-agricultural employment opportunities, thereby increasing household income and mitigating income inequality [59,60,61]. In addition to providing agricultural technical support, AIOs regularly offer various skills training programs, such as e-commerce operations, fruit grading, and cold-chain logistics management, to improve farmers’ overall capabilities. For example, in Meixian County, Shaanxi Province, a local cooperative offers e-commerce training that has enabled many farmers to independently sell kiwi fruit on platforms like Pinduoduo and Douyin. Some farmers have even expanded their businesses to include other agricultural and sideline products, leading to a substantial increase in their non-agricultural income. For low-income farmers, this type of training is particularly crucial. In the past, they generally relied on a single source of agricultural income and faced the challenge of seasonal income instability. By acquiring e-commerce skills or basic processing techniques, these farmers are now able to generate additional income during the agricultural off-season, reducing their dependence on the kiwi harvest period. According to human capital theory in New Institutional Economics, skills enhancement significantly strengthens workers’ competitiveness in diversified markets, thereby increasing income levels. Collective learning and information sharing not only improve farmers’ agricultural production capacity but also open new pathways for non-agricultural employment and entrepreneurship. Ultimately, this diversified income structure effectively narrows the income gap between high- and low-income farmers while enhancing the economic resilience and developmental capacity of rural households. Therefore, this study proposes the following hypothesis:

Hypothesis 4 (H4).

PAIO increases household income and reduces income inequality by enhancing human capital.

In summary, this paper constructs a theoretical framework to analyze and empirically test the impact of PAIO on household income and income inequality. Furthermore, it explores the underlying mechanisms through which this impact occurs, as illustrated in Figure 1.

3. Materials and Methods

3.1. Data Source

The data used in this study were obtained from a rural household survey conducted by the research team between October and November 2023 in Shaanxi and Sichuan provinces. The selection of these regions is based on the following reasons: First, Shaanxi and Sichuan provinces are in central and western China and are among the primary production areas for kiwi fruit, ranking among the top three provinces in annual kiwi production. However, the complex geographical environment in these regions, characterized by mountainous and hilly terrain, coupled with a low level of agricultural mechanization, poses challenges to agricultural productivity. As a result, there are significant income disparities among kiwi farmers. Second, kiwi farmers in Shaanxi and Sichuan generally operate on a small scale, relying primarily on traditional agricultural practices and sales methods. These farmers urgently need to enhance their production technologies and marketization through PAIO to promote income growth. Third, in recent years, both provinces have introduced a series of policies encouraging farmers to participate in AIOs, aiming to increase farmers’ incomes and reduce income inequality through the driving effect of these organizations. Based on these factors, selecting these regions for the survey provides strong representativeness and reflects the typical relationship between farmers’ PAIO and their income. The survey areas are shown in Figure 2.

To ensure the scientific validity and reliability of the data, the survey questionnaire was rigorously designed, and experts in relevant fields were invited multiple times to review and revise it. The questionnaire covered various aspects, including farmers’ personal characteristics, household characteristics, agricultural production conditions, PAIO, and household income structure. Additionally, experienced investigators were recruited from the university, and they received centralized training to ensure familiarity with the survey procedures and questionnaire content. During the survey, a multi-stage stratified random sampling method was followed. The specific sampling process was as follows: in Shaanxi Province, the survey areas included Yangling County, Wugong County, Zhouzhi County, and Meixian County, and in Sichuan Province, Dujiangyan County and Pujiang County were selected. In each county, 2–3 townships were randomly selected, and in each township, 3–5 villages were randomly chosen. From each village, 10–25 households were randomly selected for one-on-one questionnaire interviews. To ensure data quality, multiple rounds of review and cross-checking were conducted during the survey process, resulting in 1234 valid questionnaires. The distribution of the samples is shown in

Table 1. Samples and distribution.

Province	City	County	The Number of Farmers	Percentage of Sampled Farmers (%)
Sichuan	Chengdu	Dujiangyan	255	20.66
		Pujiang	240	19.45
Shaanxi	Xianyang	Yangling	104	8.43
		Wugong	86	6.97
	Xi’an	Zhouzhi	196	15.88
	Baoji	Meixian	353	28.61
Total			1234	100

.

3.2. Variables and Descriptive Statistics

3.2.1. Dependent Variables

The dependent variables in this paper include household income and income inequality. Household income is measured using the gross household income of the sample farmers for the year 2022. Specifically, it is calculated as the sum of agricultural income, wage income, self-employment income, property income, and transfer income, minus production and operating expenses.

Income inequality is represented by the Kakwani Index, which measures per capita income inequality among the sample farmers. Compared to other commonly used inequality indices (such as the Theil Index or the Atkinson Index), the Kakwani Index is specifically designed to assess the progressivity of policies or institutions. It effectively captures changes in income distribution as income increases, making it particularly suitable for examining whether the income growth resulting from farmers’ PAIO is accompanied by an increase or decrease in income inequality. The value of the index ranges from [0, 1], with higher values indicating greater income inequality faced by farmers. The specific calculation formula is as follows:

$$K_m={\displaystyle \frac{1}{n{\theta }_I}}\sum _{i=m+1}^n\left(I_i-I_m\right)={\vartheta }_{I_m}^{+}{\displaystyle \frac{\left({\theta }_{I_m}^{+}-I_k\right)}{{\theta }_I}}$$

(1)

$m$ and $i$ denote the $m$th and $i$th sample farmers, respectively; $K_m$ denotes the degree of income inequality of sample farmer $m$ as measured by the Kakwani Index; the total number of samples is $n$, and the corresponding income vector is $I$, $I=\left(I_1,I_2,\dots ,I_3\right)$, in ascending order of per capita incomes; ${\theta }_{I_m}^{+}$ is the mean value of sample per capita incomes in the samples exceeding $I_m$; ${\vartheta }_{I_m}^{+}$ is the proportion of the number of samples with per capita income exceeding $I_m$ in the total sample to the total number of samples $n$; ${\theta }_I$ is the mean per capita income of the total sample. The calculation yields a per capita income of 28,320.000 yuan for the sample farmers and a mean income inequality of 0.387.

3.2.2. Core Explanatory Variable

The core explanatory variable in this study is farmers’ PAIO, focusing on farmers’ decision-making behavior regarding whether to participate in such organizations. This is a binary discrete variable. As noted in the theoretical analysis, in the regions where data were collected, the main types of AIOs that promote shared benefits, risk-sharing, and the inclusion of farmers are leading agribusiness companies and agricultural cooperatives. Therefore, if the interviewed farmer participated in either of these two types of organizations, the variable is assigned a value of 1. If the farmer did not participate in either, the variable is assigned a value of 0.

3.2.3. Control Variables

To identify the income effects of farmers’ PAIO more accurately, this study selects control variables from three dimensions: characteristics of the household decision-maker, household characteristics, and village characteristics. The characteristics of the household decision-maker include age, gender, health status, education level, years of farming, and social capital. Household characteristics include whether the household head is a village cadre, household size, number of laborers, and land area. Village characteristics include the availability of express delivery services and the elevation of the village. Additionally, provincial dummy variables are also controlled for in the analysis.

3.2.4. Mechanism Variables

To examine the production promotion mechanism, this paper uses the technology adoption variable to reflect whether the sample farmers adopted cost-saving and efficiency-enhancing production technologies through AIOs. In kiwi farming, water-saving irrigation technology is considered a capital-intensive technology, and individual farmers often cannot afford the adoption costs alone, requiring organizational support to install pipelines and build pumps. Therefore, this variable is measured based on the farmers’ adoption of water-saving irrigation technology. If a sample farmer adopted the technology in agricultural production, the technology adoption variable is assigned a value of 1; if not, it is assigned a value of 0.

To examine the sales premium mechanism, this paper uses the market access variable to reflect whether sample farmers entered higher-level agricultural markets through AIOs. This variable is measured based on whether farmers used the organization’s brand when selling their agricultural products. If a sample farmer sold products using the organization’s brand, the market access variable is assigned a value of 1; if not, it is assigned a value of 0.

To examine the human capital enhancement mechanism, this paper uses the off-farm employment variable to reflect whether sample farmers gained non-agricultural employment opportunities through training provided by AIOs. This variable is measured based on whether any household members engaged in off-farm work. If a sample household had members working off-farm, the variable is assigned a value of 1; if not, it is assigned a value of 0.

Table 2. Variable definitions and descriptive statistics.

Variables	Definition	Full Sample (N = 1234)		Participation Group (N = 606)		Non-Participating Group (N = 628)
		Mean	SD	Mean	SD	Mean	SD
Dependent Variable
Household income	Sum of farm income, part-time income, self-employment income, property income, and transfer income minus productive expenditures of farm households in 2022	11.937	13.282	13.798	15.915	10.141	9.795
Income inequality	Degree of inequality in per capita income of farm households in 2022, as measured by the Kakwani Index	0.387	0.155	0.359	0.137	0.414	0.167
Core Explanatory Variable
PAIO	Whether farmers participate in AIOs (1 = Yes, 0 = No)	0.491	0.500	—		—
Control Variables
Age	Age of head of household (years)	59.257	9.491	58.757	9.883	59.739	9.079
Gender	Gender of head of household (1 = male, 0 = female)	0.826	0.379	0.814	0.390	0.838	0.369
Health status	Health status of head of household (1 = poor, 2 = fair, 3 = good)	2.402	0.672	2.399	0.667	2.404	0.677
Education level	Years of education of head of household (years)	7.664	3.304	8.011	3.379	7.330	3.198
Years of farming	Years of farming experience of head of household (years)	35.355	11.979	34.906	11.659	35.788	12.274
Social capital	Number of mobile phone contacts for head of household (number)	194.055	452.280	237.901	562.194	151.745	306.007
Village cadre	Any village cadres among household members (1 = Yes, 0 = No)	0.100	0.301	0.097	0.297	0.104	0.305
Household size	Total population of household (persons)	4.529	1.721	4.533	1.580	4.525	1.849
Number of laborers	Number of household laborers (persons)	2.780	1.329	2.921	1.245	2.643	1.393
Land area	Total area of land cultivated by the household (mu)	7.538	12.100	8.971	16.043	6.155	5.972
Village delivery services	Availability of courier service points in the farmer’s village (1 = Yes, 0 = No)	0.763	0.426	0.804	0.398	0.723	0.448
Village elevation	Elevation of sample farmer’s village (meters)	595.366	139.929	609.469	133.189	581.758	144.948
Shaanxi	Whether the farmer is in Shaanxi Province (1 = Yes, 0 = No)	0.600	0.490	0.607	0.489	0.592	0.492
Sichuan	Whether the farmer is in Sichuan Province (1 = Yes, 0 = No)	0.400	0.490	0.393	0.489	0.408	0.492
Instrumental Variables
Distance to main road	Distance of the farmer’s residence from the nearest main transport artery (miles)	1.721	2.717	1.224	1.427	2.201	3.476
Awareness of AIOs	Farmer’s knowledge of the organization’s rules and regulations (1 = not at all, 2 = not very much, 3 = fairly, 4 = fairly, 5 = very much)	2.531	1.311	3.347	1.064	1.744	1.012
Mechanism Variables
Technology adoption	Whether the farmer has adopted water-saving irrigation technology (1 = yes, 0 = no)	0.229	0.420	0.309	0.462	0.151	0.359
Access market	Whether the agricultural products sold have an organizational brand (1 = yes, 0 = no)	0.218	0.413	0.323	0.468	0.116	0.321
Non-agricultural employment	Whether the household has any non-agricultural employment persons (1 = Yes, 0 = No)	0.736	0.441	0.810	0.392	0.664	0.473

Note: Social capital and village elevation variables are logarithmic in later regressions.

presents the descriptive statistics of the variables used in the empirical analysis.

3.3. Model Construction

3.3.1. Baseline Regression Model

The baseline regression model in this paper is specified as follows.

$$Y_{ji}=\delta +{\alpha }_0{PD}_i+{\beta }_0{C}_i+{\epsilon }_i$$

(2)

$Y_{ji}$ is the explanatory variable of this paper, $j$ takes the value of 1 and 2, $Y_{1i}$ denotes the household income of farmer $i$, and $Y_{2i}$ denotes the degree of income inequality of farmer $i$; ${PD}_i$ is the core explanatory variable of this paper, which denotes the participation of farmer $i$ in the agricultural industrial organization; $C_i$ is the control variable; $\delta$ is the constant term; ${\alpha }_0$ and ${\beta }_0$ are the coefficients to be estimated; and ${\epsilon }_i$ is the random perturbation term. Given that heteroskedasticity in cross-sectional data may lead to biased parameter estimates, robust standard errors are employed in all regressions to address this issue.

3.3.2. Mechanism Testing Model

In existing research, stepwise regression is commonly used to test mechanisms. However, this approach, which incorporates both mechanism variables and core explanatory variables into the model, carries the risk of introducing bad controls, potentially leading to biased estimation results [62]. Therefore, building on the theoretical analysis of the mechanism pathways discussed earlier, this paper examines the effect of core explanatory variables on mechanism variables to explore the pathways through which PAIO generates income effects for farmers [63]. The model is specified as follows:

$$M_{ni}={\gamma }_1{PD}_i+{\gamma }_2{C}_i+{\tau }_i$$

(3)

$M_{ni}$ is the mechanism variable, $n$ takes the values of 1, 2 and 3, $M_{1i}$ denotes the technology adoption variable, $M_{2i}$ denotes the brand sales variable, and $M_{3i}$ denotes the outbound labor variable; ${\gamma }_1$ and ${\gamma }_1$ are the coefficients to be estimated; and ${\tau }_i$ is the stochastic perturbation term, which is the same with the variable meanings in Equation (2).

4. Results

4.1. Baseline Regression Results

Based on model (1), we conducted regression analysis using the Ordinary Least Squares (OLS) method to examine the impact of PAIO on household income and income inequality. The estimation results are shown in

Table 3. Baseline regression results.

Variables	Household Income			Income Inequality
	(1)	(2)	(3)	(4)	(5)	(6)
PAIO	3.657 ***	1.741 ***	1.957 ***	−0.054 ***	−0.038 ***	−0.041 ***
	(0.755)	(0.566)	(0.596)	(−0.009)	(−0.009)	(−0.009)
Age		−0.058	−0.052		0.001 **	0.001 *
		(0.041)	(0.040)		(−0.001)	(−0.001)
Gender		0.482	0.406		−0.013	−0.012
		(0.934)	(0.941)		(−0.010)	(−0.010)
Health status		−0.479	−0.496		0.003	0.004
		(0.530)	(0.528)		(−0.007)	(−0.007)
Education level		0.361 *	0.371 *		−0.003 **	−0.004 **
		(0.191)	(0.189)		(−0.001)	(−0.001)
Years of farming		−0.004	−0.006		−0.000	0.000
		(0.028)	(0.028)		(−0.001)	(−0.001)
Social capital		−0.145	−0.159		−0.001	0.000
		(0.264)	(0.265)		(−0.004)	(−0.004)
Village cadre		0.414	0.417		0.0107	0.011
		(0.869)	(0.871)		(−0.016)	(−0.016)
Household size		1.171 ***	1.243 ***		0.011 ***	0.010 ***
		(0.267)	(0.280)		(−0.003)	(−0.003)
Number of laborers		0.541 *	0.498 *		−0.019 ***	−0.018 ***
		(0.298)	(0.299)		(−0.004)	(−0.004)
Land area		0.518 ***	0.504 ***		−0.001 ***	−0.001 ***
		(0.151)	(0.154)		(−0.000)	(0.000)
Village delivery services		1.093 *	1.127 *		−0.022 **	−0.023 **
		(0.582)	(0.579)		(−0.011)	(−0.011)
Village elevation		−0.787	−3.445 **		−0.050 **	−0.012
		(−1.170)	(1.452)		(−0.022)	(−0.026)
Regional dummy variables	Control	Control	Control	Control	Control	Control
Constant term	10.141 ***	6.743	24.364 **	0.414 ***	0.711 ***	0.457 **
	(0.391)	(7.791)	(11.354)	(−0.007)	(−0.157)	(−0.181)
N	1234	1234	1234	1234	1234	1234
$R^2$	0.019	0.307	0.310	0.031	0.093	0.098

Note: ***, **, and * denote 1%, 5%, and 10% significance levels, respectively. Robust standard errors in parentheses. Regional dummy variables use the Sichuan sample as the control group.

. Columns 1 and 4 present results without control variables, Columns 2 and 5 incorporate control variables, and Columns 3 and 6 further include regional dummy variables.

The results show that after controlling for household decision-maker characteristics, household characteristics, village characteristics, and regional dummy variables, PAIO has a significant positive impact on household income at the 1% statistical level. At the same time, it has a significant negative impact on income inequality at the 1% statistical level. As shown in Columns 3 and 6, the coefficients for the PAIO variable are 1.957 and −0.041, respectively. This indicates that, for kiwi farmers, PAIO not only increases overall household income but also improves income distribution among household members. Specifically, households engaged in AIOs earn, on average, 19,570 yuan more annually than those who do not participate. This income growth is primarily attributable to the advantages provided by AIOs, including unified branding, technical support, access to stable markets, and stronger bargaining power, which enable farmers to secure higher price premiums and more stable income streams. At the same time, the income inequality index decreases by an average of 4.1%, suggesting that AIOs help farmers diversify their income sources. As a result, households are no longer overly dependent on non-agricultural wage income or temporary jobs earned by a single member. Instead, household members are more engaged in collaborative agricultural production and labor-sharing, leading to a more balanced and equitable household income structure. For families with initially lower incomes and greater vulnerability to external shocks, PAIO not only enhances their risk resilience but also improves intra-household economic equity. Therefore, Hypothesis H1 is supported.

Regarding control variables, age has a significant positive effect on income inequality, suggesting that older farmers may face higher levels of income inequality. This may be due to the declining productivity of older farmers, making it difficult for them to keep up with technological advancements or market opportunities, thereby widening income disparities. The education variable has a significant positive effect on household income and a significant negative effect on income inequality. This may be because farmers with higher education levels are better able to access agricultural technologies, market information, and non-agricultural employment opportunities, enhancing their income-earning ability and effectively reducing income inequality. Household size and the number of laborers have significant positive effects on household income and significant negative effects on income inequality. This could be because households with more members who can participate in agricultural or non-agricultural labor increase their total income, thereby reducing per capita income inequality across households. Land area also has a significant positive effect on household income and a significant negative effect on income inequality, indicating that larger landholdings allow for greater agricultural production scale, leading to higher production and sales income and reducing income inequality. The presence of village express delivery points has a significant positive effect on household income and a significant negative effect on income inequality. This suggests that logistics infrastructure, such as the establishment of delivery points, improves the conditions for selling and transporting kiwi fruit, helping to increase farmers’ income and reduce income inequality. Village elevation has a significant negative effect on household income, indicating that agricultural production conditions are generally worse in higher-altitude areas, with higher production costs and lower market accessibility, resulting in significantly lower incomes for farmers in these areas compared to those in low-altitude areas.

4.2. Endogeneity Discussion

When evaluating the impact of farmers’ PAIO on household income and income inequality, potential endogeneity issues may arise. In simple terms, not all farmers have equal opportunities or willingness to participate in AIOs. In practice, farmers with better resource endowments, higher education levels, stronger risk tolerance, or greater market awareness are more likely to engage in AIOs. However, these unobservable individual characteristics—such as entrepreneurial intentions, risk preferences, or social networks—can simultaneously influence both their likelihood of participation and their income levels. In other words, farmers who choose to participate in AIOs may inherently possess a higher income growth potential, leading to biased estimates when directly comparing participants and non-participants. Additionally, farmers’ participation decisions may be influenced by factors such as geographic location, cultural norms, or social relations within villages. These factors not only affect farmers’ likelihood of joining AIOs but may also directly impact their income, resulting in omitted variable bias. To address these concerns, this study employs the instrumental variable (IV) approach. By introducing an external variable that affects farmers’ willingness to participate in AIOs but does not directly influence household income, the IV method allows for a more accurate identification of the causal effects of AIO participation on household income and income inequality—separating the true impact of participation from the confounding effects of self-selection.

The instrumental variables selected are the distance from the farmer’s residence to the main road and the farmer’s level of knowledge about AIOs. First, the distance to the main road is an exogenous geographic variable that does not directly affect household income or income inequality. The geographical location in rural areas is determined by natural conditions, and a greater distance to transportation routes may limit farmers’ access to markets and resources, thereby influencing their decision to participate in AIOs. Thus, this variable is strongly correlated with the decision to participate in AIOs, but its direct impact on income or inequality is negligible, satisfying the exogeneity requirement for an instrumental variable. Second, the farmer’s level of knowledge about AIOs is also a key factor influencing their participation decision. A higher level of knowledge may increase the likelihood of participation, but it does not directly determine the farmer’s income level or income inequality. Therefore, using the level of knowledge as an instrumental variable captures whether the farmer has sufficient information to participate in an organization, while avoiding direct influence on the dependent variables. This makes it a well-suited exogenous variable for the model.

Table 4. Results of instrumental variable validity tests.

Test Categories and Statistics	Household Income	Income Inequality
Under-identification test: Kleibergen–Paap rk LM statistic	405.418 (0.000)
Weak instrumental variables test: Cragg–Donald Wald F statistic	362.305
Over-identification test: Hansen J statistic	0.009 (0.926)	0.866 (0.352)
Sargan statistic	0.005 (0.946)	1.191 (0.275)
Endogeneity test of endogenous regressors:	3.390 (0.066)	3.425 (0.064)

Note: p-values are in parentheses.

reports the validity tests for the instrumental variables. In the under-identification test, the Kleibergen–Paap rk LM statistic is 405.418, with a p-value of less than 0.001, rejecting the null hypothesis of “under-identification”. In the weak instrument test, the Cragg–Donald Wald F statistic is 362.305, which is greater than the 10% bias critical value of 19.93, rejecting the null hypothesis of “weak instruments”. In the over-identification test, the p-values for the Hansen J statistic and Sargan statistic under heteroskedasticity conditions are both greater than 0.1, accepting the null hypothesis that “all instruments are exogenous”. Additionally, OLS estimations were performed to assess the effect of the two instrumental variables on household income and income inequality, and the estimation results for both variables were insignificant (p-values of 0.718, 0.119, 0.121, and 0.119, respectively). In the endogeneity test, the endogenous regression factors are 3.390 and 3.425, both significant at the 10% level, indicating that the model does indeed have endogeneity issues.

In conclusion, the instrumental variables—distance to the main road and knowledge of AIOs—passed all validity tests, demonstrating their suitability as instrumental variables.

To address potential endogeneity issues, such as reverse causality and omitted variables, this paper employs the Two-Stage Least Squares (2SLS) method for regression analysis. The results are presented in

Table 5. 2SLS regression results.

Variables	First Stage	Second Stage
		Household Income	Income Inequality
PAIO	—	3.328 ***	−0.063 ***
		(0.936)	(0.015)
Distance to main road	−0.024 ***	—	—
	(0.004)
Awareness of AIOs	0.221 ***	—	—
	(0.008)
Control variables	Control	Control	Control
Regional dummy variables	Control	Control	Control
N	1234	1234	1234
$R^2$	0.435	0.617	0.875

Note: *** represents significance at the 1% statistical level. Robust standard errors are in parentheses. The control variables and regional dummy variables are the same as in the baseline regression.

. It can be observed that in the first stage, the instrumental variables are highly correlated with farmers’ PAIO. In the second stage, PAIO remains significant at the 1% statistical level in relation to both household income and income inequality. This indicates that after addressing endogeneity issues, the regression results remain largely consistent with the baseline regression outcomes.

4.3. Robustness Test

4.3.1. Alternative Estimation Methods

Although the previous discussion has addressed potential endogeneity issues caused by omitted variables and reverse causality, particular attention must also be paid to farmers’ self-selection behavior. In reality, farmers who choose to participate in AIOs often possess better resource endowments, such as higher production capacity, stronger market awareness, or more extensive social networks. This means that participation is not random but is instead influenced by farmers’ inherent characteristics. If this self-selection is not properly accounted for, the estimated benefits of AIO participation may mistakenly capture farmers’ pre-existing advantages rather than the true effects of participation, thereby leading to biased estimates—either overstating or understating the real impact. To address this issue, this study further adopts the Conditional Mixed Process (CMP) model. In essence, the CMP model jointly estimates two processes: the decision to participate in AIOs and the impact of participation on household income and income inequality. This approach captures the unobserved correlation between the two processes, particularly by incorporating the influence of latent factors—such as risk preferences and information acquisition capacity—from the participation decision into the income outcome equation through the error terms. In doing so, the model effectively corrects for self-selection bias and provides more accurate causal inference.

Given that the core explanatory variable is binary, using OLS in the first stage of the regression is not appropriate. Therefore, the first-stage model is modified to a Probit model. The results are presented in

Table 6. CMP model regression results.

Variables	Household Income		Income Inequality
	First Stage	Second Stage	First Stage	Second Stage
PAIO	—	3.893 ***	—	−0.066 ***
		(1.184)		(0.014)
Distance to main road	−0.117 ***	—	−0.121 ***	—
	(0.024)		(0.024)
Awareness of AIOs	0.744 ***	—	0.745 ***	—
	(0.040)		(0.039)
Control variables	Control	Control	Control	Control
Regional dummy variables	Control	Control	Control	Control
N	1234	1234	1234	1234
$atanhrho\_12$	−0.165 **		0.161 **
	(0.084)		(0.073)

Note: *** and ** represent significance at the 1% and 5% statistical level, respectively. Robust standard errors are in parentheses. The control variables and regional dummy variables are the same as in the baseline regression.

. The endogeneity test parameters from the CMP regression are all significant at the 10% statistical level, rejecting the hypothesis that the organization participation variable is exogenous. This confirms the presence of endogeneity, consistent with the results from the previous endogeneity tests. Additionally, the organization participation variable has a significant positive effect on household income and a significant negative effect on income inequality at the 1% statistical level. This indicates that the baseline regression results are robust.

4.3.2. Removing Extreme Value Samples from the Dependent Variable

In the statistical analysis of household income, the dependent variable was found to exhibit a right-skewed distribution. Therefore, we removed 124 samples with extremely high household income values, accounting for 10% of the total sample, leaving 1110 valid samples. The 2SLS method was then used for re-estimation. The results, shown in

Table 7. 2SLS second-stage regression results after removing extreme value samples.

Variables	Household Income	Income Inequality
PAIO	1.587 ***	−0.059 ***
	(0.455)	(0.016)
Control variables	Control	Control
Regional dummy variables	Control	Control
N	1110	1110
$R^2$	0.816	0.879

Note: *** represents significance at the 1% statistical level. Robust standard errors are in parentheses. The control variables and regional dummy variables are the same as in the baseline regression.

, indicate that compared to the baseline regression, the coefficients, direction, and significance of the impact of PAIO on household income and income inequality did not change significantly. This demonstrates that the baseline regression results are robust.

4.4. Mechanism Test Results

According to the theoretical analysis, farmers’ PAIO can promote agricultural production, increase sales premiums, and enhance human capital, thereby increasing household income and alleviating income inequality. Following this theoretical framework, this paper uses a single-step method to test the mechanism effects. When the core explanatory variable has a significant impact on the mechanism variables, it indicates that the mechanism effect is significant.

Table 8. Mechanism test results.

Variables	Technology Adoption		Access Market		Non-Agricultural Employment
	(1)	(2)	(3)	(4)	(5)	(6)
	OLS	2SLS	OLS	2SLS	OLS	2SLS
PAIO	0.137 ***	0.156 ***	0.184 ***	0.240 ***	0.128 ***	0.093 **
	(−0.024)	(−0.038)	(0.240)	(0.040)	(0.024)	(0.038)
Control variables	Control	Control	Control	Control	Control	Control
Regional dummy variables	Control	Control	Control	Control	Control	Control
N	1234	1234	1234	1234	1234	1234
$R^2$	0.107	0.314	0.104	0.296	0.213	0.792

Note: *** and ** represent significance at the 1% and 5% statistical level. Robust standard errors are in parentheses. The control variables and regional dummy variables are the same as in the baseline regression. The instrumental variables are the same as in Table 5.

reports the mechanism test results for the impact of farmers’ PAIO on income and income inequality. The regression results in Column 1 show that PAIO significantly increases the adoption of agricultural technologies by farmers, leading to higher agricultural production efficiency, increased household income, and a reduction in income inequality. Column 2 further demonstrates that the results remain robust after accounting for endogeneity, thereby confirming Hypothesis H2.

The regression results in Column 3 indicate that PAIO significantly facilitates farmers’ access to higher-level agricultural markets, enabling them to capture product brand premiums, which in turn increases agricultural income. Regarding market access, farmers who are more socially and economically disadvantaged tend to benefit more from the assistance provided by AIOs. Column 4 further shows that the results remain robust after addressing endogeneity, thus confirming Hypothesis H3.

The regression results in Column 5 reveal that PAIO significantly increases the likelihood of household members engaging in off-farm work. By participating in these organizations, farmers enhance their human capital and, through collective actions, reduce labor requirements in agriculture, allowing surplus labor to be transferred to non-agricultural sectors to earn non-agricultural income. This leads to increased household income and helps alleviate income inequality. Column 6 further confirms that the results remain robust after addressing endogeneity, thereby confirming Hypothesis H4.

4.5. Heterogeneity Analysis

To further investigate whether there are differences in the income effects of different types of AIOs, this paper classifies the sample farmers for further analysis. The cooperative sample consists of farmers who did not participate in any organization and those who participated only in cooperatives, while the leading enterprise sample consists of farmers who did not participate in any organization and those who participated only in leading agribusiness companies. In this part of the study, we excluded 30 farmers who participated in both types of AIOs. The regression results are presented in

Table 9. Heterogeneity analysis regression results.

Variables	Household Income
	Cooperative Sample		Leading Enterprise Sample
	OLS	2SLS	OLS	2SLS
PAIO	1.642 ***	3.191 ***	2.865 ***	5.361 ***
	(0.609)	(1.065)	(1.078)	(1.735)
Control variables	Control	Control	Control	Control
Regional dummy variables	Control	Control	Control	Control
N	1013	1013	819	819
$R^2$	0.360	0.644	0.189	0.576

Note: *** represents significance at the 1% statistical level. Robust standard errors are in parentheses. The control variables and regional dummy variables are the same as in the baseline regression. The instrumental variables are the same as in Table 5.

. In the cooperative sample, PAIO has a significant effect on increasing farmers’ income. In the OLS regression, the coefficient is 1.642, and after considering endogeneity in the 2SLS regression, the coefficient increases to 3.191, both significant at the 1% level. This indicates that farmers’ participation in cooperatives significantly improves their income. In the leading enterprise sample, the income effect of participating in leading agribusiness companies is even greater. In the OLS regression, the coefficient for participating in leading enterprises is 2.865, and in the 2SLS regression, the coefficient further increases to 5.361, indicating a more pronounced effect of leading enterprises on raising farmers’ income.

This result suggests that while both organizational forms help farmers increase their income, the income-enhancing effect of participating in leading agribusiness companies is much greater than that of cooperatives. This may be due to the stronger market capacity, resource integration, and brand effect of leading enterprises, which enables them to provide higher returns to farmers.

5. Discussion

Through rigorous empirical analysis, this study finds that farmers’ PAIO significantly increases household income and effectively mitigates income inequality among farmers. This conclusion not only corroborates the findings of previous studies on the income-enhancing effects of agricultural organization both in China and globally [64,65,66,67], but also provides micro-level empirical evidence in support of several key United Nations SDGs, including “No Poverty,” “Reduced Inequalities,” and “Decent Work and Economic Growth”. However, the statistical correlations revealed by the data reflect more complex underlying economic and social mechanisms. The mechanism analysis further demonstrates that farmers benefit from PAIO through three primary pathways. First, in the production stage, technological integration and resource sharing significantly reduce production costs and lower technological barriers for smallholders. Second, in the sales stage, improved market bargaining power enables farmers to obtain higher price premiums. Third, skills training and expanded non-agricultural employment opportunities help diversify household income sources.

Field interviews also reveal that many kiwi farmers reported that prior to joining AIOs, their household income mainly depended on one or two family members working off-farm, while the remaining members engaged in low-efficiency agricultural activities with limited income. After joining AIOs, farmers were able to share advanced agricultural technologies, participate in unified marketing systems, and secure stable order contracts through the organization. This enabled more household members to engage in value-added activities such as farming, processing, and e-commerce, resulting in a more diversified and balanced household income structure. In addition, AIOs help reduce the risks associated with market fluctuations through resource pooling and standardized production processes. This is particularly critical for low-income farmers who previously lacked bargaining power and investment capacity. These qualitative findings not only offer a practical explanation for the quantitative results but also reinforce the critical role that AIOs play in improving household income and optimizing income distribution.

Although this study is based on micro-level survey data from kiwi farmers in China, the findings possess strong external applicability and provide valuable insights for other developing countries characterized by smallholder-dominated agricultural systems. In regions across Asia, Africa, and Latin America, agriculture faces common structural challenges, including land fragmentation, small-scale production, high barriers to market entry, and significant transaction costs [68,69,70,71]. Smallholders cultivating high-value cash crops in these regions often lack stable market channels and effective risk management mechanisms [72,73]. The mechanisms identified in this study—where AIOs reduce transaction costs, integrate resources, and enhance farmers’ bargaining power to promote income growth and alleviate income inequality—are equally relevant in these contexts. For example, smallholder farmers in Africa’s coffee and cocoa sectors, Southeast Asia’s rubber and palm oil industries, and Latin America’s tropical fruit production all face similar issues of small production scale and high exposure to market volatility [74,75,76,77,78]. The challenge of connecting smallholders to modern markets through organizational means is a widely shared development problem [79,80,81]. Therefore, this study not only provides micro-level evidence for agricultural modernization and rural poverty reduction in China but also offers valuable lessons for developing countries worldwide. It demonstrates how AIOs can serve as a vehicle for increasing farmer incomes, improving income distribution, and promoting sustainable rural development. Nevertheless, given the differences in market environments, institutional frameworks, and social structures across countries, the application of this approach should be adapted flexibly to local contexts.

Furthermore, this study finds that compared to cash crop farmers participating in agricultural cooperatives, those participating in leading agribusiness companies experience a greater increase in income. Agricultural cooperatives are typically farmer-initiated and aim to achieve economies of scale through collective action, reducing production costs and improving bargaining power [82]. Leading agribusiness companies, on the other hand, are more commercialized organizations with stronger financial strength and market resources, providing more comprehensive support to farmers [83]. The income effect differences between these two types of organizations can be explained from two perspectives. First, in terms of risk-sharing and protection mechanisms, cooperatives mainly rely on collective action to reduce individual risks. However, due to their smaller scale, cooperatives often find it difficult to cope with large-scale market fluctuations or natural disasters [84,85,86], which can expose farmers to greater risks when market prices fall or production fails. In contrast, farmers working with leading enterprises can sign fixed-price or guaranteed-price contracts, reducing market risk. This model ensures that farmers earn stable incomes during the production process, reducing the uncertainty caused by market price fluctuations [87], which explains why farmers participating in leading enterprises see more significant income gains. Second, in terms of marketization and product positioning, cooperatives typically have a lower level of marketization, with members relying primarily on collective sales channels, often limited to local or regional markets, and focused on mid-to-low-end products. This limits the potential of cooperatives to raise farmers’ income. Leading agribusiness companies, with their stronger market capabilities, usually target higher-end or export markets, achieving a higher level of marketization. As a result, farmers can sell their products at higher prices through these companies, leading to greater income growth [88]. Moreover, leading companies often have brand recognition and stronger bargaining power, helping farmers secure a larger market share and higher economic returns.

This finding raises a deeper question regarding the sustainable development of smallholder farmers: Is reliance on large agribusinesses necessarily superior to dependence on farmer-led organizations such as cooperatives? From the perspective of short-term economic returns and risk mitigation, depending on enterprises indeed appears to be safer than relying on farmer self-organized groups. Enterprises possess stronger capital strength, broader market networks, and more advanced risk management capabilities. Through mechanisms such as contract farming, guaranteed minimum purchase prices, and order-based agriculture, they can provide farmers with stable market access and relatively predictable income, effectively shielding them from risks associated with market price volatility and sales uncertainties [89,90,91]. In addition, enterprises typically offer comprehensive services, including technical support, advance payments, and input supply, further reducing production risks for farmers [92,93]. However, this form of security is fundamentally rooted in farmers’ dependence on enterprises. Positioned at the upper tiers of the supply chain, enterprises often hold stronger bargaining power and greater authority in contract negotiations, leaving farmers with limited decision-making power. If an enterprise decides to adjust its production strategy or unilaterally terminate contracts during market downturns, farmers are left highly vulnerable to external risks. Therefore, in the long run, over-reliance on enterprises may actually undermine farmers’ capacity for autonomous development and weaken their resilience to risks. In contrast, while cooperatives—formed through grassroots farmer initiatives—may be relatively weaker in terms of financial capacity, market resilience, and managerial efficiency, their governance structures are based on collective decision-making. This ensures that farmers maintain autonomy in income distribution, price negotiation, and production arrangements [94,95]. Particularly when cooperatives are well-governed, they can accumulate social capital through collective action, thereby enhancing farmers’ bargaining power and adaptability to changing markets [96]. Thus, while reliance on enterprises offers greater short-term security, dependence on farmer-led organizations is more conducive to strengthening farmers’ independent development capacity and collective risk resilience from a long-term sustainable development perspective.

While this study provides valuable insights into the relationship between farmers’ PAIO, household income, and income inequality, there are some limitations. First, the study uses cross-sectional data and evaluates the income effects and mechanisms using the 2SLS method. Although most endogeneity issues have been addressed, selection bias due to time factors may still exist. Future research could employ more rigorous causal identification strategies to examine these issues. Second, due to budget constraints, the sample is limited to two provinces in China, which imposes some limitations on the study’s conclusions and policy recommendations. Future research could collect data from more fragmented farmland regions in China to explore these issues further. Finally, the analysis is limited to kiwi farmers. Since the application of agricultural technology and inputs varies significantly across crops, and the organizational models of AIOs differ, caution is needed when generalizing the findings to non-cash crop farmers. Therefore, future research should incorporate multi-crop datasets and multi-period samples to conduct systematic analyses across different crops and regions. Such an approach would enable a deeper exploration of the boundaries and applicability of AIOs in various agricultural subsectors and development contexts. This would provide more comprehensive empirical evidence to support the role of AIOs in enhancing farmer income, reducing income inequality, and promoting sustainable rural development.

6. Conclusions

This paper uses rural household survey data collected in Shaanxi and Sichuan provinces between October and November 2023. Based on a theoretical analysis of the income effects of kiwi farmers’ PAIO, the study employs the IV method to empirically test the impact of participation on household income and income inequality. The key findings are as follows:

First, PAIO significantly increases the household income of kiwi farmers and alleviates income inequality, demonstrating the shared prosperity effect of AIOs.

Second, PAIO promotes agricultural production, increases sales premiums, and enhances human capital, thereby increasing household income and mitigating income inequality among farmers.

Third, compared to kiwi farmers participating in agricultural cooperatives, those participating in leading agribusiness companies experience a larger increase in income. This finding has significant reference value for other regions and countries with cash crop farmers.

Based on the research findings, the primary policy priority should focus on accelerating the development of AIOs and strengthening their role in promoting income growth and improving income distribution among farmers. This represents a fundamental pathway to addressing the dual challenges of enhancing smallholder incomes and reducing income inequality. Governments should prioritize supporting leading agribusiness enterprises and farmer cooperatives through targeted measures such as financial subsidies, tax reductions, and low-interest loans. At the same time, it is essential to improve relevant laws and regulations to standardize the governance structures of AIOs, incentivize their active inclusion of smallholders, and enhance their capacity for market integration and risk-sharing. These efforts not only help farmers obtain higher market premiums but also significantly reduce production and transaction costs.

In addition, to strengthen the endogenous development capacity of AIOs, it is critical to simultaneously invest in agricultural technology promotion and human capital development. Building a comprehensive agricultural service system that provides targeted and specialized technical training—along with the widespread adoption of smart farming, digital agriculture, and green production technologies—can effectively improve farmers’ production efficiency and ability to respond to increasingly complex market conditions. Particularly in a volatile market environment, enhancing farmers’ technical skills and management capabilities is key to transforming the external support provided by AIOs into sustained income growth.

Furthermore, it is recommended to foster a complementary development model that integrates “cooperatives + leading enterprises”. Cooperatives can support farmers on the production side by offering agricultural inputs, technical guidance, and primary processing services, while leading enterprises, leveraging their brand strength and market networks, can assist farmers in accessing high-value markets. This synergistic model enables both economies of scale and brand premium enhancement. Governments should facilitate this process through platform construction, financial support, and policy guidance to promote deeper collaboration between cooperatives and leading enterprises, thereby creating a more efficient and integrated agricultural value chain.

On this basis, optimizing the agricultural market environment and promoting product standardization and branding are equally crucial. Governments should develop unified production standards, improve agricultural product certification and traceability systems, and actively support the development of regional brands. These measures will enhance the market recognition and competitiveness of specialty agricultural products such as kiwi fruit, thereby further expanding the value-added potential derived from agricultural organizations.

Finally, as a supporting measure, it is imperative to improve agricultural social security systems to provide risk buffers for farmers participating in the market. Establishing mechanisms such as price insurance, income protection, and natural disaster compensation can effectively mitigate the adverse impacts of market fluctuations on smallholders. This will not only enhance farmers’ stability in participating in AIOs but also contribute to sustained improvements in income distribution alongside income growth.