Due to its relatively low input requirements, short production cycle, and rapid market responsiveness, the edible fungi industry has gradually emerged as an important agricultural sector for increasing farmers' income and optimizing the structure of agricultural earnings. With the continuous expansion and professionalization of the industry, it is necessary to systematically examine its impact on farmers' economic benefit evaluations from a micro-level perspective.
Based on questionnaire survey data collected in 2023 from 814 farm households in typical edible fungi cultivation areas of Hebei Province, this study takes farmers' economic return satisfaction as the outcome variable and constructs a binary choice model. Using a Logistic regression approach, the analysis investigates the effect of participation in the edible fungi industry on the probability of farmers attaining a satisfactory level of economic returns, while controlling for individual characteristics, household conditions, and production and management factors.
The empirical results indicate that participation in the edible fungi industry significantly increases the likelihood that farmers enter a state of “satisfied or above” economic returns, with the overall model being statistically significant at the 5% level. Substantial heterogeneity is observed in the effects of individual characteristics on economic return satisfaction. Specifically, age (coefficient = 1.487,
Overall, the findings confirm that participation in the edible fungi industry plays a significantly positive role in enhancing farmers' economic return satisfaction, providing empirical evidence to support the formulation of targeted industrial support and technology promotion policies.
Against the backdrop of agricultural income diversification, farmers' subjective satisfaction with economic returns serves as an important indicator of welfare improvement and plays a critical role in shaping production decisions and sustained industrial participation (
Existing studies have examined the effects of agricultural industry participation on farmers' income perceptions across multiple applied contexts and generally concur that industrial participation enhances farmers' subjective evaluations of household economic conditions. For instance, empirical evidence indicates that farmers engaged in specialty agriculture or high value-added industries report significantly higher levels of economic return satisfaction than non-participants, suggesting that industrial choices not only alter income levels but also reshape farmers' perceptions and evaluations of economic outcomes (
Although the aforementioned studies provide an important foundation for understanding the relationship between industrial participation and income perceptions, the existing literature has largely focused on whether income increases or the magnitude of income improvement, while offering relatively limited quantitative identification of economic return satisfaction as a subjective outcome variable. Moreover, research on the edible fungi industry has predominantly remained at the level of case descriptions or single-factor analyses, lacking a systematic examination of the probability of farmers entering a satisfactory economic return state after controlling for individual characteristics and production and management conditions. Against the backdrop of rising input risks and increasing market volatility in the edible fungi industry, clarifying its actual impact on farmers' economic return satisfaction has become a matter of practical urgency. Building upon the existing literature, this study therefore treats economic return satisfaction as the core outcome variable and constructs an econometric model to systematically examine the effect of participation in the edible fungi industry on the probability of farmers attaining a satisfactory level of economic returns. By controlling for farmers' individual characteristics and operating conditions, the analysis further identifies the key factors shaping the formation of economic return satisfaction, thereby addressing the gap in the literature regarding the quantitative assessment of income perceptions in the context of the edible fungi industry. By quantitatively identifying the impact of edible fungi industry participation on economic return satisfaction from a farmer-centered perspective, this study enables a more accurate evaluation of the industry's actual welfare effects and provides empirical evidence to support the precision and effectiveness of industrial support policies.
The remainder of this paper is organized as follows. Section 1 presents the introduction, outlining the research background and key issues addressed in this study. Section 2 provides a literature review that systematically synthesizes existing research on the edible fungi industry. Section 3 describes the research design, including the study sample, data sources, variable definitions, and model specification. Section 4 reports and analyzes the empirical results, employing a Logistic regression model to examine the impact of participation in the edible fungi industry on farmers' economic return satisfaction and to explore the roles of related factors. Section 5 discusses the implications of the findings in light of the empirical evidence. Section 6 concludes the paper by summarizing the main results and outlining directions for future research.
As an important economic crop sector within modern agriculture, the edible fungi industry has been rapidly promoted and developed worldwide—particularly in China—owing to its efficient production model and strong potential for resource recycling (
The role of the edible fungi industry in promoting rural economic development and increasing farmers' income has been widely recognized. Owing to its high return potential, edible fungi production has played an important role in rural economic transformation, particularly in enhancing farmers' income and improving living standards (
The diffusion and dissemination of technology are critical to the sustainable development of the edible fungi industry. Technological diffusion depends not only on innovation itself but is also shaped by farmers' acceptance, access to technical training, and the structure of social networks (
Overall, previous studies provide a solid theoretical foundation for this research, particularly with respect to the impacts of the edible fungi industry on farmers' economic benefits, resource recycling, and technology diffusion. While this body of literature highlights the industry's potential in facilitating rural economic transformation, most existing studies remain focused on macro-level policy analysis and offer limited quantitative assessments of farmers' individual economic outcomes. To address this gap, the present study draws on field survey data from Hebei Province and applies a Logistic regression model to conduct an empirical analysis, with the aim.
The data for this study were collected through questionnaires and interviews with farmers in the major edible fungi production areas of Hebei Province, conducted between May and August 2023. The survey covered Xingtai, Baoding, and their surrounding areas, which are characterized by typical agricultural production patterns and have favorable climate conditions for the growth of edible fungi. These areas also have a long history of mushroom cultivation, with farmers having accumulated substantial planting experience. Agricultural production in these regions primarily follows small-scale, family-based operations, offering a broad representativeness. Additionally, the high degree of marketization in Xingtai and Baoding, with diverse sales channels and strong government policy support, has contributed to the rapid development of the edible fungi industry, providing a solid foundation for research.
In terms of data collection, this study employed a mixed-method approach combining online questionnaires with offline field surveys. The offline surveys were conducted by the research team in the selected regions through household interviews and on-site questionnaire administration, while the online questionnaires were distributed via local agricultural cooperatives and farmer communication platforms. Sample selection followed several criteria. Surveyed households were required to be permanent local agricultural operators with agricultural income as part of their household earnings. Participation in edible fungi cultivation was not imposed as a prerequisite, ensuring that both participating and non-participating farmers were included in the sample. This design enhances sample comparability and satisfies the requirements for subsequent regression analysis.
A total of 900 questionnaires were distributed and successfully collected in this study. During data cleaning, all responses were subjected to systematic checks for completeness and internal consistency. A total of 86 questionnaires were excluded due to missing key information, apparent logical inconsistencies in responses, or the presence of unverifiable outliers. Consequently, 814 valid questionnaires were retained for the final analysis, resulting in an effective response rate of 90.5%. The questionnaire was designed based on relevant literature and expert consultations, covering key aspects such as basic household information (e.g., age, education level, family size, and planting experience), production costs (including raw materials, labor costs, equipment investment, and technical training), sales methods, production output, and price fluctuations. This ensured the scientific rigor and relevance of the research data.
Edible Fungi Industry (FungiInd): The edible fungi industry is a key component of Hebei Province's agricultural economic development, playing a crucial role in increasing farmers' income. Therefore, this study uses “participation in the edible fungi industry” as the explanatory variable to assess the relationship between farmers' economic returns and the development of the edible fungi industry. Specifically, farmers who participate in edible fungi cultivation are coded as 1, while those who do not are coded as 0. This variable setting effectively reflects the impact of the edible fungi industry on farmers' economic returns.
This study adopts farmers' economic return satisfaction (Income Satisfaction) as the dependent variable to capture farmers' subjective evaluations of their economic outcomes. Economic return satisfaction reflects farmers' overall perceptions and assessments of their economic conditions after jointly considering income levels, production costs, market fluctuations, and risk-related uncertainties. This variable is derived from survey responses regarding farmers' satisfaction with their current economic returns. Accordingly, a binary variable is constructed, where farmers reporting being “satisfied” or above with their economic returns are coded as 1, and all other responses are coded as 0.
To comprehensively analyze the main factors affecting farmers' economic returns, this study also incorporates a series of subjective and objective factors, which are detailed in
Symbols and descriptions of influencing factors.
| Subjective factors | Age | Age | Actual age of the farmer (years), which affects their ability to accept new technologies and willingness to participate. |
| Education level | Edu | Years of education (years), influencing the farmer's ability to acquire and understand agricultural technical information. | |
| Planting experience | Exp | Years of participation in edible fungi cultivation (years), determining decision-making capacity and risk management ability. | |
| Oak wood area | Area | Area of oak wood cultivation (mu), which affects growing conditions for edible fungi and improves soil quality, thus influencing yield and quality. | |
| Objective factors | Number of family members | Fam | Number of family members involved in agricultural production, affecting labor input and economic burden. |
| Planting area | Area | Land area used for edible fungi cultivation (mu), directly determining production capacity and potential income. | |
| Market price fluctuation | Pri | Standard deviation of market prices over the past year, influencing farmers' production decisions. | |
| Climate factors | Wea | Farmer's subjective evaluation of climate conditions (rated on a scale), affecting the growth and production cycle of edible fungi. | |
| Logistics cost | Log | Average cost of transporting edible fungi, directly influencing profit levels. | |
| Production cost | Pro | Production cost per kilogram of edible fungi, including raw materials and labor costs. | |
This study employs a Logistic regression model to analyze the impact of the edible fungi industry on farmers' economic returns. The Logistic regression model is well-suited for handling binary dependent variables, effectively analyzing the relationship between farmers' participation in the edible fungi industry and their economic returns. This method is appropriate for comparative studies like this one, which includes both participant and non-participant groups, and can estimate the average treatment effect of participating in the edible fungi industry on farmers' income while controlling for other influencing factors.
The basic equation of the Logistic regression model is as follows:
where
In conducting the Logistic regression analysis, it is necessary to standardize the data to eliminate the influence of differing variable scales on the model results. The data standardization process can effectively improve the accuracy and reliability of the model, ensuring that different features are compared on the same scale, thereby enhancing the model's fit.
This study adopts the Min-Max normalization method, with the specific formula as follows:
Among them,
Descriptive statistics for all variables are reported in
Descriptive statistics.
| lnY | 10.66 | 0.29 | 9.62 | 11.23 |
| Age | 45.31 | 9.63 | 25.00 | 70.00 |
| Edu | 9.52 | 3.27 | 0.00 | 16.00 |
| Lab | 3.12 | 1.14 | 1.00 | 6.00 |
| lnPro | 9.34 | 0.49 | 8.01 | 10.31 |
| Tra | 0.64 | 0.48 | 0.00 | 1.00 |
| Pla | 8.42 | 4.31 | 1.50 | 20.00 |
| Cha | 0.58 | 0.49 | 0.00 | 1.00 |
| Pri | 9.63 | 1.25 | 6.50 | 12.50 |
| Gov | 0.32 | 0.47 | 0.00 | 1.00 |
| Tec | 0.47 | 0.51 | 0.00 | 1.00 |
In terms of production and operational characteristics, the mean value of the logarithm of production cost is 9.34, corresponding to an original input level of approximately 12,500 yuan, with a relatively large standard deviation. This suggests notable differences among farmers in equipment investment and the allocation of production factors. The mean planting area (Pla) is 8.42 mu, indicating that the production scale of sampled farmers ranges from small-scale to medium-scale operations and exhibits considerable diversity. The sales price (Pri) fluctuates between 6.50 yuan and 12.50 yuan, showing that edible fungi prices are strongly influenced by market supply–demand conditions and seasonal factors.
Regarding institutional and technical conditions, the mean value of the technical training variable (Tra) is 0.64, indicating that approximately 64% of farmers have participated in relevant technical training programs. The mean values of government subsidies (Gov) and technical support (Tec) are 0.32 and 0.47, respectively, suggesting that while some farmers benefit from policy and technical support, such measures have not yet achieved comprehensive coverage. Overall, the sampled farmer's exhibit pronounced heterogeneity in income levels, production scale, cost inputs, and access to policy and technical support, providing a necessary empirical basis for the subsequent regression analysis examining the effects of various factors on farmers' economic returns.
To avoid significant bias in the research results, it is necessary to test for multicollinearity among the explanatory variables. This study uses the Variance Inflation Factor (VIF) and Tolerance values to assess potential multicollinearity issues. A higher VIF value indicates more severe multicollinearity between variables, while a lower Tolerance value (closer to 0) suggests the presence of multicollinearity. Generally, a VIF value greater than 10 or a Tolerance value less than 0.1 is considered to indicate serious multicollinearity issues.
As shown in the results from
Collinearity statistics.
| Age | 4.800 | 0.208 |
| Education level | 4.500 | 0.222 |
| Planting experience | 1.700 | 0.588 |
| Oak wood area | 2.900 | 0.345 |
| Number of family members | 2.200 | 0.455 |
| Market price fluctuation | 3.200 | 0.312 |
| Climate factors | 1.900 | 0.526 |
| Logistics cost | 1.500 | 0.667 |
| Production cost | 2.600 | 0.385 |
In the regression analysis of farmers' satisfaction with economic returns, the constant term of the model is significant at the 5% level, indicating that the overall model is statistically valid and that at least one explanatory variable is significantly associated with the probability of farmers entering a state of economic satisfaction. As shown in
Regression analysis results of factors affecting farmers' satisfaction with economic returns.
| Constant | −0.556** | 0.241 | 5.210 | – |
| Age | 1.487** | 0.392 | 6.185 | 0.366 |
| Education level | −1.482*** | 0.395 | 7.913 | 0.371 |
| Planting experience | 0.785** | 0.297 | 4.082 | 0.843 |
| Family size | 0.754* | 0.344 | 3.800 | 1.587 |
| Oak wood area | −0.684*** | 0.354 | 6.000 | 0.830 |
| Technical cost | 0.615** | 0.224 | 4.500 | 1.218 |
| Production cost | −0.590* | 0.396 | 3.000 | 1.259 |
| Logistics cost | 0.485* | 0.265 | 3.684 | 0.849 |
| Sales cost | 0.675** | 0.286 | 5.201 | 0.887 |
| Weather conditions | 0.387 | 0.251 | 2.999 | 1.010 |
***
Planting experience has a significant positive effect on farmers' economic satisfaction (coefficient = 0.785,
To verify the robustness of the regression analysis results, a robustness test was conducted to ensure the stability and reliability of the model under different conditions. The purpose of the robustness test is to confirm the credibility of the regression results and to validate the effectiveness of the influencing factors using different methods. This study employed the following two methods for robustness testing: the bootstrap method and the alternative variable method.
By repeatedly resampling the original dataset (set to 1,000 iterations), the distribution and confidence intervals of the model parameters were assessed to test the stability of the regression coefficients. The bootstrap method results are summarized in
Bootstrap method regression coefficients and 95% confidence intervals.
| Age | 1.487** | [0.726, 2.259] |
| Education level | −1.482*** | [−2.150, −0.814] |
| Planting experience | 0.785** | [0.034, 1.536] |
| Family size | 0.754* | [0.001, 1.507] |
| Technical cost | 0.615** | [0.093, 1.137] |
***
The results from the bootstrap method indicate that the 95% confidence intervals for all variables do not include zero, demonstrating that the effects of these factors on farmers' satisfaction with economic returns are statistically significant.
In the alternative variable method, certain independent variables were replaced to observe changes in the model results. For example, “technical cost” was replaced with “market price fluctuation” to test the consistency of the findings. The regression results are shown in
Regression results from alternative variable method.
| Age | 1.520*** |
| Education level | −1.350*** |
| Planting experience | 0.840** |
| Family size | 0.700** |
| Market price fluctuations | −0.400** |
***
The regression results showed that, although the model structure changed, the impact of key variables remained significant, demonstrating the robustness of the model.
Through the robustness tests described above, the study's findings further validate the significant influence of the edible fungi industry on farmers' economic returns. The results of both the bootstrap method and the alternative variable method consistently indicate that factors such as age, education level, planting experience, and family size have a significant impact on farmers' satisfaction with economic returns, thereby enhancing the credibility of the study's conclusions.
Through the robustness tests described above, the empirical results of this study further confirm the stability of the relationship between participation in the edible fungi industry and farmers' economic satisfaction. The results from both the bootstrap resampling method and the alternative variable method consistently indicate that factors such as age, education level, planting experience, and family size are significantly associated with farmers' satisfaction with economic returns. This consistency across different robustness checks suggests that the main findings of the study are robust to alternative specifications and testing approaches, thereby strengthening the credibility of the conclusions.
This study takes farmers' satisfaction with economic returns as a binary outcome variable and applies a Logistic regression model to compare the probability that farmers who participate in edible fungi cultivation, vs. those who do not, enter a state of “economic satisfaction or above.” The regression results show that, after controlling for farmers' age, education level, planting experience, family size, and production conditions, the participation variable of the edible fungi industry exhibits a significant positive probability effect in the model. This indicates that participation in the edible fungi industry significantly increases the likelihood that farmers form a higher level of satisfaction with their economic returns. This finding corresponds directly to the statistical significance of the core explanatory variable in the regression results, confirming that participation in the edible fungi industry increases, in a statistical sense, the probability that farmers enter a satisfied income state. From a probabilistic perspective of income perception, these results reveal the positive role of the edible fungi industry in improving farmers' subjective economic evaluations and, from a micro-level behavioral response perspective, highlight its important position within the rural economic system.
The results further indicate that factors such as farmers' age, education level, planting experience, and family size have significant effects on the probability of entering a higher level of economic satisfaction, reflecting substantial heterogeneity in farmers' income evaluations across different individual characteristics and household conditions. Specifically, older farmers are more likely to report satisfaction with their economic returns, suggesting that increasing age raises the probability of entering a satisfied income state. This effect may be associated with long-term accumulation of production experience and dynamic adjustment of income expectations, and is directionally consistent with the findings of
In addition, planting experience shows a significant positive probability effect on economic satisfaction, suggesting that experience accumulation helps farmers form more favorable income evaluations when facing production uncertainty and market volatility. This finding is consistent with
Despite the systematic analysis of the relationship between participation in the edible fungi industry and farmers' economic satisfaction based on micro-level survey data, several limitations should be acknowledged. First, the sample is drawn exclusively from Hebei Province, China, which has distinct characteristics in terms of climate conditions, agricultural structure, policy support, and market organization. As a result, the conclusions primarily reflect the mechanisms through which the edible fungi industry influences farmers' income perception in a specific regional context and cannot be directly generalized to other provinces or countries. Second, although economic satisfaction serves as a useful subjective indicator capturing farmers' overall perception of income outcomes, the study does not further disentangle psychological expectations and risk attitudes underlying different satisfaction levels, and these subjective factors are not fully captured in the model. Third, while the Logistic regression model is appropriate for analyzing binary outcomes, it may not fully capture potential non-linear relationships or latent heterogeneity effects among variables. Finally, the analysis does not systematically incorporate dynamic external factors such as policy adjustments or changes in market structure, which to some extent limits a deeper understanding of the long-term income effects of the edible fungi industry.
Based on field survey data from 814 farming households in Hebei Province, China, this study employs a Logistic regression model to quantitatively evaluate the effect of participation in the edible fungi industry on farmers' satisfaction with economic returns. The main conclusions are summarized as follows.
(1) Participation in the edible fungi industry significantly increases the likelihood of higher economic satisfaction among farmers. The regression results show that the constant term of the model is significant at the 5% level, indicating that the overall model is statistically valid. After controlling for other factors, farmers who participate in the edible fungi industry are more likely to report higher satisfaction with their economic returns. This finding suggests that the edible fungi industry plays a significant positive role in improving farmers' perceived economic outcomes.
(2) Farmers' individual characteristics exhibit significant heterogeneous effects on economic satisfaction. Both age and planting experience have a significant positive impact on satisfaction with economic returns, with an age coefficient of 1.487 (
(3) Production conditions and cost constraints play an important role in shaping farmers' economic satisfaction. With respect to operational factors, oak wood area is found to have a significant negative effect on economic satisfaction (coefficient = −0.684,
Future research may further deepen and extend the present analysis in several directions. First, in terms of research context, expanding the sample to other provinces in China, or incorporating countries and regions with comparable agricultural structures, would help test the cross-regional applicability of the findings from Hebei Province and more closely embed the conclusions within the international literature on the edible fungi industry and agricultural income. Second, regarding outcome variables, future studies could retain economic satisfaction while further disaggregating subjective evaluation dimensions, or integrate psychological factors such as farmers' risk preferences and income expectations, to more comprehensively explain the relationship between industrial participation decisions and income perception. Third, at the methodological level, alternative analytical frameworks—such as ordered Logit models, mixed-effects models, or structural equation models—could be employed to better capture complex relationships and potential heterogeneity among variables. Finally, future research may incorporate external factors such as policy support intensity, changes in market pricing mechanisms, and the evolution of industrial chain structures, adopting a dynamic perspective to analyze the long-term mechanisms through which the edible fungi industry affects farmers' economic returns and satisfaction, thereby providing more generalizable empirical evidence for agricultural policy design under diverse contexts.
Given that participation in the edible fungi industry has a significant effect on farmers' satisfaction with economic returns, and that individual characteristics and operational conditions play an important role in the formation of income evaluations, the findings of this study provide useful insights for the development of specialty agriculture, the enhancement of farmers' capabilities, and the optimization of related support policies.
First, industry support policies oriented toward specialty agriculture should be strengthened, positioning the edible fungi industry as an important vehicle for the stable improvement of farmers' incomes. The empirical results indicate that participation in the edible fungi industry significantly increases the probability that farmers achieve a higher level of economic satisfaction, demonstrating the industry's tangible role in improving farmers' income outcomes. Accordingly, in the process of adjusting agricultural industrial structures, the edible fungi industry can be treated as a key specialty agriculture sector. By optimizing industrial layout, improving infrastructure, and stabilizing market expectations, policymakers can guide farmers to participate in edible fungi production in an orderly manner, thereby enhancing the sustainability of income growth.
Second, capacity-building mechanisms centered on technical training and experience accumulation should be reinforced to enhance farmers' perceived gains from industrial participation. The regression results show that planting experience and technology-related inputs have significant positive effects on farmers' satisfaction with economic returns, suggesting that merely expanding production scale is insufficient to improve income perception, while capability-related factors play a critical role. At the policy level, greater emphasis should be placed on farmer-oriented technical training and knowledge dissemination. Through continuous technical guidance, experience-sharing platforms, and demonstration-based extension, uncertainty in production and operation can be reduced, thereby strengthening farmers' stable expectations regarding industrial returns.
Third, industry support policies should balance cost constraints with income expectations, guiding farmers toward more sustainable production and management decisions. The study finds that production costs and the scale of resource inputs tend to weaken the likelihood that farmers form higher levels of economic satisfaction, indicating an important trade-off between cost pressure and income expectations. Therefore, in policy design, it is necessary to take into account the practical constraints faced by farmers in terms of input costs, market volatility, and risk exposure. By optimizing input structures, improving circulation and logistics conditions, and strengthening complementary services, policymakers can alleviate operational pressure and enhance farmers' confidence in the long-term development of the edible fungi industry.
The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding author.
The animal study was approved by Experimental Animal Ethics Committee, Hebei Agricultural University. The study was conducted in accordance with the local legislation and institutional requirements.
XZ: Conceptualization, Funding acquisition, Investigation, Resources, Software, Visualization, Writing – original draft, Writing – review & editing. RZ: Data curation, Formal analysis, Methodology, Project administration, Supervision, Validation, Writing – review & editing.
The author(s) declared that this work was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
The author(s) declared that generative AI was not used in the creation of this manuscript.
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