Perceived Benefit, Policy Incentive and Farmers’ Organic Fertilizer Application in Protected Areas

Abstract

The green production behaviors of farmers in protected areas (PAs) can reduce environmental disturbances and contribute to the effectiveness of PAs. Based on a survey of 708 farmers in Wuyishan National Park (WNP) and Crested Ibis Nature Reserve (CINR), we studied the influence of perceived benefit and policy incentive on farmers’ organic fertilizer application. Results: (1) Perceived benefit, subsidies policy, and propaganda policy significantly promoted farmers’ organic fertilizer application, with perceived benefit having the strongest effect, followed by subsidies policy. The influence of restriction policy was not significant. (2) Perceived benefit, subsidies policy, and propaganda policy indirectly influenced organic fertilizer application through ecological awareness. (3) In WNP, perceived benefit had the strongest effect on organic fertilizer application, and subsidies policy significantly enhanced farmers’ ecological awareness and influenced their behaviors. In contrast, subsidies policy had the strongest effect on organic fertilizer application, but did not significantly enhance farmers’ ecological awareness in CINR. These results offer valuable insights for promoting green production behaviors of farmers in PAs. This study implies that there must be a pathway for the realization of ecological value in ecological protection. Ecological value realization is the endogenous motivation for farmers’ sustainable green production behaviors. However, government subsidies and guidance are also essential in the early stage.

1. Introduction

Protected areas (PAs) have become important models for biodiversity and ecosystem protection [1]. The construction of national parks is essential for achieving ecological civilization in China. However, PAs are not only expanding areas and enhancing ecosystem services. Alleviating conflicts between conservation and development is still a core challenge [2]. The contradiction is reflected in the development of socio-economic and environmental protection, such as the impact caused by anthropogenic activities in the economic development of regions [3]. This can influence the goals and effectiveness of PAs.

As a large agricultural country, China uses 2.73 times more fertilizers for crops than the world average [4]. Many activities of the farmers in PAs have a negative impact on the environment, such as the application of chemical fertilizers and pesticides. Excessive chemical fertilizers and pesticides have polluted water, soil, and the atmospheric environment, resulting in serious non-point source pollution [5]. It can also damage or even kill rare species [6]. Therefore, farmers’ non-green production behaviors are an important threat to ecosystem and biodiversity conservation.

Farmers’ behaviors determine the level of green development. It is especially important to promote farmers’ green production behaviors in PAs. There are a great number of studies on the green production behaviors of farmers. The influencing factors are divided into four main categories, individual and family characteristics, government policies, and market factors and farmers’ perceptions. In terms of individual and family characteristics, studies show that age, gender, education, labors and off-farm income ratio can influence farmers’ green production behaviors [7,8,9,10]. Among government policies, studies show that government subsidies and government propaganda will promote farmers’ adoption of green production behaviors [7,11,12]. In terms of government restriction, research argues that restriction policy has a significant impact on the willingness to adopt green production behaviors, while others have found that restriction policy does not significantly promote green production behaviors [13,14] and is less effective in restraining farmers’ behaviors. As for market factors, perceived benefits are the main factor influencing green production behaviors, which reflects farmers’ rational economic motivation [7,15]. At the same time, costs are not conducive to green production behaviors [16,17]. Among farmers’ cognition, studies show that farmers’ ecological cognition and environmental crisis awareness promote green production behaviors [12,18].

A larger number of studies have conducted empirical analyses and laid the foundation for research. However, it is still worth exploring how farmers adopt green production behaviors in PAs. The objective of this study is to understand the mechanism of the green production behavior of farmers in the context of enjoying the benefits of protecting the environment, while being influenced by policies. By including intrinsic benefit perceptions and extrinsic policies, we explore the intrinsic motivation of farmers’ green production behavior, as well as differences in the role of certain factors.

Therefore, in the case of organic fertilizers, we use perceived benefit, policy incentive, ecological awareness and other variables to reveal the generation mechanism of farmers’ green production behaviors. Three main issues are explored: (1) evaluating the impact of perceived benefit, restriction policy, subsidies policy, and propaganda policy on farmers’ organic fertilizer application; (2) identifying the impact of these factors on organic fertilizer application in WNP and CINR; (3) clarifying the mediating role of ecological awareness. This study elucidates the endogenous dynamics of organic fertilizer applications for farmers in the context of policy restraints and beneficial environments. Based on this, we propose recommendations to promote green development in PAs.

There are two novelties in this paper: (1) it explores the intrinsic motivation of farmers’ organic fertilizer application; (2) by reflecting the environment value, it is an exploration of the transformation of the ecological value of protected areas. It provides scientific evidence to stimulate the endogenous motivation of farmers’ organic fertilizer application and provides important insights for coordinating the protection and development in Pas, and thus promoting farmers’ green production behaviors in PAs.

2. Theoretical Analysis Framework

According to the theory of the “rational man”, farmers’ behavior is a choice following cost–benefit analysis and taking into account their own endowment [15,19]. The motivation of farmers’ behaviors is the pursuit of economic returns [20]. If the benefits of organic fertilizer application are higher than those of traditional production, farmers tend to adopt such behaviors. Also, government policies play an important role in farmers’ organic fertilizer application. PAs are important areas for biodiversity and ecosystem conservation. First of all, in order to reach the goal of protection, the government enacts laws and policies to restrain farmers’ production behaviors. If farmers violate the rules, they will be punished, and they will make rational choices regarding benefits and costs [21]. Secondly, because of the high costs of organic fertilizer application [17], the government can provide subsidies to offset costs. This will increase farmers’ expected returns and motivate them to adopt organic fertilizer [22]. Finally, the publicity for ecological protection and environment management can increase farmers’ inherent cognition and is conducive to organic fertilizer application [23]. To sum up, perceived benefit, subsidies policy, restriction policy, and propaganda policy can directly promote farmers’ organic fertilizer application.

However, in addition to direct impacts, there may also be indirect impacts. Due to the development of green industries in PAs, the perceived benefits brought by a good environment will deepen our understanding of ecological protection and economic development, enhance ecological awareness, and promote the application of organic fertilizer. In addition, the restriction policy makes farmers realize the importance of environmental protection. It leads farmers to change crude production methods and standardize their production behaviors [14]. The subsidy policy can make farmers clearly recognize the environmental problems and policy guidance, enhance their ecological cognition, and thus promoting the organic fertilizer application [24]. At the same time, environmental education in PAs will increase the knowledge and awareness of the importance of environmental protection. It will also help farmers understand the seriousness of current pollution and enhance their ecological awareness, which will indirectly influence their production behaviors [25].

Therefore, this study believes that perceived benefit, restriction policy, subsidies policy, and propaganda policy all directly affect farmers’ organic fertilizer application. Additionally, these also affect their behaviors through ecological awareness. Based on this, our study establishes an analytical framework (Figure 1) and verifies the above argument through empirical analysis.

3. Materials and Methods

3.1. Study Area

The study areas included Wuyishan National Park (WNP) and the Crested Ibis Nature Reserve (CINR), as shown in Figure 2. WNP was established as a pilot national park in 2016 and officially established in 2021. WNP preserves the primary forest ecosystem. The forest ecosystem is the most intact, typical, and largest area in the middle subtropical zone and is located along the same latitude. It is a key area for global biodiversity conservation. The livelihoods of 80% of the community are based on tea, which is the main source of income for the local people. The contradiction between conservation and development is prominent here. To address this effectively, ecological tea gardens were developed, mainly by adopting organic fertilizers, which in turn improved the quality of the tea and enhanced market recognition. This increased ecological protection for the national park and provided significant perceived benefits for farmers.

The CINR was approved in 2005. The main object of protection is the Crested Ibis, which is a famous bird in the CINR, and its critical habitat, where most local residents grow rice. The Crested Ibis mostly forages in artificial or natural wetlands, such as rice fields, streams, rivers, and swamps. Chemical fertilizers have a great impact on the survival environment of the Crested Ibis, with the excessive use of fertilizers causing the bird’s infertility. In order to protect the population of Crested Ibis, the local government strictly restricts the application of chemical fertilizers and pesticides. This puts ecological costs on farmers. In this case, through government policy support, Yangxian County focuses on building the “Crested Ibis Brand”, planting organic rice, and developing other green products. It aims to promote the perceived benefits of ecological conservation and achieve a “win-win” situation for both conservation and development.

3.2. Data Collection and Variable Selection

Survey data were collected from March to April 2022 and in July 2023. We designed a structured questionnaire and conducted a social survey of farmers in WNP and CINR, by combining stratified sampling with random sampling. By combining pre-survey and communication with local forestry and protected areas departments, representative communities were selected. Specifically, 10 communities were selected in WNP, with about 25 farming households randomly selected from each community; 16 communities were selected in CINR, with about 30 farming households randomly selected from each community. A total of 708 questionnaires were valid, including 245 from WNP and 463 from CINR.

The questionnaire mainly covered basic demographic information, family resource conditions, production and management situations, and farmers’ cognition. The investigators were experienced graduate students. Before the survey, the questionnaire designer conducted questionnaire training for the investigators to ensure that there was a consistent understanding of the questionnaire; in addition, the designer answered the relevant questions raised by the investigators. Each questionnaire took 30–40 min. Before asking the questions, the investigators explained the purpose and questions in the survey to the participants to minimize potential miscommunication.

The adoption of organic fertilizer and the amount of organic fertilizer were considered dependent variables. The first variable measures whether farmers adopt organic fertilizer, and the second variable, which is different from those in the existing research, measures the degree of organic fertilizer as a ratio of organic fertilizers to total fertilizers. There are four core explanatory variables. These variables were measured with reference to Yang et al., Xie and Huang, Bopp et al. and Zhang et al. [7,14,26,27]. The detailed breakdown was as follows: “I believe that adopting organic fertilizer can increase income” represented perceived benefit, “Government subsidies of organic fertilizer are high” represented subsidies policy, “Government restriction of organic fertilizer is high” represented restriction policy, and “Government propaganda of organic fertilizer is high” represented propaganda policy. “Organic fertilizer application is good for the environment” represented ecological awareness. Ecological awareness was the intermediary variable between perceived benefit, policy factors, and farmers’ behaviors. These variables were measured using a 5-point Likert scale (1 to 5 scale, from strongly disagree to strongly agree).

Based on existing data, we also controlled for variables such as individual characteristics, family characteristics, and the cost perception of organic fertilizer application. Among these, cropland area and forestland area were evaluated logarithmically to reduce heteroscedasticity and to better fit the model. All variables and descriptive statistics are shown in

Table 1. Variable definitions and measurements.

Variable	Abbreviation	Definition	Measurement	Min	Max
The adoption of organic fertilizer	AOF	If the farmer has adopted organic fertilizer.	1 = Yes, 0 = No	0	1
The degree of organic fertilizer	DOF	The ratio of organic fertilizers to total fertilizers.	Actual values	0	1
Perceived benefit	PB	I believe that adopting organic fertilizer can increase income.	“1–5” stands for “strongly disagree to strongly agree”	1	5
Subsidies policy	SP	Government subsidies of organic fertilizer are high.	“1–5” stands for “strongly disagree to strongly agree”	1	5
Restriction policy	RP	Government restriction of organic fertilizer is high.	“1–5” stands for “strongly disagree to strongly agree”.	1	5
Propaganda policy	PP	Government propaganda of organic fertilizer is high.	“1–5” stands for “strongly disagree to strongly agree”.	1	5
Ecological awareness	EA	Organic fertilizer application is good for the environment	“1–5” stands for “strongly disagree to strongly agree”.	1	5
Gender	GEN	Gender of household head.	1 = male; 0 = female	0	1
Age	AGE	Age of household head.	Actual values	26	88
Education	EDU	Years of education of the household head.	Actual values	0	16
Health	HEA	Health condition of household head.	1 = good; 2 = average; 3 = minor disease; 4 = serious disease	1	4
Labour	LAB	The amount of household labourers	Actual values	0	10
Off-farm income ratio	OIR	Household non-farm income as a percentage of total income	Actual values	0	1
Cropland area	CA	Total cropland area (ha)	Actual values	0	8
Forestland area	FA	Total forestland area (ha)	Actual values	0	24.9
Protected area	PA	Living inside PAs	1 = Yes; 0 = No	0	1
Cost increasing	CI	I believe that organic fertilizer application will increase cost.	“1–5” stands for “strongly disagree to strongly agree”.	2	5
Risk preference	RP	Level of risk preference.	“1–10” stands for “low to high”	1	10
Province	PRO	The province is Shaanxi	1 = Yes; 0 = No	0	1

, and each variable is represented by its abbreviation in the following tables.

3.3. Methods

We used a logit model to study the factors influencing farmers’ adoption of organic fertilizer. The observed dependent variable is a discrete dichotomous variable (0,1) that does not meet the application conditions of the traditional least squares (OLS) method, assuming that the dependent variable is linearly continuous. Therefore, the maximum likelihood estimation (MLE) was used in this study. The specific model expression is given by Equation (1).

$$\mathit{log}it\left(P(\left.Y=1\right|x_1,\cdots ,x_n)\right)={\beta }_0+{\beta }_1{x}_1+\cdots {\beta }_n{x}_n$$

(1)

Y is a dummy variable. Y = 1 means that the farmer adopts organic fertilizer, and Y = 0 means that the farmer does not adopt organic fertilizer; $x_1\dots \dots x_n$ indicates the characteristic variables that influence farmers’ organic fertilizer application; β is the parameter to be estimated.

The Tobit model was used to analyze the factors influencing the degree of organic fertilizer. If farmers do not adopt organic fertilizer, the ratio of organic fertilizer to total fertilizers is 0. It can be seen that the dependent variable is roughly continuously distributed at higher than zero, but contains a proportion of observations that are zero. Therefore, Y is limited dependent variable, in which case, the Tobit model is valid [5,28,29]. The model is expressed by Equation (2).

$$\left\{\begin{array}{c}{Y}^{*}={\beta }_0+{\beta }_1{x}_1+\cdots {\beta }_n{x}_n+\mu \\ Y=\mathrm{Max}\left(0,Y^{*}\right)\end{array}\right.$$

(2)

$Y^{*}$ is the potential variable, $x_1\dots \dots x_n$ are the characteristic variables that affect the degree of organic fertilizer, and ${\beta }_0\dots \dots {\beta }_n$ are the parameters to be estimated, μ is the random disturbance term that follows the standard normal distribution.

A structural equation model (SEM) was used to test the intermediary effect. The estimation of the structural equation model is statistically unbiased; therefore, it is widely used in statistical inference research, especially in impact path analysis [30,31]. Structural equations were used to verify the mediating role of ecological awareness between the main independent variables and the dependent variables. There are no potential variables in this model, and all variables are directly observable explicit variables. The general expression of the structural equation model with explicit variables is shown in Equation (3).

$$Y=BY+\Gamma X+\xi$$

(3)

Y represents the dependent variable, which is endogenous; X is the explanatory variable of the model, which is exogenous; $\xi$ is an error term; and $B$ and $\Gamma$ are coefficient matrices.

4. Results

4.1. Analysis of Differences in Farmers’ Organic Fertilizer Application

We briefly described farmers’ adoption of organic fertilizer (

Table 2. Descriptive statistical analysis.

Variable	Full Sample		Inside PAs		Outside PAs		Diff 1	WNP		CINR		Diff 2
	Mean	SD	Mean	SD	Mean	SD		Mean	SD	Mean	Mean
AOF	0.453	0.498	0.524	0.500	0.383	0.487	0.000	0.433	0.496	0.464	0.499	0.420
DOF	0.243	0.362	0.316	0.409	0.170	0.292	0.000	0.354	0.445	0.184	0.294	0.000

Note: Diff 1 denotes the difference between the mean of the sample outside and insider PAs; Diff 2 denotes the difference between the mean of the WNP and CINR.

). Among the sample farmers, 45.3% chose to adopt such behaviors, which accounted for 24.3% of the total fertilizers. This indicates that there is still room for organic fertilizer application. The proportion of farmers using organic fertilizer was higher inside PAs, accounting for 52.4%, while the proportion outside the PAs was 31.6%.

The probability of farmers inside PAs adopting organic fertilizer was significantly higher than that of farmers outside PAs. The ratio of organic fertilizers to total fertilizers was 31.6% inside PAs and 17% outside PAs. The degree of organic fertilizers inside the PAs was significantly higher than that outside the PAs. By comparing the farmers’ organic fertilizer application in and around WNP with that of CINR, it is found that the proportion of organic fertilizer used by farmers in WNP was significantly higher than that of CINR, which has a significant relationship with the management intensity of PAs and agricultural practice.

4.2. Factors Influencing Farmers’ Organic Fertilizer Application

Correlations among independent variables could distort the model estimation. Therefore, we calculated the variance inflation factor (VIF) to assess multicollinearity. The calculated VIF was less than 2 and the average VIF was 1.22 (

Table 3. Multicollinearity test.

Variables	VIF
PB	1.18
SP	1.19
RP	1.07
PP	1.35
AGE	1.37
GEN	1.10
EDU	1.24
HEA	1.19
LAB	1.07
OIR	1.19
CA	1.12
FA	1.34
PA	1.05
CI	1.10
RP	1.13
PRO	1.86
Mean VIF	1.22

), implying that there was no significant multicollinearity between the independent variables.

We estimated the impact of perceived benefit, subsidies policy, restriction policy and propaganda policy on organic fertilizer application, using Stata 15.0 software. The results are shown in

Table 4. Regression results impacting farmers’ organic fertilizer application.

Variable	Model 1		Model 2
	AOF		DOF
	Coefficient	Marginal Effect	Coefficient
PB	0.862 ***	0.152 ***	0.116 ***
	(0.125)	(0.019)	(0.015)
RP	0.007	0.001	0.004
	(0.097)	(0.017)	(0.012)
SP	0.673 ***	0.118 ***	0.051 ***
	(0.113)	(0.018)	(0.013)
PP	0.533 ***	0.094 ***	0.031 **
	(0.130)	(0.022)	(0.016)
AGE	0.007	0.001	0.000
	(0.010)	(0.002)	(0.001)
GEN	0.234	0.041	0.001
	(0.274)	(0.048)	(0.034)
EDU	0.032	0.006	0.005
	(0.033)	(0.006)	(0.004)
HEA	−0.101	−0.018	−0.027
	(0.132)	(0.023)	(0.017)
LAB	−0.062	−0.011	−0.004
	(0.070)	(0.012)	(0.009)
OIR	−0.089	−0.016	−0.044
	(0.291)	(0.051)	(0.038)
CA	0.307 ***	0.054 ***	0.009
	(0.113)	(0.019)	(0.015)
FA	−0.158 **	−0.028 **	−0.002
	(0.069)	(0.012)	(0.009)
PA	0.546 ***	0.096 ***	0.099 ***
	(0.185)	(0.032)	(0.024)
CI	−0.292 **	−0.051 **	−0.019
	(0.126)	(0.022)	(0.016)
RP	0.186 ***	0.033 ***	0.023 ***
	(0.041)	(0.007)	(0.005)
PRO	−0.176	−0.031	−0.135 ***
	(0.263)	(0.046)	(0.033)
Constant	−8.471 ***		−0.474 ***
	(1.203)		(0.139)
Observations	708		708
Chi-square	233.1		217.85
Prob > chi2	0.000		0.000
Pseudo R2	0.239		0.382

Note: *** p < 0.01, ** p < 0.05. The values in parentheses are standard errors.

. Model 1 analyzed the factors influencing the adoption of organic fertilizer. Perceived benefit, subsidies policy, and propaganda policy positively influenced the adoption of organic fertilizer at a significance level of 1%. The restriction policy positively influenced the adoption of organic fertilizer; however, this effect was not significant. Marginal effects indicated that the probability of adopting organic fertilizer increased by 15.2%, 11.8%, and 9.4% for a 1-unit increase in the perception of perceived benefits, green subsidies and propaganda policies, respectively. The increase in cost negatively influenced the adoption of organic fertilizer at a significance level of 5%. The increasing cost was not conducive to adopting organic fertilizer. The estimated marginal effect indicated that the probability of organic fertilizer application increased by 9.6% if farmers were located in PAs. In addition, risk preference positively influenced the adoption of organic fertilizer at a significance level of 1%. Crop land positively influenced farmers’ behaviors at a significant level of 1% and negatively influenced forest land at a 5% significant level.

The Tobit model was used to analyze the factors influencing the degree of organic fertilizer, and the results are shown in Model 2. The perceived benefit and subsidies policy promoted organic fertilizer application at a significance level of 1%. Propaganda policy positively influenced the degree of organic fertilizer at a 5% level. Similarly, risk preference and PA policy positively influenced the degree of organic fertilizer at a 1% level. The factor of province had a negative influence on organic fertilizer application, which indicated that the proportion of organic fertilizers is high in WNP. The increasing cost negatively influenced the degree of organic fertilizer; however, this effect was not significant.

Overall, perceived benefit, subsidies policy, and propaganda policy positively promoted farmers’ organic fertilizer application. High-cost perception was not conducive to organic fertilizer application. Farmers in PAs apply more organic fertilizer. The higher the risk preference of the farm, the higher the adoption of organic fertilizer.

To compare WNP and CINR, the regression results are presented in

Table 5. Regression results impacting farmers’ organic fertilizer for different regions.

	WNP			CINR
Variable	Model 3		Model 4	Model 5		Model 6
	AOF		DOF	AOF		DOF
	Coefficient	Marginal Effect	Coefficient	Coefficient	Marginal Effect	Coefficient
PB	1.013 ***	0.163 ***	0.133 ***	0.784 ***	0.133 ***	0.088 ***
	(0.226)	(0.031)	(0.029)	(0.168)	(0.026)	(0.016)
RP	0.345 *	0.056 **	0.018	−0.203	−0.034	−0.015
	(0.177)	(0.028)	(0.027)	(0.129)	(0.022)	(0.013)
SP	0.349 *	0.056 *	0.056 *	0.914 ***	0.155 ***	0.065 ***
	(0.199)	(0.031)	(0.030)	(0.154)	(0.022)	(0.014)
PP	0.734 ***	0.118 ***	0.042	0.465 ***	0.079 ***	0.040 ***
	(0.251)	(0.038)	(0.038)	(0.160)	(0.027)	(0.016)
AGE	−0.026	−0.004	−0.003	0.022 *	0.004 *	0.001
	(0.017)	(0.002)	(0.003)	(0.013)	(0.002)	(0.001)
GEN	1.065	0.171	0.074	0.090	0.015	−0.005
	(0.678)	(0.170)	(0.091)	(0.323)	(0.055)	(0.032)
EDU	0.007	0.001	0.003	0.032	0.005	0.002
	(0.069)	(0.011)	(0.010)	(0.040)	(0.007)	(0.004)
HEA	0.338	0.054	0.033	−0.163	−0.028	−0.034 **
	(0.325)	(0.052)	(0.046)	(0.152)	(0.026)	(0.016)
LAB	−0.087	−0.014	−0.001	−0.077	−0.013	−0.001
	(0.111)	(0.018)	(0.017)	(0.105)	(0.018)	(0.011)
OIR	−0.338	−0.054	−0.037	−0.547	−0.093	−0.142 ***
	(0.439)	(0.070)	(0.064)	(0.483)	(0.081)	(0.050)
CA	0.328 **	0.053 **	0.049 **	0.252	0.043	−0.065 ***
	(0.164)	(0.026)	(0.025)	(0.198)	(0.033)	(0.020)
FA	−0.041	−0.007	0.013	−0.393 ***	−0.066 ***	−0.014
	(0.114)	(0.018)	(0.016)	(0.111)	(0.018)	(0.011)
PA	0.750 **	0.121 ***	0.144 ***	0.524 **	0.089 **	0.057 **
	(0.341)	(0.053)	(0.053)	(0.237)	(0.039)	(0.024)
CI	−0.290	−0.047	−0.046	−0.375 **	−0.063 **	−0.022
	(0.258)	(0.041)	(0.038)	(0.160)	(0.026)	(0.016)
RP	0.421 ***	0.068 ***	0.053 ***	0.156 ***	0.026 ***	0.016 ***
	(0.098)	(0.014)	(0.013)	(0.048)	(0.008)	(0.005)
Constant	−10.781 ***		−0.803 **	−7.779 ***		−0.292 *
	(2.624)		(0.333)	(1.625)		(0.155)
Observations	245			463
Chi-square	97.6		84.93	170.35		137.99
Prob > chi2	0.000		0.000	0.000		0.000
Pseudo R2	0.291		0.286	0.266		0.770

Note: *** p < 0.01, ** p < 0.05, * p < 0.1. The values in parentheses are standard errors.

. For WNP, perceived benefit and propaganda policy had a strong influence on adopting organic fertilizer. For CINR, perceived benefit and subsidies policy had a strong influence on the adoption of organic fertilizer. In contrast to WNP, the effects of perceived benefit are weaker, and the effects of subsidies are stronger. In WNP, restriction policy positively influenced the adoption of organic fertilizer at the 5% level. However, it negatively influenced behavior in CINR, but not significantly. Increasing cost negatively influenced the adoption of organic fertilizer at a significance level of 5% in CINR. In WNP, there is also a negative effect, but it is not significant.

4.3. Robustness Test

To ensure the validity of the models, we performed a robustness test using Stata 15.0 for OLS. The regression results are presented in

Table 6. Robustness test.

Variable	Total		WNP		CINR
	AOF	DOF	AOF	DOF	AOF	DOF
PB	0.147 ***	0.116 ***	0.144 ***	0.133 ***	0.127 ***	0.088 ***
	(0.021)	(0.015)	(0.032)	(0.030)	(0.028)	(0.017)
RP	0.001	0.004	0.053 *	0.018	−0.027	−0.015
	(0.017)	(0.013)	(0.030)	(0.027)	(0.022)	(0.013)
SP	0.116 ***	0.051 ***	0.056	0.056 *	0.153 ***	0.065 ***
	(0.019)	(0.014)	(0.034)	(0.031)	(0.023)	(0.014)
PP	0.090 ***	0.031 *	0.122 ***	0.042	0.072 ***	0.040 **
	(0.022)	(0.016)	(0.043)	(0.039)	(0.026)	(0.016)
Constant	−0.909 ***	−0.474 ***	−1.044 ***	−0.803 **	−0.828 ***	−0.292 **
	(0.192)	(0.141)	(0.377)	(0.344)	(0.259)	(0.158)
Control variables	Controlled	Controlled	Controlled	Controlled	Controlled	Controlled
Observations	708	708	245	245	463	463
Prob > F	0.000	0.000	0.000	0.000	0.000	0.000
R-squared	0.277	0.265	0.321	0.293	0.293	0.258

Note: *** p < 0.01, ** p < 0.05, * p < 0.1. The values in parentheses are standard errors.

. The test shows that the results are generally consistent with the above, indicating that the results had good robustness.

4.4. Intermediary Effect Analyses

Structural equations were tested for mediating effects. The fit indices for structural equation explicit variable models are generally good, so the focus is on analyzing effects [32]. Since restriction policy did not have a significant effect on the adoption of organic fertilizer, the intermediary effect was not tested.

The results are listed in

Table 7. Intermediary effect analyses in full sample.

Variable		Model 7	Model 8
	EA	AOF	DOF
EA		0.219 ***	0.191 ***
		(0.033)	(0.034)
PB	0.118 ***	0.244 ***	0.302 ***
	(0.036)	(0.032)	(0.033)
SP	0.110 ***	0.194 ***	0.096 ***
	(0.038)	(0.034)	(0.035)
PP	0.176 ***	0.126 ***	0.008
	(0.037)	(0.034)	(0.036)
Control variables	Controlled	Controlled	Controlled
Observations	708	708	708

Note: *** p < 0.01. The values in parentheses are standard errors.

. Figure 3 shows the intermediary path. As can be seen from the results, perceived benefit, subsidies policy, and propaganda policy significantly and positively influenced farmers’ ecological awareness. Therefore, perceived benefit can enhance farmers’ ecological awareness. Government subsidies and propagation can also enhance farmers’ ecological awareness. In Model 7, perceived benefit, subsidies policy, and propaganda policy significantly and positively influenced the adoption of organic fertilizer. Additionally, ecological awareness also positively influenced behavior at a 1% level. Model 8 indicates that perceived benefit and subsidies policy significantly and positively influenced the degree of organic fertilizer. Ecological awareness also positively influenced the degree of organic fertilizer at the 1% level. These results suggest that ecological awareness has a mediating effect.

Overall, perceived benefit, subsidies policy, and propaganda policy directly promoted farmers’ organic fertilizer application. In addition, these indirectly influenced organic fertilizer application through ecological awareness.

The mediating effects were tested separately for WNP and CINR, and the results are shown in

Table 8. Intermediary effect analyses in different PAs.

Variable		WNP			CINR
		Model 9	Model 10		Model 11	Model 12
	EA	AOF	DOF	EA	AOF	DOF
EA		0.268 ***	0.299 ***		0.200 ***	0.155 ***
		(0.056)	(0.056)		(0.040)	(0.042)
RP	−0.072	0.117 **	0.058
	(0.061)	(0.055)	(0.056)
PB	0.214 ***	0.262 ***	0.268 ***	0.144 ***	0.174 ***	0.224 ***
	(0.060)	(0.055)	(0.055)	(0.048)	(0.043)	(0.044)
SP	0.191 ***	0.078	0.089	0.046	0.274 ***	0.203 ***
	(0.060)	(0.057)	(0.057)	(0.050)	(0.042)	(0.045)
PP	0.061	0.189 ***	0.094 *	0.144 ***	0.112 ***	0.106 **
	(0.061)	(0.053)	(0.055)	(0.048)	(0.042)	(0.044)
Observations	245	245	245	463	463	463

Note: *** p < 0.01, ** p < 0.05, * p < 0.1. The values in parentheses are standard errors.

. Figure 4 shows the intermediary path of WNP. As can be seen from Model 9 and Model 10, perceived benefit and subsidies policy positively influenced farmers’ ecological awareness at the 1% level. Additionally, ecological awareness and perceived benefit significantly and positively influence the application of organic fertilizer. However, the effect of subsidies policy was not significant. Ecological awareness played a partly mediating role in the effect of perceived benefit on organic fertilizer application and a fully mediating role in the effect of subsidies policy on organic fertilizer application. It was worth noting that restriction policy had a negative effect on farmers’ ecological awareness.

Figure 5 shows the intermediary path of CINR. According to Model 11 and Model 12, perceived benefit and propaganda policy significantly and positively influenced farmers’ ecological awareness. Additionally, ecological awareness, perceived benefit, and propaganda policy positively influenced organic fertilizer application at a 1% level. Therefore, ecological awareness played a partly mediating role in the effect of perceived benefit and propaganda policy on farmers’ organic fertilizer application.

5. Discussion

Encouraging and ensuring farmers’ green production behaviors in PAs is important. This study examined the effects of perceived benefit, subsidies policy, restriction policy, and propaganda policy on farmers’ organic fertilizer application and focused on the mediating role of ecological awareness. The crux of the paper is that the product premium for the development of green agriculture in PAs allows farmers to recognize the value of protecting the environment, which is the endogenous motivation for farmers and more effective than relying solely on policy constraints and incentives. However, in the early stage, government subsidies and propaganda are indispensable. This study provides meaningful insights into promoting farmers’ green production behaviors.

Perceived benefit, subsidies policy, and propaganda policy positively promoted farmers’ organic fertilizer application, with perceived benefit having the strongest effect, followed by subsidies policy. This indicates that economic benefit is the primary factor in promoting farmers’ organic fertilizer application [13,21]. This is in line with their economic motivation to pursue profits. The influence of restriction policy was not significant, and some previous studies reached the same conclusion [14]. It is clear that a single restraint does not always achieve good results. However, some studies have concluded that restraint policy significantly and positively influences green production behavior. This may be related to the degree of regulation [13,33]. The increasing cost negatively influenced the adoption of organic fertilizer but had no significant influence on the degree of organic fertilizer. This implies that farmers pay attention to short-term benefits of green production at an early stage because uncertainty and the high cost of organic fertilizer application are not conducive to its adoption [33]. However, once farmers have accepted organic fertilizer, they are more focused on long-term benefits. Therefore, increasing cost did not have a significant impact on the degree of organic fertilizer. Perceived benefit, subsidies policy, and propaganda policy significantly increased ecological awareness, which in turn promoted farmers’ organic fertilizer application.

The similarity between the two PAs was that perceived benefit both directly and indirectly promoted farmers’ organic fertilizer application significantly enhancing farmers’ ecological awareness. Differently, perceived benefit had the strongest effect on organic fertilizer application, and subsidies policy significantly enhanced farmers’ ecological awareness and influenced their behaviors in WNP. In contrast, subsidies policy had the strongest effect on organic fertilizer application, but did not significantly enhance farmers’ ecological awareness in CINR. Aside from that, increasing cost significantly and negatively influenced the adoption of organic fertilizer in CINR, but in WNP, there was no such significant effect.

The fundamental reason for these differences is the different ecological premiums. Both WNP and CINR are developing green industries to protect the environment, resulting in a premium for products. However, the degree of premium varies. In WNP, by adopting green production techniques to create ecological tea gardens and improve the quality of tea by one grade, the price of the tea has increased by about 10% per kilogram [34]. Consumers have a high degree of recognition and trust for tea produced in WNP, resulting in significantly higher prices and bringing a higher product premium. CINR has created the “Crested Ibis brand” and planted Crested Ibis rice, mainly by increasing the adoption of organic fertilizer and reducing the use of chemical fertilizers. The unit price of Crested Ibis rice is about 60% higher than that of ordinary rice, but it is only CNY 1.614 higher. The total income benefit to farmers is not significant. In WNP, there is a higher degree of premium and a strong drive of perceived benefits. Thus, the perceived benefit effect is greater. Meanwhile, comparing the subsidy is more of a welfare incentive for farmers’ green behaviors; while in CINR, the subsidy is more of a compensation for the cost. Therefore, subsidies can enhance farmers’ ecological awareness in WNP, while that of CINR cannot. Similarly, increasing cost had a stronger negative impact on farmers’ green behaviors in CINR than those in WNP.

Therefore, the ecological value is more fully reflected and realized through the market in WNP. Farmers recognize the importance of environmental protection. The value provided by an excellent environment is the endogenous motivation for farmers’ organic fertilizer application. In CINR, the realization of ecological value is relatively low, which reflects the importance of subsidies. Farmers’ organic fertilizer application needs to be subsidized and regulated. There must be a pathway for the realization of ecological value in ecological protection, so as to guarantee the sustainability of farmers’ green production behaviors. However, government subsidies and guidance are also essential in the early stage. In conclusion, the results reflect the consistency between industrial policy objectives and conservation objectives, and the mutual benefits between natural ecosystems and agricultural and forestry systems [35]. Additionally, this study also confirms that the development of green industry can effectively coordinate the conservation and development of PAs [36,37,38].

This study has some limitations. There are differences in standards within the industry owing to different products, which may have some impact on the results. Additionally, our results refer to one year of data; therefore, the impacts of weather annual price changes in tea or rice on the market cannot be assessed, nor can the impact of input prices.

6. Conclusions and Policy Recommendations

The results of the study show that benefit realization is the important factor for farmers’ green production behaviors in the case of organic fertilizer. Therefore, for sensitive regions such as PAs, it is important to emphasize the realization of ecological value along with ecological protection. This will enable farmers to realize the benefits of a good environment, and thus adopt the green production behaviors actively. At the same time, government subsidies and guidance are also very important. In response to these findings, the following policy recommendations are made:

Increasing market recognition of green products: By building brands and labels, market recognition can be enhanced, and product premiums can be increased. The income of farmers demonstrating green production behaviors should be increased through premiums for ecological products. This is an important way to promote farmers’ green production behaviors in PAs, as it links income with the environment, making farmers aware of the value of the environment.

Enhancing green subsidies: The government should provide more support for products with a lower premium, such as financial subsidies and technical support. This can offset production costs for farmers.

Strengthening environmental education and providing guidance: A comprehensive environmental education system for PAs should be built to increase the awareness of the relationship between the ecological and economic systems. It is important to emphasize that conservation and development are not opposed to each other and that it is possible to achieve a “win-win” situation. This would enhance both farmers’ ecological awareness and their enthusiasm to participate in ecological protection.