Exploring Impacts of Perceived Value and Government Regulation on Farmers’ Willingness to Adopt Wheat Straw Incorporation in China

Abstract

In China, wheat straw incorporation (WSI) is the most popular way of utilizing wheat straw. WSI can manage agricultural residues to improve soil quality and avoid open burning in fields. However, farmers have been reluctant to implement WSI, which hinders sustainability. This study collected first-hand data about 1027 wheat growers, and used a Logit model to explore the influence of perceived value, government regulation, and their interaction on farmer willingness to adopt WSI. The results also reveal the differences between farmers with different farm sizes, as well as differences in other characteristics impacting WSI willingness. The study found that implementing government regulations and increasing the positive perceived value by farmers can effectively improve farmer willingness to adopt WSI. For example, government subsidies and farmers’ perceptions about cost-related risks impact farmer willingness. There is an interaction effect between government regulation and perceived value with respect to farmer willingness. Policy outreach could effectively strengthen the positive impacts of farmers’ perception of social benefits on farmer willingness. Government subsidies could effectively weaken the negative impacts of farmers’ perception of cost-related and time-related risks on farmer willingness. Farmers with different sized farms are influenced differently by government regulation and perceived value. The willingness of large-scale farmers to adopt WSI is generally influenced by government regulation and perceived value; in contrast, the willingness of traditional farmers is mainly influenced by policy outreach and perceived economic benefits.

1. Introduction

Agricultural production depends highly on resource conditions and, in turn, directly affects the ecological environment. Wheat straw is a byproduct of agricultural production, with important reuse value [1]. The utilization of crop straw relates closely to the green development of agriculture and improvements in rural ecological environments. However, many developing countries have low straw utilization rates, and open field burning is the most common straw disposal mode. Crop straw is very plentiful in China [2], with an estimated annual production close to 900 million tons. It is estimated that 200 million tons or more go unused [3]. Turning to wheat as an example, China is the largest wheat producer in the world. China produced 140 million tons of wheat straw in 2016; however, approximately 20% was discarded or burned in open fields (Ministry of Environmental Protection of China, 2018). Rapid increases in rural labor costs and changes in the rural energy consumption structure have led many farmers to burn significant amounts of straw in China [4]. This wastes significant resources, and further aggravates the environmental pollution of water, soil, and air [5]. Given this, straw resource utilization is of significant societal interest.

WSI is a protective cultivation technology that avoids the open field burning of wheat straw, improves environmental quality, and improves soil fertility. As an alternative to burning, a proportion of straw could be left on the ground or incorporated mechanically into the soil, which can improve the physical and chemical nature of soil, reduce soil erosion as well as nutrient and water losses [6]. Furthermore, previous studies have shown that WSI can enrich soil organic nitrogen [7] and soil organic carbon [8], and thus improve soil quality and possibly increase yields of subsequent crops [9]. In recent years, the Chinese government has issued a series of policy documents, and has invested significant labor and material resources to encourage farmers to adopt straw incorporation. These include the “pilot project for encouraging the resource utilization of agricultural waste (2016)” and “opinions on innovating system and mechanism to encourage agricultural green development (2017)”. By the end of 2016, approximately 57% of China’ total straw was applied as fertilizer by returning it to the soil (China’s People’s Daily, 2017); this proportion has gradually improved in recent years. However, while China has achieved some success in encouraging straw incorporation, challenges remain, including difficulties in motivating farmers to voluntarily adopt straw incorporation. Open field burning and the random disposal of crop straw are common, and some farmers are still not willing to adopt straw incorporation [10].

Currently, a growing body of literature has investigated farmer’s actual decision-making behavior on straw incorporation. Cost–benefit, technical attributes, and policy effect are the focus of the discussion. Since China belongs to a highly intensive agricultural region [11], most farmers must implement two straw incorporations in a year under the rotation system, leading to many problems involved in carrying out and maintaining actions of crop straw incorporation. For example, crop straw incorporation needs additional mechanical crushing and deep ploughing, which considerably increases the production cost burdened by farmers [12]. Moreover, farmers may encounter obstacles at the technical side. The difficulties in implementing crop straw incorporation includes issues such as poor quality of mechanical crushing and slow decomposition of crop straw. Excessive stalk in soil results in poor root–soil contact, affecting roots taking and crop survival [13]. On the other hand, scholars have found that many farmers seem disinterested in straw incorporation, passively adopting it only because of China’s strict command-and-control policy against straw burning [12,14]. However, these studies overlook farmer willingness and related psychological factors in the analysis of the farmers’ actual decision-making behavior. There is still a lack of clear evidence to understand why/how farmers voluntarily adopt straw incorporation. Scholars have shown that a farmer’s willingness to participate has a significant impact on the sustainability of reusing agricultural waste [15,16]. Intermittent or short-term adoption of straw incorporation does not add the most value; it is when farmers are fully willing to adopt straw incorporation in the long term that it has the most impact and efficacy.

Without considering the psychological issues, we may not fully understand the behaviors of farmers in adopting sustainable practices. Prokopy [17] and Getz [18] found that psychological awareness was an important factor affecting whether farmers adopt best management practices. Sattler [19] also concluded that farmers’ environmental perceptions were an essential factor determining their decision to adopt conservation tillage technology. Wu [20] also concluded that farmers’ judgments and decisions about straw incorporation are largely affected by surrounding farmers. Researchers mainly use classic theories, such as technology acceptance theory, planned behavior theory, and innovation diffusion theory, to explore users’ adoption of a new technology or service [21,22,23,24]. Although these theories provide reference to explain farmers’ behavior, they ignore the individual willingness and the internal causal relationship between individual perception and willingness. Farmers’ attitude was the primary factor affecting their behavioral willingness, while perceived value was the most direct reason for forming farmers’ attitude, which indicated that farmers’ technology adoption willingness was largely influenced by their perceived value. As such, there has been little work to explore farmers’ willingness to adopt WSI, nor the perception factors influencing that willingness. As the main body of agricultural production in China, farmers are vital decision-makers and the final implementors of WSI; the process cannot be smoothly conducted if farmers are unwilling to do it [12]. Farmers need to be truly motivated to continuously adopt WSI technology to reduce the administrative costs.

To address the challenges above, this paper makes the following key contributions to the field. First, this paper creates an analytical framework, integrating perceived value, government regulation, and farmers’ willingness to adopt straw incorporation. The study also identifies the impact of perceived value and government regulation on farmers’ willingness to adopt straw incorporation. Second, the study explores the interactive effect of these two factors on farmers’ willingness. Third, the study explores the differences between farmers with different sized farms with respect to the effect of internal perceptions and external regulation on farmers’ willingness to adopt straw incorporation. Wheat is one of China’s three most plentiful crop straws (rice, wheat, and maize straw). Given the large volume of wheat straw burned in open fields, this study particularly focused on farmers’ willingness to adopt WSI [12].

2. Materials and Methods

2.1. Analytical Framework

A farmer’s willingness to adopt WSI is shaped by both internal perceptions and external regulation. A few studies have explored the impact of attitude, subjective norms, and perceived behavioral control on farmer adoption behavior [25,26,27]; however, the relationship between farmer perception and willingness to adopt WSI has not been addressed. Attitudes impact behavior, and perceived value can directly affect that attitude [28]. A growing number of studies have confirmed the effect of consumers’ perceived value on purchasing behavior; this is generally considered to be a trade-off between two antecedent factors of perceived value: perceived gains (i.e., benefits) and losses (i.e., risks) [29,30,31]. However, few studies have considered the farmer’s perceived value in the WSI decision-making process. A farmer’s choice to adopt WSI is a type of purchasing behavior for an agricultural technology or service. The weighing of perceived benefits and risks informs a farmers’ willingness to participation. Thus, understanding the impact of perceived value on farmers’ willingness to adopt WSI is an area deserving in-depth research.

Turning to the government’s role, due to the strong negative externalities associated with the improper disposal of straw, including straw burning and casual disposal, the government has issued a series of policies to vigorously advocate for straw incorporation [32]. The government has also taken strong command-and-control measures to oversee the open-field burning of straw. Government regulations impact farmer willingness and decisions regarding straw incorporation, because it is a form of public policy where the government intervenes in microcosmic behaviors using two types of regulation: (1) restrictive policies, such as penalties and shutdowns; and (2) motivational policies, such as subsidies and technological training and outreach.

Farmers’ behaviors occur in the context of these government regulatory policies, and analyzing WSI motivation and participation cannot be separated from the current policy landscape. This highlights the need to analyze both factors affecting farmer willingness to adopt WSI: perceived value, and the impact of government regulation. Considering these factors together is important for avoiding omissions and errors in setting the measurement model; it also facilitates the assessment of any interactive effect between perceived value and government regulation on farmer willingness to adopt WSI.

Turning to another dynamic, studies have identified differences in production inputs and technology adoption behaviors between farmers who have different sized farms [33,34]. Farmers operating at different scales may have different attributes with respect to personal appeal, value evaluation, behavioral logic, and other variables. The study compared traditional farmers and scale farmers. Traditional farmers are defined as farmers with a farm size that is smaller than the average farm size the local town; scale farmers are defined as those with farms that are larger than that average level. It is hypothesized that scale farmers may pay more attention to the long-term benefits of agricultural production. WSI is an inter-temporal agricultural technology; as such, farmer differences with respect to scale may be associated with differences in time-based preferences and the uncertainty of benefits. This could, in turn, impact perceived value and the willingness to adopt WSI. This aspect of farmer adoption of WSI has not yet been explored.

Figure 1 is the analytic framework for considering farmer willingness to adopt WSI, constructed from the perspectives of perceived value and government regulation.

2.2. Data Sources

Wheat production regions in China can be divided into three major zones, i.e., the Northern winter wheat district, Southern winter wheat district, and Spring wheat district, which produce, respectively, 65%, 25%, and 10% of China’s total wheat production in 2017 (National Bureau of Statistics of China, 2018). The Northern winter wheat district is mainly located south of the Great Wall, and north of Qinling Mountains and Huaihe River. It is the largest concentrated wheat producing area and consumption area in China. The Southern winter wheat district is mainly located south of Qinling Mountains and Huaihe River. Since most people in this area live on rice, the commodity rate of wheat in this area is high. The Spring wheat district is mainly located north of the Great Wall. The temperature in this area is generally low and the production season is short, so in this area, spring wheat is the main choice which could only be harvested for one time per year.

The three major wheat cropping districts in China are: the Northern winter wheat district, Southern winter wheat district, and Spring wheat district (see Figure 2). The Northern and Southern winter districts are the main wheat cropping areas [35], with primary crop rotation systems that include winter wheat–summer maize and winter wheat–summer rice, respectively. The Northern and Southern districts produced 65% and 25% of China’s total wheat production, respectively, in 2017 (National Bureau of Statistics of China, 2018). Thus, this study was conducted in three provinces aligned with these two main wheat cropping districts: Shandong, Henan, and Anhui provinces. Shandong and Henan Provinces produce more than half of the total wheat in the Northern winter wheat district, and Anhui Province produces approximately one-third of the total wheat in the Southern winter wheat district.

Study data were collected using a questionnaire survey of farmers, conducted by the research team, in the three provinces from July to August 2018. In addition to being large wheat production areas, these provinces are suitable areas and pilot provinces for WSI, and there is known to be significant straw burning there. This makes these provinces effective areas when studying farmer willingness to adopt WSI.

A stratified random sampling method followed by Sharafi et al. [36] was used for selecting the farmers to be surveyed. First, the study team randomly selected 4 counties in each province. Next, in each county, the team randomly selected 3 townships and 3 villages within each township, followed by a random selection of 10 wheat production farmers in each of the villages. The study questionnaire survey conducted with these farmers used face-to-face interviews to survey the head of the household, or the main family members making production decisions. The survey asked about personal characteristics of the respondent, basic household situation, policies and regulation related to WSI technology, WSI participation status, the farmer’s perceived value, and other aspects. A total of 360 questionnaires were distributed in each province, for a total of 1080 distributed questionnaires. After excluding questionnaires with missing data and inconsistent information, 1027 valid questionnaires were obtained, reflecting an effective response rate of 95.1%.

2.3. Model Construction

This study mainly evaluates the impact of perceived value and government regulation on famer willingness to adopt (FWA). Therefore, the explained variable is “FWA”, set as a 0–1 variable in this study (1 is willing; 0 is not willing). A Logit model was applied at the individual level to perform the regression, using the following expression:

$$P_i=F\left(y\right)=\frac{1}{1+e^{-y}}$$

(1)

where P_i is the probability that farmers are willing to adopt WSI. The variable y represents FWA: when y = 1, the farmer is willing to adopt WSI; when y = 0, the farmer is not willing to adopt WSI. In formula (1), y is a linear combination of variables X, Z, and G:

$$y=b_0+\chi X+\lambda Z+\theta G$$

(2)

where X is the control variable, including the respondent’s age, years of education, and household labor and so on; Z is the government regulation variable, including outreach, subsidies, and penalties; G is the perceived value variable, including perceived economic benefits, perceived environmental benefits, perceived social benefits, perceived cost-related risks, and perceived time-related risks. The variable b₀ is a constant.

Next, expressions (1) and (2) are transformed to obtain an expression of the binary Logit model:

$$\ln \frac{P_i}{1-P_i}=b_0+\chi X+\lambda Z+\theta G+\epsilon$$

(3)

where ε is the random error term.

2.4. Variable Selection

Table 1. Definitions and descriptive statistics of the variables.

Variables	Definition	Mean	S.D.	Expected Direction
Willingness to adopt	FWA: 1 = Yes; 0 = No	0.533	0.493
Perceived value
Perceived economic benefits	Adopting WSI will increase farmers’ income: 1 = very or rather agree; 0 = otherwise	0.385	0.486	+
Perceived environmental benefits	Adopting WSI will help protect the environment: 1 = very or rather agree; 0 = otherwise	0.861	0.345	+
Perceived social benefits	Adopting WSI will help win social recognition: 1 = very or rather agree; 0 = otherwise	0.812	0.383	+
Perceived cost-related risks	Adopting WSI will require more money investment: 1 = very or rather agree; 0 = otherwise	0.595	0.495	-
Perceived time-related risks	Adopting WSI will require more time investment: 1 = very or rather agree; 0 = otherwise	0.257	0.481	-
Government regulation
Outreach	Is the number of types of outreach methods to encourage WSI received by the respondent larger than the local average? 1 = Yes; 0 = No	0.585	0.317	+
Subsidy	The local government has set up subsidies related to WSI: 1 = Yes; 0 = No	0.358	0.473	+
Punitive measures	The local government has implemented severe penalties for banning straw burning: 1 = Yes; 0 = No	0.556	0.489	+
Control variables
Age	The age of the respondent (in years)	54.879	11.644	?
Educational attainment	The years of schooling (in years)	7.075	3.524	+
Village cadre	Whether the family has village cadre: 1 = Yes; 0 = No	0.079	0.316	+
Household labor	Number of household labor in 2017 (number)	2.689	1.186	+
Farm size	Household farm size in 2017 (in hectare)	0.630	0.403	?
Annual agricultural income	Household agricultural income in 2017 (ten thousand yuan)	4.813	4.117	+
Region (Henan)	1 = yes; 0 = other provinces	0.216	0.407	-
Region (Anhui)	1 = yes; 0 = other provinces	0.597	0.493	-
Instrumental variable
Technical familiarity	Whether the farmer understand WSI technologies: 1 = Yes; 0 = No	0.756	0.434

provides a detailed definition of each variable.

2.4.1. FWA

FWA was selected as the explained variable because of the prominent practical problem: the passivity of WSI adoption by farmers under the government command-and-control policies. Specifically, FWA was measured with the following yes or no question for farmers: “Are you willing to adopt wheat straw incorporation technology”?

2.4.2. Perceived Value

Perceived value contains both rational and irrational factors, based on the farmers’ rational judgment of gains and losses with respect to WSI, and irrational factors such as personal preference. Porter [37] began the analysis of perceived value, defining perceived value as a trade-off between perceived performance and perceived cost. Zeithaml [38] proposed that perceived value is the overall evaluation of the utility of a product by customers based on their own gains and losses. Moore and Benbasat [39] proposed that the ratio of perceived benefits to perceived costs is the perceived value. Despite some differences between researchers, there is general consensus that perceived value is “the trade-off between perceived benefits and perceived risks.” [40] Therefore, the perceived value in this study refers to the subjective comprehensive evaluation completed by farmers in the WSI decision-making process, including a consideration and comparison of perceived benefits and perceived risks. The study combined the trait of WSI technology, and the actual situation of farmers’ WSI behavior. Perceived benefits include three dimensions: economic benefits, social benefits, and environmental benefits. Perceived risks include: cost-related risks and time-related risks.

2.4.3. Government Regulation

Most studies have measured government regulation from two perspectives: the farmers’ acceptance of regulation, and government behavior [41]. Government regulation reflects the external environment of FWA from multiple dimensions [42]. The government’s behavioral orientation is a key factor impacting farmer adoption of agricultural technology [43]. Farmer acceptance of government regulation is also an important driver in farmer willingness to adopt WSI. This verified the use of existing methods to measure government regulation, taking the dual perspective of farmer acceptance and government behavior. As such, outreach, subsidies, and penalties measure government regulation. Outreach is measured by the actual outreach methods implemented by the local government to advocate the mechanized straw mechanization technology received by the farmers. The more types of outreach methods farmers receive, the stronger the intensity of policy outreach promoted by the government are. The subsidy is measured based on whether the local government has established an agricultural subsidy related to the WSI technology from the farmers’ perspective. Penalties are measured by whether the local government has implemented severe adverse actions to ban straw burning.

2.4.4. Control Variables and Tool Variables

Many studies have found that individual characteristics and family characteristics significantly affect farmers’ agricultural technology adoption behavior. To control other factors that may affect farmer willingness to adopt WSI, the control variables were set as: age, educational attainment, farm size, household labor, annual agricultural income, whether the family has village cadre, and the region. In addition, to consider the possible endogenous problems of the model, this article assessed technical familiarity (“Do you understand the wheat straw incorporation technology?”) as an instrumental variable. It can only affect farmers’ willingness through the perceived value. This variable may affect farmers’ perceived value of WSI, but is unlikely to affect FWA. This is consistent with the relevance and exogenous requirements when selecting instrumental variables.

3. Results

3.1. Variable Descriptive Statistics

Table 1 provides the descriptive statistics of the study variables. The average respondent age was approximately 55 years old. The number of respondents’ household labor was approximately 2.7. The average respondent’s length of education was approximately 7 years, with 53.6% of the respondents having an education level of junior high school or higher. In addition, there was a small number of households with village cadres in the sample area; approximately 8% of the households reported village cadres in the home. Among the farmers, 53.3% of the farmers were reported being willing to adopt WSI. As the actual adoption rate of WSI is 90% in the three provinces (Ministry of Environmental Protection of China, 2018), the current proportion of farmers willing to adopt WSI is not high.

3.2. The Main Effect of Perceived Value and Government Regulation on FWA

Stata version 13.0 (Stata Corporation, College Station, TX, USA) was used to analyze survey data. Estimation results of the logit model for FWA are presented in

Table 2. The main effect of perceived value and government regulation on FWA.

Variable	Logit
	Coefficient	Marginal Effect
Perceived economic benefits	0.366 ** (0.160)	0.059 ** (0.014)
Perceived environment benefits	0.399 * (0.215)	0.035 * (0.009)
Perceived social benefits	0.428 ** (0.184)	0.069 ** (0.054)
Perceived cost-related risks	−0.194 * (0.109)	−0.129 * (−0.396)
Perceived time-related risks	−0.534 *** (0.163)	−0.093 *** (−0.152)
Outreach	0.263 *** (0.045)	0.108 *** (0.081)
Subsidy	0.765 *** (0.165)	0.143 *** (0.217)
Punitive measures	0.583 *** (0.149)	0.048 *** (0.311)
Age	0.005 (0.007)	0.001 (0.143)
Educational attainment	0.083 *** (0.024)	0.012 *** (0.074)
Village cadre	0.325 * (0.183)	0.061 * (0.049)
household labor	0.113 (0.065)	0.021 (0.044)
Farm size	0.157 * (0.006)	0.011 * (0.035)
Annual agricultural income	0.061 *** (0.017)	0.013 *** (0.041)
Henan	1.233 *** (0.217)	0.230 *** (0.115)
Anhui	0.945 *** (0.177)	0.176 *** (0.214)
Constant term	−4.019 *** (0.554)	—
Pseudo R2	0.187	—

Notes: ***, **, * represent significance at the 1%, 5%, and 10% levels, respectively. Bracketed numbers indicate standard error.

.

3.2.1. The Effect of Perceived Value on FWA

Perceived value significantly affects farmer willingness to adopt WSI. Specifically, farmers’ perceived economic, environmental, and social benefits of WSI are significant at 5%, 10%, and 5% levels, respectively; all positively impact FWA, with marginal effects of 5.9%, 3.5%, and 6.9%, respectively. This indicates that the higher the farmers’ perceived benefits of WSI, the greater the probability that the farmer reports being willing to participate in WSI. Farmers’ perceived cost-related risks significantly negatively impact farmer willingness to participate in WSI. When other conditions are constant, if farmers believe that WSI implementation increases cost inputs, the probability of being willing to adopt WSI decreases by 12.9%. Perceived time-related risks also significantly negatively impact FWA, with a marginal effect of −9.3%. In other words, when other variables are constant, if farmers believe WSI technology is cumbersome to implement and troublesome to adopt, the probability of adopting WSI decreases by 9.3%. Farmers’ perceived time-related risks are particularly critical. The determination of whether the technology is simple and convenient is a particularly important factor affecting FWA.

3.2.2. The Effect of Government Regulation on FWA

Table 2 shows that the three variables related to government regulation, outreach, subsidies, and penalty measures are all significant at a 1% level. This indicates that government regulation has a significant positive impact on FWA. The more the government distributes outreach materials, the more likely farmers are willing to adopt WSI under the influence of outreach. When other variables are constant, for every additional outreach approach, the probability of farmers being willing to adopt WSI increases by 10.8%. With respect to subsidies, when other variables are constant, when compared to when there is no relevant subsidy, the government establishing a subsidy related to WSI increases the probability that farmers are willing to participate in WSI by 14.3%. This shows that establishing a WSI subsidy can effectively drive farmers to actively and continuously adopt the WSI technology. Finally, when other variables are constant, compared to when there is with no penalty policies, the presence of government penalties increase the probability of FWA by 4.8%.

For most farmers, WSI is a relatively economical and feasible way to treat wheat straw, apart from burning it. The government’s stringent straw burning prohibition and penalty measures should force farmers to abandon straw open field burning, thereby encouraging farmers to adopt WSI technology. In summary, of the three types of government regulation, subsidies have the highest marginal effect. This indicates that compared with outreach and penalties, subsidies can more effectively encourage WSI adoption by farmers.

3.2.3. The Effect of Control Variables

The estimated results of the control variables were consistent with previous studies [26,28,44]. Education attainment, village cadre, farm size, and annual agricultural income significantly affect FWA. A higher level of education means that a farmer has a certain knowledge reserve, increasing the ability to understand the mechanism involved in WSI. This makes it easier for them to solve problems when implementing WSI technology, increasing the probability of adoption. Village cadres are facilitators of policy implementation, tend to have a higher political consciousness, and are more supportive of the government’s advocacy of WSI, increasing the willingness to adopt WSI. A larger farmland area was beneficial to economies of scale, and it could reduce the potential cost of some intermediate links. Farmers with higher annual agricultural incomes are more capable of mitigating risks and may be more confident in adopting WSI technology.

3.2.4. Endogenous Test for Farmers’ Perceived Value of WSI

It is important to be careful when explaining the impact of perceived value on FWA. FWA may lead to a higher perceived value: that is, there may be a two-way causal relationship. However, the farmers’ perceived value and their willingness to adopt WSI may be simultaneously affected by unexplored variables. In addition, measurement errors could bias the estimation results. Therefore, the model may have endogenous problems. Possible endogenous problems would result in inaccuracy of the estimated coefficients, and a common solution for this type of problem is instrumental variable estimation. For WSI is discrete variable, Instrumental variable methods based on continuous variables are no longer effective. We try to use the method of conditional mixed process (CMP) proposed by Roodman [45] to carry out the endogeneity test. It should be noted that CMP is developed from seemingly unrelated regression (SUR) and based on maximum likelihood estimation, and it is a kind of two-stage regression model by building recursive equations. The basic process of endogeneity test based on CMP can be divided into two stages: The first stage is to find the instrumental variable of the core explanatory variables and to assess the relevance between the two. The second stage is to bring the instrumental variable into the CMP for regression. If the endogeneity test parameter is significantly different from 0, indicating that the endogenous problems exist and the estimated results of CMP are better than logit model. In contrast, if the endogeneity test parameter is not significantly different from 0, indicating that the endogenous problems do not exist.

To address these problems, based on the relevant literature, a new variable is constructed: “positive perception value.” When, and only when, three variables measuring perceived benefits are valued at 1 and two variables measuring perceived risks are simultaneously valued at 0, the positive perceived value assignment is 1. In other cases, the positive perceived assignment is 0. Given these conditions, the technical familiarity variable (whether farmers understand the technology of WSI) serves as an instrumental variable to assess positive perception value, using a CMP two-stage regression.

Table 3. The effect of positive perceived value on FWA.

	CMP
Variables	The First Stage		The Second Stage
	Coefficient	SE	Coefficient	SE
Positive perceived value	—	—	0.351 **	0.085
Technical familiarity	0.055 **	0.233	—	—
Other variables	Not control		Control
Atanhrho_12	−0.034	−0.488	—	—
Wald Chi-square	376.28 ***
Observations	1027

Note: ① the explanatory variable for the first stage of CMP is the positive perceived value; the second stage of the explanatory variable is FWA; ② ***, ** represent significance at the 1%, 5% levels, respectively.

shows that, according to the regression results of the first stage of CMP, the effect of technical familiarity on the positive perceived value of farmers is significant at 1% level, the results meet the requirements of correlation for instrumental variable. According to the second stage of CMP, after controlling the possible endogeneity bias, positive perceived value of farmers has significant positive effects on WSI at 5% level. Meanwhile, endogeneity test parameter Atanhrho_12 cannot reject the hypothesis that positive perceived value is exogenous variable. It indicates that there are no serious endogeneity problems in the benchmark analyses of this paper.

3.3. The Effect of Interaction Terms of Government Regulation and Perceived Value on FWA

Although individual willingness and behavior take the pursuit of interests as the starting point and maximize the interests as the goal, they are subject to the constraints of systems and policies in practice. Simultaneously, the impact of government regulations on farmers’ willingness to adopt WSI is also affected by farmers’ perceptions. Government regulation and perceived value are likely to have an interactive impact on FWA. Therefore, we construct interaction terms between government regulation and perceived value, and add it to the model for testing.

Table 4. The effect of interaction terms on FWA.

Variables	Logit
	Coefficient	SE
Perceived economic benefits	0.221 **	0.153
Perceived environmental benefits	0.161 *	0.215
Perceived social benefits	0.235 **	0.184
perceived cost-related risks	−0.214 **	0.309
Perceived time-related risks	−0.334 ***	0.263
Outreach	0.126 ***	0.345
Subsidy	0.385 ***	0.165
Punitive measures	0.412 ***	0.149
Outreach*Perceived social benefits	0.931 *	0.560
Subsidy*Perceived cost-related risks	0.807 *	0.469
Subsidy*Perceived time-related risks	0.650 **	0.315
Pseudo R2	0.181	—

Note: Given space constraints, estimates that have a lower significant impact are not listed; the other variables are consistent with Table 1, and the estimated results are omitted; ***, **, * represent significance at the 1%, 5% and 10% levels, respectively.

shows the estimated results.

Table 4 shows that none of the interaction terms constructed by the penalty measures and different variables measuring perceived value pass the significance test. However, the interaction item of outreach and perceived social benefits significantly positively impact FWA, indicating that policy outreach could effectively strengthen the positive impacts of farmers’ perception of social benefits on FWA. For farmers with higher perceived social benefits, the greater the intensity of outreach, the more they may be willing to adopt WSI. Farmers with high perceived social benefits already have sufficient endogenous motivation to participate in WSI. As such, outreach can further stimulate their motivation to implement WSI and solidify the FWA.

The interactions of subsidy with perceived cost-related and time-related risks were positively significant, indicating that subsidy could effectively weaken the negative impacts of farmers’ perception of cost-related and time-related risks on FWA. From another perspective, for farmers with lower perceived cost-related and time-related risks, establishing a local subsidy may further stimulate their willingness to adopt WSI. Farmers with low perceived cost-related and time-related risks already have sufficient endogenous motivation to participate in WSI. As such, establishing a local subsidy can further stimulate their motivation to implement WSI and solidify the FWA.

3.4. The Effect of Perceived Value and Government Regulation on the Willingness to Adopt WSI Based on Farm Scale

The rapid development of urbanization and industrialization has led to significant changes in the structure of China’s agricultural labor force [46]. There is currently a significant outflow of young and middle-aged laborers in rural China, and the increases in non-agricultural income for rural households have accelerated the rapid development of rural land transfer markets [47,48]. The change is seen in this study: traditional farmers are currently the main decision-makers with respect to WSI. However, as time passes and with further changes in the urban and rural population structure, the proportion of scale farmers engaged in first-line agricultural production may gradually increase.

The perceived value and actual demand of farmers with different sized farms differ, and should not be treated the same way. This study groups farmers with different sized farms (traditional and scale), to assess their differences in perceptions related to government regulation and perceived value, and differences in factors affecting their willingness to adopt WSI. Of the study respondents, 22.8% are scale farmers and 77.2% are traditional farmers. The willingness to adopt WSI for scale farmers was higher than traditional farmers: 42.1% of traditional farmers were willing to adopt WSI; 56.6% of scale farmers were willing to do so (Figure 3).

Government regulation is generally outside the farmer’s control; in theory, there should be no difference with respect to government regulation implementation between traditional and scale farmers in the same area. However, the acceptance and understanding of those government regulations by farmers do differ at different operating scales. For this study, the variable of government regulation is measured through the perspective of “audience”; that is, the implementation of government regulation is judged by the farmer’s answer. The differentiated answers of farmers to broadly implemented policies may more truthfully reflect the implementation of government regulations. Based on this, this study assesses differences in perceptions related to government regulation between traditional and scale farmers.

Table 5. Descriptive statistics of perceived value and government regulation for different scale farmers.

Variable	Traditional Farmers		Scale Farmers		t Test
	Mean	S.D.	Mean	S.D.	t Value
FWA	0.421	0.495	0.566	0.493	−0.767
Perceived value
Perceived economic benefits	0.345	0.479	0.397	0.487	−1.066
Perceived environmental benefits	0.846	0.367	0.865	0.343	−0.801
Perceived social benefits	0.748	0.432	0.832	0.379	−2.215 **
Perceived cost-related risks	0.647	0.481	0.580	0.492	1.417
Perceived time-related risks	0.271	0.445	0.254	0.481	−2.535 **
Government regulation
Outreach	0.548	0.314	0.596	0.351	0.169
Subsidy	0.322	0.480	0.368	0.470	0.788
Punitive measures	0.626	0.518	0.535	0.487 *	−1.792 *

Note: **, * represent significance at the 5%, 10% levels, respectively.

indicated that, in terms of government regulation, scale farmers have a slightly better understanding of incentive regulations, such as outreach and subsidies, compared to traditional farmers. Traditional farmers have a better understanding of constraint regulations, such as penalty measures. From the perspective of perceived value, these two types of farmers reported significant differences in the perceived time-related risks and perceived social benefits. Compared with traditional farmers, scale farmers recognize the simplicity and ease of use of WSI technology and its practical advantages in reducing cost inputs, and pay more attention to the environmental and social benefits of WSI technology. In contrast, traditional farmers pay more attention to the economic benefits of WSI technology.

Table 6. The effect of perceived value and government regulation on the willingness to adopt WSI for different scale farmers.

Variables	Traditional Farmers		Scale Farmers
	Coefficient	SE	Coefficient	SE
Perceived economic benefits	1.071 *	0.597	0.310 *	0.173
Perceived environmental benefits	0.197	0.576	0.453 *	0.237
Perceived social benefits	0.425	0.615	0.463 **	0.207
perceived cost-related risks	−0.435	0.491	−0.210 *	0.124
Perceived time-related risks	−0.134	0.654	−0.566 ***	0.174
Outreach	0.496 ***	0.189	0.242 ***	0.045
Subsidy	0.638	0.635	0.797 ***	0.168
Punitive measures	1.028	0.751	0.588 ***	0.157
Pseudo R2	0.310	—	0.194	—

Note: the control variables introduced in the model are consistent with Table 1, and the estimated results are omitted; ***, **, * represent significance at the 1%, 5%, and 10% levels, respectively.

shows that the influence of government regulation and perceived value on the willingness to adopt WSI differs for different scale farmers. Subsidies and penalties have no significant impact on traditional farmers’ willingness, but have a significant positive impact on scale farmers’ willingness. Outreach is significant at the 1% level and has a significant positive impact on WSI willingness for both traditional and scale farmers. This may be because most traditional farmers in China today do not treat agricultural production as the main source of income. Instead, time and effort are mainly invested in other occupations, such as non-agriculture employment or business. Compared with subsidies, traditional farmers are more sensitive to direct and clear outreach. However, traditional farmers more easily accept different and relatively mild outreach measures, compared with compulsory penalty measures. As such, outreach can effectively encourage traditional farmers to adopt WSI.

Perceived value generally has a significant impact on the scale farmers’ willingness to adopt WSI, while the willingness of traditional farmers is mainly affected by the perceived economic benefits. While the main source of income for most traditional farmers is not agricultural production, traditional farmers are still rational, and adopting agricultural production techniques with economic benefits is consistent with economically rational motives. In particular, for traditional farmers operating in the context of rapid urbanization and continuous consumer market expansion, compared with scale farmers, the impact of perceived economic benefits on the traditional farmers is more prominent. Therefore, the good economic benefits of WSI can form a positive incentive, encouraging traditional farmers to adopt WSI.

4. Discussion

Promoting WSI technology should not be confined to the simple act of farmer adoption; instead, building the willingness to adopt WSI can achieve a “win-win” situation, where there is comprehensive utilization of straw resources and protection of cultivated land quality. Results revealed that farmers’ willingness to adopt WSI in the surveyed area were 53.3%, which was much lower than the farmer actual WSI adoption rate (90%) of the three provinces (Ministry of Environmental Protection of China, 2018). This view had been elucidated by some studies about sustainable farming practices [12,14]. Although farmers’ willingness to adopt sustainable farming practices was influenced by many factors [25], recognizing the role of perceived value and government regulation can largely contribute to predict the extent to which they would take practical actions [49,50].

In the process of implementing WSI, it was essential to note the farmers’ perceived value and how the identified factors influenced the farmers’ willingness. As the key subjects of WSI, Farmers could truly and directly perceive whether WSI could bring benefits or risks to people. Results showed that farmers’ perceived value had direct effects on their willingness. In particular, perceived economic, environmental and social benefits had significantly positive effects on FWA, and perceived cost-related and time-related risks had significantly negative effects on FWA. According to the marginal effect, perceived cost-related and time-related risks had the greatest impacts on FWA. This was basically consistent with the research results of Yang et al. [12], which emphasized that economic issues such as the cost of straw incorporation are high, and technical issues such as straw incorporation technology has a few shortcomings might be the main factors restricting farmers to return stalk to the field. Hence, it could be predicted that reducing farmers’ perceived risks, especially their perceptions of cost-related and time-related risks, was conducive to enhancing FWA. It was worth noting that, the marginal effect of perceived environmental benefits was the smallest in different variables measuring perceived value. This shows that farmers did not have a deep understanding of the environmental significance of WSI, which resulted in their relatively deficient perception ability of environmental value. The main reason might be that WSI brings various environmental benefits, but farmers only receive a small part of these benefits [14].

In terms of government regulations, subsidy and outreach had significantly positive effects on the improvement of FWA, with marginal effects at 14.3% and 10.8%, respectively. The more the government paid attention to technical publicity and outreach, the more in time it gave certain subsidies to farmers, the more effectively it could improve farmers’ willingness to adopt WSI. These results are in line with those of previous studies. Hou et al. [51] argued that subsidizing residue choppers and providing conservation demonstration is effective in promoting the sustainable use of crop residue. Song et al. [52] pointed out that giving certain subsidy could increase the risk tolerance in green production. Qian et al. [53] also found that outreach could deepen farmers’ understanding of green production, and enhance their willingness to apply green fertilizer. These conclusions revealed the effectiveness of outreach and subsidy in different fields, and also demonstrated the improvement effects of these government regulations on FWA. However, the model results showed that the marginal effect of punitive measures was the smallest (4.8%) in different variables measuring government regulation. At present, WSI is the most popular way of wheat straw utilization, mainly because the Chinese government has been effectively implementing the command-and-control policy of crop straw burning ban since 1995, and farmers could not decide what practices they used and have to utilize wheat straw passively. Farmers have no choice other than to follow the government targets. Currently, subsidy policies of WSI are still in the exploration stage in China, and in most provinces, there is no such a subsidy policy for WSI and the production costs of WSI are fully burdened by farmers which has caused great discontent among farmers. This was consistent with the research findings of Huang et al. [14] that if policies for wheat straw utilization rely more on economic incentive regulation rather than binding regulation, famers will be more likely to adopt WSI with less compensation.

In addition to the direct effects of perceived value and government regulation, this study also focused on the interaction between them. Results found that the interaction between outreach and perceived social benefits was positively significant, indicating that outreach could effectively strengthen the positive impacts of farmers’ perception of social benefits on FWA. A possible explanation for this might be that the outreach implemented by the government could strengthened the social legitimacy for WSI, which could enhance the farmers’ expectation for receiving social recognition by adopting WSI. On the other hand, as perceived social benefits are a sign of social responsibility, farmers considering adopting WSI will help win social recognition would be more aware of the outreach. This study also found that the interactions of subsidy with perceived cost-related and time-related risks were positively significant, indicating that subsidy could effectively weaken the negative impacts of farmers’ perception of cost-related and time-related risks on FWA. These results indicated that subsidy policies could have great effects on alleviating farmers’ worry about the increased operating and labor costs for adopting WSI. To make WSI more sustainable, economic incentive policies need to be implemented to counteract these negative effects of WSI on crop production [14].

In this study, the control variables, such as education level, village cadre, farm size, and annual agricultural income had significantly positive impacts on FWA. The higher educated farmers had a stronger awareness of green production, so it was easier for them to understand the significance of WSI [44]. Moreover, in China’s environmental governance policy, village cadres play an exemplary and supervisory role in banning straw burning. Having a village cadre in the family can improve FWA [1]. In addition, the larger the arable area owned by farmers, the higher their willingness to adopt WSI would be. Yang et al. [12] also found that farmers owning large cultivation areas of corn are more likely to get relatively lower expectations of compensated amount than in those small areas. Results also showed that farmers with higher agricultural income were more willing to adopt WSI. With the rapid development of China’s economy, a considerable part of the income of farmers came from off-farm employment rather than agricultural production [26]. The higher the agricultural income is, the stronger the dependence on agriculture and the more attention paid to straw incorporation.

This study also grouped farmers with different sized farms (traditional and scale) to assess the differences in government regulation and perceived value affecting their willingness to adopt WSI. Results showed that perceived value and government regulation generally had significant impacts on the scale farmers’ willingness to adopt WSI, while the willingness of traditional farmers was mainly affected by the perceived economic benefits and outreach. However, this result has not previously been described. There are several possible explanations for the result. Scale farmers have larger farm sizes, greater crop straw yields, a lower average cost for straw incorporation, and a higher opportunity cost for burning straw [1]. These caused the difference in perception between scale farmers and traditional farmers to a large extent. Due to the relatively large farm area, scale farmers need to invest more capital and labor into agricultural production, and be more professional in agricultural production than traditional farmers, so they must put more focus on the technological improvement in agricultural production. In this case, scale farmers’ perceptions related to government regulation and perceived value could be more grounded in reality and consequently more consistent with each other, causing the internal perceptions and external regulation more significantly impact their willingness of WSI. For most traditional farmers in China today, time and effort are mainly invested in other occupations, so they are more sensitive to direct outreach policy and clear perceived economic benefits.

The findings of this study provided some policy implications. First, the government should make the most of TV, radio, and news network media to widely publicize the positive effects of WSI in improving soil fertility, pest control, and land quality. WSI is a typical intertemporal agricultural technology, and its benefits are reaped in the future. The government should strengthen the propaganda of technology use on environmental improvement and expected output stability. Second, in the past, the government relied too heavily on rigid regulation, and administrative and economic penalties. In the future, the policies for WSI should rely more on economic incentive rather that command-and-control approaches. Government should also can consider how to decrease the charging level for WSI, such as compensate the machinery manipulators to lower the farmers’ compensation in future. Third, the government should speed up the renewal and upgrades to farming machinery and equipment and supporting implementation technology. In addition, the technical standard of straw incorporation to guide how much return amount, how crushing quality, and what methods to accelerate the decomposition rate should be regulated. Providing technology support, such as subsidizing residue choppers and providing conservation demonstration, is effective in promoting WSI. Fourth, the rapid development of the land transfer market and the differences in land endowment among farmers in China should not be ignored. Scale farmers will be the main force driving the adoption of WSI. During this process, the government should develop some relevant incentives to guide scale farmers to actively adopt WSI. At the same time, the government should strongly encourage the transfer and integration of adjacent plots, expand the plot area of farmers and reduce the cost of straw incorporation per unit area.

5. Conclusions

Using survey data from 1027 farmers in Shandong, Henan, and Anhui provinces in China in 2018, this study analyzes the effect of perceived value, government regulation, and their interaction on FWA. The study also considers differences between traditional and scale farmers in the willingness to adopt WSI. After addressing the endogenous problem, the study reveals three key conclusions.

First, farmers’ willingness to adopt WSI was not high on the whole, while the implementation of government regulation and the positive perceived value can effectively encourage farmers to adopt WSI. Among dimensions of perceived value, the perceived cost-related risks and perceived time-related risks had the strongest impacts with marginal effects of −12.9% and −9.3%, respectively. Among dimensions of government regulation, the subsidy had stronger promoting impacts with a marginal effect of 14.3%, followed by the outreach with a marginal effect of 10.8%.

Second, government regulation and perceived value have an interactive effect on FWA. More precisely, the effect of perceived social benefits on FWA is somewhat affected by the outreach. The outreach may stimulate farmers with higher perceived social benefits to more willingly to adopt WSI. The negative effects of the perceived time-related risks and perceived cost-related risks on FWA are also affected by the subsidy. Establishing a subsidy may help encourage farmers with higher perceived time-related and cost-related risks to more willingly adopt WSI.

Third, there are differences between traditional and scale farmers when reporting their willingness and the impact of government regulation and perceived value on FWA. The willingness of adopting WSI for scale farmers was higher than traditional farmers. Scale farmers are generally affected by government regulation and perceived value, while traditional farmers are more affected by outreach regulation and perceived economic benefits.

The findings of this study have a number of important implications for the government to guide farmers to participate in WSI. It is conducive to the adjustment and improvement of straw resource utilization policies. However, there are several limitations of this study to note. Firstly, this paper only investigated farmer willingness to adopt WSI, yet the actual WSI behaviors were not involved. Although willingness could predict behaviors to a certain extent, the two variables were quite different [54,55]. A more comprehensive analysis that consider both farmers’ adoption willingness and behaviors should be conducted in future study. Secondly, this study took wheat growers in major wheat-producing provinces of China as samples, the results may be not robust enough for application in different crops and regions. Hence, future research could expand the research scope for different types of farmers and supplement more investigation samples with different regional attributes.