Farmers’ Willingness to Engage in Ecological Compensation for Crop Rotation in China’s Black Soil Regions

Abstract

Assessments of farmer satisfaction can be a crucial tool for effectively implementing an appropriate ecological compensation policy. This study evaluates the effectiveness of an ecological compensation policy for crop rotation in China’s black soil regions from the perspective of farmer satisfaction. Specifically, utilizing an improved entropy TOPSIS, logistic regression, and the obstacle degree model, this empirical study analyzes the performance of the ecological compensation policy for crop rotation in black soil regions and identifies barriers in Heilongjiang Province. The findings indicate that gender significantly and positively affects outcomes at the 10% level, while age has a notable influence at the 5% level. Additionally, the family labor force and the quality of cultivated land, both significant at the 5% level with negative coefficients, suggest a diminished likelihood of farmers participating in these ecological compensation projects. The family’s source of income, significantly influential at the 1% level, also indicates a lower propensity among farmers to engage. Performance analysis reveals that the values for variables of satisfaction with the project’s publicity (A1), satisfaction with the protection of farmers’ rights and interest (A3), overall satisfaction with the effectiveness of the project (B1), satisfaction with ecological compensation methods (B3), and satisfaction with household income after the implementation of the project (C1) are above the average. In contrast, values for variables of satisfaction with the supervision and management (A2), satisfaction with the payment of ecological compensation funds (A4), satisfaction level with black soil rotation ecological compensation standards (B2), satisfaction with the welfare level of family life after the implementation of the project (C2), and satisfaction with farmers’ proactive participation in the project (D1) fall below the average of 3.03. Therefore, this study provides a comprehensive framework for assessing the effectiveness of the ecological compensation policy for crop rotation in China’s black soil regions and offers recommendations for enhancing its performance.

1. Introduction

Sustainable food systems are required to feed a growing world population [1]. Cultivated land is the primary resource and the basis for human survival and development [2,3]. Cultivated land, a strategic resource, offers essential services such as the production of food, fiber, and fuel for humans. Additionally, it delivers ecosystem services such as soil and water conservation, along with regulation of the climate [4,5]. Considering the critical role of cultivated land for humans, the Food and Agriculture Organization of the United Nations reported that nearly 12% of the global population experienced severe food insecurity in 2020 [6]. Therefore, the debate over the planning of sustainable land use development has had a long history and has attracted prioritized academic attention since the widespread acceptance of the ideas of sustainability [7]. In essence, suitable resource management and sustainable practices of agriculture [8], such as implementing suitable land management systems and analyzing the farmers’ knowledge, are essential for cultivated land, particularly in the black soil in Northeast China.

The black soil region of Northeast China is one of the most fertile soil areas of soil in the world and serves as a crucial grain-producing region in China [9]. Moreover, China’s black soil region is a vital grain-producing area and a traditional stronghold of heavy industry [10]. Black soils have the highest organic matter content in the world, and they are also important terrestrial carbon reservoirs. The black soil of Northeast China, renowned for its high fertility and suitability for agriculture, is often referred to as the “giant panda of cultivated land” [11]. It produces one-quarter of the grain and one-third of the commercial grain in China and is essential for food security and the development of sustainable socio-ecological system development [12].

Consequently, numerous studies across various fields have highlighted the decline in black soil fertility. In other words, due to their external characteristics, a decline in black soil’s fertility decline can adversely affect the ecosystem’s health and the land’s productivity [13]. These factors include intensive cultivation, traditional tillage, and monoculture [14,15]. Consequently, the black soil regions of China should initiate comprehensive development policies for the farmland environment to establish a fertile soil layer in an ecologically sound and disaster-resistant manner [16].

Consequently, to effectively utilize and protect black soil, China’s Ministry of Agriculture, in collaboration with various departments, has implemented a suitable project. The project aims to maintain black soil’s long-term fertility by promoting crop rotation and fallow systems at the household level through subsidies. In 2016, the government introduced a pilot crop rotation program known as “One Main and Four Auxiliaries” in Northeast China, offering a subsidy of CNY 150 per mu per year to farmers who voluntarily implement crop rotation. In this sense, crop rotation is an inevitable step to implementing ecological civilization policies and protecting cultivated land [17]. Therefore, subsidizing crop rotation on black soil serves as an ecological compensation for protecting and maintaining the soil’s fertility. Evaluating the performance of ecological compensation for crop rotation can provide insights into the utility of the compensation and identify solutions within the process of ecological compensation for black soil. Additionally, it can guide the government in crafting policies to protect black soil, offer a theoretical framework, and foster restoration and sustainable development.

Accordingly, the increasing pressure on agricultural production systems to achieve global food security and prevent environmental degradation necessitates a transition toward more sustainable practices [18]. By the end of 2020, more than 60% of cropland was medium- to low-yielding farmland [19]. From then on, the protection of black land has long been a central topic of academic studies. Many findings have concluded that implementing compensation for crop rotation and fallowing is an effective method for safeguarding soil fertility and human well-being. For instance, according to Krasuska [20], the implementation of compensation for fallowing restores the fertility of the soil, which aids in increasing food production, ensuring food security, and alleviating poverty. However, the crop rotation project offers several benefits within socio-economic and ecological frameworks, but these results are still nuanced. For instance, potential drawbacks of crop rotation and fallow compensation, such as how fallowing will reduce the operation of agricultural production activities [21], can negatively impact food security. Compensation for crop rotation facilitates the transfer of China’s rural labor force and increases farmers’ opportunities for non-farm employment [17]. Additionally, the changes in market prices of agricultural products and modifications in planting structures can reduce farmers’ willingness to participate in the project effectively. Thus, it appears that the implementation of the ecological compensation project may be influenced by socio-economic factors, such as farmers’ willingness to make it less misunderstood.

In this context, encouraging farmers to protect the quality of arable land is a focus of the current Chinese government [22]. Therefore, the effective implementation of ecological compensation through subsidies for crop rotation needs to be explored further, specifically by examining farmers’ satisfaction and willingness to participate in the project. Specifically, to address this gap, our study chooses Heilongjiang Province as the study area. It seeks to understand (1) the farmers’ willingness to participate in the project and (2) interpret their satisfaction concerning the project. This research unveils significant insights for improving the satisfaction level of farmers, increasing their enthusiasm to protect black soil consciously, and protecting the ecological functions of black soil.

2. Literature Review and Research Hypothesis

Cultivated land is the main body of farmland ecosystems, and it has a comprehensive effect on the natural environment and social economy [23]. It has been noted that the most significant barriers are related to the market environment, which greatly restricts the feasibility of growing many crops [24]. In the context of climate change, rising temperatures and drought pose significant challenges to the natural environment [25], such as the declining fertility of the soil. Furthermore, chemical fertilizer inputs contribute to the development of food production [26]. However, excessive application of nitrogen fertilizer has caused several environmental problems [27], including a decline in cultivated land quality [28], soil degradation, and loss of agrobiodiversity [29]. Thus, finding economical, reasonable, and feasible technical strategies to control contaminated arable land has recently been one of the hottest topics [30]. Therefore, ecological compensation for cultivated land is a prominent means of coordinating the protection and utilization of cultivated land ecosystems [4]. Moreover, compensation measures are perceived as a means to reconcile development with conservation [31]. In essence, the ecological compensation mechanism is an innovative institutional arrangement that can effectively realize the coordinated development of social–economic growth and ecological protection [32].

Furthermore, as the population has grown, urbanization and the demand for food have increased, and the pressure on cultivated land resources has intensified, leading to a decline in soil fertility [33,34]. In other words, a continuously growing pressure to increase food, fiber, and fuel production to meet worldwide demand and achieve zero hunger has put severe pressure on soil resources [33]. So, the policy’s effect on the quality protection of farmland has always been a topic of concern in the academic community in terms of achieving sustainable agricultural development [35]. Globally, land issues are becoming increasingly complex due to economic development and population growth [34]; accordingly, implementing land-protection policies can significantly impact agricultural production. A reasonable crop planting pattern can effectively contribute to maintaining soil fertility and ensuring stable crop growth in the black soil regions of China [36]. Further, conventional and scientific cropping patterns are important in realizing the sustainable utilization of black soil and promoting the high-quality development of agriculture [37]. Hence, when farmers perceive the importance of improving land quality, they are more likely to adopt crop rotation. In addition, previous studies align with this assertion and concede that farmers may be able to diversify their sources of income by adopting diversified crop rotations [38]. In a nutshell, farmers’ perceptions of the importance of improving cultivated land quality are crucial for the successful performance of ecological compensation via crop rotation, as these perceptions drive adoption, implementation, and support for sustainable agricultural practices.

Hypothesis 1 (H1).

Farmers’ awareness of the benefits of cultivated land quality improvement increases the adoption of sustainable agricultural practices such as crop rotation.

The agricultural sector plays a vital role in food security and the socio-economic development of various nations [39]. Consequently, positive perceptions of improvements in the quality of cultivated land align with the broader goals of sustainable development, ensuring that agricultural practices contribute to ecological compensation and socio-economic well-being. In addition to farmers’ perception, their socio-economic status indicates several important factors in agriculture. For example, age and sex play a significant role in agricultural modernization [40]. In other words, gender can influence the roles individuals play in agricultural activities, such as expectations that impact climate change [41]. For instance, gender shapes access to productive resources and opportunities, with women having less access to many assets, inputs, and services across a wide range of contexts [42]. In essence, the combined influence of sex, age, education, and household labor can determine the overall success of crop rotation practices and the effectiveness of ecological compensation in protecting black soil. Beyond age and sex, farmers whose primary profession is agriculture are likely to have a deeper understanding and more practical experience with crop-rotation and soil-management practices.

Moreover, in Yulin City, for example, there are significant differences in the contributions of livelihood income across the counties [43]. Therefore, as highlighted in the previous study, farm incomes are substantially less than the average income of all professions [44]. From then on, household labor and income may have shaped the socio-economic dynamics that influence the effectiveness of crop-rotation practices and the protection of black soil. In other words, the level of financial compensation and the anticipated benefits have a profound impact on decisions regarding adoption [45]. Therefore, financial compensation is the most urgently needed compensation [46]. In summary, the combined influence of household labor, age, education, profession, and income determines the overall capacity and willingness of households to participate in and benefit from the ecological compensation project.

Hypothesis 2 (H2).

The socio-economic status of farmers, influenced by factors such as age, gender, education, and primary profession, affects their ability to implement effective crop rotation.

Hypothesis 3 (H3).

Positive perceptions of improvements in the quality of cultivated land may be correlated with the adoption of agricultural practices that support ecological compensation and enhance socio-economic well-being.

Ecological compensation affects farmers’ livelihoods, as well as sustainability and social equity [43]. Therefore, farmers’ behavior regarding cultivated land use is the result of a rational choice [47]. As noted in the previous research, the farmers’ decisions regarding biodiversity management are complex and often non-directional processes shaped by numerous external and internal factors [45]. Farmers’ cognition in the agricultural field can refer to their knowledge, awareness, perceptions, and understanding of various agricultural practices, technologies, and policies. Satisfaction refers to the perceived value of a product or service in relation to its expected value, compared with a benchmark index. In the farming system, household satisfaction with innovative agricultural technologies is crucial in determining their widespread adoption [40]. Previous studies have shown that farmers’ behavior can be affected by farmers’ cognition, which should be considered in the study of farmers’ behavior [48]. The cognition level represents farmers’ willingness, while the cultivated land area is related to the scale economy of agricultural production [17]. Alternatively, the economy of agriculture is strongly linked to soil quality. Therefore, farmers’ socio-economic status, such as land tenure regularization, gained importance in the literature on agricultural productivity [49]; therefore, it is compulsory to protect cultivated land quality. In Heilongjiang Province, China, due to the differences in the land ownership system, there are obvious differences in land-use and management systems [50]. In addition, compared to younger, older farmers with more traditional farming practices, they might be less adaptable to new methods like crop rotation, influencing their confidence in the project’s outcomes. In summary, farmers’ satisfaction with the project can be interpreted as the difference between their perceptions before and after the implementation of the ecological compensation policy. Farmers’ confidence in the performance of ecological compensation is influenced by their perceptions of the short-term and long-term benefits of crop rotation for soil protection and productivity. Farmers’ willingness is closely tied to their satisfaction and exerts a substantial impact on the implementation of the ecological compensation project and economic development. Several indicators, such as policy cognition level, returned farmland area, and participation in other Eco compensation projects, have been demonstrated to significantly affect farmers’ willingness to participate [51].

Hypothesis 4 (H4).

Farmers’ confidence in the effectiveness of ecological compensation policies may be influenced by their perceptions of the immediate and future benefits of these policies for protecting the soil and productivity.

3. Materials and Methods

3.1. Study Area and Data Sources

Heilongjiang Province, a crucial grain production area, is situated in Northeast China, spanning from 43°26′ to 53°33′ N and from 121°11′ to 135°05′ E. The black soil area of Heilongjiang Province covers an area of 4,824,727 hectares [16] and is one of the three major black soil regions in the world [11]. It is situated in the northeastern and western areas of the three provinces, encompassing 39 cities, counties, and districts. The topography of the region predominantly consists of plains and terraces. Heilongjiang Province has a temperate continental monsoon climate, and the annual average temperature ranking is between −5 °C and 5 °C [2]. The annual rainfall ranges from 400 to 700 mm. The land use type in the study area is dominated by arable land, accounting for 58.83% of the total land area. However, in recent years, the intensive cultivation of land has led to a worrying decline in the quality of black soil. Therefore, to protect the black soil, the Party Central Committee of China has emphasized the promotion of the national black soil Protection Project since 2015. Over the past decade, Heilongjiang Province has been one of China’s key grain-producing regions. The overall grain output has increased and is expected to exceed 75 billion kg in 2021, contributing significantly to national food security [52]. Consequently, selecting the Heilongjiang Province as the study area to explore the performance of ecological compensation is crucial for effective cultivated land management and for developing strategies to combat the decline in soil fertility.

Social surveys can provide insights into how different stakeholders, particularly local communities and farmers, perceive the impact and effectiveness of ecological compensation policies. Therefore, to assess the implementation status of the ecological compensation policy for crop rotation on black soil, a social survey was conducted in Bayquan, Wangkui, and Jixian counties, which are the pilot areas for the project’s crop rotation in Heilongjiang Province (Figure 1B). Specifically, this study employed a random sampling method, focusing on the household level. The sample exclusively focuses on farmers and does not encompass other entities like agricultural enterprises and cooperatives. The face-to-face method was adopted, with careful attention to ethical considerations, particularly regarding sensitive responses such as agricultural income. In total, 347 questionnaires were collected in August 2021 from farmers who participated in the ecological compensation policy project. Before data processing, the reliability and validity of the questionnaire were first tested. For instance, Cronbach’s α coefficients are important for assessing the quality of survey data. They indicate how well the items in a survey measure the same underlying construct. Therefore, after testing, the Cronbach’s α coefficients and KMO values were all greater than 0.6, indicating that the questionnaire had a high degree of reliability and good structural validity. Accordingly, it could effectively reflect the process analysis of the performance of the implementation of ecological compensation policy for crop rotation on black soil.

3.2. Determining the Indexes of Farmers’ Willingness and Satisfaction

3.2.1. Identifying the Key Indexes of Farmers’ Willingness

In the context of crop rotation, it is necessary to resolve the contradiction between increasing yields and preserving the environment [53]. Accordingly, this study investigates the willingness of farmers to participate in cropland rotation ecological compensation projects. In other words, it seeks to understand the manner in which the implementation of crop rotation can improve the ecological environment while simultaneously taking into account the farmer’s interests. Prior studies examining the relationship between gender, age, and satisfaction with the effectiveness of agricultural technologies have yielded inconsistent results [40]. Moreover, agricultural training and education are essential in enhancing farmers’ adoption of climate-smart agriculture [54]. In addition, the world has undergone numerous changes over the years as a result of industrial development and technological advancements [55]. From this point forward, in the context of agricultural modernization and the adoption of new technologies, skills such as education and professional expertise are essential for maintaining soil fertility and enhancing crop production yields. In other words, agricultural land protection and quality improvement have become unavoidable prerequisites for reducing ecological and environmental pressures and ensuring long-term agricultural development [56].

Given that the family characteristics of the respondents could influence their willingness to participate in cropland rotation ecological compensation projects, this study identifies several variables for evaluation. These include gender, age, education level, type of occupation, family labor dynamics, family income level, and sources of family income, all of which are used to assess farmers’ readiness to engage in the project. Furthermore, we assume that farmers possess knowledge about the current state of cultivated land quality, as well as an understanding and recognition of the impact of the cultivated land rotation compensation policy. So, these variables are also added to evaluate the effect of their willingness to participate in the project. Globally, as shown in

Table 1. Variable setting and descriptive statistics.

	Variables	Variables and Description	Mean Value	Standard Deviation
Dependent Variable	Willingness to participate (y)	Willing = 1; unwilling = 0	0.69	0.46
Explanatory variables	Sex (X1)	Male = 1; female = 0	1.31	0.46
	Age (X2)	Actual age of the respondent in years	43.17	11.33
	Education (X3)	Elementary school and below = 1; middle school = 2; high school or junior college = 3; Bachelor’s degree or college = 4; Master’s degree or above = 5	2.58	1.18
	Profession (X4)	Student = 1; farming = 2; self-employed businessperson = 3; business employee = 4; government worker = 5	2.41	1.07
	Labor size (X5)	Number of the labor force in a household (persons)	2.08	0.81
	Household income (X6)	Household income/CNY 10,000	7.05	9.80
	Household income sources (X7)	farming = 1; part-time job = 2; business = 3; wage income = 4; Retirement = 5; other = 6	1.8	1.39
	Cultivated land quality level (X8)	Very poor = 1; poor = 2; Moderate = 3; better = 4; Very good = 5	3.32	0.88
	Importance in improving the cultivated land quality (X9)	Not necessary = 1; Not very necessary = 2; Indifferent = 3; Necessary = 4; Very necessary = 5	3.93	0.93
	The project’s knowledge level (X10)	Do not know at all = 1; Do not know much = 2; Know generally = 3; Comparatively aware = 4; Very aware = 5	3.30	1.28
	Have confidence in the project (X11)	Very skeptical = 1; Somewhat skeptical = 2; Not really concerned = 3; Quite confident = 4; Very confident = 5	3.84	0.99
	Identify oneself as part of the project (X12)	Disagree fully = 1; Disagree somewhat = 2; General = 3; Somewhat agree = 4; Completely agree = 5	3.53	1.14

, this paper has chosen 12 variables as explanatory factors to align with our objectives. Further, we assess satisfaction with participation in this project by using the choice of willingness or unwillingness to participate as the dependent variable.

3.2.2. Indexes of Farmers’ Satisfaction

This study uses farmer satisfaction as a theoretical reference model. It develops an evaluation index system for assessing farmer satisfaction across four dimensions: policy support, farmer satisfaction, quality of life, and farmer expectations (see

Table 2. Indicator system for evaluating the level of satisfaction.

Objective	Indicator Level I	Indicator Level II
Performance of ecological compensation policy for black soil crop rotation	Policy support (A)	Satisfaction with the project’s publicity (A1)
		Satisfaction with the supervision and management (A2)
		Satisfaction with the protection of farmers’ rights and interests (A3)
		Satisfaction with the payment of ecological compensation funds (A4)
	Overall farmer satisfaction (B)	Overall satisfaction with the effectiveness of the project (B1)
		Satisfaction level with black soil rotation ecological compensation standards (B2)
		Satisfaction with ecological compensation methods (B3)
	Quality of life (C)	Satisfaction with household income after the implementation of the project (C1)
		Satisfaction with the welfare level of family life after the implementation of the project (C2)
	Farmers’ expectations (D)	Satisfaction with farmers’ proactive participation in the project (D1)

). Knowledge of innovations provides the foundation for the adoption of innovative technologies among farmers [57]. Hence, (1) Policy support includes government policy promotion, supervision, and management of the project’s implementation; protection of farmer interests; and payment of compensation funds. (2) The primary factors that determine farmer satisfaction include their overall satisfaction with the project, their satisfaction with the ecological compensation standards, and their satisfaction with the ecological compensation method of cultivated land rotation. (3) We measure the quality of life using two aspects: the impact on family income and the impact on family living and welfare levels following the implementation of ecological compensation for cultivated land rotation. (4) Farmers’ expectations: farmers’ active participation in cultivated land rotation ecological compensation policy illustrates their expectations for compensation. In the model, perceived value is primarily gauged through the quality of life. These indicators are measured across five levels, corresponding to very dissatisfied, somewhat dissatisfied, relatively satisfied, satisfied, and very satisfied.

3.3. Research Methodology

3.3.1. Logistic Regression

The logistic regression model is highly suited for detailed analysis of individual behavioral choices and their influencing factors. A logistic regression model predicts the probability of the occurrence or non-occurrence of the event [43]. It can screen out the more significant factors affecting the occurrence or not of the event and, at the same time, eliminate the insignificant factors and generate regression coefficients for each significant factor [58]. In this study, the decision-making behavior of multiple subjects’ willingness to participate in ecological compensation for cropland rotation includes two predictable outcomes, i.e., “one is willing” and “two is unwilling”. Therefore, the sum of the probability of these two events was 1. In parallel, the dependent variable “y” can be defined as a binary categorical variable with the values of willingness to 1 and unwillingness to 0 and obeys the binomial distribution. As shown in Equation (1), this study chooses the binomial categorical logistic regression model to describe the farmer behaviors.

$$y=f\left(\alpha +{\displaystyle \sum _{j=1}^n{\beta }_j{x}_{ij}}\right)={\displaystyle \frac{1}{1+e^{-\left(\alpha +{\displaystyle \sum _{j=1}^{\mathrm{n}}{\beta }_j{x}_{ij}}\right)}}}$$

(1)

In the equation above, y is the willingness of the multivariate subject i to participate in the ecological compensation for rotation of cultivated land, i.e., the explained variable; xij represents the j-th factor, i.e., the explanatory variable, that influences the multivariate subject to choose to be willing to pay or to be compensated for the cost of ecological compensation of arable land rotation; βj is the regression coefficient of the explanatory variables in the model; α is the constant term; and n is the number of the explanatory variables.

3.3.2. TOPSIS Method Based on Improvement of Entropy Weight

The TOPSIS method is widely used in the comprehensive evaluation of overall efficiency [59]. It is the technique for ordering preferences by their similarity to the ideal solution, and it shows the value of closeness to the positive ideal solution [60]. TOPSIS offers several advantages, including the comprehensive evaluation of multiple criteria, which is crucial for understanding both performance and willingness factors. Additionally, TOPSIS is simple and intuitive, making it accessible to researchers and practitioners without advanced expertise in complex statistical methods. This simplicity also facilitates better communication of results to stakeholders, including farmers and policymakers. Specifically, in this study, the TOPSIS method enhances the analysis of factors affecting farmers’ willingness to participate in ecological compensation, leading to more effective policy recommendations.

In essence, the choice of Entropy and Min-Max Weighting methods for this study ensures a comprehensive, objective, and balanced analysis of farmers’ willingness to engage in ecological compensation for crop rotation in China’s black soil regions. Entropy weighting leverages the variability and information content of each factor, while Min-Max weighting normalizes diverse data for comparability. Together, these methods provide robust insights, facilitating effective policy development and targeted interventions to promote sustainable agricultural practices.

Its function is to create a two-dimensional data space that represents the distances between chosen evaluation indices and both the optimal and the worst solutions. It then ranks a set number of evaluation objects based on their proximity to the optimal solution and their distance from the worst solution. The actual value of each evaluation indicator is determined by using the extreme value standardization method to assign weights to each indicator based on their importance (Equation (2)).

$$Y_{ij}={\displaystyle \frac{X_{ij}-X_{min}}{X_{max}-X_{min}}}$$

(2)

where X_ij represents the actual value of the j-th evaluation index for the i-th interviewed farmer; X_max and X_min denote the maximum and minimum values, respectively, of the j-th evaluation index. Y_ij is the standardized value of the indicator. From the standardized values Y_ij, a standardized decision matrix Y can be constructed, where i = 1, 2, 3, ..., n, j = 1, 2, 3, ..., m.

Then, the entropy weight decision matrix is constructed. So, once the weight wj of each indicator is determined, the weighted normalized decision matrix vij can be constructed.

$$v_{ij}=w_{ij}\times y_{ij}$$

(3)

Following, we calculate the positive ideal solution M⁺ and the negative ideal solution M⁻ using the weighted normalized decision matrix vij.

$$M^{+}=\left\{\max \right.v_{ij}|j=1,2,\dots ,\left.m\right\}=\left\{v_1^{+}\right.,v_2^{+},\dots ,\left.v_m^{+}\right\}{M}^{-}=\left\{\min v_{ij}\right.|j=1,2,\dots ,\left.m\right\}=\left\{\left.v_1^{-},v_2^{-},\dots ,v_m^{-}\right\}\right.$$

(4)

After this step, using the Euclidean distance formula, we calculate the distances D⁺ and D⁻ from the evaluation vector to the positive ideal solution and the negative ideal solution, respectively.

$$D^{+}=\sqrt{{\displaystyle \sum _{j=1}^m{\left(v_{ij}-v_j^{+}\right)}^2}}{D}^{-}=\sqrt{{\displaystyle \sum _{j=1}^m{\left(v_{ij}-v_j^{-}\right)}^2}}\left(i=1,2,\dots n\right)$$

(5)

Here, D⁺ and D⁻ represent the proximity levels of the evaluation vector to the optimal and worst solution targets, respectively. The smaller D⁺ is, the closer the evaluation of the farmer’s perception of the black soil rotation ecological compensation policy is to the ideal solution, indicating a better evaluation result. The smaller D⁻ is, the closer the evaluation of the farmer’s perception of the black soil rotation ecological compensation policy is to the negative ideal solution, indicating a poorer evaluation result.

Finally, we calculate the closeness Ci between the evaluation vector and the ideal solution using Equation (6).

$$C_i={\displaystyle \frac{D_i^{-}}{D_i^{+}+D_i^{-}}}\left(i=1,2,\dots ,n\right)$$

(6)

where 0 ≤ Ci ≤ 1. When Ci = 1, vj = M⁺, indicating that farmers perceive the black soil rotation ecological compensation policy very positively, resulting in a better evaluation. When Ci = 0, vj = M⁻, indicating that farmers have a poor perception of the black soil rotation ecological compensation policy, suggesting that the policy does not meet the expected needs of the farmers. Currently, there is limited research and evaluation on the performance of the ecological compensation policy for black soil rotation. Consequently, as shown in

Table 3. Evaluation criteria of ecological compensation.

Performance Level	Proximity
Poor	0~0.3
Moderate	0.3~0.6
Good	0.6~0.8
Excellent	0.8~1

, the closeness Ci classification references the work conducted by Yu Liang-Liang and Wang Huiji [61,62]. Categorizing the closeness of the equation into qualitative categories like poor, moderate, good, and excellent provides a practical, consistent, and effective method for performance evaluation. In other words, these ranging methods can provide an overall assessment of the policy’s performance, highlighting areas of strength and those needing improvement.

3.3.3. Determination of the Obstacle Degree

The obstacle model employs a specific method that involves making a comprehensive judgment by introducing factor contribution, index deviation, and obstacle degree. In this model, the factor contribution Fj represents the contribution of a single indicator to the overall goal and is expressed by the weight Wj. The indicator deviation degree Ij represents the distance between the actual value of each indicator and the target performance of the black soil rotation ecological compensation. Specifically, it is the difference between the standardized value of a single achieved performance and the ideal performance, typically expressed as 100%. The obstacle degree Uj represents the impact of a specific indicator on the implementation performance of the black soil rotation ecological compensation policy. The formula to calculate this is as follows:

$$U_j={\displaystyle \raisebox{1ex}{$I_j{W}_j$}\!\left/ \!\raisebox{-1ex}{$\left({\displaystyle \sum _{j=1}^n{I}_j{W}_j}\right)$}\right.}$$

(7)

where Ij = 1 − Yj, Yj is the standardized extreme value of the individual indicator obtained by the extreme value-normalization method.

In a nutshell, the combination of entropy TOPSIS, logistic regression, and the obstacle degree model is innovative for this research subject as it integrates multiple analytical methods for a comprehensive assessment. This multi-faceted analysis enhances the understanding of farmers’ motivations and barriers, leading to more targeted and effective policy recommendations.

4. Results

Table 4. Logistic regression model estimates of the outcome of the compensation project.

Explanatory Variables		Coef.	Std. Err.	z	p > |z|
Sex (X1)		0.645 *	0.342	1.89	0.059
Age (X2)		−0.046 **	0.021	−2.20	0.028
Education (X3)		0.042	0.233	0.18	0.856
Profession (X4)		−0.089	0.028	−0.33	0.740
Labor size (X5)		−0.438 **	0.207	−2.12	0.034
Household income (X6)		0.038	0.028	1.37	0.170
Household income sources (X7)		−0.320 ***	0.102	−3.13	0.002
Cultivated land quality level (X8)		−0.449 **	0.199	−2.26	0.024
Importance of improving the cultivated land quality (X9)		0.782 ***	0.172	4.54	0.000
The project’s knowledge level (X10)		0.278 *	0.166	1.68	0.094
Have confidence in the project (X11)		0.377 **	0.185	2.04	0.041
Identify oneself as part of the project (X12)		0.260	0.169	1.54	0.123
-cons		0.721	1.690	0.43	0.669
Log likelihood	−167.74	LR chi2(12)		78.27
Pseudo R2	0.189	Prob > chi2		0.000

Note: ***, **, * denote 1%, 5%, and 10% levels of significance in statistical tests, respectively.

reveals that age, family labor force, and income negatively affect the willingness to engage in ecological compensation for cropland rotation projects. On the other hand, factors such as education level and family income level positively correlated with the willingness to participate, showing favorable impacts. Additionally, the analysis shows that gender has a significant positive influence at the 10% level, whereas age demonstrates significance at the 5% level. However, age and gender can play a significant role in crop rotation. For instance, conversely, older farmers and younger farmers might be more open to adopting new practices and may see ecological compensation as an opportunity for innovation. Understanding these demographic implications, namely, age and gender, can help tailor policies and programs to address the specific needs and perspectives of different farmer groups, improving the effectiveness of ecological compensation schemes. The family labor force and the quality of cultivated land, both significant at the 5% level and displaying negative coefficients, along with the family’s source of income, significant at the 1% level, suggest a decreased willingness among farmers to participate in ecological compensation for cropland rotation projects. Conversely, farmers’ recognition of the importance of enhancing cultivated land quality, which is significant at the 1% level with a positive coefficient, indicates that farmers acknowledge the necessity of improving cultivated land quality. In a nutshell, these results indicate that while there is a general trend toward recognizing the benefits of ecological compensation, significant hurdles remain due to demographic characteristics and economic dependencies that influence farmer behavior. Addressing these through targeted policies and education could enhance participation and effectiveness of ecological compensation schemes.

4.1. Analysis of Performance Evaluation Indicators

Table 5. Description of performance evaluation indicators.

Indicator	A1	A2	A3	A4	B1	B2	B3	C1	C2	D1	Mean
Minimum value	1.00	1.00	1.00	1.00	1.00	1.00	1.00	1.00	1.00	1.00	1.00
Maximum value	5.00	5.00	5.00	5.00	5.00	5.00	5.00	5.00	5.00	5.00	5.00
Mean value	3.40	2.94	3.28	3.01	3.46	2.11	3.44	3.45	2.59	2.61	3.03
Standard deviation	1.20	1.28	1.26	1.28	1.17	1.05	1.15	1.01	1.22	0.99	1.16

Note: Satisfaction with the project’s publicity (A1); Satisfaction with the supervision and management (A2); Satisfaction with the protection of farmers’ rights and interests (A3); Satisfaction with the payment of ecological compensation funds (A4); Overall satisfaction with the effectiveness of the project (B1); Satisfaction level with black soil rotation ecological compensation standards (B2); Satisfaction with ecological compensation methods (B3); Satisfaction with household income after the implementation of the project (C1); Satisfaction with the welfare level of family life after the implementation of the project (C2); Satisfaction with farmers’ proactive participation in the project (D1).

shows that the performance evaluation indicators of the ecological compensation policy have an average of 3.03, with a minimum value of 1 and a maximum value of 5. The analysis reveals that the values of variables A1, A3, B1, B3, and C1 are all above the average. Conversely, the values of variables A2, A4, B2, C2, and D1 fall below the average of 3.03. Further analysis of the B1 indicator shows that the ecological compensation for black soil rotation is rated at 3.46. Among the surveyed farmers, 24.9% reported being relatively satisfied, 27.5% satisfied, and 23.2% very satisfied with the overall effectiveness of the policy. This indicates that farmers acknowledge the positive impact of the government’s black soil rotation ecological compensation policy, which supports its potential for broader promotion and implementation in the future. In summary, the ecological compensation policy for black soil rotation seems to be moderately successful, with certain aspects performing particularly well. The satisfaction levels among farmers suggest that the policy is making a positive impact, warranting continued support and possibly broader implementation. However, the areas scoring below the average should be examined for potential adjustments to enhance the overall effectiveness of the policy.

4.2. Normalized Decision Matrices Based on Positive and Negative Ideal Solutions

The weighted normalized decision matrix of the 10 indicators of the text is constructed, and its positive and negative ideal solutions are calculated. In

Table 6. Normalized decision matrix and positive and negative ideal solutions.

Indicators	A1	A2	A3	A4	B1	B2	B3	C1	C2	D1
Positive ideal solution	0.086	0.119	0.099	0.117	0.076	0.142	0.076	0.055	0.145	0.085
Negative ideal solution	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000

Note: Satisfaction with the project’s publicity (A1); Satisfaction with the supervision and management (A2); Satisfaction with the protection of farmers’ rights and interests (A3); Satisfaction with the payment of ecological compensation funds (A4); Overall satisfaction with the effectiveness of the project (B1); Satisfaction level with black soil rotation ecological compensation standards (B2); Satisfaction with ecological compensation methods (B3); Satisfaction with household income after the implementation of the project (C1); Satisfaction with the welfare level of family life after the implementation of the project (C2); Satisfaction with farmers’ proactive participation in the project (D1).

, it can be seen that the positive ideal solution of each evaluation index is the maximum value of the corresponding evaluation index, the negative ideal solution is its minimum value, and the negative ideal solutions are all 0 due to the standardization of the indexes using the extreme value method. In summary, the use of maximum and minimum values as positive and negative benchmarks, respectively, helps in clearly delineating the best and worst possible scenarios, facilitating informed decision-making.

4.3. Evaluation of the Performance Evaluation and Proximity Analysis of the Project

We assessed the overall performance value and proximity to the ideal positive solution for ecological compensation related to black soil crop rotation. According to

Table 7. Indexes of ecological compensation performance and proximity.

Comprehensive Performance	0.47
Proximity	0.43
Performance Level	Moderate

, the comprehensive performance value for this compensation is 0.47, and the closeness to the ideal positive solution stands at 0.43. Consequently, as categorized in Table 1, this performance value falls within the moderate range of 0.3 to 0.6. Given this moderate closeness and the low overall satisfaction of farmers with the ecological compensation for black soil rotation, there is a clear need for substantial improvements. In summary, while the ecological compensation policy for black soil crop rotation is functioning to a moderate degree, the low satisfaction among farmers and moderate closeness to the ideal indicate that there is considerable room for policy refinement and implementation improvements.

4.4. Analysis of Factors Hindering the Performance of the Project

The analysis of farmers’ satisfaction performance is primarily conducted at Indicator Level I. As shown in

Table 8. Hurdle factors of ecological compensation performance (%).

Barrier Factors	Degree of Impairment
Policy support	32.43
Farmers’ satisfaction	37.85
Quality of life	20.29
Farmers’ expectations	9.45

, three obstacles impact the performance evaluation of black soil crop rotation ecological compensation. Compared to farmers’ expectations (9.45%), these factors include policy support (32.43%), farmers’ overall satisfaction (37.85%), and quality of life (20.29%). This variation in obstacles may stem from multiple causes. Firstly, the survey data indicate that during the implementation phase, problems like inadequate publicity, poor supervision and management, and delays in disbursing compensation funds have substantially impacted farmers’ satisfaction with policy support. Farmers are the main participants in the black soil crop rotation policy. Influenced by factors like individual family and social characteristics, the current ecological compensation policy for crop rotation has not markedly enhanced family income or living standards.

Consequently, this has led to low satisfaction among farmers regarding improvements in their quality of life. Ultimately, farmland continues to be a crucial support for farmers’ production and livelihood. Nonetheless, the government has set a compensation standard of CNY 150 per mu without consulting farmers. This lack of dialogue and disregard for farmers’ input has led to dissatisfaction with the compensation standards. These issues have significantly hindered the enhancement of ecological compensation performance for black soil crop rotation projects.

At Indicator Level II, this study examines the five most significant obstacles listed in Table 3. The results indicate that the factors affecting the evaluation of the performance of rotational ecological compensation in black soil include satisfaction with the compensation standard (B2), satisfaction with family life and welfare after project implementation (C2), satisfaction with supervision and management (A2), satisfaction with the allocation of funds (A4), and satisfaction with active participation (D1) (

Table 9. Barrier factors to the performance of ecological compensation (%).

Category	Ranking of Indicators
	1	2	3	4	5
Barrier factor	B2	C2	A2	A4	D1
Degree of impairment	28.87	17.28	10.69	9.69	9.45

).

The primary obstacle identified is the farmers’ dissatisfaction with the compensation standard, scoring an average of 2.11. This is the lowest rating among all indicators, indicating widespread dissatisfaction with the ecological compensation standard for crop rotation. The second most significant obstacle is the farmers’ satisfaction with changes in family living welfare after implementing crop rotation ecological compensation, with an average satisfaction score of 2.59. This suggests that while promoting crop rotation, there is also a need to focus on improving family welfare levels.

The third obstacle concerns farmers’ satisfaction with government supervision and management of the crop rotation ecological compensation, with an average satisfaction of 2.94. This indicates that the government’s oversight and management regarding this policy have been lacking. The fourth-ranked obstacle is farmers’ satisfaction with the distribution of compensation funds during the implementation of crop rotation ecological compensation. With an average satisfaction score of 3.01, farmers are dissatisfied with the current method of distributing ecological compensation funds for crop rotation. Furthermore, it has been discovered that farmers prefer the funds to be distributed on a monthly or quarterly basis. Farmers’ satisfaction with their proactive participation in crop rotation ecological compensation ranks as the fifth obstacle, with an average satisfaction score of 2.61. This low score indicates that farmers are dissatisfied with their level of active involvement in the crop rotation ecological compensation policy in the past. The policy implementation has not lived up to the farmers’ expectations.

This preference also suggests that farmers lack confidence in the sustained implementation of crop rotation ecological compensation policies in the future. These challenges have substantially impeded the improvement of ecological compensation performance for the black soil crop rotation project. If the government were to actively assist farmers in resolving practical livelihood issues, offer technical guidance, and implement comprehensive process-wide supervision during the crop rotation ecological compensation policy, it could significantly enhance the project’s effectiveness. In a nutshell, by actively addressing these issues, particularly through practical support and technical guidance, the government can improve the acceptance and success of ecological compensation measures, leading to more sustainable agricultural practices in black soil regions.

In summary, these results support the increase in crop production. The ecological compensation policy for crop rotation in Heilongjiang Province has started to show positive outcomes since the state introduced the crop rotation and fallow policy in 2016. Since 2000, Heilongjiang Province has produced over 20% of the total corn in the Northeast region, with this share surpassing 30% between 2010 and 2020, according to the data from the 2000–2020 national and provincial statistical yearbooks.

4.5. Robustness of the Analysis

As demonstrated in the process outlined in

Table A1. Indicator system for evaluating the satisfaction performance.

Objective	Indicator Level I	Weight	Indicator Level II	Weight
Performance of ecological compensation policy for black soil crop rotation (A)	Policy Support (A1)	1/4	Satisfaction with the project’s publicity (A11)	1/4
			Satisfaction with the supervision and management (A12)	1/4
			Satisfaction with the protection of farmers’ rights and interests (A13)	1/4
			Satisfaction with the payment of ecological compensation funds (A14)	1/4
	Overall Farmer Satisfaction (A2)	1/4	Overall satisfaction with the effectiveness of the project (A21)	1/3
			Satisfaction level with black soil rotation ecological compensation standards (A22)	1/3
			Satisfaction with ecological compensation methods (A23)	1/3
	Quality of Life (A3)	1/4	Satisfaction with household income after the implementation of the project (A31)	1/2
			Satisfaction with the welfare level of family life after the implementation of the project (A32)	1/2
	Farmers’ Expectations (A4)	1/4	Satisfaction with farmers’ proactive participation in the project (A41)	1

and

Table A2. Affiliation degree of performance evaluation indexes.

Indicator	Evaluation Level of Affiliation
	Very Dissatisfied	Somewhat Dissatisfied	Relatively Satisfied	Satisfied	Very Satisfied
Satisfaction with the project’s publicity (A11)	0.030	0.056	0.269	0.353	0.292
Satisfaction with the supervision and management (A12)	0.042	0.208	0.250	0.212	0.289
Satisfaction with the protection of farmers’ rights and interests (A13)	0.033	0.100	0.259	0.292	0.317
Satisfaction with the payment of ecological compensation funds (A14)	0.041	0.186	0.230	0.261	0.283
Overall satisfaction with the effectiveness of the project (A21)	0.013	0.112	0.218	0.320	0.338
Satisfaction level with black soil rotation ecological compensation standards (A22)	0.146	0.397	0.271	0.049	0.137
Satisfaction with ecological compensation methods (A23)	0.018	0.102	0.189	0.423	0.268
Satisfaction with household income after the implementation of the project (A31)	0.003	0.117	0.238	0.417	0.225
Satisfaction with the welfare level of family life after the implementation of the project (A32)	0.102	0.156	0.297	0.361	0.084
Satisfaction with farmers’ proactive participation in the project (A41)	0.024	0.407	0.216	0.270	0.083

, this study used a fuzzy comprehensive evaluation (FCE) to assess the reliability or robustness of the results. FCE is an artificial evaluation technique based on fuzzy mathematics [63]. The robustness of the FCE method lies in its ability to handle uncertainties and imprecise data, maintain consistency, and provide reliable results through systematic fuzzy logic principles. Therefore, by assigning values to each evaluation element in the rubric set and then fuzzifying the objective rubric set, the final comprehensive evaluation value for the performance of the ecological compensation policy rotation system in black soil regions is calculated as E = 1 × 0.043 + 2 × 0.221 + 3 × 0.240 + 4 × 0.301 + 5 × 0.195 = 3.383. Accordingly, this score falls within the middle–upper range, indicating a level of performance between “comparative satisfaction” and “satisfaction”. These findings align closely with results obtained using the entropy power method. Additionally, the improved TOPSIS method produces similar outcomes, demonstrating the robustness of the study’s results.

5. Discussion

5.1. Factors Influencing Farmers’ Willingness to Engage in Ecological Compensation Policy for Crop Rotation Projects

The long-term intensive use of arable land has caused soil quality degradation and threatened the sustainable development of agriculture [64]. Therefore, the evaluation of ecological protection compensation policies is the main basis for the formulation, adjustment, and improvement of the policy [65]. As the direct beneficiaries of the ecological compensation policy for black soil crop rotation, farmers have the most immediate perceptions of the policy’s implementation and its impact on their production and livelihood. The effectiveness of the ecological compensation policy for crop rotation directly affects farmers’ satisfaction. Accordingly, using farmers’ satisfaction as a measure to evaluate the performance of the ecological compensation policy for black soil crop rotation not only serves as a benchmark for assessing the policy’s success but also enhances research on ecological compensation.

Population increase in many parts of the world, such as China, has a consequential effect on agricultural resources because an excessive growth of population can drastically minimize agricultural land throughout the world [66,67]. In essence, the losses to agricultural lands due to urban expansion have progressively posed serious challenges to the global food system [68] and, therefore, can affect crop rotation systems and cultivated land quality. According to the performance evaluation, farmers’ satisfaction with the ecological compensation policy for black soil crop rotation is 0.47, with the closeness to the positive ideal solution being 0.43, indicating a general performance level. Utilizing the entropy weight enhancement of the TOPSIS method and the obstacle degree model to analyze the performance of the black soil rotation ecological compensation policy and its influencing factors proves to be logical and can significantly improve the policy’s implementation effectiveness in the future. This is because the degree of coupling coordination between agricultural modernization and black soil protection in Heilongjiang Province exhibits differentiated characteristics [16]. In the impact of demographic and socio-economic factors, older farmers, those with a larger family labor force, and those dependent on farming for income are less willing to participate in ecological compensation projects. This could be due to the higher risks they perceive in changing established agricultural practices. Further, the decision to adopt or try an innovation in the traditional farming system requires knowledge and analytical capacity to understand such an innovation and its added value [69]. The negative coefficient for the quality of cultivated land affecting willingness negatively is particularly interesting. This could imply that farmers who currently have better-quality land might see less benefit in changing their practices compared to those with degraded land, who might view these projects as essential for restoring land fertility accordingly. So, as the crop rotation system is highly consistent with China’s green development system [70], the government should improve the farmer’s needs regarding cultivated land quality means. In a nutshell, despite government subsidies, local farmers can choose the most suitable varieties accordingly in order to maximize yield production while maintaining relatively high yield stability [71].

Understanding the spatiotemporal dynamic of crop cover types and the driving forces of cropping patterns in Northeast China is essential for establishing suitable and sustainable cropping patterns [72]. From the perspective of guideline-level obstacles, the three most significant ones affecting the evaluation of ecological compensation performance for crop rotation on black soil are policy support, overall farmer satisfaction, and quality of life. For instance, in this study, obstacles to implementing the project include satisfaction with the distribution of funds for ecological compensation (A4) and satisfaction with active participation in ecological compensation (D1). This situation has been highlighted in previous studies. In fact, only a few farmers were strongly satisfied with the funding use requirement of the cultivated land protection fund [73]. At the same time, the quality of cultivated land had continued to decline due to the overuse of farmland and the overdraft of its productive capacity [19]. From that point forward, redistributing funds among farmers continues to pose a significant challenge for ecological compensation associated with crop rotation projects implemented in China’s Black Soil Regions. In addition, it has been noted that counties with different labor force structures may have different impacts on farmers’ incomes after enjoying ecological compensation in key national ecological function areas [74]. These results collaborate with our research findings, which indicate satisfaction with the welfare level of family life after implementing ecological compensation for crop rotation (C2). This situation remains the same, with satisfaction with the supervision and management of the ecological compensation policy (A2). Intensive agriculture practiced without adherence to scientific principles and ecological aspects has led to the loss of soil health and the depletion of freshwater resources and agrobiodiversity [75].

5.2. Policy Implications and Research Perspectives

The above findings and discussion underscore the complexity of factors influencing agricultural decision-making and suggest that successful implementation of ecological compensation schemes requires a multifaceted approach that addresses economic, educational, and social dimensions. Further, to establish effective ecological compensation standards for crop rotation, it is essential to develop these standards based on the economic conditions of the area and promote the establishment of a strong supervision mechanism. Furthermore, the local government should also communicate with transparency [76] regarding the fund process distribution. To achieve this, the administration should strengthen government supervision and increase transparency in the management and distribution of funds. Following the implementation of the crop rotation ecological compensation policy, the government should create more employment opportunities for farmers to assist them with the production challenges that arise from the policy.

In summary, the government should encourage farmers to actively engage in ecological compensation for black soil crop rotation by providing education on crop rotation techniques and promoting the importance of ecological responsibility. Additionally, it should enhance financial support and implement diverse cropping systems that boost local biodiversity, climate resilience, and economic opportunities, such as eco-tourism. In other words, by enhancing such a program, farmers can potentially increase their incomes and reduce costs associated with soil degradation. These measures will help ensure that ecological compensation programs are effective, practical, and advantageous for both farmers and the environment.

This study may have certain limitations. This study’s survey data were collected during the early stages of the trial implementation of crop rotation on black soil and only in three counties. Since a performance evaluation of the ecological compensation policy in the trial area has not yet been conducted, the available references and data seem limited. In other words, the sample size of surveyed farmers directly involved in the project appears to be insufficient.

In addition, in the methodology framework, numerous methods implement robustness checks. In essence, although robustness checks are common in applied economics, their use is subject to numerous pitfalls [77]. In this study, Fuzzy Comprehensive Evaluation (FCE) has been used to check the validity analysis and, therefore, may present potential limits. Consequently, the evaluation indicators used in this study require further examination using new ways, such as the Comprehensive Sensitivity Analysis Method (COMSAM) [78]. The TOPSIS method has several limitations, including its focus on the relative importance of indicators without considering the interactions and impacts between them. Therefore, future studies should acknowledge these potential limitations.

6. Conclusions

Understanding farmers’ perceptions and behaviors regarding ecological compensation policies for crop rotation is a prerequisite for maintaining long-term cultivated land quality. Accordingly, this study focuses on Heilongjiang Province as the research area, utilizing survey data to explore farmers’ satisfaction and willingness to participate in the ecological compensation for cropland rotation projects. This current finding showed that gender has a significant positive influence at the 10% level, whereas age demonstrates significance at the 5% level. The family labor force and the quality of cultivated land, both significant at the 5% level and displaying negative coefficients, along with the family’s source of income, significant at the 1% level, suggest a decreased willingness among farmers to participate in ecological compensation for cropland rotation projects. Concerning the performance, the analysis reveals that the values of the variables of satisfaction with the project’s publicity (A1), satisfaction with the protection of farmers’ rights and interests (A3), overall satisfaction with the effectiveness of the project (B1), satisfaction with ecological compensation methods (B3), and satisfaction with household income after the implementation of the project (C1) are all above the average. Conversely, the values of variables satisfaction with the supervision and management (A2), satisfaction with the payment of ecological compensation funds (A4), satisfaction with black soil rotation ecological compensation standards (B2), satisfaction with the welfare level of family life after the implementation of the project (C2), and satisfaction with farmers’ proactive participation in the project (D1) fall below the average of 3.03. Given the moderate performance and low satisfaction levels, there is a clear indication that substantial improvements are necessary. Enhancements could involve increasing compensation amounts, improving the transparency and management of the distribution process, or better aligning the policy with the specific needs and conditions of the farmers in the black soil regions. Further research should explore how economic constraints influence farmers’ decisions and what specific financial incentives or support mechanisms could counteract these negative influences. Future research can focus on exploring regional variations in the willingness to participate and satisfaction levels, investigating why satisfaction with farmers’ proactive participation in the project is low, and examining the reasons for dissatisfaction with the payment of ecological compensation funds. By actively addressing these issues, particularly through practical support and technical guidance, the government can improve the acceptance and success of ecological compensation measures, leading to more sustainable agricultural practices in black soil regions.