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Article

Analysis of Farmers’ Crop Rotation Intention and Behavior Using Structural Equation Modeling: Evidence from Heilongjiang Province, China

by
Rui Zhang
1,
Guoming Du
1,2,*,
Bonoua Faye
2 and
Haijiao Liu
1
1
School of Economics and Management, Northeast Agricultural University, Harbin 150030, China
2
School of Public Administration and Law, Northeast Agricultural University, Harbin 150030, China
*
Author to whom correspondence should be addressed.
Land 2025, 14(1), 158; https://doi.org/10.3390/land14010158
Submission received: 21 October 2024 / Revised: 4 January 2025 / Accepted: 11 January 2025 / Published: 14 January 2025
(This article belongs to the Section Land Socio-Economic and Political Issues)
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Abstract

:
Exploring the factors driving farmers’ crop rotation intention and behavior can provide a scientific foundation for enhancing crop rotation policies. Utilizing 448 micro-research samples from three counties in Heilongjiang Province, a structural equation model was developed to examine the factors influencing farmers’ crop rotation intention and behavior. This analysis focused on farmer cognition, family endowment, and the external environment. The study results indicate that farmer cognition and family endowment positively impact their intention to rotate. The rotation intention, in turn, is a crucial driver of actual rotation behavior. However, the external environment tends to affect rotational behavior negatively. Then, positive effects were noted, and factors such as the benefits of crop rotation, policy awareness, social support, larger cropland area, narrower return, the profit gap between maize and soybean, lower spatial connectivity standard, and higher crop rotation subsidy standard significantly encourage farmers’ crop rotation intention and behavior. In a nutshell, to effectively promote the area of rotation, it is crucial to enhance policy communication, target subsidy to larger households, and establish differentiated subsidy standards. These measures are essential to boosting farmers’ motivation and increasing the adoption of crop rotation in the region.

1. Introduction

Agricultural activities are essential for human survival, providing vital resources and supporting economies [1,2]. Understanding the spatiotemporal patterns and driving mechanisms behind the evolution of cropping practices tailored to local conditions is key to the efficient use of black soil in Northeast China and the advancement of agricultural development [3]. The northeast black soil region plays a significant role in the nation’s grain production, contributing a quarter of the total grain output and a third of grain exports, largely due to its rich black soil [4]. However, unsustainable farming practices have led to the depletion of black soil, causing a decline in the productivity and functionality of cropland [5,6]. In May 2016, the Chinese government introduced the Pilot Program for Exploring the Implementation of Cropping and Fallowing System on Cropland, targeting crop rotation in Northeast China. This program suggests a rotation plan, mainly based on maize and soybean rotation to sustain grain production while preserving black soil. Crop rotation aims to integrate short-term and long-term needs, balancing production with ecological health and land maintenance, and is central to effectively using and conserving black soil [7,8]. Farmers are the micro-actors in the implementation of crop rotation and are the essential stakeholders in crop rotation activities, which determines the need to focus on the wishes and needs of farmers [9]. Thus, it is essential to analyze the factors influencing farmers’ intention to adopt crop rotation and their related behaviors. This analysis is crucial for developing a scientifically sound cropland rotation system and crop rotation subsidy policy, which will aid in the restoration of black land and the sustainable development of agriculture.
Crop rotation and fallow practices are two types of agricultural cropping systems that essentially involve farmers’ choices in crop selection during production [10]. Consequently, the factors influencing farmers’ decisions on both crop rotation and fallow practices share some similarities. Existing research focuses more on the factors influencing farmers’ decisions to fallow land rather than on crop rotation. Therefore, this study reviews both crop rotation and fallow practices together to provide a more comprehensive overview of the research. Overall, the driving factors affecting farmers’ intention to adopt crop rotation and fallow practices are categorized into two groups: subjective factors, such as policy cognition, environmental cognition, and policy satisfaction [11,12]. Among them, policy awareness, friend suggestions, behavioral attitudes, and perceived value can enhance farmers’ intention to rotate and fallow practices [13], and policy satisfaction positively influences farmers’ intention to rotate crops and fallow practices [14]. Objective factors influencing farmers’ intention to adopt crop rotation and fallow practices include personal and family characteristics, cropland features, production conditions, fallow policy attributes, and natural environmental factors [15,16]. Key elements such as farmers’ age, degree of part-time work, and cropland quality positively affect their intention to fallow land [17,18].
Additionally, the amount of subsidy is positively related to this intention [19,20], and varying natural environmental conditions can cause differences in fallow goals and behaviors across regions [9]. Research on fallowing, which is closely related to crop rotation, is relatively extensive. Scholars have primarily examined the factors driving farmers’ intention and behavior regarding both crop rotation and following from both subjective and objective perspectives. Although crop rotation and fallowing share some theoretical and practical similarities, they differ significantly in their macro-institutional frameworks and micro-mechanisms. While findings on fallowing provide a valuable theoretical foundation and research ideas, it is essential to adapt and innovate these insights to suit the specific context of crop rotation.
Diversified crop rotation improves the efficiency of farming systems all over the world [21]. Heilongjiang Province is a typical annual cropping system agricultural area; farmers must evaluate both subjective and objective factors each year to adjust their crop allocation plans. In the transition zone between maize and soybean cultivation in this region, crop selection is highly sensitive to climate change, market prices, and policy adjustments. This sensitivity has led to a variety of planting practices, including maize continuous cropping, soybean continuous cropping, and maize–soybean rotation [19]. Moreover, little is known about how farmers make decisions that shape crop diversity [22]. Therefore, in the transition zone between maize and soybean, based on the difference in accumulated temperature, this study selected three counties for investigation, obtained 448 micro survey sample data, and used structural equation modeling as the research method. The goal was to empirically examine the factors influencing farmers’ intention to adopt crop rotation and their behavior. The findings aim to provide a scientific foundation for optimizing the crop rotation system and improving regional crop rotation practices.

2. Theoretical Analysis and Research Hypotheses

According to the research results of planned behavior theory, crop rotation intention is a pre-variable of crop rotation behavior, which is the individual’s tendency to perform specific behaviors, and can directly determine behavioral response. Crop rotation intention is an individual’s intention judgment to adopt crop rotation behavior, which can also be understood as the subjective probability of individuals adopting crop rotation behavior. The stronger their behavioral intention, the greater the possibility of adopting crop rotation behavior, and there is a high correlation between the two [23].
Over the decades, agri-food security has become one of the most critical concerns in the world [24]. Cropland rotation behavior, a key area in studying farmers’ actions, involves various complex factors. These include subjective decision-making tendencies, such as farmer cognition, which refers to the degree of understanding of rotation characteristics and policies among farmers. It is also constrained by objective environmental conditions, such as family endowment and the external environment. Among these, family endowment belongs to the objective conditions that farmers can control, namely human capital, economic capital, and social capital; whereas the external environment belongs to the objective conditions that farmers find difficult to control, such as market environment, policy environment, etc. (Figure 1).
Crop rotation, a traditional farming practice, plays a crucial role in enhancing soil health by introducing crop diversity, which helps mitigate soil nutrient depletion [7]. By alternating different crops, farmers can optimize soil structure, leveraging the varied growing periods and biological characteristics of each crop [25,26]. This practice improves nutrient cycling, reduces pest and disease buildup, and enhances soil organic matter, ultimately leading to more sustainable agricultural production. Since the pilot implementation of crop rotation policy in China in 2016, this approach has evolved from a simple practice to a comprehensive agricultural management system. The government has actively promoted its adoption through subsidies, aiming to encourage widespread implementation.
Behavioral intention refers to individuals’ motivation to adopt a specific behavior [27]. Therefore, farmers who are well-informed about the subsidy policy, including specific details, such as subsidy rates, application procedures, and eligibility criteria, are more inclined to participate. These incentives make crop rotation a more appealing option for farmers, encouraging them to adopt sustainable farming practices that contribute to long-term soil health and productivity. As a result, crop rotation has become a strategic component of modern agriculture, fostering both economic and environmental benefits by promoting efficient land use and crop diversity. Based on this understanding, the following research hypotheses are proposed:
Hypothesis 1 (H1). 
The farmer’s cognition may influence the formation of rotation intention.
Hypothesis 2 (H2). 
The farmer’s cognition may influence the formation of rotation behavior.
The concept of family endowment can be expanded into three key dimensions: human, economic, and social capital [28]. Human capital encompasses farmers’ knowledge, skills, and experience, which are essential for mastering and implementing crop rotation techniques. Previous studies highlighted that the combined influence of sex, age, education, and household labor could determine the overall success of crop rotation practices and the effectiveness of ecological compensation in protecting black soil [29]. Farmers who possess higher levels of education and technical know-how are better positioned to adopt, innovate, and effectively manage crop rotation practices, as they can understand and apply more advanced agricultural methods.
Agricultural finance supports farming modernization and enhances global food security [30]. Economic capital consists of resources generated by production and distribution systems [31]. In this context, economic capital plays a crucial role in influencing a farmer’s ability to manage risks associated with crop rotation. Challenges such as uncertain crop yields, fluctuating market prices, and potential losses deter farmers from experimenting with new practices. Further, the lack of equipment and profitability dilute producers’ interest in diversified crop rotation practices [9]. However, those with sufficient financial resources can better absorb these risks, invest in necessary inputs, and sustain their operations through periods of instability, thereby ensuring the long-term stability and sustainability of crop rotation practices.
Social capital comprises resources generated through networks and community connections [31]. Social capital also significantly impacts crop rotation adoption. Farmers with strong social networks and information sources are more likely to receive cooperation, support, and collaborative opportunities, such as sharing resources or coordinating crop rotation with neighboring farms. Moreover, their broader social connections facilitate access to the latest policies, technological advances, and market information, helping them enhance the efficiency and effectiveness of their crop rotation efforts. Overall, a robust family endowment across these three dimensions increases the likelihood of successful and sustainable crop rotation practices. Based on this understanding, the following research hypotheses are proposed:
Hypothesis 3 (H3). 
Family endowment may influence the formation of rotation intention.
Hypothesis 4 (H4). 
Family endowment may influence the formation of rotation behavior.
External environmental factors, such as market conditions and policy frameworks, significantly impact farmers’ decisions [32]. Grain cultivation is vital to farmers’ livelihoods, making profitability a crucial consideration in adopting crop rotation practices. Crop rotation improves soil health, leading to higher grain yields and increased profits, which motivates farmers to continue this approach. While spontaneous crop rotation offers benefits, government subsidies further enhance farmers’ enthusiasm by providing financial incentives. These subsidies help offset costs, making crop rotation more economically viable. Thus, fostering a supportive policy environment is likely to positively affect farmers’ willingness to adopt and maintain crop rotation practices. Based on this understanding, the following research hypotheses are proposed:
Hypothesis 5 (H5). 
The external environment may influence the formation of rotation intention.
Hypothesis 6 (H6). 
The external environment may influence the formation of rotation behavior.
Actions are typically guided by deliberate choice and planning, where a stronger intention leads to a higher likelihood of corresponding behaviors [33]. In the context of agriculture, farmers’ decisions to adopt crop rotation practices are influenced by their intention to rotate crops. The stronger this intention, the more likely they are to engage in consistent crop rotation. Understanding this relationship is essential for developing strategies to promote sustainable agricultural practices. Based on this insight, the following research hypothesis is proposed:
Hypothesis 7 (H7). 
A stronger rotation intention may positively impact the likelihood of engaging in rotation behavior.

3. Materials and Methods

3.1. Study Area and Data Sources

Crop rotation and diversification are crucial strategies in sustainable agriculture, offering multiple benefits to both farmers and the environment [25]. In selecting variables, this study defines farmers’ cognition using indicators such as their knowledge of crop rotation benefits and policies, social support for crop rotation, previous crop rotation experience, and policy satisfaction. Family endowment is characterized by indicators such as farmers’ age, health, literacy, number of agricultural workers, share of family agricultural income, cropland area, and number of plots. The external environment is represented by indicators including the crop rotation subsidy rate, requirements for obtaining subsidy, profit difference between maize and soybean, duration of land transfer, and the level of regional agricultural machinery specialization.
Between July and August 2022, this study conducted questionnaire surveys across 16 townships in three counties in Heilongjiang Province. Using a combination of whole clusters and a simple random sampling method ensured data reliability. One-on-one interviews were carried out with farm household decision-makers, with researchers filling out the questionnaires on-site to ensure accuracy and completeness. A total of 490 farm households were surveyed, including 149 from Baiquan County, 192 from Wangkui County, and 149 from Jixian County. After excluding questionnaires with missing key variables or inconsistencies, 448 valid responses were obtained, resulting in an overall validity rate of 91.43% (Figure 2).
Heilongjiang Province, a key grain-producing region, is located in Northeast China, spanning from 43°26′ to 53°33′ N and from 121°11′ to 135°05′ E [29,34]. In 2022, soybean and maize were the primary crops in this area, covering 70% of the total land, with each alternating as the most dominant crop in Northeast China [35]. The three counties selected for this study are situated in the transition zone between maize and soybean cultivation, where cumulative temperature conditions are close to the lower threshold for optimal maize growth. This makes them ideal for observing marginal changes in crop rotation. Baiquan County has the lowest annual average temperature and a relatively high income from soybean cultivation. Wangkui County experiences moderate annual temperatures, where the profitability of maize and soybean cultivation is roughly equal. Jixian County has the highest annual average temperature, making maize cultivation the most profitable.

3.2. Data Processing and Modeling

In this study, SPSS 26.0 software was used to assess the reliability of the data, yielding a Cronbach’s α of 0.802, which indicates satisfactory overall reliability. The KMO (Kaiser–Meyer–Olkin) statistic test and Bartlett’s spherical test were also performed, resulting in a KMO value of 0.657 (>0.5) and a Bartlett’s test value of 0.000 (<0.001). Both values were significant, confirming the suitability for factor analysis. This study used principal component analysis to reduce the dimensionality of variables, and the model converged after four iterations of orthogonal rotation. Variables were removed based on the criterion that “loading on any component is less than 0.5 or loading on multiple components is more than 0.5” [36]. This process resulted in 9 factors and 3 principal components, accounting for a cumulative explained variance of 55.715% (Table 1).
Based on exploratory factor analysis, this study employed the structural equation model (SEM) for confirmatory factor analysis, which involved identifying latent and observed variables. The factors influencing farmers’ intention and behavior regarding crop rotation were categorized into three second-order latent variables. Consequently, the rotation intention and rotation behavior were defined as two first-order latent variables (Table 2).
SEM is a multivariate data analysis method for analyzing complex relationships among constructs and indicators [37]. SEM is capable of analyzing the relationships between latent variables and their indicators simultaneously [38], making it effective for capturing subjective factors that are difficult to observe directly, such as farmer cognition and rotation intention. This approach clearly outlines the decision-making process involved in farmers’ crop rotation behavior. The model includes five latent variables and thirteen measurement variables covering farmer cognition, family endowment, external environment, rotation intention, and rotation behavior. This aligns with the model’s general structure and requirements. Therefore, this study employs SEM to explore the driving factors behind farmers’ decisions regarding crop rotation behavior as follows:
η = B η + Γ ξ + ζ
x = Λ x ξ +
y = Λ y η + ε
Equation (1) is the structural equation that defines the linear relationships between latent variables. Equations (2) and (3) are the measurement equations that determine the linear relationships between latent variables and observed variables. In these equations, ξ represents the exogenous latent variable, which includes three exogenous latent variables in this study: farmer cognition, family endowment, and external environment. η represents the endogenous latent variable, referring to the rotation intention and rotation behavior in this study. Λx and Λy are the factor loading matrices of the observed variables on the latent variables. x are the exogenous observed variables, including cognition of rotation benefits, cognition of rotation policies, level of social support, cropland area, number of plots, age of farmers, maize–soybean profit gap, standard for contiguous cropland, crop rotation subsidy standard. x represents the exogenous and endogenous observed variables, including family crop rotation program, suggested crop rotation for others, recent crop rotation experience, cropland rotation ratio. B and Γ are path coefficients, while ζ, δ, and ε are error terms.

4. Results

4.1. Descriptive Statistics for Key Indicators

The descriptive statistics of the sample data revealed the following characteristics (Table 3). For the observed variable related to the latent variable of farmer cognition (FC), most farmers acknowledge that crop rotation can enhance soil fertility and increase yield. Their family members and friends generally have a positive view of crop rotation. However, 33% of farmers still lack sufficient knowledge about crop rotation subsidy standards and application methods.
In the observed variables for the latent variable family endowment (FE), there is significant variation in the cropland area managed by different farmers, with an average of 7.876 hm2. The average number of plots is 8.99, and the average age of farmers is 54.70 years, indicating a relatively older demographic.
Regarding the observed variables for the latent variable external environment (EE), farmers generally support measures to enhance their rotation intention, such as reducing the profit gap between maize and soybean, lowering the contiguous standard for obtaining crop rotation subsidy, and increasing the subsidy standard. Additionally, 53.57% of farmers plan to practice crop rotation in the future, and 52.01% are willing to recommend crop rotation to others. Overall, more than half of the farmers show a strong intention to engage in crop rotation. Since the implementation of the crop rotation subsidy policy in 2016, 63.62% of farmers have had experience with crop rotation. In 2022, 50.89% of farmers practiced crop rotation on their land, with the average rotation area accounting for 26% of their total cropland.

4.2. Model Fitting and Correction

In this study, the initial model of structural equations was constructed in AMOS 24.0 software and with the help of path diagrams describing the logical relationships between each latent variable and between latent variables and observed variables. Then, the model was tested for estimation using the data that passed the reliability and validity tests (Figure 3).
Before performing maximum likelihood estimation on the structural equation model, it is essential to test the goodness of fit of the sample variables. This study employed the following fitness indices to evaluate the model: the absolute adaptability indices (GFI, AGFI, RMSEA, X2/df), the relative adaptability indices (NFI, TLI, CFI), and the parsimonious adaptability indices (PGFI, PNFI, PCFI). The research data were entered into AMOS software to validate the initial model. Based on the fitness criteria for each index, the model’s fit was found to be unsatisfactory, necessitating adjustments (Table 4).
According to the modification indicators of the initial model, the modification index (M.I.) for the path between farmer cognition (FC) and external environment (EE) is relatively high. Increasing the covariance in this path can enhance the model’s explanatory power. There is a strong logical correlation in this path due to the interaction between farmer cognition and the external environment within the cropland utilization system. The external environment serves as a crucial source of information for farmers’ agricultural decisions, influencing the formation and shaping of their cognition. Conversely, farmers adjust their resource allocation in agricultural production based on information from the external environment, thereby impacting the external environment itself. After incorporating this path, the model was re-evaluated and found to meet the adaptation standards, indicating good adaptability (Figure 4).

4.3. Hypothesis Testing of the Model

This study employed maximum likelihood (ML) estimation in SEM to validate the seven hypotheses in the conceptual model (Table 5). The results indicated that hypotheses H2, H4, and H5 did not pass the significance test. Specifically, for Hypothesis 2 (H2), the influence of farmer cognition on rotation behavior was not significant when compared to rotation intention. Farmers develop cognitive judgments about crop rotation based on their understanding of its benefits, knowledge of crop rotation policy, and the attitudes of their social circles. However, the translation of these cognitive judgments into actual crop rotation behaviors is influenced by the constraints of their family endowments and the external environment. For Hypothesis 4 (H4), this study used the cropland area, the number of plots, and the age of farmers as dimensions of family endowment to reflect the agricultural production resources available to the household. Before engaging in actual crop rotation, farmers typically evaluate their resource deployment capabilities and predict the benefits of crop rotation as part of their production decisions. This assessment process influences their rotation intention, which acts as a mediating variable between family endowment and rotation behavior, thereby weakening the direct relationship between family endowment and rotation behavior. For Hypothesis 5 (H5), this study assessed the impact of three indicators of the external environment—the profit gap between maize and soybean, the cropland continuous standard for obtaining a crop rotation subsidy, and the crop rotation subsidy standard—on the rotation intention. However, the results indicated that these factors did not have a significant impact on rotation intention. The likely reason for this is that, at the micro-level, farmers can only adapt passively to macro-factors such as food market conditions and agricultural policies. Consequently, when deciding on crop rotation, farmers primarily focus on optimizing their resource allocation based on the family resources they can control and their ability to exercise subjective initiatives. Additionally, the inclusion of the path “farmer cognition (FC) ↔ external environment (EE)” indicates that farmer cognition mediates the relationship between the external environment and their rotation intention.
In addition, the data analysis results verified hypotheses H1, H3, H6, and H7, in which farmer cognition significantly and positively affects rotation intention, with a standardized regression coefficient of 0.689, reflecting the importance of the farmer cognition level in enhancing rotation intention. Family endowment significantly and positively affects rotation intention, with a standardized regression coefficient of 0.121, revealing that family endowment plays a role in mobilizing rotation intention. The external environment has a positive impact on rotation intention, with a standardized regression coefficient of 0.689. The external environment has a significant negative effect on rotation behavior, with a standardized regression coefficient of −0.182, which indicates that external environmental constraints such as grain market prices and government policy implementation play an inhibitory role in farmers’ crop rotation behavior. Rotation intention has a significant positive effect on rotation behavior, with a standardized regression coefficient of 0.687.

4.4. Model Path Analysis

The modified model fit results (Table 6) indicate that among the factors affecting farmer cognition, social support has the most significant impact, with a standardized regression coefficient of 0.587. This is followed by farmers’ cognition of crop rotation policies and the benefits of crop rotation. Positive attitudes from family and friends, as part of farmers’ social networks, strongly encourage crop rotation. Additionally, the agronomic benefits of crop rotation and increased understanding of subsidy policies further enhance farmers’ intention to engage in crop rotation.
Among the family endowment factors, the area of cropland and the number of plots both positively influence crop rotation intention, with the area of cropland having a more significant impact. This is because a larger land area encourages diversification to manage production risks and stabilize returns, increasing the likelihood of crop rotation. Additionally, the interaction between crops in neighboring plots, such as the negative impact of tall maize on nearby soybeans, can also promote flexible crop choices and rotation. Conversely, older farmers tend to have lower crop rotation intention due to decreased physical strength and the tendency to favor less labor-intensive crops, such as soybeans. Younger farmers, however, are more likely to respond positively to crop rotation subsidy policies and implement crop rotation.
Among the external environment variables, the crop rotation subsidy standard, the standard for contiguous cropland, and the maize–soybean profit gap all significantly and positively impact farmers’ crop rotation behavior, with standardized regression coefficients of 0.757, 0.699, and 0.418, respectively. As economic interests fundamentally drive crop cultivation, farmers prioritize maximizing their economic returns. First, the crop rotation subsidy acts as a policy tool that directly impacts farmers’ crop rotation behavior. The subsidy standard affects the income levels for different crops, thereby influencing farmers’ crop choices and promoting crop rotation. Second, the contiguous area standard determines farmers’ eligibility for crop rotation subsidy. A lower standard for contiguous areas allows more farmers to qualify for subsidies, indirectly boosting the proportion of crop rotation. Finally, due to the time lag between agricultural supply and demand, farmers consider the previous period’s maize–soybean profit gap when choosing current crops. A more significant profit gap increases the likelihood of selecting a particular crop, whereas a smaller gap reduces this tendency.

5. Discussion

5.1. Resources and External Factors Limit Farmers’ Intentions for Crop Rotation

China is among the world’s agricultural producer giants [39]. Accordingly, maintaining long-term soil fertility in Northeast China requires a clear understanding of farmers’ intentions and behavior regarding crop rotation [29]. This study found that external factors, such as profit gaps between different crops and subsidy standards, had no significant impact on farmers’ rotation intentions. This highlights the complexity of farmers’ decision-making processes, which occur within a dynamic environment shaped by economic, political, social, and ecological changes [40]. This suggests that at the micro-level, farmers have limited ability to adapt to or influence broader macroeconomic factors and agricultural policies. Instead, they tend to prioritize the management of family resources and internal capabilities, suggesting that resource considerations drive their decisions more than policy incentives. This underscores the need for agricultural policies to address practical, resource-based constraints when designing effective incentives for crop rotation.
The lack of a significant relationship between farmer cognition and rotation behavior, as observed in Hypothesis 2 (H2), suggests that although farmers may be well aware of the benefits of crop rotation, possess knowledge of relevant policies, and are influenced by prevailing social attitudes, these cognitive factors alone are insufficient to drive behavioral change. This implies that even when farmers understand the importance of crop rotation and its positive impacts, such understanding does not necessarily translate into the consistent adoption of rotation practices without additional motivational or external factors. Instead, their actual behaviors are moderated by more tangible constraints, such as family resources and external environmental conditions, which were perceived as significant limitations by all growers [41].
Hypothesis 4 (H4) showed that family endowment factors such as cropland area, number of plots, and farmer age influence rotation intention but do not direct rotation behavior. This suggests that farmers assess their resources before deciding on crop rotation. Hypothesis 5 (H5) revealed a minimal impact of external factors (profit gaps, subsidies) on rotation intention, indicating that farmers prioritize internal resources over external incentives. These findings suggest that effective crop rotation policies must address farmers’ practical constraints, rather than just providing subsidies. Enhancing farmer awareness alone is insufficient; policies should also strengthen the practical means for farmers to implement crop rotation, ensuring alignment with their existing capabilities and priorities. Further, education improves a person’s resources and capacity to carry out tasks, but it also broadens their understanding of their options and the benefits anticipated from their actions [42]. Accordingly, implementing agricultural training to promote the adoption of crop rotation will be beneficial.
Consequently, this study contributes new insights by showing that while farmers may understand the benefits of crop rotation and relevant policies, cognitive awareness alone does not ensure adoption. Farmers’ decisions are more influenced by internal resources and practical constraints than by external incentives. Effective agricultural policies should address resource-based limitations, providing practical support that aligns with farmers’ capabilities. Education and training can further enhance farmers’ skills and adaptability, encouraging sustainable crop rotation practices.

5.2. Policy Implications

According to our research results, approaches such as broadening the promotional channels for policies, maintaining a subsidy tilt towards large-scale farmers, and establishing a differentiated subsidy standard may help motivate farmers’ enthusiasm for crop rotation and increase the area under regional crop rotation.
(1) Expand policy publicity channels: to effectively promote rotation behavior, it is crucial to enhance farmer cognition and rotation intention through broader and more diverse publicity channels. This study identified that limited information access, especially among older farmers, hinders the effectiveness of policy communication [43]. Leveraging younger family members who are adept with modern information tools and utilizing social networks can improve access to policy information and influence farmer cognition positively.
(2) Continue to prioritize subsidies for large-scale farmers: the highly specialized cultivation of crops is inevitably accompanied by large-scale continuous cropping. Inter-annual crop rotation on farmland is based on intra-annual diversified planting, and a larger farmland area is crucial for the realization of diversified planting [44]. Compared to small farmers, new agricultural management entities typically possess larger farmland areas. By establishing thresholds for the farmland operation area to qualify for the crop rotation subsidy, more new agricultural management entities can be incentivized to obtain these subsidies, potentially enabling larger-scale crop rotation.
(3) Implement differentiated subsidy standards: to encourage crop rotation, subsidy standards should reflect regional and crop-specific economic conditions. Geographic and market supply–demand disparities result in varied returns for different crops and regions. Establishing differentiated subsidy standards can help balance returns between crops, making crop rotation more attractive to farmers by addressing these economic imbalances [45]. To improve crop rotation, adjust the subsidy standard based on the market price differences between maize and soybean and regional yield differences. Align the producer subsidy and the crop rotation subsidy, dynamically balance yields, and tailor subsidy standards to local conditions to better support regional crop rotation practices.

6. Conclusions

This study integrated and analyzed 448 micro-level data samples from three case counties in Heilongjiang Province. Building on existing research on farmers’ fallow intentions and behavior, it formulated the research hypotheses. It constructed a structural equation model to investigate the driving factors behind farmers’ intention to rotate crops and their actual rotation behavior. The main conclusions are as follows:
(1) Despite a generally positive attitude towards crop rotation among farmers and their surrounding social networks, they face several external constraints. These include an aging farmer population, fragmented cropland, inadequate dissemination of crop rotation policies, a significant disparity in returns between maize and soybean, high cropland contiguous standards for obtaining crop rotation subsidies, and low subsidy amounts. As a result of these factors, while 63.62% of farmers have engaged in crop rotation since the policy’s inception in 2016, by 2021, only half of them continued this practice, with an average crop rotation ratio of 26%. This indicates significant potential for expanding crop rotation in Heilongjiang Province.
(2) The latent variables influencing the intention and behavior of crop rotation include farmer cognition and family endowment, both of which significantly positively impact farmers’ intention to rotate crops. Notably, farmer cognition plays a particularly critical role in shaping this intention. Both the rotation intention and the external environment significantly affect crop rotation behavior; however, rotation intention has a strong positive influence, while the external environment exerts a relatively weak negative impact. Additionally, the external environment significantly negatively affects farmer cognition.
(3) Among the observed variables, while farmers’ age has a notable adverse effect on both crop rotation intention and behavior, several factors can positively influence these aspects. Enhancing farmers’ awareness of crop rotation benefits and policies, leveraging supportive social networks, increasing the cropland area and the number of plots, reducing the profit gap between maize and soybean, lowering the criteria for obtaining crop rotation subsidy, and raising the subsidy standards—all contribute to a greater intention to rotate crops and encourage rotational behavior.

Author Contributions

Conceptualization, R.Z. and G.D.; methodology, R.Z.; software, R.Z.; validation, R.Z., G.D. and B.F.; formal analysis, R.Z.; investigation, R.Z.; resources, R.Z. and G.D.; data curation, R.Z and G.D.; writing—original draft preparation, R.Z.; writing—review and editing, B.F. and H.L.; visualization, R.Z.; supervision, G.D.; project administration, G.D.; funding acquisition, G.D. All authors have read and agreed to the published version of the manuscript.

Funding

This work was supported by the National Social Science Foundation of China (Grant No. 21BJY209).

Data Availability Statement

The data that support our research findings are available from the corresponding author on request due to privacy concerns (survey data).

Conflicts of Interest

The authors declare no conflicts of interest.

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Figure 1. Conceptual model of this study.
Figure 1. Conceptual model of this study.
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Figure 2. The localization of the study area and sample strategy.
Figure 2. The localization of the study area and sample strategy.
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Figure 3. Initial model of structural equations: e1 to e15 are the residuals of the 13 observed variables, as well as “rotation intention” and “rotation behavior”. The numbers on the arrows represent “fixed parameters”.
Figure 3. Initial model of structural equations: e1 to e15 are the residuals of the 13 observed variables, as well as “rotation intention” and “rotation behavior”. The numbers on the arrows represent “fixed parameters”.
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Figure 4. Corrected model path diagram: the numbers on the arrows are “standardized regression coefficients”, indicating “how many standard deviations the dependent variable changes for each standard deviation increase in the independent variable”.
Figure 4. Corrected model path diagram: the numbers on the arrows are “standardized regression coefficients”, indicating “how many standard deviations the dependent variable changes for each standard deviation increase in the independent variable”.
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Table 1. Rotated component matrix.
Table 1. Rotated component matrix.
NVariablesElement 1Element 2Element 3
1Cognition of rotation benefits−0.033−0.0260.769
2Cognition of rotation policy0.0160.2300.518
3Level of social support0.0550.1480.679
4Cropland area0.8520.040−0.003
5Number of plots0.783−0.016−0.021
6Age of farmers−0.6400.061−0.047
7Maize–soybean profit gap0.0410.773−0.102
8Standard for contiguous cropland−0.0850.7110.310
9Crop rotation subsidy standard−0.0380.7180.349
Table 2. Description of the variables.
Table 2. Description of the variables.
VariablesDescription
Level ILevel II
Farmer cognition (FC)Cognition of crop rotation benefits (FC1)Farmers’ level of agreement on the benefits of crop rotation (improved soil fertility, guaranteed yields, etc.) (1 = completely disagree; 2 = mostly disagree; 3 = not sure; 4 = mostly agree; 5 = completely agree).
Cognition of crop rotation policy (FC2)Farmers’ knowledge of crop rotation subsidy policy (subsidy standard, subsidy method, etc.) (1 = completely disagree; 2 = mostly disagree; 3 = not sure; 4 = mostly agree; 5 = completely agree)
Level of social support (FC3)Degree of support from the farmer’s surrounding social relations (family, friends, etc.) for practicing crop rotation (1 = not at all; 2 = not much; 3 = not sure; 4 = moderate; 5 = complete)
Family endowment (FE)Cropland area (FE1)Total area of family-run cropland (hm2) (actual input data)
Number of plots (FE2)Number of plots of family-run cropland (actual input data)
Age of farmers (FE3)Age of actual agricultural production decision-makers in the household (actual input data)
External environment (EE)Maize–soybean profit gap (EE1)Closing the maize–soybean profit gap would favor crop rotation (1 = completely disagree; 2 = mostly disagree; 3 = not sure; 4 = somewhat agree; 5 = completely agree)
Standard for contiguous cropland (EE2)Lowering the criteria for accessing crop rotation subsidy for concentrated patches would tend to favor crop rotation (1 = completely disagree; 2 = mostly disagree; 3 = not sure; 4 = somewhat agree; 5 = completely agree)
Crop rotation subsidy criteria (EE3)Increasing crop rotation subsidy rates would tend to favor crop rotation (1 = completely disagree; 2 = mostly disagree; 3 = not sure; 4 = somewhat agree; 5 = completely agree)
Rotation intention (RI)Family crop rotation program (RI1)Whether the household has plans to rotate crops in the future (1 = completely disagree; 2 = mostly agree; 3 = not sure; 4 = somewhat agree; 5 = completely agree)
Suggested crop rotation for others (RI2)Whether farmers would advise people around them to rotate crops (1 = not at all; 2 = not much; 3 = not sure; 4 = somewhat agree; 5 = completely agree)
Rotation behavior (RB)Recent crop rotation experience (RB1)Whether there has been a crop rotation since 2016 (1 = yes; 0 = no)
Cropland rotation ratio (RB2)The proportion of family-run cropland rotated in 2022 (actual inputs)
Table 3. Statistics of the variables.
Table 3. Statistics of the variables.
VariablesMean ValueStandard Deviation
Level ILevel II
Farmer cognition (FC)Cognition of crop rotation benefits (FC1)4.450.697
Cognition of crop rotation policies (FC2)3.261.024
Level of social support (FC3)4.000.935
Family endowment (FE)Cropland area (FE1)7.8769.411
Number of plots (FE2)8.999.051
Age of farmers (FE3)54.709.828
External environment (EE)Maize–soybean profit gap (EE1)4.011.096
Standard for contiguous cropland (EE2)3.841.169
Crop rotation subsidy criteria (EE3)3.781.224
Rotation intention (RI)Family crop rotation program (RI1)3.461.263
Suggested crop rotation practices for others (RI2)3.591.033
Rotation behavior (RB)Recent crop rotation experience (RB1)0.640.482
Cropland rotation ratio (RB2)0.260.381
Table 4. Fitness of initial and modified models.
Table 4. Fitness of initial and modified models.
Statistical TestAdaptation Criteria or ThresholdsInitial Model Validation ResultsModifying Model Validation ResultsModel Adaptability Judgment
Absolute Adaptability Index (AFI)
X2/df<5.004.1773.051adaptation
RMSEA<0.080.0840.068adaptation
GFI>0.800.9260.945adaptation
AGFI>0.800.8840.912adaptation
Relative Adaptability Index
NFI>0.800.8130.866adaptation
TLI>0.800.7970.869adaptation
CFI>0.800.8490.904adaptation
Streamlining Adaptability Index
PGFI>0.500.5900.592adaptation
PNFI>0.500.6050.633adaptation
PCFI>0.500.6310.661adaptation
Table 5. Results of hypothesis testing.
Table 5. Results of hypothesis testing.
NHypothesisUnstandardized Regression CoefficientsStandard Errors of Estimated ParameterspStandardized Regression CoefficientsAccept/
Reject
H1Rotation Intention (RI) ← Farmer Cognition (FC)3.1550.9920.0010.689Accept
H2Rotation Behavior (RB) ← Farmer Cognition (FC)0.3650.363ns0.199Reject
H3Rotation Intention (RI) ← Family Endowment (FE)0.0010.0000.0340.121Accept
H4Rotation Behavior (RB) ← Family Endowment (FE)0.0000.000ns−0.096Reject
H5Rotation Intention (RI) ← External Environment (EE)0.1650.298ns0.074Reject
H6Rotation Behavior (RB) ← External Environment (EE)−0.1620.0930.083−0.182Accept
H7Rotation Behavior (RB) ← Rotation Intention (RI)0.2760.057***0.687Accept
Note: *** indicates p < 0.01; ns indicates not significant at the 0.1 level. The “unstandardized regression coefficient” indicates “how many units the dependent variable changes for each unit increase in the independent variable”, while the “standardized regression coefficient” represents “how many standard deviations the dependent variable changes for each standard deviation increase in the independent variable”.
Table 6. Observed variable loadings table.
Table 6. Observed variable loadings table.
PathwayUnstandardized Regression CoefficientsStandard Errors of Estimated ParametersStandardized Regression Coefficientp
FC1 ← Farmer Cognition (FC)1.000 0.318
FC2 ← Farmer Cognition (FC)2.5130.5520.463***
FC3 ←Farmer Cognition (FC)2.4760.4900.587***
FE1 ← Family Endowment (FE)1.000 0.894
FE2 ←Family Endowment (FE)0.0420.0070.589***
FE3 ←Family Endowment (FE)−0.0310.006−0.399***
EE1 ← External Environment (EE)1.000 0.418
EE2 ← External Environment (EE)1.7820.2470.699***
EE3 ← External Environment (EE)2.0210.2900.757***
RI1 ← Rotation Intention (RI)1.000 0.804
RI2 ← Rotation Intention (RI)0.7010.0760.689***
RB1 ← Rotation Behavior (RB)1.000 0.846
RB2 ← Rotation Behavior (RB)0.5460.0760.584***
Note: *** indicates p < 0.01.
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Zhang, R.; Du, G.; Faye, B.; Liu, H. Analysis of Farmers’ Crop Rotation Intention and Behavior Using Structural Equation Modeling: Evidence from Heilongjiang Province, China. Land 2025, 14, 158. https://doi.org/10.3390/land14010158

AMA Style

Zhang R, Du G, Faye B, Liu H. Analysis of Farmers’ Crop Rotation Intention and Behavior Using Structural Equation Modeling: Evidence from Heilongjiang Province, China. Land. 2025; 14(1):158. https://doi.org/10.3390/land14010158

Chicago/Turabian Style

Zhang, Rui, Guoming Du, Bonoua Faye, and Haijiao Liu. 2025. "Analysis of Farmers’ Crop Rotation Intention and Behavior Using Structural Equation Modeling: Evidence from Heilongjiang Province, China" Land 14, no. 1: 158. https://doi.org/10.3390/land14010158

APA Style

Zhang, R., Du, G., Faye, B., & Liu, H. (2025). Analysis of Farmers’ Crop Rotation Intention and Behavior Using Structural Equation Modeling: Evidence from Heilongjiang Province, China. Land, 14(1), 158. https://doi.org/10.3390/land14010158

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