1. Introduction
Land is the essential link that binds the interactions of ecological elements and provides the space for human production and life [1]. And cultivated land is the most fundamental resource required for human survival and development [2]. Faced with daunting global challenges such as the escalation of climate warming and food insecurity, the imperative to attain cultivated land use sustainability (CLS) has emerged as a critical and pressing concern of the contemporary era. Since the concept of sustainable land use was officially established in 1990, a plethora of studies on CLS has been conducted by experts and scholars worldwide [3]. Previous studies on CLS have primarily focused on the following three aspects. First, several studies have measured the level of sustainable use of cultivated land in countries or regions through the construction of specific evaluation indicators [4,5]. For instance, in 1997, the International Conference on Sustainable Land Use Management and Information Systems divided the evaluation indicators of sustainable use of cultivated land into environmental and technical indicators, economic indicators, and social indicators. In accordance with the United Nations 2023 Sustainable Development Goals, Lu et al. selected 43 indicators from three dimensions to construct an evaluation index system for sustainable land use [4]. Second, some studies have focused on specific regions and the analysis of countermeasures. Scholars have examined issues regarding the sustainable use of cultivated land in plain areas [6], plateau regions [7], tropical zones [8], and ecologically fragile areas [9] and have offered optimization recommendations. Third, studies have analyzed the influencing factors of the sustainable use of cultivated land. These studies have explored the impacts of urbanization [10], agricultural labor transfer [11], land tenure [12], cultivated land fragmentation [13], and food production efficiency [14] on sustainable use. Within the realm of factors influencing CLS, existing research has identified the latent effect of part-time farming on CLS.
Part-time farming is a widespread phenomenon associated with the long-term global trend of urbanization, especially in China since its reform and opening-up in 1978. As a result of the shift in agricultural labor to non-farming employment resulting from urbanization [15], coupled with the influences of seasonal agricultural production and the instability of non-farming employment opportunities [16], part-time farming is increasingly becoming the predominant production and management strategy adopted by the majority of farmers [17]. And it has long been a rational choice for Chinese farming households to mitigate risks and maximize income. Among the impact of part-time farming on CLS, it has been found that part-time farming has an impact on fertilizer and pesticide application [18], agricultural labor productivity [19], agricultural irrigation [20], cultivated land protection behavior [21], willingness to return straw to the land [22], farmland transfer [23], and the abandonment of cultivated land [24]. Research indicates that part-time farming can promote the adoption of machinery by farmers, and mechanical tillage can significantly reduce the intensity of chemical fertilizer application [18]. The reduction implies a more rational control of agricultural pollution [25], effectively enhancing the level of CLS. Moreover, part-time farming may exacerbate land abandonment through changes in household income [26], thereby impacting the economic dimension of CLS.
Protecting and enhancing the sustainability of cultivated land is of significant importance for ensuring food security and promoting the sustainable development of society. Currently, the global population has reached 8 billion people; however, nearly 800 million people still face hunger issues [27], leading to sustained pressure on cultivated land production. Despite having vast areas of arable land in China, the per capita arable land area is less than 40% of the global average, meaning that China needs to feed nearly 20% of the world’s population with only 9% of the global arable land. With rapid economic development and urbanization, food production faces the dual risks of tightening land resources and declining cultivated land quality. In light of the increasingly severe international food security situation, exploring scientific pathways to enhance China’s cultivated land sustainability (CLS) is essential. It is also important to clarify the relationship between part-time farming and CLS, as well as to elucidate the mechanism that links part-time farming and CLS.
The previous research efforts in cultivated land sustainability have yielded substantial findings. However, there still exist areas that require further investigation: (1) Currently, there is still a lack of studies concerning the holistic correlation between part-time farming and CLS. Most of the land sustainability studies have focused on the impact of part-time farming on a single dimension of CLS, including food production efficiency, agricultural non-point source pollution, and agricultural irrigation efficiency, but lack a broader understanding of the impact of part-time farming on CLS. (2) Existing studies have only partially revealed the mechanisms through which part-time farming affects the sustainability of cultivated land, leaving much to be explored in understanding the intricate dynamics of this relationship. (3) Previous studies have paid insufficient attention to the impact of spatial variables and have not effectively explored the spatial differentiation in the impact effect.
In this study, based on the data collected from a field survey in seven villages across three provinces in China, we constructed an index system for evaluating CLS and analyzed the impact and mechanisms of part-time farming on CLS. The main objective of this research is to investigate the effect of part-time farming on CLS in different regions of China. Our research provides empirical evidence that validates the relationship between part-time farming and CLS. Furthermore, the study’s findings will assist the government in developing better plans and strategies to achieve socio-environmental sustainability at local, regional, and global levels by promoting land use sustainability.
2. Framework and Hypotheses
2.1. Analytical Framework of How Farmers’ Part-Time Farming Affects CLS
Sustainability is a people-oriented concept that consists of the following three elements: ecological sustainability, economic sustainability, and social sustainability. Sustainability signifies the responsible management of resources to satisfy the current ecological, economic, and social needs without compromising the ability to meet future requirements [28]. Cultivated land is the material foundation of agricultural production activities. The sustainable utilization of cultivated land calls for the effective use of the existing land resources, the mitigation or eradication of detrimental environmental impacts, and the enhancement of yields while augmenting the flow of natural capital [29]. This involves the multi-dimensional attributes of environmental, economic, and social dimensions. The farmer’s perspective is an essential vantage point for comprehending CLS, with the influence pathways encompassing both crowding-out and income effects.
Regarding the crowding-out effects, as the key agents of cultivated land conservation, the willingness of farmers to invest in protective measures significantly influences the quality of the cultivated land [30]. The structural changes in family labor are also closely linked to land management practices [31]. The nature of part-time farming, as a process of the reallocation of family labor resources between the agricultural and non-agricultural production sectors, is characterized by a significant displacement of labor and a rapid shift of the young and middle-aged labor force to the non-agricultural sector. Consequently, on the one hand, part-time farming leads to a large-scale transfer of labor to non-agricultural sectors, affecting the transfer of cultivated land, the scale of land management [19], and the forms of agricultural organization [19]. On the other hand, it also exacerbates the phenomenon of agricultural aging. The elderly labor force’s capacity to accept new technologies and equipment, as well as their willingness to invest, will further influence the protective investment in cultivated land, the input of agricultural production materials [32], and the efficiency of agricultural production [33]. In the context of the income effect, agricultural income is a significant factor that influences the farmers’ behavior in protecting their cultivated land [34]. The income of farmers directly determines the level of investment in agricultural production and management. Consequently, the pluralistic employment behavior of farmers, which leads to changes in family income and its structure, including the increase in non-agricultural income and the expansion of income channels, will affect the livelihood strategies of farmers [17], the agricultural production methods [35], and the application of technological equipment [36], thereby impacting CLS.
At present, China’s labor supply is experiencing the “Lewis turning point”, where the labor market has shifted from a long-term surplus to a state of general scarcity, and labor wages have transitioned from long-term stability to a phase of general increase [37]. The phase differences in agricultural labor and income will lead to stage differences in CLS. Furthermore, industrialization and urbanization are the primary factors driving the phenomenon of farmers’ pluralistic employment [15]. Regional socio-economic disparities also influence the process by which farmers’ pluralistic employment affects CLS. Based on this, the analytical framework is proposed in Figure 1.
2.2. Hypothesis
The crowding-out and income effects on CLS manifest in the multiple dimensions of ecology, economy, and society, with varying impacts. Regarding the positive influence of part-time farming, on the one hand, the crowding-out of agricultural labor by pluralistic employment can optimize the allocation of labor in terms of division of labor and specialization, intensifying the degree of labor specialization in agricultural production [38]. This is conducive to accelerating land transfer, improving the concentration of plots, promoting the scale operation of land, and simultaneously fostering the development of agricultural mutual aid organizations [19], thereby enhancing the social sustainability of CLS. On the other hand, the increase in non-agricultural income brought about by a higher degree of multiple employment opportunities will provide farmers with additional income channels, significantly alleviating their financial constraints. This enables farmers to compensate for the shortage of labor by improving the level of agricultural mechanization and purchasing specialized services [39], thereby enhancing agricultural production efficiency [33], and serving the needs of economic sustainability.
Additionally, the increase in non-agricultural income may diminish the enthusiasm of farmers for engaging in agricultural activities, thereby reducing the use of pesticides and fertilizers [18], which will continue to contribute to the green production of cultivated land. In terms of the adverse effects of part-time farming, such behaviors are likely to result in a lack of sufficient labor within farming households for agricultural production. To mitigate these negative impacts on agricultural production, the households engaged in multiple employment may increase the input of pesticides and fertilizers [32] and adopt more extensive irrigation methods [40], which could pose hazards to the cultivated land ecosystem. In addition, the shortage of labor may inhibit the adoption of new technologies and equipment, thereby restricting the mechanization of agriculture [41] and the scale of land operations [42], reducing agricultural production efficiency [43]. The shift in young and middle-aged labor to non-agricultural industries may also exacerbate the aging phenomenon of the agricultural labor force [44]. Typically, the elderly labor force has a lower capacity for using water-saving technologies and a lower willingness to invest in agricultural water conservancy facilities, leading to lower irrigation efficiency [40] and a low rate of straw return to the fields [45]. The increase in non-agricultural income may also lead to a lack of emphasis on the role of cultivated land by farmers, potentially resulting in land abandonment. Accordingly, hypotheses H1 and H2 are proposed in this study.
Part-time farming affects CLS through crowding-out effects.
Part-time farming affects CLS through income effects.
Part-time farming is bound to alter agricultural production and management activities, thereby causing changes in CLS. Before the agricultural labor force reaches the “Lewis turning point”, part-time farming by farmers will not have a negative impact on agricultural production [46]. Specifically, the impact of part-time farming on CLS is mainly divided into the following two stages: First, when the degree of part-time farming is low, farmers have a strong dependence on and an emotional attachment to the land. Although part-time farming may lead to the transfer of some agricultural labor, this period is mainly characterized by seasonal farming or working outside during the agricultural off-season, which does not significantly negatively affect grain production. At the same time, part-time farming increases the level of family income, and farmers may offset the resultant labor shortage by improving the level of mechanization. The farmers who are more exposed to modern concepts are also more likely to adopt more cultivated land protection behaviors, enhancing the level of CLS. Second, when the degree of part-time farming is high, the main source of household income for farmers comes from non-agricultural industries and the economic and emotional dependence on cultivated land decreases. The labor force engaged in agricultural production gradually becomes both scarce and aging, compounded by the lower environmental awareness and weaker acceptance of new things among the elderly, which is very likely to lead to low irrigation efficiency, low straw return rate, less arable land protection behaviors, and a significant area of abandoned land. At the same time, to alleviate the negative impact of labor shortages during this period, farmers will increase the use of agricultural chemicals such as pesticides and fertilizers, thereby reducing the ecological level of CLS. Furthermore, despite a notable rise in farmers’ household income, the diminished status of agriculture may result in a suboptimal investment of production factors by farmers. Consequently, the substitution effect of agricultural mechanization and intermediate organizations has not fully compensated for the loss of the agricultural labor force, thereby dampening the overall level of CLS. In summary, hypothesis H3 is proposed in this study.
The impact pattern of the degree of part-time farming on CLS exhibits an inverted U-shape.
Additionally, due to the influence of factors such as geographical location, natural resources, national policies, and social culture, there are significant regional disparities in the levels of industrialization and urbanization in China, with the eastern regions showing higher levels compared to the central regions. In turn, the central regions surpass the western regions in the development of industrialization and urbanization. The regional disparities in these respective levels are bound to lead to regional differences in the degree of part-time farming, as well as regional disparities in the crowding-out and income effects, which in turn will cause regional differences in CLS. In summary, hypothesis H4 is proposed in this study.
There exists a significant regional heterogeneity in the impact of part-time farming on CLS.
3. Methodology and Data
3.1. Statistical Modeling
After examining the scatter plot of the sample data and evaluating several models for data fitting, the researchers determined that the quadratic regression model exhibited the best goodness of fit.
In this study, the correlation between part-time farming and CLS was analyzed using an OLS model, defined in Equation (1):
(1)
where, in (1) is CLS score of farmer ; is the independent variable; the degree of part-time farming; stands for the square number of the independent variable; represents the set of control variables; and represent the intercept and error terms, respectively.In addition, this study conducted regional heterogeneity and moderation effect analysis on the model results. We employed truncation handling, robust regression, and ridge regression analysis to perform robustness checks on the OLS model results.
The moderation effect model is defined by the following equation:
(2)
in Equation (2) is the moderating variable, with the meanings of other variables being the same as in Equation (1).
3.2. Data Collection
The data we collected were obtained from a field survey conducted from January to March 2024. According to the Economic Divisions of China, a total of seven villages were sampled for investigation from three representative provinces of China, including Shandong Province in the eastern region, Henan Province in the central region, and Shaanxi Province in the western region. The characteristics of these seven villages are quite similar to the economic and social development status of their respective regions, allowing them to accurately mirror the diverse economic and social conditions found across China. The survey mainly covered the characteristics of farmers’ individual and family structures, inputs of production factors for farmland cultivation, household income and expenses, agricultural production subsidies, as well as land transfer and family farmland area. To ensure the randomness of the sampling process, team members first developed a sampling frame based on the household registration list, then used a simple random method to select samples, and replaced households that were not available or refused to participate with random samples. In the end, a total of 246 households in seven villages were visited in this survey, and after validity testing, 237 valid questionnaires were collected. The KMO value in the validity test was 0.743, and the result of Bartlett’s sphericity test was significant (p < 0.001), indicating good data quality for this survey. Additionally, Cronbach’s alpha value of the data was 0.697, indicating good reliability of the questionnaire.
3.3. Index System for Assessing CLS
Based on the connotations of sustainability and considering the evaluation indicators for sustainable land use, this study subdivides CLS into ecological sustainability, economic sustainability, and social sustainability. Additionally, referring to the global indicator framework for the Sustainable Development Goals issued by the United Nations [47], and drawing from the research of scholars such as Lu [4], Ge [48], Ustaoglu [49], Lv [50], etc., this study establishes three-level indicators in the index system for CLS (Table 1). The specific reasons for establishing these indicators are as follows:
(1). Agricultural subsidy amount. Agricultural subsidy can be interpreted as providing certain preferential treatment or compensation in order to promote farmers’ behavior in improving the quality of cultivated land [51]. The impact of the agricultural subsidy amount on farmers’ behavior in protecting cultivated land triggers a chain reaction, ultimately affecting CLS.
(2). The number of mu (1 mu = 0.1647369 acres) of cultivated land for which agricultural insurance has been purchased. Agriculture is counted as one of the most vulnerable areas to disastrous weather; therefore, purchasing agricultural insurance is an essential approach to diversifying the risks of agricultural production and operation [52]. Purchasing agricultural insurance, a crucial strategy for enhancing farmers’ resilience against risks, serves as a significant indicator in assessing CLS.
(3). Whether the farmer has joined a farmer’s cooperative or other mutual agricultural production organization. As farmers’ engagement in mutual agricultural production organizations drives improvements in the scale management of cultivated land and the degree of agricultural mechanization, it subsequently boosts the progress of agricultural labor efficiency [53]. The fully developed mutual agricultural production organization can prevent farmers from abandoning cultivated land and enhance CLS.
(4). The average annual grain yield per mu and net income per mu of grain. The average grain yield per mu is a direct factor in measuring the productivity of cultivated land, and the net income of grain per mu is closely related to the average grain yield per mu. Both of these are important indicators of CLS.
(5). Cultivation patterns of cultivated land. Within cultivation patterns, there are three distinct categories: fully mechanized, partially mechanized, and entirely reliant on manpower. The mechanized farming pattern can enhance agricultural labor productivity [54], thereby facilitating the development of CLS.
(6). Whether to hand over the cultivated land to others for management. Handing over cultivated land to others for management is the main manifestation of cultivated land circulation. This behavior enables the transfer of cultivated land to more productive farmer through the market [55], which helps to promote the scale management of cultivated land and prevent its abandonment [56].
(7). Whether the cultivation process incorporates at least one of the following practices for the protection of cultivated land. Adopting acts of cultivated land protection during cultivation can directly improve cultivated land productivity [57] and enhance CLS.
(8). The number of mu of cultivated land treated with the return of straw to the land. Straw returning can enhance the soil’s carbon sequestration capacity, reduce carbon loss in the soil, and mitigate environmental pollution [58]. As the amount of cultivated land treated with straw returning increases, so does the potential for improving CLS.
(9). The main irrigation method and average irrigation time per mu. The use of scientific irrigation methods can improve the irrigation efficiency of cultivated land [59]. Provided that the irrigation effect is not disturbed, an increase in irrigation efficiency, manifesting as a reduction in the irrigation time per mu, positively correlates with an elevation in the degree of CLS.
(10). The amount of money spent on pesticides and fertilizer per mu of cultivated land in the last year. The excessive use of pesticides and fertilizer can lead to soil compaction, water pollution, and a decline in cultivated land fertility [60]. As the amount of pesticides and fertilizers purchased per mu increases, meaning the usage of pesticides and fertilizers is enhanced, it can result in damage to the ecological level of CLS.
After standardizing the positive and negative indicators, we constructed an indicator system for CLS using the entropy method (Table 1). In the existing research, it should be noted that farmland abandonment is an important indicator for measuring CLS. However, no instances of farmland abandonment were observed in China during the survey period. Hence, this study does not include farmland abandonment in the indicator system for CLS.
Table 1A comprehensive index system for assessing CLS.
| Level 1 | Level 2 | Level 3 | Weight | Direction of Impact |
|---|---|---|---|---|
| Cultivated | Social Sustainability | Annual agricultural subsidy amount | 2.31% | + |
| The number of mu of land for which agricultural insurance has been purchased | 9.03% | + | ||
| Whether the farmer has joined a farmer’s cooperative or other mutual agricultural production organization | 16.05% | + | ||
| Economic Sustainability | Net income per mu of grain | 1.41% | + | |
| Cultivation patterns of cultivated land | 18.69% | - | ||
| Whether to hand over the farmland to others for management | 18.25% | + | ||
| Whether the cultivation process incorporates at least one of the following practices for the protection of cultivated land | 11.78% | + | ||
| The average annual grain yield per mu | 0.09% | + | ||
| Ecological Sustainability | The number of mu of cultivated land treated with straw returning | 2.46% | + | |
| The main irrigation method | 18.47% | - | ||
| Average irrigation time per mu | 0.73% | - | ||
| The amount of money spent on pesticides per mu of cultivated land last year | 0.24% | - | ||
| The amount of money spent on fertilizer per mu of cultivated land last year | 0.49% | - |
Note: 1 mu = 0.1647369 acres.
3.4. Variables
3.4.1. Dependent Variables
The main focus of this study was to examine the impact of the degree of part-time farming behavior on CLS. Therefore, CLS served as the dependent variable in this research. Drawing on existing research findings, this study divided CLS into the following three positive sub-indicators: social sustainability, economic sustainability, and ecological sustainability. The researchers employed the entropy method to calculate the weights of each indicator and the scores of each level of indicators, ultimately determining CLS. A higher score for CLS indicated a higher level of sustainability.
3.4.2. Independent Variables
The degree of part-time farming was the independent variable in this study. According to the criteria of China’s Third Agricultural Census, full-time farming households referred to farming households whose household income was derived entirely from agricultural sources, and farming households engaged in part-time farming referred to farming households whose household income was partially derived from non-agricultural sources [48]. Following the definition of part-time farmers and with reference to the existing research results [18], this study used the proportion of non-farming income to the annual net income of households to express the degree of part-time farming. In other words, the higher the proportion of non-farming income in the annual net income of households, the higher the degree of part-time farming.
3.4.3. Control Variables
To mitigate the potential biases in the estimation results of regression models caused by omitted variables, this study, apart from the variable of farmers’ part-time farming, also took into account other variables that may affect the sustainability of cultivated land use as much as possible. Drawing on previous studies, this study included the following five control variables: (1) The health status of the household head; (2) Whether the household head has received agricultural skills training; (3) Whether the household head has other commercial insurance; (4) The total years of education for other family members besides the head of the household; (5) The distance between the rural area where the farmers reside and the nearest county seat.
3.4.4. Moderating Variables
This study conducted moderation analysis on the relationship between the core variables. The moderating variables were the number of non-agricultural labor participants in the family and the gender of the household head. Typically, the number of non-agricultural labor participants in the family directly affected the non-agricultural income, thereby influencing the degree of part-time farming. However, in contemporary China, with the upgrading of the industrial structure in major urban areas, the opportunities available for Chinese migrant workers are decreasing [61]. Whether the relationship between agricultural labor migration and non-agricultural income (degree of part-time farming) continues to be significant requires examination with the latest evidence. Additionally, the gender of the household head has a significant influence on the family; therefore, this study sets this as a moderating variable. The specific variable settings and descriptive statistics are presented in Table 2.
4. Results
4.1. Impact Pattern Analysis
The analysis of the sample data revealed that the impact of the degree of part-time farming on the level of CLS presented an inverted U-shaped pattern. Therefore, in this study, we performed a curve fitting on the core variables and established a quadratic regression model.
From Model 1, it can be inferred that the coefficient of determination for the quadratic regression model was 0.369, indicating that the variance in CLS data that can be explained by the degree of part-time farming was approximately 36.9%. Model 1 also passed the F-test (F = 18.393, p < 0.05). Specifically, the regression coefficient for the degree of part-time farming (PE) was 0.904 (p < 0.001), and the regression coefficient for the square of the degree of part-time farming (PE2) was −0.928 (p < 0.001). This implies that the degree of part-time farming has a significant “inverted U-shaped” effect on CLS. That is, in the initial stages of the increase, part-time farming continuously improves the level of CLS. However, after reaching a turning point, higher levels of the degree of part-time farming lead to lower levels of CLS (Table 3). From the model fitting of various sub-indicators of CLS, it is evident that the degree of part-time farming among farmers significantly impacts social sustainability, economic sustainability, as well as ecological sustainability, with the distribution of data mirroring that of the overall indicators, i.e., an inverted U-shape trend. (Models 2, 3, and 4).
The researchers utilized the U-shaped test method proposed by Lind et al. [62] to analyze the data, thereby obtaining the inflection point value of the degree of part-time farming on the “inverted U-shaped” impact on CLS. From the analysis results, the inflection point value was determined to be 0.45, indicating that when the degree of part-time farming exceeded 45%, the level of CLS began to decline. This validates hypothesis H3 (Table 4).
4.2. OLS Regression
This study separately established OLS regression models for CLS and three secondary indicators (SF1, SF2, SF3). Both independent and control variables were included in the models using the “enter” method, which helped to highlight the relationships between core variables more prominently in the data results. The results of Model 5, Model 6, Model 7, and Model 8 showed that after adding control variables, the variables in the degree of part-time farming (PE) and the square of the numerical value of the degree of part-time farming (PE2) remained significant in each model. The explanatory efficacy of the models also demonstrated robust performance (Table 5).
Model 5 revealed the effects of the control variables. Notably, it is clear that the better the health status of the householder head (C1), the higher the level of CLS. Additionally, households benefit from heads with agricultural skills training (C2), showing that expertise in farming positively impacts sustainability. Moreover, having other commercial insurance (C3) is shown to increase CLS, indicating the effectiveness of risk management strategies in supporting agriculture. Furthermore, the total years of education of non-head family members (C4) is a significant factor, with higher education correlating with better CLS. Lastly, the distance from the county seat (C5) also plays a role, with greater distances associated with higher levels of CLS. These results corroborate previous studies [63,64].
The reasons behind the results can be outlined as follows: a healthier householder typically demonstrates better management capabilities over cultivated land use. The heads of households who undergo agricultural skills training exhibit heightened levels of agricultural knowledge and demonstrate a greater inclination toward agricultural work. The households with commercial insurance generally enjoy higher incomes compared to those without, and higher education levels among other family members besides the head of the household often correlate with higher household incomes. Consequently, such households possess adequate financial resources to protect their cultivated land; for instance, through the adoption of practices like the application of organic fertilizers, they can sustain soil fertility [65]. The farther away the household is from the county seat, the higher the overall cost of working in the city and the greater the proportion of agricultural income in their household income. In this case, farmers’ willingness to maintain the fertility of cultivated land and increase their income from growing grain will also be stronger. Therefore, the assumption is that H2 (income effect) is true. In turn, Model 6, Model 7, and Model 8 analyzed social sustainability, economic sustainability, and ecological sustainability, and the data results were essentially consistent with those of Model 5. However, the influence of the three control variables on the secondary indicators (SF1, SF2, SF3) was weakened by the physical health status of the head of household (C1), whether the head of household had other commercial insurance (C3), and the education level of other family members (C4) (Table 5).
4.3. Heterogeneity Analysis
Considering the long-standing regional disparities in China, particularly the existence of the developed manufacturing industry in the eastern region [66], while the central and western regions account for a larger proportion of China’s total grain output and bear greater ecological protection responsibilities [67], it is necessary to conduct a test for regional heterogeneity in the relationships between the core variables. Based on common regional division methods, this study divided China into eastern, central, and western regions and sought to separately provide model estimates for each region (see Table 6 for details). The results vividly showed significant differences in the levels of CLS among the eastern region (Model 9), central region (Model 10), and western region (Model 11). From the eastern to central region and then to the western region, the level of CLS continuously and significantly decreased, with values of 1.465, 1.432, and 0.36. In all three regions, the square of the degree of part-time farming (PE2) significantly influenced the level of CLS, with negative regression coefficients. This implies that beyond the inflection point value, the level of CLS in all three regions decreases as the degree of part-time farming continues to rise. However, before reaching the inflection point value, the degree of part-time farming had a significant positive impact on the level of CLS in the eastern and central regions (1.465 ***, 1.432 ***). Conversely, the western region did not exhibit the same relationship between variables. Based on this, hypothesis H4 is confirmed.
4.4. Moderation Effect Analysis
In this study, the moderating variable of household non-farming labor participation (Re1) was added to the model for moderating effect analysis (Table 7). Model 12 showed that the interaction term (PE* Re1) between the degree of part-time farming and the number of household non-farming labor participants was significantly at the p < 0.05 level. The results showed that the household labor structure of farming households had a significant moderating effect on the relationship between the degree of part-time farming and the level of CLS. The data analysis results indicate a significant negative effect of part-time farming on the overall level of CLS. Moreover, the involvement of more family members in non-agricultural labor intensifies this negative effect. This implies that as the number of family members engaged in non-agricultural labor increases, this negative effect becomes more pronounced (Figure 2a). Based on this, we assume that hypothesis H1 is valid.
As a large number of male laborers have migrated to urban areas, women have emerged as the primary workforce in Chinese agriculture. Compared to men, women exhibit significant differences in terms of ideology, physiological conditions, and off-farm employment opportunities. Therefore, we believe it is necessary to consider the influence of the household head’s gender on the correlations between core variables. Through the introduction of the moderating variable of the household head’s gender into the analysis, Model 13 demonstrates that the interaction term between the degree of part-time farming and the household head’s gender (PE* Re2) was significant at the p < 0.01 level. This result indicates that the gender of the household head significantly moderates the relationship between part-time farming and the level of CLS. Overall, the degree of part-time farming among farmers has a significant negative effect on the level of CLS, and this effect is more pronounced in households where the head of the household is female. In other words, among two households with the same degree of non-agricultural employment, the level of CLS is lower in the household headed by a female compared to the one headed by a male, as illustrated in Figure 2b. This can be explained by the fact that female household heads engage less in land protection activities (Re2: Female = 0). This study suggests that households headed by females often indicate the absence or weakening of the husband’s role, and such households usually face more difficult livelihoods. Therefore, these farmers lack both the willingness and the capability to undertake land protection activities. Additionally, the physiological characteristic of women being physically weaker than men may also contribute to the aforementioned result, as households headed by females generally have fewer adult male laborers.
4.5. Robustness Tests
In order to validate the robustness of the research findings, three methods of robustness tests were employed in this study (Table 8). The first method involved truncating the data (Model 14). Even after truncation (at both sides 1%), the results remained significant. Namely, the higher the degree of farmers’ part-time farming activities, the lower the level of CLS. Regression coefficients, coefficient directions (positive/negative), significance levels, and collinearity indicators did not exhibit significant changes compared to the baseline model. The second method employed robust regression (Model 15), yielding consistent results. Considering the collinearity effects of the quadratic terms, the third method utilized ridge regression on core variables (Model 16). The model yielded a determination coefficient of 0.24, supported by an F-test (F = 10.358, p < 0.001). These three model results remained consistent with the OLS model, thus verifying the robustness of the results.
5. Discussion
5.1. Directing Farmers to Maintain a Rational Proportion of Part-Time Income
Sustainable development is an important goal for the advancement of society and is the central theme of our era. In the process of achieving sustainable development, sustainable thinking is essential for scholars, government officials, social workers, and the general public. In this study, grounded in sustainability thinking, we aimed to understand and elucidate the impact of part-time farming on CLS. Our goal was to promote the sustainable use of cultivated land through the obtained insights. To achieve this, we constructed an index system for CLS and analyzed the effects of part-time farming on this sustainability, thereby bridging a gap in the existing research. The findings revealed that the impact of part-time farming on CLS follows an inverted U-shaped pattern. This pattern is also observed in the economic, social, and ecological dimensions of CLS (Table 3). Some studies have suggested that part-time farming leads to a shortage of agricultural labor, resulting in decreased agricultural production efficiency and the increased use of pesticides and fertilizers [68], which in turn undermines CLS. While this reasoning is partially valid, it overlooks the fact that many rural migrants move back and forth between urban and rural areas when the degree of part-time farming among farming households is low. Specifically, these migrants work in urban areas during the off-season and return to their hometowns during peak farming periods, thus mitigating the negative impact of the “crowding out effect” on CLS. During this period, the predominant influence is the “income effect”, showing a positive correlation between the degree of part-time farming among farmers and CLS. As the degree of part-time farming surpasses 45%, the “crowding out effect” gradually intensifies, diminishing the impact of the “income effect” and consequently leading to a decline in CLS. Hence, enhancing subsidies for farming households engaged in grain cultivation and judiciously adjusting grain purchase prices should be the focal points of China’s agricultural policy in order to effectively manage the proportion of part-time income within the total household income.
5.2. Facilitating Structured Reallocation of Agricultural Labor Force and Bolstering Engagement of Female Household Heads in Cultivated Land Conservation Efforts
Since the 1980s, China’s industrialization, marketization, and urbanization have led to a significant migration of agricultural labor to cities, resulting in a demographic shift marked by an increase in the proportion of women and older workers in the agricultural sector [69]. This change indirectly affects CLS. Generally, households with a higher proportion of members engaged in non-agricultural work tend to demonstrate lower levels of attention to CLS among those with the same degree of part-time farming (Table 7). This is because more non-agricultural workers in a family increase the “crowding out effect” of part-time work on the labor force, hindering the sustainable use of cultivated land. Additionally, our findings suggest that households with female heads of household tend to demonstrate lower levels of CLS activity compared to those with male heads of household, within the same level of part-time employment (Table 7). The previous research further reinforces the arguments advanced in this paper and has revealed that women generally have lower psychological expectations regarding the value of cultivated land use compared to men, often due to disparities in education, policy advocacy, and economic and social development [70]. Additionally, women shoulder significant childcare responsibilities [71], leading them to prefer managing smaller, less resource-intensive cultivated land [72], and they exhibit less proactive behavior in conserving cultivated land. Consequently, households led by women tend to exhibit lower levels of CLS activity. Therefore, the Chinese government must strategically guide the transition of agricultural labor, encourage men to reengage in agricultural activities in their hometowns, optimize the demographic composition of rural communities, and mitigate the adverse impact of agricultural labor shortages on the sustainable use of cultivated land. In addition, the government can enhance women’s awareness of cultivated land protection and their psychological expectations of cultivated land value through ideological education, policy publicity, economic incentives, etc., so as to effectively mobilize women’s enthusiasm for cultivated land protection. For example, the Law of the People’s Republic of China on the Protection of Women’s Rights and Interests, revised in 2022, clearly emphasizes the protection of rural women’s land and their related rights and interests, which can help raise women’s psychological expectations of the value of cultivated land to encourage their effective protection of it [73].
5.3. Other Strategies to Enhance CLS
Influenced by geographical positioning, resource allocation, and government policies, China displays significant disparities in economic development among its regions, with the eastern region leading, followed by the central region, and then the western region [74]. Aligned with the level of economic development, employment opportunities and wage levels in the eastern region significantly outstrip those in the central and western regions. Consequently, in the early 21st century, a substantial rural population, particularly from the central and western regions, migrated to the urban areas in the eastern region. This phenomenon resulted in a pronounced shortage in the western region’s young and middle-aged rural labor force, alongside a substantial conversion of high-quality cultivated land in the eastern region for non-agricultural purposes. In comparison with the eastern and central regions, the rural population’s migration distances are greater in the western region. Consequently, commuting costs between urban and rural areas (i.e., between workplace and hometown) are higher, exacerbating the pronounced “crowding out effect” of part-time farming on the household labor force, with limited alleviation prospects. Therefore, at a lower degree of part-time farming, it has no significant positive impact on CLS in the western region. Furthermore, as the degree of part-time farming increases, the decline in CLS in the western region occurs at a notably slower rate (Table 6). Previous studies have also corroborated this finding, suggesting that women living in developed areas hold higher expectations regarding cultivated land [70]. Conversely, in the relatively underdeveloped western regions, female-headed households have lower expectations for cultivated land, resulting in fewer protective farming behaviors among farmer households in these areas. Hence, government policies should be tailored to the unique characteristics of different regions, including location, resource endowment, transportation conditions, and policy intensity, to implement region-specific strategies for enhancing CLS.
This study found a direct correlation between higher levels of education, improved physical health, and comprehensive agricultural skills training among farmers and the heightened CLS within their households (Table 5). Previous research has also demonstrated that higher levels of education are correlated with an increased likelihood of farmers adopting new technologies or methods to enhance CLS [75]. Moreover, initiatives such as the establishment of field schools [76] and providing agricultural skills training for farmers have been shown to encourage more proactive cultivated land conservation behaviors. In recent years, with improvements in the education and healthcare systems, the educational attainment of the Chinese population has been steadily increasing. As a result, a growing number of agricultural workers have received agricultural skill training, contributing to enhanced cultivated land-management practices. Additionally, there has been an overall improvement in the physical health of the population. Therefore, highly skilled professional farmers will become the primary participants in China’s future agricultural sector. The advances in science and technology, coupled with enhancements in human capital, have mitigated the adverse effects of an aging agricultural labor force and rural depopulation on cultivated land conservation [77]. So far, an increasing number of regions in China have embraced and implemented smart agricultural practices, not only enhancing the mechanization level of agricultural production but also integrating advanced technologies such as IoT detection, artificial intelligence analysis, and drones. As a result, the pessimistic perspectives regarding the state of cultivated land protection in China are becoming increasingly untenable.
Drawing from empirical surveys, this study thoroughly explores the relationship between the degree of part-time farming and CLS in China, revealing statistically significant findings. These results contribute to advancing sustainable development practices at the land level. However, it should be noted that this study primarily relies on the proportion of non-farming income in household net income to assess farmers’ part-time farming activities, lacking a deeper analysis regarding factors like location, timing, and the number of farmers engaged in part-time farming. Additionally, this study only examines the sustainability of cultivated land used for food crops, excluding the consideration of cultivated land for cash crops. Furthermore, this study does not analyze how farmers’ part-time farming affects the sustainability of different types of cultivated lands. Despite concerns from some scholars about widespread cultivated land abandonment in China [78], none of the farmers surveyed reported such instances. This absence may be attributed to the long-standing protection of cultivated land by the Chinese government [79], leading to cultivated land abandonment being excluded from the study’s sustainability index system. These are crucial areas for future research, as a thorough understanding of these issues can better promote sustainable development at the land level.
6. Concluding Remarks
Land relates to the sustainability of our coupled human–Earth system through various entry points. Our study takes a social–environmental system perspective on cultivated land resources and thus operationalizes CLS in a broader sense than the environmentally friendly use of cultivated land. Our research contributes by proposing a comprehensive indicator system for assessing CLS and shedding new light on the understudied connection between CLS and part-time farming, which is a long-term nationwide trend in rural China. We quantitatively assessed the impact pattern and moderating effects of part-time farming on CLS, using first-hand survey data obtained from village representatives in Eastern, Central, and Western China. Based on the rich empirical findings, we further proposed actionable suggestions for policymakers to advance CLS.
To recap, this study provides three key empirical insights. First, the impact pattern of the degree of part-time farming on CLS exhibits an inverted U-shape, with a clear turning point at 45%. When the degree of part-time farming is below 45%, CLS increases with the degree of part-time farming. However, once the degree of part-time farming exceeds 45%, CLS begins to decline with any further increases. Second, there exists significant regional heterogeneity in the impact of part-time farming on CLS. Prior to reaching the inflection point, the degree of part-time farming has a notably positive impact on CLS in the eastern and central regions, whereas no significant positive correlation is observed in the western region. Following the inflection point, CLS in all three regions experiences a significant decline with increasing degrees of farmers’ part-time farming at varying magnitudes. Third, the involvement of households in non-farming activities and the gender of the household head significantly moderate the correlation between farmers’ part-time farming and CLS. Additionally, factors such as the physical health of the household head, the cumulative years of education among family members excluding the head, access to commercial insurance, and participation in agricultural skills training play crucial roles in influencing CLS. Moreover, households with healthier heads and higher levels of education among family members (excluding the head) tend to exhibit greater awareness and protection of CLS. Similarly, farmers with access to commercial insurance and those who have undergone agricultural skills training demonstrate higher levels of understanding of and respect for CLS. We anticipate that these findings will support governments facing the long-term trend of part-time farming in formulating informed policies to advance rural and regional sustainability with CLS as an entry point.
Conceptualization, X.P.; methodology, X.P. and C.W.; software, C.W.; validation, C.W.; formal analysis, C.W.; investigation, X.P. and C.W.; resources, X.P. and X.Z.; data curation, C.W.; writing—original draft preparation, X.P. and C.W.; writing—review and editing, X.P., C.W. and X.Z.; visualization, X.P. and C.W.; supervision, X.Z.; project administration, X.P. All authors have read and agreed to the published version of the manuscript.
The data presented in this study are available upon reasonable request from the corresponding author.
We appreciate the constructive suggestions and comments from the editor and anonymous reviewers.
The authors declare no conflicts of interest.
Footnotes
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Enlarge this image.Figure 2. Moderating effect of household non-farming labor force participation and the gender of the household head. (a) The moderating effect of household non-farming labor force participation; (b) the moderating effect of the household head’s gender.
Descriptive statistics of the dependent, independent, and control variables.
| Category | Name | Definition | Notation | Min | Max | Mean | Standard Deviation |
|---|---|---|---|---|---|---|---|
| Dependent Variables | CLS | Score | SF | 0 | 0.87 | 0.17 | 0.19 |
| Social sustainability | Score | SF1 | 0 | 0 | 0.04 | 0.066 | |
| Economic sustainability | Score | SF2 | 0 | 1 | 0.09 | 0.120 | |
| Ecological sustainability | Score | SF3 | 0 | 0 | 0.04 | 0.063 | |
| Independent Variable | Degree of farmer’s part-time farming | % | PE | 0 | 1 | 0.63 | 0.29 |
| Control Variables | The health status of the household head | Very unhealthy = 1, very healthy = 5, 5 dimensions in total | C1 | 1 | 5 | 3.63 | 1.18 |
| Agricultural skills training | True = 1, false = 0 | C2 | 0 | 1 | - | - | |
| Other commercial insurance | True = 1, false = 0 | C3 | 0 | 1 | - | - | |
| Total years of education for other family members | Years | C4 | 0 | 60 | 19.17 | 10.638 | |
| The distance between the rural area and the nearest county seat | Kilometer | C5 | 9 | 25 | 13.16 | 4.89 | |
| Categorical Variable | Province | Eastern region = 1; central region = 2, western region = 3 | Prov | 1 | 3 | - | - |
| Moderating Variables | Non-farm labor force participants | Number | Re1 | 0 | 4 | 0.99 | 0.776 |
| The gender of the head of household | Male = 1, female = 0 | Re2 | 0 | 1 | - | - |
Quadratic regression of CLS against part-time farming with various model settings. Model 1: the regression results of part-time farming on CLS. Model 2: the regression results of part-time farming on the social level of CLS. Model 3: the regression results of part-time farming on the economic level of CLS. Model 4: the regression results of part-time farming on the ecological level of CLS.
| Model 1 (SF) | Model 2 (SF1) | Model 3 (SF2) | Model 4 (SF3) | |
|---|---|---|---|---|
| PE | 0.904 *** | 0.146 * | 0.586 *** | 0.150 * |
| PE2 | −0.928 *** | −0.155 ** | −0.583 *** | −0.167 ** |
| _cons | 0.047 | 0.025 | 0.002 | 0.023 |
| R2 | 0.369 | 0.183 | 0.35 | 0.225 |
| F | 18.393 *** | 4.067 * | 16.316 *** | 6.236 ** |
Note: * p < 0.05, ** p < 0.01, *** p < 0.001, standard errors are in parentheses.
Impact pattern of part-time farming on CLS (Inverted U-shaped).
| SF | SF1 | SF2 | SF3 | |
|---|---|---|---|---|
| Inflection point | 0.45 | 0.47 | 0.46 | 0.45 |
| Whether the inflection point is within the sample range | Yes | Yes | Yes | Yes |
| U-shaped test results | Inverted | Inverted | Inverted | Inverted |
OLS regression of CLS against part-time farming with various model settings. Model 5: the regression results of part-time farming on CLS. Model 6: the regression results of part-time farming on the social level of CLS. Model 7: the regression results of part-time farming on the economic level of CLS. Model 8: the regression results of part-time farming on the ecological level of CLS.
| Model 5 | Model 6 | Model 7 | Model 8 | |
|---|---|---|---|---|
| PE | 1.021 *** | 0.173 ** | 0.649 *** | 0.172 ** |
| PE2 | −1.077 *** | −0.203 *** | −0.659 *** | −0.187 *** |
| C1 | 0.024 ** | 0.006 | 0.014 * | 0.003 |
| C2 | 0.073 ** | 0.016 | 0.035 * | 0.021 * |
| C3 | 0.049 | 0.027 * | 0.033 | −0.012 |
| C4 | 0.001 | 0.002 *** | 0.001 | −0.001 |
| C5 | 0.009 *** | 0.003 *** | 0.004 ** | 0.002 |
| _cons | −0.212 *** | −0.075 *** | −0.127 *** | −0.003 |
| R2 | 0.269 | 0.238 | 0.204 | 0.097 |
| F | 12.031 *** | 10.224 *** | 8.370 *** | 3.528 *** |
Note: * p < 0.05, ** p < 0.01, *** p < 0.001, standard errors are in parentheses.
Regression results of part-time farming on CLS in China’s different regions. Model 9: Eastern China. Model 10: Central China. Model 11: Western China.
| Model 9 (Prov1) | Model 10 (Prov2) | Model 11 (Prov3) | |
|---|---|---|---|
| PE | 1.465 *** | 1.432 *** | 0.360 |
| PE2 | −1.505 *** | −1.470 *** | −0.434 + |
| C1 | 0.017 | 0.051 *** | −0.007 |
| C2 | 0.106 * | 0.073 | 0.036 |
| C3 | 0.154 | 0.044 | 0.040 |
| C4 | 0.000 | 0.003 | 0.006 ** |
| C5 | 0.006 * | 0.019 | 0.001 |
| _cons | −0.145 | −0.515 *** | −0.021 |
| R2 | 0.405 | 0.439 | 0.184 |
| F | 7.015 *** | 8.149 *** | 2.195 * |
Note: + p < 0.1, * p < 0.05, ** p < 0.01, *** p < 0.001, standard errors are in parentheses.
Moderation effect analysis results. Model 12: the moderated influence of part-time farming on CLS with the inclusion of the number of family members engaged in non-agricultural labor. Model 13: the moderated influence of part-time farming on CLS with the household head’s gender as a moderating factor.
| Model 12 | Model 13 | |
|---|---|---|
| C1 | 0.014 | 0.017 |
| C2 | 0.070 * | 0.076 ** |
| C3 | 0.042 | 0.040 |
| C4 | 0.001 | 0.002 |
| C5 | 0.007 ** | 0.008 ** |
| PE | −0.193 ** | −0.136 ** |
| Re1 | 0.02 | |
| PE* Re1 | −0.145 * | |
| Re2 | −0.026 | |
| PE* Re2 | 0.223 ** | |
| _cons | 0.003 | −0.049 |
| R2 | 0.136 | 0.145 |
| F | 4.472 *** | 4.850 *** |
Note: * p < 0.05, ** p < 0.01, *** p < 0.001, standard errors are in parentheses.
Robustness test results. Model 14: results of the censoring process for the data. Model 15: results of the robust regression tests. Model 16: the findings from the ridge regression analysis of the key variables.
| Model 14 | Model 15 | Model 16 | |
|---|---|---|---|
| PE | 1.021 ** | −0.922 ** | −0.618 ** |
| PE2 | −1.077 ** | 0.893 ** | 0.524 ** |
| C1 | 0.024 * | 0.011 | 0.021 * |
| C2 | 0.073 ** | 0.072 ** | 0.069 ** |
| C3 | 0.049 | 0.042 | 0.044 |
| C4 | 0.001 | 0.002 | 0.002 |
| C5 | 0.009 ** | 0.008 ** | 0.008 ** |
| _cons | −0.212 ** | −0.171 ** | −0.097 |
| R2 | 0.269 | 0.250 | 0.24 |
| F | 12.031 *** | 10.922 *** | 10.358 *** |
Note: * p < 0.05, ** p < 0.01, *** p < 0.001, standard errors are in parentheses.
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