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Article

Continuous Decline in Direct Incomes for Farmers Threatens the Sustainability of the Grain for Green Project

by
Luqian Li
1,2,
Xiaobo Hua
3,
Guangshuai Zhao
4 and
Erqi Xu
1,*
1
Key Laboratory of Land Surface Pattern and Simulation, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
2
University of Chinese Academy of Sciences, Beijing 100049, China
3
College of Humanities and Development Studies, China Agricultural University, Beijing 100193, China
4
Development Research Center, National Forestry and Grassland Administration, Beijing 100714, China
*
Author to whom correspondence should be addressed.
Land 2024, 13(8), 1307; https://doi.org/10.3390/land13081307
Submission received: 11 July 2024 / Revised: 14 August 2024 / Accepted: 16 August 2024 / Published: 18 August 2024
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Abstract

:
Understanding the impacts of ecological projects on household livelihoods is important in ensuring their sustainability. However, existing studies typically cover only a limited, discrete temporal point. Insufficient study duration makes it difficult to determine the full impact and dynamics of a project, affecting the veracity of the results. Representing one of the world’s largest investments in an ecosystem service programs, the Grain for Green Project (GGP) has an objective of increasing household incomes. Using data from 7112 questionnaires collected through 12 consecutive years (2007–2018) of field survey monitoring, this study examined the long-term impacts of the GGP on household livelihoods in the Beijing–Tianjin Sandstorm Source Control Project area through comparisons between participant households (PHs) and non-participant households (NPHs). The results show that GGP subsidies significantly increased household incomes only during the period 2007–2015, and their share of total household income decreased from 9.21% in 2007 to 1.62% in 2018. Subsidies for GGP cannot compensate farmers for losses due to the reduction in farmland. The above findings suggest that the effect of subsidies diminishes over time. Increased investment in jobs and skills training is needed to consolidate the GGP results. This provides new perspectives and empirical support for the study of international ecological compensation policies and sustainable development.

1. Introduction

At the beginning of the 21st century, the Beijing–Tianjin region of China witnessed a widespread outbreak of sandstorms, which adversely affected the ecological environment. In response, the Chinese government launched the Beijing–Tianjin Sandstorm Source Control Project (BTSSCP) to restore the ecological environment in the area [1]. The BTSSCP has been in place for more than 20 years and has achieved great progress in realizing its ecological goals [2,3]. In recent years, the BTSSCP has broadened its scope to include multiple goals, such as promoting social and economic development. One of the core contents of the BTSSCP is the implementation of the Grain for Green Project (GGP), under which the government provides compensation to farmers who have converted their farmland to forests or grasslands to reduce the economic losses caused by the reduction in farmland while alleviating poverty and diversifying rural livelihoods [4,5]. In practice, the impacts of the GGP on household livelihoods have been multifaceted and thus require further study [6].
Existing studies have mainly discussed the impact of the GGP on households’ livelihoods from the perspectives of four dimensions: changes in household income and sources of income [7,8], changes in livelihood strategies [9,10], changes in household production and consumption activities [11,12], and changes in rural labor arrangements [13,14]. The existing literature shows that the implementation of the GGP has provided additional off-farm employment opportunities for households, increased household incomes, and diversified their livelihood strategies [8,15]. However, other studies find that the GGP had a negative impact on households’ livelihoods. For example, government subsidies have not been able to fully compensate for economic losses due to the reduction in farmland [16], with there even being a reduction in net household incomes and livelihood diversification in some areas [6]. Taken together, it can be seen that a consensus has yet to be reached regarding the correlations among the GGP, rural livelihoods, and poverty reduction factors.
Current studies have mostly analyzed the changes in the livelihoods of participant households or project areas after the implementation of the GGP [17,18], or compared the differences between participant households (PHs) and non-participant households (NPHs) [19], to reflect the impact of the implementation of the GGP on household livelihoods. The selected indicators include household annual income [20,21], a household income diversity index [6,10], the number of migrant workers [22,23], etc. Existing studies tend to use survey data from only a few points in time. For example, Lu and Yin [24] found, through a review of 33 studies assessing the impacts of the GGP, that close to 20 studies only used one or two years of data for their analyses. However, the impacts of the GGP on households’ livelihoods tend to be lagging and long-term in character. Too few time points make it difficult to capture the full impact and dynamic changes, affecting the authenticity of the findings. Therefore, this study uses a long-series data tracking approach to explore the changes in consecutive years and at points of particular significance to analyze the impacts of the GGP on households’ livelihoods.
The Beijing–Tianjin Sandstorm Source Control Area, as an important desertification control project area in China, has a fragile ecological environment. Since the implementation of the GGP, the socioeconomic and ecological conditions of this area have changed significantly. However, the impact of the GGP on household livelihoods in this region is unclear and needs further research. Based on long-term monitoring, this study aims to answer the following research questions by establishing comparative experiments: How does GGP affect household livelihoods in the long-term? What measures can be taken to consolidate its gains for sustainable development? This study effectively solves the limitations of the existing research in the time dimension, and makes the long-term impact assessment of the GGP more comprehensive and accurate. Moreover, as one of the largest ecosystem service payment projects in the world [25], the success stories and the problems that have arisen during the GGP are of great significance for the study of global ecological compensation policies.

2. Material and Methods

2.1. Study Area

The BTSSCP was initiated in 2000 and includes two phases. Phase I covers 75 counties in Beijing, Tianjin, Hebei, and Shanxi, as well as the 5 Inner Mongolia provinces, from 2001 to 2012. Phase II was launched in 2013 and completed in 2022 to expand the scope of the project westward to include 138 counties in 6 provinces: Beijing, Tianjin, Hebei, Shanxi, Shaanxi, and Inner Mongolia (36°49′~46°40′ N, 105°12′~121°01′ E). The total area of the project increased from 458,000 to 706,000 km2 [26]. Considering the availability of data, this paper takes the 21 monitoring counties in the “Annual Tracking and Monitoring of Socioeconomic Benefits of the Beijing–Tianjin Sandstorm Source Control Project” (initiated by the Development Research Center of the National Forestry and Grassland Administration) as the study area, which covers 21 project counties (banners and cities) in the 3 provinces (autonomous regions) of Hebei, Shanxi, and Inner Mongolia (Figure 1). Most of these 21 counties are in the fragile ecological environment area; here, the sandstorm disaster had serious impacts, with agricultural production being the main source of household income. Therefore, improving the ecological environment in these areas is crucial for improving household livelihoods, and it is an ideal sample for studying the impact of the BTSSCP on household livelihoods. See Table S1 for numbered place names.

2.2. Data Collection

The data for this study come from the monitoring data published in the “Annual Tracking and Monitoring of Socioeconomic Benefits of the Beijing–Tianjin Sandstorm Source Control Project”, initiated by the Development Research Center of the National Forestry and Grassland Administration. The data include those from the “Beijing–Tianjin Sandstorm Source Control Project Farmers’ Questionnaire” (hereinafter referred to as the “Farmers’ Questionnaire”) and the “Beijing–Tianjin Sandstorm Source Control Project Farmers’ Survey” (hereinafter referred to as the “Farmers’ Survey”). The study period is 2007–2018, except in indicators that are not continuously monitored or for which monitoring was established after 2007. Both the Farmers’ Questionnaire and the Farmers’ Survey collect information on PHs and NPHs, but they differ in the number of farmers they include because they enumerate farmers differently. The Farmers’ Questionnaire collected 261 farming households, including 190 PHs and 71 NPHs, while the Farmers’ Survey collected 382 farming households, including 334 PHs and 48 NPHs.
The Farmers’ Questionnaire is shown in the form of questions in which PHs are required to answer all questions and NPHs are required to answer only some of the questions. The questionnaire mainly includes basic information regarding farmers’ participation in the GGP, their satisfaction with its ecological, social, and economic benefits, and the degree to which their family life and economic conditions have improved after participating in the GGP (the questionnaire data table is provided in the Supplementary Materials).
All questions in the Farmers’ Survey are to be completed by both PHs and NPHs. The Farmers’ Survey is divided into three sections: basic information about the household, progress made by and implementation of the GGP, and socioeconomic conditions. Each section consists of a number of questions. The first includes basic information, the ownership of property and assets, and the management of land by farming households. The second includes the area of farmland that has been returned to forest and grassland and the subsidy amounts received from the GGP. The third includes agricultural, forestry, livestock, and fishery production, other production and income, household consumption expenditures, household production expenditures, and household disaster losses.
There were no significant differences in the means of certain variables, such as education, income, source of income, household size, and labor force between PHs and NPHs (Table 1). Specifically, the sampled farming households mostly have a junior high school education, are mostly middle-income in relation to their village income, with farming as their main source of income, and have a household size of 3–4 persons per household, with an average household labor force of 2 persons.

2.3. Indicators

This paper analyzes the impacts of the GGP on households’ livelihoods along four dimensions: land management, labor force transfer, sources of income, and expenditures (Table S2). The 12 indicators used in this study were selected by reviewing the existing literature and considering the availability, rationality, and representativeness of the data. Among them, indicators 7, 8, 9, and 10 of the Level 2 indicators are analyzed using the answers from PHs in the Farmers’ Questionnaire, and the remaining indicators are evaluated by comparing the differences between PHs and NPHs using the Farmers’ Survey.

2.4. Methodology

In this study, stratified random sampling was used to select households. First, according to whether or not households participate in the GGP, households were divided into PHs and NPHs. Then, within each level, the number of samples to be drawn was determined according to the proportion of the population. Finally, a simple random sample was taken within each level, and certain percentages of samples were taken from the PHs and NPHs, respectively. But there may be some potential biases. For example, when interviewing households about their past experiences and events caused by GGP, participants may not have been able to accurately recall details, which may have led to certain recall bias. In addition, if the selected households were unwilling to participate in the survey or could not be reached, this led to non-response bias.
This study used the Herfindahl–Hirschman Index to calculate the household income diversity index [9]. Where HDI is the household income diversity index, IPh is the proportion of source of income h to total income, and n is the number of sources of income for a particular household. In this paper, household sources of income were classified into five categories: income from farming, income from forestry, income from livestock, off-farm income (including wage income, business income, property income, and income remitted by people who have been away from their home village), and subsidy income (subsidies from the GGP and other agricultural subsidies). The more sources of income one household has, the more balanced their income is; the greater the value of HDI, the lower the family’s livelihood risk is, and the more stable a household’s living standards will be.
This study used one-way ANOVA and the question “How would you rate the effect of money and food subsidies from the GGP on your family’s income? (a) Small (b) Moderate (c) Large” to analyze the impact of the GGP subsidies on farmers with different levels and sources of income. The impacts were assigned values of “1”, “2”, and “3”, respectively; the larger the value, the greater the extent of the impact of the subsidies on the household income was found to be. Then, an independent-samples t-tests was used to analyze whether there were significant differences between PHs and NPHs in terms of sources of income, household consumption, and production expenditures. A paired-samples t-test was used to analyze whether there was a significant difference in the household income of PHs with and without GGP subsidies. The rest of the indicators were analyzed by organizing the questionnaire data and then performing descriptive statistical analysis.

3. Results

3.1. Impact of the GGP on the Transfer of the Labor Force

The release of the labor force has contributed to outwork for both PHs and NPHs, but this increase has been greater for PHs. In 2007, the percentage of migrant workers in PHs and NPHs accounted for 43.97% and 44.60% of the household labor forces, respectively (Figure 2). Subsequently, the percentage of migrant workers in the household labor force increased for both PHs and NPHs, and that of PHs has been higher than that of NPHs since 2016. In 2018, the percentage of migrant workers in PHs and NPHs accounted for 51.56% and 50.11% of the household labor force, respectively.

3.2. Impact of the GGP on Agricultural Management

The implementation of the GGP has led to a greater reduction in the per capita area of farmland managed by PHs and intensified agricultural production. The per capita area of managed farmland for PHs and NPHs decreased from 8.27 and 8.22 acre/person in 2007 to 7.33 and 7.50 acre/person in 2018, respectively (Figure 3a). In 2007, output per unit area for PHs and NPHs was 589.40 and 627.34 pounds/acre, respectively (Figure 3b). From 2007 to 2018, crop output per unit area increased for both PHs and NPHs, and since 2013, PHs have produced more per unit area than NPHs have. Crop production per unit area for PHs and NPHs increased by 23.22% and 15.98%, respectively, during the study period.
PHs have intensified their agricultural activities by increasing their farming expenditures (Table S4) [12]; the motivation of this change has been to offset the negative impacts of reduced farmland and the reduced availability of agricultural labor [27]. To a certain extent, this has enhanced the cultivation of high-quality farmland, increased crop yields per unit area, and promoted intensive agriculture. It is of great significance to improve rural livelihoods and improve farmers’ living standards by helping farmers to obtain higher returns on their limited land. However, intensive agriculture may lead to the use of more fertilizers and pesticides, which will destroy biodiversity and ecological balance, and have a certain negative impact on the ecological environment [28].

3.3. Impact of the GGP on Household Income

The GGP has increased income and livelihood diversification for PHs to a greater degree than it has for NPHs. In 2007, the per capita annual income of PHs and NPHs was 7319.10 and 7702.22 CNY/person, respectively (Figure S1), with that of PHs consistently higher than that of NPHs from 2013 onwards. The household income diversity index of PHs was slightly lower than that of NPHs in 2007 and has been consistently higher than that of NPHs since 2011.
Since PHs have many sources of income, it is difficult to show all data in the figure. Therefore, the average value of each source of income from 2007 to 2018 is shown in Figure S2, and the detailed data are provided in Table S3. Looking at the average of household sources of income from 2007 to 2018, livestock income, off-farm income, and subsidy income were significantly higher for PHs than they were for NPHs; farming and forestry incomes were significantly higher for NPHs than they were for PHs (Figure S2).
The implementation of the GGP has contributed to the income transfer of PHs, whose sources of income have become more oriented toward off-farm income and less dependent on subsidies. The share of off-farm income in the total income of PHs has gradually increased, from 51.07% in 2007 to 62.22% in 2018. Subsidy income as a percentage of total income is getting smaller, falling from 9.60% in 2007 to 2.41% in 2018. The proportion of forestry income in the total income of PHs has remained at a low level. NPHs’ main income source is also off-farm income, which increased from 53.25% to 57.15% during the study period, a share slightly lower than PHs. The household income structure of farmers changed regardless of whether they participated in the GGP, but compared with NPHs, the project greatly promoted the increase in PHs’ off-farm income (Figure S3).

3.4. Impact of the GGP on Household Expenditures

The structure of household consumption and expenditures of PHs and NPHs (Figure S4) shows that PHs have significantly higher expenditures for education, livestock, and industry than NPHs do; meanwhile, NPHs have significantly higher home repair costs than PHs do. Household consumption expenditures are dominated by living expenditures (including transportation, clothing, shoes, hats, medical care, and interpersonal communication) and similar for both PHs and NPHs. Production expenditures are dominated by farming expenditures for both PHs and NPHs; and although those of NPHs are higher than those for PHs, there is no statistically significant difference between the two. PHs have been investing more in farming, especially in 2018, when farming expenditures of PHs exceeded those of NPHs (Table S4).

3.5. Changes in GGP Subsidies and Comparison of Land Benefits before and after Returning Farmland

The GGP subsidies significantly increased the income of PHs in 2007–2015, especially for low-income households and those whose primary source of income is farming. In 2007–2015, the average annual household income of PHs with subsidies was significantly higher than that of PHs without subsidies, and in 2016–2018, the average annual household income of PHs with subsidies, while higher than that without subsidies, was not statistically significantly different (Figure 4). The proportion of subsidies from the GGP in the total household income of PHs has decreased from 9.21% in 2007 to 1.62% in 2018 and is no longer a major contributor to household income. Among the three groups of farming households with different incomes, the impact of the subsidies on low-income families is significantly higher than that on middle-income and high-income families. Among the three groups of farming households with different sources of income, the impact of the GGP subsidies was the greatest on households whose main source of income was farming and the smallest on those whose main source of income was industrial line work (Figure S5).
The income from retired farmland after retiring is lower than that before retiring, and the retired farmland subsidies do not make up for the losses incurred by farming households as a result of retiring their farmland. In addition to planting ecological forests on retired farmland to reduce soil erosion and wind and sand disasters, the GGP also increases the income of households by compensating them for planting economic forests (e.g., mountain apricots, lemons, chestnuts). Although the income from retired farmland is increasing, it is still low, and it remains less than that generated by the farmland before it is retired. The subsidies from retired farmland can reduce farmers’ losses to some extent, but even with the addition of the subsidies, the total income after the farmland is retired is lower than the income before it is retired. Compared with farmland before it was retired, the income of the retired farmland subsidies and the income from retired farmland decreased by 26.58% to 36.38%; this was especially the case in 2018, when the income of the retired farmland declined the most (Figure 5). Thus, the current economic benefits of retired farmland are too low to be a viable alternative source of income for most households.
The main reason for the economic benefits of retired farmland being low is its lack of output and low levels of production. The absolute share of unproductive PHs on retired farmland averaged about 70% for all PHs in 2009–2018. The proportion of productive retired farmland to total retired farmland was also low, averaging only 25.61% (Table 2). The output of retired farmland is dominated by grass, which accounts for 51.03% of the retired farmland and is mostly used as livestock feed; thus, it brings negligible economic benefits to households (Table S5).

4. Discussion

4.1. Direct and Indirect Influences

The implementation of the GGP has led to an increase in forest area, grasslands, and other vegetation, as well as to improvements in a variety of ecological services, such as soil and water conservation, biodiversity, etc. [29,30]. However, it is crucial to maintain the restorative effects of the GGP over a long-term period and to safeguard the incomes of PHs [23]. The implementation of the GGP has had both direct and indirect influences on the incomes of PHs [31,32]. The direct influence is mainly reflected in the government’s subsidies to PHs to compensate for the income lost due to the reduction in farmland as well as the benefits of retiring farmland, while the indirect impact is mainly reflected in the income brought about by adjusting households’ production and employment strategies.
Some studies have identified that the GGP subsidies are the main driver of PHs’ revenue growth. Wang and Yue [33] believe that the reason why the GGP helps farmers increase their income is mainly attributed to the subsidies; if the subsidies are not taken into account, then the effect of increasing income is not obvious for the time being. However, this study found that this was the case only from 2007 to 2015. In 2018, the proportion of the GGP subsidies to the household income of PHs was only 1.62% (Figure 4). The reason for this discrepancy may be that Wang and Yue [33] focused on the period 2006–2010, whereas this study looked at more recent years and looked at a longer period; thus, the results are more accurate. In addition, the income from retired farmland in the current project area is lower than that before it is retired, even with the subsidy (Figure 5), which is an observation that is also proved by Wang et al. [16]. Therefore, the direct impact of the implementation of the GGP on the income of PHs is minimal. On the contrary, this study found that the impacts of the GGP on households’ livelihoods are usually indirect. The implementation of the GGP has reduced the total farmland area, lowered the demand for agricultural labor, and promoted outwork, thereby increasing off-farm incomes and increasing the diversification of household income. Liu and Lan [9] showed that rural livelihood diversity is significantly and negatively associated with household farmland area and that households with more farmland are less likely to be involved in off-farm employment, thereby resulting in lower income diversity index values for such households. The increased diversity of household income livelihoods has largely increased household resilience to risk, providing them with the ability to recover from stresses and shocks [34]. This suggests that the policy has some potential to promote the structural transformation of the rural economy and income growth. Based on this finding, the government should pay more attention to improving the off-farm employability and skill levels of households within the implementation of the policy, in order to help them better adapt to market changes and realize sustainable livelihood development.

4.2. Advantages of Using Long-Series Household Survey Data

This study analyzes the impacts of the GGP on households’ livelihoods by collecting household survey data from 2007 to 2018. During these 12 years, indicators such as the percentage of migrant workers in the household labor force, the per capita annual income, and the household income diversity index for both PHs and NPHs show inconsistent results, which implies that changes in the indicators at two or more discrete points in time cannot be representative of changes over the entire study period. In particular, there are many factors that influence households’ livelihoods, and non-ecological project factors such as socioeconomic development may have a significant effect. For example, the percentage of migrant workers in the household labor force was higher for NPHs than for PHs in both 2007 and 2015, which might indicate that NPHs consistently had a higher percentage of migrant workers than PHs during 2007–2015, but the opposite effect was observed during 2010–2012 (Figure 2).
Therefore, when assessing the impacts of the GGP on households’ livelihoods, it is necessary to pay close attention to year-over-year changes as well as those occurring in special nodes [35,36]. Continuous household survey data are more valuable to decision making and to accurately identifying the dynamic characteristics of the data.

4.3. Alternative Livelihoods and the Sustainability of Ecological Projects

Currently, government subsidies to retired households are given in the form of cash compensation. However, cash compensation is only a temporary solution [37]. When cash compensation is reduced, households that rely excessively on cash compensation will fall back into poverty [38]. Given the ecological and socioeconomic importance of the GGP, it is necessary to take measures to ensure that this compensation has lasting effects [39]. For example, the GGP could implement compensation for employment opportunities and compensation for job skills training [40], which is in effect an alternative to the cash subsidy policy for returned farmland [41]. Through an analysis of the rural areas in Western China that implemented the GGP, Howell [25] found that villages with more job training opportunities were better able to withstand the adverse socioeconomic impacts caused by the reduction in subsidies and the cessation of job training.
By organizing regular centralized training courses and inviting industry experts, the government provides farmers with training in employment skills covering a wide range of areas, such as agricultural production techniques (e.g., efficient planting and breeding techniques), agricultural product processing techniques, ecommerce, rural tourism services, and so on. The government takes the lead in establishing long-term and stable cooperative relationships with agricultural enterprises and vocational colleges [42]. Qualified farmers are provided with employment referral services to help them connect with labor-using enterprises and achieve stable employment. Such practices encourage farmers to develop new industrial models such as forest economy and forest farming [18]. These can help households shift away from an over-reliance on agriculture to a more sustainable mix of income sources (e.g., off-farm employment, commercial activities, livestock, forest products), while making lasting changes to the livelihoods of households that support ecological engineering efforts [4].

4.4. Shortcomings and Future Research Prospects

This comparative study of PHs and NPHs suggests that the GGP promotes off-farm employment in a broad sense. However, the unavailability of the types of off-farm work held by participants and the lack of projections and long-term tracking data on changes in the occupational choices of households after the end of the program have resulted in an inability to accurately assess the impact of the GGP on the long-term labor market. The project may be a temporary substitute for agricultural work [13]. For example, through an analysis of out-of-work farmers in Luanping and Fengning counties in Hebei province, Yang et al. [43] found that only 22% were engaged in off-farm work, while the others lacked stable off-farm incomes. Such farmers are likely to return to farming after the project comes to an end [13]. If farmers permanently transition to other jobs, especially off-farm jobs with relatively stable incomes, then they may decide not to engage in agricultural production. In such cases, the ecological, social, and economic benefits generated by the project will continue well after the project comes to an end [44,45]. In the future, there is a need for a long-term follow-up study on the changes in occupational choices of households after the end of the GGP. Such a study would collect detailed data on the type of off-farm work undertaken by participants, including occupation, nature of work, skill requirements, etc. It would also involve an in-depth study of the impact of the GGP on the livelihood strategies and occupational choices of farmers. And, based on the long-term follow-up results, such a study could propose policy adjustments and optimization possibilities to enhance its sustainable support for household livelihoods.

5. Conclusions

Through long-term monitoring and a comparative experiment, this study analyzed the impact of the GGP on household livelihoods. The direct impacts of the GGP implementation on households’ livelihoods are minimal. The results show that GGP subsidies significantly increased PHs’ incomes only in 2007–2015 and its share of the total income of PHs decreased from 9.21% in 2007 to 1.62% in 2018. The GGP subsidies do not compensate PHs for the loss of farmland. On the contrary, the GGP indirectly improves household income and income diversity by promoting off-farm employment.
This study points out that, although the GGP boosts off-farm employment, it is difficult to assess its long-term impact on the labor market due to the lack of long-term tracking data. Therefore, in the future, long-term follow-up studies should be conducted on the changes in farmers’ occupational choices after the GGP comes to an end. At the same time, the current form of government subsidies needs to be optimized, and cash compensation is only a temporary measure; focus and investment should be shifted to employment opportunities and skills training compensation to ensure that the compensation policies have a positive long-term effect. This study provides empirical evidence for the government to adjust and optimize the ecological compensation policy, which is of great significance for the implementation of similar ecological compensation policies in other countries and regions.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/land13081307/s1, Figure S1. Per capita annual income and income diversity indices for PHs and NPHs. Figure S2. Sources of household income for PHs and NPHs. Figure S3. Proportion of each income to total household income for PHs and NPHs. Figure S4. Consumption and production expenditures of PHs and NPHs. Figure S5. Effects of the GGP subsidies on households with different income levels (a) and different sources of income (b). Table S1. Study area range number and place name correspondence table. Table S2. Indicators. Table S3. Comparison of sources of income of PHs and NPHs (CNY/household). Table S4. Comparison of PHs and NPHs household expenditures (CNY/household). Table S5. Main outputs of retired farmland.

Author Contributions

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

Funding

This work was financed by National Natural Science Foundation of China (42271277, 72221002), entrusted project of the Development Research Center, National Forestry and Grassland Administration (JYC-2022-0005) and Youth Innovation Promotion Association of the Chinese Academy of Sciences (2021052).

Data Availability Statement

The data presented in this study are available on request from the corresponding author. The data are not publicly available due to privacy restrictions.

Conflicts of Interest

The authors declare no conflicts of interest.

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Land 13 01307 g001
Figure 1. Boundary of the study area (BTSSCP: Beijing–Tianjin Sandstorm Source Control Project).
Figure 1. Boundary of the study area (BTSSCP: Beijing–Tianjin Sandstorm Source Control Project).
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Figure 2. The percentage of migrant workers in the household labor forces of PHs and NPHs.
Figure 2. The percentage of migrant workers in the household labor forces of PHs and NPHs.
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Figure 3. Area of farmland operated per capita by PHs and NPHs; (a) crop output per unit area for PHs and NPHs (b).
Figure 3. Area of farmland operated per capita by PHs and NPHs; (a) crop output per unit area for PHs and NPHs (b).
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Figure 4. Average annual household income of PHs with and without GGP subsidies (Bar chart) and the proportion of the GGP subsidies to total household income (Line chart) (note: * p < 0.05, ** p < 0.01; ns indicates no significance).
Figure 4. Average annual household income of PHs with and without GGP subsidies (Bar chart) and the proportion of the GGP subsidies to total household income (Line chart) (note: * p < 0.05, ** p < 0.01; ns indicates no significance).
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Figure 5. Comparison of income before and after farmland is retired (land output and subsidies).
Figure 5. Comparison of income before and after farmland is retired (land output and subsidies).
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Table 1. Description of basic characteristics of farming households.
Table 1. Description of basic characteristics of farming households.
No. aVariableDefinitionPHsNPHsT-Test
Mean (SD b)Mean (SD)TP
1EducationEducational level of the head of household c1.99 (0.70)2.03 (0.65)−0.410.69
2Income Economic status of the household of the respondent d1.75 (0.50)1.73 (0.58)0.200.84
3Income sourceMain sources of household income e1.30 (0.54)1.31 (0.52)−0.130.90
4Household sizeRespondent’s household size3.58 (1.06)3.58 (0.87)−0.020.98
5Labor forcePersons over 16 years of age in sample households who participate in productive activities and receive income in kind or in money2.16 (0.85)2.08 (0.87)0.560.58
Notes: a Nos. 1, 2, and 3 are based on the Farmers’ Questionnaire, and Nos. 4 and 5 are based on the Farmers’ Survey. b SD means standard deviation. c 1 is primary school and below, 2 is middle school, and 3 is high school and above. d 1 is low-income families, 2 is middle-income families, and 3 is high-income families (households’ incomes are allocated by local village cadres). e 1 is farming, 2 is part-time work, and 3 is industrial line work.
Table 2. Outputs from retired farmland.
Table 2. Outputs from retired farmland.
Proportion of PHs with Unproductive Retired Farmland (%)Proportion of PHs with Productive Retired Farmland (%)Proportion of Productive Retired Farmland to Total Retired Farmland (%)
200959.00%41.00%35.65%
201073.72%26.28%24.42%
201169.44%30.56%26.80%
201268.72%31.28%27.13%
201370.23%29.77%20.84%
201478.72%21.28%19.45%
201567.86%32.14%27.76%
201660.65%39.35%36.16%
201760.00%40.00%36.23%
201878.99%21.01%21.33%
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Li, L.; Hua, X.; Zhao, G.; Xu, E. Continuous Decline in Direct Incomes for Farmers Threatens the Sustainability of the Grain for Green Project. Land 2024, 13, 1307. https://doi.org/10.3390/land13081307

AMA Style

Li L, Hua X, Zhao G, Xu E. Continuous Decline in Direct Incomes for Farmers Threatens the Sustainability of the Grain for Green Project. Land. 2024; 13(8):1307. https://doi.org/10.3390/land13081307

Chicago/Turabian Style

Li, Luqian, Xiaobo Hua, Guangshuai Zhao, and Erqi Xu. 2024. "Continuous Decline in Direct Incomes for Farmers Threatens the Sustainability of the Grain for Green Project" Land 13, no. 8: 1307. https://doi.org/10.3390/land13081307

APA Style

Li, L., Hua, X., Zhao, G., & Xu, E. (2024). Continuous Decline in Direct Incomes for Farmers Threatens the Sustainability of the Grain for Green Project. Land, 13(8), 1307. https://doi.org/10.3390/land13081307

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