How Part-Time Farming Affects Cultivated Land Use Sustainability: Survey-Based Assessment in China
Abstract
:1. Introduction
2. Framework and Hypotheses
2.1. Analytical Framework of How Farmers’ Part-Time Farming Affects CLS
2.2. Hypothesis
3. Methodology and Data
3.1. Statistical Modeling
3.2. Data Collection
3.3. Index System for Assessing CLS
- (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.
Level 1 Indicators | Level 2 Indicators | Level 3 Indicators | Weight Coefficient | Direction of Impact |
---|---|---|---|---|
Cultivated Land use Sustainability | 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% | - |
3.4. Variables
3.4.1. Dependent Variables
3.4.2. Independent Variables
3.4.3. Control Variables
3.4.4. Moderating Variables
4. Results
4.1. Impact Pattern Analysis
4.2. OLS Regression
4.3. Heterogeneity Analysis
4.4. Moderation Effect Analysis
4.5. Robustness Tests
5. Discussion
5.1. Directing Farmers to Maintain a Rational Proportion of Part-Time Income
5.2. Facilitating Structured Reallocation of Agricultural Labor Force and Bolstering Engagement of Female Household Heads in Cultivated Land Conservation Efforts
5.3. Other Strategies to Enhance CLS
6. Concluding Remarks
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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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 | - | - |
Model 1 (SF) | Model 2 (SF1) | Model 3 (SF2) | Model 4 (SF3) | |
---|---|---|---|---|
PE | 0.904 *** (0.179) | 0.146 * (0.066) | 0.586 *** (0.115) | 0.150 * (0.063) |
PE2 | −0.928 *** (0.164) | −0.155 ** (0.061) | −0.583 *** (0.105) | −0.167 ** (0.057) |
_cons | 0.047 (0.042) | 0.025 (0.015) | 0.002 (0.027) | 0.023 (0.015) |
R2 | 0.369 | 0.183 | 0.35 | 0.225 |
F | 18.393 *** | 4.067 * | 16.316 *** | 6.236 ** |
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 U-shaped | Inverted U-shaped | Inverted U-shaped | Inverted U-shaped |
Model 5 | Model 6 | Model 7 | Model 8 | |
---|---|---|---|---|
PE | 1.021 *** (0.168) | 0.173 ** (0.061) | 0.649 *** (0.112) | 0.172 ** (0.062) |
PE2 | −1.077 *** (0.155) | −0.203 *** (0.056) | −0.659 *** (0.103) | −0.187 *** (0.057) |
C1 | 0.024 ** (0.009) | 0.006 (0.003) | 0.014 * (0.006) | 0.003 (0.004) |
C2 | 0.073 ** (0.026) | 0.016 (0.009) | 0.035 * (0.017) | 0.021 * (0.010) |
C3 | 0.049 (0.032) | 0.027 * (0.011) | 0.033 (0.021) | −0.012 (0.012) |
C4 | 0.001 (0.001) | 0.002 *** (0.000) | 0.001 (0.001) | −0.001 (0.00) |
C5 | 0.009 *** (0.002) | 0.003 *** (0.001) | 0.004 ** (0.001) | 0.002 (0.001) |
_cons | −0.212 *** (0.059) | −0.075 *** (0.021) | −0.127 *** (0.039) | −0.003 (0.022) |
R2 | 0.269 | 0.238 | 0.204 | 0.097 |
F | 12.031 *** | 10.224 *** | 8.370 *** | 3.528 *** |
Model 9 (Prov1) | Model 10 (Prov2) | Model 11 (Prov3) | |
---|---|---|---|
PE | 1.465 *** (0.323) | 1.432 *** (0.285) | 0.360 (0.243) |
PE2 | −1.505 *** (0.295) | −1.470 *** (0.257) | −0.434 + (0.232) |
C1 | 0.017 (0.026) | 0.051 *** (0.013) | −0.007 (0.015) |
C2 | 0.106 * (0.044) | 0.073 (0.047) | 0.036 (0.041) |
C3 | 0.154 (0.079) | 0.044 (0.057) | 0.040 (0.040) |
C4 | 0.000 (0.002) | 0.003 (0.002) | 0.006 ** (0.002) |
C5 | 0.006 * (0.003) | 0.019 (0.011) | 0.001 (0.005) |
_cons | −0.145 (0.118) | −0.515 *** (0.146) | −0.021 (0.106) |
R2 | 0.405 | 0.439 | 0.184 |
F | 7.015 *** | 8.149 *** | 2.195 * |
Model 12 | Model 13 | |
---|---|---|
C1 | 0.014 (0.010) | 0.017 (0.010) |
C2 | 0.070 * (0.028) | 0.076 ** (0.028) |
C3 | 0.042 (0.035) | 0.040 (0.034) |
C4 | 0.001 (0.001) | 0.002 (0.001) |
C5 | 0.007 ** (0.002) | 0.008 ** (0.002) |
PE | −0.193 ** (0.060) | −0.136 ** (0.043) |
Re1 | 0.02 (0.023) | |
PE* Re1 | −0.145 * (0.062) | |
Re2 | −0.026 (0.024) | |
PE* Re2 | 0.223 ** (0.083) | |
_cons | 0.003 (0.065) | −0.049 (0.052) |
R2 | 0.136 | 0.145 |
F | 4.472 *** | 4.850 *** |
Model 14 Truncating | Model 15 Robust Regression | Model 16 Ridge Regression | |
---|---|---|---|
PE | 1.021 ** (0.168) | −0.922 ** (0.146) | −0.618 ** (0.088) |
PE2 | −1.077 ** (0.155) | 0.893 ** (0.159) | 0.524 ** (0.095) |
C1 | 0.024 * (0.009) | 0.011 (0.009) | 0.021 * (0.010) |
C2 | 0.073 ** (0.026) | 0.072 ** (0.024) | 0.069 ** (0.026) |
C3 | 0.049 (0.032) | 0.042 (0.030) | 0.044 (0.032) |
C4 | 0.001 (0.001) | 0.002 (0.001) | 0.002 (0.001) |
C5 | 0.009 ** (0.002) | 0.008 ** (0.002) | 0.008 ** (0.002) |
_cons | −0.212 ** (0.059) | −0.171 ** (0.056) | −0.097 (0.050) |
R2 | 0.269 | 0.250 | 0.24 |
F | 12.031 *** | 10.922 *** | 10.358 *** |
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Pei, X.; Zheng, X.; Wu, C. How Part-Time Farming Affects Cultivated Land Use Sustainability: Survey-Based Assessment in China. Land 2024, 13, 1242. https://doi.org/10.3390/land13081242
Pei X, Zheng X, Wu C. How Part-Time Farming Affects Cultivated Land Use Sustainability: Survey-Based Assessment in China. Land. 2024; 13(8):1242. https://doi.org/10.3390/land13081242
Chicago/Turabian StylePei, Xinwei, Xinger Zheng, and Cong Wu. 2024. "How Part-Time Farming Affects Cultivated Land Use Sustainability: Survey-Based Assessment in China" Land 13, no. 8: 1242. https://doi.org/10.3390/land13081242