Journal of Resources and Ecology ›› 2020, Vol. 11 ›› Issue (6): 580-588.DOI: 10.5814/j.issn.1674-764x.2020.06.005
Special Issue: 中国耕地资源与粮食安全
• Land Resource and Land Use • Previous Articles Next Articles
Received:
2020-05-11
Accepted:
2020-07-30
Online:
2020-11-30
Published:
2020-10-25
Contact:
WANG Xue
Supported by:
WANG Xue, LI Xiubin. Impacts of Land Fragmentation and Cropping System on the Productivity and Efficiency of Grain Producers in the North China Plain: Taking Cangxian County of Hebei Province as an Example[J]. Journal of Resources and Ecology, 2020, 11(6): 580-588.
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URL: http://www.jorae.cn/EN/10.5814/j.issn.1674-764x.2020.06.005
Variable | Description | Mean | S.D. | Min. | Max. |
---|---|---|---|---|---|
Grain output | Per hectare grain output of the household (kg ha-1) | 7371.00 | 3701.68 | 750.00 | 20250.00 |
Labor | Per hectare input of family labor (person-day ha-1) | 31.80 | 19.83 | 3.00 | 123.96 |
Seed | Per hectare cost of seed (yuan ha-1) | 1437.42 | 790.62 | 197.37 | 6800.00 |
Machinery | Per hectare cost of machinery (yuan ha-1) | 3340.38 | 766.44 | 900.00 | 4986.67 |
Fertilizer | Per hectare cost of fertilizer (yuan ha-1) | 4121.32 | 1572.53 | 900.00 | 10016.13 |
Pesticide | Per hectare cost of pesticide (yuan ha-1) | 209.51 | 231.46 | 6.00 | 2000.00 |
Irrigation | Per hectare cost of irrigation (yuan ha-1) | 518.39 | 484.88 | 30.00 | 3290.00 |
Irrigation users | Dummy (1=Used irrigation, 0=No) | 0.73 | 0.45 | 0.00 | 1.00 |
Average_size | Total agricultural land area divided by number of plots (ha) | 0.16 | 0.06 | 0.05 | 0.40 |
MCI | Multiple cropping index | 1.55 | 0.37 | 1.00 | 2.00 |
Qland | Average quality of farmland weighted by area | 2.01 | 0.68 | 1.00 | 4.00 |
Age | Age of household head (year) | 56.92 | 10.55 | 24.00 | 87.00 |
Education | Education status of the household head (1=illiterate; 2=primary school; 3=junior middle school; 4=senior middle school; 5=college or above) | 2.29 | 0.79 | 1.00 | 5.00 |
Ragrilabor | Ratio of agricultural labor to family size | 0.49 | 0.25 | 0.10 | 1.00 |
Routlabor | Ratio of off-farm labor to total labor | 0.35 | 0.28 | 0.00 | 0.83 |
Aincome | Average income per off-farm labor (×104 yuan person-1 yr-1) | 1.24 | 0.86 | 0.00 | 4.56 |
Table 1 Summary statistics of variables
Variable | Description | Mean | S.D. | Min. | Max. |
---|---|---|---|---|---|
Grain output | Per hectare grain output of the household (kg ha-1) | 7371.00 | 3701.68 | 750.00 | 20250.00 |
Labor | Per hectare input of family labor (person-day ha-1) | 31.80 | 19.83 | 3.00 | 123.96 |
Seed | Per hectare cost of seed (yuan ha-1) | 1437.42 | 790.62 | 197.37 | 6800.00 |
Machinery | Per hectare cost of machinery (yuan ha-1) | 3340.38 | 766.44 | 900.00 | 4986.67 |
Fertilizer | Per hectare cost of fertilizer (yuan ha-1) | 4121.32 | 1572.53 | 900.00 | 10016.13 |
Pesticide | Per hectare cost of pesticide (yuan ha-1) | 209.51 | 231.46 | 6.00 | 2000.00 |
Irrigation | Per hectare cost of irrigation (yuan ha-1) | 518.39 | 484.88 | 30.00 | 3290.00 |
Irrigation users | Dummy (1=Used irrigation, 0=No) | 0.73 | 0.45 | 0.00 | 1.00 |
Average_size | Total agricultural land area divided by number of plots (ha) | 0.16 | 0.06 | 0.05 | 0.40 |
MCI | Multiple cropping index | 1.55 | 0.37 | 1.00 | 2.00 |
Qland | Average quality of farmland weighted by area | 2.01 | 0.68 | 1.00 | 4.00 |
Age | Age of household head (year) | 56.92 | 10.55 | 24.00 | 87.00 |
Education | Education status of the household head (1=illiterate; 2=primary school; 3=junior middle school; 4=senior middle school; 5=college or above) | 2.29 | 0.79 | 1.00 | 5.00 |
Ragrilabor | Ratio of agricultural labor to family size | 0.49 | 0.25 | 0.10 | 1.00 |
Routlabor | Ratio of off-farm labor to total labor | 0.35 | 0.28 | 0.00 | 0.83 |
Aincome | Average income per off-farm labor (×104 yuan person-1 yr-1) | 1.24 | 0.86 | 0.00 | 4.56 |
Variables | Model 1 | Model 2 | Model 3 | Model 4 | ||||
---|---|---|---|---|---|---|---|---|
Production frontier | Coef. | Std. Err. | Coef. | Std. Err. | Coef. | Std. Err. | Coef. | Std. Err. |
ln(Labor) | 0.197*** | 0.040 | 0.213*** | 0.039 | 0.167*** | 0.042 | 0.175*** | 0.041 |
ln(Seed) | 0.122*** | 0.032 | 0.128*** | 0.031 | 0.098*** | 0.030 | 0.104*** | 0.029 |
ln(Machinery) | 0.108 | 0.166 | -0.107 | 0.161 | -0.069 | 0.170 | -0.090 | 0.167 |
ln(Fertilizer) | 0.077 | 0.047 | 0.052 | 0.047 | 0.074 | 0.046 | 0.057 | 0.045 |
ln(Pesticide) | -0.020** | 0.009 | -0.015* | 0.008 | -0.018** | 0.004 | -0.015** | 0.007 |
ln(Irrigation) | 0.009** | 0.004 | 0.009** | 0.004 | 0.008*** | 0.004 | 0.007* | 0.004 |
Average_size | - | - | 0.926*** | 0.224 | - | - | 0.757*** | 0.225 |
MCI | - | - | - | - | 0.241*** | 0.052 | 0.229*** | 0.051 |
Constant | 7.120*** | 0.450 | 7.012*** | 0.409 | 7.363*** | 0.441 | 7.326*** | 0.429 |
Inefficiency predictors | ||||||||
Age | 0.005** | 0.002 | 0.007* | 0.004 | 0.007* | 0.004 | 0.011 | 0.008 |
Education | -0.003 | 0.027 | 0.002 | 0.038 | 0.006 | 0.039 | 0.016 | 0.058 |
Ragrilabor | 0.128 | 0.152 | 0.159 | 0.215 | 0.185 | 0.228 | 0.226 | 0.065 |
Routlabor | 0.219* | 0.115 | 0.281* | 0.140 | 0.308* | 0.305 | 0.148 | 0.371 |
Aincome | -0.029 | 0.026 | -0.024 | 0.035 | -0.040 | 0.039 | -0.034 | 0.055 |
Qland | 0.150*** | 0.038 | 0.195*** | 0.068 | 0.211*** | 0.075 | 0.283** | 0.137 |
Average_size | -1.521*** | 0.493 | - | - | -1.916*** | 0.776 | - | - |
MCI | -0.293*** | 0.092 | -0.381*** | 0.150 | - | - | - | - |
Constant | 0.275 | 0.271 | -0.276 | 0.493 | -0.613 | 0.564 | -1.638 | 1.246 |
Model diagnostics | ||||||||
${{\sigma }^{2}}=\sigma _{\mu }^{2}+\sigma _{v}^{2}$ | 0.077*** | 0.016 | 0.100*** | 0.032 | 0.107*** | 0.035 | 0.148** | 0.071 |
$\gamma =\sigma _{\mu }^{2}/(\sigma _{\mu }^{2}+\sigma _{v}^{2})$ | 0.815*** | 0.062 | 0.834*** | 0.053 | 0.862*** | 0.049 | 0.887*** | 0.049 |
H0: No inefficiency component (Prob. ≤ z): | ||||||||
0.000 | 0.000 | 0.000 | 0.000 | |||||
Total number of observations | 350 | 350 | 350 | 350 |
Table 2 Parameter estimation results for the Cobb-Douglas production frontier and technical inefficiency models
Variables | Model 1 | Model 2 | Model 3 | Model 4 | ||||
---|---|---|---|---|---|---|---|---|
Production frontier | Coef. | Std. Err. | Coef. | Std. Err. | Coef. | Std. Err. | Coef. | Std. Err. |
ln(Labor) | 0.197*** | 0.040 | 0.213*** | 0.039 | 0.167*** | 0.042 | 0.175*** | 0.041 |
ln(Seed) | 0.122*** | 0.032 | 0.128*** | 0.031 | 0.098*** | 0.030 | 0.104*** | 0.029 |
ln(Machinery) | 0.108 | 0.166 | -0.107 | 0.161 | -0.069 | 0.170 | -0.090 | 0.167 |
ln(Fertilizer) | 0.077 | 0.047 | 0.052 | 0.047 | 0.074 | 0.046 | 0.057 | 0.045 |
ln(Pesticide) | -0.020** | 0.009 | -0.015* | 0.008 | -0.018** | 0.004 | -0.015** | 0.007 |
ln(Irrigation) | 0.009** | 0.004 | 0.009** | 0.004 | 0.008*** | 0.004 | 0.007* | 0.004 |
Average_size | - | - | 0.926*** | 0.224 | - | - | 0.757*** | 0.225 |
MCI | - | - | - | - | 0.241*** | 0.052 | 0.229*** | 0.051 |
Constant | 7.120*** | 0.450 | 7.012*** | 0.409 | 7.363*** | 0.441 | 7.326*** | 0.429 |
Inefficiency predictors | ||||||||
Age | 0.005** | 0.002 | 0.007* | 0.004 | 0.007* | 0.004 | 0.011 | 0.008 |
Education | -0.003 | 0.027 | 0.002 | 0.038 | 0.006 | 0.039 | 0.016 | 0.058 |
Ragrilabor | 0.128 | 0.152 | 0.159 | 0.215 | 0.185 | 0.228 | 0.226 | 0.065 |
Routlabor | 0.219* | 0.115 | 0.281* | 0.140 | 0.308* | 0.305 | 0.148 | 0.371 |
Aincome | -0.029 | 0.026 | -0.024 | 0.035 | -0.040 | 0.039 | -0.034 | 0.055 |
Qland | 0.150*** | 0.038 | 0.195*** | 0.068 | 0.211*** | 0.075 | 0.283** | 0.137 |
Average_size | -1.521*** | 0.493 | - | - | -1.916*** | 0.776 | - | - |
MCI | -0.293*** | 0.092 | -0.381*** | 0.150 | - | - | - | - |
Constant | 0.275 | 0.271 | -0.276 | 0.493 | -0.613 | 0.564 | -1.638 | 1.246 |
Model diagnostics | ||||||||
${{\sigma }^{2}}=\sigma _{\mu }^{2}+\sigma _{v}^{2}$ | 0.077*** | 0.016 | 0.100*** | 0.032 | 0.107*** | 0.035 | 0.148** | 0.071 |
$\gamma =\sigma _{\mu }^{2}/(\sigma _{\mu }^{2}+\sigma _{v}^{2})$ | 0.815*** | 0.062 | 0.834*** | 0.053 | 0.862*** | 0.049 | 0.887*** | 0.049 |
H0: No inefficiency component (Prob. ≤ z): | ||||||||
0.000 | 0.000 | 0.000 | 0.000 | |||||
Total number of observations | 350 | 350 | 350 | 350 |
Variables | Model 1 | Model 2 | Model 3 | Model 4 |
---|---|---|---|---|
Mean | 0.737 | 0.778 | 0.776 | 0.802 |
S. D. | 0.135 | 0.128 | 0.130 | 0.124 |
Min. | 0.330 | 0.352 | 0.369 | 0.352 |
Max. | 0.965 | 0.967 | 0.966 | 0.967 |
Table 3 Technical efficiency (TE) scores
Variables | Model 1 | Model 2 | Model 3 | Model 4 |
---|---|---|---|---|
Mean | 0.737 | 0.778 | 0.776 | 0.802 |
S. D. | 0.135 | 0.128 | 0.130 | 0.124 |
Min. | 0.330 | 0.352 | 0.369 | 0.352 |
Max. | 0.965 | 0.967 | 0.966 | 0.967 |
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