Journal of Geographical Sciences >
Impacts and effects of government regulation on farmers’ responses to drought: A case study of North China Plain
Author: Li Xiaoyun (1987-), PhD, specialized in man-land relationship and regional development. E-mail: lixy.15b@igsnrr.ac.cn;
Received date: 2017-02-09
Accepted date: 2017-03-07
Online published: 2017-12-10
Supported by
National Natural Science Foundation of China, No.41430636, No.41590841
National Program on Key Basic Research Project (973 Program), No.2012CB95570001
Copyright
Frequent extreme weather events like drought, etc. in the context of climate change present huge challenges to agricultural production. To find out if farmers have taken measures against them and identify governments’ impact on their response measures are the foundation of and key to further improving relevant policies and farmers’ responsiveness. Taking the North China Plain as an example, the study analyzes farmers’ responses to frequent climate change-induced drought, and assesses the impacts of governments’ early- warning, policy support and other factors on farmers’ responses based on questionnaire survey data and an econometric approach. The results show that: (1) Farmers are responsive to drought, and they are more likely to take measures as the degree of drought deepening. (2) Governments’ regulation affects farmers’ responses, although only part of its regulation measures has remarkable effects. Governments’ early-warning messages can increase the possibility of farmers’ responding to drought, however, only when they get the early-warning in all the processes including before, during, and after disasters can the effects be significant. Currently, as the primary channel through which early-warning information is released, television cannot change farmers’ behaviors significantly. Early-warning is most effective when spread via two or more types of channels. In addition, governments’ (especially town and village level institutions’) policy support has certain impacts on farmers’ responses to drought, yet with less prominent effects in disaster years than in normal years; to provide subsidies, as a regulation measure, can encourage farmers’ initiative a lot in adopting response measures, but most of the farmers haven’t got support from any institutions. Both the structure and strength of government regulation need to be improved. (3) Farmers with different conditions respond differently. Farmer households in irrigation areas, those whose farmland is lower fragmented, and those with numerous agricultural family members tend to take response measures. The study can provide scientific reference to the making of relevant regulation policies under the background of acidifications.
LI Xiaoyun , YANG Yu , LIU Yi , LIU Hui . Impacts and effects of government regulation on farmers’ responses to drought: A case study of North China Plain[J]. Journal of Geographical Sciences, 2017 , 27(12) : 1481 -1498 . DOI: 10.1007/s11442-017-1448-7
Figure 1 Location of the North China Plain and the survey sites |
Table 1 Farmer households’ responding measures |
Number of measures taken | Normal years | Disaster years | |||
---|---|---|---|---|---|
Number of farmer households taking measures | Percentage (%) | Number of farmer households taking measures | Percentage (%) | ||
Number of farmer households not taking measures | 133 | 24.63 | 93 | 17.22 | |
Number of farmer households taking measures, among whom: | 407 | 75.37 | 447 | 82.78 | |
1 measure | 263 | 48.70 | 185 | 34.26 | |
2 measures | 112 | 20.74 | 112 | 20.74 | |
3 measures | 29 | 5.37 | 95 | 17.59 | |
4 measures | 3 | 0.56 | 42 | 7.78 | |
5 measures | 0 | 0.00 | 13 | 2.41 | |
Total | 540 | 100.00 | 540 | 100.00 | |
Variance analysis | F value | 9.011 | |||
Sig. | 0.003 |
Figure 2 Major channels for releasing early-warning information |
Table 2 Farmer households’ access to early-warning information |
Got information or not | Pre-disaster | Amid- and post-disaster | |||||||
---|---|---|---|---|---|---|---|---|---|
Normal years | Disaster years | Normal years | Disaster years | ||||||
Number of farmer households | Percentage (%) | Number of farmer households | Percentage (%) | Number of farmer households | Percentage (%) | Number of farmer households | Percentage (%) | ||
No | 292 | 65.32 | 291 | 56.84 | 282 | 63.66 | 286 | 55.75 | |
Yes | 155 | 34.68 | 221 | 43.16 | 161 | 36.34 | 227 | 44.25 | |
Number of channels | 1 | 132 | 29.53 | 185 | 36.13 | 140 | 31.60 | 188 | 36.65 |
2 | 21 | 4.70 | 33 | 6.45 | 19 | 4.29 | 34 | 6.63 | |
3 | 1 | 0.22 | 2 | 0.39 | 1 | 0.23 | 4 | 0.78 | |
4 | 1 | 0.22 | 1 | 0.20 | 1 | 0.23 | 1 | 0.19 | |
Total | 447 | 100 | 512 | 100 | 443 | 100 | 513 | 100 |
Figure 3 Relationship between early-warning and farmer households’ responding measures |
Table 3 Supports by different levels of government institutions |
Being supported | Normal years | Disaster years | ||
---|---|---|---|---|
Number of samples | Proportion (%) | Number of samples | Proportion (%) | |
No | 406 | 76 | 403 | 75 |
Yes, by: | 129 | 24 | 131 | 25 |
Village | 32 | 23.40 | 37 | 26.60 |
Town | 39 | 28.50 | 37 | 26.60 |
Upper-level institutions | 63 | 46.00 | 62 | 44.60 |
Others | 3 | 2.20 | 3 | 2.20 |
Total number of supports | 137 | 100 | 139 | 100 |
Total number of samples | 535 | 100 | 534 | 100 |
Figure 4 Forms of government policy supports (multiple choices) |
Table 4 Forms of government supports and corresponding share of farmer households taking actions |
Being supported | Share of farmer households taking actions (%) | |
---|---|---|
Normal years | Disaster years | |
No | 72.17 | 81.39 |
Yes | 85.07 | 86.86 |
Technical guidance | 100.00 | 87.50 |
Financial subsidies | 85.29 | 86.96 |
Material subsidies | 83.02 | 88.00 |
Labor support | 100.00 | 100.00 |
Table 5 The regression models of farmer households’ responses (Dependent variable: taking measures=1, not taking measures=0) |
Independent variables | Model 1 | Model 2 | Model 3 | |||
---|---|---|---|---|---|---|
Coefficients | Exp (B) | Coefficients | Exp (B) | Coefficients | Exp (B) | |
Internal factors | ||||||
Total number of family members | -0.063 | 0.939 | -0.061 | 0.941 | -0.062 | 0.940 |
Age of household head | -0.01 | 0.99 | -0.01 | 0.99 | -0.012 | 0.988 |
Education level of household head | 0.028 | 1.028 | 0.026 | 1.026 | 0.018 | 1.018 |
Farmland area of the household (mu) | -0.051** | 0.951 | -0.049** | 0.953 | -0.040* | 0.961 |
Farmland fragmentation level (mu/piece) | -0.119** | 0.888 | -0.119** | 0.887 | -0.082 | 0.922 |
Total value of house (ten thousand yuan) | 0.004 | 1.004 | 0.004 | 1.004 | 0.006 | 1.006 |
Joined cooperation organization (Yes=1, No=0) | 0.221 | 1.247 | 0.184 | 1.202 | 0.023 | 1.023 |
Family member is/are village cadre/s (Yes=1, No=0) | -0.329* | 0.719 | -0.344* | 0.709 | -0.523** | 0.593 |
External factors | ||||||
Topography (plain=1, others=0) | 19.523 | 3.012 | 19.595 | 3.236 | 19.538 | 3.056 |
Inirrigation area (Yes=1, No=0) | 0.727*** | 2.07 | 0.699*** | 2.012 | 0.809*** | 2.246 |
Average underground water level (m) | 0.001 | 1.001 | 0.002 | 1.002 | 0.003 | 1.003 |
Policy factors | ||||||
Received early-warning information (Yes=1, No=0) | 0.412** | 1.51 | ||||
Only received pre-disaster warning | 0.076 | 1.079 | ||||
Only received post-disaster warning | 0.216 | 1.241 | ||||
Received both pre- and post-warning | 0.615*** | 1.85 | ||||
Only received warning via TV | -0.342 | 0.711 | ||||
Only received warning via one channel apart from TV | 0.499 | 1.646 | ||||
Received warning via two or more channels | 0.491** | 1.633 | ||||
The village organizes disaster prevention activities (Yes=1, No=0) | 0.326* | 1.386 | 0.321* | 1.379 | 0.223 | 1.250 |
Received government supports (Yes=1, No=0) | 0.374* | 1.454 | ||||
Supported by town-level or lower levels of institutions | 0.667** | 1.949 | ||||
Only technical guidance | 0.197 | 1.218 | ||||
Only financial subsidies | 2.057** | 7.821 | ||||
Only material subsidies | 0.019 | 1.019 | ||||
Only labor supports | 0.744 | 2.105 | ||||
Two or more types of supports | 1.100 | 3.003 | ||||
Supported by above-town level institutions | 0.308 | 1.361 | ||||
Disaster years (Yes=1, No=0) | 0.410** | 1.507 | 0.412** | 1.51 | 0.426** | 1.532 |
Constants | 1.025 | 2.786 | 1.043 | 2.838 | 1.400** | 4.057 |
Accuracy of prediction | 79.10% | 79.20% | 78.80% | |||
Degrees of freedom | 15 | 18 | 21 | |||
Number of samples | 1070 | 1070 | 936 |
Notes: *, ** and *** indicate significance at confidence level 0.1, 0.05 and 0.01 respectively. |
The authors have declared that no competing interests exist.
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