Abstract
The Indian marine fishing community faces multiple climate and non-climate related stresses, such as unpredictable and extreme weather events, declining fish stocks and pollution. Prompted by these changes, some community members have adopted strategies such as motorization and mechanization of their boats and using GPS (Global Positioning System) for navigation, to ensure a greater fish catch as well as safety in the sea. Capacity to adapt is crucial for retaining livelihoods. This study attempts to measure and develop indices denoting the strength of adaptation and adaptive capacity of the community, using suitable indicators. Percentage of the community implementing the adaptation strategies are used to develop the adaptation index. According to the Sustainable Livelihoods Approach, capacity to adapt can be determined by access to different capitals. Hence, adaptive capacity of the community is measured through indicators for human, physical, economic and social capitals. These community-level indices are developed for seventy Indian coastal districts. The results suggest that Srikakulam in Andhra Pradesh and Rajkot in Gujarat have the lowest adaptation and adaptive capacity index respectively. Further, the indices are validated by evaluating the relation between adaptation and adaptive capacity through regression and Monte Carlo simulation. Results indicate that adaptive capacity has low but significant influence on adaptation levels in the community, concluding that the indices are quite adept. The results of the study facilitate identification of the coastal districts in urgent need of policies and actions to develop adaptation and adaptive capacity of the community.
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Notes
- 1.
The average percentage use of GPS in India is 2.7% (Appendix).
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Appendix
Appendix
Descriptive statistics of indicators (inclusive of 70 districts used in the study)
S. no. | Indicator | Average | Minimum | Maximum |
|---|---|---|---|---|
1 | Motorized (in %) | 39.1 | 0.0 | 88.8 |
2 | Mechanized (in %) | 33.2 | 0.0 | 100.0 |
3 | Life saving equipment (in %) | 18.6 | 0.0 | 97.1 |
4 | GPS (in %) | 2.7 | 0.0 | 33.5 |
5 | Schooled fisherfolk population (in %) | 51.96 | 0.19 | 89.1 |
6 | Families below poverty line (in %) | 53.8 | 1.0 | 99.9 |
7 | No. of landing centres | 21.9 | 1 | 100 |
8 | No. of banks | 22.2 | 0 | 190 |
9 | No. of community centres | 32.0 | 0 | 285 |
10 | Membership in fishing cooperative (in %) | 15.0 | 0.0 | 53.9 |
Adaptation and adaptive capacity indices of the districts
S. no. | State | Coast | District | Adaptation index | Adaptation grade | Adaptive capacity index | Adaptive capacity grade | Is adaptive capacity index higher than adaptation index? |
|---|---|---|---|---|---|---|---|---|
1 | Maharashtra | West | Ratnagiri | 0.209 | Low | 0.583 | Highest | Yes |
2 | Maharashtra | West | Sindhudurg | 0.261 | Medium | 0.549 | Highest | Yes |
3 | Maharashtra | West | Raigad | 0.253 | Medium | 0.523 | Highest | Yes |
4 | Maharashtra | West | Thane | 0.246 | Medium | 0.501 | Highest | Yes |
5 | Tamil Nadu | East | Kanyakumari | 0.387 | High | 0.498 | Highest | Yes |
6 | Maharashtra | West | Greater Mumbai | 0.267 | Medium | 0.461 | High | Yes |
7 | Tamil Nadu | East | Ramanathapuram | 0.204 | Low | 0.431 | High | Yes |
8 | Kerala | West | Alappuzha | 0.134 | Lowest | 0.401 | High | Yes |
9 | Karnataka | West | Uttara Kannada | 0.225 | Low | 0.399 | High | Yes |
10 | Goa | West | SouthGoa | 0.284 | Medium | 0.398 | High | Yes |
11 | Tamil Nadu | East | Nagapattinam | 0.347 | High | 0.388 | High | Yes |
12 | Kerala | West | Thiruvananthapuram | 0.210 | Low | 0.384 | High | Yes |
13 | Kerala | West | Kozhikode | 0.260 | Medium | 0.376 | Medium | Yes |
14 | Kerala | West | Ernakulam | 0.264 | Medium | 0.376 | Medium | Yes |
15 | Goa | West | NorthGoa | 0.529 | Highest | 0.371 | Medium | No |
16 | Puducherry | East | Puducherry | 0.229 | Low | 0.362 | Medium | Yes |
17 | Tamil Nadu | East | Tuticorin | 0.321 | Medium | 0.357 | Medium | Yes |
18 | Andaman and Nicobar | East | South Andaman | 0.293 | Medium | 0.355 | Medium | Yes |
19 | Tamil Nadu | East | Pudukkottai | 0.431 | Highest | 0.354 | Medium | No |
20 | Tamil Nadu | East | Thiruvallur | 0.271 | Medium | 0.350 | Medium | Yes |
21 | Kerala | West | Kannur | 0.273 | Medium | 0.347 | Medium | Yes |
22 | Karnataka | West | Udupi | 0.339 | High | 0.346 | Medium | Yes |
23 | Puducherry | East | Mahe | 0.288 | Medium | 0.338 | Medium | Yes |
24 | Daman & Diu | West | Daman | 0.283 | Medium | 0.337 | Medium | Yes |
25 | Kerala | West | Thrissur | 0.300 | Medium | 0.333 | Medium | Yes |
26 | Tamil Nadu | East | Cuddalore | 0.392 | High | 0.323 | Medium | No |
27 | Karnataka | West | Dakshina Kannada | 0.397 | High | 0.322 | Medium | No |
28 | Tamil Nadu | East | Kanchipuram | 0.373 | High | 0.322 | Medium | No |
29 | Puducherry | East | Karaikal | 0.279 | Medium | 0.322 | Medium | Yes |
30 | Odisha | East | Balasore | 0.210 | Low | 0.314 | Medium | Yes |
31 | Tamil Nadu | East | Tirunelveli | 0.409 | High | 0.312 | Medium | No |
32 | Gujarat | West | Valsad | 0.330 | Medium | 0.300 | Medium | No |
33 | Kerala | West | Kasaragod | 0.213 | Low | 0.298 | Medium | Yes |
34 | Gujarat | West | Porbander | 0.380 | High | 0.298 | Medium | No |
35 | Kerala | West | Kollam | 0.284 | Medium | 0.295 | Medium | Yes |
36 | Gujarat | West | Junagadh | 0.432 | Highest | 0.295 | Medium | No |
37 | Andaman and Nicobar | East | N&M Andaman | 0.274 | Medium | 0.291 | Medium | Yes |
38 | Daman and Diu | West | Diu | 0.288 | Medium | 0.289 | Medium | Yes |
39 | Tamil Nadu | East | Thanjavur | 0.455 | Highest | 0.285 | Medium | No |
40 | Lakshadweep | West | Lakshadweep | 0.220 | Low | 0.278 | Low | Yes |
41 | Odisha | East | Kendrapara | 0.087 | Lowest | 0.265 | Low | Yes |
42 | Odisha | East | Jagatsinghpur | 0.209 | Low | 0.263 | Low | Yes |
43 | West Bengal | East | Purba Medinipur | 0.175 | Low | 0.254 | Low | Yes |
44 | Gujarat | West | Navsari | 0.192 | Low | 0.248 | Low | Yes |
45 | Kerala | West | Malappuram | 0.254 | Medium | 0.247 | Low | No |
46 | Andhra Pradesh | East | Nellore | 0.188 | Low | 0.244 | Low | Yes |
47 | Gujarat | West | Surat | 0.213 | Low | 0.240 | Low | Yes |
48 | Gujarat | West | Kutch | 0.249 | Medium | 0.239 | Low | No |
49 | Gujarat | West | Bhavnagar | 0.243 | Medium | 0.236 | Low | No |
50 | Andhra Pradesh | East | Visakhapatnam | 0.121 | Lowest | 0.231 | Low | Yes |
51 | Tamil Nadu | East | Villupuram | 0.291 | Medium | 0.230 | Low | No |
52 | Gujarat | West | Jamnagar | 0.339 | High | 0.227 | Low | No |
53 | West Bengal | East | South 24 Parganas | 0.330 | Medium | 0.223 | Low | No |
54 | Gujarat | West | Amreli | 0.521 | Highest | 0.216 | Low | No |
55 | Andhra Pradesh | East | Srikakulam | 0.035 | Lowest | 0.207 | Low | Yes |
56 | Odisha | East | Puri | 0.185 | Low | 0.205 | Low | Yes |
57 | Tamil Nadu | East | Thiruvarur | 0.218 | Low | 0.204 | Low | No |
58 | Odisha | East | Ganjam | 0.115 | Lowest | 0.201 | Low | Yes |
59 | Odisha | East | Bhadrak | 0.252 | Medium | 0.194 | Low | No |
60 | Andaman and Nicobar | East | Nicobar | 0.261 | Medium | 0.190 | Low | No |
61 | Gujarat | West | Bharuch | 0.228 | Low | 0.188 | Low | No |
62 | Andhra Pradesh | East | Krishna | 0.116 | Lowest | 0.172 | Lowest | Yes |
63 | Andhra Pradesh | East | Prakasam | 0.149 | Low | 0.171 | Lowest | Yes |
64 | Tamil Nadu | East | Chennai | 0.272 | Medium | 0.167 | Lowest | No |
65 | Andhra Pradesh | East | East Godavari | 0.180 | Low | 0.165 | Lowest | No |
66 | Andhra Pradesh | East | West Godavari | 0.099 | Lowest | 0.150 | Lowest | Yes |
67 | Gujarat | West | Anand | 0.255 | Medium | 0.125 | Lowest | No |
68 | Andhra Pradesh | East | Guntur | 0.245 | Medium | 0.123 | Lowest | No |
69 | Andhra Pradesh | East | Vizianagaram | 0.119 | Lowest | 0.077 | Lowest | No |
70 | Gujarat | West | Rajkot | 0.114 | Lowest | 0.059 | Lowest | No |
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Malakar, K., Mishra, T., Patwardhan, A. (2017). Developing Indices for Adaptation and Adaptive Capacity in Indian Marine Fishing. In: Leal Filho, W. (eds) Climate Change Research at Universities. Springer, Cham. https://doi.org/10.1007/978-3-319-58214-6_26
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