Community resilience against natural hazards in rice farming systems: a social network analysis (Record no. 291189)
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| 000 -LEADER | |
|---|---|
| fixed length control field | 06205nam a22002177a 4500 |
| 082 ## - DEWEY DECIMAL CLASSIFICATION NUMBER | |
| Classification number | 630.71 |
| Item number | SUD/CO PG |
| 100 ## - MAIN ENTRY--PERSONAL NAME | |
| Personal name | Suddamalla Manoj Kumar Reddy |
| 245 ## - TITLE STATEMENT | |
| Title | Community resilience against natural hazards in rice farming systems: a social network analysis |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. (IMPRINT) | |
| Place of publication, distribution, etc | Vellanikkara |
| Name of publisher, distributor, etc | Department of agricultural extension, college of agriculture |
| Date of publication, distribution, etc | 2023 |
| 300 ## - PHYSICAL DESCRIPTION | |
| Extent | 139p. |
| 502 ## - DISSERTATION NOTE | |
| Dissertation note | Msc |
| 520 3# - SUMMARY, ETC. | |
| Abstract | With the world falling back in keeping the global temperature rise below the two degrees Celsius limit, compared to the pre-industrial levels, the vagaries of climate change are becoming more prevalent than ever. Agriculture, which depends on climatic factors, such as rainfall, temperature, humidity, etc., will be affected by the erratic play of these factors with higher intensity and frequency of extreme weather events. Hazards of these natural events in the form of heavy rains and floods, droughts, pests and diseases result in crop losses, leading to distress to farmers at the individual level and affecting production at the national level. Hence, it is imperative to make agri-food systems, especially rice systems, resilient, as they form the major source of calories for the Indian population. In this context, the present study was undertaken to understand the factors that contribute to resilience and assess the level of resilience of paddy farmers in the states of Kerala and Andhra Pradesh (AP). Purposive sampling was used to select East Godavari and Kurnool districts of AP and Palakkad and Thrissur districts of Kerala. The criteria used are the area of production under paddy and exposure of the districts to natural hazards. Proportionate random sampling was used in selecting 60 farmers per district of AP, and 30 farmers per district of Kerala. Analysis of the natural hazards trend in the districts has revealed no distinct trend in the intensity of natural hazards. But, a presence of spatial and temporal variation across the four districts was evident. It was observed that heavy rains and floods were the hazard that recurred in all the years from 2019 to 2022 in all the regions. While heavy rains and floods were the only natural hazards that affected the districts of AP during the period 2019-2022, in Kerala, instances of droughts and severe pest and disease attacks were also seen. In Palakkad, heavy rains and floods resulted in more than 80 per cent of the total losses for 2019, 2020 and 2022. Though Palakkad is traditionally considered a drought-prone area, the losses due to droughts were very low compared to those of heavy rains and floods. In the case of Thrissur, more than 65 per cent of the losses were contributed by heavy rains and floods, followed by pests, diseases, and droughts. Overall, there was a total average production loss in paddy of 5.9, 7.2, 8.9, and 2.7 per cent in East Godavari, Kurnool, Palakkad and Thrissur districts, respectively, over the period 2019-2022. The community resilience of the farmers in the four regions was assessed using Community Resilience Index (CRI), and the results revealed that the Palakkad farming community had the highest resilience with an index score of 0.78, followed by Thrissur with 0.64, Kurnool with 0.48 and East Godavari with the lowest score of 0.33. The results indicated lower levels of resilience among the paddy farming community of AP districts compared to Kerala farming communities. Paddy farmers in AP were found lacking in many indicators affecting resilience, such as annual income, adoption of crop insurance, credit sources, farm size, type of tenancy, education levels, community action, training exposure, etc. The results implied regional-specific interventions and policies to improve the resilience of farming communities of these regions. Social network analysis of the farmers in the four regions also revealed the same picture concerning resilience. The network measures of districts of Kerala were highly favourable in relation to resilience compared to those of AP districts. The social capital of the Palakkad region was 5.05, the highest among all the four districts, followed by 4.90 for Thrissur, 4.48 for Kurnool and 4.26 for East Godavari farming communities. In the case of information networks, the farmers of Kerala districts were sourcing their information from institutional sources such as Krishi Bhavan, Kerala Agricultural University (KAU) and Krishi Vigyan Kendra (KVK), while farmers in AP were highly relying on input dealers with lower presence of institutional actors. In support networks, the presence of linking actors such as MLA and panchayat president was seen in the network of Kerala farmers, which was lacking in those of AP networks. Also, the presence of bridging actors is relatively scarce in AP networks. These results imply implementing policies that improve social participation among the farmers, leading to enhanced cohesion and social capital among the farming community that contributes to resilience. Grey Relation Analysis of the social networks indicated the ranking of social networks based on their average eigen-centrality values. The information network of the Palakkad region was considered the best among the networks of other regions. The region's broader presence of institutional actors and contact farmers contributed to the highest rank. In the case of support networks, the network of Thrissur was considered the best among others. This may be attributed to the presence of important bridging actors such as MLA and panchayat president in the network of Thrissur. Finally, in resource networks, the network of Kurnool farmers was the best. This could be attributed to multiple actors that would spread the influence among them rather than relying on a few significant actors. In conclusion, the type of actors and connections in the best networks in the three types of networks should be replicated in all the other regions to improve their resilience. |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name as entry element | climate change |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name as entry element | resilience |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name as entry element | rice |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name as entry element | paddy farming |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name as entry element | grey relation analysis |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name as entry element | agricultural extension |
| 700 ## - ADDED ENTRY--PERSONAL NAME | |
| Personal name | Binoo Benny, P (Guide) |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) | |
| Source of classification or shelving scheme | |
| Item type | Theses |
| Not for loan | Collection code | Permanent location | Current location | Shelving location | Date acquired | Full call number | Barcode | Date last seen | Koha item type |
|---|---|---|---|---|---|---|---|---|---|
| Not For Loan | Thesis | KAU Central Library, Thrissur | KAU Central Library, Thrissur | Theses | 2024-04-16 | 630.71 SUD/CO PG | 176007 | 2024-04-16 | Theses |