Abstract
The Intergovernmental Panel on Climate Change (IPCC) has mentioned that coastal areas would be the worst sufferers of climate change-induced variabilities and extremes, severely affecting the farming community, particularly in developing countries. Farmers are developing different field-based and livelihood-based adaptive mechanisms depending on several socio-economic, institutional and locational factors. Previous studies were concentrated on agriculture and its adaptation strategies against climate change, but considering coastal agriculture in the context of climate variability is largely unexplored. This study aims to find controlling factors of coping mechanisms against climate variability for coastal agriculture on the east coast of India. A questionnaire survey and focused group discussion have been conducted to collect and validate farmers’ perceptions of climate variability. The study has applied a binary logit model and established that socio-economic farming system attributes and locational factors influence farmers’ decision to adopt farm-level and livelihood adaptations. Most farmers (> 80%) have perceived that rainfall variability has increased, which is a major issue for agriculture in this area. The logistic regression models successfully predicted nearly 70% of the variables in each model. The model indicated that variables like experience, education, land ownership, involvement with marine fishing and distance from the coast influenced adaptation mechanisms against climate variability. The findings of the study have underlined the factors that need more attention for better management of coastal agriculture in the context of climate variability and can help to formulate better climate adaptation policies in the coastal areas of India and areas with similar backgrounds.
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Data availability
The datasets generated during and/or analysed during the current study are available from the corresponding author upon reasonable request.
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Appendix
Appendix
Figures 6, 7, 8, 9, 10, 11 and 12
Factors influencing crop calendar change. ***Significant at probability level, p ≤ 0.01. **Significant at p ≤ 0.05. *Significant at p ≤ 0.1
Factors influencing water conservation. ***Significant at probability level, p ≤ 0.01. **Significant at p ≤ 0.05. *Significant at p ≤ 0.1
Factors influencing planting vegetables. ***Significant at probability level, p ≤ 0.01. **Significant at p ≤ 0.05. *Significant at p ≤ 0.1
Factors influencing crop insurance. ***Significant at probability level, p ≤ 0.01. **Significant at p ≤ 0.05. *Significant at p ≤ 0.1
Factors influencing increasing off-farm activities. ***Significant at probability level, p ≤ 0.01. **Significant at p ≤ 0.05. *Significant at p ≤ 0.1
Factors influencing leasing land. ***Significant at probability level, p ≤ 0.01. **Significant at p ≤ 0.05. *Significant at p ≤ 0.1
Factors influencing migrating for job. ***Significant at probability level, p ≤ 0.01. **Significant at p ≤ 0.05. *Significant at p ≤ 0.1
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Panja, S., Mukhopadhyay, S. An investigation of small and marginal holder farmers’ adaptation strategies to climate variability and its determinants in coastal agriculture: evidence from east coast of India. Mitig Adapt Strateg Glob Change 29, 21 (2024). https://doi.org/10.1007/s11027-024-10118-4
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DOI: https://doi.org/10.1007/s11027-024-10118-4