Scholarlink

<p>Agricultural communities are facing a higher level of vulnerability as a result of the emerging challenges posed by climate change. Climate change-related stressors are adversely impacting agricultural production and food security. Developing region-specific climate-smart strategies demands an integrated climate change vulnerability assessment in agriculture considering regional disparities. This study aimed to evaluate climate change vulnerability of agriculture in the coastal districts of India. A comprehensive set of district-wise 30 indicators concerning sensitivity, exposure, and adaptive capacity indicators was systematically gathered for this region. The indicators were normalized using a linear scoring technique and their relative weights were determined using both entropy and principal component analysis (PCA). Subsequently, the normalized indicators were multiplied by their respective weights, resulting in the computation of sensitivity, exposure, and adaptive capacity indices which were, in turn, used for the vulnerability index calculation for each district. Entropy and PCA methods are completely data-driven and do not require additional inputs beyond the dataset itself making the analysis free from expert opinion bias. The results indicated that entropy is a better method for weight assignment as compared to PCA as it dynamically adjusts weights based on the distribution and uncertainty of the data, ensuring that indicators with significant variations receive more importance. So, this method was more sensitive to regional variations, making it a preferred approach for differentiating vulnerabilities among the districts. Based on entropy analysis, Kachchh (0.3183) is found to be the most vulnerable, whereas Uttara Kannada (0.0263) is the least vulnerable district of coastal India. The average vulnerability for the entire coastal region of India is calculated at 0.1148. PCA analysis too, ranked Kachchh (0.2146) as the most vulnerable district and Ganjam (0.0506) as the lowest vulnerable district with a mean vulnerability of 0.1311 for the entire coastal region. These findings can be used for targeted strategies and development activities in climate-vulnerable areas to facilitate effective adaptation measures.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Assessment of climate change vulnerability of agriculture in coastal districts of India using principal component analysis and entropy-based methods

  • 期刊论文

  • 发表日期：

    2025年7月5日

查看全文
  • Bappa Das,
  • Sonam Sah,
  • K. V. Reddy,
  • S. J. Balaji,
  • Sujeet Desai,
  • Gopal Mahajan,
  • Pooja Singh,
  • Saurav Saha,
  • Uthappa AR,
  • Parveen Kumar,
  • V. K. Sehgal,
  • Ram N. Singh,
  • Dikshita A. Shetkar

摘要

Agricultural communities are facing a higher level of vulnerability as a result of the emerging challenges posed by climate change. Climate change-related stressors are adversely impacting agricultural production and food security. Developing region-specific climate-smart strategies demands an integrated climate change vulnerability assessment in agriculture considering regional disparities. This study aimed to evaluate climate change vulnerability of agriculture in the coastal districts of India. A comprehensive set of district-wise 30 indicators concerning sensitivity, exposure, and adaptive capacity indicators was systematically gathered for this region. The indicators were normalized using a linear scoring technique and their relative weights were determined using both entropy and principal component analysis (PCA). Subsequently, the normalized indicators were multiplied by their respective weights, resulting in the computation of sensitivity, exposure, and adaptive capacity indices which were, in turn, used for the vulnerability index calculation for each district. Entropy and PCA methods are completely data-driven and do not require additional inputs beyond the dataset itself making the analysis free from expert opinion bias. The results indicated that entropy is a better method for weight assignment as compared to PCA as it dynamically adjusts weights based on the distribution and uncertainty of the data, ensuring that indicators with significant variations receive more importance. So, this method was more sensitive to regional variations, making it a preferred approach for differentiating vulnerabilities among the districts. Based on entropy analysis, Kachchh (0.3183) is found to be the most vulnerable, whereas Uttara Kannada (0.0263) is the least vulnerable district of coastal India. The average vulnerability for the entire coastal region of India is calculated at 0.1148. PCA analysis too, ranked Kachchh (0.2146) as the most vulnerable district and Ganjam (0.0506) as the lowest vulnerable district with a mean vulnerability of 0.1311 for the entire coastal region. These findings can be used for targeted strategies and development activities in climate-vulnerable areas to facilitate effective adaptation measures.