Can the approach of vulnerability assessment facilitate identification of suitable adaptation models for risk reduction?

Local agriculture in India is highly vulnerable to a wide range of anthropogenic-induced natural disasters. Most studies regarding the vulnerability of farmers in India are performed at coarse spatial scales. These studies fail to assess the spatial distribution of vulnerability status in local regions where small and marginal farmers own lands. Consequently, the adaptation measures fail to pass on to the grassroots level. This study attempts to 1) assess the vulnerability status of local agricultural sector among the Sub-district Administrative Units (SDAUs) of Gaya district in Bihar, and 2) identify suitable adaptation models using two aggregation methods to reduce potential risks. Both aggregation methods are used to compute indices of exposure, sensitivity and adaptive capacity followed by their classification under five vulnerability categories. The degree of transition among categories is analyzed for each SDAU to find a suitable adaptation model, i.e., incremental, systemic and transformational. Our results show that, in the case of exposure, only three SDAUs shifted their categories. In sensitivity and adaptive capacity cases, 41.66% and 45.83% SDAUs are found to shift their categories respectively. Moreover, SDAUs facing higher exposure require systemic model of adaptation. SDAUs facing higher sensitivity need both systemic and transformational models, while SDAUs with lower adaptive capacity found the systemic adaptation model to be the best suited. Such a vulnerability assessment of a local area, which also facilitates the identification of suitable adaptation models, can assist agricultural agencies in reducing the risks of potential disasters by implementing efficient adaptation strategies.

Automated summary

This study aims to assess the vulnerability of the local agricultural sector in the Gaya district of Bihar, India, and identify suitable adaptation models using two aggregation methods. The results show that certain sub-district administrative units require systemic or transformational adaptation models based on their exposure, sensitivity, and adaptive capacity levels. Such vulnerability assessments can assist agricultural agencies in implementing efficient adaptation strategies to reduce the risks of potential disasters in local areas.