English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Dryland farming is highly affected by climate variability as its coefficient of variation with precipitation is high. The vulnerability assessment is a useful and effective tool for determining the impact of climate change on dryland farmers towards adaptation. The factors assessment plays an important role in designing and implementing policy research on a local scale in developing nations. The study aims to calculate the vulnerability level and factors affecting it in the dryland community in Southern India using Vulnerability index calculation and Machine Learning (ML) techniques. The primary data were collected with a structured questionnaire from randomly selected 200 farming households from ten villages. This study introduced new contextual variables in the vulnerability assessment and compared different ML techniques to analyse the vulnerability factors. The study showed that the dryland community is moderately vulnerable because of its higher adaptive capacity. The results of the ML techniques showed that the factors “Involvement in the awareness programmes, income sources for food, farm size, and education status of the dryland farmers” had a significant impact on the vulnerability level of the community. Potential coping strategies were identified to reduce the vulnerability level of the dryland farmers. The present study showed a pathway that can help the policy research and development plan for the sustainable livelihood of dryland farmers at a micro-level in the climate-changing scenario.

Climate change vulnerability assessment of dryland farmers and factors Identification using machine learning techniques