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Area of Science:

  • Agricultural Science
  • Environmental Science
  • Data Science

Background:

  • Climate change and extreme weather events pose significant threats to global agriculture, leading to crop production declines.
  • Natural disasters like floods and wildfires exacerbate agricultural vulnerabilities, increasing the need for robust disaster management strategies.
  • The integration of information technology into emergency management is a growing area of academic and governmental focus.

Purpose of the Study:

  • To review the current state of research in agricultural disaster risk management (ADRM).
  • To analyze the role and significance of big data in ADRM.
  • To provide a bibliometric analysis of ADRM research over the past decade, focusing on disaster types and big data utilization.

Main Methods:

  • Bibliometric analysis of research publications from the last ten years.
  • Assessment of annual publication growth, topic categories, and productivity.
  • Evaluation of data flux and its impact on forecasting performance.
  • Case study incorporating proposed ADRM mechanisms for flood prediction using Indian Meteorology Department data.

Main Results:

  • Identified trends in ADRM research, including disaster types and the application of big data.
  • Demonstrated the critical role of comprehensive data analysis in improving disaster forecasting.
  • Showcased the effectiveness of the proposed ADRM mechanism in enhancing early flood prediction capabilities.

Conclusions:

  • Big data analytics are crucial for advancing agricultural disaster risk management.
  • The proposed ADRM framework, integrating meteorological data, significantly improves flood prediction accuracy and lead time.
  • Continued research and technological integration are vital for building agricultural resilience against climate change impacts.