VULNERABILITY IN AGRICULTURE – DATA ANALYSIS

This paper explores the vulnerability in agriculture in Serbia. Agricultural sector is extremely important for both country and the farmers. The country’s exposure to hazards has a direct effect on the economy, since around 20% of the population are farmers. This sector is exposed to all kinds of hazards and climate changes. When a hazard occurs, it is essential to act quickly and determine recovery and risk reduction requirements. This paper shows monetary losses and recorded damaged/destroyed goods in agricultural sector. Data of previously recorded hazards is obtained from United Nations DesInventar Open Source Initiative database. The analysis is based on 1493 agricultural events recorded from 1981. to 2023. Data is analysed using Python programming language. It is processed with Python’s DOM parser xml.dom.minidom. inside a Django web application. Visualisation of analysed data is implemented using Chart.js library.

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Publication Details

Published
2025-01-01
DOI
https://doi.org/10.24867/future-bme-2024-062
Primary Topic
Agricultural risk and resilience
Type
article
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article

VULNERABILITY IN AGRICULTURE – DATA ANALYSIS

Srđan Popov, Ljiljana Popović, Tanja Vranić, Milena Zeljković
Agricultural risk and resilience
article

VULNERABILITY IN AGRICULTURE – DATA ANALYSIS

Srđan Popov, Ljiljana Popović, Tanja Vranić, Milena Zeljković
article en

Abstract

This paper explores the vulnerability in agriculture in Serbia. Agricultural sector is extremely important for both country and the farmers. The country’s exposure to hazards has a direct effect on the economy, since around 20% of the population are farmers. This sector is exposed to all kinds of hazards and climate changes. When a hazard occurs, it is essential to act quickly and determine recovery and risk reduction requirements. This paper shows monetary losses and recorded damaged/destroyed goods in agricultural sector. Data of previously recorded hazards is obtained from United Nations DesInventar Open Source Initiative database. The analysis is based on 1493 agricultural events recorded from 1981. to 2023. Data is analysed using Python programming language. It is processed with Python’s DOM parser xml.dom.minidom. inside a Django web application. Visualisation of analysed data is implemented using Chart.js library.

Vol. 1
Openalex Percentile: Top 93%
Agricultural risk and resilience
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