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Growing Science » Decision Science Letters » Analysing the decision making for agricultural risk assessment: An application of extreme value theory

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Decision Science Letters

ISSN 1929-5812 (Online) - ISSN 1929-5804 (Print)
Quarterly Publication
Volume 10 Issue 3 pp. 351-360 , 2021

Analysing the decision making for agricultural risk assessment: An application of extreme value theory Pages 351-360 Right click to download the paper Download PDF

Authors: Riaman Riaman, Sukono Sukono, Sudradjat Supian, Noriszura Ismail

DOI: 10.5267/j.dsl.2021.2.003

Keywords: Agricultural Insurance, Risk Assessment, Climate Variables, Extreme Value Theory

Abstract: As the most contributed sectors in agriculture, rice farming is facing various risks, namely uncertainty such as crop failure caused by climate change, including air temperature, weather, rainfall and others. Indonesia is categorised as an agricultural country with a tropical climate. By this season, the farmers can plant the rice. Rice farming is currently an inseparable part of most agricultural societies in Indonesia, especially in West Java. However, changes in air temperature, weather and annual rainfall, can increase the uncertainty and upward the risk of crop failure. Thus, the current study seeks to investigate the decision making for agricultural risk assessment (climate variable) through the formulation of a risk model for agricultural insurance in Indonesia. This study utilised the climate variables, which consist of air temperature, wind speed, maximum and minimum temperatures, and rainfall. For determining the magnitude of risk, we applied the Block Maxima method and Peak Over Threshold. The results of this study found that the highest risk of losses occurred in November, December, January, February and March with a value of 0.17485.

How to cite this paper
Riaman, R., Sukono, S., Supian, S & Ismail, N. (2021). Analysing the decision making for agricultural risk assessment: An application of extreme value theory.Decision Science Letters , 10(3), 351-360.

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Journal: Decision Science Letters | Year: 2021 | Volume: 10 | Issue: 3 | Views: 1540 | Reviews: 0

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