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<p>Challenges arising from climate variations and other factors in the agricultural insurance market complicate the pricing of insurance products for insurers and affect consumers. Although different methodologies are available in the literature, there is growing demand for more accurate predictive models. In this context, the main aim of this paper is to use distributional regression models to investigate the relationships between climatic factors and insured crop features in terms of both the probability and severity of claims for soybean crops in Minas Gerais, Brazil. The results obtained indicate that the proposed and adopted methodology is crucial, as it allowed both for the identification of covariates that affect only the probability of a claim occurring and variables that affect only the severity of a claim, given that it occurs, and the detection of non-linear relationships that would not be possible based on traditional models.</p>

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Agricultural insurance and climate variability: analysing soybean yield risks in Brazil using distributional regression models

  • 期刊论文

  • 发表日期：

    2026年1月23日

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  • Luiz O. O. Pala,
  • Luiz R. Nakamura,
  • Elias M. Sabe,
  • Thiago G. Ramires

摘要

Challenges arising from climate variations and other factors in the agricultural insurance market complicate the pricing of insurance products for insurers and affect consumers. Although different methodologies are available in the literature, there is growing demand for more accurate predictive models. In this context, the main aim of this paper is to use distributional regression models to investigate the relationships between climatic factors and insured crop features in terms of both the probability and severity of claims for soybean crops in Minas Gerais, Brazil. The results obtained indicate that the proposed and adopted methodology is crucial, as it allowed both for the identification of covariates that affect only the probability of a claim occurring and variables that affect only the severity of a claim, given that it occurs, and the detection of non-linear relationships that would not be possible based on traditional models.