Desafios do Seguro Rural no Contexto das Mudanças Climáticas

The document examines the gap between soybean production and rural insurance coverage in Brazil, highlighting how climate change exacerbates risks through extreme events. Using LASSO machine learning models, researchers demonstrate that while insurance data is scarce in regions like the North and Northeast, agricultural yield data can effectively identify climate variables—primarily precipitation, fire risk, and wind—that drive production losses. The brief argues for expanding insurance to vulnerable regions like MATOPIBA and improving the solvency of insurers through reinsurance and public-private funds to support a low-carbon agricultural transition.

• Climate change is increasing the frequency of extreme events in Brazil, such as prolonged droughts, hail, and frost, which has led to a substantial rise in rural insurance claims. In the 2021/2022 agricultural year, severe drought caused insurance indemnities to increase more than fourfold compared to the previous harvest.
• Rural insurance in Brazil is highly concentrated in the South and Midwest regions, failing to keep pace with the expansion of soybean production in other areas. For example, in the MATOPIBA region (Maranhão, Tocantins, Piauí, and Bahia), the growth of insurance coverage has not been proportional to the increase in production.
• Machine learning models (LASSO) reveal that precipitation, fire risk, and wind are the most relevant climate variables for explaining agricultural losses across Brazil. The models show that the correlation between these variables and losses varies by region according to planting and harvesting cycles; for instance, the South shows higher correlation in September and November (planting), while the Midwest peaks in March (harvest).
• There is a significant data gap in the North and Northeast regions that hinders the ability of insurance-based models to predict claims. However, when using average yield data from PAM/IBGE instead of insurance records, the models can effectively explain production losses in the Northeast, suggesting that the lack of insurance is due to high risk and low data availability rather than an inability to model the climate impact.
• The Brazilian reinsurance market is growing but remains highly concentrated. While IRB Brasil held 89% of premiums in the 2011/12 harvest, this dropped to 38% by 2021/22 as foreign companies increased their participation, which helps dilute risks globally.

APA

Souza, P., Oliveira, W., & Stussi, M. (2023). Desafios do Seguro Rural no Contexto das Mudanças Climáticas. Climate Policy Initiative. https://www.climatepolicyinitiative.org/wp-content/uploads/2023/04/PB-Desafios-do-Seguros-Rural-1.pdf

Chicago

Souza, Priscila, Wagner Oliveira, and Mariana Stussi. Desafios do Seguro Rural no Contexto das Mudanças Climáticas. Climate Policy Initiative, 2023. https://www.climatepolicyinitiative.org/wp-content/uploads/2023/04/PB-Desafios-do-Seguros-Rural-1.pdf.

Wikipedia

{{cite report |last1=Souza |first1=Priscila |last2=Oliveira |first2=Wagner |last3=Stussi |first3=Mariana |title=Desafios do Seguro Rural no Contexto das Mudanças Climáticas |publisher=Climate Policy Initiative |date=April 2023 |url=https://www.climatepolicyinitiative.org/wp-content/uploads/2023/04/PB-Desafios-do-Seguros-Rural-1.pdf |access-date=17 August 2026 |via=Climate Insights Directory}}

BibTeX

@techreport{souza2023desafios, author = {Souza, Priscila and Oliveira, Wagner and Stussi, Mariana}, title = {{Desafios do Seguro Rural no Contexto das Mudanças Climáticas}}, institution = {Climate Policy Initiative}, year = {2023}, month = apr, url = {https://www.climatepolicyinitiative.org/wp-content/uploads/2023/04/PB-Desafios-do-Seguros-Rural-1.pdf}, urldate = {2026-08-17}, note = {Indexed by Climate Insights Directory} }