Challenges of Rural Insurance in the Context of Climate Change: The Case of Soybeans

The report examines the gap between soybean production distribution and rural insurance coverage in Brazil, highlighting how climate change—manifesting as severe droughts, hail, and frost—has led to unprecedented insurance claim payments. Using LASSO machine learning models, researchers found that while insurance data is insufficient to predict losses in the North and Northeast regions, agricultural yield data from PAM/IBGE can effectively identify climate variables (precipitation, fire risk, and wind) that correlate with losses. The authors recommend expanding insurance supply to vulnerable regions like MATOPIBA, utilizing production history for pricing in underserved areas, and strengthening the reinsurance market and public-private funds to ensure insurer liquidity.

• Climate change is increasing the frequency of extreme weather events in Brazil, leading to a surge in rural insurance claims. In the 2021/2022 agricultural year, severe drought caused claim payments to grow more than four times compared to the previous season, with total claims reaching R$ 8.8 billion, exceeding the total premiums collected.
• There is a significant mismatch between where soybeans are produced and where rural insurance is available. The Southern region holds 60% of policies and 43% of premium value, despite representing only 37% of soybean production value (2006-2018). Conversely, the Midwest accounts for 44% of production value but only 20% of insurance policies.
• The MATOPIBA region (Maranhão, Tocantins, Piauí, and Bahia) has seen an increase in soybean production share, rising from 10% in 2010 to 14% in 2020, yet insurance coverage has not grown proportionally, leaving the area vulnerable to high climate risk.
• Machine learning models (LASSO) identify precipitation, fire risk, and wind as the most relevant climate variables for explaining agricultural losses across Brazil. These variables are strongly correlated with droughts and storms, while temperature variables have lower individual explanatory power.
• Predictive models based on insurance claims data perform poorly in the North and Northeast due to limited policy history. However, when using average soybean yield data from PAM/IBGE, the models can effectively explain losses in the Northeast, suggesting that the lack of insurance is due to data scarcity and high risk rather than an inability to model the climate.
• The Brazilian reinsurance market is expanding but remains highly concentrated. While IRB Brasil held 89% of premiums in the 2011/12 crop, its share fell to 38% by 2021/22 as foreign companies increased their participation.

APA

Climate Policy Initiative (2023). Challenges of Rural Insurance in the Context of Climate Change: The Case of Soybeans. https://www.climatepolicyinitiative.org/publication/challenges-of-rural-insurance-in-the-context-of-climate-change-the-case-of-soybeans/

Chicago

Climate Policy Initiative. Challenges of Rural Insurance in the Context of Climate Change: The Case of Soybeans. 2023. https://www.climatepolicyinitiative.org/publication/challenges-of-rural-insurance-in-the-context-of-climate-change-the-case-of-soybeans/.

Wikipedia

{{cite report |author=Climate Policy Initiative |title=Challenges of Rural Insurance in the Context of Climate Change: The Case of Soybeans |date=10 April 2023 |url=https://www.climatepolicyinitiative.org/publication/challenges-of-rural-insurance-in-the-context-of-climate-change-the-case-of-soybeans/ |access-date=23 October 2026 |via=Climate Insights Directory}}

BibTeX

@techreport{climatepolicyinitiative2023challenges, author = {{Climate Policy Initiative}}, title = {{Challenges of Rural Insurance in the Context of Climate Change: The Case of Soybeans}}, institution = {Climate Policy Initiative}, year = {2023}, month = apr, url = {https://www.climatepolicyinitiative.org/publication/challenges-of-rural-insurance-in-the-context-of-climate-change-the-case-of-soybeans/}, urldate = {2026-10-23}, note = {Indexed by Climate Insights Directory} }