Insuring Weather Risks in European Agriculture

Abstract

Summary Smart insurance designs (e.g. tailoring insurances to the individual farm) and technological progress (e.g. advances in satellite technology) enable improvements in insurance schemes. Spatially and temporally more detailed information as well as better knowledge of the relationship between weather and yields losses can reduce basis risk of insurances. However, the use of new and better data does not automatically lead to better insurance schemes. The SURE‐Farm project contributes to the understanding of weather risks and new insurance mechanisms as possible tools to increase the resilience of European agriculture to weather extremes. Therefore, we aim to show how to integrate newly available data sources for different agricultural outputs, namely grassland, crop and dairy production in different European regions. In the four contributions summarised here, we evaluate currently existing index insurance schemes and provide fundamental insights for future developments of index insurances for the crop and livestock sector in Europe. We first summarise and discuss existing index‐based weather insurances. Second, we investigate the drought risk reduction potential of different drought indicators in two case studies on different crops in Eastern Germany. Third, we examine the relationship between hot and humid weather and milk yield losses for dairy producers in Flanders.

Review article: Design and Evaluation of Weather Index Insurance for Multi-Hazard Resilience and Food Insecurity

“…For farmers, climate-driven hazards such as droughts, temperature variation, excessive rainfall can decrease crop yields, cause damage to forests and cause livestock mortality (Bokusheva, 2018;Bucheli et al, 2021;Eze et al, 2020;Sacchelli et al, 2018;Vroege and Finger, 2020;Ward and Makhija, 2018). The lack of precipitation is very concerning for reservoir operation that leads to business interruption in the case of water supply and irrigation (Denaro et al, 2020;Gómez-Limón, 2020; Guerrero-Baena and Gómez-Limón, 2019; Mohor and Mendiondo, 2017).…”

“…Deterministic models were applied for income reduction impacts, especially for crop insurance. Additionally, some models were related only with one explanatory variable (one index) was the most common solution found in the in-depth analysis (Aizaki et al, 2021;Bokusheva, 2018;Bucheli et al, 2021;Hohl et al, 2020;Kath et al, 2019;Mortensen and Block, 2018;F et al, 2020;Vroege and Finger, 2020;Ward and Makhija, 2018). There is evidence that machine learning techniques improve loss modeling from different sources and present different time and spatial scales (Eze et al, 2020).…”

The Importance of Improving and Enlarging the Scope of Risk Management to Enhance Resilience in European Agriculture

“…For example, farmers can reduce uncertainty by learning and sharing experiences with other farms. Farmers can also share risks with other farmers or transfer risks to markets to complement onfarm risk management strategies (Vroege and Finger 2020). Moreover, farms are increasingly connected to other actors along the value chain that look to ensure the procurement of agricultural commodities through contractual agreementsa phenomenon referred to as contract farming (Bellemare 2018).…”

“…Even though the current toolbox of insurance schemes offered to European farmers is rich (e.g. Meuwissen et al 2018;Severini et al 2019;Vroege and Finger 2020), the availability of insurance schemes to cover extreme weather risks, e.g. against droughts or heat waves, is currently limited.…”

Risk management in crop farming

“…Figure 2: Share of farms that purchased any type of agricultural insurance in the period [2014][2015][2016][2017][2018] Source: Own edition based on Spiegel et al (2019) According to Vroege and Finger (2020), there are various types of crop insurance. For example, single or multiple peril insurance that covers one or more specific risks, and yield insurance covering production losses caused by any peril.…”