Climate change increases the need for better insurance solutions that enable farmers to cope with drought risks. We design weather index insurance using drought indices based on precipitation, soil moisture and evapotranspiration as underlying drought index and compare their risk-reducing potential for winter wheat producers in Eastern Germany. In general, we find that all drought indices can reduce financial risk exposure. However, the largest risk reduction can be achieved if the underlying drought index is tailored individually for each farm. This implies that insurers should offer insurance with farm-specific underlying drought index.

Crop producers face significant and increasing drought risks. We evaluate whether insurances based on globally and freely available satellite-retrieved soil moisture data can reduce farms’ financial drought risk exposure. We design farm individual soil moisture index insurances for wheat, maize and rapeseed production using a case study for Eastern Germany. We find that the satellite-retrieved soil moisture index insurances significantly decrease risk exposure for these crops compared to the situation where production is not insured. The satellite-retrieved index also outperforms one based on soil moisture estimates derived from meteorological measurements at ground stations. Important implications for insurers and policy makers are that they could and should develop better suited insurances. Available satellite-retrieved data can be used to increase farmers’ resilience in a changing climate.

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Ecosystem Service (ES) mapping has become a key tool in scientific assessments of human-nature interactions and is being increasingly used in environmental planning and policy-making. However, the associated epistemic uncertainty underlying these maps often is not systematically considered. This paper proposes a basic procedure to present areas with lower statistical reliability in a map of an ES indicator, the vegetation carbon stock, when extrapolating field data to larger case study regions. To illustrate our approach, we use regression analyses to model the spatial distribution of vegetation carbon stock in the Brazilian Amazon forest in the State of Pará. In our analysis, we used field data measurements for the carbon stock in three study sites as the response variable and various land characteristics derived from remote sensing as explanatory variables for the ES indicator. We performed regression methods to map the carbon stocks and calculated three indicators of reliability: RMSE-Root-mean-square-error, R -coefficient of determination -from an out-of-sample validation and prediction intervals. We obtained a ‡ § ‡ | ‡ ¶ # ¶ § 2 © Le Clec'h S et al. This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.map of carbon stocks and made explicit its associated uncertainty using a general indicator of reliability and a map presenting the areas where our prediction is the most uncertain. Finally, we highlighted the role of environmental factors on the range of uncertainty. The results have two implications. (1) Mapping prediction interval indicates areas where the map's reliability is the highest. This information increases the usefulness of ES maps in environmental planning and governance.(2) In the case of the studied indicator, the reliability of our prediction is very dependent on land cover type, on the site location and its biophysical, socioeconomic and political characteristics. A better understanding of the relationship between carbon stock and land-use classes would increase the reliability of the maps. Results of our analysis help to direct future research and fieldwork and to prevent decision-making based on unreliable maps.

Heat stress can affect milk production in several ways. We here quantify overall farm-level heat effects on annual milk revenues, veterinary expenses, and feed purchases in Swiss agriculture. We combine farm-level accountancy panel data from 1314 representative Swiss milk producers and covering 13 years with high-quality weather data in a reduced-form two-way fixed effect model. Although we find that Swiss milk producers frequently encounter heat stress, we find no significant heat effects on annual milk revenues, veterinary expenses, or feed purchases. This finding implies that Swiss milk farms are on average robust to current heat exposure.