The optimal drought index for designing weather index insurance

Bucheli, Janic; Finger, Robert

doi:10.1093/erae/jbaa014

Cited by 60 publications

(37 citation statements)

References 83 publications

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“…OLS is not the only commonly used method for estimating trends. For instance, some authors have reported that the Theil‐Sen, M and MM estimators are more robust to outliers (Finger, 2013), and quantile regression can be used to estimate the median and other quantiles (Bucheli et al, 2020; Conradt et al, 2015; Dalhaus et al, 2018). However, these methods are more difficult to include in model averaging frameworks, which is the main focus of this study.…”

Section: Data Ols Trends and Statistical Significancementioning

confidence: 99%

Dealing with trend uncertainty in empirical estimates of European rainfall climate for insurance risk management

Jewson

Dallafior

Comola

2021

Meteorological Applications

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The insurance industry uses mathematical models to estimate the risks due to future natural catastrophes. For climate-related risks, historical climate data are a key ingredient used in making the models. Historical data for temperature and sea level often show clear and readily quantified climate change driven trends, and these trends would typically be accounted for when building risk models by adjusting earlier values to render the earlier data relevant to the future climate. For other climate variables, such as rainfall in many parts of the world, the questions of whether there are climate change driven trends in the historical data, and how to quantify them if there are, are less simple to answer. We investigate these questions in the context of European rainfall with a specific focus on how to deal with the uncertainty around trend estimates. We compare 10 empirical methodologies that one might use to model and predict trends, including traditional statistical testing and alternatives to statistical testing based on standard methods from model selection and model averaging. We emphasize prediction and risk assessment, rather than detection of trends, as our goal. Viewed in terms of this goal, the methods we consider each have qualitative and quantitative advantages and disadvantages. Understanding these advantages and disadvantages can help risk modellers make a choice as to which method to use, and based on the results we present, we believe that in many common situations model averaging methods, as opposed to statistical testing or model selection, are the most appropriate.

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Section: Data Ols Trends and Statistical Significancementioning

confidence: 99%

Dealing with trend uncertainty in empirical estimates of European rainfall climate for insurance risk management

Jewson

Dallafior

Comola

2021

Meteorological Applications

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“…New insurance options, such as for example insurance schemes based on satellite imagery, can contribute to increasing the resilience of European farming systems to extreme weather risks, in both crop and livestock production. Drawing on findings from the SURE‐Farm project, this article provides an overview of recent developments in agricultural insurance research (Bucheli et al ., 2020; Vroege et al ., 2019; Vroege et al ., 2020a; Vroege et al ., 2020b).…”

Section: Weather Risk Management In Agriculturementioning

confidence: 99%

“…As a result, there are still few drought insurance options available to farmers – even though drought is an important and increasing risk for European agriculture. We thus aimed to provide insights on how differently an optimal drought insurance contract could be designed (Bucheli et al ., 2020). In this contribution, we specifically address two questions.…”

Section: Sure‐farm Contributionsmentioning

confidence: 99%

Insuring Weather Risks in European Agriculture

Vroege

Finger

2020

EuroChoices

Self Cite

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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.

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“…In reality, the timing of a crop's sensitivity to weather may vary significantly across fields due to differences in management practices, such as variety and sowing dates, as well as meteorological conditions, which affect rates of crop development [15]. Failure to consider this heterogeneity may lead to inaccurate estimation of yield losses and basis risk [16,17].…”

Section: Introductionmentioning

confidence: 99%

Improving the Performance of Index Insurance Using Crop Models and Phenological Monitoring

et al. 2021

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Extreme weather events cause considerable damage to the livelihoods of smallholder farmers globally. Whilst index insurance can help farmers cope with the financial consequences of extreme weather, a major challenge for index insurance is basis risk, where insurance payouts correlate poorly with actual crop losses. We analyse to what extent the use of crop simulation models and crop phenology monitoring can reduce basis risk in index insurance. Using a biophysical process-based crop model (Agricultural Production System sIMulator (APSIM)) applied for rice producers in Odisha, India, we simulate a synthetic yield dataset to train non-parametric statistical models to predict rice yields as a function of meteorological and phenological conditions. We find that the performance of statistical yield models depends on whether meteorological or phenological conditions are used as predictors and whether one aggregates these predictors by season or crop growth stage. Validating the preferred statistical model with observed yield data, we find that the model explains around 54% of the variance in rice yields at the village cluster (Gram Panchayat) level, outperforming vegetation index-based models that were trained directly on the observed yield data. Our methods and findings can guide efforts to design smart phenology-based index insurance and target yield monitoring resources in smallholder farming environments.

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The optimal drought index for designing weather index insurance

Cited by 60 publications

References 83 publications

Dealing with trend uncertainty in empirical estimates of European rainfall climate for insurance risk management

Dealing with trend uncertainty in empirical estimates of European rainfall climate for insurance risk management

Insuring Weather Risks in European Agriculture

Improving the Performance of Index Insurance Using Crop Models and Phenological Monitoring

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