Can Gridded Precipitation Data and Phenological Observations Reduce Basis Risk of Weather Index–Based Insurance?

Abstract

Adverse weather events occurring at sensitive stages of plant growth can cause substantial yield losses in crop production. Agricultural insurance schemes can help farmers to protect their income against downside risks. While traditional indemnity-based insurance schemes need governmental support to overcome market failure caused by asymmetric information problems, weather index–based insurance (WII) products represent a promising alternative. In WII the payout depends on a weather index serving as a proxy for yield losses. However, the nonperfect correlation of yield losses and the underlying index, the so-called basis risk, constitutes a key challenge for these products. This study aims to contribute to the reduction of basis risk and thus to the addition of risk-reducing properties of WII. More specifically, the study tests whether grid data for precipitation (vs weather station data) and phenological observations (vs fixed time windows for index determination) that are provided by public institutions can reduce spatial and temporal basis risk and thus improve the performance of WII. An empirical example of wheat production in Germany is used. No differences were found between using gridded and weather station precipitation, whereas the use of phenological observations significantly increases expected utility. However, even if grid data do not yet reduce basis risk, they enable overcoming several disadvantages of using station data and are thus useful for WII applications. Based on the study’s findings and the availability of these data in other countries, a massive potential for improving WII can be concluded.

Resilience through risk management: cooperative insurance in small-holder aquaculture systems

“…In small-holder agriculture, weather index or parametric insurance policies have grown in popularity due to efficiencies in overcoming challenges associated with false claims ( Muller et al, 2017 ). This is when the probability of flooding, and the occurrence of a flooding event are calculated and identified from historical and real-time remotely sensed data respectively ( Dalhaus and Finger, 2016 ). Weather index insurance is attractive in small-holder food production systems because it can be tailored on specific down-side events ( Conradt et al, 2015 ) and it removes the problem of moral hazard (e.g.…”

Blockchain Technology for Agriculture: Applications and Rationale

“…Three sources of basis risk can occur. Spatial basis risk marks any differences between measured and on-farm weather, e.g., through spatial distance (Ritter et al, 2014;Dalhaus and Finger, 2016). Temporal basis risk indicates that an unprecise time window was chosen for index determination, e.g., whole year rainfall vs. growing season rainfall (Conradt et al, 2015;Dalhaus et al, 2018).…”

Insurance risk assessment in the face of climate change: Integrating data science and statistics

“…Such flexibility can reduce farmers' downside risk exposure. Most index products are based on a single variable such as rainfall, county yield, and satellite‐based NDVI values, but complex weather‐based products need to be explored and validated to insure against multiple events. There shall be developed curated archives of gridded climate data at temporal and spatial resolutions applicable to insurance practice. Such data availability could significantly increase the attractiveness of weather index‐based products, without providing premium subsidies (Dalhaus & Finger, ). There is a need to further explore the use of crop yield and seasonal weather forecasts in weather index‐based insurance. Coupling crop yield and weather forecast indices provides probabilistic information on the next growing season and are anticipated to continue to increase in accuracy. Further investigation of agent‐based approaches to better understand collective behavior and dynamics with respect to socio‐economic benefits and challenges of insurance are needed. The wider availability and integration of private crop insurance data, relevant governmental databases, including RS‐based crop mapping and yield indices to enable an evaluation of the performance of deep learning (i.e., the area of machine learning in which artificial neural networks adapt and learn from vast amounts of data) at the regional and field level. There is a demand for data science models that utilize available crowdsourced data from farmers' smartphones, and models that can utilize optical and/or synthetic aperture radar (SAR) satellite‐based remote sensing data such as soil moisture and vegetation indices (LAI and NDVI) from Sentinel 1/2/3 and RADARSAT2/RADARSAT Constellation Mission (RCM) satellites.…”

“…• There shall be developed curated archives of gridded climate data at temporal and spatial resolutions applicable to insurance practice. Such data availability could significantly increase the attractiveness of weather index-based products, without providing premium subsidies (Dalhaus & Finger, 2016). • There is a need to further explore the use of crop yield and seasonal weather forecasts in weather index-based insurance.…”