Improving agricultural microinsurance by applying universal kriging and generalised additive models for interpolation of mean daily temperature

Roznik, Mitchell; Porth, C. Brock; Porth, Lysa; Roznik, Katerina

doi:10.1057/s41288-019-00127-9

Cited by 18 publications

(8 citation statements)

References 30 publications

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“…The OK method approximates values in unsampled regions by averaging nearby data and visualizing the correlations between surrounding values as a function of the geographic distance between the sites in the area of study using a weighted average of neighboring data and a variogram [93]. UK uses a trend surface that may include factors that account for variation in the global component, and it more likely to provide residuals that are more closely related to a stationary mean with identical distribution [94]. EBK automates the most time-consuming and challenging stages in creating a realistic kriging model.…”

Section: Spatial Interpolation Methods For Heavy Metalsmentioning

confidence: 99%

A Hybrid Neural Network–Particle Swarm Optimization Informed Spatial Interpolation Technique for Groundwater Quality Mapping in a Small Island Province of the Philippines

Jesus

Senoro

Cruz

et al. 2021

Toxics

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Water quality monitoring demands the use of spatial interpolation techniques due to on-ground challenges. The implementation of various spatial interpolation methods results in significant variations from the true spatial distribution of water quality in a specific location. The aim of this research is to improve mapping prediction capabilities of spatial interpolation algorithms by using a neural network with the particle swarm optimization (NN-PSO) technique. Hybrid interpolation approaches were evaluated and compared by cross-validation using mean absolute error (MAE) and Pearson's correlation coefficient (R). The governing interpolation techniques for the physicochemical parameters of groundwater (GW) and heavy metal concentrations were the geostatistical approaches combined with NN-PSO. The best methods for physicochemical characteristics and heavy metal concentrations were observed to have the least MAE and R values, ranging from 1.7 to 4.3 times and 1.2 to 5.6 times higher than the interpolation technique without the NN-PSO for the dry and wet season, respectively. The hybrid interpolation methods exhibit an improved performance as compared to the non-hybrid methods. The application of NN-PSO technique to spatial interpolation methods was found to be a promising approach for improving the accuracy of spatial maps for GW quality.

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Section: Spatial Interpolation Methods For Heavy Metalsmentioning

confidence: 99%

A Hybrid Neural Network–Particle Swarm Optimization Informed Spatial Interpolation Technique for Groundwater Quality Mapping in a Small Island Province of the Philippines

Jesus

Senoro

Cruz

et al. 2021

Toxics

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“…The index is derived from precipitation measurements collected from the nearest weather station to the insured farm. Alternatively, producers can select up to three nearby weather stations from an authorized network and weight their precipitation values to best represent their farm experience (Roznik et al , 2019). Producers can select coverage levels up to 85 percent of historical precipitation normals (Alberta Financial Services Corporation, 2019; Vroege et al , 2019).…”

Section: Forage Production Backgroundmentioning

confidence: 99%

Factors affecting the use of forage index insurance

Roznik

Porth²,

Porth

2019

AFR

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Purpose The purpose of this paper is to examine factors affecting the use of forage index insurance. Forage is a difficult crop to insure, and index insurance may be well suited for forage insurance and has been implemented in several countries, including Canada, the USA and France. Despite being a promising risk management tool, forage index insurance participation rates in Canada, and other countries are low relative to crop insurance participation rates for grain and oilseed producers. Design/methodology/approach A survey was conducted with 87 beef and cattle producers from Alberta and Saskatchewan, Canada. A probit regression model was used, and a number of variables were included to examine the use of forage index insurance. Findings In total, 6 of 11 variables in the model are found to be statistically significant in explaining forage producers’ use of forage index insurance. Results suggest that producers who maintain lower feed reserves are more likely to purchase forage index insurance. Also, producers with higher levels of knowledge of crop insurance and a more positive attitude toward forage insurance are more likely to use forage index insurance. Furthermore, producers are more likely to use forage index insurance if they perceive drought and weather risk as being of greater importance, and if they are younger. The importance of the variable forage index insurance premium price was statistically insignificant. This could be due to the effect of subsidization, reducing the importance of price for the decision to purchase. Similarly, the use of other subsidized risk management policies, including a whole-farm margin policy (e.g. the government program and AgriStability), did not reduce forage index insurance use. A possible explanation for this is that the subsidization of the policies may make it profitable to purchase both, despite the overlapping coverage. Practical implications These results may be useful for policy makers interested in increasing forage index insurance participation rates, as forage index insurance participation rates have historically been low relative to grain and oilseed producers. Originality/value This study is believed to be one of the first studies regarding the use of forage index insurance by forage producers. Producers can be exposed to catastrophic risks such as drought or other extreme weather events, and forage index insurance may be an effective means to manage these risks. Index insurance determines payments using an index that is correlated to producers’ actual yields. A downside of this method is basis risk, which is the mismatch between the insured index and the producer’s actual yield. Research has focused on basis risk and developing improved methods to reduce basis risk. However, less research has investigated the other important factors that may contribute to forage index insurance use. Producers may have a different risk management environment regarding forage production compared to other farm activities, and these differences have largely not been examined.

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“…Therefore, it is key to use weather data that best capture extreme events at the production location to keep basis risk low and the weather index insurance market functioning (Dalhaus and Finger 2016). 1 In this article, we draw from the geostatistics literature and show how interpolation (Cressie 1988;Wu and Li 2013;Roznik et al 2019) reduces the basis risk of heat index insurance. We extend the literature in three dimensions.…”

Section: Introductionmentioning

confidence: 99%

“…This research adds to previous work in this area by combining a comparison of designs of weather indices with an economic analysis of WII contracts. Roznik et al (2019) show that kriging interpolation may successfully be used to estimate weather indices, but they do not evaluate the effectiveness of insurance contracts designed this way. Based on a solid microeconomic and econometric foundation with a special focus on downside risks, our results can be useful for the development of functioning heat index insurances in the United States and elsewhere that complement the currently existing indemnity-based yield and revenue protection schemes.…”

Section: Introductionmentioning

confidence: 99%

Accounting for Geographic Basis Risk in Heat Index Insurance: How Spatial Interpolation Can Reduce the Cost of Risk

Leppert

Lagerkvist

2021

Weather, Climate, and Society

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Extreme heat events cause periodic damage to crop yields and may pose a threat to the income of farmers. Weather index insurance provides payouts to farmers in case of measurable weather extremes to keep production going. However, its viability depends crucially on the accuracy of local weather indices to predict yield damages from adverse weather conditions. So far extreme heat indices are poorly represented in weather index insurance. In this study we construct indices of extreme heat using observations at the nearest weather station and estimates for each county using three interpolation techniques: Inverse-distance weighting, ordinary kriging, and regression kriging. Applying these indices to insurance against heat damage to corn in Illinois and Iowa, we show that heat index insurance reduces relative risk premiums by 27-29% and that interpolated indices outperform the nearest-neighbor index by around 2-3% in terms of relative risk reduction. Further, we find that the advantage of interpolation over a nearest-neighbor index in terms of relative risk reduction increases as the sample of weather stations is reduced. These findings suggest that heat index insurance can work even when weather data is spatially sparse, which delivers important implications for insurance practice and policy makers. Further, our public code repository provides a rich toolbox of methods to be used for other, perils, crops and regions. Our results are therefore not only replicable but also constitute a cornerstone for projects to come.

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Improving agricultural microinsurance by applying universal kriging and generalised additive models for interpolation of mean daily temperature

Cited by 18 publications

References 30 publications

A Hybrid Neural Network–Particle Swarm Optimization Informed Spatial Interpolation Technique for Groundwater Quality Mapping in a Small Island Province of the Philippines

A Hybrid Neural Network–Particle Swarm Optimization Informed Spatial Interpolation Technique for Groundwater Quality Mapping in a Small Island Province of the Philippines

Factors affecting the use of forage index insurance

Accounting for Geographic Basis Risk in Heat Index Insurance: How Spatial Interpolation Can Reduce the Cost of Risk

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