Remote Sensing Applications for Insurance: A Predictive Model for Pasture Yield in the Presence of Systemic Weather

Porth, C. Brock; Porth, Lysa; Zhu, Wenjun; Tan, Ken Seng; Li, Kai

doi:10.2139/ssrn.3195389

Cited by 3 publications

(2 citation statements)

References 40 publications

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“…Technology is one of the main reasons for growing yields for many crop types in many countries. Technology contributes positively to long-term crop yields by making the crops more resistant to extreme weather events, such as drought and strong wind ( [27]). Ref.…”

Section: Other Variables Affect Crop Yieldsmentioning

confidence: 99%

Assessing the Effectiveness of the Actuaries Climate Index for Estimating the Impact of Extreme Weather on Crop Yield and Insurance Applications

Pan¹,

Porth

2022

Sustainability

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This paper investigates the effectiveness of the Actuaries Climate Index (ACI), a climate index jointly launched by multiple actuarial societies in North America in 2016, on predicting crop yields and (re)insurance ratemaking. The ACI is created using a variety of climate variables reflecting extreme weather conditions in 12 subregions in the US and Canada. Using data from eight Midwestern states in the US, we find that the ACI has significant predictive power for crop yields. Moreover, allowing the constituting variables of the ACI to have data-driven rather than pre-determined weights could further improve the predictive accuracy. Furthermore, we create the county-level ACI index using high-resolution climate data and investigate its predictive power on county-level corn yields, which are more relevant to insurance practices. We find that although the self-constructed ACI index leads to a slightly worse fit due to noisier county-specific yield data, the predictive results are still reasonable. Our findings suggest that the ACI index is promising for crop yield forecasting and (re)insurance ratemaking, and its effectiveness could be further improved by allowing for the data-driven weights of the constituting variables and could be created at higher resolution levels.

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Section: Other Variables Affect Crop Yieldsmentioning

confidence: 99%

Assessing the Effectiveness of the Actuaries Climate Index for Estimating the Impact of Extreme Weather on Crop Yield and Insurance Applications

Pan¹,

Porth

2022

Sustainability

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“…Kolassa et al [25] proposed an empirical model calibration method to distinguish vegetation types based on the deviation between the simulated FPAR and MODIS-observed FPAR. Furthermore, studies on crop FPAR retrieval mainly focused on crops such as wheat, maize and soybean [26,27], but limited studies were performed on canola. Monitoring canola FPAR dynamics could effectively diagnose growth status and stage nodes, providing key variables for crop productivity models.…”

Section: Introductionmentioning

confidence: 99%

Identification of Robust Hybrid Inversion Models on the Crop Fraction of Absorbed Photosynthetically Active Radiation Using PROSAIL Model Simulated and Field Multispectral Data

et al. 2023

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The fraction of absorbed photosynthetically active radiation (FPAR), which represents the capability of vegetation-absorbed solar radiation to accumulate organic matter, is a crucial indicator of photosynthesis and vegetation growth status. Although a simplified semi-empirical FPAR estimation model was easily obtained using vegetation indices (VIs), the sensitivity and robustness of VIs and the optimal inversion method need to be further evaluated and developed for canola FPAR retrieval. The objective of this study was to identify the robust hybrid inversion model for estimating the winter canola FPAR. A field experiment with different sow dates and densities was conducted over two growing seasons to obtain canola FPARs. Moreover, 29 VIs, two machine learning algorithms and the PROSAIL model were incorporated to establish the FPAR inversion model. The results indicate that the OSAVI, WDRVI and mSR had better capability for revealing the variations of the FPAR. Three parameters of leaf area index (LAI), solar zenith angle (SZA) and average leaf inclination angle (ALA) accounted for over 95% of the total variance in the FPARs and OSAVI exhibited a greater resistance to changes in the leaf and canopy parameters of interest. The hybrid inversion model with an artificial neural network (ANN-VIs) performed the best for both datasets. The optimal hybrid inversion model of ANN-OSAVI achieved the highest performance for canola FPAR retrieval, with R2 and RMSE values of 0.65 and 0.051, respectively. Finally, the work highlights the usefulness of the radiation transfer model (RTM) in quantifying the crop canopy FPAR and demonstrates the potential of hybrid model methods for retrieving the canola FPAR at each growth stage.

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Approaching rainfall-based weather derivatives pricing and operational challenges

Salgueiro

Tarrazon-Rodon

2019

Rev Deriv Res

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Remote Sensing Applications for Insurance: A Predictive Model for Pasture Yield in the Presence of Systemic Weather

Cited by 3 publications

References 40 publications

Assessing the Effectiveness of the Actuaries Climate Index for Estimating the Impact of Extreme Weather on Crop Yield and Insurance Applications

Assessing the Effectiveness of the Actuaries Climate Index for Estimating the Impact of Extreme Weather on Crop Yield and Insurance Applications

Identification of Robust Hybrid Inversion Models on the Crop Fraction of Absorbed Photosynthetically Active Radiation Using PROSAIL Model Simulated and Field Multispectral Data

Approaching rainfall-based weather derivatives pricing and operational challenges

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