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; Liu, Kai

doi:10.1080/10920277.2020.1717345

Cited by 14 publications

(7 citation statements)

References 41 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Advanced weather risk models were used to better understand the dependency of risks, for example in Zhu et al (2018) and Li et al (2021). The research team also expanded their work regarding predictive crop yield models for indexbased insurance applications, including the use of satellite remote sensing for forage insurance, which included important collaborations with government, insurers, reinsurers, and technology providers (Porth et al, 2020).…”

Section: Agricultural Insurancementioning

confidence: 99%

Keng Seng Tan 1970–2023 In Memoriam

Hardy

Boyle

2023

Ann. actuar. sci.

View full text Add to dashboard Cite

The editorial is dedicated to the memory of Ken Seng Tan, who passed away on January 1, 2023, at the height of his academic career. He was one of the most influential and prolific actuarial researchers of the last three decades.Ken Seng Tan moved to Canada from Malaysia for his undergraduate studies and remained there until 2019. In 1998, he completed his PhD in Actuarial Science at the University of Waterloo, where his doctoral thesis, Quasi-Monte Carlo Methods: Applications in Finance and Actuarial Science, won a university award for Outstanding Achievement In Graduate Studies.

show abstract

Section: Agricultural Insurancementioning

confidence: 99%

Keng Seng Tan 1970–2023 In Memoriam

Hardy

Boyle

2023

Ann. actuar. sci.

View full text Add to dashboard Cite

show abstract

“…To evaluate the value provided by using crop models to generate larger synthetic yield training datasets, we compared the performance of our models reported in Section 3.2 with plot and GP level yield estimates derived using statistical vegetation index (VI)-based models developed by using observed yield data from CCEs. This is a common approach underlying the design of many existing index insurance products in India and other parts of the world [47][48][49], and, as such, understanding what, if any, improvements in accuracy are obtained from using crop models alongside satellite data is critical to understand the value-added from more complex approaches.…”

Section: Comparison Of Statistical Models Developed Based On Vegetation Indices and Process-based Crop Model Simulationsmentioning

confidence: 99%

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

et al. 2021

View full text Add to dashboard Cite

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.

show abstract

“…To evaluate the value provided by using crop models to generate larger synthetic yield training datasets, we compare the performance of our models reported in Section 3.2 with plot and GP level yield estimates derived using statistical vegetation index (VI)-based models developed by using observed yield data from CCEs. This is a common approach underlying design of many existing index insurance products in India and other parts of the world (Brock Porth et al, 2020;Kölle et al, 2020;Turvey & McLaurin, 2012), and, as such, understanding what -if any -improvements in accuracy are obtained from using crop models alongside satellite data is critical to understand the value added from more complex approaches.…”

Section: Comparison Of Statistical Models Developed Based On Vegetation Indices and Process Based Crop Model Simulationsmentioning

confidence: 99%

Improving performance of index insurance using crop models and phenological monitoring

Afshar¹,

Foster²,

Higginbottom³

et al. 2020

Preprint

View full text Add to dashboard Cite

Extreme weather events pose significant risks to the livelihoods of smallholder farmers across Asia and Africa. Weather index-based insurance provides a potential solution to mitigate risks caused by crop failures, providing farmers with a payout in the event of a poor harvest. It also reduces costs relative to traditional indemnity insurance by eliminating the need for resource-intensive, in-situ assessment of losses. However, one challenge associated with weather index-based insurance is basis risk &#8211; where the payouts triggered by the index do not match actual crop losses. High levels of basis risk are observed across many existing weather index-based insurance products, and represent a key constraint to successful upscaling. &#160;A common feature of existing weather index-based insurance contracts is that payouts are triggered based on weather indices defined over fixed calendar periods, specified to capture the typical duration of the crop growing season or key phenological stages in a given agricultural system. In reality, however, the timing of a crop&#8217;s sensitivity to weather often varies significantly between individual plots or farmers due to differences in management practices (e.g., sowing date, variety choice) and meteorological conditions (e.g., temperature and precipitation) that affect rates of crop development. Failure to consider this heterogeneity is potentially a significant driver of basis risk, and suggests that opportunities may exist to improve the quality of index insurance by designing phenology-specific insurance contracts.&#160;In this study, we evaluate the impacts of improved monitoring of crop phenology on the performance of index-based crop yield models through a range of synthetic model-based simulated experiments for wheat and rice production in Haryana and Odisha states in India. We use a calibrated process-based crop simulation model (APSIM) to evaluate yields for a range of potential weather realizations and agricultural management practices typically observed in our case study regions. Subsequently, we develop non-linear statistical (i.e. index-based) models using non-parametric regression techniques (Multivariate adaptive regression splines; MARS) to reproduce APSIM-simulated yields as a function of rainfall and temperature conditions during key sensitive crop growth stages.&#160;Our results show that by considering field-level heterogeneity in crop phenology and development, it is possible to reliably estimate (>0.8 r-squared) wheat and rice yields. In contrast, model performance deteriorates significantly when variability in growth stage between individual simulated fields is not considered or when weather predictors are aggregated over the entire growing season as opposed to specific growth stages. These findings show that considering crop phenology can dramatically improve the performance of statistical yield models and, in turn, the accuracy of an index-based insurance product. Nevertheless, reductions in basis risk must also be balanced against the increasing complexity and implementation costs of these potential products in smallholder environments.

show abstract

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

Cited by 14 publications

References 41 publications

Keng Seng Tan 1970–2023 In Memoriam

Keng Seng Tan 1970–2023 In Memoriam

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

Improving performance of index insurance using crop models and phenological monitoring

Contact Info

Product

Resources

About