scholarly journals Contrasting Influences of Seasonal and Intra-Seasonal Hydroclimatic Variabilities on the Irrigated Rice Paddies of Northern Peninsular Malaysia for Weather Index Insurance Design

2021 ◽  
Vol 13 (9) ◽  
pp. 5207
Author(s):  
Zed Zulkafli ◽  
Farrah Melissa Muharam ◽  
Nurfarhana Raffar ◽  
Amirparsa Jajarmizadeh ◽  
Mukhtar Jibril Abdi ◽  
...  
Keyword(s):  
Pearson Correlation ◽  
Extreme Rainfall ◽  
Peninsular Malaysia ◽  
Coefficient Of Determination ◽  
Index Insurance ◽  
Weather Index ◽  
Basis Risk ◽  
Insurance Contracts ◽  
Weather Index Insurance ◽  
Streamflow Statistics

Good index selection is key to minimising basis risk in weather index insurance design. However, interannual, seasonal, and intra-seasonal hydroclimatic variabilities pose challenges in identifying robust proxies for crop losses. In this study, we systematically investigated 574 hydroclimatic indices for their relationships with yield in Malaysia’s irrigated double planting system, using the Muda rice granary as a case study. The responses of seasonal rice yields to seasonal and monthly averages and to extreme rainfall, temperature, and streamflow statistics from 16 years’ observations were examined by using correlation analysis and linear regression. We found that the minimum temperature during the crop flowering to the maturity phase governed yield in the drier off-season (season 1, March to July, Pearson correlation, r = +0.87; coefficient of determination, R2 = 74%). In contrast, the average streamflow during the crop maturity phase regulated yield in the main planting season (season 2, September to January, r = +0.82, R2 = 67%). During the respective periods, these indices were at their lowest in the seasons. Based on these findings, we recommend temperature- and water-supply-based indices as the foundations for developing insurance contracts for the rice system in northern Peninsular Malaysia.

Do high-resolution satellite indices at field level reduce basis risk of satellite-based weather index insurance?

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Wienand Kölle ◽  
Matthias Buchholz ◽  
Oliver Musshoff
Keyword(s):  
Spatial Resolution ◽  
Satellite Data ◽  
Index Insurance ◽  
Weather Index ◽  
Basis Risk ◽  
Content Type ◽  
Farm Level ◽  
Insurance Contracts ◽  
Weather Index Insurance ◽  
Field Level

PurposeSatellite-based weather index insurance has recently been considered in order to reduce the high basis risk of station-based weather index insurance. However, the use of satellite data with a relatively low spatial resolution has not yet made it possible to determine the satellite indices free of disturbing landscape elements such as mountains, forests and lakes.Design/methodology/approachIn this context, the Normalized Difference Vegetation Index (NDVI) was used based on both Moderate Resolution Imaging Spectroradiometer (MODIS) (250 × 250 m) and high-resolution Landsat 5/8 (30 × 30 m) images to investigate the effect of a higher spatial resolution of satellite-based weather index contracts for hedging winter wheat yields. For three farms in north-east Germany, insurance contracts both at field and farm level were designed.FindingsThe results indicate that with an increasing spatial resolution of satellite data, the basis risk of satellite-based weather index insurance contracts can be reduced. However, the results also show that the design of NDVI-based insurance contracts at farm level also reduces the basis risk compared to field level. The study shows that higher-resolution satellite data are advantageous, whereas satellite indices at field level do not reduce the basis risk.Originality/valueTo the best of the author’s knowledge, the effect of increasing spatial resolution of satellite images for satellite-based weather index insurance is investigated for the first time at the field level compared to the farm level.

Quantifying spatial basis risk for weather index insurance

2012 ◽  
Vol 14 (1) ◽  
pp. 20-34 ◽  
Author(s):  
Michael T. Norton ◽  
Calum Turvey ◽  
Daniel Osgood
Keyword(s):  
Index Insurance ◽  
Weather Index ◽  
Basis Risk ◽  
Weather Index Insurance

Near real-time identification of extreme events for weather index insurance using machine learning algorithms

2021 ◽  
Author(s):  
Luigi Cesarini ◽  
Rui Figueiredo ◽  
Beatrice Monteleone ◽  
Mario Martina
Keyword(s):  
Machine Learning ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Index Insurance ◽  
Model Parameters ◽  
Weather Index ◽  
Basis Risk ◽  
Weather Index Insurance ◽  
Extreme Flood

<p>A steady increase in the frequency and severity of extreme climate events has been observed in recent years, causing losses amounting to billions of dollars. Floods and droughts are responsible for almost half of those losses, severely affecting people’s livelihoods in the form of damaged property, goods and even loss of life. Weather index insurance is an innovative tool in risk transfer for disasters induced by natural hazards. In this type of insurance, payouts are triggered when an index calculated from one or multiple environmental variables exceeds a predefined threshold. Thus, contrary to traditional insurance, it does not require costly and time-consuming post-event loss assessments. Its ease of application makes it an ideal solution for developing countries, where fast payouts in light of a catastrophic event would guarantee the survival of an economic sector, for example, providing the monetary resources necessary for farmers to sustain a prolonged period of extreme temperatures. The main obstacle to a wider application of this type of insurance mechanism stems from the so-called basis risk, which arises when a loss event takes place but a payout is not issued, or vice-versa.</p><p>This study proposes and tests the application of machine learning algorithms for the identification of extreme flood and drought events in the context of weather index insurance, with the aim of reducing basis risk. Neural networks and support vector machines, widely adopted for classification problems, are employed exploring thousands of possible configurations based on the combination of different model parameters. The models were developed and tested in the Dominican Republic context, leveraging datasets from multiple sources with low latency, covering a time period between 2000 and 2019. Using rainfall (GSMaP, CMORPH, CHIRPS, CCS, PERSIANN and IMERG) and soil moisture (ERA5) data, the machine learning algorithms provided a strong improvement when compared to logistic regression models, used as a baseline for both hazards. Furthermore, increasing the number of information provided during model training proved to be beneficial to the performances, improving their classification accuracy and confirming the ability of these algorithms to exploit big data. Results highlight the potential of machine learning for application within index insurance products.</p>

Factors affecting farmers’ willingness to purchase weather index insurance in the Hainan Province of China

2015 ◽  
Vol 75 (1) ◽  
pp. 103-113 ◽  
Author(s):  
Jia Lin ◽  
Milton Boyd ◽  
Jeffrey Pai ◽  
Lysa Porth ◽  
Qiao Zhang ◽  
...  
Keyword(s):  
Background Information ◽  
Index Insurance ◽  
Probit Regression ◽  
Weather Index ◽  
Basis Risk ◽  
Content Type ◽  
Factors Affecting ◽  
Weather Index Insurance ◽  
Main Challenge ◽  
Willingness To Purchase

Purpose – The purpose of this paper is to explain the factors affecting farmers’ willingness to purchase weather index insurance for crops in China, in the Province of Hainan, and to also provide additional background information on weather index insurance. Design/methodology/approach – A survey of 134 farmers was undertaken in Hainan, China, regarding their willingness to purchase weather index insurance. A probit regression model was used, and a number of variables were included to explain willingness of farmers to purchase weather index insurance. Findings – In total, 11 of 15 variables in the model are found to be statistically significant in explaining farmers’ willingness to purchase weather index insurance. Research limitations/implications – First, farmers’ interest in weather index insurance may be limited due to basis risk. Second, some farmers may not sufficiently understand weather index insurance and so may not purchase it, and a considerable portion of farmers may also require a subsidy if they are to purchase weather insurance. Practical implications – Weather index insurance may provide a lower cost alternative than traditional crop insurance, however, basis risk remains a main challenge. Originality/value – This is the first study to quantitatively study the factors affecting the willingness of farmers to purchase weather index insurance for agriculture in the province of Hainan, China.

scholarly journals Improving performance of index insurance using crop models and phenological monitoring

2020 ◽  
Author(s):  
Mehdi H. Afshar ◽  
Timothy Foster ◽  
Ben Parkes ◽  
Koen Hufkens ◽  
Francisco Ceballos ◽  
...  
Keyword(s):  
Management Practices ◽  
Growing Season ◽  
Multivariate Adaptive Regression Splines ◽  
Extreme Weather Events ◽  
Index Insurance ◽  
Growth Stages ◽  
Crop Phenology ◽  
Weather Index ◽  
Basis Risk ◽  
Insurance Contracts

<p>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 – 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.  </p><p>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’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. </p><p>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. </p><p>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.</p>

scholarly journals Addressing Fractional Dimensionality in the Application of Weather Index Insurance and Climate Risk Financing in Agricultural Development: A Dynamic Triggering Approach

2019 ◽  
Vol 11 (4) ◽  
pp. 901-915 ◽  
Author(s):  
Calum G. Turvey ◽  
Apurba Shee ◽  
Ana Marr
Keyword(s):  
Agricultural Development ◽  
Seasonal Rainfall ◽  
Climate Risk ◽  
Index Insurance ◽  
Weather Index ◽  
Basis Risk ◽  
Climate Risks ◽  
Weather Index Insurance ◽  
The Mean ◽  
Rainfall Patterns

Abstract Climate risk financing programs in agriculture have caught the attention of researchers and policy makers over the last decade. Weather index insurance has emerged as a promising market-based risk financing mechanism. However, to develop a suitable weather index insurance mechanism it is essential to incorporate the distribution of underlying weather and climate risks to a specific event model that can minimize intraseasonal basis risk. In this paper we investigate the erratic nature of rainfall patterns in Kenya using Climate Hazards Group Infrared Precipitation with Station Data (CHIRPS) rainfall data from 1983 to 2017. We find that the patterns of rainfall are fractional, both erratic and persistent, which is consistent with the Noah and Joseph effects that are well known in mathematics. The erratic nature of rainfall emerges from the breakdown of the convergence to a normal distribution. Instead we find that the distribution about the average is approximately lognormal, with an almost 50% higher chance of deficit rainfall below the mean than adequate rainfall above the mean. We find that the rainfall patterns obey the Hurst law and that the measured Hurst coefficients for seasonal rainfall pattern across all years range from a low of 0.137 to a high above 0.685. To incorporate the erratic and persistent nature of seasonal rainfall, we develop a new approach to weather index insurance based upon the accumulated rainfall in any 21-day period falling below 60% of the long-term average for that same 21-day period. We argue that this approach is more satisfactory to matching drought conditions within and between various phenological stages of growth.

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