This paper investigates the accuracy of corn yield forecasts using machine learning with satellite and weather data. In addition, the study examines the incremental value of these forecasts to augment the World Agricultural Supply and Demand Estimates (WASDE) forecast. To illustrate the potential of machine learning methods for agricultural forecasting, publicly available data are collected from 1984 to 2021 for national corn yield, state corn yield, satellite variables, and weather variables and used with the XGBoost algorithm. The results show that the XGBoost model performed about the same but did not outperform the WASDE corn yield forecasts over a 12-year out-of-sample period. The incremental value analysis results suggest that the XGBoost and WASDE forecasts capture similar information, and no incremental information exits. Although the XGBoost model does not outperform the WASDE August forecast, it is near real-time and can be produced using publicly available data. The results indicate that the XGBoost machine learning models can produce reasonably accurate crop yield forecasts.

This research contributes to the literature by investigating if and by how much higher resolution satellite imagery improves crop yield estimation accuracy at the county level when paired with a high-resolution cropland mask. Satellite imagery is an interesting big data source that has potential applications in agriculture. When applying satellite imagery for crop yield estimation, practitioners choose which resolution (i.e., grid size) of images to use. Processing higher resolution images requires greater computing resources compared to lower resolution images. Practitioners may choose to use lower resolution images, but there may be a loss in crop yield model estimation accuracy. The cost of computation has decreased significantly with the advent of cloud computing and open access computing portals such as Google Earth Engine. These technologies have made satellite image processing more economical. The objective of this research is to quantify the crop yield estimation accuracy improvement that could be achieved by using higher resolution normalized difference vegetation index (NDVI) with a cropland mask. NDVI (a measure of crop greenness) data was collected for 48 U.S. states for four crops over 11 years. The crops investigated were corn, soybeans, spring wheat, and winter wheat. Each crop yield regression model estimation showed improved accuracy (R-2) as the satellite NDVI resolution increased. Results suggest that using higher resolution satellite NDVI provides more accurate crop yield estimation compared to lower resolution satellite NDVI. This study is believed to be the most comprehensive study to date using NDVI to estimate crop yield, analyzing 48 states in the U.S. and four crops over 11 years using three resolution levels.

Weather index insurance is a relatively new alternative to traditional agricultural insurance such as individual yield-based crop insurance. It is still mostly at the experimental stage, rather than in widespread use like traditional crop insurance. A major challenge for weather insurance is basis risk, where the loss estimated by the index differs from the actual loss, and this is generally believed to be the main limitation in the use of weather index insurance for crops. Variable basis risk is an important type of basis risk that occurs when there are incorrect variables or missing variables for the design of the weather index. In agriculture, there is a relatively small sample size of yields, and therefore, as the number of considered weather variables increases, the problems of limited degrees of freedom for predictive models must be overcome. The objective of this article is to demonstrate two possible approaches that could be used to construct a multivariable weather index to reduce variable basis risk. Forage insurance is used as an example, and a main focus of the research is on reducing the dimensionality of the predictive model and resolving the problem of multicollinearity among weather variables. The research uses daily weather information and county-level forage yield data from Ontario, Canada. Two multivariable indices are developed based on principal component regression (PCR) and partial least squares regression (PLSR) methods, and they are evaluated against a single-variable benchmark index based on cumulative precipitation (SVCP) using several basis risk metrics. The results show that both the PCR and PLSR models are superior compared to the single-variable weather index based on cumulative rainfall (SVCR) index and can be used to achieve the objective of reducing the dimensionality of the weather variable matrix and addressing the issue of multicollinearity. Though the PLSR indices perform better than the PCR and SVCR indices in terms of average value of basis risk (EoBRLossTHORN),(rom) the PCR method produces a smaller percentage of mismatch, suggesting that the PCR method may be superior in correctly detecting when the insurance payment should be triggered. The methods demonstrated in this article will assist in the development of weather index-based crop insurance.

Agricultural microinsurance has the potential to protect farmers against crop loss caused by extreme adverse weather conditions. Microinsurance policies for smallholder farmers are often designed on the basis of weather indices, whereby weather insurance variables are measured at ground weather stations and then interpolated to the location of the farm. However, a low density of weather stations causes interpolation error, which contributes to basis risk. The objective of this paper is to investigate whether agricultural microinsurance can be improved by reducing interpolation error through advanced interpolation methods, including universal kriging (UK) and generalised additive models (GAM) used with land surface temperature, elevation, and other covariates. Results indicate that for areas with a lower density of weather stations, UK with elevation substantially improves air temperature interpolation accuracy. The approach developed in this paper may help to improve interpolation and could therefore reduce basis risk for agricultural microinsurance in regions with a low density of weather stations, such as in developing countries.

Weather index insurance for crops is at the developmental stage, however, this type of insurance is particularly susceptible to the problem of spatial basis risk. Spatial basis risk occurs when the weather observed at weather stations does not match the weather experienced on the farmer’s property, causing improper indemnities to be paid to the farmer. However, spatial basis risk may be reduced through the use of averaging and spatial interpolation techniques, such as inverse distance weighting and kriging. These techniques make it possible to incorporate multiple weather stations in the estimation process rather than using only the single closest station, potentially resulting in more accurate estimations and thereby reducing spatial basis risk. Therefore, the objective of this study is to examine the extent to which the choice of spatial interpolation techniques can influence the amount of spatial basis risk in a weather-based insurance model. Using forage crops from the province of Ontario, Canada, as an example, a weather insurance index is developed based on cooling degree days. The weather index represents the heat stress that the crops receive over the growing season. This insurance index is used to determine to what extent spatial basis risk can be reduced by the insurer’s choice of spatial interpolation technique. Seven different interpolation methods are applied to temperature data from Ontario, and theoretical indemnities are calculated for forage producers across the province. By analyzing the correlation between the estimated indemnities and reported forage yields, the amount of spatial basis risk in each model is quantified. The results of this study highlight the importance of choosing an appropriate method based on the characteristics of the target region (and data). Operationally this is important because insurers typically apply the same interpolation methods across an entire region. While one finding of this research may suggest that governments and/or insurance companies may wish to invest in additional weather stations to improve the accuracy of the interpolation method and index, this may not be feasible in practice. Given this, future research may consider utilizing satellite-based remote sensing weather estimates to augment the weather station data and reduce basis risk.

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.

A robust predictive model for crop yield is essential for designing a commercially viable index-based insurance policy. Index-based insurance for crops is still at a relatively infant stage, and more research and development is needed in order to address a main limitation, which is referred to as basis risk. Traditionally, ground weather station measurements have been the most common approach used in weather indices, and this approach has often led to high levels of basis risk. Recent advances in satellite-based remote sensing provide new opportunities to use publicly available and transparent "big data," to potentially make index-based insurance policies more relevant by reducing basis risk. This is the first article to provide a comprehensive comparison of 13 pasture production indices (PPIs), including those developed based on satellite-derived vegetation and biophysical parameter indices using data products from the Moderate Resolution Imaging Spectroradiometer (MODIS). A validation protocol is established, and a unique dataset covering the period 2002 to 2016 from a network of pasture clip sites in the province of Alberta, Canada, is used to demonstrate new applications for insurance based on remote sensing–derived data. The results of the satellite-derived PPIs are compared to PPIs based on ground weather station data as benchmarks, which to date are the most common design for index-based insurance. Overall, the satellite-based indices report higher correlations with the ground truth forage yield data compared to the weather station PPIs. As an example, in 2015 and 2016 the best performing satellite-based PPIs produced correlations close to 90%. When considering the whole sample period, the highest overall average correlation is 62.0% for the biophysical parameter PPI based on FPAR 500-m MODIS, and the lowest overall average correlation is 43.8% for the vegetation PPI based on EVI 250-m MODIS. Comparatively, the highest correlation reported for the weather station indices is for precipitation, at 12.51%. Other predictive models based on principal component analysis and shrinkage methods (such as lasso, ridge regression, and elastic net) are considered to address the issues of variable selection and dimension reduction in insurance design. This research makes an important contribution to the field of actuarial science and insurance, because it highlights potential new opportunities for insurance design and predictive analytics using large and comprehensive satellite datasets that remain relatively unexplored to date in insurance practice. Though forage is used as the example here, the research could be extended to other crops, and other areas in the property and casualty sector, including for fire and flood.

Agricultural insurance is often faced with the challenge of systemic risk, arising from weather risks that tend to be correlated within a specific region in extreme situations, resulting in large crop losses within the region. However, across many regions, especially if regions are considerable distances apart, weather may be quite different and losses may be much less correlated. The objective of this paper is to improve the diversification of a crop insurance portfolio, through developing a new alternative risk management approach (Model 3) that pools crop risks across all provinces in a country to form a Canada-wide joint insurance pool. This is in contrast to the current approach used in Canada, where crop risks are pooled only within an individual province. Then using a simulated annealing optimization approach, the most suitable combination of the 150 crop types in the portfolio is identified for either retaining in the joint insurance pool or for ceding to reinsurers, such that the variance of the loss coverage ratio of the portfolio is minimized. This model overcomes the problem of insufficient diversification that makes pooling of systemic weather risk challenging. It achieves diversification at a lower cost by using a more efficient combination of pooling and selective reinsurance, resulting in overall higher surplus, higher survival probability, and lower deficit at ruin.

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.

A major problem facing livestock producers is animal mortality risk. Livestock mortality insurance is still at the initial stages, and premium computation approaches are still relatively new and will require more research. This study seeks to provide a first step for developing a better understanding of livestock insurance as a solution to mortality risk, as it explores improved methods for livestock mortality insurance modeling procedures, and premium computation, using credibility analysis. The purpose of this study is to develop improved estimates for livestock mortality insurance premiums for Canada under a credibility framework. We illustrate our approach through one example using livestock data from 1999 to 2007.

Some insurance firms challenged with a portfolio of high-variance risks face the classic trade-off between risk spreading and risk retaining. Using crop insurance as an example, a new solution to this problem is undertaken to uncover an improved reinsurance design. Joint self-managed reinsurance pooling and private reinsurance are combined in a portfolio approach utilizing combinatorial optimization with a genetic algorithm (Model C), achieving high surplus, high survival probability, and low deficit at ruin. This portfolio model may also be useful for other large natural disaster and weather-related insurance portfolios, and other portfolio applications.

Purpose - The purpose of this research is examine the development of livestock mortality insurance, and associated challenges, in order to provide an improved understanding regarding the operation of livestock mortality insurance.Design/methodology/approach - In a many countries, livestock mortality insurance has been either unavailable or underdeveloped. A descriptive analysis is provided regarding the background and development of livestock mortality insurance, along with an example.Findings - Livestock mortality insurance is considerably more complex than crop insurance, and some of the complexities of livestock mortality insurance include multi-stage production, consequential losses, occasional large event losses, animal health management, moral hazard, and adverse selection.Originality/value - This study provides background and development information regarding livestock mortality insurance, and also highlights a number of important differences between livestock mortality insurance and crop insurance.

PurposeThe purpose of this paper is to explain the factors affecting crop insurance purchases by farmers in Inner Mongolia, China.Design/methodology/approachA survey of farmers in Inner Mongolia, China, is undertaken. Selected variables are used to explain crop insurance purchases, and a probit regression model is used for the analysis.FindingsResults show that a number of variables explain crop insurance purchases by farmers in Inner Mongolia. Of the eight variables in the model, seven are statistically significant. The eight variables used to explain crop insurance purchases are: knowledge of crop insurance, previous purchases of crop insurance, trust of the crop insurance company, amount of risk taken on by the farmer, importance of low crop insurance premium, government as the main information source for crop insurance, role of head of village, and number of family members working in the city.Research limitations/implicationsA possible limitation of the study is that data includes only one geographic area, Inner Mongolia, China, and so results may not always fully generalize to all regions of China, for all situations.Practical implicationsCrop insurance has been recently expanded in China, and the information from this study should be useful for insurance companies and government policy makers that are attempting to increase the adoption rate of crop insurance in China.Social implicationsCrop insurance may be a useful approach for stabilizing the agricultural sector, and for increasing agricultural production and food security in China.Originality/valueThis is the first study to quantitatively model the factors affecting crop insurance purchases by farmers in Inner Mongolia, China.

Firms within various sectors of an economy are often faced with a number of risks. These risks can be relatively sudden and large, especially if they are weather related. In agriculture, risk often has natural causes such as weather, and therefore losses can be large and costly in particular years. Crop insurance has been commercially available in many developed countries for a number of decades, though it is only now starting to become more commercially available in a number of developing countries such as China. When facing these risks without crop insurance, farmers may use fewer inputs and invest less in crop production, resulting in lower yields and lower production. As well, lenders may be reluctant to extend credit to farmers, if farmers have not purchased crop insurance. Crop insurance has been one of the most successful risk management and longest running stabilization programs for farmers in many parts of the world. The purpose of this article is to explain the main principles underlying crop insurance, with implications for China. Challenges for crop insurance development are also pointed out, along with some possible solutions. Some North American experience with crop insurance is also discussed, including the case of Canada.

ABSTRACT Area-based yield insurance (AYI) is a kind of index insurance that can eliminate asymmetric information problems (adverse selection and moral hazard) and reduce transaction cost. It is important in managing crop risk for developing countries such as China, which has many small farms. However, the biggest challenge in index insurance is basis risk. In this regard, can AYI be used in China? Is AYI competitive with traditional multi-peril crop insurance (MPCI)? What are the principal factors affecting the effectiveness of AYI? Following and extending the approaches suggested by Miranda in 1991, this article compares the effectiveness of AYI and MPCI in terms of risk reduction per premium. An empirical model was established, indicating the relationship between the efficiency of AYI and the explanatory variables, using data from a survey of 108 wheat farmers from two counties in Hebei Province, China. The results provide salutary suggestions to area-based index insurance design and development in China.

Purpose - In the USA, private insurance companies serve as an integral part of the delivery and risk sharing of the federal crop insurance program. Governed by the Standard Reinsurance Agreement (SRA), private crop insurance companies must designate an eligible crop insurance contract to the assigned risk, developmental, or commercial funds. While the SRA restricts the private sector delivery system in a number of ways, the assignment of contracts to crop insurance funds, however, is left solely to the discretion of individual crop insurance companies. Thus, as to the companies' profitability viewpoint, the optimal selection of the crop insurance funds is the most important task. Therefore, the purpose of this paper is to provide a decision framework for crop insurance companies to make optimal decisions regarding the purchases of crop reinsurance. This information and framework may also be useful for crop insurance firms in China when considering crop reinsurance decisions.Design/methodology/approach - The paper studied three commonly used parametric loss distributions and presented a general guideline to choose the most profitable fund within the company's risk bearing level.Findings - The paper finds many important features in the commonly used loss distributions, which are useful to maximize the company's underwriting returns.Originality/value - The paper provides a general decision framework for optimally ceding risks to reinsurance. While this paper focused on agricultural insurance decisions by firms, the concept could be applied to general reinsurance decisions.