Optimal Crop Insurance Under Climate Variability: Contrasting Insurer and Farmer Interests

Abstract

ABSTRACT. This study illustrates the potential synergies and conflicts of interest between farmers and insurers in the selectionlimatic variability significantly affects agricultural production, profitability, and risk (Hansen, 2002;Chen and Chang, 2005). Several studies have shown that agricultural management has been improved during the last years based on the prediction of seasonal climate (Baigorria et al., 2008;Podestá et al., 2002;Nnaji, 2001). Predictability of seasonal climate variations can help in reducing farm risk by tailoring agricultural management strategies to mitigate the impacts of adverse conditions or to take advantage of favorable conditions (Letson et al., 2005;Hill and Mjelde, 2002). In this respect, crop insurance offers farmers economic stability under the uncertainty of future random events, including climate (Mahul, 2001). However, optimal crop insurance choices for farmers differ from those of insurers. In addition, once farmers buy crop insurance, they have a greater incentive to engage in risky behavior; clearly moral hazard can cause farmers' and insurers' interests to diverge. Predictable climate variations may offer an opportunity to close this gap by improving some monitoring features of the crop insurance market (Fraser, 2004).Previous research on the value of climate forecasts on the crop insurance market has been performed from either the farmer's or the insurer's viewpoints, but none have investigated the potential interactions between them. Thus, this article adds to the literature by evaluating optimal crop insurance strategies for farmers and insurers based on different climatic scenarios and levels of risk aversion. The goal of this study is to offer a thorough picture of the value of climate forecasts for the whole crop insurance market. Our hypothesis is that both conflicts and synergies exist between farmers and insurers regarding crop insurance selection and that they are influenced by climate variability.To reach our goal, we analyze the case of a representative 40 ha, rainfed, cotton-peanut farm located in Jackson County, Florida. The southeastern U.S. offers an illustrative setting for studying the interaction of climate variability and crop insurance strategies. Several studies have shown that the El Niño Southern Oscillation (ENSO) is a strong driver of seasonal climate variability that impacts cotton and peanut crop yields in this geographical area (e.g., Hansen, 2002;Jones et al., 2000). In this study, we integrate biophysical simulation models and stochastic non-linear whole-farm optimization models to identify an optimum crop insurance product for farmers and insurers based on different scenarios for ENSO and levels for risk aversion. The riskiness of the decision strategies is evaluated using a constant relative risk aversion utility function for farmers (Letson et al., 2005) and a conditional value-at-risk model (CVaR) for insurers (Liu et al., 2008). These results are then contrasted to evaluate the synergies and conflicts between the two groups under stu...

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“…More 55 recent literature has explored the use of seasonal forecasts related to index based crop insurance (e.g. Cabrera et al, 2006Cabrera et al, , 2009. Carriquiry and Osgood (2012) demonstrate how to couple the provision of loans for covering cropping losses in an index insurance scheme to seasonal forecasts.…”

Predicting the seed cotton yield with value added medium range weather forecast data using CROPGRO-Cotton model at Bhathinda, Punjab

“…Farmers can leverage weather forecasts to make informed decisions concerning crop selection, the timing of planting, and the implementation of protective measures for their crops. This strategic approach aims to maximize crop yields, enabling farmers to capitalize on favorable weather patterns and minimize the detrimental effects of adverse climate conditions on their agricultural endeavors (Cabrera et al, 2009).…”

“…Despite the existence of national and regional agro advisory services, there is a growing emphasis on establishing district-level agro advisory services to ensure that farmers can benefit to the fullest extent from weather-based agricultural advisory services (Lunagaria et al, 2009). Consequently, the significance of weather forecasting has gained importance due to the impact of climate change on crop production, both at the level of individual farmers and in crop-related decision-making (Cabrera et al, 2009). More than 60% of farmers have recognized the utility of weather predictions and Agromet Advisory Services (AAS) for determining irrigation timing, optimizing fertilizer application, managing pests and diseases, and planning crop harvesting (Khichar and Bishnoi, 2003).…”