Crop revenue insurance is unique, because it involves a guarantee subsuming yield risk and highly systematic price risk. This study examines whether crop insurers could use options instead of, or in addition to, assigning policies to the Commercial Funds of the USDA Federal Crop Insurance Corporation (FCIC) as per the Standard Reinsurance Agreement (SRA) to hedge the price risk of revenue insurance policies. The behavioral model examines the optimal hedge ratio for a crop insurer with a book of business consisting of corn Revenue Protection (RP) policies. Results show that a mix of put and call options can hedge the price risk of the RP policies. The higher optimal hedge ratios of call options as compared to put options imply that the risk of increased liability due to upside price risk can be hedged using options better than downside price risk. This study also analyzed the combination of options with the SRA at 35, 50, and 75% retention levels. The zero optimal hedge ratios at each retention level and the negative correlation between RP indemnities and the option returns when the crop insurer mixed options and SRA suggest that the purchasing of options provides no additional risk protection to crop insurers beyond what is provided by the SRA despite retention limits.

Experience across many countries shows that, without large premium subsidies, crop insurance uptake rates are generally low. In this article, we propose to use the cumulative prospect theory to design weather insurance products for situations in which farmers frame insurance narrowly as a stand-alone investment. To this end, we introduce what we call “behavioral weather insurance” whereby insurance contract parameters are adjusted to correspond more closely with farmers’ preferences. Depending on farmers’ preferences, we find that a stochastic multiyear premium increases the prospect value of weather insurance, while a zero deductible design does not. We suggest that insurance contracts should be tailored precisely to serve farmers’ needs. This offers potential benefits for both the insurer and the insured.

We evaluate the performance of area yield crop insurance (AYCI) and farm yield crop insurance (FYCI) using farm‐level yield data from China, focusing on their effects on farmers' welfare, and their cost‐effectiveness in terms of government subsidy. Given a subsidy rate sufficient to generate a politically acceptable participation level, the price advantage of AYCI may no longer offset its higher basis risk, and consequently FYCI may be preferred by farmers. From the government's perspective, AYCI is the cheapest option to maintain reasonable farmer participation in insurance, but is not necessarily the most cost‐effective choice. Our findings suggest that, contrary to an assumption that informs many developing country agricultural insurance programmes, AYCI schemes are not necessarily preferred to FYCI. Decisions on the structure of a national agricultural insurance programme should be based on careful consideration of local conditions.

The USDA produces yield and supply estimates for many crops that influence commodity markets and are used for implementing the Title I program, Agriculture Risk Coverage. Precision agriculture advances have increased the potential for the private sector to capture near-real time yield data, however, it is unclear whether they provide advantages in setting market positions since the samples are typically non-random. Here, we use yield histories from a large population of corn farms to quantify biases associated with different non-random sampling schemes for estimating aggregate yield, and demonstrate the effectiveness of benchmarking procedures for removing systematic prediction error.

The Stacked Income Protection Plan (STAX) county-level insurance product is analyzed for cotton producers in Texas. In contrast to studies based on representative farms, this analysis uses actual farm-level yield data, which allows one to observe the heterogeneity of STAX effectiveness across farms in a county. The findings indicate that, for most farms, STAX is not a very effective alternative to farm-level crop insurance. However, contrary to observed behavior, the findings suggest that many cotton producers in Texas would benefit from using STAX as a complement to their farm-level crop insurance.

Likely climate change impacts include damages to agricultural production resulting from increased exposure to extreme heat. Considerable uncertainty remains regarding impacts on crop insurance programs. We utilize a panel of U.S. corn yield data to predict the effect of warming temperatures on the mean and variance of yields, as well as crop insurance premium rates and producer subsidies. While we focus on corn, we demonstrate that the subsidy impacts are likely to carry over to other major program crops. We find that warming decreases mean yields and increases yield risk on average, which results in higher premium rates. Under a 1 degrees C warming scenario, we find that premium rates at the 90% coverage level will increase by 39% on average; however, there is considerable statistical uncertainty around this average as the 95% confidence interval spans from 22% to 61%. We also find evidence of extensive cross-sectional differences as the county-level rate impacts range from a 10% reduction to a 63% increase. Results indicate that exposure to extreme heat and changes in the coefficient of variation are large drivers of the impacts. Under the 1 degrees C warming scenario, we find that annual subsidy payments for the crop insurance program could increase by as much as $1.5 billion, representing a 22% increase relative to current levels. This estimate increases to 3.7 billion (57%) under a 2 degrees C warming scenario. Our results correspond to a very specific counterfactual: the marginal effect of warming temperatures under current technology, production, and crop insurance enrollments. These impacts are shown to be smaller than the forecasted impacts under a commonly used end-of-century general circulation model for even the most optimistic CO2 emissions projection.

This paper considers the current and possible institutions (programs, policies, participants, etc.) that govern public Business Risk Management (BRM) in Canada. This is an important policy topic for two reasons: BRM spending accounts for the vast majority of public monies funneled to Canadian agricultural producers and the upcoming Canadian Agricultural Partnership includes a mandated BRM review and thus presents an opportunity to change these institutions in meaningful ways. We pay particular attention to the rhetoric surrounding greater involvement of private insurance, the lack of rhetoric regarding the use of crown corporations, and issues of subsidization. We conclude with policy recommendations favoring commodity-specific revenue versus whole-farm net margin insurance, a possible reduction in subsidy levels, and a call to reconsider the role of crown corporations. We also make programming recommendations regarding the discontinued use of private reinsurance, a reduction in the level of program reserves, and greater transparency.

Recent advances in precision agriculture technology have increased the potential to capture near-real time data such as planting and yield information. It is well established that information on crop acreage and yield can have value in commodity markets. That is why the USDA conducts farm surveys and freely reports such information. In this study we use a large sample (just over 1.5 million observations) of farm-level corn yield data to consider if it is possible to use non-random farm yield data (such as might be available to providers of precision agriculture services) to accurately predict the national corn yield. Specifically, we examine scenarios where a forecasting agent has data that is not representative either because it is from a limited region or because it consists primarily of large farms. In general, we conclude that large volumes of data can, to some degree, overcome forecasting bias caused by non-representative samples. Moreover, if the forecaster can benchmark against an unbiased estimator, it may be possible to remove much of the bias from estimates generated by non-representative samples.

Whole-farm revenue insurance is frequently suggested as a conceptually attractive alternative to commodity-specific insurance, but attempts to deliver farm-level whole-farm revenue insurance (FWFI) have been fraught with underwriting and actuarial challenges. This study develops customizable area-based whole-farm insurance (CAWFI) that overcomes some known impediments to existing designs. Certainty equivalents are generated for representative farms in Kansas, North Dakota, Illinois, and Mississippi. We find that a restricted CAWFI design generates significant risk reduction at much lower cost than FWFI.

Response to adverse weather conditions by cotton and other major crops are likely to be heterogeneous across varieties, but it is unclear whether this translates into yield risk heterogeneity across varieties. Crop insurance is the dominant agricultural policy instrument and will play an important role for any potential adaptation path to climate change. However, the impact of climate change on the performance of crop insurance programs is not well established and currently the Risk Management Agency does not offer alternative premium rates across varieties. This study utilizes Mississippi cotton variety trial data for the period 1998 to 2013 to identify whether there are heterogeneous crop insurance premium rates across varieties using a moment-based model. Warming impacts on these rates will then be measured. Our results identified heterogeneities for both the mean and variance of cotton yields across varieties. These differences extended to the coefficient of variation – a commonly used measure of yield risk – as well as actuarially fair premium rates, which capture a producer’s exposure to downside risk. Our findings provide evidence of yield risk heterogeneity across varieties. The finding of heterogeneous premium rates across varieties presents an interesting problem for the FCIP.

A multi-year drought has taken a severe toll on the agricultural economy of California's Central Valley. Index insurance is an instrument with the potential to protect water users from economic losses due to periodic water shortages. An index insurance product based on the Sacramento Index and adapted to the Central Valley Project water supply is proposed. To address the potential for intertemporal adverse selection, three product designs are suggested: (1) "early bird" insurance; (2) variable premium insurance; and (3) variable deductible insurance. The performance of the designs are assessed using loss functions from the Westlands Water District in the San Joaquin Valley.

This study estimates the site-specific crop yield response function using varying coefficient models. It is widely recognized that the parameters of yield response function vary dramatically across space and over time. Previous studies usually capture this variability of response by using locational and time dummy variables. While that approach reveals the existence of the response variability, the exact pattern of the variability is unknown, and the capacity of ex ante prediction of such models are limited. This study takes a step forward to explicitly explain how the response varies with the actual site characteristic variables, such as soil, water, topography, weather, and other factors that are commonly available to producers. By using the varying coefficient model, the parameters of the response function are specified to change continuously with those site variables. Based on a simulation data set, the varying coefficient model is demonstrate to outperform the site-dummy model by creating better variable rate application (VRA) fertilizer prescriptions. We further propose to apply the model to large sample of high resolution production data, and create ex ante spatially explicit optimal VRA fertilizer recommendations. The ultimate goal is to develop a precision decision system which can statistically turn the soil testing and weather forecasting information into input application prescriptions for producers.

The Federal Crop Insurance Products offered for major field crops are either yield-based or revenue-based and offered at either the unit-level (farm or sub-farm) or county-level. The 2014 Farm Bill created the Supplemental Coverage Option (SCO) and Stacked Income Protection Plan (STAX) insurance products. These products provide county-level coverage against “shallow-losses” that can be added to the coverage provided by a unit-level yield or revenue insurance product. Historically, county-level insurance products have been based on National Agricultural Statistics Service (NASS) county yield estimates. However, in recent years NASS has reduced the number of counties for which it reports county yield estimates. As a result, the Risk Management Agency (RMA) is now basing all county-level insurance products (including SCO and STAX) on aggregated (to the county-level) farm-level yield data obtained from unit-level yield and revenue insurance policies sold. This paper analyzes how the performance of county-level insurance products might be impacted by this change. Specifically, the paper analyzes how differences across counties in factors such as unit-level insurance participation, the characteristics of producers purchasing unit-level insurance, and spatial yield correlation affect the performance of county-level insurance products based on aggregated unit-level insurance yield data.

The Federal Crop Insurance Program (FCIP) is designed to provide agricultural producers with insurance coverage against crop damage caused by natural disasters. This article provides some historical background on the FCIP; analyzes the financial performance of the FCIP; describes recent changes in the FCIP; and considers what the recent changes may mean for the future of the FCIP.

“Farm bill” is a colloquial term for omnibus legislation that authorizes various government programs related to agriculture, food, and rural areas. Some of these programs have their roots in New Deal legislation. Others were initially authorized after the New Deal and subsequently included in farm bills. Some debate exists about exactly which omnibus legislation was the precursor of modern-day farm bills. However, since at least 1973, farm bills have included titles related to farm programs, trade, rural development, farm credit, conservation, agricultural research, food and nutrition programs, and marketing. Beginning in 2008, crop insurance-authorizing language was also included in the farm bill.Farm bills generally have a life of approximately five years. In the case of farm support programs (typically authorized in Title 1), the farm billtemporarily amends permanent legislation. When the farm bill expires, theseprograms revert to permanent legislation (from the 1930s and 1940s) unless a new farm bill is adopted that again temporarily amends permanent legislation. The permanent legislation would put in place price supports, at extremely high levels, for many agricultural commodities, distorting markets and greatly increasing federal costs. The specter of reverting to permanent legislation has, through the years, been used by Congress to ensure that future Congresses will replace expiring farm bills with new legislation.

Purpose– The purpose of this paper is to examine international experience with multiple-peril crop insurance (MPCI). Named peril crop insurance is available in most countries but MPCI is less common. While named peril insurance is widely successful, MPCI has a checkered history. In most cases, MPCI actuarial experience has been poor and large premium subsidies have been required to incentivize purchasing.Design/methodology/approach– International experience with MPCI is reviewed with a particular focus on the USA which has the largest MPCI program in the world. Rationales for government involvement in facilitating MPCI offers are examined and future challenges are explored.Findings– In most cases, MPCI actuarial experience has been poor and large premium subsidies have been required to incentivize purchasing. MPCI purchasing has increased dramatically in recent years but so have government expenditures to support MPCI programs. Significant challenges remain with providing cost-effective MPCI coverage for crop farmers.Originality/value– While previous articles have reviewed MPCI in the USA, this paper also considers experiences in other countries. Future challenges and research needs are described.

Introduction : The context of row crop risk management continues to grow more complex. While the magnitude of price and yield risk changes over time, the development of sophisticated risk management tools and complex government policies may improve growers’ ability to manage risk -- if these instruments are used correctly. Conversely, these instruments may actually increase risk exposure if used incorrectly. Gone are the days when growers had access only to individual yield insurance and national triggered price programs. In 1996, revenue insurance became available for many crop growers. For most major crops, the acreage covered by revenue insurance now far exceeds that covered by yield insurance. The 2008 farm bill created the complex risk policies of ACRE and SURE (Ubilava et al.). Mitchell et al. argue that ACRE, which subsumed multiple revenue risks and integrated with other risk instruments, was difficult for growers to understand and difficult for county USDA officials to implement. Current farm bill proposals are now focused on various shallow loss programs such as Agricultural Risk Coverage (ARC), Stacked Income Protection Plan (STAX) and Supplemental Coverage Option (SCO) which layer risk protection on top of crop insurance. Thus, producers are likely to continue to be confronted with complex risk management tools which may overlap or leave gaps in risk protection. Further, the decision becomes even more complex when one considers the possibility of also using futures or forward contracts.

American Journal of Agricultural EconomicsVolume 95, Issue 2 p. 498-504 AAEA Meeting Invited Paper Session Why Do We Subsidize Crop Insurance? Keith H. Coble, Corresponding Author Keith H. Coble [email protected] [email protected]Search for more papers by this authorBarry J. Barnett, Barry J. BarnettSearch for more papers by this author Keith H. Coble, Corresponding Author Keith H. Coble [email protected] [email protected]Search for more papers by this authorBarry J. Barnett, Barry J. BarnettSearch for more papers by this author First published: 05 November 2012 https://doi.org/10.1093/ajae/aas093Citations: 64 Keith Coble ([email protected]) is Giles Distinguished Professor and Barry Barnett ([email protected]) is Professor, Department of Agricultural Economics, Mississippi State University. This article was presented in an invited paper session at the 2012 AAEA Annual meeting in Seattle, WA. The articles in these sessions are not subjected to the journal's standard refereeing process. Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Citing Literature Volume95, Issue2January 2013Pages 498-504 RelatedInformation