Search results

The purpose of this paper is to investigate changes proposed in 2012 to commodity programs for the new Farm Bill. Both the Senate and House Agriculture Committee versions of the new Farm Bill eliminate current commodity programs including direct payments, create new revenue‐based commodity program options designed to cover “shallow” revenue losses, and also introduce supplemental crop insurance coverage for shallow revenue losses.

This paper documents the payment functions for the new revenue programs proposed in both the Senate and House Ag Committee Farm Bills, and also estimates expected payments for each using a model based on historical county yield data, farmer‐level risk rates from RMA, and commodity price levels from the March 2012 CBO baseline projections.

The authors find significant variation in expected per acre payment across programs, crops, and regions. In general, the Senate's bill would be expected to be preferred over the House's bill for corn and soybean producers, particularly those in the Midwest. Also, the RLC program in the House's Bill typically would be projected to pay much less than the Senate's SCO or ARC programs for most producers in the Midwest.

This study develops an extensive nationwide model of county and farm yield and price risks for the five major US crops and employs the model to evaluate expected payment rates and the distribution of payments under the House and Senate Farm Bill proposals. These analyses are important for program evaluation and should be of great interest to producers and policymakers.

The purpose of this paper is to bring out the development of a flexible model for nonstationary crop yield distributions and its applications to decision-making in crop insurance.

The authors design a nonparametric Bayesian approach based on Gaussian process regressions to model crop yields over time. Further flexibility is obtained via Bayesian model averaging that results in mixed Gaussian processes.

Simulation results on crop insurance premium rates show that the proposed method compares favorably with conventional estimators, especially when the underlying distributions are nonstationary.

Unlike conventional two-stage estimation, the proposed method models nonstationary crop yields in a single stage. The authors further adopt a decision theoretic framework in its empirical application and demonstrate that insurance companies can use the proposed method to effectively identify profitable policies under symmetric or asymmetric loss functions.

This study evaluates the impacts on gross revenue distributions of the use of alternative crop insurance products across different coverage levels and across locations with differing yield risks. Results are presented in terms of net costs, values‐at‐risk, and certainty equivalent returns associated with five types of multi‐peril crop insurance across different coverage levels. Findings show that the group policies often result in average payments exceeding their premium costs. Individual revenue products reduce risk in the tails more than group policies, but result in greater reductions in mean revenues. Rankings based on certainty equivalent returns and low frequency VaRs generally favor revenue products. As expected, crop insurance is associated with greater relative risk reduction in locations with greater underlying yield variability.

The purpose of this paper is to examine the reduction of crop yield uncertainty using rainfall index insurances. The insurance payouts are determined by a transparent rainfall index rather than actual crop yield of any producer, thereby circumventing problems of adverse selection and moral hazard. The authors consider insurances on rainfall indexes of various months and derive an optimal insurance portfolio that minimizes the income variance for a crop producer.

Various regression models are considered to relate crop yield to monthly mean temperature and monthly cumulative precipitation. A bootstrapping method is used to simulate weather indexes and corn yield in a future year with the correlation between precipitation and temperature incorporated. Based on the simulated scenarios, the optimal insurance portfolio that minimizes the income variance for a crop producer is obtained. In addition, the impact of correlation between temperature and precipitation, availability of temperature index insurance and geographical basis risk on the effectiveness of rainfall index insurance is examined.

The authors illustrate the approach with the corn yield in Illinois east crop reporting district and weather data of a city in the same district. The analysis shows that corn yield in this district is negatively influenced by excessive precipitation in May and drought in June–August. Rainfall index insurance portfolio can reduce the income variance by up to 51.84 percent. Failing to incorporate the correlation between temperature and precipitation decreases variance reduction by 11.6 percent. The presence of geographical basis risk decreases variance reduction by a striking 24.11 percent. Allowing for the purchase of both rainfall and temperature index insurances increases variance reduction by 13.67 percent.

By including precipitation shortfall into explanatory variables, the extended crop yield model explains more fluctuation in crop yield than existing models. The authors use a bootstrapping method instead of complex parametric models to simulate weather indexes and crop yield for a future year and assess the effectiveness of rainfall index insurance. The optimal insurance portfolio obtained provides insights on the practical development of rainfall insurance for corn producers, from the selection of triggering index to the demand of the insurance.

The USDA’s Risk Management Agency (RMA) made several changes to the crop insurance products available to organic growers for the 2014 crop year. Most notably, a 5 percent premium surcharge was removed and organic-specific transitional yields (t-yields) were issued for the first time. The purpose of this paper is to use farm-level organic crop yield data to analyze the impact of these reforms on producer insurance outcomes and compare the insurance options for new organic growers.

This study uses a unique panel data set of organic corn and soybean yields to analyze the impact of organic crop insurance reforms. Actual Production History values and premium rates are calculated for each farm and crop yield sequence. Producer loss ratios and subsidized premium wedges are compared for yield, revenue and area-risk products before and after the instituted reforms.

Results indicate that RMA succeeded in improving the actuarial soundness of the organic insurance program, though further refinement of organic t-yields may be necessary to accurately reflect the yield potential of organic producers and avoid reductions in program participation.

This paper provides insight into the effectiveness of reforms intended to improve the actuarial soundness of organic crop insurance and demonstrates the effect that the reforms are likely to have on new and existing organic farms. Because this analysis uses data collected independently of RMA and includes farms that may or may not have purchased crop insurance, it avoids the self-selection problems that might affect analyses using crop insurance program data.

The purpose of this paper is to propose an improved reinsurance pricing framework, which includes a crop yield forecasting model that integrates weather variables and crop production information from different geographically correlated regions using a new credibility estimator, and closed form reinsurance pricing formulas. A yield restatement approach to account for changing crop mix through time is also demonstrated.

The new crop yield forecasting model is empirically analyzed based on detailed farm-level data from Manitoba, Canada, covering 216 crop varieties from 19,238 farms from 1996 to 2011. As well, corresponding weather data from 30 stations, including daily temperature and precipitation, are considered. Algorithms that combine screening regression, cross-validation and principal component analysis are evaluated for the purpose of achieving efficient dimension reduction and model selection.

The results show that the new yield forecasting model provides significant improvements over the classical regression model, both in terms of in-sample and out-of-sample forecasting abilities.

The empirical analysis is limited to data from the province of Manitoba, Canada, and other regions may show different results.

This research is useful from a risk management perspective for insurers and reinsurers, and the framework may also be used to develop improved weather risk management strategies to help manage adverse weather events.

This is the first paper to integrate a credibility estimator for crop yield forecasting, and develop a closed form reinsurance pricing formula.

The purpose of this paper is to analyze the effects of data aggregation and farm‐level crop acreage on the level of natural hedge, i.e. the level of price‐yield correlations, which is an important issue in risk modeling and management.

Swiss FADN data for five crops covering the period 2002‐2009 are used to estimate price‐yield correlations at the farm‐ as well as on an aggregated level. Tobit regressions are used to estimate empirical relationships between the level of natural hedge and the underlying crop acreage.

Price‐yield correlations differ significantly between farm‐ and aggregated‐level. More specifically, the natural hedge observed at the farm‐level is much smaller, i.e. correlations are closer to zero. Taking correlations from aggregated levels thus leads to an underestimation of farm‐level revenue variability. Furthermore, it is found that larger farms have a stronger natural hedge. For instance, a 1 percent increase in area under maize and intensive barley leads to a change in the correlation by −0.02 and −0.08, respectively.

The natural hedge is often approximated with correlations observed at more aggregated levels, e.g. the county level. The results show that this implies errors in risk assessment and modeling as well as insurance applications. Thus, farm‐level estimates should be used. The here presented relationship between price‐yield correlations and farm‐level crop acreage can be used to derive better information on levels of the natural hedge.

Even though the effects of data aggregation on price‐yield correlations have been discussed in earlier research, this paper is the first to also account for on‐farm effects of underlying crop acreage on levels of natural hedge. It is found that this simple relationship can be useful in risk management and modeling applications.

This paper aims to evaluate the effects of climate variables on the mean yield and yield variability of major pulse crops in the Telangana state of India.

Authors have estimated the Just and Pope (1978, 1979) production function using panel data at the district level of four major pulses in nine former districts of Telangana for 36 years during 1980–2015. A three-stage feasible generalized least squares estimation procedure has been followed. The mean yield and yield variance functions have been estimated individually for each of these study crops, namely, Bengal gram, green gram, red gram and horse gram.

Results have shown that changes in climatic factors such as rainfall and temperature have significant influences on the mean yield levels and yield variance of pulses. The maximum temperature is observed to have a significant adverse impact on the mean yield of a majority of pulses, and it is also a risk-enhancing factor for a majority of pulses except horse gram. However, the minimum temperature is positively related to the mean yields of the study crops except for Bengal gram, and it is having a risk-reducing impact for a majority of study crops. Rainfall is observed to have a negative impact on the mean yields of all pulses, but it is a risk-enhancing factor for only one crop, i.e. Bengal gram. Thus, rising temperatures and excess rainfall are not favorable to the productivity of pulses in study districts.

The present study is based on the secondary data at the district level and is considering only one state. Season-wise primary data, including farm-specific characteristics, could have been better. The projected climate change and its impact on the mean yields and yield variance of pulses need to be considered in a future study.

According to the best of our knowledge, this is the first study to empirically evaluate the impact of climatic variables on the mean yields and yield variability of major pulses in Telangana using a panel data for major pulses and nine districts of 36 years time-series during 1980–2015. The study has given useful policy recommendations.

At the beginning of the production year producers face a complex risk management decision environment given by risks specific to their operation, multiple crop insurance contracts and hedging opportunities. The purpose of this paper is to provide a producer-level framework for risk management decision making, focusing on the interaction between crop insurance and hedging.

The authors develop a Monte Carlo simulation model that generates a producer’s net income (NI) distribution that incorporates historical producer risk, price-yield correlation via a copula, price risk, and production costs. The authors evaluate the NI distribution through a modified Modern Portfolio Theory (MPT) decision framework. The authors use the modified MPT decision framework to explore tradeoffs between expected NI and farm ruin (defined as 1 or 5 percent expected shortfall) from different crop insurance contracts and pre-harvest hedging options.

Only revenue protection and the highest two levels of coverage level exist on the efficient frontier. The level of hedging on the efficient frontier ranges from 0 to 55 percent of Actual Production History. The authors find that increasing coverage level 5 percent (from 80 to 85 percent) negatively impacts the optimal hedging amount by 26 percentage points (from 35 to 9 percent).

The model provides the precise identification of financial benefits from different risk management strategies by incorporating producer-level historical yield data, using a copula to capture yield-price dependency structure and producer production cost in generating the NI distribution. This model can be applied to any producer’s characteristics and data.

The purpose of this paper is to develop methods for inferring if crop insurance premiums imply yield distributions that are valid according to standard laws of probability and broadly consistent with observed empirical evidence. The authors also survey current premium-implied distributions both before and after conditioning on the producer’s choice of coverage level.

Under an assumption of actuarial fairness, the authors derive expressions for upper and lower bounds for premium-implied yield cumulative distribution functions (CDFs) at loss thresholds for each coverage level. When observed premiums imply a CDF that exceeds one or is not non-decreasing, the authors conclude that premiums cannot be actuarially fair. The authors additionally specify very weak conditions for premium-implied yield CDFs to be consistent with two possible reasonable parametric distributions.

The authors evaluate premiums for the year 2018 for 19,104 county-crop-type-practice combinations, both before and after conditioning on producer’s choice of coverage level. The authors find problems in roughly one-third of cases. Problems are exhibited for all crops evaluated, and are strongly associated with areas with lower expected yields and higher yield variability. At least 40m acres are currently insured under premium schedules that cannot possibly be consistent with valid probability distributions.

The authors make two primary contributions. First, the premium-implied yield CDF bounds the authors derive requires fewer assumptions than previous similar work, while simultaneously placing more stringent conditions on premiums to be consistent with actuarial fairness. Second, the authors show that current US crop insurance premiums cannot possibly be actuarially fair for many cases, reflecting tens of millions of insured acres, which implies sub-optimal producer risk mitigation and inequitable expenditures for producers and taxpayers.