Search results

This article aims to explore recent trends in farmland rental markets using data for the state of Illinois. Trends in the types of rental agreements used and the relationship between the rental rate for those contracts, land values, crop revenues, production costs, and farm returns are examined.

Data from various sources and at different levels of aggregation for the state of Illinois are used to provide illustrations of historical trends in farmland rental agreements and rental rates, and how they are related to various market and industry factors. Focus is placed on the more recent period since 2005 characterized by high commodity price levels and volatility.

The majority of farmland in the Midwest is controlled under rental agreements which are increasingly of the fixed cash rent type. Rental rates have increased, but at a slower rate than farm returns. Average rental and interest rates imply that land values are consistent with the current market environment. Aggregate rental rates mask considerable variation in farm‐level rents, only a portion of which can be explained by differences in soil productivity. Given the current level of price volatility, the tenure position of a farm operation has a significant effect on downside risk exposure.

The illustrations provided in this paper should be of interest to researchers working in the area of farmland values and rental agreements, as well as to practitioners including farmers, landowners, and professional farm managers. The findings should motivate additional research and recognition of the importance of tenure position to the performance and risk exposure of grain farms.

Area-based insurance plans trigger payments based on losses which may not match actual loss experience at the farm level, an issue often referred to as basis risk. The purpose of this paper is to quantify the basis risk associated with the Supplemental and Enhanced Coverage Option (SCO and ECO) crop insurance programs, and the risk reduction that can be achieved when these area-based plans are added to farmers’ risk management portfolios.

This study utilizes simulation techniques to build a stylized model for representative farms at the county-level for non-irrigated corn and soybean production. We model farms for each county in the 17 states included in USDA’s Crop Progress Reports for corn and soybeans, which comprise more than 90% of planted acreage for those crops. Yield and price data from the USDA’s National Agricultural Statistics Service (NASS), futures price data and insurance premiums from the Risk Management Agency are used to calibrate the simulation model.

Area-based plans may provide (1) insufficient coverage for actual losses, which is a risk management concern or (2) payments exceeding actual losses, which is a program efficiency concern given federal support for the insurance program. The risk of insufficient coverage (under-compensation) can be reduced by increasing the coverage level of the area plans, but that also increases the likelihood of support exceeding actual loss experience (over-compensation). The scale of basis risk associated with the area plans differs by region and crop due to differences in yield risk. Area plans do have the potential to provide additional risk reduction; however, risk reduction is inversely related to the level of basis risk.

To the best of the authors’ knowledge, this study is the first to focus on quantifying the basis risk associated with the relatively new supplemental area options (SCO, ECO) currently available in the US federal crop insurance program. It provides important insights which could inform current and future Farm Bill debates as policymakers consider modifications and enhancements to commodity and crop insurance programs. It also provides useful information to help educate farmers and other stakeholders about the use of SCO and ECO in their risk management plans.

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.

This study seeks to evaluate the impacts of land rental arrangements on crop insurance and grain marketing decisions.

The analysis is conducted in an Illinois corn‐soybean setting in which optimal marketing and crop insurance decisions are estimated for a risk‐averse producer under typical cash rent and share rent agreements using numerical simulation methods.

Results indicate that the availability of crop insurance impacts the intensity of use of put options under both cash and share rent arrangements. Similar to previous work in this area, revenue insurance is found to cause a substitution away from marketing using put options, while yield insurance is complementary to price risk management alternatives. However, while insurance and marketing play a role under both types of land tenure arrangements, shifting from a cash rent to a share rent agreement provides a relatively greater degree of risk reduction.

The results suggest that additional research is needed to explain trends in land rental contracts. Crop insurance and other federal programs may provide incentives to switch from share leases to cash rent arrangements. Changes to the design of these programs could facilitate risk management for producers more efficiently.

The unique contribution of this study is the comparison of insurance and marketing decisions under both cash rent and share rent agreements for crop land.

The purpose of this paper is to contribute to the empirical evidence about crop yield distributions that are often used in practical models evaluating crop yield risk and insurance. Additionally, a simulation approach is used to compare the performance of alternative specifications when the underlying form is not known, to identify implications for the choice of parameterization of yield distributions in modeling contexts.

Using a unique high-quality farm-level corn yield data set, commonly used parametric, semi-parametric, and non-parametric distributions are examined against widely used in-sample goodness-of-fit (GOF) measures. Then, a simulation framework is used to assess the out-of-sample characteristics by using known distributions to generate samples that are assessed in an insurance valuation context under alternative specifications of the yield distribution.

Bias and efficiency trade-offs are identified for both in- and out-of-sample contexts, including a simple insurance rating application. Use of GOF measures in small samples can lead to inappropriate selection of candidate distributions that perform poorly in straightforward economic applications. The β distribution consistently overstates rates even when fitted to data generated from a β distribution, while the Weibull consistently understates rates; though small sample features slightly favor Weibull. The TCMN and kernel density estimators are least biased in-sample, but can perform very badly out-of-sample due to overfitting issues. The TCMN performs reasonably well across sample sizes and initial conditions.

Economic applications should consider the consequence of bias vs efficiency in the selection of characterizations of yield risk. Parsimonious specifications often outperform more complex characterizations of yield distributions in small sample settings, and in cases where more demanding uses of extreme-event probabilities are required.

The study helps provide guidance on the selection of distributions used to characterize yield risk and provides an extensive empirical demonstration of yield risk measures across a high-quality set of actual farm experiences. The out-of-sample examination provides evidence of the impact of sample size, underlying variability, and region of the probability measure used on the performance of candidate distributions.

The purpose of this paper is to discuss the growth and rising costs association with the Federal Crop Insurance program in the USA, justifications for public support, and recent reforms that have been implemented or proposed to reduce program costs. It also analyzes a specific policy to reduce premium assistance spending.

Data from the Risk Management Agency are used to illustrate historical trends in crop insurance program costs and to analyze the impacts of imposing a per acre cap on premium assistance.

Imposing a per acre cap on premium assistance could achieve significant savings. A $20 per acre cap is estimated to reduce premium subsidy expenditures by more than 40 percent. However, the impact of such a policy would be most severe on crops currently receiving the largest subsidies per acre, which happen to be some of the largest program crops in the USA.

This paper adds to the literature analyzing potential reform in crop insurance industry. The subsidy cap considered has been proposed and considered by policy makers, and this paper provides estimates for its potential savings.

The purpose of this paper is to compare the characteristics of farms who participate in farm management associations to the wider population of farms at the state level.

Farm-level records obtained from the USDA's Agricultural Resource Management Survey (ARMS) are compared to similar data obtained from farm management associations in three states: Illinois, Kansas, and Kentucky.

Data collected through farm management associations tend to represent larger farms and a greater share of crop producers as compared to livestock producers. Association data, however, capture a greater share of younger farm operators.

This is the first study to compare farm statistics from several farm management associations to ARMS, and the study confirms the findings of existing studies of prior USDA surveys.

While the demand for weather‐based agricultural insurance in developed regions is limited, there exists significant potential for the use of weather indexes in developing areas. The purpose of this paper is to address the issue of historical data availability in designing actuarially sound weather‐based instruments.

A Bayesian rainfall model utilizing spatial kriging and Markov chain Monte Carlo techniques is proposed to estimate rainfall histories from observed historical data. An example drought insurance policy is presented where the fair rates are calculated using Monte Carlo methods and a historical analysis is carried out to assess potential policy performance.

The applicability of the estimation method is validated using a rich data set from Iowa. Results from the historical analysis indicate that the systemic nature of weather risk can vary greatly over time, even in the relatively homogenous region of Iowa.

The paper shows that while the kriging method may be more complex than competing models, it also provides a richer set of results. Furthermore, while the application is specific to forage production in Iowa, the rainfall model could be generalized to other regions by incorporating additional climatic factors.

The idea of implementing supply chain management (SCM) principles for the construction industry was embraced by construction stakeholders to enhance the sector's performance. The analysis from the literature revealed that the implementation of SCM in the construction industry enhances the industry's value in terms of cost-saving, time savings, material management, risk management and others. The construction supply chain (CSC) can be managed using the pull or push system. This chapter also discusses the origin and proliferation of SCM into the construction industry. The chapter revealed that the concept of SCM has passed through five different eras: the creation era, the use of ERP, globalisation stage, specialisation stage and electronic stage. The findings from the literature revealed that we are presently in the fourth industrial revolution (4IR) era. At this stage, the SCM witnesses the adoption of technologies and principles driven by the 4IR. This chapter also revealed that the practice of SCM in the construction industry is centred around integration, collaboration, communication and the structure of the supply chain (SC). The forms and challenges hindering the adoption of these practices were also discussed extensively in this chapter.

The purpose of this paper is to evaluate the risk management benefits provided by the supplemental coverage option (SCO) insurance plan which was created in the 2014 Farm Bill. Specifically, the marginal expected utility benefits are compared with the potential additional subsidy cost introduced by the new program for a stylized example of a corn producer.

The paper uses a stylized simulation model examines the preferred insurance program choice for a typical Midwestern corn farmer. The expected utility of the farmer is calculated under their preferred insurance program choice both with and without the availability of the SCO program, and compared to the case where crop insurance is not available. Scenarios are examined for a range of farmer risk aversion levels, different levels of correlation between farm-level and county-level corn yields, and case with and without insurance premium subsidies.

The SCO program is found to enter into the preferred insurance program choice for risk averse farmers. As risk aversion increases, farmers are estimated to prefer higher coverage levels for individual products along with SCO coverage. While the availability of existing crop insurance programs are shown to substantially increase the expected utility of farmers, the marginal impact of adding SCO to the crop insurance program is relatively small. Furthermore, the additional expected benefits generated by SCO are shown to include both risk management and expected return components. With subsidies removed, the estimated marginal benefits provided by SCO are reduced significantly.

The findings of this paper can help inform the policy debate for future farm bills as agricultural support programs continue to evolve. The results in this paper can also be used to help explain farm-level decision making related to crop insurance program choices.

This paper contributes to the literature by documenting a new, federally supported risk management programs made available to farmers in the 2014 Farm Bill and evaluates the marginal benefits the SCO program offers US crop producers.