Search results

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.

Recently, USDA-RMA introduced a Trend Adjusted-Actual Production History (TA-APH) program, which increases APH by a trend factor to cover yield changes over time. The purpose of this paper is to examine the effects of the TA-APH program on farmer participation, coverage election, and risk by analyzing data before and after the program.

Since the program was carried out in selected counties, the authors employ a difference in differences approach doing comparisons of insurance participation and coverage levels between eligible and ineligible counties.

The authors find that farmers within the counties where the TA-APH program was available experienced an increase in insured acres of 3 percent for corn and 5 percent for soybeans. The authors also find the farmers eligible for the program purchased lower coverage levels relative to those not eligible. However, the magnitude of that negative effect is relatively small, −0.9 percent in corn and −1.3 percent in soybeans. Collectively the evidence shows the TA-APH program does increase the guaranteed yield level mitigating farmer risk.

The data set used only permitted analysis at the county level, thus the authors could not look at the individual farmer choices.

The results suggest that if a greater level of farmer risk protection is desired from crop insurance, the authors find that the trend adjustment as implemented was a successful way to achieve this.

This paper contributes to the literature on the crop insurance by evaluating the program controlling for a non-participating groups, farming experience, liability rates, and subsidy rates. In doing this, it fulfills an identified need to study the actual impact on participation rates and coverage levels elected.

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 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 use of laser direct imaging (LDI) processing for outer layer circuit pattern generation and the benefits already obtained with this process have encouraged attention to be focussed on the possibility of using laser direct imaging soldermask (LDISM) in the secondary imaging stage of printed circuit board (PCB) fabrication. As feature sizes on advanced interconnects have continued to diminish and the accompanying spacing between circuit features decreases, the requirement for accurately placing and scaling solder dams and apertures has become more critical in order to ensure that a high yield of finished product can be obtained. Consequently the traditional process of creating photographic artworks for soldermask exposure is rapidly becoming a crucial step that can have significant yield implications due to both environmental conditions and registration issues. The use of LDISMs in combination with specially developed LDI exposure systems is an enabling technology which can offer the benefits of a “standard” mask application process and the positional accuracy and individual image scaling required for guaranteeing improved yields for high density interconnect (HDI) panels.

The purpose of this research is to study the weather risk management practices of agriculture producers. In particular, the authors look at the extent to which farmers use weather derivatives to complement insurance. Unlike insurance, weather derivatives mitigate risk associated with low intensity, high probability events and therefore offer the potential of a more complete hedge than insurance alone.

The authors conducted a survey of grain farmers in the province of Saskatchewan, Canada, a typical jurisdiction in which farmers tend to face weather events that are high in frequency but low in severity, to study the usage of weather derivatives compared to insurance and identify the hurdles to their usage.

The authors find that fewer than 10 percent of their respondents use weather derivatives. Consistent with previous literature in other contexts, they identify participation costs, especially lack of awareness, to be the most significant hurdle to their usage.

A limitation of this study is that the data were collected using a survey methodology and are therefore subject to the usual risks of bias associated with that approach. Moreover, because the authors' survey was delivered online, it may have favoured the participation of farmers that were more comfortable with technology and some bias may have also been introduced into the data as a result.

The authors' findings suggest that there is significant potential to improve farmers' ability to hedge weather risk and thereby improve economic outcomes if the major barriers to the usage of weather derivatives can be overcome. The study paves the way for further research to support the development of public policy strategies that could help farmers take advantage of weather derivatives as part of their inventory of risk management tools.

To the authors' knowledge this is the first study that quantifies the usage of weather derivatives by agriculture producers and identifies the hurdles.

Malting barley is an important specialty crop in the Northern Plains and growers mitigate risk with federally subsidized crop insurance and production contracts. The purpose of this paper is to quantify risks growers face due to “coverage gaps” in crop insurance that result in uncertain indemnity payments when their crop does not meet contract specifications.

A stochastic dominance model is developed to evaluate alternative strategies for growers with differing risk attitudes and production practices (irrigation vs dryland).

The results illustrate how alternative crop insurance provisions affect efficient choice sets for growers. Risk premiums for irrigated growers all point to valuations favoring more coverage, contracts, and malting option B. As the crop insurance industry matures in the functions it performs, it will become increasingly more important to address quality attributes.

This paper addresses quality issues and coverage gaps in crop insurance provisions.

Federal crop insurance programs are the primary risk management programs of the US farm programs. Currently, these programs have been criticized for being disproportionally in favor of the riskier areas. Despite previous researchers having widely speculated its existence, a formal study of the scale, spatial pattern and fiscal impacts of such misrating phenomenon is still missing in the literature.

This paper first purposes an empirically testable definition of misrating, and then detects the scale of the misrating phenomenon by using over two million actuarial records collected by United States Department of Agriculture (USDA's) risk management agency since 1989. Furthermore, multiple spatial statistics methods have been adopted to study the spatial patterns of the misrating statuses. Finally, the paper builds a simple theoretical model to study the potential fiscal impacts of any policy attempts to mitigate the misrating issue.

The result reveals that roughly 40% of the counties display some degree of misrating. Furthermore, the distribution of misrating displays a significant pattern of positive global spatial autocorrelation, which reflects the existence of regional clusters of premium rate mispricing. Last but not least, the paper concludes that whether an attempt toward fair rating decreases the total program outlay or not relies on the demand elasticity of crop insurance in both overrated and underrated regions.

This paper offers the first attempt to quantify the scale, identify the spatial pattern and evaluate the fiscal impact of the premium misrating in federal crop insurance programs.

The purpose of this study is to investigate the determinants of agricultural insurance adoption by farmers of the state of São Paulo, Brazil.

Primary data from the 2015/2016 crop season was collected from a sample of 175 farmers. Logit econometric models were applied to identify the variables that affect the probability of agricultural insurance adoption.

The empirical results show that the education level, access to technical assistance, use of management tools and farm size positively affect the probability of adopting agricultural insurance. In addition, farmers who produce soybean and/or corn are more likely to use insurance. On the other hand, the higher the farmers’ propensity to take risk the lower the likelihood of using insurance.

The empirical analysis is based on cross-sectional data of a sample of 175 farmers of the state of São Paulo. The use of panel data with a larger sample of farmers, considering a period of years, could provide additional information.

To the best of the knowledge, this is the first empirical analysis about determinants of agricultural insurance adoption by Brazilian farmers, considering behavioral factors. The findings provide useful insights for policymakers in formulating risk management programs in the Brazilian agricultural markets. A better understanding about the determinants of insurance adoption is also relevant for private companies that sell insurance to farmers. Therefore, the paper may contribute with the diffusion of rural insurance as risk management tool in Brazilian agriculture.

The purpose of this paper is to establish mechanisms for process improvement so that production efficiency and product quality can be expected, and create a sustainable development in terms of circular economy.

The authors obtain a critical value from statistical hypothesis testing, and thereby construct a process capability indices chart, which both lowers the chance of quality level misjudgment caused by sampling error and provides reference for the processes improvement in poor quality levels. The authors used the bottom bracket of bicycles as an example to demonstrate the model and methods proposed in this study.

This approach enables us to plot multiple quality characteristics, despite varying attributes and specifications, onto the same process capability analysis chart. And it therefore increases accuracy and precision to reduce rework and scrap rates (reduce), increase product availability, reduce maintenance frequency and increase reuse (reuse), increase the recycle rates of components (recycle) and lengthen service life, which will delay recovery time (recovery).

Parts manufacturers in the industry chain can upload their production data to the cloud platform. The quality control center of the bicycle manufacturer can utilized the production data analysis model to identify critical-to-quality characteristics. The platform also offers reference for improvement and adds the improvement achievements and experience to its knowledge management to provide the entire industry chain. Feedback is also given to the R&D department of the bicycle manufacturer as reference for more robust product designs, more reasonable tolerance designs, and selection criteria for better parts suppliers, thereby forming an intelligent manufacturing loop system.