Search results

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.

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.

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.

While previous articles have reviewed MPCI in the USA, this paper also considers experiences in other countries. Future challenges and research needs are described.

The purpose of this paper is to determine which factors influence the choice to adopt subsidized multi-peril crop insurance (MPCI) in the Netherlands and whether prior hail insurance uptake is one of the determinants of MPCI adoption. In addition, it is analyzed whether subsidized MPCI has reduced disaster relief spending.

Cross-sectional survey with 512 respondents using a stratified design comprising MPCI adopters and non-adopters sampled from the Dutch national census data base. The national census, including information on subsidized MPCI adoption from 2010 up to and including 2015, was supplemented with information on (prior) traditional market-based hail insurance uptake, and other underlying determining factors were elicited. Logistic regression analysis was used to determine which factors influence the choice to adopt MPCI.

Analysis of MPCI adoption reveals that subsidized MPCI mainly substituted for market-based hail insurance uptake up to now. Growers who did not insure against hail in the past were hardly reached. Approximately, three-quarter of MPCI adopters insured hail prior to market introduction of MPCI. In the arable sector, MPCI adoption was 2.89 (p<0.01) more likely for prior hail insurance adopters compared to non-adopters, while it was 9.67 (p<0.01) more likely in the fruit sector.

In the arable sector, it is expected that MPCI uptake in the coming years will reach more prior non-adopters of hail insurance as demand is expected to increase. Prior hail insurance adopters in the arable sector can be seen as the early MPCI adopters. In the fruit sector, adoption rates are already at a relative high level and a further significant increase by targeting non-adopters of hail insurance is not likely.

Governmental support has crowded out to some extend traditional market-based hail insurance in the Netherlands. Since the Common Agricultural Policy of the European Union is creating more momentum to subsidize crop insurance more member states with a long history of a mature hail insurance market may be confronted with similar crowding-out effects.

The purpose of this paper is to evaluate the Dual Use (DU) Option – a crop insurance policy created by the 2018 Farm Bill – relative to other policies available to dual-purpose annual forage producers. The new policy combines existing rainfall-based policies for annual forage crops and multi-peril policies for grain, allowing coverage for multiple crop uses on the same acres during the same growing season.

The paper uses a simulation model to examine crop insurance choices for a typical Texas dual-purpose wheat farm. The certainty equivalent (CE) of wealth is used to rank choices within and between three insurance plans and to analyze the effects of those choices over a range of producer risk aversion levels and for three cases of yield expectations.

The DU Option is more preferred as risk aversion increases, but it is not universally preferred. Therefore, while the policy can be a viable risk management tool, certain restrictions may be limiting its effectiveness.

The findings of this paper can help explain farm-level decision making related to dual-purpose annual forage crop insurance program choices.

This paper contributes to the literature by documenting a new crop insurance program made available in the 2018 Farm Bill and provides insights into producers' possible choices by evaluating extensive scenarios.

This paper aims to propose fuzzy-regression-particle swarm optimization (PSO) based hybrid optimization approach for getting maximum weld quality in terms of weld strength and bead depth of penetration.

The prediction of welding quality to achieve best of it is not possible by any single optimization technique. Therefore, fuzzy technique has been applied to predict the weld quality in terms of weld strength and weld bead geometry in combination with a multi-performance characteristic index (MPCI). Then regression analysis has been applied to develop relation between the MPCI output value and the input welding process parameters. Finally, PSO method has been used to get the optimal welding condition by maximizing the MPCI value.

The predicted weld quality or the MPCI values in terms of combined weld strength and bead geometry has been found to be highly co-related with the weld process parameters. Therefore, it makes the process easy for setting of weld process parameters for achieving best weld quality, as there is no need to finding the relation for individual weld quality parameter and weld process parameters although they are co-related in a complicated manner.

In this paper, a new hybrid approach for predicting the weld quality in terms of both mechanical properties and weld geometry and optimizing the same has been proposed. As these parameters are highly correlated and dependent on the weld process parameters the proposed approach can effectively analyzing the ambiguity and significance of each process and performance parameter.

The study has been aimed to search an appropriate process environment for simultaneous optimization of quality‐productivity favorably. Various surface roughness parameters (of the machined product) have been considered as product quality characteristics whereas material removal rate (MRR) has been treated as productivity measure for the said machining process.

In this study, three controllable process parameters, cutting speed, feed, and depth of cut, have been considered for optimizing material removal rate (MRR) of the process and multiple surface roughness features for the machined product, based on L₉ orthogonal array experimental design. To avoid assumptions, limitation, uncertainty and imprecision in application of existing multi‐response optimization techniques documented in literature, a fuzzy inference system (FIS) has been proposed to convert such a multi‐objective optimization problem into an equivalent single objective optimization situation by adapting FIS. A multi‐performance characteristic index (MPCI) has been defined based on the FIS output. MPCI has been optimized finally using Taguchi method.

The study demonstrates application feasibility of the proposed approach with satisfactory result of confirmatory test. The proposed procedure is simple, and effective in developing a robust, versatile and flexible mass production process.

In the proposed model it is not required to assign individual response weights; no need to check for response correlation. FIS can efficiently take care of these aspects into its internal hierarchy thereby overcoming various limitations/assumptions of existing optimization approaches.

The article examines the impact of policy change on enterprise unit subsidies that took place in 2009 on the quantity demanded for crop insurance.

The analysis covers corn, soybeans, and wheat that are grown in six economic regions and uses various measures of purchasing such as acres insured, unit structure, coverage levels, as well as crop hail use as proxies for the quantity demanded.

The analysis first employs time series econometric tools to analyze whether the time path of the share of enterprise units within buyup acres is influenced by the policy change in enterprise unit subsidies. It then comparatively examines the insurance experience between 2008 (right before the change) and 2015 (well after the change).

For corn, soybean, and wheat, the analysis establishes that the time path of the share of enterprise units within buyup coverage acres is statistically and economically influenced by the intervention. The analysis further quantifies the intervention's immediate and long-term impacts and finds that farmers' unit choices are highly responsive (elastic) to subsidy rates in those units.

Between 2008 and 2015, the insurance experience generally indicates that the share of enterprise units within buyup coverage surged, the share of acres under catastrophic coverage declined, and the share acres in high coverage levels increased. Meanwhile, growers have increasingly utilized crop-hail policies.

This appears to be the first study (1) quantifying the sensitivity of farmers' unit choices with respect to subsidy rates in those units and finding that such choices are actually highly responsive (elastic), and (2) pointing out the interaction between MPCI and crop-hail products and offering insights as to their combined use. The findings should be of considerable value to policymakers, academics, bankers, and producers in regards to the design and use of risk management tools.

The purpose of this paper is to present the development of cumulative rainfall deficit (CRD) indices for corn in Shandong Province, China, based on high-resolution weather (county, 1980-2011) and yield data (township, 1989-2010) for five counties in Tai’an prefecture.

A survey with farming households is undertaken to obtain local corn prices and production costs to compute the sum insured. CRD indices are developed for five corn-growth phases. Rainfall is spatially interpolated to derive indices for areas that are outside a 25 km radius from weather stations. To lower basis risk, triggers and exits of the payout functions are statistically determined rather than relying on water requirement levels.

The results show that rainfall deficits in the main corn-growth phases explain yield reductions to a satisfying degree, except for the emergence phase. Correlation coefficients between payouts of the CRD indices and yield reductions reach 0.86-0.96 and underline the performance of the indices with low basis risk. The exception is SA-Xintai (correlation 0.71) where a total rainfall deficit index performs better (0.87). Risk premium rates range from 5.6 percent (Daiyue) to 12.2 percent (SA-Xintai) and adequately reflect the drought risk.

This paper suggests that rainfall deficit indices can be used in the future to complement existing indemnity-based insurance products that do not cover drought for corn in Shandong or for CRD indices to operate as a new insurance product.

A common feature of certain kinds of data is a high level of statistical dependence across space and time. This spatial and temporal dependence contains useful information that can be exploited to significantly reduce the uncertainty surrounding local distributions. This chapter develops a methodology for inferring local distributions that incorporates these dependencies. The approach accommodates active learning over space and time, and from aggregate data and distributions to disaggregate individual data and distributions. We combine data sets on Kansas winter wheat yields – annual county-level yields over the period from 1947 through 2000 for all 105 counties in the state of Kansas, and 20,720 individual farm-level sample moments, based on ten years of the reported actual production histories for the winter wheat yields of farmers participating in the United States Department of Agriculture Federal Crop Insurance Corporation Multiple Peril Crop Insurance Program in each of the years 1991–2000. We derive a learning rule that combines statewide, county, and local farm-level data using Bayes’ rule to estimate the moments of individual farm-level crop yield distributions. Information theory and the maximum entropy criterion are used to estimate farm-level crop yield densities from these moments. These posterior densities are found to substantially reduce the bias and volatility of crop insurance premium rates.

The purpose of this paper is to explore the impact of the levels of inaccuracy associated with three different premium estimation methods, one of which attempts to mimic the protocol currently used by the Risk Management Agency (RMA), on the actuarial performance of the US crop insurance program.

The analyses are conducted using empirically‐grounded simulation and other computational methods, under various plausible assumptions about the producer's risk aversion behavior and knowledge of his/her actuarially fair premium.

Regardless of the assumed producer knowledge and behavior, it is concluded that the persistently high government subsidy levels required to keep the program solvent could be solely explained by the inaccuracy in the RMA's premium estimates. In other words, the observed need for large subsidies does not necessarily imply that the program is systematically favoring less efficient farmers or particular crops or production areas. Also, contrary to the commonly accepted “adverse selection” argument, it is shown that farmers having more information about their actuarially fair premiums than the insurer is not the reason why high subsidies are needed. Actuarial performance, however, could be improved by using the more elaborate methods exemplified in the paper, as well as larger sample sizes for premium estimation.

The paper provides conclusions and recommendations that could substantially reduce the amount of public subsidies needed to keep the US crop insurance program solvent.

In the present study, wire electro-discharge machining (WEDM) of Inconel 625 (In-625) is performed with the machining parameter such as spark-on time, spark-off time, wire-speed, wire tension and servo voltage. The purpose of this study is to find the most favorable machining parameter setting with respect to WEDM performance such as material removal rate (MRR) and surface roughness (RA).

Taguchi’s L27 orthogonal array has been used to design the experiments with varying machining parameters into three-level four factors. A hybrid multi-optimization technique has been purposed with grey relation analysis and fuzzy inference system integrated with teaching learning-based optimization to achieve optimum machinability (MRR and RA in present case). The obtained result has been compared with two evolutionary optimization tools via a genetic algorithm and simulated annealing.

It has been found that proposed hybrid technique taking minimum computational time, provide better solution and avoid priority weightage calculation by decision-makers. A confirmation test has been performed at single and multi-optimal parameter settings. The decision-makers have been chosen to select any single or multi-parameter setting as per the industry’s demand.

The proposed optimization technique provides better machinability of In-625 using zinc-coated brass wire electrode during WEDM operation.