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Defines data mining as the extraction of potentially useful information from large databases. Shows how data mining can be applied to detecting anomalous behaviour in American agriculture and thus support the Risk Protection Agency in its compliance mission to detect fraud in crop insurance, using corn as the crop studied and percentage of acres harvested as the key indicator for “proof of concept”. Indicates potential areas of improvement, such as the development of a single data warehouse, and the role of social scientists with knowledge of data analysis and agricultural management. Concludes that data mining could be more effective than the current technique of random selection for investigation of individual entities.

This article seeks to examine the reference yield calculation method used in crop insurance rating and provides recommendations that could potentially improve actuarial performance of the Federal crop insurance program.

Conceptual, numerical, and statistical analysis is utilized to evaluate the reference yield calculation method used in the US Federal crop insurance program.

The results suggest that reference yields, which at the time of this study are calculated using National Agricultural Statistics Service (NASS) data, do not accurately represent the average actual yields of the insured pool of producers in the Federal crop insurance program. In addition, it is found that not regularly updating these NASS‐based reference yields exacerbates this problem because these reference yields do not appropriately represent the current state of technological progress.

The empirical analysis leads this paper to recommend a reference yield calculation procedure that utilizes county‐average yields from the risk management agency (RMA) participation database and an approach that uses spatially aggregated average yields in cases when data for a particular county are sparse.

No previous study has investigated the reference yield calculation method in the Federal crop insurance program using both RMA and NASS data sets. Moreover, this study contributes to the small literature that examines various aspects of the actual production history (APH) rating platform and suggests refinements to improve actuarial performance.

The purpose of this research is to investigate the degree to which trends and structural change may have altered crop insurance expected loss cost ratios across time. Because loss experience is used to set rates for the program, these changes can impact the premiums paid by producers and cost to the government.

County level adjusted loss cost data was merged with climate division weather data for the 1980‐2009 period. Crop‐specific regional‐level regression models were estimated to test for trends and structural changes in the loss experience for major crops (corn, soybeans, sorghum, cotton, winter wheat, and spring wheat). Climate data was used to control for the effect of weather.

For several crops and regions, a significant break point in the loss cost data is found at 1995. This is consistent with the policy changes that occurred in in the program due to the 1994 legislative change. In most instances loss experience prior to 1995 is higher than more recent years even when controlling for the effect of weather. The exception is in winter wheat where it appears recent experience may be worse rather than older experience.

This paper provides a large‐scale assessment of the magnitude of improved crop insurance loss experience across time.

The purpose of this paper is to identify the factors that determine demand for crop insurance in Poland.

To examine the determinants of decisions regarding crop insurance, the authors used logistic regression. The base source of data for the analysis was the 2013 FADN sample. The scale of yield losses, the indemnities received and the Arrow-Pratt risk aversion coefficient were examined in a representative sample of farms in consecutive years in the period 2004-2013.

Losses are the major determinants of crop insurance uptake. Additionally, it was observed that the economic determinants are in line with the expected utility theory, while contrary to expectations, farmer’s characteristics such as education level, age or even risk aversion did not prove to have any influence on crop insurance uptake.

The FADN sample is representative as regards the type of farming, economic size of farm and location of the farm. Every farm in the sample represents a specific number of similar farms in the population. However, it must be emphasised that the representativeness of the sample with respect to other determinants, e.g., yield losses in previous years, using crop insurance or the farmers’ age and education has not been verified due to lack of data characterizing the general population with regard to these factors.

It could be argued that the system of crop insurance subsidies should be targeted to encourage the farmers who previously had not used insurance to join the system.

The paper presents the analysis of crop insurance uptake in a country with a strongly polarised agriculture. The Polish farm sector consists of 1.4 million farms with sizes ranging from 1 ha to over a few thousands hectares. The research is based on a data set of 5,202 farms which contains data from ten years (2004-2013). The novelty of the methodological approach is that it includes information on the number of farms represented by every farm in the FADN sample in the Horvitz-Thompson estimator in order to achieve results which are valid for the general population of Polish farms.