Crop Insurance Data Research Articles

Bt corn and cotton planting may benefit peanut growers by reducing aflatoxin risk.

Decades of studies have shown that Bt corn, by reducing insect damage, has lower levels of mycotoxins (fungal toxins), such as aflatoxin and fumonisin, than conventional corn. We used crop insurance data to infer that this benefit from Bt crops extends to reducing aflatoxin risk in peanuts: a non-Bt crop. In consequence, we suggest that any benefit-cost assessment of how transgenic Bt crops affect food safety should not be limited to assessing those crops alone; because the insect pest control offered by Bt crops affects the food safety profile of other crops grown nearby. Specifically, we found that higher Bt corn and Bt cotton planting rates in peanut-growing areas of the United States were associated with lower aflatoxin risk in peanuts as measured by aflatoxin-related insurance claims filed by peanut growers. Drought-related insurance claims were also lower: possibly due to Bt crops' suppression of insects that would otherwise feed on roots, rendering peanut plants more vulnerable to drought. These findings have implications for countries worldwide where policies allow Bt cotton but not Bt food crops to be grown: simply planting a Bt crop may reduce aflatoxin and drought stress in nearby food crops, resulting in a safer food supply through an inter-crop "halo effect."

Unwelcomed guests: Impact of deer harvest on corn and soybean wildlife damage

While wildlife damages are a relatively small part of crop insurance claims in the United States, reported damages from wildlife are growing and producers are concerned this issue is getting worse. Wildlife agencies advocate for hunting as a control to wildlife damages to crops but to our knowledge, no study has econometrically explored if hunting could reduce crop damage. Therefore, we analyze if county-level white-tailed deer (Odocoileus virginianus) harvest and other factors like habitat and field fragmentation are associated with corn and soybean acres being indemnified due to wildlife damages in Tennessee. We estimate a fractional probit model using crop insurance data to measure crop damages and a county-level white-tailed deer harvest dataset. We find a higher coverage level of a policy increases the likelihood of both corn and soybean acres being indemnified due to wildlife damage. We also find white-tailed deer harvest during the period post-harvest in the previous crop year (November through January) slightly decreases the likelihood of a claim being made due to wildlife damage for corn but was not found to mitigate losses to soybeans. The whitetail hunting season appears to align better with corn production to reduce crop losses than with soybeans. This study will be useful for wildlife and crop agencies in state and federal governments to develop policies to more effectively utilize hunting to reduce crop insurance claims from wildlife damages.

Protecting land and livelihood under climate risks: What hinders crop insurance adoption?

Small-scale agriculturists in developing countries face continuous weather-related risks, which are exacerbated by the growing issues of climate change (See Shah et al., 2018 for Pakistan; Zakaria et al. (2020) for Sub Sahara Africa). Despite efforts to introduce formal crop insurance at a subsidized rate by many governments, including in India, insurance penetration rates remain low in less developed countries (LDCs). One reason for this is continued reliance on informal credit as a risk-coping mechanism in poorer regions in times of agricultural distress. This prompts the question as to why farmers rely on these methods when apparently less expensive formal crop insurance is available. To answer, we investigate the link between the conditionalities of informal credit and the decision to adopt crop insurance together with other factors such as farmers’ socioeconomic class, education, and attitude to risk. Thereby, we derive policy implications for enhancing insurance penetration among poor farmers. A theoretical model is first constructed to provide a motivation for the empirical relation examined. An analysis of the decision to adopt crop insurance and the extent of insurance coverage availed is undertaken using a two-part fractional logistic regression with crop insurance data from India (provided by the National Sample Survey Office). The results demonstrate that informal interest rates are significantly and positively correlated with the probability of taking up crop insurance, and disparities between economic and social classes in the adoption of crop insurance exist. Although the ills of usurious informal interest rates are often highlighted (Bhaduri, 1997), this indirect positive effect on crop insurance penetration has not been established heretofore. The exercise also looks at the possibility of loss expected by a farmer and their insurance adoption behaviour by relating it to Prospect Theory (Kanheman, Tversky, 1979).

Understanding the effect of cover crop use on prevented planting losses

AbstractCover cropping has the potential to improve resilience of agriculture to climate‐change‐induced extreme weather events. However, rigorous quantitative evidence on the resilience effect of cover crops is still lacking. Using a novel data set that combines satellite‐based cover crop information and county‐level crop insurance data, we examine the impact of planting cover crops on prevented‐planting‐related losses that are typically caused by heavy rainfall events. The US federal crop insurance program offers “prevented planting” coverage, which pays indemnities if insured growers are unable to plant their crop due to adverse weather. Linear fixed effects models, instrument‐based estimation methods, long‐difference models, and a number of other robustness checks are utilized in the empirical analysis to achieve the study objective. Our findings suggest that counties with higher cover crop adoption rates tend to have lower levels of crop insurance losses due to prevented planting. The resulting reduction in prevented planting risk also becomes larger with longer term, multiyear cover crop use. These results support the notion that cover crops improve soil conditions such that the likelihood and magnitude of prevented planting losses decrease. We posit that the ability of cover crops to handle excess moisture (i.e., through better water absorption and improved water infiltration in the soil) is the main factor in its ability to reduce prevented planting losses in the US Midwest.

Holt-Winters Forecast on Seasonal Time Series Crop Insurance Data with Structural Outliers (2000-2018) Using Ms Excel

Time series data on the number of farmers enrolled in different crop insurance schemes such as NAIS, MNAIS and PMFBY in the state of Tamil Nadu, India was analysed for its seasonality, outliers and forecast. The data was found to be seasonal and exponentially increasing. Seasonality is innate as the crop insurance itself was registered separately for two seasons (Kharif and Rabi). Multiplicative Holt-Winters Model should be applied in order to do short range forecast for an exponentially increasing dataset. The model was run in Ms-Excel in order to understand the basics of times series forecasting. Minimum MSE value was the criteria used to find the better fitting smoothing values. The residual of the model was examined for the fit of the model. Residual mean value was close to zero. Residuals are tested for autocorrelation with Durbin-Watson test and Runs test. Histogram of residuals implies a normal distribution. Presence of outliers are detected using 3IQR method and the identified outliers are part of the structure of data and need not be removed. However, alternate models of Holt-Winters itself which are robust to work with outliers are reviewed and RHW model of Gelpers et al. [1] was suggested.

Cloud Seeding and Crops Yields: Evaluation of the North Dakota Cloud Modification Project

AbstractThe North Dakota Cloud Modification Project was established in 1951 to reduce severe hail damage and increase precipitation in specific counties in North Dakota. Every year, participating counties receive cloud seeding treatment during the months of June, July, and August. Although some atmospheric studies have examined the efficacy of the treatment, few have used statistical procedures to determine how the program affected crop yields and crop losses. We use the panel nature of historical cloud seeding participation and crop data to estimate a two-way fixed effects regression with county-specific time trends to examine the effect of cloud seeding on wheat and barley yields. In addition, we use federal crop insurance data to estimate the effect of cloud seeding on losses for those same crops. Our evaluation indicates that the cloud seeding program had significant positive effects on crop yields and improved loss ratios.

Estimation of wildlife damage from federal crop insurance data.

Wildlife damage to crops is a persistent and costly problem for many farmers in the USA. Most existing estimates of crop damage have relied on direct assessment methods such as field studies conducted by trained biologists or surveys distributed to farmers. In this paper, we describe a new method of estimating wildlife damage that exploits federal crop insurance data. We focused our study on four crops: corn, soybean, wheat, and cotton, chosen because of their economic importance and their vulnerability to wildlife damage. We determined crop-raiding hot spots across the USA over the 2015-2019 period and identified the eastern and southern regions of the USA as being the most susceptible to wildlife damage. We estimated lower bounds for dollar and percent losses attributable to wildlife to these four crops. The combined loss across four crops was estimated at $592.6 million. The highest total estimated losses to wildlife were incurred by soybeans ($323.9 million) and corn ($194.0 million) and the highest percentage losses were estimated for soybeans (0.87%) and cotton (0.72%). We believe the proposed method is a reliable way to evaluate geographic and temporal heterogeneity in damages for the coming years. Accurate information on damages benefits various management agencies by allowing them to allocate management resources to crops and regions where the problem is relatively severe. A better understanding of damage heterogeneity can also help guide research and development of new management techniques.

Calibration of agricultural risk programming models using positive mathematical programming

Mathematical programming models of farmers’ cropping decisions must first be calibrated before they can be used to examine agricultural producer responses to policy changes. In this paper, we compare three calibration approaches for disentangling the risk parameter from the parameters of the cost function: one assumes a logarithmic utility function, while the others employ an exponential utility function. Historical crop insurance data for southern Alberta, Canada, are used to assess the calibration performance of the three approaches, and sensitivity analysis is implemented to test whether the changes in the optimal land allocation caused by the changes in the values of the parameters are practically reasonable. Only one of the three models is of practical use for policy analysis because it can recover the true values of the parameters and the results of sensitivity analysis are reasonable.

Are Actuarial Crop Insurance Rates Fair?: An Analysis Using a Penalized BivariateB-Spline Method

SummaryIn this paper, we investigate whether the yield insurance premium rates given by the US Department of Agriculture's Risk Management Agency are actuarially fair by comparing the conditional yield density inferred from premium data with the conditional yield density inferred from yield data. A procedure is developed to estimate the conditional yield density by using premium data through partial derivatives of the premium rate function, as fitted by penalized bivariate tensor product B-splines. We study the asymptotic properties of partial derivatives of a penalized bivariate tensor product B-spline estimator and provide variance estimates. The conditional yield density inferred from premium data and its variance estimator are evaluated through simulation studies. The procedure is also applied to a crop insurance data set from the state of Iowa to examine the actuarial fairness of the premium rates. On average, premium rates are close to our estimates and this is true for each coverage level. However, premiums for low productivity land are generally too low whereas those for high productivity land are generally too high. Even after subsidies, premiums for the more productive land are generally substantially higher than are our estimates of the corresponding actuarially fair rates.

Excessive rainfall leads to maize yield loss of a comparable magnitude to extreme drought in the United States.

Increasing drought and extreme rainfall are major threats to maize production in the United States. However, compared to drought impact, the impact of excessive rainfall on crop yield remains unresolved. Here, we present observational evidence from crop yield and insurance data that excessive rainfall can reduce maize yield up to −34% (−17 ± 3% on average) in the United States relative to the expected yield from the long‐term trend, comparable to the up to −37% loss by extreme drought (−32 ± 2% on average) from 1981 to 2016. Drought consistently decreases maize yield due to water deficiency and concurrent heat, with greater yield loss for rainfed maize in wetter areas. Excessive rainfall can have either negative or positive impact on crop yield, and its sign varies regionally. Excessive rainfall decreases maize yield significantly in cooler areas in conjunction with poorly drained soils, and such yield loss gets exacerbated under the condition of high preseason soil water storage. Current process‐based crop models cannot capture the yield loss from excessive rainfall and overestimate yield under wet conditions. Our results highlight the need for improved understanding and modeling of the excessive rainfall impact on crop yield.

Missouri Spring Freeze Event and Crop Loss Analysis for the Years 1982-2015

A spring freeze is an unexpected freeze event occurring in late spring that can result in severe domestic crop loss. These events coincide with seasonal weather conditions that promote early crop development. As a consequence, early growth is most susceptible to freeze damage. Spring temperature fluctuations pose significant threat to crop yield and growth, when compared to onsistently cold springtime temperatures. In this study, a thirty-four-year span (1982 to 2015) of historical climate, agricultural and crop insurance data for Missouri was analyzed. Risk analysis was performed for each Missouri county to determine statistical relationships between crop loss and springtime warm-cold temperature fluctuations. Statewide, several regions were identified as having an increased susceptibility to freeze events, with Lafayette County emerging as the state’s leading county in agricultural losses due to freeze events. The window for peak freeze damage for Lafayette County was identified as the last week of March through the first week of April. The years 1985, 1996 and 2007 stood apart in the dataset as they correspond to the highest crop damages on record. Each of these years also corresponds to a doubling of severity in the reported crop losses.

Modelling the Sustainability of the Canadian Crop Insurance Program: A Reserve Fund Process Under a Public–Private Partnership Model

The objective of this paper is to model the multilayer risk-sharing structure of the crop insurance system in Canada and, in doing so, is the first paper to provide a quantitative assessment of the sustainability of the program. A reserve fund process is developed using actual crop insurance data from Manitoba to model the surplus of the crop insurance company. Based on simulation, a number of actuarial risk measures are calculated, including mean surplus, expected shortfall, value at risk and conditional tail expectation. The results point to a potential fault with the design of the current risk-sharing structure, where the federal–provincial reinsurance fund premium rate is designed to increase as the mean surplus of the insurer becomes less. This potentially penalises an insurance company that purchases reinsurance, even though reinsurance can reduce the risk exposure to the federal government by more than 58 per cent in some cases.

A Spatial Cross-Sectional Credibility Model with Dependence Among Risks

A Bühlmann-Straub type credibility model with dependence structure among risk parameters and conditional spatial cross-sectional dependence is studied. Predictors of future losses for the model under both types of dependence are derived by minimizing the expected quadratic loss function, and nonparametric estimators of structural parameters are considered in the spatial statistics context. Predictions and estimations made for the proposed model are examined and compared to other models in an application with crop insurance data and in a simulation study.

Geography of crop yield skewness

AbstractAnalysis of crop yield distributions provides insights into better understanding how natural resources shape agricultural productivity. This study seeks to provide a rigorous theoretical and empirical understanding of the effects of exogenous geographic and climatic factors on the first three moments of crop yields with focus on the third moment. We hypothesize that exogenous factors having beneficial effects on crop production should make crop yield distributions less positively or more negatively skewed. We employ a large crop insurance data set for corn, soybean, and wheat to find general support for our natural‐resources‐determines‐skewness hypothesis. The proposed statistical method optimally uses correlations between the first three moments. It significantly improves estimation performance over existing methods, including the linear moment model which has been commonly applied in the literature.

Improving the spatial resolution and ecostratification of crop yield estimates in Canada

Du, Y., Huffman, T., Daneshfar, B., Green, M., Feng, F., Liu, J., Liu, T. and Liu, H. 2015. Improving the spatial resolution and ecostratification of crop yield estimates in Canada. Can. J. Soil Sci. 95: 287–297. Canada's terrestrial ecostratification framework provides nested spatial units for organizing national data related to soils, landforms and land use. In the agricultural domain, the lack of national, uniform crop yield data on the ecostratification framework severely hinders our ability to evaluate the biophysical data with respect to economic and climatic conditions. We developed a national crop yield database at the regional (ecodistrict) level by aggregating individual records of an existing but very broad-level sample-derived yield database according to the ecostratification hierarchy. Issues related to the different sampling frameworks and the need for confidentiality of individual records were resolved in order to generate an ecostratified crop yield dataset at a reasonably detailed spatial scale. Sixty crops were first statistically arranged into 37 agronomically similar crop groups in order to increase class size, and these crop groups were aggregated into increasingly large spatial units until confidentiality was assured. The methodology maintained data quality and confidentiality while producing crop yield estimates at the ecodistrict level. Comparison to independent crop insurance data confirmed that the resulting crop yield data are valid where estimates were derived from data released at the level of an ecodistrict or an ecoregion, but not at the ecoprovince level. Our crop yield estimates offer a reasonably high level of spatial precision while remaining within standard confidentiality constraints.

Rates of yield gain of hard red spring wheat in western Canada

Thomas, J. B. and Graf, R. J. 2014. Rates of yield gain of hard red spring wheat in western Canada. Can. J. Plant Sci. 94: 1–13. The Manitoba and Saskatchewan Seed Guides dating back to 1972 represent an unused source of yield comparisons to re-examine current progress in western Canadian spring wheat cultivar yields. Adjusting for the shift in check cultivars over time showed that the yield rise due to new cultivars could be divided into two periods. Prior to the early 1990s, yields rose at a rate of about 0.33% per year; these low early rates agree with other published estimates from this period and were possibly influenced by a strong emphasis on replicating the quality of previous cultivars. From the early 1990s to 2013, yields rose by about 0.7% per year; this doubling of the earlier rate was significant based on the non-overlap of confidence intervals of comparable slopes. To compare rates published in the literature with these new rates, all slopes were adjusted to a common benchmark where mean yield = 100%. Following these adjustments, current rates in western Canada (about 0.67% per year) were comparable with a world average estimated to be about 0.62% per year. Variation in performance among Canada Western Red Spring cultivars based on the Seed Guides was significantly correlated with their on-farm yields based on Manitoba Management Plus Program (MMPP) crop insurance data (r = 0.81, n = 42). Beginning in 1991, on-farm yields rose by an average of about 1.4% per year both in Manitoba (Manitoba Management Plus Program data) and across the entire western wheat area (Statistics Canada data). This compares favorably with a world-wide rate of yield increase for wheat since 1991 of 1.16% per year. Although western Canadian on-farm yield gains were attributed to a combination of new cultivars and upgraded agronomy, the two influences were not separable in the Manitoba crop insurance data set. Opinions published in the farming press that rates of yield gain among western Canadian wheat cultivars are comparatively low were not supported by the evidence presented here.

Analysis of hail damages and temperature series for peninsular Spain

Abstract. Hail is a serious concern for agriculture on the Iberian Peninsula. Hailstorms affect crop yield and/or quality to a degree that depends on the crop species and the phenological time. In Europe, Spain is one of the countries that experience relatively high agricultural losses related to hailstorms. It is of high interest to study models that can support calculations of the probabilities of economic losses due to hail damage and of the tendency over time for such losses. Some studies developed in France and the Netherdlands show that the summer mean temperature was highly correlated with a yearly hail severity index developed from hail-related parameters obtained for insurance purposes. Meanwhile, other studies in the USA point out that a highly significant correlation between both is not possible to find due to high climatic variability. The aim of this work is to test the correlation between average minimum temperatures and hail damage intensity over the Spanish Iberian Peninsula. With this purpose, correlation analyses on both variables were performed for the 47 Spanish provinces (as individuals and single set) and for all crops and four individual crops: grapes, wheat, barley and winter grains. Suitable crop insurance data are available from 1981 until 2007 and based on this period, temperature data were obtained. This study does not confirm the results previously obtained for France and the Netherlands that relate observed hail damage to the average minimum temperature. The reason for this difference and the nature of the cases observed are discussed.

Heterogeneity and Distributional Form of Farm‐Level Yields

Representing farm‐level crop yield heterogeneity and distributional form is critical for risk and crop insurance research. Most studies have used county data, understating both systematic and random variation. Comparison of systematic versus random intra‐county variation is lacking. Few studies compare the various distributional forms that have been proposed. This study utilizes the extensive potential of government farm‐level crop insurance data. Results show that systematic intra‐county variation is surprisingly strong. A newly applied reverse lognormal distribution is preferred when county‐wide variation is removed, but the normal distribution fits surprisingly well in the crop insurance relevant percentiles when county‐wide variation is not removed.

Hail frequency, distribution and intensity in Northern Greece

Hail numerical and physical parameters are examined for an assessment of frequency, spatial and temporal distribution and intensity of hailfalls in Central Macedonia periphery, Northern Greece. Data from weather stations of the Hellenic National Meteorological Service (HNMS) indicated a mean yearly point frequency varying between 1 and 2 hail days for Central Macedonia. Crop insurance data from the Hellenic National Agricultural Insurance Organization (ELGA) showed a mean regional frequency of 22.3 hail days for the total cultivated area of Central Macedonia, about 6,500 km 2, for the warm season, from April to September. Hailpad data representing almost one third of the total cultivated area of Central Macedonia, indicated a regional average of 8 hail days and 45 hailpads recording hail for the warm season. Spatial analysis over the hailpad area revealed a large variability of hail occurrence, with local maxima at the higher altitude and close to mountain barrier areas and a decreasing frequency over areas closer to the sea. The size distribution of a sample of 22,000 hailstones measured by the hailpads, showed a great majority, of 85.7%, having diameters up to 11 mm. An inter-comparison of hail impact energies according to crop damaging potential indicated Central Macedonia hailfalls as the less intense compared to those of areas of Northern America. Application of the revised TORRO hail intensity scale extending to classes from H0 to H10, permitted a classification of Central Macedonia hailfalls appropriate for comparisons and correspondence between areas and countries about the severity and damaging of hailfalls.

Determinants of Anomalous Prevented Planting Claims: Theory and Evidence from Crop Insurance

This study examines the factors that determine the likelihood of submitting a potentially fraudulent prevented planting claim. A theoretical model is developed and the theoretical predictions are empirically verified by utilizing a binary choice model and crop insurance data from the southern United States. The empirical results show that insured producers with higher prevented planting coverage, lower dollar value of expected yield, and a history of submitting prevented planting claims are more likely to submit an anomalous prevented planting claim. The empirical model also suggests revenue insurance plans may be more vulnerable to prevented planting fraud than the traditional yield-based insurance plan. Results of this study can be valuable to compliance offices in their efforts to find “indicators” of fraudulent behavior in crop insurance, especially with regard to prevented planting.