Crop-Yield Distributions Revisited
TLDR
This paper revisited the issue of crop-yield distributions using improved model specifications, estimation, and testing procedures that address the concerns raised in recent literature, which could have invalidated previous findings of yield nonnormality.Abstract:
This article revisits the issue of crop-yield distributions using improved model specifications, estimation, and testing procedures that address the concerns raised in recent literature, which could have invalidated previous findings of yield nonnormality. It concludes that some aggregate and farm-level yield distributions are nonnormal, kurtotic, and right or left skewed, depending on the circumstances. The advantages of utilizing nonnormal versus normal probability distribution function models, and the consequences of incorrectly assuming crop-yield normality are explored.read more
Citations
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Journal ArticleDOI
Probabilistic estimates of drought impacts on agricultural production
TL;DR: This paper developed a multivariate probabilistic model that uses projected climatic conditions (e.g., precipitation amount or soil moisture) throughout a growing season to estimate the probability distribution of crop yields.
Journal ArticleDOI
Heterogeneity and Distributional Form of Farm-Level Yields
Roger Claassen,Richard E. Just +1 more
TL;DR: In this article, the authors used the extensive potential of government farm-level crop insurance data and evaluated a broader set of parametric distributional possibilities than previously, finding that the systematic intra-county variation is surprisingly strong.
Journal ArticleDOI
Crop Yield Distributions: A Reconciliation of Previous Research and Statistical Tests for Normality
TL;DR: This paper revisited the large but inconclusive body of research on crop yield distributions using competing techniques across 3,852 crop/county combinations and found that corn and soybeans yields are negatively skewed while they tend to become more normal as one moves away from the Corn Belt.
Journal ArticleDOI
More than Mean Effects: Modeling the Effect of Climate on the Higher Order Moments of Crop Yields
TL;DR: In this article, the authors proposed the use of moment functions and maximum entropy techniques as a flexible approach for estimating conditional crop yield distributions, which is easily estimated using standard econometric estimators.
Journal ArticleDOI
Relaxing Heteroscedasticity Assumptions in Area-Yield Crop Insurance Rating
TL;DR: In this paper, the effect of differing heteroscedasticity assumptions on derived premium rates of area-yield crop insurance was examined using both in-sample and out-of-sample measures.
References
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Journal ArticleDOI
Probability Distributions of Field Crop Yields
TL;DR: In this paper, statistical analyses of several experimental series of field crop yields are presented, and the Pearson system of probability density functions is applied to the data, which are then applied to obtain the type I (generalized Beta) skewed function of limited range.
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Nonparametric Estimation of Crop Insurance Rates Revisited
TL;DR: In this article, a nonparametric kernel density estimator was used to estimate conditional yield densities and derive the insurance rates for the U.S. crop insurance program, which is one of the only government-subsidized, income stabilizing mechanisms available to agricultural producers.
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U.S. Soybean Yields: Estimation and Forecasting with Nonsymmetric Disturbances
TL;DR: In this article, the authors proposed that national average soybean yields are skewed with a relatively high chance of low yields and that revised forecasts which account for skewed yields are positioned higher than forecasts based on the illusion of a symmetric distribution.
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Estimation and Use of a Multivariate Parametric Model for Simulating Heteroskedastic, Correlated, Nonnormal Random Variables: The Case of Corn Belt Corn, Soybean, and Wheat Yields
TL;DR: The authors developed a multivariate, non-normal density function that can accurately and separately account for skewness, kurtosis, heteroskedasticity, and correlation among the random variables of interest.
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Two Practical Procedures for Estimating Multivariate Nonnormal Probability Density Functions
TL;DR: In this article, two procedures for empirically estimating non-normal joint probability density functions (pdf's) that are operational with small samples are presented; one procedure empirically estimates marginal distributions, and the second approach exploits the identity that a joint distribution is the product of a conditional pdf and a marginal pdf.
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