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Journal ArticleDOI

Crop-Yield Distributions Revisited

01 Feb 2003-American Journal of Agricultural Economics (Oxford University Press)-Vol. 85, Iss: 1, pp 108-120
TL;DR: 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.

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Citations
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Journal ArticleDOI
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.
Abstract: Increases in the severity and frequency of drought in a warming climate may negatively impact agricultural production and food security. Unlike previous studies that have estimated agricultural impacts of climate condition using single-crop yield distributions, we develop 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. We demonstrate the model by an analysis of the historical period 1980–2012, including the Millennium Drought in Australia (2001–2009). We find that precipitation and soil moisture deficit in dry growing seasons reduced the average annual yield of the five largest crops in Australia (wheat, broad beans, canola, lupine, and barley) by 25–45% relative to the wet growing seasons. Our model can thus produce region- and crop-specific agricultural sensitivities to climate conditions and variability. Probabilistic estimates of yield may help decision-makers in government and business to quantitatively assess the vulnerability of agriculture to climate variations. We develop a multivariate probabilistic model that uses precipitation to estimate the probability distribution of crop yields. The proposed model shows how the probability distribution of crop yield changes in response to droughts. During Australia's Millennium Drought precipitation and soil moisture deficit reduced the average annual yield of the five largest crops.

127 citations

Journal ArticleDOI
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.
Abstract: Representing farm-level crop yield heterogeneity and distributional form is critical for risk and crop insuranceresearch.Moststudieshaveusedcountydata,understatingbothsystematicandrandomvariation. 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. Representing and accounting for both spatial and temporal heterogeneity is a major problem in agricultural economics and policy analysis due to the fact that most data are aggregated to at least the county level (e.g., Gardner and Kramer 1986; Just and Pope 1999). Both systematic and random components of crop yields are major factors in intra-county farm-level heterogeneity that are critical for modeling risk, producer behavior, and crop insurance participation. Most studies have either used aggregate (at least county-level) data or relied on relatively few farm-level observations. The former makes results primarily illustrative, while the latter limits statistical significance. This article characterizes intra-county crop yield heterogeneity both spatially and temporally with the most extensive dataset utilized to date and evaluates a broader set of parametric distributional possibilities than previously.

81 citations

Journal ArticleDOI
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.
Abstract: This study revisits the large but inconclusive body of research on crop yield distributions. Using competing techniques across 3,852 crop/county combinations we can reconcile some inconsistencies in previous studies. We examine linear, polynomial, and ARIMA trend models. Normality tests are undertaken, with an implementable R-test and multivariate testing to account for spatial correlation. Empirical results show limited support for stochastic trends in yields. Results also show that normality rejection rates depend on the trend specification. Corn Belt corn and soybeans yields are negatively skewed while they tend to become more normal as one moves away from the Corn Belt.

76 citations

Journal ArticleDOI
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.
Abstract: This article proposes the use of moment functions and maximum entropy techniques as a flexible approach for estimating conditional crop yield distributions. We present a moment‐based model that extends previous approaches, and is easily estimated using standard econometric estimators. Predicted moments under alternative regimes are used as constraints in a maximum entropy framework to analyze the distributional impacts of switching regimes. An empirical application for Arkansas, Mississippi, and Texas upland cotton demonstrates how climate and irrigation affect the shape of the yield distribution, and allows us to illustrate several advantages of our moment‐based maximum entropy approach.

75 citations

Journal ArticleDOI
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.
Abstract: This article focuses on the effect of differing heteroscedasticity assumptions on derived premium rates of area-yield crop insurance. Tests of the proportional and absolute heteroscedasticity assumptions are conducted using both in-sample and out-of-sample measures. Our results suggest that arbitrarily imposing a specific form of heteroscedasticity or homoscedasticity in insurance rate calculations limits actuarial soundness. Our results have practical implications for the federal crop insurance programs, as we reject the traditional rating assumptions for many cotton regions and lower-yielding/higher-risk corn and soybean counties but not in the heart of the Cornbelt. Copyright 2011, Oxford University Press.

72 citations

References
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Book
01 Jan 1994
TL;DR: Continuous Distributions (General) Normal Distributions Lognormal Distributions Inverse Gaussian (Wald) Distributions Cauchy Distribution Gamma Distributions Chi-Square Distributions Including Chi and Rayleigh Exponential Distributions Pareto Distributions Weibull Distributions Abbreviations Indexes
Abstract: Continuous Distributions (General) Normal Distributions Lognormal Distributions Inverse Gaussian (Wald) Distributions Cauchy Distribution Gamma Distributions Chi-Square Distributions Including Chi and Rayleigh Exponential Distributions Pareto Distributions Weibull Distributions Abbreviations Indexes

7,270 citations

Book
01 Jan 1963
TL;DR: In this article, a tabular summary of parametric families of distributions is presented, along with a parametric point estimation method and a nonparametric interval estimation method for point estimation.
Abstract: 1 probability 2 Random variables, distribution functions, and expectation 3 Special parametric families of univariate distributions 4 Joint and conditional distributions, stochastic independence, more expectation 5 Distributions of functions of random variables 6 Sampling and sampling distributions 7 Parametric point estimation 8 Parametric interval estimation 9 Tests of hypotheses 10 Linear models 11 Nonparametric method Appendix A Mathematical Addendum Appendix B tabular summary of parametric families of distributions Appendix C References and related reading Appendix D Tables

4,571 citations

Journal ArticleDOI
01 Jul 1966
TL;DR: This chapter discusses distributions in the context of Bayesian Statistics, which aims to clarify the role of randomness in the construction of statistical inference.
Abstract: 1. Probability and Distributions. 2. Multivariate Distributions. 3. Some Special Distributions. 4. Some Elementary Statistical Inferences 5. Consistency and Limiting Distributions 6. Maximum Likelihood Methods. 7. Sufficiency. 8. Optimal Tests of Hypotheses. 9. Inferences about Normal Models. 10. Nonparametric and Robust Statistics. 11. Bayesian Statistics. Appendix A. Mathematical Comments Appendix B. R-Functions. Appendix C. Tables of Distributions Appendix D. List of Common Distributions Appendix E. References Appendix F. Answers to Selected Exercises Index

299 citations

Journal ArticleDOI
TL;DR: In this paper, the evidence for nonnormality of crop yields is reassessed and three methodological problems are identified in typical yield distribution analyses: misspecification of the nonrandom components of yield distributions, missreporting of statistical significance, and use of aggregate timeseries (ATS) data to represent farm-level yield distributions.
Abstract: The evidence for nonnormality of crop yields is reassessed. Three methodological problems are identified in typical yield distribution analyses: (i) misspecification of the nonrandom components of yield distributions, (ii) missreporting of statistical significance, and (iii) use of aggregate timeseries (ATS) data to represent farm-level yield distributions. One or more of these problems infect virtually all evidence against normality to date. The positive contribution of the article is a set of principles that must be followed in any valid investigation of yield normality.

294 citations

Journal ArticleDOI
TL;DR: In this article, nonparametric density estimation procedures were used to evaluate county-level crop yield distributions and their implications for rating area-yield crop insurance contracts were discussed, and the procedures developed are used to measure yield risk and calculate insurance premium rates for wheat and barley in the 1995-96 Group Risk Program.
Abstract: We use nonparametric density estimation procedures to evaluate county-level crop yield distributions. Implications for rating area-yield crop insurance contracts are discussed. The procedures developed are used to measure yield risk and calculate insurance premium rates for wheat and barley in the 1995-96 Group Risk Program.

280 citations