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Modeling of soybean yield using symmetric, asymmetric and bimodal distributions: implications for crop insurance

Author

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  • Gislaine V. Duarte
  • Altemir Braga
  • Daniel L. Miquelluti
  • Vitor A. Ozaki

Abstract

Over the years, many papers used parametric distributions to model crop yields, such as: normal (N), Beta, Log-normal and the Skew-normal (SN). These models are well-defined, mathematically and also computationally, but its do not incorporate bimodality. Therefore, it is necessary to study distributions which are more flexible in modeling, since most of crop yield data in Brazil presents evidence of asymmetry or bimodality. Thus, the aim of this study was to model and forecast soybean yields for municipalities in the State of Paran, in the period from 1980 to 2014, using the Odd log normal logistic (OLLN) distribution for the bimodal data and the Beta, SN and Skew-t distributions for the symmetrical and asymmetrical series. The OLLN model was the one which best fit the data. The results were discussed in the context of crop insurance pricing.

Suggested Citation

  • Gislaine V. Duarte & Altemir Braga & Daniel L. Miquelluti & Vitor A. Ozaki, 2018. "Modeling of soybean yield using symmetric, asymmetric and bimodal distributions: implications for crop insurance," Journal of Applied Statistics, Taylor & Francis Journals, vol. 45(11), pages 1920-1937, August.
    • Handle: RePEc:taf:japsta:v:45:y:2018:i:11:p:1920-1937
    DOI: 10.1080/02664763.2017.1406902
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    Cited by:

    1. Lima Miquelluti, Daniel & Ozaki, Vitor & Miquelluti, David J., 2020. "An application of geographically weighted quantile LASSO to weather index insurance design," 2020 Annual Meeting, July 26-28, Kansas City, Missouri 304288, Agricultural and Applied Economics Association.
    2. Addey, Kwame Asiam & Shaik, Saleem & Nganje, William, 2022. "DEVELOPMENT OF FARM MODEL FOR ND and NGP Prediction of Corn and Soybean Yields in the Presence of Random Shocks," Agribusiness & Applied Economics Report 320066, North Dakota State University, Department of Agribusiness and Applied Economics.

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