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

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.

KEYWORDS:

• Asymmetry
• bimodality distribution
• crop insurance
• heavier tails
• risk management

Disclosure statement

No potential conflict of interest was reported by the authors.

ORCID

Gislaine V. Duarte  http://orcid.org/0000-0003-4372-7499.

Daniel L. Miquelluti  http://orcid.org/0000-0002-6335-3618.

Notes

1 According to Quiggin et al. [42], ‘Adverse selection means that people who are more likely to present claims will be more willing to insure at a given rate’.

2 Moral hazard refers to the fact that the insured may take certain actions which the insurer is unable to monitor, leading to an increase in production risk. For example, after buying insurance the producer may use less fertilizers or pesticides, causing the yield to decline [42].

3 According to Ozaki and Silva [38] these rates are based on the relationship between the average loss of the insured and this method does not take into account robust statistical analysis.

4 We present several coverage levels in order to compare with the equivalent coverage level in our model

Additional information

Funding

We thank the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES) for financial support.