Seasonal Autoregressive Models for Estimating the Probability of Frost in Rafsanjan

Subject Areas : Microbiology

A. Hosseini ¹ , M.S. FallahNezhad ² , Y. ZareMehrjardi ³ , R. Hosseini ⁴

1 - Department of industrial engineering, Yazd University, Yazd, Iran
2 - Department of industrial engineering, Yazd University, Yazd, Iran
3 - Department of industrial engineering, Yazd University, Yazd, Iran
4 - Division of Biostatistics, University of Southern California, USA

Received: 2015-10-08 Accepted : 2015-10-08 Published : 2012-12-01

Keywords: Pistachio, Frost, Weather derivative, Minimum temperature, Time-varying autoregressive coefficients,

Abstract :

This work develops a statistical model to assess the frost risk in Rafsanjan, one of the largest pistachio production regions in the world. These models can be used to estimate the probability that a frost happens in a given time-period during the year; a frost happens after 10 warm days in the growing season. These probability estimates then can be used for: (1) assessing the agroclimate risk of investing in this industry; (2) pricing of weather derivatives. Autoregressive models with time-varying coefficients and different lags are compared using AIC/BIC/AICc and cross validation criterions. The optimal model is an AR (1) with both intercept and the “autoregressive coefficients” vary with time. The long-term trends are also accounted for and estimated from data. The optimal models are then used to simulate future weather from which the probabilities of appropriate hazard events are estimated.