Determining Spatial Dependency of Systematic Risk of Dryland Wheat Yield in Iran: Application of Spatial Autoregressive Models

Tahami Pour, Morteza; Salami, Habibollah; Yazdani, Saeed; Chizari, Amir Hossein

doi:10.22059/ijaedr.2013.50223

Determining Spatial Dependency of Systematic Risk of Dryland Wheat Yield in Iran: Application of Spatial Autoregressive Models

Document Type : Research Paper

Authors

Morteza Tahami Pour ¹

Habibollah Salami ²

Saeed Yazdani ²

Amir Hossein Chizari ³

¹ PhD Student, Agricultural Economics, Department of Economics and Agricultural Development, University of Tehran, Tehran, Iran

² Professor, Agricultural Economics, Department of Economics and Agricultural Development, University of Tehran, Tehran, Iran

³ Assistance Professor, Agricultural Economics, Department of Economics and Agricultural Development, University of Tehran, Tehran, Iran

10.22059/ijaedr.2013.50223

Abstract

The purpose of this study is to determine spatial dependency pattern of systematic risk of dry land wheat production in Iran, using spatial autoregressive models. To this end, spatial weighted contiguity matrix was constructed based on the Delaunay Triangularization method, and correlation coefficient among these neighbors were estimated using spatial autoregressive models. In addition, the role of precipitation and temperature variables in explaining yield variations in wheat production cities was determined. Results indicated that: First, yield risk of dry land wheat production has a systematic nature and covers a considerable numbers of wheat producing cities. Second, the intensity of spatial correlation varies among neighbor’s cities. Precipitation and temperature variations play important role in explaining these differences such that the more variation in these variables is associated with the more variations in neighbor’s wheat yields. This is a useful piece of information that can be used in developing appropriate insurance portfolio of agricultural products to reduce risk of the insurers in Iran.

Keywords

dry land wheat

spatial autoregressive models

systematic risk