Spatial Relations Between Climatic Variables and Wheat Yield in Iran

Abstract

In this study relation between average minimum and maximum of temperature and rainfall as independent variables of 223 synoptic meteorological stations in Iran and wheat yield as dependent variable in 223 regions during the statistical period of 2017-2017 in the monthly time scale of the growing season and the total growth period was investigated. In data analysis, spatial statistics analysis methods in Arc/GIS 10.4.1 software were used, using Moran autocorrelation test, and geographic weighted regression their spatial relationships were tested. The result of Moran index showed that spatial distribution of wheat yield follows cluster pattern. Analysis Geographic weighted regression has showed rainfall has remarkable effect on wheat yield. In addition, analysis showed rainfall has positive effects on wheat yield especially in dry and warm regions of Central, and Southeastern. The results showed the positive effect of temperature increase on wheat yield, which gradually decreased toward the southern parts in proportion to the decrease in altitude and increase in temperature. So that the effect of increasing daily temperatures (maximum) in cold mountainous areas is positive, while in east, central and southern parts of Iran negative effects of temperature over wheat yield were seen due to heat stress. Therefore, in hot and semi-warm regions of the country, increasing the temperature along with increasing rainfall can have a positive effect on wheat yield. In these areas, it is better to cultivate wheat in the highlands or in cold weather and used irrigation systems to reduce drought stress at critical stages of wheat growth.

Keywords

• spatial statistics
• climate changes
• geographic weighted regression
• iran
• wheat