Prioritization of the effectiveness rate of various climatic variables on the annual yield of rain-fed winter wheat using different statistical models

Abstract

The annual yield of different plant species is affected by various parameters such as soil and water properties, climatic variables, etc. Due to the importance of the role of climatic variables on the yield of rain-fed winter wheat (as a strategic and effective plant species in the food security of the people of Iran), in this research the influence of climatic variables including the mean annual maximum temperature (MAT), the mean annual minimum temperature (MIT), the mean annual sunshine (SS), the mean annual relative humidity (HU), the mean annual wind speed (W) and the annual rainfall (R) on the annual yield of rain-fed winter wheat (AYWW) were evaluated based on the records of 10 stations from 1967 to 2016 scattered in Iran. To evaluate the impact of climate variables on the AYWW, the backward time series regression (BTSR), the backward logistic generalized estimating equation (BLGEE) and the backward generalized estimating equation (BGEE) statistical methods were used. All the statistical methods showed that W and SS variables were respectively the most and the least effective variables on the AYWW. Effective variables on AYWW in the BTSR were found to be the W, MIT and R (in 5, 4 and 1 stations out of 10), Effective variables on AYWW in the BLGEE were found to be the W, MIT, R and MAT (in 5, 3, 1 and 1 stations out of 10) and effective variables on AYWW in the BGEE were found to be the W, MIT, R and HU (in 5, 3, 1 and 1 stations out of 10).