Wheat is the main focus of the economy of Kurdistan province in which the annual fluctuation of wheat yield is 4/11 times as affected by the climatic elements of the site. This study investigated the role of agro-climatic variables and indices on rainfed wheat yield in Kurdistan province. The data of planting area, amount of production, damages and yield of wheat of 31-year in 10 regions of Kurdistan along with the hourly, daily, decade, monthly, seasonal and yearly levels data of 22 synoptic stations were collected. The correlation between wheat yield and 128 independent variables was calculated. The effect of variables on yield evaluated by multivariate regression. The spatial analysis of variables was performed and the spatial model of wheat yield was introduced for province and regions. The results showed that climatic elements in various regions are different, in a 99% confidence. Most of the independent variables alone have a significant effect on wheat yield, but in the stepwise model, 7 variables such as: the number of rainy days of the year, the sum of the degree hours (of temperature less than -11 ° C) in germination and tilling stage, annual precipitation and the precipitation of November are determinants of the yield. Yield and effective independent variables have significant spatial differences even in a cluster climate type. The highest and lowest coefficient of variation of wheat yield is related to Bijar and Kamyaran areas, respectively. Kamyaran and Sanandaj regions have the highest and lowest yield, respectively. Bijar is the highest risk region of the province for wheat production.


The results of this study showed that with a 99 percent confidence, climatic elements (variables) vary in different regions. Most of the independent variables have a significant effect on wheat yield in simple linear regression, but in Stepwise method, due to the internal correlation between variables, just variables entered that have insignificant correlation with each other and have more effects than other variables. The variables affecting the performance are differentin various regions, and from the point of view of effectiveness, the arrangement of the variables in different areas vary too. In other words, even in two regions with a climatic type (based on the Modified De Martonne method), both agro-climatic indices and wheat yield are significantly different. The impact of effective variables on yield at any time and place depends on the time of year and the phonological stage of wheat. At one time the environmental conditions of different regions in terms of temperature, humidity and precipitation differ, based on phonological stages of the regions. The time of the vulnerability of wheat varies from place to place. Wheat vulnerability at flowering stage is more than other stages. The effect of independent variables on yield at different times of year is proportional to the phonological stage in years Different and different in different regions. In Kurdistan province, the number of rainy days of the year, total degree hours the temperature reaches below -11 °^C (sum of hours with below -11 °^C temperature) from germination to tillering stage, the annual precipitation, the rainfall in the fifth decade of the water year (the precipitation of 11-20 of November), annual relative humidity and total degree hours the temperature reaches above 30°^Ctemperature (sum of hours with above 30 °^C temperature) in milky and dough stage is the determinants of the production of rainfed wheat. In Baneh and Marivan areas, the coefficient of variation (CV) is lower and in Bijar and Divandareh regions CV is more than other regions. Kamyaran region has the highest yield, Baneh and Marivan were ranked secondjointly. Sanandaj and then Bijarhave the lowest yield. Each region has a model for wheat yield and determinant factors vary from region to region. Although the annual production of Bijar is higher than other areas, wheat production in the Bijar region has a higher risk than other areas.

Karstic zones play an important role in feeding the karstic aquifers؛ therefore, recognizing the factors affecting the karst mutation and its zonation in the field of studies concerned with karst water resources is of great concern. The results of these studies can be applied to quantitative and qualitative management and protection of these resources against the pollutions coming from human activities. Karst mass of Khorin with an area of 396.5 square kilometers is located in elevated Zagros zone at the boundary of two provinces, Kurdistan and Kermanshah. Because of the outcrops of carbonate rocks and the presence of other variables, this mass in which there are many springs as karst aquifer outputs is susceptible of Karstification. Thus, identifying the karstic shapes as well as studying the geomorphology of karst and its development in the karst mass of Khorin is very significant. In this relief different forms of karst landscape including a variety of Lapies, sinkholes, caves, etc. have formed. Here, lithological layers, distance from faults, precipitation, temperature, slope, elevation, direction and plants were fuzzy considering the required function for each and then were combined using 0.9 gamma operator. At the end, the zonation map of surface karst development was prepared. This map was classified into four classes of without karst, karst with low development, karst with medium development and karst with high development through natural fractures method. Regarding the need for the layer of sinkholes, to study the accuracy of karst development zonation in the area under the survey, karst sinkholes were identified and their spatial distribution map was prepared using the Fill sink method. The results of this study indicate that about 98% of sinkholes are situated in the classes of triple karst.