Developing an Algorithm for Selecting the Most Suitable Crops based on Climatic Conditions (Case Study: Soumar Plain in Kermanshah Province)

Because of the key role of the agricultural sector in achievement of food security, increasing climatic variations due to global change and the dependence of agricultural yields to climatic conditions, it is essential to study the long-term relationship between climatic conditions and agricultural yields in order to coordinate agricultural activities with climate change trend. In this study, a screener algorithm considering the climatic conditions of the region has been developed to rank the most suitable agricultural products with the climatic conditions of Soumar plain in Kermanshah province. For this purpose, the sensitivity of the defined agroecosystem to climatic conditions of the region was calculated using the Shannon-Wiener index. Then, using Multiple Linear Regression method and SPSS software, regression models were developed between climatic data and crop yield data. In the next step, the accuracy of the developed models was confirmed considering the conditions of using linear regression for all models. Afterward, the weight of effective climatic parameters was determined using pairwise comparison methods. According to the results, the minimum monthly temperature parameter with weight of 0.169 and the average monthly wind speed parameter with weight of 0.032 were considered the most and the least effective climatic parameters, respectively. Finally, crops ranking in the study area was completed using TOPSIS method and calculating Ci index which shows the score of each crop. According to the results, bean, barley and canola with the Ci of 0.601, 0.537 and 0.564 and tobacco, tomato and fodder corn with the Ci of 0.376, 0.513 and 0.518 show the most and the least compatibility with the climate conditions of the region, respectively.