## 5 Results and Discussion  
Since goal of this research was to estimate the yield of rainfed chickpea farms in Kermanshah, information about other types of land and other areas were irrelevant. Therefore, we combined the raster map of rainfed farms of the province with the raster maps of slope and altitude (Figure 3a,b) in order to filter the obtained satellite data and reduce irrelevant and outlier data. Since the crop yield data provided in the reports of the Statistical Center of Iran and the Ministry of Agriculture of Iran are at the county level, other data were also transformed into county level by the use of zonal operators. For this purpose, all pixels except those related to rainfed farms, slopes of less than 10 degrees, and non- mountainous heights were filtered out. In this way, we created an integrated database of remote sensing data, weather data, and rainfed chickpea farms in Kermanshah. Rainfed spring chickpea, which accounts for the largest portion of chickpea production in Kermanshah, is typically planted in late winter and harvested in late spring. Since the selected factors had different degrees of sensitivity to the time of data, we defined two scenarios for using the data of each month and also the combination of consecutive months (e.g., MA for March and April): (1) scenario (A), in which we used the average values obtained for the entire cultivation period from planting to harvest; (2) scenario (OC), in which we used an optimal combination of months depending on to the correlation coefficient for the relationship between the crop yield and each predictor variable. Table 4 shows the correlation coefficient for the relationship between the average value of each factor (predictor) in each month between March and June (planting, germination, growth, and harvest months for rainfed chickpea farming in the area) and the crop yield in 2010-2017. The highlighted cells in this table show the periods selected for the OC scenario on account of having the highest correlation coefficient. Table 5 illustrate the average of real and predicted values by the methods for the whole province in different years from 2010 to 2017 according to these two scenarios. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Criteria** & **March** & **April** & **May** & **June** & **MA** & **AM** & **MJ** \\ \hline Temperature min & 0.059 & 0.009 & 0.028 & 0.081 & 0.033 & 0.019 & 0.056 \\ Temperature max & 0.079 & 0.093 & 0.094 & 0.087 & 0.086 & 0.094 & 0.091 \\ Average Temperature & 0.009 & -0.018 & -0.019 & -0.018 & -0.005 & -0.018 & -0.018 \\ Precipitation & 0.072 & 0.01 & -0.366 & -0.459 & 0.05 & -0.124 & -0.377 \\ Humidity max & 0.162 & 0.176 & 0.139 & 0.039 & 0.175 & 0.155 & 0.091 \\ Humidity min & -0.317 & -0.152 & -0.013 & 0.044 & -0.227 & -0.080 & 0.008 \\ Humidity & 0.207 & 0.205 & 0.163 & 0.087 & -0.213 & 0.183 & 0.132 \\ Dry temperature & 0.103 & 0.211 & 0.294 & 0.238 & 0.151 & 0.261 & 0.269 \\ Normalized difference vegetation index & 0.458 & 0.503 & 0.142 & 0.068 & 0.0616 & 0.291 & 0.113 \\ Enhanced vegetation index & 0.361 & 0.549 & 0.141 & 0.082 & 0.624 & 0.312 & 0.120 \\ Fraction of photosynthetically active radiation & 0.415 & 0.457 & 0.132 & 0.044 & 0.547 & 0.281 & 0.104 \\ Leaf area index & 0.462 & 0.456 & 0.148 & 0.044 & 0.559 & 0.291 & 0.118 \\ Gross primary production & 0.373 & 0.407 & 0.132 & -0.005 & 0.566 & 0.260 & 0.084 \\ Evaportransporting & -0.021 & 0.235 & 0.134 & 0.041 & 0.161 & 0.180 & 0.110 \\ Surface soil moisture & 0.105 & 0.090 & 0.097 & 0.075 & 0.099 & 0.093 & 0.095 \\ Subsurface soil moisture & 0.203 & 0.202 & 0.203 & 0.221 & 0.204 & 0.204 & 0.210 \\ \hline \hline \end{tabular}  
* Highlighted cells are the maximum in each row.  
\end{table} 
Table 4: Correlation coefficient of the relationship between the yield of spring chickpea and the values of its predictors during the growing months (from planting to harvest).