## 5 Results and Discussion
After implementing SVR, RF, and KNN using the Scikit-Learn library in Python, their predictions of chickenyei yield were evaluated by computing RMSE, MBE, MAE, MAPE, and CC in the two defined scenarios (using an optimal combination of months and using the average of the growing period) with respect to the values reported by the Statistical Center and the Ministry of Agriculture of Iran. Tables 6 and 7 show the values of these validation measures in the (A) and (OC) scenarios. To compute these measures with the LOOCV technique, we generated eight sets of results for each year between 2010 and 2017. For each year, we first took the data of that year out of the dataset and trained the methods with the rest of the data. We then used the method to predict the removed data and compared the predictions with the actual data. This process was carried out for both (A) and (OC) scenarios. The results showed that among the implemented machine learning methods, RF with a CC of 0.86, RMSE of 40.71 and 43.39 (Kg/ha), and MAPE of 7-8% for the (A) and (OC) scenarios had the best accuracy in estimating the yield of rainfed chickpea farms. The least accurate method was KNN, which had a CC of 0.20 and 0.32, RMSE of 90.16 and 93.66 (Kg/ha), and MAPE of 17-19% in the two scenarios. SVR was found to have a CC of 0.46 and 0.52, RMSE of 73.57 and 80.94 (Kg/ha), and MAPE of 11-15% in the (A) and (OC) scenarios. Similar results were obtained in terms of MAE and MBE, showing RF to be the most accurate, SVR to be the second most accurate, and KNN to be the least accurate of the implemented methods. All methods performed better in all evaluation measures in the OC scenario, which indicates that this method (the optimal combination of months) is more appropriate for predicting the yield of rainfed chickpeas. Figure 5(a)-5(b) show the difference between the actual values and the prediction made by the methods in the A and OC scenarios. For both scenarios, the line drawn for RF (Figure 5(c),d) is closest to the 1:1 line, reflecting the better performance of this method in predicting the yield of rainfed chickpea farms. Given that the lowest chickpea yield has occurred in 2015 due to drought and the highest yield has occurred in 2014 because of heavy Mediterranean rainfall, KNN has performed poorly in predicting the yield of both of these years in both scenarios. This is evident in the lines plotted for KNN in Figure 5(e),f. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Methods** & **MBE (Kg/ha)** & **RMSE (Kg/ha)** & **MAE (Kg/ha)** & **MAPE (\%)** & **CC** \\ \hline SVR & 48.92 & 73.57 & 49.71 & 11.94 & 0.46 \\ RF & 32.31 & 40.71 & 33.00 & 7.96 & 0.86 \\ KNN & 72.85 & 90.16 & 73.72 & 17.18 & 0.20 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Validity assessment of the implemented machine learning methods in the OC scenario. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Year** & **Real Value** & **Prediction by** & **Prediction by** & **Prediction by** & **Prediction by** & **Prediction by** \\
**of Production** & **RF(OC)** & **SVROC** & **KNN(OC)** & **RF(A)** & **SVR(A)** & **KNN(A)** \\
**Kg/ha** & **Kg/ha** & **Kg/ha** & **Kg/ha** & **Kg/ha** & **Kg/ha** & **Kg/ha** \\ \hline
2017 & 542.76 & 535.27 & 509.31 & 524.56 & 514.13 & 495.45 & 484.85 \\
2016 & 539.82 & 534.15 & 519.13 & 521.92 & 528.545 & 516.89 & 522.44 \\
2015 & 281.85 & 320.31 & 368.36 & 395.32 & 341.79 & 392.87 & 413.74 \\
2014 & 568.83 & 525.92 & 486.75 & 462.44 & 524.11 & 453.12 & 447.45 \\
2013 & 475.52 & 454.20 & 446.63 & 424.78 & 457.32 & 428.88 & 446.95 \\
2012 & 470.49 & 442.52 & 449.05 & 432.69 & 449.09 & 445.45 & 437.84 \\
2011 & 379.82 & 412.23 & 429.82 & 438.33 & 416.40 & 433.05 & 437.43 \\
2010 & 408.51 & 434.36 & 430.45 & 442.61 & 432.1 & 438.66 & 472.62 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Average of real and predicted values by the methods for Kermanshah in different years.