# Crop Yield Prediction Based on Agrometeorological Indexes and Remote Sensing Data
## 5 Discussion  
RF is a very efficient machine learning algorithm. Compared with traditional regression model, random forests modeling method has good stability and high accuracy and does not need to check whether the interaction of the variables is significant. It has high tolerance to the outliers and noises, and does not easily appear over fitting phenomenon. Although machine learning modeling methods perform well in yield prediction [80; 81; 82], the modeling process of them is a \"black box\", and the yield estimation model constructed cannot be directly expressed by the functional relationship between input variables and yield. However, compared with other machine learning modeling methods, RF has a prominent advantage; that is, it can estimate the importance of variables. Therefore, it can be used to identify variables that are more important for crop yield estimation and analyze and explain the factors affecting crop yield according to the differences of variables. However, the ability of RF simulation to explain crop physiology is still limited. The crop growth model can simulate the physiological growth process of crops, and has a better mechanism [7; 83]. The combination of machine learning and the crop growth model could be a new direction for crop yield estimation [84]. We proposed a new strategy for the yield estimation model development in which remote sensing indexes, agrometeorological indexes, and technical yield are used together, and modeling variables are selected by a two-stage importance evaluation method. The new strategy was tested in two different provinces: Jilin and Liaoning. Although both provinces are the main maize producing provinces, and located in Northeast China, there are some differences between them. First, technical yield is the dominant component of yield. It is also the most important modelling variable. The average coefficient of variation of maize technical yield during the  
Figure 12: Variance in the technical yield in Jilin (**a**) and Liaoning (**b**) Provinces. study period was 8.94% in Jilin Province and 10.86% in Liaoning Province (Figure 12), indicating the stability of the technical yield in Jilin Province is higher than that in Liaoning Province. This makes the overall modelling accuracy in Jilin Province higher than that in Liaoning Province. Second, rainfed cropland is the main cropland type in both Liaoning and Jilin Provinces. The average proportion of irrigated farmland area to farmland area is 0.32 in Jilin Province and 0.42 in Liaoning Province, respectively (Figure 13). The crop yield in rainfed cropland is easily affected by meteorological conditions. The climate yield is defined as the difference between technical yield and yield and determined by short-term climate variations. The range, standard deviation, and mean deviation of climate yield during our study period are 2893.13 kg/ha, 661.96 kg/ha, and 522.75.96 kg/ha in Jilin Province, and they are 641.34 kg/ha, 800.46 kg/ha, and 603.42 kg/ha in Liaoning province, respectively (Figure 14). The climate yield fluctuation of Liaoning Province is larger than that of Jilin Province. Thus, the agrometeorological indexes play a greater role in improving the yield estimation accuracy in Liaoning Province than in Jilin Province. Third, the altitude and latitude of Jilin Province are higher than those of Liaoning Province (Figure 1), and the growth period of maize is later than that of Liaoning Province. Therefore, the months of the selected modelling variables are different, and the months of the important temperature indicators affecting the yield in Jilin are delayed. For example, the ATT8 and ATT9 were important variables in Jilin Province, while ATT6 and ATT7 were the most important variables in Liaoning Province. The validation results at the municipal level in these two provinces show that the MARE of the established models is lower than 0.15 (including disaster years), which indicates that our method has good application prospects. Our method can select the best modeling variable for each province.