## 4 Discussion  
In this study, we proposed and applied a new model named GDM-DEA model for the evaluation of cultivation measures in maize. We also compared the performance of the GDM-DEA model with other models. Taking the JLASTU experiment as an example, nine groups of CCR-effective maize cultivation measures can be determined by the CCR model. Three groups of effective cultivation measures can be selected by the GDM-DEA model, and the three groups of cultivation measures meet the desired level of CCR effectiveness. Only by analyzing the cultivation measures and yield of the three groups, it could be determined that group 19 is the best cultivation measure for the JLASTU experiment, which has an unchanged yield, planting density and phosphorus, and potassium application at the previous standard level in the same region, while its nitrogen application is at the \(-2\) level. The optimal cultivation measures are the same as the measures that were determined by the CCR model. Therefore, the evaluation results of the GDM-DEA model and CCR model are the same, but they can reduce the workload of determining the optimal cultivation measures. As demonstrated in the JLASTU experiment and the three independent experiments, the GDM-DEA model outperforms the CCR model when determining an optimized estimation. Therefore, the GDM-DEA model is good enough for predicting the maize yield output under different cultivation measures. It also may be applicable for other crops, although it needs to be tested in the future. The CCR model is commonly used to evaluate performance. However, the CCR model can give only a general range of planting density and fertilization data but not specific planting guidance. The CCR model is only dependent on the original data and analyzes the data objectively. In the CCR model, each DMU selects the subjective weights that are propitious to their own. It means that a DMU only needs the original data of the input index and output index when the CCR model evaluates the DMU. The solution results of the model include both the efficiency value of the DMU and the corresponding weight of each input and output index. When evaluating a group of cultivation measures, in view of the \"uncertain weight\" of the CCR model, the cultivation measures will choose a weight to maximize their own efficiency. In other words, when evaluating a group of cultivation measures of four indicators, the greater the input, the smaller the index weight, and the smaller the input, the greater the index weight, so that this group of cultivation measures will achieve the maximum efficiency value. Therefore, when evaluating maize cultivation performance, it should be noted that there are many CCR-effective cultivation measures, and the cost of calculation will be relatively high in the process of determining the optimal cultivation measures. GDM-DEA could solve the above problems. The principle of this model is to add an average decision unit as the \"control group\". When calculating the GDM-DEA efficiency value of a group of cultivation measures, the group of cultivation measures should be removed from the model, and the remaining original cultivation measures and mean cultivation measures will form a new group. If the removed cultivation measure has a great influence, the efficiency of the average decision unit will change greatly and vice versa. In short, the removal and retention of the removed cultivation measure could reflect the degree of its impact on the efficiency of new groups, which could also reflect the role of the removed cultivation measure. Therefore, by optimizing the index composition of the average decision unit in the new evaluation set, and then taking the sum of the optimal weighting coefficient of the average decision unit as the standard for the evaluated cultivation measures, we can not only establish a relationship between the average cultivation measures and the evaluated cultivation measures, but also make the evaluation resultsmore reasonable.