Based on the given feature "Repetition" and the task of predicting the soybean cultivar's grain yield as either low or high, I will assume that the feature "Repetition" represents the number of times the experiment was repeated for a specific cultivar. 

To analyze the relationship between the feature and the task, we can consider the following:

1. High grain yield: If a cultivar is associated with a high grain yield, it is likely that the experiment was repeated multiple times to obtain reliable results. Therefore, we can expect "Repetition" to have higher values for cultivars with high grain yield.

2. Low grain yield: If a cultivar is associated with a low grain yield, it might indicate that the cultivar performs poorly or inconsistently. In such cases, fewer repetitions of the experiment may have been conducted. Hence, we can expect "Repetition" values to be lower for cultivars with low grain yield.

Based on this analysis, we can create a dictionary with the possible values of the feature "Repetition" for each target class:

```json
{
	"low": [1.0, 1.0, 1.0, 2.0, 2.0],  
	"high": [5.0, 4.0, 6.0, 3.0, 5.0]  
}
```

In this example, I have generated 5 possible values for each target class ('low' and 'high') for the feature "Repetition". These values are just for illustration purposes and should be replaced with appropriate values based on the actual dataset and knowledge of the domain.