Based on the given feature "Repetition" and the task of predicting the grain yield of a soybean cultivar as either low or high, let's analyze the relationship between the feature and target.

Assuming that the feature "Repetition" represents the number of times the experiment was repeated for each soybean cultivar, we can infer that higher repetition values may lead to more accurate and reliable measurements for determining the grain yield.

Here is a dictionary that shows the possible values of the feature "Repetition" for each target class:

```json
{
	"low": [1.0, 2.0, 3.0, 4.0, 5.0],
	"high": [6.0, 7.0, 8.0, 9.0, 10.0]
}
```

Please note that the specific values for "Repetition" provided in the dictionary are just examples and might not reflect the actual data. You should update the values according to your prior knowledge or relevant dataset.