Based on the given feature description ("Repetition") and the task of predicting the grain yield of a soybean cultivar as either low or high, we need to analyze the relationship between the feature and target variable.

Since the feature "Repetition" is not described in detail, we can make some assumptions based on common knowledge of soybean cultivation. "Repetition" could potentially refer to the number of times a specific cultivar has been planted in a given area or experimental setup. 

To analyze how the feature "Repetition" relates to the target variable (grain yield), we can examine historical data or conduct experiments with different levels of repetition to determine if there is any correlation or pattern. However, without specific data or prior knowledge regarding the relationship between "Repetition" and grain yield, we cannot definitively determine the appropriate ranges of values for the feature.

Given this uncertainty, we can still create a dictionary with some example values for the feature "Repetition" for each target class (low and high grain yield). However, it's important to note that these values are hypothetical and may not reflect the true relationship between the feature and target:

```json
{
	"low": [1.0, 1.5, 2.0, 3.0, 4.0],
	"high": [5.0, 6.0, 7.0, 8.0, 9.0]
}
```

It's important to conduct further analysis and gather more information or data to understand the true relationship between the feature "Repetition" and the grain yield target variable.