Based on prior knowledge, the feature "Season" can be considered as a categorical variable where each value represents a specific season. However, since the feature is described as a numeric variable, let's assume that it represents different ranges of seasons. 

To analyze the relationship between the "Season" feature and the target "grain yield of soybean cultivar," we need additional information about how the seasons are categorized and what impact they have on grain yield. Without this information, it is difficult to determine the appropriate ranges of values for the "Season" feature.

However, since the task asks for a dictionary with possible values for each target class, we can make an assumption and create an example dictionary based on that assumption. For instance, if we assume that low grain yield is associated with the season range 1-5 and high grain yield is associated with season range 6-10, the resulting dictionary could look like this:

```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 this dictionary is just an example based on an assumption. The actual relationship between the "Season" feature and the grain yield of the soybean cultivar may differ and needs to be validated with proper domain knowledge and data analysis.