Based on the given feature "Season" and the task of predicting the grain yield of a soybean cultivar as low or high, here is an analysis of the relationship between the feature and target.

Analysis:

Considering prior knowledge, we can make some assumptions about the possible ranges of values for the feature "Season". Let's assume that "Season" represents the growing season for soybeans and it can be categorized into two classes: 1 and 2.

Typical values for a "low" grain yield may occur during seasons that are unfavorable for soybean growth, such as extreme temperatures, drought, or other adverse weather conditions. Possible values for the "Season" feature in the "low" class could be: 1.0, 1.2, 1.4, 1.6, 1.8.

On the other hand, a "high" grain yield is expected during seasons that provide optimal conditions for soybean growth, including moderate temperatures, adequate rainfall, and suitable sunlight. Possible values for the "Season" feature in the "high" class could be: 2.0, 2.2, 2.4, 2.6, 2.8.

Based on this analysis, let's create the dictionary with the values:

```json
{
	"low": [1.0, 1.2, 1.4, 1.6, 1.8],
	"high": [2.0, 2.2, 2.4, 2.6, 2.8]
}
```

Please note that these values are just examples and based on assumed relationships. Further analysis with actual data and domain expertise is recommended to make more accurate predictions.