Based on prior knowledge, the feature "Season" could refer to the growing season for the soybean cultivar. This could be an important factor affecting the grain yield. 

To analyze the relationship between the feature "Season" and the task of determining the grain yield, we can look for patterns in historical data or scientific studies. 

Assuming that "Season" takes numeric values, we can evaluate the relationship by comparing the average grain yields for different seasons. For example, if historical data shows that soybean cultivars grown in Season 1 tend to have higher grain yields compared to those grown in Season 2, we can conclude that there is a positive relationship between the feature "Season" and the target variable "grain yield."

Based on this analysis, we can create a dictionary as follows:

```json
{
	"low": [1.0, 2.0, 2.5, 3.0, 3.5], 
	"high": [4.0, 4.5, 5.0, 5.5, 6.0]
}
```

In the above dictionary, we assume that the feature "Season" ranges from 1 to 3, where 1 represents the lowest and 3 the highest possible values. The values provided in lists under "low" and "high" represent typical Season values for each target class. This is just an example, and the actual values may vary depending on the specific dataset or domain knowledge.