Based on prior knowledge, we can analyze the relationship between the "Season" feature and the target variable, "grain yield of soybean cultivar".

Assuming that "Season" represents the time of year when the soybean crop is planted and harvested, we can infer that it might have an impact on the grain yield. 

Analyzing the relationship between the feature and target based on prior knowledge, we can make the following observations:

- Season 1 (lower values) might correspond to winter or colder months, which could potentially result in lower grain yield.
- Season 2 (higher values) might correspond to spring or summer months, which could potentially result in higher grain yield.

However, the specific relationship between the "Season" feature and the target variable can vary based on various factors such as region, climate, and specific cultivar characteristics.

Based on this analysis, we can create a dictionary with possible values of the "Season" feature for each target class:

```json
{
    "low": [1.0, 1.2, 1.5, 1.8, 2.0],
    "high": [2.5, 2.8, 3.0, 3.2, 3.5]
}
```

Please Note: The values presented in the dictionary are arbitrary and for illustrative purposes only. In a real-world scenario, these values should be based on actual data or domain-specific knowledge.