Based on the provided feature description, the "Season" feature is a numeric variable with values of 1 or 2. We need to analyze how the "Season" feature relates to the task of predicting the grain yield of a soybean cultivar as low or high.

To perform this analysis, we need to have prior knowledge or domain expertise about soybean cultivation. Without specific knowledge about the relationship between season and grain yield, it is challenging to infer the relationship accurately. However, assuming that higher values of the "Season" feature correspond to a particular season or time period that typically leads to high grain yield and lower values correspond to a different season associated with low yield, we can provide a general analysis.

Based on this assumption, we can generate the dictionary as follows:

```json
{
  "low": [1.0, 1.0, 1.0, 1.0, 1.0],  
  "high": [2.0, 2.0, 2.0, 2.0, 2.0]  
}
```

In this example, we assume that a "Season" value of 1 corresponds to a low grain yield and a "Season" value of 2 corresponds to a high grain yield. Therefore, for the target class "low," we include five typical values of "Season," all set to 1. Similarly, for the target class "high," we include five typical values of "Season," all set to 2.

Please note that this is a simplified example, and in a real-world scenario, the relationship between the "Season" feature and grain yield would be determined with more accurate domain knowledge or through analysis of relevant data.