Based on prior knowledge, we need to analyze the relationship between the feature "Season" and the task "grain yield of soybean cultivar".

To analyze this relationship, we can consider the general characteristics of each season and how they might impact grain yield. Here are some assumptions we can make based on prior knowledge:

1. Season 1 (e.g., winter): Generally, the grain yield of soybean cultivars tends to be lower in winter due to factors such as lower temperatures, shorter daylight hours, and potential frost damage.

2. Season 2 (e.g., summer): The grain yield of soybean cultivars tends to be higher in summer as it is the optimal growing season for soybeans. Longer daylight hours, higher temperatures, and more favorable weather conditions contribute to higher grain yield.

Now, let's create the dictionary with the requested format:

```json
{
	"low": [1.0, 1.5, 1.8, 2.0, 2.2],
	"high": [2.5, 2.7, 2.9, 3.0, 3.2]
}
```

In this example, we have included 5 typical values of the "Season" feature for each target class. These values are just illustrative and can be adjusted based on specific domain knowledge or dataset analysis.