Based on my prior knowledge, the Season feature is likely to have an influence on the grain yield of soybean cultivars. However, without any specific domain knowledge, it is difficult to determine the appropriate ranges of values for this feature.

To analyze the relationship between the feature Season and the target task of grain yield (high or low), we can consider some hypothetical scenarios:

1. If the grain yield of soybean cultivars is generally higher during a specific season, for example, during the summer (Season = 1), we can expect a higher grain yield for cultivars with Season values around 1.

2. On the other hand, if the grain yield is generally lower during a specific season, such as winter (Season = 2), we can expect a lower grain yield for cultivars with Season values around 2.

Based on these scenarios, we can analyze the relationship between the feature and the target. However, as I don't have specific information about the soybean cultivars or the target task, I cannot provide exact values for the dictionary at this time.

Here is an example of how the output dictionary could be structured, assuming we have more information:

```json
{
	"low": [1.5, 1.7, 1.3, 1.8, 1.6],
	"high": [2.2, 2.0, 2.5, 2.3, 2.1]
}
```

In this example, the values [1.5, 1.7, 1.3, 1.8, 1.6] represent typical values of the Season feature for soybean cultivars with a low grain yield, while the values [2.2, 2.0, 2.5, 2.3, 2.1] represent typical values for cultivars with a high grain yield.

Please note that these values are purely hypothetical and should not be considered as accurate without further domain-specific information.