Based on my prior knowledge, I will analyze the relationship between the feature "Season" and the task of predicting the grain yield of a soybean cultivar as low or high.

Analysis:
In this case, without any specific information, we can assume that the feature "Season" could represent the growing season of the soybean crop. The season could be influenced by factors such as temperature, rainfall, and daylight duration, which can impact the grain yield. However, the specific ranges of the values are not provided, so we will provide a general analysis using a feature value range of 1 to 2.

Based on the assumption that different seasons can have varying effects on the grain yield, the relationship between the feature and the target variable could be as follows:

1. Low Grain Yield:
   - Features:
     - Season: 1, 1.2, 1.4, 1.6, 1.8
   - Explanation:
     - The low grain yield could be associated with the growing season having unfavorable conditions for soybean cultivation. This could include extremely hot or cold temperatures, insufficient rainfall, or other adverse weather conditions.

2. High Grain Yield:
   - Features:
     - Season: 2, 1.3, 1.5, 1.7, 1.9
   - Explanation:
     - The high grain yield could be associated with the growing season having optimal conditions for soybean cultivation. This could include moderate temperatures, adequate rainfall, and the right amount of sunlight.

Based on this analysis, we can create the following dictionary:

```json
{
  "low": [1, 1.2, 1.4, 1.6, 1.8],
  "high": [2, 1.3, 1.5, 1.7, 1.9]
}
```

Please note that this is a general analysis based on assumptions, and for a more accurate analysis, it would be beneficial to have more specific information about the soybean cultivation conditions and the provided range of values for the "Season" feature.