Analysis:

To understand the relationship between the feature "Repetition" and the target variable "grain yield of soybean cultivar", we need to consider the following:

1. The "Repetition" feature represents the number of times a specific soybean cultivar has been repeated or replicated in an experiment or study. It is a numeric variable, so it can take any continuous value.

2. There is no standard range for the "Repetition" feature as it depends on the experimental design and the specific study. However, based on general knowledge and prior experience, we can assume that the "Repetition" values would typically range from 1 to 10 in most experiments.

3. To determine the relationship between "Repetition" and the target variable "grain yield of soybean cultivar", we need access to a dataset that includes both features. By analyzing this dataset, we can identify typical "Repetition" values associated with low and high grain yield.

4. Without a specific dataset, we can make some assumptions based on general knowledge. It is possible that higher repetition values indicate more reliable and accurate results, leading to a better understanding of the soybean cultivar's growth and higher grain yield. Therefore, it is reasonable to expect a positive relationship between the "Repetition" feature and the target variable "grain yield of soybean cultivar". In other words, as the "Repetition" value increases, the grain yield might also increase.

Based on the above analysis, we can now create the requested dictionary with example values:

```json
{
	"low": [1.0, 2.0, 3.0, 4.0, 5.0],
	"high": [6.0, 7.0, 8.0, 9.0, 10.0]
}
```

In this example dictionary, we assume that the range of "Repetition" values associated with low grain yield is from 1 to 5, and the range of "Repetition" values associated with high grain yield is from 6 to 10. However, it is important to note that these values are arbitrary and should be verified with real data.