Based on prior knowledge, we can analyze the relationship between the feature "Number of stems (unit)" (NLP) and the target variable "grain yield of soybean cultivar" to determine the appropriate ranges of values.

Feature: NLP - Number of stems (unit)

A high number of stems is generally associated with increased grain yield in soybean cultivars. However, it is important to note that the relationship between NLP and grain yield may vary depending on various factors such as environmental conditions, cultivation practices, and genetic characteristics of the cultivar.

Based on this analysis, we can create a dictionary with possible values of NLP for each target class:

```json
{
	"low": [1.0, 1.5, 2.0, 2.5, 3.0],
	"high": [5.0, 5.5, 6.0, 6.5, 7.0]
}
```

In the above dictionary, the "low" class includes typical NLP values in the range of 1.0 to 3.0, suggesting a lower grain yield. On the other hand, the "high" class includes typical NLP values in the range of 5.0 to 7.0, indicating a higher grain yield.

Please note that these values are just examples and may not represent the actual ranges in your specific dataset. It is crucial to consider domain knowledge and the context of the dataset to determine appropriate ranges for the feature.