Based on the given information, we can analyze the relationship between the "Cultivar" feature and the task of predicting the grain yield of soybean. Since there is no direct information about the grain yield in the feature descriptions, we need to examine other features and their potential correlation with the target variable.

One possible approach is to look for patterns or trends in the provided data and make inferences based on that. For example, we can observe the "Seeds per meter/linear" and "Density per meter/linear" values for each cultivar and see if there is any relationship with the likely grain yield.

Let's create a dictionary to summarize the relationship between the "Cultivar" feature and the task:

```json
{
	"low": ["NEO 760 CE", "MANU IPRO", "77HO111I2X - GUAPORÉ", "NK 7777 IPRO", "ADAPTA LTT 8402 IPRO", "FTR 4280 IPRO", "ELISA IPRO", "FTR 3868 IPRO", "MANU IPRO"],
	"high": ["NEO 790 IPRO", "NK 8770 IPRO", "82I78RSF IPRO", "SYN2282IPRO", "FORTALEZA IPRO", "BRASMAX OLIMPO IPRO", "BRASMAX BÔNUS IPRO", "97Y97 IPRO", "GNS7900 IPRO - AMPLA", "96R29 IPRO", "74K75RSF CE", "LAT 1330BT", "GNS7700 IPRO", "NK 8100 IPRO", "83IX84RSF I2X", "TMG 22X83I2X", "MONSOY 8330I2X", "FORTALECE L090183 RR", "FTR 3179 IPRO", "EXPANDE LTT 8301 IPRO", "TMG 22X83I2X", "MONSOY 8330I2X", "NK 8770 IPRO", "FTR 3190 IPRO"]
}
```

In the above dictionary, the "low" key contains a list of cultivar names that are likely to have a low grain yield, while the "high" key contains a list of cultivar names that are likely to have a high grain yield. Note that the lists are not exhaustive and may contain duplicates. The values are presented as strings in lists.

Please note that this analysis is based on the given information and assumptions made from it. Additional data or domain-specific knowledge might provide more insight into the relationship between the "Cultivar" feature and the task of predicting grain yield.