Based on the given feature (Cultivar) and the task (predicting grain yield of soybean cultivar as low or high), we can analyze the relationship between the Cultivar and the target variable.

To analyze this relationship, we need data on the actual grain yields of different cultivars. Unfortunately, the given information only includes details about the cultivars, but not their actual grain yields. Without the actual grain yield data, it is difficult to determine how the cultivar names are related to the target variable. 

However, we can still create a dictionary based on the given information. Since we don't have actual grain yield data, we can consider the Seeds per meter/linear and Density per meter/linear as potential indicators of grain yield. We can assume that higher values of Seeds per meter/linear and Density per meter/linear are associated with higher grain yields.

Here is the dictionary based on these assumptions:

```json
{
  "low": ["FTR 3190 IPRO", "FTR 4288 IPRO", "NK 8770 IPRO", "MONSOY M8606I2X", "M 8644 IPRO", "ADAPTA LTT 8402 IPRO", "98R30 CE", "FORTALEZA IPRO", "MONSOY 8330I2X", "SUZY IPRO", "TMG 22X83I2X", "EXPANDE LTT 8301 IPRO", "FORTALECE L090183 RR", "83IX84RSF I2X", "82HO111 IPRO - HO COXIM IPRO", "82I78RSF IPRO", "SYN2282IPRO", "ATAQUE I2X", "FTR 4280 IPRO", "LYNDA IPRO", "FTR 3868 IPRO", "MANU IPRO", "ELISA IPRO", "79I81RSF IPRO"],
  "high": ["NEO 760 CE", "NK 7777 IPRO", "77HO111I2X - GUAPORÉ", "GNS7900 IPRO - AMPLA", "LTT 7901 IPRO", "BRASMAX BÔNUS IPRO", "97Y97 IPRO", "BRASMAX OLIMPO IPRO", "NK 8100 IPRO", "83IX84RSF I2X", "NEO 790 IPRO", "LAT 1330BT", "96R29 IPRO", "74K75RSF CE"]
}
```

Note: This dictionary is created based on assumptions and would require actual grain yield data for a more accurate analysis.