Demographic: DTN value derived from and combined from FSA, CDL, CA DPR sources for describing Contains Crop, Income, Livestock, Summary, etc. data.
GFI: Gross Farm Income
Household: An association/total of acres to a single household based on multiple data points and algorithms, which have been tested over many years. The intent is to, as best as possible, associate acres of land to a single household best identified as the primary responsible. Essentially, every piece of land has a single household identified as the primary. This process creates unique acres at the address thus there is no duplication of acres across households.
Individual: An association/total of acres to one or more individuals based on multiple data points and algorithms, which have been tested over many years. The intent is to as best as possible associate acres of land to the correct individuals. This process creates duplication across individuals, as more than one individual can be associated with a single piece of land. An individual may have more acres associated with them than the household that he/she is part of since some individual acres may be associated to a different household because a different individual is the primary for those acres.
Equipment:
- Last UCC Update: Last update to UCC Record
- Current UCC Status: Last status of the UCC Record, which occurred on the Last UCC Update
- Initial UCC Year: The initial year that this UCC record was recorded
- Comments: Any additional comments regarding the UCC record
- Company: Lienholder on the UCC Record
- State: Condition of the equipment
- Year: Production year of the equipment
- Manufacturer: Manufacturer of the equipment
- Model: Model of the equipment
- Description: General description of the equipment
- Serial Number: Serial number of the equipment
- Value: Dollar value of the equipment
In-Season: (August and September) Crop identification covering corn, soybean, wheat, cotton, sorghum, and rice using satellite-based remote sensing using machine learning.
Predictive: Q4 prediction for the upcoming cropping year, on a field level, for corn, soybean, wheat, cotton, sorghum, and rice in 31 states. Leverages eight year field-level crop rotation, crop yields, commodity prices, and weather data employing a multivariate regression analysis and statistical model to project field level planting decision.