USDA · USDA-047 · record R1348
Predictive Cropland Data Layer
Department of Agriculture · Research, Education and Economics
Overview
- Development stage
- DeployedSource: c) Deployed – The use case is being actively authorized or utilized to support the functions or mission of an agency.
- High-impact designation
- Not high-impactSource: c) Not high-impact
- Impact justification
- Not reported
- Start date
- 2021-02-15 (day precision)Source: 2/15/2021
- Withheld from public reporting
- Not reported
Mission
- Topic area
- Administrative Functions
- Operational functions
- No function tag matched Derived by keyword rules; see methodology
Problem the AI is intended to solve
The purpose of this system is to predict crop rotations.
Expected benefits
Benefits include improving data quality of area-based surveys and providing assistance to field office personnel.
System outputs
The system outputs predictions of the types of crops in specific locations within the Conterminous United States (CONUS).
Technology
- AI classification
- Classical / predictive MLSource: Classical/Predictive Machine Learning: Models trained on data to make predictions or classifications based on identified patterns or relatio…
- System name(s)
- Not reported
- Custom-developed code
- Yes
- Public source code
- Not reported
Sourcing
- How it was built
- Developed in-houseSource: b) Developed in-house
- Vendor (as reported)
- Not reported
- Vendors (standardized)
- None on the standard list
A vendor is the supplier named by the agency. It does not identify the underlying model or AI technology.
Data and privacy
- Involves PII
- No
- Privacy Impact Assessment
- Not reported
- Authorization to Operate
- No
- Demographic features
- k) None of the above
- Training and evaluation data
- The data is derived from Cropland Data Layer (CDL) and Farm Service Agency (FSA) administrative data. CDL is produced using satellite images and extensive agricultural ground reference data.
- Federal Data Catalog
- Not reported
Governance
0 of 8 minimum-practice questions answered. A blank answer means the agency reported nothing; it does not mean the practice is absent.
Pre-deployment testingNo answer reported
AI impact assessmentNo answer reported
Independent reviewNo answer reported
Ongoing monitoringNo answer reported
Operator trainingNo answer reported
Fail-safeNo answer reported
Appeal processNo answer reported
User and public consultationNo answer reported
Potential impacts and how they were identified
Not reported