2_use_case_name: Acreage and Crop Type Validation
3_agency: Department of Agriculture
3_abr: USDA
4_bureau: RMA: Risk Management Agency
8_topic_area: Law & Justice
10_commercial_ai: None of the above.
11_purpose_benefits: This project uses satellite images and plant growth models to check if the reported sizes of farm fields and the types of crops grown match what is actually seen on the ground in the U.S. Midwest. This data helps the agency independently verify the accuracy of reported field sizes and crop types, supporting efforts to ensure the integrity of their programs.
12_outputs: The output is a validation of reported acreage and validation of reported crop type for corn, soybean, and winter wheat on farm fields.
16_dev_stage: Acquisition and/or Development
17_impact_type: Rights-Impacting

18_date_initiated: 9/1/2022
19_date_acq_dev_began: 9/1/2022
22_dev_method: Developed with both contracting and in-house resources.
23_contract_piids: This work was developed under a Research Support Agreement with the University of Illinois (RMA22CPT0012747)
24_hisp_support: No
30_saop_review: No
31_data_catalog: Yes
33_agency_data: The data is created from satellite imagery that uses planted acreage and crop type (corn, soybean, and winter wheat) features as an independent data validation source. Data is not publicly available.
34_data_docs: Documentation has been partially completed: Some documentation exists (detailing the composition and any statistical bias or measurement skew for training and evaluation purposes), but documentation took place within this use case’s development.
40_has_ato: No
42_dev_tools_wait: Less than 6 months
49_existing_reuse: Re-use production level code from a different use-case: This use case re-uses production level code and/or internally developed code libraries for model development from existing AI systems or repositories inside the agency.
50_internal_review: Limited documentation for review: Some information regarding the model’s performance on specific benchmarks are available or have been shared with specific stakeholders within the agency.
51_extension_request: No – Agency did not request an extension for this use case.
52_impact_assessment: Planned or in-progress.
53_real_world_testing: Benchmark evaluation: Testing of the AI model has involved the use of benchmarks (either publicly available or internally created) to estimate performance in real-world settings, but has not been tested in an operational environment. 
54_key_risks: The current key risks for the planting date model results are Type I and Type II errors.  For this particular use case, a False Positive (Type I error) would be where the acreage detected by the model was outside the margin of error, when in fact the acreage reported was within the margin of error and False Negatives (Type II errors) where the acreage detected by the model was inside the margin of error, when in fact the acreage was outside the margin of error.  Similar logic would be applied to the crop type detection. Model results will not be used without independent review before a potential compliance action takes place. The model produces a field specific observation and is not tied to a demographic group.
55_independent_eval: Planned or in-progress
56_monitor_postdeploy: Automated and Regularly Scheduled Updates: Some aspects of the plan to monitor the AI system post-deployment are automated including re-training of models after detecting drift; however, data science teams are still significantly involved in the monitoring and re-deployment process.
57_autonomous_impact: No - Some individual decisions or actions require direct human oversight.
59_ai_notice: N/A - individuals are not interacting with the AI for this use case
61_adverse_impact: Yes
62_disparity_mitigation: No analysis specific to this use case has been conducted as the model is focused on the detection of a particular land use-land cover class and agronomic variable.
63_stakeholder_consult: None of the above
65_appeal_process: No – Law, operational limitations, or governmentwide guidance precludes an opportunity for an individual to appeal.
66_no_appeal_reason: Models are currently in development & broader plans about the use of the results are happening internally.
67_opt_out: No – This AI use case is not subject to the opt-out requirement because the AI functionality is solely used for the prevention, detection, and investigation of fraud or cybersecurity incidents, or the conduct of a criminal investigation.