2_use_case_name: Cover Crop Mapping
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 aims to measure the use of cover crops on farms in the U.S. Midwest by creating yearly maps showing cover crops in the fall and spring. These maps are made using satellite images and models of plant growth. This data helps the agency independently check and track how many farmers are using cover crops, reducing the need for on-site visits to determine if cover crops are present on a field.
12_outputs: The output is a state-level map of detected cover crops by year, classified by planting date (fall, spring).
16_dev_stage: Acquisition and/or Development
17_impact_type: Rights-Impacting

18_date_initiated: 9/2/2022
19_date_acq_dev_began: 9/2/2022
22_dev_method: Developed with both contracting and in-house resources.
23_contract_piids: This work is supported 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 crop insurance cover crop subsidy data 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
43_infra_provisioned: No
45_compute_request: No
47_timely_resources: Other
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 Cover Crop detection are Type I and Type II errors.  For this particular use case, a False Positive (Type I error) would be where cover crop was detected by the model, when in fact none was planted by the producer and False Negatives (Type II errors) where cover crop was planted and emerged, but the model did not detect cover crop.  Model results will not be used without independent review before a potential compliance action takes place.
55_independent_eval: Planned or in-progress
56_monitor_postdeploy: No monitoring protocols have been established: Necessary infrastructure needed to perform monitoring of AI systems in production is not available and/or a plan to monitor models has not yet been established.
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: No
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.
63_stakeholder_consult: Direct user testing
65_appeal_process: Yes
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.