Document and maintain records of precision agriculture information.

“Document and maintain records of precision agriculture information.” is a core task performed by Precision Agriculture Technicians. Among the occupation's 22 rated tasks, workers place it 22nd by importance (#1 most important). About 100% of workers say it is relevant to their job.

This is a single occupation-specific task statement from O*NET. The figures below describe how central the task is to the job and what independent studies measure about AI and this kind of work — not a prediction that the task will be automated.

Work activities this task rolls up to

O*NET groups concrete tasks into broader work activities shared across many occupations.

• Maintain operational records. in Documenting/Recording Information

AI exposure

The OpenAI / Eloundou “GPTs are GPTs” study rates this task E1. Direct exposure — a language model could plausibly cut the time to do this task by at least half.

Exposure measures whether a model could meaningfully speed the task up — it is an estimate of overlap with model capabilities, not a measure of whether the work will be done by software. The study's intermediate score (β) for this task is 1.00. Automation potential label: T3.

How AI is actually used on this kind of task

The Anthropic Economic Index observes how people actually use AI on tasks like this one across millions of real conversations.

• 0.005% share of AI-use records mapped to this task

Observed AI use describes people choosing to use AI as a tool on this kind of task today. It is augmentation and assistance, not a measure of jobs replaced.

Other tasks in this occupation

• Collect information about soil or field attributes, yield data, or field boundaries, using field data recorders and basic geographic information systems (GIS). · importance 4.2
• Use geospatial technology to develop soil sampling grids or identify sampling sites for testing characteristics such as nitrogen, phosphorus, or potassium content, pH, or micronutrients. · importance 4.2
• Divide agricultural fields into georeferenced zones, based on soil characteristics and production potentials. · importance 4.1
• Install, calibrate, or maintain sensors, mechanical controls, GPS-based vehicle guidance systems, or computer settings. · importance 4.0
• Create, layer, and analyze maps showing precision agricultural data, such as crop yields, soil characteristics, input applications, terrain, drainage patterns, or field management history. · importance 4.0
• Compare crop yield maps with maps of soil test data, chemical application patterns, or other information to develop site-specific crop management plans. · importance 4.0
• Analyze geospatial data to determine agricultural implications of factors such as soil quality, terrain, field productivity, fertilizers, or weather conditions. · importance 4.0
• Identify spatial coordinates, using remote sensing and Global Positioning System (GPS) data. · importance 4.0
• Analyze data from harvester monitors to develop yield maps. · importance 4.0
• Apply precision agriculture information to specifically reduce the negative environmental impacts of farming practices. · importance 3.9
• Demonstrate the applications of geospatial technology, such as Global Positioning System (GPS), geographic information systems (GIS), automatic tractor guidance systems, variable rate chemical input applicators, surveying equipment, or computer mapping software. · importance 3.8
• Draw or read maps, such as soil, contour, or plat maps. · importance 3.7
• Recommend best crop varieties or seeding rates for specific field areas, based on analysis of geospatial data. · importance 3.7
• Prepare reports in graphical or tabular form, summarizing field productivity or profitability. · importance 3.6

See all tasks on the Precision Agriculture Technicians page.

Sources for this page

Every figure above traces to a named public dataset and the exact release below — not hand-written opinion. See the full methodology for what each measure does and does not mean.

• O*NET 30.3 U.S. Department of Labor / National Center for O*NET Development
• Anthropic Economic Index v4 (2026-01-15) + v2 (2025-03-27) Anthropic
• “GPTs are GPTs” (Eloundou et al.) arXiv 2303.10130 OpenAI / academic

Data compiled June 2, 2026. Figures are estimates, not advice.

@misc{singulariki-task-18295,
  title  = {Document and maintain records of precision agriculture information.},
  author = {{Singulariki}},
  year   = {2026},
  note   = {O*NET 30.3; Anthropic Economic Index v4 (2026-01-15) + v2 (2025-03-27); “GPTs are GPTs” (Eloundou et al.) arXiv 2303.10130. Accessed June 7, 2026},
  url    = {https://singulariki.com/tasks/task-18295}
}