Precision Agriculture Technicians
Life, Physical, and Social Science Occupations- O*NET-SOC コード
- 19-4012.01
Apply geospatial technologies, including geographic information systems (GIS) and Global Positioning System (GPS), to agricultural production or management activities, such as pest scouting, site-specific pesticide application, yield mapping, or variable-rate irrigation. May use computers to develop or analyze maps or remote sensing images to compare physical topography with data on soils, fertilizer, pests, or weather.
職業名とタスク記述は、公表された英語のまま表示します。ラベル（タスクの種類を含む）は翻訳しています。
AI露出度
- データ出典: BLS公表時点: 2026-08
高い· 相対
低い4段階の相対区分非常に高い職業群単位の値
尺度・母数・出典
4段階の相対区分（低い / 中程度 / 高い / 非常に高い）
BLS雇用見通し表の詳細職業831件が母数。値は NEM(全国雇用マトリクス)コード単位で付与されるため、同じ NEM コードの職業は同じバンドになります
- データ出典: Anthropic公表時点: 2026-03
0.006
0.000ここに掲載された値の範囲0.745職業群単位の値
尺度・母数・出典
観測エクスポージャー指数、公開されたまま0–1
O*NETタスクへの対応づけが基準
SOC 2018の職業単位で公表された値で、同じSOC 2018コードを持つO*NET職業はすべてこの値になります
- データ出典: ILO公表時点: 2025
0.26
0.09ここに掲載された値の範囲0.70職業群単位の値
尺度・母数・出典
生成AI露出度指数、公開されたまま0–1
ISCO-08の細分類（4桁）単位で公表された値です。米国労働統計局（BLS）の公式対応表（ISCO-08→2010 SOC、2010 SOC→2018 SOC）を公表どおり適用してこの職業に結び付けており、対応は全部または一部です
この出典がどのような性格の値か
BLSの区分は絶対水準ではなく相対順位であり、一次測定でもありません。複数の既存研究が付けた職業別パーセンタイル順位を4段階にまとめた値です。雇用や賃金の予測でもなく、導入確率でもなく、自動化と増強を区別しません。
AI 曝露度（OpenAI ルーブリック）
評価済みタスク 22 件 · β ≥ 0.5 のタスク 20 件（90.9%）
β = 直接的な露出（E1）+ 0.5 × ツール利用時の露出（E2）。原典リポジトリの定義に従います。
- 原典の単位：O*NET 27.2 のタスク → O*NET 31.0 の職業コード
- 評価済みタスクはすべて O*NET 31.0 のタスク一覧にあります。
- 出典
- OpenAI "GPTs are GPTs" exposure rubric
- 版
- gh-main-0471612
- ライセンス
- MIT License, Copyright (c) 2024 OpenAI
タスク
O*NET® 31.0 Database のタスク記述です。コアタスクを先に表示します。
| タスク | 種類 | β（OpenAI） |
|---|---|---|
| Program farm equipment, such as variable-rate planting equipment or pesticide sprayers, based on input from crop scouting and analysis of field condition variability. | コア | 0.5 |
| Compare crop yield maps with maps of soil test data, chemical application patterns, or other information to develop site-specific crop management plans. | コア | 0.5 |
| Install, calibrate, or maintain sensors, mechanical controls, GPS-based vehicle guidance systems, or computer settings. | コア | 0 |
| Collect information about soil or field attributes, yield data, or field boundaries, using field data recorders and basic geographic information systems (GIS). | コア | 0.5 |
| Identify spatial coordinates, using remote sensing and Global Positioning System (GPS) data. | コア | 0.5 |
| Divide agricultural fields into georeferenced zones, based on soil characteristics and production potentials. | コア | 0.5 |
| Identify areas in need of pesticide treatment by analyzing geospatial data to determine insect movement and damage patterns. | コア | 0.5 |
| Recommend best crop varieties or seeding rates for specific field areas, based on analysis of geospatial data. | コア | 0.5 |
| Create, layer, and analyze maps showing precision agricultural data, such as crop yields, soil characteristics, input applications, terrain, drainage patterns, or field management history. | コア | 0.5 |
| Contact equipment manufacturers for technical assistance, as needed. | コア | 1 |
| Analyze remote sensing imagery to identify relationships between soil quality, crop canopy densities, light reflectance, and weather history. | コア | 0.5 |
| Document and maintain records of precision agriculture information. | コア | 1 |
| Draw or read maps, such as soil, contour, or plat maps. | コア | 0.5 |
| Advise farmers on upgrading Global Positioning System (GPS) equipment to take advantage of newly installed advanced satellite technology. | コア | 0.5 |
| Apply precision agriculture information to specifically reduce the negative environmental impacts of farming practices. | コア | 0.5 |
| Participate in efforts to advance precision agriculture technology, such as developing advanced weed identification or automated spot spraying systems. | コア | 0.5 |
| Provide advice on the development or application of better boom-spray technology to limit the overapplication of chemicals and to reduce the migration of chemicals beyond the fields being treated. | コア | 0.5 |
| 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. | コア | 0.5 |
| 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. | コア | 0 |
| Analyze data from harvester monitors to develop yield maps. | コア | 0.5 |
| Analyze geospatial data to determine agricultural implications of factors such as soil quality, terrain, field productivity, fertilizers, or weather conditions. | コア | 0.5 |
| Prepare reports in graphical or tabular form, summarizing field productivity or profitability. | コア | 0.5 |
職業情報
出典と帰属表示
This page includes information from the O*NET® 31.0 Database (https://www.onetcenter.org/database.html) by the U.S. Department of Labor, Employment and Training Administration (USDOL/ETA). Used under the CC BY 4.0 license (https://creativecommons.org/licenses/by/4.0/). O*NET® is a trademark of USDOL/ETA. AI Changing Work has modified all or some of this information: the O*NET-SOC code, title and task statements are reproduced in English without change; task-type labels are shown in the page's language and tasks are listed core first; any Korean occupation title shown on the Korean-language page is AI Changing Work's translation; any KSCO-8 unit groups linked to this occupation were paired with it by AI Changing Work's judgment, and the relation labels and statuses are AI Changing Work's additions. USDOL/ETA has not approved, endorsed, or tested these modifications.
Any AI exposure figures on this page are published by third parties, not by AI Changing Work, and none is part of the O*NET information. OpenAI publishes task-level scores (MIT License) for O*NET 27.2 task statements; each is shown next to the O*NET 31.0 task statement with the same task ID, whose wording can differ from the 27.2 statement that was scored. OpenAI also publishes occupation-level scores for O*NET-SOC codes in the same release, and any such score is shown on the O*NET occupation with the same code. Anthropic publishes an observed exposure index in the Anthropic Economic Index (CC-BY), and the U.S. Bureau of Labor Statistics publishes relative AI exposure categories (public domain); both are published per SOC code, and each value is shown on every O*NET occupation with that code. The International Labour Organization publishes a generative AI exposure index in ILO Working Paper 140 (CC BY 4.0) for ISCO-08 unit groups; AI Changing Work links those groups to O*NET occupations by applying the U.S. Bureau of Labor Statistics ISCO-08 to 2010 SOC and 2010 SOC to 2018 SOC crosswalks as published, without case-by-case selection, and these crosswalks match many groups only in part. Where several unit groups are linked, each group's published value is listed, and any summary shows only the lowest and highest of those values with the number of groups; no exposure figure is averaged or recalculated. Any employment figures are published by the U.S. Bureau of Labor Statistics for the SOC group containing this occupation. Each source is credited where its figures are shown.
O*NET OnLine: 19-4012.01 Precision Agriculture Technicians
KSCO 코드·명칭: 한국표준직업분류(제8차 개정) — 통계청 고시 제2024-328호 (2024-07-01 고시, 2025-01-01 시행). 저작권법 제7조 제2호의 고시 항목이다. 명칭 표기(가운뎃점·띄어쓰기)는 해설서 2차 정오 반영판의 표기를 따랐으며, 고시 항목표와는 18개 명칭에서 가운뎃점 글리프나 띄어쓰기만 다르다. 통계청은 2025년 10월 국가데이터처로 개편되었다. 이 페이지의 KSCO 연결은 통계청·국가데이터처의 공식 연계표가 아니다.