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.

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
出典データセット（XLSX ファイルのダウンロード）
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
出典データセット（CSV ファイルのダウンロード）
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
出典データセット（PDF ファイルのダウンロード）

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。

Task-level exposure

露出のある作業のみ

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

O*NET-SOC 2010 の職業 19-4099.02 Precision Agriculture Technicians の作業です。当サイトが O*NET-SOC 2010→2019 公式クロスウォークによりこの職業に結び付けました

Task (O*NET 20.1)	Claude.aiRaw / share %	APIRaw / share %
Contact equipment manufacturers for technical assistance, as needed. 19-4099.02	0.012276.6	—0
Identify spatial coordinates, using remote sensing and Global Positioning System (GPS) data. 19-4099.02	0.002213.6	0.0021100.0
Provide advice on the development or application of better boomspray technology to limit the overapplication of chemicals and to reduce the migration of chemicals to areas other than the fields being treated. 19-4099.02	0.00169.7	—0
Not observed on any surface — 20 task(s) — These tasks have no row in the source for this release. The 0 in the share row is a display-stage composition ratio; absence is what the — in the raw row states.
Program farm equipment, such as variable-rate planting equipment or pesticide sprayers, based on input from crop scouting and analysis of field condition variability.	—0	—0
Compare crop yield maps with maps of soil test data, chemical application patterns, or other information to develop site-specific crop management plans.	—0	—0
Install, calibrate, or maintain sensors, mechanical controls, GPS-based vehicle guidance systems, or computer settings.	—0	—0
Collect information about soil or field attributes, yield data, or field boundaries, using field data recorders and basic geographic information systems (GIS).	—0	—0
Demonstrate the uses and 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, and computer mapping software.	—0	—0
Divide agricultural fields into georeferenced zones, based on soil characteristics and production potentials.	—0	—0
Compile and analyze geospatial data to determine agricultural implications of factors such as soil quality, terrain, field productivity, fertilizers, and weather conditions.	—0	—0
Develop soil sampling grids or identify sampling sites, using geospatial technology, for soil testing on characteristics such as nitrogen, phosphorus, and potassium content, pH, and micronutrients.	—0	—0
Identify areas in need of pesticide treatment by analyzing geospatial data to determine insect movement and damage patterns.	—0	—0
Recommend best crop varieties or seeding rates for specific field areas, based on analysis of geospatial data.	—0	—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.	—0	—0
Prepare reports summarizing field productivity and profitability in graphical or tabular form.	—0	—0
Process and analyze data from harvester monitors to develop yield maps.	—0	—0
Analyze remote sensing imagery to identify relationships between soil quality, crop canopy densities, light reflectance, and weather history.	—0	—0
Apply knowledge of government regulations when making agricultural recommendations.	—0	—0
Document and maintain records of precision agriculture information.	—0	—0
Draw or read maps, such as soil, contour, or plat maps.	—0	—0
Advise farmers on upgrading Global Positioning System (GPS) equipment to take advantage of newly installed advanced satellite technology.	—0	—0
Apply precision agriculture information to specifically reduce the negative environmental impacts of farming practices.	—0	—0
Participate in efforts to advance precision agriculture technology, such as developing advanced weed identification or automated spot spraying systems.	—0	—0

Values in this tab are predicted labels, not observations. Eloundou et al. (2023) published two rating regimes — human raters and GPT-4 — and the β shown here is derived from the GPT-4 rater basis alone; the same task can take a different value under the other regime. The unit and the meaning differ from the observed shares (%) in the other tabs, so do not place them on the same axis.

Task (O*NET 27.2)	βE1 + 0.5 × E2
Contact equipment manufacturers for technical assistance, as needed. O*NET Task ID 18290	1.0
Document and maintain records of precision agriculture information. O*NET Task ID 18295	1.0
Program farm equipment, such as variable-rate planting equipment or pesticide sprayers, based on input from crop scouting and analysis of field condition variability. O*NET Task ID 18278	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. O*NET Task ID 18279	0.5
Collect information about soil or field attributes, yield data, or field boundaries, using field data recorders and basic geographic information systems (GIS). O*NET Task ID 18281	0.5
Identify spatial coordinates, using remote sensing and Global Positioning System (GPS) data. O*NET Task ID 18282	0.5
Divide agricultural fields into georeferenced zones, based on soil characteristics and production potentials. O*NET Task ID 18284	0.5
Identify areas in need of pesticide treatment by analyzing geospatial data to determine insect movement and damage patterns. O*NET Task ID 18287	0.5
Recommend best crop varieties or seeding rates for specific field areas, based on analysis of geospatial data. O*NET Task ID 18288	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. O*NET Task ID 18289	0.5
Analyze remote sensing imagery to identify relationships between soil quality, crop canopy densities, light reflectance, and weather history. O*NET Task ID 18293	0.5
Draw or read maps, such as soil, contour, or plat maps. O*NET Task ID 18296	0.5
Advise farmers on upgrading Global Positioning System (GPS) equipment to take advantage of newly installed advanced satellite technology. O*NET Task ID 19800	0.5
Apply precision agriculture information to specifically reduce the negative environmental impacts of farming practices. O*NET Task ID 19801	0.5
Participate in efforts to advance precision agriculture technology, such as developing advanced weed identification or automated spot spraying systems. O*NET Task ID 19802	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. O*NET Task ID 19803	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. O*NET Task ID 20872	0.5
Analyze data from harvester monitors to develop yield maps. O*NET Task ID 20874	0.5
Analyze geospatial data to determine agricultural implications of factors such as soil quality, terrain, field productivity, fertilizers, or weather conditions. O*NET Task ID 20875	0.5
Prepare reports in graphical or tabular form, summarizing field productivity or profitability. O*NET Task ID 20876	0.5
他 10 件の作業を表示 ▾折りたたむ ▴
Install, calibrate, or maintain sensors, mechanical controls, GPS-based vehicle guidance systems, or computer settings. O*NET Task ID 18280	0.0
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. O*NET Task ID 20873	0.0

β = E1 + 0.5 × E2 · E1 = tasks where direct LLM access alone cuts time by at least 50%, E2 = tasks where software built on top of an LLM cuts time by at least 50%. Values take only 0 / 0.5 / 1.0.

Data sources & licenses — O*NET®, Anthropic Economic Index, Eloundou et al. (2023): see full notices on the Credits page

O*NET 31.0 のタスク全件（22件）

この職業の O*NET 31.0 のタスク全件（英語原文、種別はコア・補足）。露出値はこの一覧には載せず、上の表で各出典が用いた O*NET 版の文言の横にあります。

タスク（O*NET 31.0）	種類
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
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
Install, calibrate, or maintain sensors, mechanical controls, GPS-based vehicle guidance systems, or computer settings.	コア
Collect information about soil or field attributes, yield data, or field boundaries, using field data recorders and basic geographic information systems (GIS).	コア
Identify spatial coordinates, using remote sensing and Global Positioning System (GPS) data.	コア
Divide agricultural fields into georeferenced zones, based on soil characteristics and production potentials.	コア
Identify areas in need of pesticide treatment by analyzing geospatial data to determine insect movement and damage patterns.	コア
Recommend best crop varieties or seeding rates for specific field areas, based on analysis of geospatial data.	コア
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
Contact equipment manufacturers for technical assistance, as needed.	コア
Analyze remote sensing imagery to identify relationships between soil quality, crop canopy densities, light reflectance, and weather history.	コア
Document and maintain records of precision agriculture information.	コア
Draw or read maps, such as soil, contour, or plat maps.	コア
Advise farmers on upgrading Global Positioning System (GPS) equipment to take advantage of newly installed advanced satellite technology.	コア
Apply precision agriculture information to specifically reduce the negative environmental impacts of farming practices.	コア
Participate in efforts to advance precision agriculture technology, such as developing advanced weed identification or automated spot spraying systems.	コア
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
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
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
Analyze data from harvester monitors to develop yield maps.	コア
Analyze geospatial data to determine agricultural implications of factors such as soil quality, terrain, field productivity, fertilizers, or weather conditions.	コア
Prepare reports in graphical or tabular form, summarizing field productivity or profitability.	コア

Anthropic「What work can robots do?」2026-09

0.35/ 3· ロボット露出指数

0 不可3 非定型の環境

尺度・母数・出典

身体的なタスクそれぞれに、実際に導入・販売・実演されたロボットがこなせる環境に応じて 0–3 の等級を付け（0 不可 · 1 フェンスで囲われた機械セルや瓶詰めラインのようなロボット専用設備 · 2 病院の薬剤部やコンテナ港のような管理された施設 · 3 一般家庭や建設現場のような非定型の環境）、勤務時間に占める割合で平均した値

この職業の O*NET 29.3 タスクのうち身体的なタスクが対象

等級と時間の割合は Anthropic が Claude Opus 5（ウェブ検索）で推定しました（2026年時点）。

出典データセット

ロボット露出指数は、このサイトの AI 露出度とは別の指標であり、足し合わせたり比べたりすることはできません。実際の導入やコスト競争力を示すものではなく、0 には身体的なタスクのない職業も含まれます。

身体的なタスクごとの等級

ロボット環境等級（E0–E3）

E0 ロボット不可詳細
実際に導入・販売・実演されたロボットでは、ロボット専用設備でもこなせないと評価
E1 ロボット専用設備詳細
フェンスで囲われた機械セルや瓶詰めラインのように、機械を中心に作られた設備でこなせると評価
E2 管理された施設詳細
病院の薬剤部やコンテナ港のように、機関が作業基準を保っている人の作業施設でこなせると評価
E3 非定型の環境詳細
一般家庭や建設現場のように、作業基準を保つ機関がない環境でこなせると評価

身体的なタスクに分類されたタスクのみを掲載しています。その他のタスクには出典で等級がないため載せていません。

タスク（O*NET 29.3）	ロボット環境等級
Install, calibrate, or maintain sensors, mechanical controls, GPS-based vehicle guidance systems, or computer settings.	E0 ロボット不可
Collect information about soil or field attributes, yield data, or field boundaries, using field data recorders and basic geographic information systems (GIS).	E3 非定型の環境
Participate in efforts to advance precision agriculture technology, such as developing advanced weed identification or automated spot spraying systems.	E3 非定型の環境
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.	E0 ロボット不可

雇用見通し（BLS）

この O*NET 職業だけの値ではなく、SOC 19-4012 グループ（Agricultural technicians）全体の値です。

雇用者数、2025: 20K
雇用者数見通し、2035: 21K
変化率 (2025–35): +5.4%
年間賃金の中央値、2025: $49,630
入職時に通常必要な学歴: Associate's degree

最低 $30,890BLS EP 2025–35 の詳細職業825件の賃金中央値の範囲最高 $559,030

出典: U.S. Bureau of Labor Statistics, Employment Projections 2025–35 — Employment by detailed occupation, 2025 and projected 2035

韓国職業分類との対応（KSCO）

AI Changing Work が作成した Korea Occupation Direct Crosswalk v1.1 現行版の対応です。関係の値は O*NET-SOC 職業の側から読みます。

KSCO コード	KSCO 名称	関係	状態
21321	농업 시험원	関連（対応ではない）	確定

関係と状態の定義(kr-direct v1.1)

ILO（国際労働機関）の職業説明

以下のISCO-08職業群は、AI Changing Workが米国労働統計局（BLS）の発行するクロスウォーク（2018 SOC→2010 SOC、2010 SOC→ISCO-08）を個別の選択なしにそのまま適用して、この職業に結び付けたものです。結び付きは全体的な対応の場合も部分的な対応の場合もあります。

ISCO-08 3142Agricultural Technicians
農業技術者

出典: 総務省「国際標準職業分類（ISCO）2008年改定版（仮訳）分類項目名一覧（和英対照）」— 原著：ILO ISCO-08 © International Labour Organization（日本語訳は総務省がILOの許可を得て作成）

原文を見る

以下の定義・作業・職業例は、この職業ではなく、当該ISCO-08職業群についてのILOによる説明です。

Agricultural technicians perform tests and experiments, and provide technical and scientific support to agricultural scientists, farmers and farm managers.

作業

(a) preparing materials and equipment for experiments, tests and analyses;
(b) collecting and preparing specimens such as soils, plant or animal cells, tissues or parts or animal organs for experiments, tests and analyses;
(c) assisting with and performing experiments, tests and analyses applying methods and techniques such as microscopy, histochemistry, chromatography, electrophoresis and spectroscopy;
(d) identifying pathogenic micro-organisms and insects, parasites, fungi and weeds harmful to crops and livestock, and assisting in devising methods of control;
(e) analysing produce to set and maintain standards of quality;
(f) conducting or supervising operational programmes such as fish hatchery, greenhouse and livestock production programmes;
(g) analysing samples of seeds for quality, purity and germination rating;
(h) collecting data and estimating quantities and costs of materials and labour required for projects;
(i) organizing maintenance and repairs of research equipment.

職業例

Dairy Technician · Field Crop Technician · Herd Tester · Horticultural Technician · Poultry Technician

Sources: ILO ISCO-08 — details: Credits

ESCO 職業語彙

以下の各項目は、公開されているESCO-O*NET Crosswalk（バージョン1、2022年）で、この職業と一つずつ対応付けられたものです。対応の種類はそのクロスウォークによるもので、表現はAI Changing Workによるものです。

agricultural engineerESCO 2144.1.2重なるが同じではない
Agricultural engineers intervene in a variety of matters within the agriculture field in combination with engineering concepts. They design and develop machinery and equipment for an efficient and sustainable exploitation of the land. They advise on the use of resources in agricultural sites comprising the usage of water and soil, harvesting methods, and waste management.
agricultural scientistESCO 2132.1重なるが同じではない
Agricultural scientists research and study soil, animals and plants with the objective of improving agricultural processes, the quality of agricultural products or the impact of agricultural processes on the environment. They plan and implement projects such as development projects on behalf of clients or institutions.

出典: European Commission, DG EMPL — ESCO API 2026-08-08 (bridge v1.2.1)

ライセンス: CC BY 4.0 (EU Reuse Notice)

原文を見る

ESCO reuse terms and the modification notice are in Credits.

フランス職業分類との対応（ROME）

この職業と ROME 4.0 の職業票（fiche）との対応で、AI Changing Work が AI モデルを用いて判定したものです。関係はこの職業の側から読みます。「暫定」と表示した対応は、同じモデル系列（Claude）の二人の判定者による判定のみ、またはその判定に運営者の規則を適用して採用したものであり、確定した対応より根拠が弱くなります。

ROME コード	ROME 名称（フランス語）	関係
A1301	Technicien / Technicienne en agriculture	関連（対応ではない）
A1304	Conseiller / Conseillère technique agricole	関連（対応ではない）
M1895	Géomaticien / Géomaticienne	関連（対応ではない）

Source: France Travail — Répertoire Opérationnel des Métiers et des Emplois (ROME 4.0), version 61, last updated 15 June 2026 (https://www.data.gouv.fr/datasets/repertoire-operationnel-des-metiers-et-des-emplois-rome). Licence Ouverte 2.0.

ROME codes and titles are reproduced unchanged; the links to O*NET-SOC occupations are AI Changing Work's own judgment and are not a France Travail correspondence.

(AI Changing Work link release: fr-direct v1)

スペイン職業分類との対応（CNO-11）

この職業と CNO-11 の職業との対応で、AI Changing Work が AI モデルを用いて判定したものです。関係はこの職業の側から読みます。

CNO-11 コード	CNO-11 名称（スペイン語）	関係
2424	Ingenieros técnicos agrícolas	関連（対応ではない）
3142	Técnicos agropecuarios	関連（対応ではない）

Source: Instituto Nacional de Estadística (INE), National Classification of Occupations 2011 (CNO-11, Royal Decree 1591/2010) — www.ine.es · Licensed CC BY 4.0. CNO-11 codes and Spanish titles are reproduced unchanged; the links to O*NET occupations are AI Changing Work's own judgment and are not an INE correspondence.

(AI Changing Work link release: es-direct v1)

ドイツ職業分類との対応（KldB）

この職業と KldB の職業との対応で、AI Changing Work が AI モデルを用いて判定したものです。関係はこの職業の側から読みます。

KldB コード	KldB 名称（ドイツ語）	関係
11113	Berufe in der Landtechnik - komplexe Spezialistentätigkeiten	関連（対応ではない）
11183	Berufe in der Landwirtschaft (sonstige spezifische Tätigkeitsangabe) - komplexe Spezialistentätigkeiten	関連（対応ではない）暫定

Source: © Statistik der Bundesagentur für Arbeit — Klassifikation der Berufe 2010 – überarbeitete Fassung 2020, https://statistik.arbeitsagentur.de. KldB codes and titles are reproduced unchanged; the links to O*NET occupations are AI Changing Work's own mapping (eigene Zuordnung) and are not a Bundesagentur für Arbeit correspondence.

(AI Changing Work link release: de-direct v1)

日本職業分類との対応（JSCO）

この職業と JSCO の職業との対応で、AI Changing Work が AI モデルを用いて判定したものです。関係はこの職業の側から読みます。

JSCO コード	JSCO 名称（日本語）	関係
061	農林水産技術者	この職業のほうが狭い

Source: Ministry of Internal Affairs and Communications (MIC), Japan Standard Occupational Classification (Dec. 2009, MIC Notification No. 555). JSCO codes and Japanese titles are reproduced unchanged; the links to O*NET occupations are AI Changing Work's own judgment and are not a MIC correspondence.

(AI Changing Work link release: jp-direct v1)

このページのデータの出典・ライセンスと AI Changing Work による改変・翻訳の告知：クレジットと出典