Field Crop and Vegetable Growers

← The GenAI exposure gradient

On the International Labour Organization's 2025 global study, the 11 task statements that define Field Crop and Vegetable Growers (ISCO-08 6111) score an average of 0.18 on a 0–1 exposure scale — more exposed than about 26% of the 427 placed occupations. Roughly 0% of its tasks fall somewhere on the exposed part of the gradient, and the typical task lands in the Not exposed band.

Exposure is task overlap, not a verdict. A high score means a generative-AI model can do part of the content of these tasks — it says nothing about whether the work is automated, whether anyone uses AI for it today, or whether jobs are lost. The gradient is scored on the international ISCO-08 system; the rest of Singulariki is U.S. O*NET/SOC, bridged below by an approximate, many-to-many crosswalk.

How its tasks split across the gradient

Each of the 11 scored tasks for this occupation, sorted into the six exposure bands — cool (human ground) to hot (almost fully assistable).

The most-exposed task

“Monitoring market activity and conditions, determining types and quantities of crops to be grown, and planning and coordinating production accordingly;”

Scores 0.43 on the 2025 scale. The task of monitoring market activity and conditions, determining types and quantities of crops to be grown, and planning and coordinating production accordingly is complex, involving strategic decision-making supported by extensive data analysis. Generative AI can significantly contribute to this task by processing large amounts of market data, predicting trends, and optimizing crop selection and production planning, similar to tasks like "Planning the types and varieties of cultivated plants, breeds and utility types of animals, and the scale of production," which received a score of 0.4. Despite AI's capabilities in data-driven decision-making, the task still requires human expertise to interpret nuanced market dynamics, apply localized knowledge, and make final strategic decisions, aligning it more closely with higher-scored planning-related tasks in the cluster. Given the context of a high-income country such as Poland, where access to advanced technologies is common, an adjusted score of 0.41 reflects the balance between significant AI assistance and the indispensable need for human oversight.

Moving fastest, 2023 → 2025

“Training and supervising workers in crop production, maintenance duties and health and safety precautions, and hiring and discharging workers and contractors.”

Model capability on this task changed by +0.16 in two years — the gradient is not static, it is filling in.

U.S. occupations this maps to

The American O*NET/SOC roles that crosswalk to ISCO-08 6111, biggest by employment first, via the published (approximate, many-to-many) IBS O*NET-SOC ↔ ISCO-08 correspondence. These are the closest U.S. matches — not an asserted one-to-one identity.

• Agricultural Equipment Operators
• First-Line Supervisors of Farming, Fishing, and Forestry Workers

In context

Part of the 6 - Skilled agricultural, forestry and fishery workers major group. Return to the full gradient to see how the whole group sits.

Field Crop and Vegetable Growers sit at the 26th percentile of the global GenAI exposure gradient

• Across 427 international occupations scored by the ILO, Field Crop and Vegetable Growers rank in the 26th percentile for GenAI task exposure — overlap with what generative AI can attempt, not a projection of displacement. (ILO / Gmyrek et al. (2025) GenAI exposure gradient)
• About 0% of this occupation's tasks fall into an exposed gradient band. (ILO / Gmyrek et al. (2025))
• Mean task exposure rose by 0.01 between the 2023 and 2025 model-capability snapshots. (ILO / Gmyrek et al. (2025), 2023→2025)
• Its most-exposed task: "Monitoring market activity and conditions, determining types and quantities of crops to be grown, and planning and coordinating production accordingly;". (ILO / Gmyrek et al. (2025))

Source: Singulariki — "Field Crop and Vegetable Growers". https://singulariki.com/gradient/6111-field-crop-and-vegetable-growers.html
Note: AI task overlap measures what today's AI can attempt, not automation, job loss, or a forecast.