Crop Farm Labourers

← The GenAI exposure gradient

On the International Labour Organization's 2025 global study, the 8 task statements that define Crop Farm Labourers (ISCO-08 9211) score an average of 0.09 on a 0–1 exposure scale — more exposed than about 1% 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 8 scored tasks for this occupation, sorted into the six exposure bands — cool (human ground) to hot (almost fully assistable).

The most-exposed task

“Grading, sorting, bunching and packing produce into containers;”

Scores 0.13 on the 2025 scale. The task of grading, sorting, bunching, and packing produce into containers involves significant manual labor, dexterity, and physical presence, which current Generative AI cannot perform directly. Similar tasks in the context, such as "Sorting and preparing for further processing of fiber waste" (adjusted score: 0.125) and "Packing goods and preparing them for shipment" (adjusted score: 0.153), also involve manual handling and have lower automation potential. This task is physically oriented with minimal room for automation via AI, aside from potential logistical optimizations like providing instructions based on quality standards or generating packing lists. The predominant manual and sensory components, as observed in similar tasks, highlight the limited automation potential, justifying a low adjusted score. In a high-income country like Poland, while technology can assist in process optimization, the core physical tasks remain largely unaffected by AI automation. Thus, the score of 0.1 reflects the realistic capabilities of Generative AI in such tasks.

Moving fastest, 2023 → 2025

“Digging and shovelling to clear ditches or for other purposes;”

Model capability on this task changed by +0.03 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 9211, 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.

• Farmworkers and Laborers, Crop, Nursery, and Greenhouse
• Agricultural Workers, All Other

In context

Part of the 9 - Elementary occupations major group. Return to the full gradient to see how the whole group sits.

Crop Farm Labourers sit at the 1st percentile of the global GenAI exposure gradient

• Across 427 international occupations scored by the ILO, Crop Farm Labourers rank in the 1st 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 fell by 0.01 between the 2023 and 2025 model-capability snapshots. (ILO / Gmyrek et al. (2025), 2023→2025)
• Its most-exposed task: "Grading, sorting, bunching and packing produce into containers;". (ILO / Gmyrek et al. (2025))

Source: Singulariki — "Crop Farm Labourers". https://singulariki.com/gradient/9211-crop-farm-labourers.html
Note: AI task overlap measures what today's AI can attempt, not automation, job loss, or a forecast.