Mixed Crop and Livestock Farm Labourers

← The GenAI exposure gradient

On the International Labour Organization's 2025 global study, the 11 task statements that define Mixed Crop and Livestock Farm Labourers (ISCO-08 9213) score an average of 0.11 on a 0–1 exposure scale — more exposed than about 5% 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 livestock, reporting on their condition;”

Scores 0.15 on the 2025 scale. The task of monitoring livestock and reporting on their condition primarily involves physical presence, real-time observation, and hands-on interaction with animals. Generative AI can support this task by providing data analysis and generating reports based on collected data, but it cannot autonomously replace the nuanced decision-making and observational skills required. Semantically similar tasks, like "Monitoring the progress of a veterinary patient" and "Recognizing symptoms of diseases based on livestock appearance and behavior," have adjusted scores of 0.215 and 0.335 respectively, reflecting limited automation potential due to the need for human expertise in these areas. Additionally, tasks such as "Maintaining necessary breeding and economic documentation" have scores around 0.275, but these involve more structured data management, which AI handles better than physical tasks. Considering these aspects, and the typical infrastructure available in Poland, a score of 0.16 accurately reflects generative AI's limited but supportive role in automating this task.

Moving fastest, 2023 → 2025

“Watering, thinning and weeding crops by hand or using hand tools;”

Model capability on this task changed by +0.09 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 9213, 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
• Farmworkers, Farm, Ranch, and Aquacultural Animals
• 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.

Mixed Crop and Livestock Farm Labourers sit at the 5th percentile of the global GenAI exposure gradient

• Across 427 international occupations scored by the ILO, Mixed Crop and Livestock Farm Labourers rank in the 5th 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 0.02 between the 2023 and 2025 model-capability snapshots. (ILO / Gmyrek et al. (2025), 2023→2025)
• Its most-exposed task: "Monitoring livestock, reporting on their condition;". (ILO / Gmyrek et al. (2025))

Source: Singulariki — "Mixed Crop and Livestock Farm Labourers". https://singulariki.com/gradient/9213-mixed-crop-and-livestock-farm-labourers.html
Note: AI task overlap measures what today's AI can attempt, not automation, job loss, or a forecast.