Mixed Crop Growers

← The GenAI exposure gradient

On the International Labour Organization's 2025 global study, the 11 task statements that define Mixed Crop Growers (ISCO-08 6114) score an average of 0.17 on a 0–1 exposure scale — more exposed than about 23% 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

“Promoting and marketing products, arranging the sale, purchase and transportation of produce and supplies and maintaining and evaluating records of activities and transactions;”

Scores 0.42 on the 2025 scale. The task of promoting and marketing products, arranging the sale, purchase, and transportation of produce and supplies, and maintaining and evaluating records involves a mix of automation-friendly activities and those requiring human intervention. Generative AI can significantly assist in automating tasks such as data entry, generating marketing content, analyzing sales data, and optimizing logistics routes, similar to tasks like "Promoting manufactured eco-friendly products" (Score: 0.385) and "Organizing the sale of fruits" (Score: 0.15). However, managing complex negotiations, strategic decision-making, and building personal relationships with clients are areas where human skills are indispensable, akin to tasks such as "Organizing promotional events for the salon" (Score: 0.365). Given the high-income country context like Poland where technology and digital infrastructure are advanced, the potential for automation is higher but still requires significant human oversight, particularly for strategic and relationship-building elements. Therefore, based on the semantically similar tasks and considering the capabilities of Generative AI for automatable aspects while recognizing the necessity of human judgment, an adjusted score of 0.395 is appropriate.

Moving fastest, 2023 → 2025

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

Model capability on this task changed by +0.21 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 6114, 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.

Mixed Crop Growers sit at the 23rd percentile of the global GenAI exposure gradient

• Across 427 international occupations scored by the ILO, Mixed Crop Growers rank in the 23rd 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 0.00 between the 2023 and 2025 model-capability snapshots. (ILO / Gmyrek et al. (2025), 2023→2025)
• Its most-exposed task: "Promoting and marketing products, arranging the sale, purchase and transportation of produce and supplies and maintaining and evaluating records of activities and transactions;". (ILO / Gmyrek et al. (2025))

Source: Singulariki — "Mixed Crop Growers". https://singulariki.com/gradient/6114-mixed-crop-growers.html
Note: AI task overlap measures what today's AI can attempt, not automation, job loss, or a forecast.