Key Takeaways
- 52% of workers say they need to learn new skills due to technology changes
- 54% of workers say they need to learn new skills due to the introduction of new technology (2023 survey across OECD countries)
- 41% of workers say they are willing to take training/learning opportunities to adapt to new demands (OECD, 2019)
- 27% of employers globally report challenges finding workers with skills needed to implement technology/digital transformation (World Economic Forum, 2023)
- $2.8 billion global agriculture technology (agtech) training and advisory ecosystem spend estimated for 2023 (per ITC / agri training market mapping, 2024)
- In the EU, 5.4 million people were trained through the European Social Fund (ESF) related support in 2021 (European Commission ESF+ reporting)
- EU’s 2021-2027 European Social Fund+ has 26% of total budget earmarked for social inclusion and poverty reduction and also supports skills and training, with €99.3 billion allocated to ESF+ (EC overview)
- In the US, 76% of employers provided some form of training for their workforce in 2022 (BLS National Compensation Survey / Employer provided training estimates)
- In Australia, 58% of workplaces provided some type of training to employees in 2021 (NCVER, 2022 workplace training survey)
- 2.4x higher knowledge retention with spaced learning versus traditional cramming (peer-reviewed meta-analysis, 2019)
- 18% average productivity gain after implementing effective training programs (meta-analysis across industries, 2019)
- 43% of adults in a skills-training evaluation reported improved job performance after training (OECD 2021 evaluation synthesis)
- In OECD countries, 9% of jobs are at high risk of automation according to risk assessments summarized in OECD (2019)
- €5.4 billion total EU CAP spending (2021-2027) is earmarked for knowledge transfer and advisory services (European Commission, CAP overview)
- In the EU, the Digital Europe Programme has a budget of €7.5 billion for 2021-2027, supporting digital skills initiatives (European Commission)
Agricultural jobs are rapidly changing, but workers and employers need faster upskilling to adopt technology.
Related reading
- Upskilling And Reskilling In IndustryUpskilling And Reskilling In The Agriculture Industry Statistics
- Upskilling And Reskilling In IndustryUpskilling And Reskilling In The Material Handling Industry Statistics
- Upskilling And Reskilling In IndustryUpskilling And Reskilling In The Food Processing Industry Statistics
- Upskilling And Reskilling In IndustryUpskilling And Reskilling In The Automotive Aftermarket Industry Statistics
01 · Category
Workforce Skills Gap4 stats
Workforce Skills Gap Interpretation
02 · Category
Market Size5 stats
Market Size Interpretation
03 · Category
Training Adoption4 stats
Training Adoption Interpretation
More related reading
04 · Category
Performance Metrics7 stats
Performance Metrics Interpretation
05 · Category
Policy And Technology8 stats
Policy And Technology Interpretation
Technology-driven skills needs in agriculture
A large share of workers and employers report technology-related skills gaps, and most agricultural job roles are expected to change with automation—highlighting the urgency of upskilling and reskilling.
Cite This Report
This report is designed to be cited. We maintain stable URLs and versioned verification dates. Copy the format appropriate for your publication below.
Elif Demirci. (2026, February 13). Upskilling And Reskilling In The Agricultural Industry Statistics. Gitnux. https://gitnux.org/upskilling-and-reskilling-in-the-agricultural-industry-statistics
Elif Demirci. "Upskilling And Reskilling In The Agricultural Industry Statistics." Gitnux, 13 Feb 2026, https://gitnux.org/upskilling-and-reskilling-in-the-agricultural-industry-statistics.
Elif Demirci. 2026. "Upskilling And Reskilling In The Agricultural Industry Statistics." Gitnux. https://gitnux.org/upskilling-and-reskilling-in-the-agricultural-industry-statistics.
Sources & references
28 datasets cited across this report · attribution is report-level
+12 additional datasets cited (not shown individually)

