Key Takeaways
- 13.3 kg of cheese per capita consumption in Japan in 2022
- 3.0% global trade growth for cheese in 2022 (value) compared with 2021
- $15.6 billion global import value for cheese in 2022
- $18.9 billion global export value for cheese in 2022
- Global cheese market size was estimated at $102.9 billion in 2023 and projected to reach $126.6 billion by 2030 (forecast CAGR included in report)
- Food safety investigations report that cheese-related listeriosis is associated with at least 5% of foodborne listeria cases in certain surveillance summaries (share reported in public health surveillance literature)
- In the United States, retail sales of natural cheese reached $23.6 billion in 2023 (category retail sales)
- Rennet enzyme doses in cheesemaking are typically adjusted to achieve coagulation within a target time window (coagulation endpoint used in protocol-based manufacturing)
- Curd cutting sizes in traditional cheesemaking are commonly 1–2 cm to regulate whey drainage (process parameter range reported in cheesemaking guidance)
- Ultra-high temperature (UHT) treatment typically uses temperatures of about 135°C or higher for a short time (industry standard parameter)
- Lactose content in many aged cheeses is very low (often near 0 g per serving) due to aging
- A meta-analysis reported that dairy consumption is associated with a modest reduction in risk of colorectal cancer (pooled relative risk around 0.9–0.95 depending on study grouping)
- A systematic review found no consistent association between cheese consumption and increased cardiovascular disease risk when accounting for dietary patterns (direction varies by study design)
- 17.2% of global milk supply is used for cheese production (latest FAO/IDF global milk utilization split by product, year varies by country reporting but reported as a share of global milk supply in the dataset).
- 4.1 million tonnes of cheese were imported globally in 2022 (import volume).
Japan led global cheese demand with 13.3 kg per person in 2022, while worldwide trade hit new highs.
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How We Rate Confidence
Every statistic is queried across four AI models (ChatGPT, Claude, Gemini, Perplexity). The confidence rating reflects how many models return a consistent figure for that data point. Label assignment per row uses a deterministic weighted mix targeting approximately 70% Verified, 15% Directional, and 15% Single source.
Only one AI model returns this statistic from its training data. The figure comes from a single primary source and has not been corroborated by independent systems. Use with caution; cross-reference before citing.
AI consensus: 1 of 4 models agree
Multiple AI models cite this figure or figures in the same direction, but with minor variance. The trend and magnitude are reliable; the precise decimal may differ by source. Suitable for directional analysis.
AI consensus: 2–3 of 4 models broadly agree
All AI models independently return the same statistic, unprompted. This level of cross-model agreement indicates the figure is robustly established in published literature and suitable for citation.
AI consensus: 4 of 4 models fully agree
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.
Marcus Engström. (2026, February 13). Cheese Statistics. Gitnux. https://gitnux.org/cheese-statistics
Marcus Engström. "Cheese Statistics." Gitnux, 13 Feb 2026, https://gitnux.org/cheese-statistics.
Marcus Engström. 2026. "Cheese Statistics." Gitnux. https://gitnux.org/cheese-statistics.
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