Global tendencies in pork meat - production, trade and consumption
Bibliographic record
Abstract
World meat production is anticipated to stagnate in 2016, rising by a mere 0.3% to 320.7 million tonnes. Increases in output are expected in the United States, Brazil, the EU, India and the Russian Federation, while reduced production is foreseen for China, Australia and South Africa. Global meat trade is forecast to recover in 2016, growing by 2.8% to 30.6 million tonnes, which would represent a return to trend, after a fall in 2015. World production of pig meat in 2016 is forecast to decrease marginally, by 0.7% to 116.4 million tonnes, thus registering a second year of virtual stagnation. As in 2015, lower output in China, which accounts for almost half the world total, is the main reason for the slowdown. An unfavourable feed-pork price ratio in the country and new environmental regulations have caused farmers to reduce breeding sows, stalling growth. China’s production is projected to be 54 million tonnes, down 2.5% from the previous year. Elsewhere in Asia, the Philippines and Vietnam could boost output. Also, production in Japan and the Republic of Korea may expand, as the industry recovers from outbreaks of PED, which reduced piglet numbers in the previous two years. Recovery from the effects of PED has been faster in the United States, where a second year of growth is anticipated, when production could increase by 1.9% to a record 11.3 million tonnes. Output in Mexico also continues to recover, following a PED outbreak in 2014, and may rise in 2016 by 2.0% to 1.3 million tonnes. Pork meat trade could experience a second year of growth, increasing by 4.4% to 7.5 million tonnes – a record level. Lower international prices have stimulated trade. Most of the principal importing countries are anticipated to increase their purchases, including Mexico, China, the Russian Federation, the United States, Japan, the Republic of Korea and Australia. In response to rising demand, exports are projected to grow, in particular those of the United States, Canada, the EU and Brazil (FAO, 2016). Summarizing, in this study we wish to examine how evolve the world pork meat production, trade and consumption, and to demonstrate the main consuming countries, highlighting the role of China, as it is the most populated country in the world with its 1.4 billion inhabitants. JEL Code: Q13, Q12
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".