Recent patterns of egg production and trade: a status report on a regional basis
Bibliographic record
Abstract
In this paper the dynamics of global egg production between 1990 and 2007 and patterns of trade in 2006 are analysed. This time period was chosen, as the political landscape has changed considerably since the early 1990s. The dynamics of the global poultry industry over the past 17 years has been remarkable. No other branch of animal production has shown comparable growth rates. Global egg production increased from 35.2 million tons to 62.6 million tons or by 78%. The report shows that the growth has not been homogeneous but that regional shifts occurred which have changed the spatial pattern of egg production and of egg trade considerably. Whereas Asia has become the most dynamic growth centre and is dominating global egg production, Europe and North America have lost importance. More than 75% of the absolute growth of global egg production between 1990 and 2007 were contributed by China and India. The paper also presents a detailed analysis of global and regional trade patterns. In addition to a global overview, regions with the highest egg surplus and egg deficit are identified and characterised. In 2006, Western Europe was the region with the highest egg deficit. It was the most attractive market for shell eggs, with Germany in a leading position. The second major market for shell eggs was Western Asia. In this region, the United Arab Emirates, Iraq, Kuwait and Oman were the main egg importing countries. Other important egg deficit regions were Northern Europe, in particular the United Kingdom, Middle Africa with Angola as the leading importer, and Central Asia with Tajikistan and Kazakhstan. The highest egg surplus showed Southern Europe with Spain as the leading egg exporting country. Southern Asia ranked second with India and Iran as major exporters. Another egg surplus region was Eastern Europe with Poland and Belarus as leading egg exporting countries. In North America, the USA had the highest surplus, in South-East Asia Malaysia and Thailand. It can be expected that the recent spatial pattern of egg trade will not change very much in the near future. Egg trade will be dominated by European countries, most of the trade will, however, be intra-EU trade. The banning of cages in the EU could even make higher imports from adjacent non-EU countries necessary. A second cluster of egg trade will be located in Asia with Southern and South-Eastern Asia as surplus and Western as well as Central Asia as deficit regions. The third cluster will be the NAFTA member countries with the USA as exporting and Canada and Mexico as importing countries.
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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 | 0.001 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".