Scrutinizing Pork Price Volatility in the European Union over the Last Decade
Bibliographic record
Abstract
The aim of this study was to analyze the factors that can influence pork prices, particularly the effects of various types of fluctuations on the volatility of pork prices in the European Union as a whole market and individual EU countries. The research material consisted of monthly time series of pork prices collected from 2009 to 2020. These data originated from the Integrated System of Agricultural Information coordinated by the Polish Ministry of Agriculture. Information on global pork production volumes is from the Food and Agriculture Organization Statistics (FAOSTAT) database. Time series of prices were described by the multiplicative model, and seasonal breakdown was performed using the Census X-11 method. The separation of the cyclical component of the trend was performed using the Hodrick-Prescott filter. In 2019, pork production in the European Union totaled 23,954 thousand tonnes, which accounted for 21.8% of global pork production. The largest producers were Germany, Spain, and France, supplying more than half of the pork to the entire European Union market. Pork prices in the EU, averaged over the 2009-2020 period were Euro (EUR) 154.63/100 kg. The highest prices for pork were recorded in Malta, Cyprus, Bulgaria, and Greece, whereas the lowest prices in Belgium, the Netherlands, Denmark, and France. The breakdown of the time series for pork prices confirmed that, in the period from 2009 to 2020, pork prices exhibited considerable fluctuations of both a long-term and medium-term nature as well as short-term seasonal and irregular fluctuations. Prices were higher than average in summer (with a peak in June-August) and lower in winter (January-March). Overall, the proportions of different types of changes in pork prices were as follows: random changes-7.9%, seasonal changes-36.6%, and cyclical changes-55.5%.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".