Analyzing the State of the Agricultural Land Market in the World and in Ukraine
Bibliographic record
Abstract
The article is aimed at studying the international experience of using indices relating to agricultural markets; identifying global trends in the value of agricultural land in different world countries; analyzing the state of the agricultural land market in Ukraine since its opening. It is determined that at the international level a number of indices are being calculated, allowing to obtain assessments of both the state and the trends in the development of agricultural markets. Among them are The Indxx Global Agriculture Index (IGAI); FAO Food Price Index (FFPI); Global Farmland Index offered by Savills. It is determined that the Global Farmland Index Savills is calculated according to the average cost of agricultural land/arable land in US dollars per hectare in 15 key agricultural land markets – Argentina, Australia, Brazil, Great Britain, Denmark, Ireland, Canada, Germany, New Zealand, Poland, Romania, USA, Hungary, Uruguay, and France. The basis for comparison are the value of the year of 2002 (2002 = 100). Analysis of the agricultural land market in 15 countries showed that the highest land prices are in Germany, New Zealand, Ireland, the United Kingdom and Denmark – more than 20 thousand USD per hectare. The lowest land prices are observed in South America, as well as in Hungary and Romania. When analyzing the state of the agricultural land market since its opening on July 1, 2021, Ukraine indicates a constant increase in the number of land operations, an increase in the volume of land sold and a decrease in the weighted average value of land.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".