The Most Expensive Agricultural Land Prices in Europe: An Economic Analysis of Tenerife, Canary Islands, Spain
Bibliographic record
Abstract
Agriculture in the Canary Islands has greater limitations than in the rest of Spain due to the cultivation areas being geographically limited and the abrupt orography of the archipelago. As a result, in certain situations, tillage of the land is more complex and costs increase. This study focuses on the island of Tenerife and aims to identify the determining variables that directly affect the price of agricultural land, considering the type of crop. For this purpose, a survey was designed for farm managers on the island of Tenerife and, after analysing the responses, we focused on tubers, legumes, vineyards, bananas, and cereals. A multilinear regression model showed that the highest land price corresponds to those farms destined for banana production, with a value of 16.52 €/m2. The price of agricultural land on the island of Tenerife was found to be eight times higher than the European average. The main factors impacting this value are irrigation, the orography of the land, and the presence of farm buildings.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".