Profitability of Mexican Avocado Production in the Face of an Increase in Exports to the Canadian Market
Bibliographic record
Abstract
Mexico is the main exporter of avocado in the world, while Canada is the seventh importer. To meet its domestic demand, Canada imports about 93.73% of avocados from Mexico. It is worth mentioning that, between 2003 and 2018, the average annual growth rate of Mexican avocado imports in Canada was 14.72%; while, from 2019 to 2021, this same rate averaged -4.65, that is, in this period Mexican avocado imports in Canada fell at an average annual rate of 4.65%. This research aims to determine the viability of increasing the annual growth rate of Mexican avocado exports to the Canadian market. To carry out the work, the avocado market between Mexico and Canada was represented in an econometric model and, with the results, a partial equilibrium analysis was carried out, simulating a 50% annual increase in exports. The results show that an increase in the amount of Mexican avocado exported to the Canadian market that results in an average annual growth rate of 50% is viable in terms of income. The simulation shows that the Benefit/Cost Ratio (B/C R) in avocado production for the states of Jalisco, Michoacan and the State of Mexico, in the hypothetical scenario, would be 1.4831, 1.4257 and 1.5322 respectively.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".