The economic implications of combining fibre flax contracting along with futures and options to control for farm revenue instability in Quebec /
Bibliographic record
Abstract
Due to a rising interest in natural fibres for textiles as well as environmental concerns, the demand for fibre flax has increased in recent decades. It was, therefore, with great enthusiasm that Canadian farmers welcomed, in 1997, the opening of a flaxprocessing unit in the region of Salaberry-de-Valleyfield, Quebec. The purpose of this study was to investigate the economic viability of fibre flax contracting as an alternative activity for field-crop producers in Quebec. A risk-programming model called minimization of total absolute deviation (MOTAD) was developed to better approach this issue. The MOTAD takes into account the variability in income that stems from uncertainty in commodity-market prices and yields. In addition, five different marketing strategies for pricing grain corn and soybeans were included in the model. These pricing techniques combined the use of futures and options markets. In a global agricultural system, where international commitments force governments to cut subsidies, reducing income variability for risk-averse farmers becomes a critical challenge. This study offered to assess the contribution of both contracting and futures markets as alternative market instruments for risk management. Five portfolio farm plans were identified for 200- and 300-hectare farm sizes. The results showed that gains through fibre flax contracting, in terms of risk reduction, exist only for the farm plans with lower levels of income and risk. Moreover, simulations demonstrated that the use of futures and options markets can help maximize overall net farm return.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".