Net Return Risk for Malting Barley Production in Western Canada as Influenced by Production Strategies
Bibliographic record
Abstract
The objective of this study was to evaluate the impact of agronomic practices on net return (NR) risk for malting barley (Hordeum vulgare L.) production. This study used data from two field experiments conducted from 2005 to 2008 at eight rainfed locations in western Canada. The first part of this study included 30 production strategies of barley type, seeding rate, and N rate for four regions. The second part of this study included 10 production strategies of seeding date and seeding rate for four regions. A stochastic simulation model was specified to compute the NR. Yield, protein, plumpness, and price were random in the model, drawn from multivariate distributions based on field data and historical price data. The malting cultivar CDC Copeland had higher NR than AC Metcalfe or feed barley. Seeding early at a rate of 200 to 300 seeds m−2 had higher NR than late seeding or higher seeding rates. A fertilizer rate of 60 to 90 kg N ha−1 had higher NR. A producer with high risk aversion preferred strategies that were less risky including: less N fertilizer, growing feed barley in regions that have high protein and smaller price premiums for malting, and seeding later.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".