Economic Returns to Feed Barley Yield‐increasing and Disease Resistance Research at the Alberta Field Crop Development Centre
Bibliographic record
Abstract
The Alberta Field Crop Development Centre (FCDC) at Lacombe has carried out an extensive research and development program on feed barley since 1973. Prior to 2002, FCDC released 11 hulled and six hull‐less barley varieties that have been adopted by farmers. The primary objective of this study is to estimate an economic rate of return to the FCDC barley research and development program from 1973 to 2001. A secondary objective is to include benefits arising from research that improved feed barley disease resistance in new cultivars in addition to benefits from purely higher‐yielding cultivar research. The analysis uses an ex post economic surplus methodology. Benefits are identified and empirically investigated for three separate FCDC feed barley research thrusts: • benefits arising from FCDC research that developed new, higher‐yielding feed barley cultivars that give a yield advantage • benefits arising from FCDC research that improved feed barley disease resistance in new cultivars that result in yield loss avoidance from disease • benefits arising from FCDC research that developed new feed barley cultivars that yield higher silage production. Of the total benefits from research on feed grain varieties, 52% can be attributed to yield advantage research and 48% to yield loss avoidance research. The overall internal rate of return with base elasticity parameters is estimated at 27%, ranging between 23% and 31%, depending on the assumptions made about the yield advantage and base variety. The IRR was sensitive to changes in supply elasticities and ranges from 20% (∈= 1.5) to 54% (∈= 0.1).
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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 | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".