Agronomic practices for bioethanol production from spring triticale in Alberta
Bibliographic record
Abstract
McKenzie, R. H., Bremer, E., Middleton, A. B., Beres, B., Yoder, C., Hietamaa, C., Pfiffner, P., Kereliuk, G., Pauly, D. and Henriquez, B. 2014. Agronomic practices for bioethanol production from spring triticale in Alberta. Can. J. Plant Sci. 94: 15-22. Triticale (×Triticosecale Wittmack) is an attractive crop for biofuel production due to its high grain yield potential, weed competitiveness, and drought tolerance. Field plot studies were conducted at seven locations across Alberta from 2008 to 2010 to determine optimum agronomic practices (seeding date, seeding rate and N fertilizer rate) for grain and starch production of spring triticale. The yield penalty from delayed seeding was variable, with an average yield decline of only 0.1% per day. Significant yield benefits from increasing seeding rates from 100 to 500 viable seeds m-2 were obtained at site-years with more than 200 mm of growing season precipitation, but were inconsistent or negligible at site-years with less than 200 mm of growing season precipitation. Optimum N fertilizer rates for grain production increased with growing season precipitation, but were not correlated with pre-seeding soil extractable NO3-N levels. Starch concentrations were either unaffected or only slightly affected by seeding date, seeding rate or N fertilizer rate. Thus, agronomic practices that were optimum for triticale grain production were also optimum for starch production. Under good growing conditions, grain production of spring triticale was optimum when seeded at 350 to 450 seeds m-2 and N fertilizer rates of 90 to 150 kg N ha-1.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".