Miscanthus Production in Eastern Canada as Affected by Genotypes and Nitrogen Levels
Bibliographic record
Abstract
Interest in new biomass and biofuel crops has soared in the last few years due to the changing climate and the search for renewable energy options. Under eastern Canada conditions, perennial C4 grasses can produce 8-11 Mg dry matter ha-1 year-1 [1,2]. However, Miscanthus spp. seems to be more promising with dry matter production values exceeding 30 Mg ha-1 year-1 [3], [4]. In this study, four genotypes of hybrid miscanthus (Miscanthus sinensis x M. sacchariflorus) were planted in Kemptville, Ontario, Canada in 2009. Genotypes “M1 Select”, “Nagara”, “Polish” and “Amuri” were established from rhizomes spaced at 75 cm squares. The effect of four nitrogen rates (0, 40, 80 and 120 kg N ha-1 year-1) was studied on growth and production parameters. The first summer and winter (with minimum temperatures reaching -40 °C) were the most crucial for the establishment of the crop, and any plants that survived that first year have successfully established and regrew every year. Dry matter production generally increased with the level of nitrogen application and ranged between 25-50 Mg ha-1 year-1 in 2014. Genotype “Amuri” was the most productive while “M1 Select” was the least productive. Leaf area index increased with nitrogen application rate and was highest for “Nagara”. Similarly, leaf SPAD absorbance increased with nitrogen levels and was lower in “M1 Select” than the three other genotypes. Our results show the potential of miscanthus production under Canadian conditions with low nitrogen inputs.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 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".