Nonstructural Carbohydrate Concentrations in Timothy as Affected by N Fertilization, Stage of Development, and Time of Cutting
Bibliographic record
Abstract
Forages with increased total nonstructural carbohydrate (TNC) concentrations improve the N‐use efficiency of dairy cows. This study determined the effect of time of cutting (0700 vs. 1500 h), stage of development (heading and anthesis), and N fertilization (30, 50, 70, 90, and 110 kg N ha−1 as NH4NO3) on the fermentable carbohydrate concentration of timothy (Phleum pratense L.) grown in northern Ontario, Canada. Forage dry matter (DM) yield and concentrations of N, starch, sucrose, glucose, fructose, and fructans were determined. Concentration of soluble carbohydrates (SC) was estimated by the sum of sucrose, glucose, and fructose, while TNC was obtained by adding SC and starch. Nitrogen fertilization did not affect forage carbohydrate concentrations, but slightly increased DM yield. Forage had higher TNC, starch, sucrose, and fructose concentrations (+9 to 63%) but lower glucose concentration (−27%) when harvested at anthesis compared with heading. Concentration of high degree of polymerization (HDP) fructans was close to 0 at heading and increased to 64.3 mg g−1 DM at anthesis. The afternoon‐cut forage had higher TNC (+53%), SC (+60%), and sucrose (+87%) concentrations than the morning‐cut forage; this positive effect was greater when timothy was harvested at heading compared with anthesis. Starch and HDP fructan concentrations were similar for both times of cutting, whereas results for glucose and fructose were inconsistent. Delayed cutting during the day and an extended growth period increased timothy TNC and HDP fructan concentrations to an extent likely to improve the N use efficiency of dairy cows.
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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 | 0.000 | 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) | 0.001 | 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".