Diurnal Variations of Nonstructural Carbohydrates and Nutritive Value in Alfalfa
Bibliographic record
Abstract
ABSTRACT Increasing the nonstructural carbohydrate (NSC) concentration of forages improves N use efficiency by dairy cows. We studied the diurnal variations of NSC concentration and other nutritive value attributes in alfalfa (Medicago sativa L.) to determine the best time during the day to cut alfalfa for maximizing NSC concentration. Field‐grown alfalfa was cut every 2 h between 0600 and 2000 h on six different days around the early flower stage of development during spring growth and summer regrowth at two sites in eastern Canada. Concentrations of NSC [soluble carbohydrates (SC) plus starch], neutral detergent fiber (NDF), acid detergent fiber (ADF), and N, along with in vitro true digestibility (IVTD) of dry matter (DM) and in vitro digestibility of NDF were determined. The NSC concentration increased during the day in all growth cycles and sites due mostly to an increase in starch concentration. The diurnal increase of NSC concentration, however, varied with growth cycles and sites from 15.5 to 41.9 g kg−1 DM, and it was accompanied by a decrease of 1 to 2 g kg−1 DM in N concentration. At the site where the increase in alfalfa NSC concentration was greater than 30 g kg−1 DM, concentrations of ADF and NDF also decreased by 9 to 27 g kg−1 DM while IVTD increased by 3 to 16 g kg−1 DM. Greatest NSC concentrations were reached between 11 and 13 h after sunrise and the best time during the day to cut alfalfa for maximizing NSC concentration in eastern Canada is between 1600 and 1800 h.
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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) | 0.000 | 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 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".