Forage Nonstructural Carbohydrates and Nutritive Value as Affected by Time of Cutting and Species
Bibliographic record
Abstract
Total nonstructural carbohydrates (TNC) provide readily fermentable energy to rumen microbes and their increased concentration in forages improves N use efficiency in dairy cows (Bos taurus). This study was conducted to compare TNC concentration of grass and legume forage species and to determine how variations of TNC concentration caused by time of cutting during the day differ among forage species and how these variations are related to other attributes of forage nutritive value. Six grass and two legume species were cut at 0900 h (AM) and 1530 h (PM) in the spring growth and summer regrowth of two harvest years. The TNC concentration was estimated by the sum of sucrose, glucose, fructose, pinitol, fructans, and starch. Red clover (Trifolium pratense L.) and tall fescue [Schedonorus phoenix (Scop.) Holub] had the greatest TNC concentration [94.2 g kg−1 of dry matter (DM) across time of cutting and growth periods] whereas reed canarygrass (Phalaris arundinacea L.) had the lowest TNC concentration (65.5 g kg−1 DM). Concentration of TNC of all species increased with PM cutting but the extent of this increase varied among forage species. This increase, averaged across growth periods, went from 13% in smooth bromegrass (Bromus inermis Leyss; 67.0–73.9 g kg−1 DM) to 68% in reed canarygrass (49.6–81.4 g kg−1 DM). Increased TNC concentration with PM‐cutting resulted in significant but small decreases in N, acid detergent fiber (ADF), and neutral detergent fiber (aNDF) concentrations and a small increase in in vitro true digestibility (IVTD). Both species selection and PM cutting can be used to increase forage total nonstructural carbohydrates (TNC) concentrations.
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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".