Biomass Partitioning, Forage Nutritive Value, and Yield of Contrasting Genotypes of Timothy
Bibliographic record
Abstract
Forage nutritive value and dry matter (DM) yield are negatively related. Hence, the improvement of both DM yield and nutritive value requires the identification of genotypes that deviate from that negative relationship. Our objectives were to evaluate the potential of simultaneously selecting for high forage yield and high nutritive value in timothy (Phleum pratense L.), and to study the relationship between DM yield, nutritive value, and biomass partitioning. Nine genotypes, and a reference cultivar, Champ, were studied in a growth room, with limiting and nonlimiting N rates. At both N rates, some genotypes differed significantly in forage (FDM) and total biomass (TBDM) DM yield, leaf weight ratio (LWR), and root weight ratio, but did not differ in forage (FNC) and total biomass (TBNC) N concentration. Genotypes differed in neutral detergent fiber concentration, in vitro true digestibility, and in vitro cell wall digestibility under limiting N only. Significant interaction (P < 0.05) was found between genotype and N rate for DM yield and for most of the other measured parameters. Principal component analysis indicated that, for most genotypes, the differences in FDM resulted from differences in TBDM and not only from changes in biomass partitioning between shoots and roots. Also, variability in the relationship between FDM and LWR indicated the possibility of selecting genotypes having high yield with high LWR. Consequently, it is possible to break the linkage between high DM yield and declining nutritive value parameters and select for high‐ yielding genotypes with superior forage nutritive value.
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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".