High‐energy alfalfa (<i>Medicago sativa</i> L.) developed by recurrent phenotypic selection for nonfiber carbohydrate concentration in stems
Bibliographic record
Abstract
Abstract In forages, genetic improvement in readily fermentable energy can improve the energy‐to‐protein balance, thus reducing N losses to the environment. This study aimed to evaluate the effects of recurrent selection targeting high nonfiber carbohydrate (NFC) concentrations in alfalfa stems on nutritive value and biomass yield. Populations developed after one to three cycles of recurrent selection for NFC (NFC1, NFC2, and NFC3) and a control population (NFC0) were evaluated in a field trial at three sites across Canada, and in a greenhouse trial along with the population developed after a fourth cycle of selection for NFC (NFC4). When comparing NFC3 to NFC0, increases in NFC concentration of 14 and 28 g kg −1 dry matter (DM) were observed in field and greenhouse trials, respectively. This increase reached 45 g kg −1 DM for NFC4 compared to NFC0 in the greenhouse trial. Crude protein (CP) concentration was similar among populations in both trials, resulting in an increase in their NFC/CP ratio. Fiber concentrations were lowered, which resulted in an increase in in vitro DM digestibility of more than 10 g kg −1 DM for NFC3 in the field and for NFC4 in the greenhouse trials, as compared with NFC0. None of the selected populations displayed significant annual yield differences. The recurrent phenotypic selection for high stem NFC concentrations is an effective approach to improve alfalfa NFC concentration while increasing its energy‐to‐protein balance and digestibility, and maintaining its biomass productivity.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.002 |
| 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 teacher head, 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".