Effects of Soil Moisture Deficit on Forage Quality, Digestibility, and Protein Fractionation of Kura Clover
Bibliographic record
Abstract
Abstract Kura clover (Trifolium ambiguum M.B.) is a persistent rhizomatous forage legume with great potential for permanent pastures. The effects of a soil moisture deficit on forage quality, digestibility and protein fractionation of two cultivars of Kura clover (Endura and Rhizo) were investigated in this study for 1 year. The responses of alfalfa (Medicago sativa L.) and red clover (Trifolium pratense L.) were also characterized. Stands of each species were field‐grown and submitted to two soil water regimes promoting soil moisture deficits and well‐watered (i.e. control) conditions. There were no interactions between species and soil water regimes. Soil moisture deficit increased acid detergent fibre (ADF) but reduced acid detergent lignin (ADL) content and consequently increased forage digestibility. It had only minor effects on protein content and fractionation. Species varied for most parameters measured. Kura clover generally had the lowest neutral detergent fibre (NDF), ADF and ADL contents, and consequently the greatest digestibility (83.9 %). Species also differed in their crude protein fractionation. Kura clover and red clover had a lower proportion of non‐protein nitrogen (NPN; A fraction) and a greater proportion of true protein (B fraction) (30.9 and 64.5 %, respectively) than alfalfa (36.4 and 57.4 %, respectively). Kura clover also had the lowest proportion (i.e. 4.7 %) of acid detergent insoluble protein (ADIP; C fraction) of all species tested. Endura Kura clover often had a higher forage quality than Rhizo. This study confirms that Kura clover produces high‐quality forage and provides the first estimates of protein fractionation in this species.
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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".