PSXI-15 Evaluation of low-lignin alfalfa ‘Hi-Gest® 360’ on the Canadian prairies: productivity, nutrient profile, and rumen degradation kinetics
Bibliographic record
Abstract
Abstract The study was conducted at Saskatoon, SK, Canada in the Dark Brown soil zone (52°07′N,106°38′W), to compare low-lignin Hi-Gest®360 alfalfa (Hi-Gest; Medicago sativa L.) to a conventional alfalfa (AC Grazeland) under two cutting regimes. Each cultivar was seeded in 2017 as both a monoculture and binary mixture (binary; Hi-Gest+HB and Grazeland+HB) with AC Success hybrid bromegrass (HB). Plot size was 1.2m×6m with 4 rows. Three years (2018–2020) result suggested that the first cut DMY was 1695±241 and 2151±276 kg/ha for the Hi-Gest and Grazeland, respectively. Hi-Gest had greater (P < 0.05) TDN (68.4 vs. 66.6%, DM basis), NEm (1.7 vs. 1.6 Mcal/kg), IVNDFD48: (42.9 vs. 37.8%), but lower second cut DMY (1288 vs. 1560 kg/ha), ADF (26.3 vs. 28.7%), NDF (34.7 vs. 36.6%), compared to AC Grazeland. Under two cutting, Hi-Gest was numerically lower in total DMY (2983 vs. 3712 kg/ha; P = 0.114) relative to Grazeland. In first cut forage, Hi-Gest also was numerically lower in ADL (5.9 vs. 6.5%; P = 0.57) relative to AC Grazeland. Hi-Gest was similar (P = 0.15) with AC Grazeland in effectively degradable dry matter (EDDM; 622 g/kg DM), effectively degradable CP (EDCP; 689 g/kg CP). Hi-Gest alfalfa had greater (P = 0.05) effectively degradable neutral detergent fiber (EDNDF; 19% more; 340 vs. 285 g/kg NDF) relative to AC Grazeland alfalfa. In binary system, Hi-Gest+HB had (P = 0.02) greater EDCP (12% more; 526 vs. 468 g/kg CP), and numerically greater EDNDF (4.4% more; 259 vs. 248 g/kg NDF; P = 0.10) but had similar EDDM (409 g/kg DM) relative to Grazeland+HB mixture. Each percentage unit increase in alfalfa ADL concentration decreased EDNDF by 2.3 percentage units (EDNDF, % NDF = 46.0–2.93×ADL, % DM, r2=0.26, P < 0.01). Although Hi-Gest yielded less (~25% less) than AC Grazeland, Hi-Gest had greater nutritive value compared to AC Grazeland; differed little for yield and quality in binary mixture, suggesting Hi-Gest can be a viable alternative legume for western Canadian prairies.
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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 | 0.001 | 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".