Mid-summer annual forage performance in organic, grass-fed production
Bibliographic record
Abstract
Grass-fed ruminant production does not have the convenience of feeding easily-storable grains during periods of low forage availability. This study examined the forage yield, quality, and utilization of warm- and cool-season annual forages grown under organic management during the mid-summer “feed gap” period. Annual ryegrass (Lolium multiflorum Lam. cv. Tetra Brand), winter triticale (× Triticosecale Wittmack cv. common), oat (Avena sativa L. cv. Souris), millet (Panicum miliaceum L. cv. Crown Proso), corn (Zea mays L. cv. BMR84 and CM440 Canamaize), and sorghum-sudangrass (Sorghum bicolor [L.] Moench × Sorghum sudanense [Piper] Stapf cv. common) were grown in Carman, Manitoba, over 3 site-years in 2018 and 2019. Combined forage and weed dry matter (DM) yield was 7159 kg·ha−1 for sorghum-sudangrass (29% weeds), 5506 kg·ha−1 for corn (36% weeds), 4687 kg·ha−1 for oat (45% weeds), 4617 kg·ha−1 for annual ryegrass (95% weeds), 4542 kg·ha−1 for millet (28% weeds), and 2945 kg·ha−1 for winter triticale (51% weeds); significant differences in crop and weed biomass were observed. All forage systems were palatable to sheep with utilization rates from 47% to 65%. When all quality parameters were considered, corn, winter triticale, millet, and oat displayed adequate quality for mid-summer grazing, while sorghum-sudangrass had suboptimal crude protein concentrations. Direct measurements of forage quality on weeds showed that weeds did not compromise forage quality. This Canadian first study demonstrated the potential of forage production for mid-summer grazing in an organic, grass-fed regime with oat, millet, and corn resulting in the best combination of yield and quality.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".