Effects of stage of maturity at harvest of cereal crops on biomass and quality, estimated forage dry matter intake, beef cow performance, and system economics
Bibliographic record
Abstract
The objective of this study was to evaluate the effects of maturity at harvest of cereal crops on biomass, quality, cow performance, and economics. In each of 3 yr, a 48-ha field was divided into 3 paddocks (16 ha per treatment) and seeded to either barley (Hordeum vulgare CDC Maverick, 135 kg/ha), oat (Avena sativa CDC 29 SO1, 135 kg/ha), or triticale (Triticosecale Taza, 135 kg/ha). Half of each field (8 ha) was swathed at either soft dough (SDo) or hard dough (HDo) stage for grazing. Each year, 120 dry pregnant beef cows (631 kg) were randomly allocated to 1 of 6 replicated (n = 2) paddocks for 90, 106, and 88 d in yr 1, 2, and 3, respectively. Triticale had 19 and 23% greater total yield than oat and barley, respectively. Barley CP was greater (P < 0.05; 10.9%) than oat (9.3%) or triticale (9.8%) at HDo stage. The animal unit months (AUM) per hectare were greater for HDo (11.5 ± 0.8 AUM/ha) than SDo (9.15 ± 0.8 AUM/ha) stage. Cow ADG was greater (P = 0.01) for barley (0.61 kg/d) compared with triticale (0.39 kg/d). Total crop cost averaged Can$426, Can$437, and Can$458/ha for oat, barley, and triticale, respectively. The HDo stage had Can$0.40 lower dollars per cow per day in-field costs (P = 0.02) than the SDo stage and 10% (Can$26.25 per cow/d) lower feeding costs (P = 0.02) over 3 yr. Delaying harvest to hard dough can increase yield and reduce feeding costs without affecting cow performance.
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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 | 0.001 |
| 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".