248 Validating the stage of maturity at harvest for barley, oat, and triticale for swath grazing
Bibliographic record
Abstract
The objective was to determine the effect of harvest maturity (SOM) for barley, oat, and triticale on grazing days, nutrient composition, DMI, cow rib and rump fat, and production costs. Three 16-ha fields were seeded to either barley (Hordeum vulgare; cv. CDC Maverick), oat (Avena sativa; cv. CDC SO1), or triticale (Triticosecale; cv. Taza). Each forage was cut at an early (EAL; late milk for oat and soft dough for barley and triticale) and late (LAT; hard dough) SOM. One hundred twenty Angus cows (640 kg ± 1.13 kg) were randomly allocated to 1 of the 6 replicated (n = 3) treatments and allowed to graze the paddock (3-d forage allocations) until all forage had been allocated. Cow BW (conceptus adjusted) and rib and rump fat were measured at the start and end of the study. Forage DM yield was determined using randomly sampled 0.25-m2 quadrats of pre-swath biomass and regrowth biomass. Data were analyzed as a completely randomized design with a 3 × 2 factorial arrangement, using the mixed model of SAS. Forage yield for EAL and LAT oat, barley, and triticale were (kg/ha) 7,634, 8,240, 8,242, 7,372, 8,773, and 11,080, respectively. Grazing days increased from 50 to 89 d for LMO to HDO; however, there were no effects of SOM for barley or triticale (crop type × SOM interaction, P < 0.01). Crude protein differed by crop type with barley (12.1%) having greater (P < 0.01) CP than oat (11.0%) and triticale (11.4%). Harvesting at more advanced maturity decreased CP (12.1 vs. 11.0%, P < 0.01) and TDN (58 vs. 51%, P < 0.01). Crop type (P = 0.19) and maturity (P = 0.36) did not affect BW change. However, the change in rib fat (mm) was greater (P < 0.01) for oat (0.43 mm) than barley (-0.27) and triticale (-0.15). Cows fed EAL oat increased rump fat while LAT oat decreased: no change in rump fat was observed for other crops (crop × maturity, P = 0.01). Cows fed LAT oat had greater DMI than EAL oat (13.0 vs. 6.0 kg/d), but intake did not differ among the other treatments, averaging 9.5 kg/d (crop × maturity, P = 0.01). Cost ranged from $1.16/cow/d for LAT triticale to $2.43/cow/d for EAL oat. The results of this study suggest that harvesting whole crop annuals at a later SOM may improve yield and reduce costs without negative effects on animal 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 | 0.001 | 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) | 0.002 | 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".