PSVIII-28 Forage pea-cereal mixtures for greenfeed production in Saskatchewan.
Bibliographic record
Abstract
The inclusion of forage pea (Pisum sativum L.) in mixtures with forage barley (Hordeum vulgare L.) or oat (Avena sativa L.) for greenfeed or silage production is gaining interest in western Canada. A twelve-different combination of monocultures and mixtures were evaluated for forage dry matter (DM) yield and forage quality in three soil zones of Saskatchewan for 2 years. Forage pea cvs. ‘CDC Horizon’ and ‘40-10’, barley cv. ‘CDC Maverick,’ and oat cv. ‘CDC Haymaker’ were used in the study. Treatments were allocated in a split-plot in RCBD with nitrogen fertilizer treatment (25 vs. 60kg ha-1) as main plot, and mixtures (at 100, 100:30 and 50:50% of normal seeding rate) as a subplot, with 4 replications. Plots were harvested on the maturity of the cereal in the mixture. Data were analyzed using the PROC MIXED of SAS. Year, site, nitrogen treatment, mixtures and their interactions were fixed effects; block, block, and nitrogen treatment interaction were random effects. Nitrogen fertilizer treatment did not affect DM (P=0.33), and contents of CP (P=0.25), ADF (P=0.78), NDF (P=0.73) and starch (P=0.41). Average DM yield for mixtures was significantly (P-1), intermediate in Dark Brown soil (8778 kg ha-1), and lowest in Brown soil zones (7156 kg ha-1). CP concentration was significant (P-1 (barley) to 137 g kg-1 (pea). Pea-cereal mixture had 25% more CP than that monoculture cereal stands. ADF and NDF concentration for mixtures averaged 316 and 530 g kg-1, respectively. Starch content ranged from 31.6 (7) to 53.4 g kg-1 (3), with an average of 40.4g kg-1.The pea-cereal mixture productivity varied soil zones but generally produced similar or fewer yields with greater forage quality than monocultures.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".