Cropping systems for annual forage production in northeast Saskatchewan
Bibliographic record
Abstract
Herbage production for silage and pasture production of annual species was investigated near Melfort in northeastern Saskatchewan. Barley (Hordeum vulgare L.) and oats (Avena sativa L.) were seeded as spring monocrops (SMC) and in binary intercrop (IC) mixtures with fall species including winter wheat (Triticum aestivum L.), fall rye (Secale cereale L.), winter triticale (X Triticosecale Wittmack L.), biennial Italian ryegrass (Lolium multiflorum Lam.) and annual Westerwolds ryegrass (Lolium multiflorum Lam.). Fall species were also seeded as monocrops (FMC). Silage Spring harvest occurred when barley (early-silage cut) and oats (late-silage cut) reached soft dough stage and again late in the autumn. An additional deferred grazing (DG) treatment containing each one fall species was harvested once in the autumn. Mean ranking of spring herbage silage yield was Oat-SMC (100%) > Oat-IC (91%) > Barley-SMC (83%) = Barley-IC (78%) > late-cut FMC (53%) > early-cut FMC (42%) (P ≤ 0.001). SilageSpring herbage yield of IC combinations was similar, but FMCs containing annual ryegrass were 26% to 34% (P ≤ .01) greater than other treatments. Crude protein content (g kg-1) was 14 to 35% higher (P ≤ 0.001) in IC systems than the corresponding SMC. Neutraleutral detergent fibre (NDF) and acid detergent fibre (ADF) content (g kg-1) of barley based systems was 15 and 22% lower (P ≤ 0.001) than those with oats. Ranking and relative productivity for fall pasture was DG (100%) > early-cut FMC (67%) > late-cut FMC (49%) > Barley-IC (30%) > Oat-IC (24%) = Barley-SMC (14%) (P ≤ 0.001). Cropping systems that contained no spring cereal produced 2.37-fold higher (P ≤ 0.001) fall pasture yield than those with spring cereals. Among FMCs, ICs and DG systems, mean yield of ryegrass treatments were generally higher (P ≤ 0.05) than that of fall cereals. Key words: Annual forage, deferred grazing, intercrop, monocrop
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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 | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".