Postheading Biomass Distribution for Monocrops and Mixtures of Small Grain Cereals
Bibliographic record
Abstract
Biomass distribution during the harvest period can affect the yield and quality of silage produced from cereal crops. Our objectives were to determine the changes in biomass distribution among morphological structures and how management practices could affect those changes. Three field studies were conducted to evaluate the productivity of barley (Hordeum vulgare L.), oat (Avena sativa L.), triticale (× Triticosecale rimpaui Wittm.), and rye (Secale cereale L.) grown as monocrops and mixtures. Seeding rates ranging from 250 to 750 seeds m−2 were evaluated to determine their effects on biomass distribution from heading to the soft‐dough growth stages. While seeding rate had a profound effect on per plant biomass, it had little effect on biomass per unit land area or the distribution of the biomass between leaves, stems, and spikes. During the postheading period for all tests, the leaf component declined and the spike component increased. The stem component declined for all tests, but variation was found for the tests harvested on the basis of the oat and triticale components. Composition biomass weights from our spring cereal tests averaged across the three sampling times (heading to soft dough) were 18% leaf, 50% stem, and 31% head for ‘Noble’ barley; 18% leaf, 44% stem, and 37% head for ‘AC Mustang’ oat; and 22% leaf, 43% stem, and 35% head for ‘Wapiti’ triticale. Plant populations and total, leaf, stem, and spike biomass per plant for mixtures were found to be intermediate to the monocrops. Total biomass quantity and distribution among leaves, stems, and spikes were affected by genotype, production practices, and time of harvest, with the latter having the greatest effect. Understanding cultivar, species, and management effects is important for optimum feed quantity and quality.
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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 | 0.000 | 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".