Forage and seed yield of winter turnip rape established as a mixed crop with cereals
Bibliographic record
Abstract
SUMMARY Cultivation of winter turnip rape (Brassica rapa L. ssp. oleifera (DC.) Metzg.) in Finland has been limited because of its reputation as an unreliable crop and its mid-season sowing time of July, when fields are already sown to other crops. An alternative management practice for winter turnip rape is proposed whereby it would be sown as a mixed crop simultaneously with spring cereals. The growth and yield formation of winter turnip rape grown in mixed stands with four different spring cereals was studied in two field experiments conducted in 2009–11. Pure and mixed stands of winter turnip rape and spring cereals were established in May at two different cereal and winter turnip rape stand densities. Subsequent to cereal harvest, one-third of each winter turnip rape plot was harvested for biomass in autumn, before cessation of growth. Three plant stand types, May- and July-sown monocrops and a mixed crop with oat (Avena sativa L.) were sampled for forage analysis. Plant stand densities were monitored from establishment until maturity. Winter turnip rape yield and its quality, including oil content, protein content and thousand seed weight, were determined. Following favourable overwintering conditions, winter turnip rape established with cereals yielded comparably to that of pure stands in terms of both quantity and quality. However, a pure stand of winter turnip rape out-yielded mixed crop stands after unfavourable overwintering conditions. Leaf removal decreased plant survival and seed yield. Establishing winter turnip rape with a cereal in May is an alternative to sowing it as a monocrop in July. A higher seeding rate is needed for under-sown winter turnip rape. Furthermore, autumn-harvested winter turnip rape monocrop forage potentially represents a high-protein supplement for ruminants.
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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 | 0.000 |
| 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".