Soybean Yields and Biomass Production of Winter Cover Crops in the Southwest of Parana – Brazil
Bibliographic record
Abstract
The use of winter cover crops is an important practice for the management and conservation of soil and water in southern Brazil. The objective of this work was to evaluate the accumulation of green mass and dry mass in the aerial part of winter cover crops in monocropping and intercropping in southwestern Paraná, in the years 2016 and 2017. Randomized blocks were used as experimental design with a subdivided plot scheme of 50 m² with five replications. The cover crops used in monocropping were black oats and wheat, while for the intercropping black oats + turnips + rye + white lupin (AP+NF+C+TB), black oats + turnips (AP+NF) and black oats + turnips + rye + vetches + white lupin + buckwheat (AP+NF+C+E+TB+TM) were used. The data were subjected to analysis of variance and the means were compared by the Tukey test at 5 % probability. The intercrop composed of black oats + turnips + rye + vetches + white lupin + buckwheat had a higher production of green mass, with an average value of 50.880 kg ha-1. For the production of dry mass, the monocrop of black oats had higher yields, with an average value of 5.168 kg ha-1. The highest yields were obtained in the coverage area with black oats, turnips, rye, vetches, white lupin and buckwheat, presenting a yield in 2017 of 4.487 kg ha-1.
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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".