Nitrogen and Potassium Topdressing in Maize Intercropped with Brachiaria Ruziziensis
Bibliographic record
Abstract
Maize has been intercropped with different forage species, especially Brachiaria spp., for different purposes and at different times: after maize harvest, as a food source for cattle; and exclusively for the production of straw to maintain a no-tillage system, which is the main purpose of intercropping in western Paraná. The objective was to investigate the effects of different rates of nitrogen (N) and potassium (K) topdressing on grain yield and components of yield for maize intercropped with Brachiaria ruziziensis. The experiment was conducted in Paraná State, Brazil. The experiment used a completely randomized block factorial design (4 × 3) with four replications. Treatments for the first factor consisted of sowing maize intercropped with Brachiaria ruziziensis with four rates of N topdressing (0, 80, 100 and 120 kg ha−1) applied as urea. For the second factor were rates of K topdressing (0, 30 and 50 kg ha−1), applied in the form of potassium chloride. During at sowing fertilization was performed with of 30, 26 and 50 kg ha−1 of N, phosphorus and K, respectively. The following variables were evaluated: plant height, first ear height, stem diameter, number of tillers per plant, mass of 1,000 grains, grain yield. The results showed that application of N topdressing promoted increased grain yield of maize intercropped with Brachiaria ruziziensis until N rate of 85 kg ha−1. Isolated application of K did not influence the variables studied.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".