Inoculation of Maize Seeds With Azospirillum and Magnesium Through Foliar Application to Enhance Productive Performance
Bibliographic record
Abstract
The present study aimed to evaluate the productive performance of maize crop when seed inoculated with A. brasilense, associated with different foliar doses of magnesium in the crop vegetative stages. For this, two essays were conducted in field conditions, one located in Laranjeiras do Sul-PR and the other in Entre Rios do Oeste-PR. A randomized blocks scheme was used, with a 3 × 2 factorial, being the treatments with magnesium (Mg): magnesium sulphate; magnesium oxide and without magnesium, and the presence or absence of seed inoculation with A. brasilense. The magnesium sources were supplied via foliar at the V4 stage of the crop, using doses of 6 kg ha-1. Evaluations were carried at R1 determining the SPAD index and stem diameter and, at the end of the productive cycle, were evaluated production components and yield. In both sites no significant effects of foliar application with Mg were observed over the evaluated parameters. The A. brasilense inoculation provided an increase of 9.66% and 6.32% in stem diameter and of 6.8% and 6.24% in the SPAD index in Laranjeiras do Sul and Entre Rios do Oeste respectively, however, they did not increase production components and yield. It is concluded that the inoculation with A. brasilense increases of stem diameter values and SPAD index, in turn the foliar fertilization with different sources of magnesium do not interfere in the development and productivity of corn crop.
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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".