Agronomic Efficiency of Grammy Crop® (Azospirillum brasilense) Under Different Application Methods in First- and Second-Crop Maize
Bibliographic record
Abstract
The nutritional requirements of maize are some of the most studied factors in the production of this crop. It is known that nitrogen (N) is the major nutrient required by maize. Products based on microorganisms such as Azospirillum spp. can promote N fixation, providing yield gains. This study aimed to investigate the agronomic efficiency and feasibility of applying Grammy Crop® (Azospirillum brasilense-based product) by different methods to first- and second-crop maize. Experiments were conducted in two seasons between 2019 and 2020. In the first crop, a 4 × 7 factorial design was used, in which the first factor was locality (Candói, PR; Guarapuava, PR; Santa Maria do Oeste, PR; and Sertão, RS) and the second factor was treatment (untreated control, 75% N control, 100% N control, commercial A. brasilense-based inoculant, Grammy Crop® liquid seed treatment, Grammy Crop® peat-based seed treatment, and Grammy Crop® liquid foliar treatment). In the second crop, the factorial design was 3 × 7, with three localities (Pitanga, PR; São Miguel do Iguaçu, PR; and Serranópolis do Iguaçu, PR) and the same treatments. At physiological maturity, plants were evaluated for yield (kg ha-1), thousand grain weight (g), plant height (cm), shoot dry weight (kg ha-1), leaf N content (g kg-1), and grain N content (g kg-1). All treatments improved productive, vegetative, and nutritional parameters compared with the untreated control for all localities and crops. Grammy Crop® was efficient in improving maize yield under all methods of application, providing similar benefits as N fertilizer and a commercial inoculant.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".