Inoculation of Plant Growth Promoting Bacillus spp. in N-Fertilized Maize Crop in Soils With High Organic Matter Content in South Brazil
Bibliographic record
Abstract
Inoculation of seeds with plant growth promoting bacteria (PGPB) often increases maize yields in N-deficient soils. However, would yields increase if inoculation with PBPB was made in soils with high organic matter contents receiving N-fertilizer? In this article, we report the results of four field-experiments performed in the Southern Brazil (Campo Largo-PR and Lapa-PR, Brazil) during the growing seasons of 2017/2018 and 2018/2019, including treatments with three doses of N (0, 50 and 100 kg of N ha-1) supplied as (NH4)2SO4, and inoculation of Azospirillum brasilense Abv05 and Abv06, or, of new strains of Bacillus sp. (LGMB 143, LGMB 152, LGMB 319 or LGMB 326). Application of 50 kg N ha-1 increased yields by 32% in Campo Largo, and 16% in Lapa, in relation to non-fertilized plots, but doubling the N fertilization or including PGPB inoculants did not affect crop growth and productivity. The average yields in the plots with N and bacterial inoculation was 8,255 kg ha-1 in Campo Largo and 11,311 kg ha-1 in Lapa. Increases of grain yields were related to increases in plant height, shoot dry matter, ear length and diameter and 1000 grains mass. This study adds to the fact that scientists and farmers should rethink the paradigm of excessive doses of N fertilization on maize. Furthermore, inoculation of maize seeds with PGPB do not increase yields when the N demand of the crop is satisfied via N-fertilization.
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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) | 0.000 | 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".