Co-inoculation with diazotrophic bacteria in soybeans associated to urea topdressing
Bibliographic record
Abstract
ABSTRACT Increased grain yield can be obtained via an interaction between plants and growth-promoting microorganisms. The Bradyrhizobium spp. are capable of fixing atmospheric nitrogen in soybeans [Glycine max (L.) Merril], and Azospirillum spp. induce the synthesis of phytohormones. The aim of this study was to evaluate inoculation with Bradyrhizobium and co-inoculation with Bradyrhizobium + Azospirillum brasilense in soybeans in combination with the application a topdressing of 0, 75 or 150 kg of N ha-1 of urea during the reproductive stage. Three soybean cultivars (BMX Ativa, TEC 6029 and BMX Potência), were tested in field experiments in Santa Maria, RS, Brazil, during two agricultural years (2013/2014 and 2014/2015) and two sowing times. Morphological, nodulation and yield components were evaluated. Co-inoculation increased the grain yield by 240 kg ha-1 compared with conventional inoculation. When co-inoculated, cultivars BMX Ativa, TEC 6029 and BMX Potência showed increased grain yields of 6, 4 and 12%, respectively. The application of 150 kg ha-1 of N as a topdressing increased the grain yield by 300 kg ha-1 in the co-inoculated cultivars TEC 6029 and BMX Potência, but without a financial return. When inoculated only with Bradyrhizobium, the cultivars did not respond positively to the application of urea.
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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 | 0.000 | 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".