Field co-inoculation of <i>Bradyrhizobium</i> sp. and <i>Pseudomonas</i> increases nutrients uptake of <i>Vigna radiata</i> L. from fertilized soil
Bibliographic record
Abstract
Nitrogen (N) and phosphorus (P) deficiency in soils limit plant growth. Consequently, chemical fertilizers are applied to fulfill crop nutrient demands. However, low nutrient utilization can make these fertilizers unprofitable for low-income smallholding farmers. Therefore, we studied the influence of Bradyrhizobium sp. and phosphate-solubilizing bacteria (Pseudomonas) on N and P utilization by mungbean crop from fertilized nutrient-deficient soil. We inoculated novel Bradyrhizobium sp. TAL-377 and Pseudomonas sp. 54RB strains alone or their combination with seeds of mungbean varieties (C-MUNG, NM-06, and NM-11). A basal dose of chemical fertilizers was applied at the rate of 60 kg N and 90 kg P ha−1 in the form of urea and single super phosphate in all plots before crop sowing. All inocula increased soil mineral N and P compared to control (P < 0.05). Co-inoculation increased mineral N by 30%, 18%, and 18% than Bradyrhizobium sp. alone and increase in P was 22, 29 and 43% than Pseudomonas sp. in C-MUNG, NM-06, and NM-11, respectively. This resulted in 35%, 45%, and 36% higher shoot N uptakes than Bradyrhizobium sp., and 46%, 84%, and 62% higher shoot P uptake than Pseudomonas sp. in the varieties. Similarly, root nodulation, mungbean grains, and biological yields were higher in co-inoculation treatments than single strain (p < .05). Hence, co-inoculation of Bradyrhizobium sp. and phosphorus solubilizing bacteria (Pseudomonas sp.) is a vital strategy to increase mungbean growth, yield, N and P utilization from chemical fertilizers applied in nutrient-deficient soils.
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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 | 0.001 |
| 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".