Growth promotion and yield attribute improvement of five groundnut (Arachis hypogaea L.) varieties by the application of plant growth promoting rhizobacteria
Bibliographic record
Abstract
The aim of this investigation was to evaluate the symbiotic performance of a collection of ten plant growth-promoting rhizobacteria (PGPR) strains on the growth and yield attributes of five groundnut varieties. The screening was conducted in the greenhouse using seeds of the varieties Essamay, Amoul Morom, 55-437, Fleur 11 and Sunu Gaal grown in 1.5 kg pots with Sangalkam soils. Leaf chlorophyll content, plant height, number of branches, biomass production, number and mass of pods were the parameters evaluated in response to groundnut inoculation. Overall, the results highlighted the effectiveness of inoculation with certain PGPRs on the growth and yield parameters. However, the effects of the inoculants were highly dependent on the variety used and the parameter studied. In particular, the strains RF8, SI12, SR6, SI16, SI27 and SS10 were the most efficient in improving the plant growth and yield attributes of the varieties Essamay, Amoul Morom and Sunu Gaal. In contrast, Fleur 11 and 55-437 were less responsive in terms of yield attributes, demonstrating that the response to groundnut inoculation is variety dependent. In addition, a significant increase in the estimated leaf chlorophyll content was only observed in Fleur 11 and Sunu Gaal, which was not expected and could be attributed to a lower N2 fixation capacity of the indigenous rhizobia. Nevertheless, the increases in growth and yield parameters obtained in this study can be considered as a promising data for the use of bacterial biofertilisers in groundnut cultivation. Key words: Peanut cultivars, biofertilizer, plant growth-promoting rhizobacteria (PGPR), inoculation, growth and yield parameters.
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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) | 0.001 | 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".