Inoculation effectiveness of native and exotic Bradyrhizobium species strains in a Senegalese agricultural soil: A comparison on modern and traditional peanut (Arachis hypogaea L.) cultivars
Bibliographic record
Abstract
Peanut is a key component of Senegal’s predominantly cereal-based farming systems, but its production is challenged by low soil fertility. Rhizobial inoculation is a promising strategy to improve crop yield and reduce the use of chemical nitrogen fertilizers. The aim of this study was to isolate the most specific and effective bradyrhizobial strain for peanut, and to determine the degree of variability in the response of peanut cultivars to inoculation. The seeds of five cultivars: 55-437, Fleur 11, Sunu Gaal, Amoul Morom and Essamaay were inoculated individually with ten bradyrhizobial strains (LMG9283 and USDA3187, which are the reference strains; ISRA400, ISRA453, ISRA454, ISRA519, ISRA534 and ISRA538, isolated from Fleur 11 in Senegal, and ORS3640 and ORS3644, isolated from herbaceous species that are commonly found in Senegalese farmers’ fields). The plants were grown under greenhouse conditions in a mixture of sandy soil and vermiculite (1/1, v/v). The results obtained in terms of nodule formation, plant growth and yield parameters showed a positive effect of bradyrhizobial inoculation. However, these data indicated that the response of peanut to inoculation was cultivar dependent, with the traditional cultivars 55-437 and Fleur 11 showing the greatest increase in plant growth and yield parameters. Our results also highlighted the need for cultivar-specific selection of Bradyrhizobium to improve inoculation success in peanut, with the indigenous isolates being specifically more effective than the reference strains. According to this study, it would be beneficial to promote the use of native isolates that perform well with the peanut cultivars studied. Key words: Peanut (Arachis hypogaea), inoculation, indigenous and exotic Bradyrhizobium strains, nodulation, growth, 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) | 0.000 | 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) | 0.001 | 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".