Response of Cowpea, Soya Beans and Groundnuts to Non-Indigenous Legume Inoculants
Bibliographic record
Abstract
The use of inoculants is a critical strategy in legume production. In Zambia, inoculants are particularly used for the production of non-promiscuous genotypes of soya beans, but rarely for cowpeas and groundnuts. This study evaluated the response of soya beans, cowpeas and groundnuts to Biofix legume inoculants. Seeds were inoculated at the recommended or double the recommended rate at planting. Plants were grown under greenhouse conditions in a Completely Randomized Design for 7 weeks. Control, non-inoculated seeds were also planted and plants grown under the same conditions. At 7 weeks, nodule number and fresh weight per plant, nodule effectiveness (pinkness/redness), and above ground biomass were determined. Biologically fixed nitrogen was determined using the Nitrogen Difference Method. Nodule number and fresh weight per plant were higher at the recommended rate of Biofix application for soya beans and at both rates for groundnuts, while there was no effect on nodule fresh weight at either rate in cowpeas. All representative nodules assessed were effective. There was no significant benefit in inoculating seeds of the three legumes with Biofix on above ground biomass and biological nitrogen fixation. These results could suggest that possibly, the introduced strains though with a stronger nodulation competitiveness, were not as effective at fixing nitrogen as the indigenous strains in the soils in which soya beans, cowpeas and groundnuts have been repeatedly grown before. This could be an indication that sufficient and appropriate effective strains are already present in this soil. In general, the results suggest that to obtain the full benefits of biological nitrogen fixation, legume growers need to be provided with the correct inoculant, where required. Further work under field conditions is recommended to confirm these findings.
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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".