Nodulation, Nitrogen Yield and Fixation by Bambara Groundnut (Vigna Subterranea (L.)Verdc.) Landraces Intercropped with Cowpea and Maize in Southern Guinea Savanna of Nigeria
Bibliographic record
Abstract
Two separate field experiments were undertaken during the rainy seasons (August – December) of 2010 and 2011 at the Teaching and Research Farm of the Federal University of Agriculture, Makurdi, Nigeria. The objective of the study was to evaluate some landraces of bambara groundnut intercropped separately with cowpea and maize at varying planting densities for nodulation, nitrogen (N) yield and fixation. Each experiment was a 2 x 3 x 3 split-split plot set out in a randomized complete block design with three replications. Intercropping decreased the number and weight of nodules; nitrogen derived from the atmosphere and fixed in both bambara groundnut/maize and bambara groundnut/cowpea intercropping systems. No significant differences were noticed between the landraces in N content of shoot and roots, but ‘Okirikiri’ had significantly higher N content (3.11 %) of pod with seed than the other two landraces. ‘Okirikiri’ and ‘Adikpo’ landraces fixed more N than ‘Karo’. N fixed increased with decline in bambara groundnut planting density. Mean percentage of plant N derived from atmosphere varied from 49.80 in the bambara groundnut/maize systems to 56.80 in the bambara groundnut/cowpea intercrops, while N fixed was 11.27 kg/ha and 34.90 kg/ha in the respective intercrop systems. The expectation of enormous contribution of nitrogen fixation to bambrara groundnut yields and its residual effects on soil medium for ensuing crops may be an illusion with the use of the landraces tested in this work, except, probably when inoculated with the appropriate rhizobia?
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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) | 0.000 | 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".