Symbiotic Efficiency of Native and Exotic Rhizobium Strains Nodulating Lentil (Lens culinaris Medik.) in Soils of Southern Ethiopia
Bibliographic record
Abstract
Lentil plays a major role in the food and nutritional security of low income Ethiopian families because of the high protein content of their seed; however, their productivity typically is low largely due to soil fertility limitations. Field and pot experiments were conducted during the 2011 cropping season to determine the effectiveness of Rhizobium strains on two cultivars of lentil in Southern Ethiopia. Six rhizobial inoculant treatments (four indigenous and two commercial inoculants), a nitrogen (N) fertilizer treatment (50 kg·urea·ha−1) and an absolute control (non-inoculated non-fertilized) were used. Inoculated plants produced significantly higher nodule number, nodule dry weight, grain yield and yield components than non-inoculated non-fertilized plants. Inoculation of field grown lentil with rhizobia strain Lt29 and Lt5 enhanced seed yield by 59% and 44%, respectively. Whereas urea fertilization enhanced yields by 40%. Similarly, grain yields were increased during the pot experiment by 92% and 67% over the control treatments by inoculation with Lt29 and Lt5, respectively. The highest levels of N fixation were achieved in plants inoculated with Lt29 (65.7% Ndfa). Both field and pot investigations indicate that inoculation of lentil with native rhizobial strains replace the need for inorganic N fertilization to optimize lentil yields.
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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".