Effect of rhizobial inoculants on yield and yield components of faba bean (Vicia fabae L.) on vertisol of Wereillu District, South Wollo, Ethiopia
Bibliographic record
Abstract
Abstract Background Nitrogen fixation by legumes like faba bean is a cheap way of fixing atmospheric nitrogen to plant available form. However, the inoculation of grain legumes with rhizobium bacteria are poorly researched in Amhara Region of Ethiopia. Methods Thus, a study to examine the effects of rhizobium leguminosarum (var vicae) strains on nodulation, growth, and yield of faba bean was conducted in Wereillu district of Amhara Region, Ethiopia during the rainy season of 2018. The treatments comprised of four levels of faba bean Rhizobium strains (un-inoculated, EAL-1018, EAL-1035 and EAL-17) arranged in a randomized complete block design with three replications. The collected data on yield and yield-related parameters were analyzed using Statistical Analysis System (Statistical Analysis System, version 9.1, SAS Institute Inc, Cary, 2003), version 9.1 and subjected to Duncan’s Multiple Range Test for mean separation when the analysis of variance was significant. Results The result revealed that the effect of EAL-1018 brought significantly (P ≤ 0.05) higher difference on nodule number, nodulation volume, nodule dry weight, biomass yield and grain yield compared to the control. Faba bean strain, EAL-1018 gave 45.6, 27 and 11.6% grain yield advantage over the control, EAL1017 and EAL 1035 respectively. Conclusion Biologically as well as Economically EAL 1018 brought the maximum yield and net benefit (47020.7) compared to the other treatments. Hence, EAL-1018 is recommended for the study area and similar agro—ecologies.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".