Response of chickpea (Cicer arietinum L.) to inoculation with native and exotic Mesorhizobium strains in Southern Ethiopia
Bibliographic record
Abstract
A series of pot and two consecutive crop-year field experiments were conducted from 2011 to 2012 in Southern Ethiopia to determine the effectiveness of Mesorhizobium strains on two cultivars of chickpea (Shasho and Nattoli).The eight treatments included: Six rhizobial inoculants, the four best indigenous strains (Cp8, Cp41, Cp97 and Cp105); CpNSTC (National Soils Testing Center inoculant); and CpSK (Canadian inoculant), Nitrogen fertilizer and a control.The results from the field and pot experiments indicated that chickpea crop yield can be improved using proper Mesorhizobium inoculation.Inoculation had a pronounced effect on grain yield, yield component, total N uptake, grain protein content, percentage N derived from the atmosphere (%Ndfa) for the seed, and amount of seed N fixed compared to non-inoculated treatments.In the pot experiment, significant difference was recorded among the mesorhizobial strains used with the indigenous strain Cp41 highly effective in shoot dry weight (41%) mg -1 plant, grain yield (50%), total N uptake (117%), and %Ndfa (67.9%) followed by CpSK, Cp8 and Cp97.In the second crop-year field experiment, the indigenous Mesorhizobium strain Cp41 also proved highly effective in-nodule dry weight (786%) mg -1 plant, grain yield (66%), total N uptake (100%), and %Ndfa (53.7%).The maximum seed protein content was recorded during the second cropyear field experiment in Cp41 (20%), followed by N fertilizer added treatment and CpSK (18%).The chickpea indigenous rhizobial strain Cp41, was superior inoculant for almost all parameters.Thus, there are potential advantages to be gained from using efficient rhizobial inoculants under rain fed conditions in Ethiopia.
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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".