Effect of formulation and placement of Mesorhizobium inoculants for chickpea in the semiarid Canadian prairies
Bibliographic record
Abstract
The use of bacterial inoculants can increase root nodulation and the seed yield of annual legumes. A six site-year study was conducted to determine the effect of formulations (peat-based powder vs. granules) and placement in the soil (seed-row vs. side-band) of Mesorhizobium inoculants on plant establishment and seed yield of chickpea (Cicer arietinum L.) in the semiarid Canadian prairies. Two market classes of chickpea, namely desi and kabuli, were grown on silt loam and heavy clay soils in southwestern Saskatchewan from 1999 to 2002. Inoculation reduced plant population by 10% for desi chickpea, but had no effect on kabuli chickpea. However, the use of inoculant increased seed yield by an average of 35% for desi chickpea and 7% for kabuli chickpea. On the heavy clay, soil inoculation increased seed yield by 16% for desi and 9% for kabuli compared with seed inoculation, whereas the yield increase due to soil inoculation, over seed inoculation, was 3% when the crops were grown on the silt loam. Granular inoculant applied in the seed row produced similar seed yields to side-banded inoculant. Inoculation delivery systems had a marginal impact on plant height, with no effect on the lowest pod height from the soil surface or days to maturity. Regardless of placement, soil inoculation with a granular form of Mesorhizobium was preferred over seed inoculation because of its greater positive impacts on plant establishment and seed yield for both desi and kabuli chickpea in this semiarid region. Key words: Cicer arietinum, seed weight, heavy clay, silt loam, harvestability
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".