Evaluation of Rhizobial Inoculation Methods for Chickpea
Bibliographic record
Abstract
Rhizobia inoculated onto legume seeds are often exposed to adverse environmental conditions, which can affect survival and subsequent effectiveness. Hence, soil‐applied granular inoculants have received much attention recently. We examined the efficacy of various inoculation methods at four sites in Saskatchewan, Canada, in 1997 and 1998 using desi‐ and kabuli‐type chickpea ( Cicer arietinum L.). Seed inoculation treatments (liquid or peat‐based powder) were compared with soil inoculation (granular inoculant) either placed in the seed furrow or side‐banded (i.e., 2.5 cm to the side) at depths of either 2.5 or 8 cm below the seed. Nodule formation in the seed inoculation treatments was restricted to the crown region of the root system, whereas soil inoculation enhanced nodulation on the lateral roots. In 1997, granular inoculant placed below the seed increased kabuli seed yield by 36 and 14% over the liquid and peat‐based inoculants, respectively, whereas desi seed yield increased 17 and 5%, respectively. Seed yield responses were inconsistent in 1998. Seed protein concentration, percentage N derived from the atmosphere (%Ndfa), and amount of N 2 fixed were typically lower for the liquid inoculant than for the peat and granular inoculants, which did not differ. The dry weight of lateral‐root nodules was highly correlated with yield parameters, suggesting that the lateral‐root nodules contributed significantly to N 2 fixation and yield. Although the peat and granular inoculants were equally effective in establishing successful symbiosis, placing granular inoculant 2.5 to 8.0 cm below the seed may improve yield and quality.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".