Response of lentil to microbial inoculation and low rates of fertilization in the semiarid Canadian prairies
Bibliographic record
Abstract
The use of microbial inoculation may increase nodulation and seed yield of annual legumes. A study was conducted to determine the effect of formulations (seedapplied powder vs. soil-applied granular inoculants), placement of granular inoculants in soils (applied in the seed-row vs. sidebanded), and low rates of fertilizers in comparison to P-solubilizing microbes Penicillium bilaii on plant establishment, maturity, and seed yield of lentil (Lens culinaris Medik.) in the semiarid Canadian prairies. Green lentil was grown on a silt loam and a heavy clay soil in southwestern Saskatchewan from 1999 to 2002. Inoculated lentil with Rhizobium increased seed yield by 45% averaged across all 6 site-years. Granular soil inoculants increased lentil seed yield by 19% over seed-applied inoculants. Placement of soil inoculants in the seed row or side-bands produced similar results. On the silt loam soil, the use of rhizobial inoculants increased lentil seed yield by 15%, while the yield increase was 70% on the heavy clay. Starter N applied at a rate of 15 kg ha-1 increased seed yield by 13% for lentil grown on the heavy clay, but there was no effect on the silt loam. Phosphorus fertilizer applied at a rate 15 kg P ha-1 did not influence lentil establishment, growth or seed yield. Similarly, the P-solubilizing microbes P. bilaii did not influence plant growth or development, nor did it affect the seed yield of lentil. Soil granular rhizobial inoculants are preferred over seed-applied inoculants, fertilizers, or P-solubilizing microbes in lentil because of their strong and consistently positive impact on plant growth and seed yield in the semiarid Canadian prairies. Key words: Lens culinaris, nodulation, seed yield, Penicillium bilaii, P-solubilizing microbes
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".