Evaluation of <i>Sinorhizobium meliloti</i> strains and commercial rhizobial inoculants for fenugreek production in Canada
Bibliographic record
Abstract
Fenugreek ( Trigonella foenum-graecum L.) is a versatile annual legume valued for its nutritional, medicinal, and agricultural uses. Like other legumes, fenugreek can conduct biological nitrogen fixation (BNF). Because fenugreek lacks a dedicated commercial rhizobial inoculant, this study aimed to evaluate the effectiveness of six commercial rhizobial inoculants registered for alfalfa and sweet clover, alongside 15 Sinorhizobium meliloti strains, on two fenugreek varieties, CDC Canafen and Fenucold. Using laboratory inoculation tests and greenhouse experiments, the symbiotic potential of these products and strains was assessed through nodule formation, BNF, dry matter yield, and the amount of nitrogen fixed. There was variability in symbiotic performance across the strain–variety combinations, with the commercial inoculant RIZOLIQ TOP-Alfalfa and the pure strains USDA1811 and USDA1150 demonstrating superior nodulation, % nitrogen derived from the atmosphere, and dry matter production. CDC Canafen generally exhibited greater nitrogen fixation compared to Fenucold, and the varieties could fix up to 58% and 47% of their required nitrogen, respectively. The study identifies a registered commercial inoculant that can be used on common fenugreek varieties grown in Canada and shows the potential of other S. meliloti strains for future improvements to inoculant offerings.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".