Inoculant formulation and fertilizer nitrogen effects on field pea: Crop yield and seed quality
Bibliographic record
Abstract
Appropriate rhizobial inoculation and fertility management can increase field pea (Pisum sativa) seed yield and improve yield stability in western Canada. The objective of this study was to determine the effect of inoculation method and N fertilizer application on pea seed yield and quality. The effects of soil inoculant (granular) and seed-applied inoculant (peat powder or liquid) used with and without urea-N application on field pea were investigated in the Peace River region of Western Canada. At low applied N rates, field pea biomass was significantly higher for soil-applied inoculant as compared to seed-applied inoculant. Soil-applied inoculant resulted in 15, 18, 9 and 0% higher pea biomass yield at the flatpod stage than seed-applied inoculant at 0, 20, 40 and 80 kg N ha-1, respectively. Averaged over all N rates, soil-applied inoculant resulted in 17, 50, and 56% higher pea seed yield than peat inoculant, liquid inoculant, or the uninoculated check, respectively. Soil-applied inoculant increased the proportion of the biological yield converted to seed compared to seed-applied inoculant. Seed protein concentration increased by 12 and 15% when inoculant was soil-applied compared with seedapplied or uninoculated pea, respectively. Without N fertilizer, soil-applied inoculant increased field pea biomass, seed yield and protein concentration and contributed to increasing yield stability compared with seed-applied inoculant. Key words: Granular inoculant, Pisum sativum, field pea, inoculation, seed protein, seed yield
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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) | 0.001 | 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".