Inoculant formulation and fertilizer nitrogen effects on field pea: Nodulation, N<sub>2</sub> fixation and nitrogen partitioning
Bibliographic record
Abstract
Field pea (Pisum sativum L.) acreage has expanded rapidly in the past 10 yr in the Peace River Region of Alberta as well as western Canada. Understanding nitrogen dynamics of Rhizobium inoculants and applied N will provide farmers opportunities to improve N nutrition of field pea. Field experiments were conducted (a) to compare the effects of soil inoculation using granular inoculant, and seed inoculation using peat powder and liquid inoculants with an uninoculated check, on field pea nodulation and N2 fixation, and (b) to determine whether starter N is required by field pea to enhance N2 fixation. The effects of inoculant formulation on nodule number, N accumulation and N2 fixation were in the order: granular > peat powder > liquid = uninoculated. Field pea, from soil-applied inoculant, accumulated more N prior to and during podfilling than field pea with seed-applied inoculant. Fertilizer N application rates < 40 kg N ha-1 had no significant effects on biomass N at flatpod, indicating that starter N was not necessary. Application rates greater than 40 kg N ha-1 reduced nodulation, but the total amounts of N accumulated by plants did not vary. The close proximity of a highly concentrated band of N fertilizer had a greater impact on nodulation and subsequent N2 fixation than the residual soil N level. Under field conditions, soil-applied inoculant improved N nutrition of field pea compared to seed-applied inoculation, with or without applied urea-N. Key words: Granular inoculant, Pisum sativum, Rhizobium, inoculation, field pea, nodulation, N2 fixation
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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) | 0.001 | 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".