Rhizobial inoculant formulations and soil pH influence field pea nodulation and nitrogen fixation
Bibliographic record
Abstract
Crop production systems that include field pea (Pisum sativum L.) in rotation are important for sustainable agriculture on acid soils in northwestern Canada. Greenhouse experiments were conducted to compare the ability of liquid inoculant applied to the seed, powdered peat inoculant applied to the seed, and granular inoculant applied in a band with the seed to establish effective nodulation on field pea grown at soil pH(H2O) 4.4, 5.4 and 6.6. Plants were grown to the flat pod stage, and then total plant biomass dry weight, dry weight of nodules, number of nodules, plant nitrogen content, and proportion of plant nitrogen derived from the atmosphere (%Ndfa) were measured. Granular and powdered peat inoculants produced greater nodule numbers and weight, plant nitrogen content, %Ndfa and total biomass than liquid inoculant in at least two of the three experiments. Only granular inoculant was effective in establishing nodules at soil pH 4.4, but granular and powdered peat inoculants were effective at pH 5.4, and all three formulations were effective at pH 6.6. The results showed that granular inoculant has potential for effective nodulation of field pea grown on acid soil. Key words: Rhizobium, inoculant formulations, field pea, nodulation, acid soil
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".