Rhizobium inoculant and seed-applied fungicide effects on field pea production
Bibliographic record
Abstract
Field pea has been shown to benefit from the use of rhizobium inoculation and seed-applied fungicides under intensive production. The objective of this research was to determine the effect of seed- or soil-applied rhizobium (Rhizobium leguminosarum bv. viciae) inoculants and seed-applied fungicides on field pea (Pisum sativum “Carneval”) production on fields with no previous history of the crop. The study was conducted at seven locations in Saskatchewan in each of 2 yr. Fungicide treatments were Apron FL (metalaxyl), Apron FL + Thiram 75WP (dithiocarbamate) and an untreated check in 1997 and a fourth treatment, Thiram 75WP, was added in 1998. Rhizobium treatments were seed-applied liquid inoculant, soil-applied granular inoculant and a non-inoculated check. Inoculation with rhizobium increased nodulation (5 of 10 sites), seed yield (6 of 13 sites), and protein content (3 of 8 sites), but occasionally appeared to reduce emergence (3 of 14 sites). Granular inoculant had more beneficial effects than the liquid inoculant. Fungicide treatments had few positive effects on production, and occasionally negative effects such as reduced emergence. Interaction effects were rare between seed-applied fungicide and seed-applied liquid or soil-applied granular rhizobium inoculants, which indicated compatibility of these products. Variability in the effects of inoculant types and fungicides suggests that responses are dependent on local soil and environmental conditions. Key words: Apron (metalaxyl), Thiram (dithiocarbamate), Pisum sativum, zero tillage
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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) | 0.001 | 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 | 0.001 |
| 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".