Evaluation of coated seeds as a <i>Rhizobium</i> delivery system for field pea
Bibliographic record
Abstract
Greenhouse and field experiments were conducted with field peas (Pisum sativum, L.) in soils of pH 4.4 to 6.8 to determine the best rate of inoculation with rhizobium and to evaluate pre-inoculated (coated) seeds as an alternative to the traditional seed inoculation method of using sticking agents. Inoculation rates higher than 105 cells seed–1 were usually required for high nodulation, nitrogen fixation and grain yields. Therefore, Canadian standards, which require that 105 nodulating rhizobia be delivered per seed for large-seed legumes like peas, may need to be increased. Counts of rhizobia on coated seeds were about 3 log units lower than those on freshly inoculated seeds, but coated seeds significantly outperformed standard seed-inoculated seeds in nodulation and crop yield in acid soils (pH 4.4 and 4.7). However, field pea yields were too low to have commercial value at these low pH levels. In soils with higher pH, standard inoculation resulted in greater nodulation and yield, but the differences were not always significant. It is concluded that the use of coated seeds provides a yield advantage for field pea grown on acid soils, but liming would probably be a better option. Use of coated seeds on other soils will depend on the trade-off between the time and money saved in inoculation in order to seed early and a possible reduction in yield due to insufficient numbers of rhizobia being applied. Key words: Nitrogen fixation, nodulation, Pisum sativum, pre-inoculated seeds
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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".