<i>Ascophyllum</i> extract application can promote plant growth and root yield in carrot associated with increased root-zone soil microbial activity
Bibliographic record
Abstract
Alam, M. Z., Braun, G., Norrie, J. and Hodges, D. M. 2014. Ascophyllum extract application can promote plant growth and root yield in carrot associated with increased root-zone soil microbial activity. Can. J. Plant Sci. 94: 337–348. Root growth and soil microbial activity were examined in two cultivars of carrot following treatment with Ascophyllum nodosum marine-plant extract. Field experiments were established in grower-managed fields of Maverick and Pronto carrots during 2010 and 2011. Soluble Ascophyllum extract powder (SAEP) was applied weekly, bi-weekly or tri-weekly at rates of 0 (control), 0.25, 0.50, 0.75 or 1.0 g L−1 over 11 to 13 wk. Results indicate that SAEP treatment increased root yields of Maverick and Pronto by about 20 and 15%, respectively, reduced proportion of smaller roots and improved harvest index (HI). Maximum yield was found at or above 0.50 g L−1 SAEP for Maverick and at 0.75 g L−1 for Pronto. Soil microbial colony counts, respiration and metabolic activity increased following SAEP applications, but varied with SAEP rate and application frequency. Using the Biolog microbial analysis system, maximum average well colour development (AWCD), substrate diversity (H), substrate evenness (E), and substrate richness (S) responses to extract treatment generally showed successive increases at 0.50, 0.75 and 1 g L−1 SAEP at tri-weekly application frequencies. With more frequent applications, rates below 1 g L−1 led to greater microbial growth, respiration and functional activities. Principal component analysis (PCA) showed a strong relationship between carrot growth, soil microbial populations and activity parameters. These results suggest that seaweed extract application can result in an increase in soil microbial activity associated with increased yield in carrots.
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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) | 0.001 | 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 | 0.001 |
| 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".