Effects of different management systems on root distribution of maize
Bibliographic record
Abstract
Wang, Z., Ma, B.-L., Gao, J. and Sun, J. 2015. Effects of different management systems on root distribution of maize. Can. J. Plant Sci. 95: 21-28. Characterization of root distribution in maize (Zea mays L.) is important for optimizing agronomic management to match crop requirements, while maximizing grain yield, especially under intensive management. The objectives of this study were to examine the differences in maize root distribution between two management systems and to identify root-related factors that could be adjusted for further yield improvement. A 4-yr field experiment examined maize root distribution under two management systems: farmers' practices (FP: low plant density, unbalanced fertilization) and high yield strategies (HY: high plant density, sufficient fertilization). Root mass distribution within the soil profile was more restricted horizontally within 10 cm from the stalk base and vertically below 20 cm in HY compared with FP. HY had a greater proportion of fine roots (diameter ≤ 0.5 mm) and more roots per 100 kernels than FP. However, per-plant root weight was not significantly affected by type of management system. Yield was positively correlated with total root number and the ratio of root mass below 20 cm to total root mass. Our data indicate that HY maize overcame the negative effect of crowding stress by producing more roots with smaller root diameters, and maize root systems became narrower and were distributed deeper under intensive management compared with traditional famers' practices.
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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) | 0.000 | 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".