Soil tillage and phosphorus fertilization effects on root distribution and morphology Soybean (Glycin max, L.)
Bibliographic record
Abstract
No-till (NT) management is used under various cropping systems in the context of conservation agriculture. NT could modify the soil physico-chemical and biological properties including phosphorus (P) stratification along soil profile, and consequently, the crop root development. This study aimed to investigate the effects of different tillage and P fertilization rates on soybean (Glycin max, L.) root distribution and morphology. A corn-soybean rotation field experiment was established in 1992 at L’Acadie, Eastern Canada. It is a split-plot design (four replicates) with main and sun-plot factors as tillage (moldboard plough (MP) and NT) and P fertilization (0 (0P), 17.5 (0.5P) and 35 (1P) kg P ha-1 every two years). Soybean roots and shoots were sampled in 2015 at flowering stage. Root samples were taken by collecting 8-cm diameter cores to a depth of 40 cm at 5 cm, 15 cm and 25 cm perpendicularly to the crop row. Cores were then cut at 0-5, 5-10, 10-20, 20-30, 30-40 and 40-60 cm. After washing, root lengths (RLs) were determined with WinRHIZO. Shoot biomass was weighted. Tillage had significantly effects on vertical root partition, especially for 0-20 cm. Soybean in NT had an average RLD of 1.95 cm cm-3. And roots mostly accumulated at 0-10 cm with 44% of the total RL. Whereas, MP had a lower RLD (1.55 cm cm-3), with the highest root length proportion at 10-20 cm (36%). Moreover, 0P and 0.5P treatments had, respectively, +16% and +18% higher (not significantly) RLD compared to 1P. The higher proportion of roots in surface layers in NT and reduced RLD with 1P might indicate a high sensibility of soybean roots to P sources. However, more roots and higher root/shoot in NT signified a P acquisition pressure for soybean roots, which could be related to the water availability in the surface layers.
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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".