Soil tillage and P fertilization efects on root distribution and morphology of corn (Zea mays, L.) and soybean (Glycin max, L.)
Bibliographic record
Abstract
In context of conservation agriculture, various soil properties modifications including phosphorus (P) stratification along soil profile induced by no-till practice (NT) might affect crop root development. This study aimed to investigate tillage and P fertilization effects on root distribution and morphology of corn and soybean. A field corn-soybean rotation experiment established in 1992 at l’Acadie is a split-plot design (4 replicates) with 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) as main and sub-plot factors. Soils and corn roots were sampled in 2014 at silking stage by collecting soil cores to 60-cm depth (0-5, 5-10, and every 10 cm) at 5, 15 and 25 cm perpendicularly to the row. Soybean roots were sampled in 2015 at flowering stage. Root length (RL) was determined with WinRHIZO. Corn root length density (RLD) was higher in MP (1.48 cm·cm-3) than in NT (1.28 cm·cm-3). 0P and 0.5P treatments (1.29 and 1.23 cm·cm-3, respectively) significantly reduced RLD compared to 1P (1.62 cm·cm-3). Corn roots mostly accumulated at 0-5 and 5-10 cm. Tillage and P fertilization had no effect on corn root vertical distribution. However, tillage had significant effects on soybean root vertical partition. Soybean in NT had a RLD of 1.95 cm·cm-3 on average; and roots mostly accumulated at 0-10 cm with 44% of total RL. MP had lower RLD (1.55 cm·cm-3), with the highest RL proportion (36%) at 10-20 cm. Additionally, soybean had relatively higher RLDs in 0P and 0.5P than 1P.Compared to corn, the higher proportion of soybean roots in top layers in NT might indicate a higher sensibility of soybean roots to the soil P stratification. While the reduction of corn roots in NT could be more related to a higher weed competition.
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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".