Vertical distribution profiles and temporal growth patterns of roots in selected oilseeds, pulses and spring wheat
Bibliographic record
Abstract
Knowledge of rooting systems and their vertical distribution in the soil profile for field crops is required for designing crop rotation systems that allow for effective use of soil water and nutrients. This study determined the vertical distribution in the upper 100 cm of the soil profile and the temporal growth patterns of roots for three pulses (Cicer arietinum L. chickpea, Pisum sativum L. dry pea, and Lens culinaris Medik. lentil) and three oilseeds (napus canola, juncea mustard, and Linum usitatissimum flax) and spring wheat (Triticum aestivum L.). Crops were grown in 150-mm-diameter, 1-m-long lysimeters under low- (rainfall only) and high- (rainfall + irrigation) water availabilities in a 2-year (2006–07) field study, in Swift Current, Canada. Root volumes increased significantly from the seedling stage, reached the maximum at the late-flowering, and declined to maturity; this temporal growth pattern was independent with water availability. On average, ~44% of the root volume was in the top 20-cm soil layer, 70% in the top 40 cm, and 90% in the top 60 cm. About 5% of the roots in pulse crops were located below 60 cm in depth, significantly less than the 12% for wheat, 13% for mustard, and 18% for canola. Under low-water conditions, Brassica oilseeds had greater root volume (33 mm3 cm–3) than wheat (27 mm3 cm–3), but under high-water conditions, root volumes were 27 mm3 cm–3 for the Brassicas and 32 mm3 cm–3 for wheat. Pulses had greatest root volumes under both low- (40 mm3 cm–3) and high- (42 mm3 cm–3) water conditions, with largest root diameters among crops evaluated, whereas flax the smallest. Rotating the shallower but larger diameter, thicker-rooting pulses with deeper but smaller diameter, thinner-rooting oilseeds or wheat may increase water- and nutrient-use efficiency at the system level.
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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".