Root Traits and Carbon Input in Field‐Grown Sweet Pearl Millet, Sweet Sorghum, and Grain Corn
Bibliographic record
Abstract
Little information exists on root morphological characteristics of agricultural crops under field conditions, which can be a major determinant of plant N uptake efficiency and C input to soil. Sweet pearl millet [Pennisetum glaucum (L.) R.BR.] and sweet sorghum [Sorghum bicolor (L.) Moench] are envisioned as energy crops in eastern Canada, to complement corn starch ethanol. The aims of this study were to characterize and compare root biomass and root traits of field‐grown sweet pearl millet, sweet sorghum, and grain corn (Zea mays L.), and to estimate their annual C input to soil. At two sites in eastern Canada, root samples recovered from 30‐cm deep soil cores in sandy loams were weighed and scanned. Image analysis was performed with the WinRhizo software. Roots and shoots were analyzed for C concentration. Estimated C input to soil at harvest was higher for corn (243 g C m−2) than for sorghum and millet (197 and 131 g C m−2, respectively). In contrast, millet and sorghum had the greatest specific root lengths (83, 39, and 22 m g−1 for millet, sorghum, and corn, respectively), and a larger contribution of very fine roots (<0.5 mm diam.) to the total root surface (60–63% for millet and sorghum, and 45–55% for corn). The longer and finer roots of millet and sorghum could contribute to their high N uptake efficiency. However, compared to grain corn, their lower C input to soil needs to be recognized to ensure a balanced C budget.
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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 | 0.001 | 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".