Rhizoma peanut root‐rhizome mass, growth, and decomposition under grazing or clipping management
Bibliographic record
Abstract
Abstract Belowground plant structures are integral to nutrient cycling in grassland ecosystems. However, relative to herbage responses, few studies have examined the belowground dynamics of warm‐season perennial forages under different management practices. This study evaluated root‐rhizome responses and decomposition dynamics of a perennial legume (rhizoma peanut [RP; Arachis glabrata Benth. ‘Ecoturf’]) under continuous stocking (Grazing) and 56‐day clipping (Haying) intervals across three 56‐day periods in 2018 and 2019. In 2019, root‐rhizome mass was greater under Haying than Grazing in two out of three periods, peaking at 14,980 kg organic matter (OM) ha−1. Conversely, root‐rhizome N concentration was lower with Haying than Grazing (12 vs. 14 g kg−1). Root‐rhizome growth rate was greater in 2018 than in 2019 (18.0 vs. 10.5 kg OM ha−1 day−1). In 2019, Grazing exhibited greater biomass (0.0013 vs. 0.0010 g g−1 day−1) and N (0.0016 vs. 0.0011 g g−1 day−1) decay rates than Haying. Root‐rhizome N pools for 2018 and 2019 averaged 159 and 192 kg N ha−1, with 86% and 93% N remaining post‐incubation, respectively. During a 56‐day period, N disappearance was 22.3 kg N ha−1 in 2018 and 13.4 kg N ha−1 in 2019, equating to 70 and 40 kg N ha−1, respectively, over the 168‐day growing season. With RP covering 30% of the pasture, root‐rhizomes contribute an estimated 12–21 kg N ha−1 per season. Root‐rhizome dynamics in RP were influenced by defoliation management, though responses varied between years.
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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".