The role of dung beetle species in nitrous oxide emission, ammonia volatilization, and nutrient cycling
Bibliographic record
Abstract
Abstract This study evaluated the role of dung beetle species alone or associated under different species on nitrous oxide (N 2 O) emission, ammonia volatilization, and the performance of pearl millet [ Pennisetum glaucum (L.)]. There were seven treatments, including two controls (soil and soil + dung without beetles), single species of Onthophagus taurus [Shreber, 1759] (1), Digitonthophagus gazella [Fabricius, 1787] (2), or Phanaeus vindex [MacLeay, 1819] (3); and their assemblages (1 + 2 and 1 + 2 + 3). Nitrous oxide emission was estimated for 24 days, when pearl millet was planted in sequence to assess growth, nitrogen yield (NY), and dung beetle activity. Dung beetle species presented greater N 2 O flow of dung on the 6th day (80 g N 2 O-N ha −1 day −1 ) compared to soil and dung (2.6 g N 2 O-N ha −1 day −1 ). Ammonia emissions varied with the presence of dung beetles ( P < 0.05), and D. gazella had less NH 3 − N on days 1, 6, and 12 with averages of 2061, 1526, and 1048 g ha −1 day −1 , respectively. The soil N content increased with dung + beetle application. Dung application affected pearl millet herbage accumulation (HA) regardless of dung beetle presence, and averages ranged from 5 to 8 g DM bucket −1 . A PCA analysis was applied to analyze variation and correlation to each variable, but it indicated a low principal component explanation (less than 80%), not enough to explain the variation in findings. Despite the greater dung removal, the largest species, P. vindex and their species combination, need to be more studied to get a better understanding about their contribution on greenhouse gases. The presence of dung beetles prior to planting improved pearl millet production by enhancing N cycling, although assemblages with the three beetle species enhanced N losses to the environment via denitrification.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".