Effect of Nitrification Inhibitor and Cutting Heights on Degradability of Pearl Millet
Bibliographic record
Abstract
Pearl millet crop has been increasingly growing in Brazilian Savanna and it is already being used as cover crop between annual crops. The plant has great forage potential, besides being a nutrient recycling plant due to its peculiar root system. This study was developed in order to assess the pearl millet nutritional value when submitted to nitrogen fertilizer. It was evaluated the effect of nitrogen fertilizer (ammonium sulfate nitrate) treated with the nitrification inhibitor 3,4-dimethyl pyrazole phosphate (DMPP) on the ruminal degradability of two pearl millets’ cultivars, under four nitrogen fertilization levels (0, 45, 90 and 180 kg ha-1) and pre-cutting heights (0.70, 0.80 and 0.90 m). The experimental design was a randomized block design in a factorial 3 × 4 (3 cutting heights × 4 nitrogen doses) with three replications. Data were submitted to analysis of variance and means were compared by Tukey test at 5% probability. The DMPP treated Nitrogen, in high doses, increased the dry matter, crude protein and neutral detergent insoluble fiber degradability in pearl millet handled at 0.90 m. The combination of fertilization with 45 or 90 kg ha-1 of nitrogen treated with DMPP, with the management of millet at 0.70 or 0.80 m did not favored the forage nutritional quality, indicating that in these treatments, the ratio between the availability of nitrogen in ammonium and nitrate forms may have been detrimental to the plants.
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".