Effects of Manure Management and Nitrogen Levels on Soil Organic Carbon in the Northern Guinea Savanna, Nigeria
Bibliographic record
Abstract
A two years study was carried out in two locations at Institute for Agricultural Research and the Samaru College of Agriculture Farms (Lat. 11° 11’’ and Long. 7° 38’’) in the Northern Guinea Savanna zone of Nigeria. The study aimed at investigating the effects of cow dung management practices, time of application when combined with inorganic fertilizer (urea) and their direct and residual effects on organic carbon content of the soil. The treatments consisted of three cow dung management practices, four different storage times after one month ageing and two levels of nitrogen. There was a control treatment where no cow dung or nitrogen fertilizer was applied. The study was a factorial experiment with three factors, laid out in a randomized complete block design replicated three times. The manure amended treatments were generally higher than the control treatments in the two years and at both direct and residual effects. This showed that the addition of cow dung actually increased the organic carbon content of the soil. However, the highest organic carbon value for 2003 and 2004 years of direct effects, at 4 WAP were on treatments pit covered May (54.5 g kg -1 and 49.0 g kg -1 respectively), while the lowest values were observed on the control treatments (30.7 g kg -1 and 24.7 g kg -1 respectively). The management practices and the time (month) of application did not show any significant effect on the content of the soil organic matter of the soil.
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 | 0.001 | 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".