Poultry Farming Practices Affect the Chemical Composition of Poultry Manure and Its C and N Mineralization in a Ferric Acrisol
Bibliographic record
Abstract
Industrial poultry farming is a booming sector in Africa. This activity generates a significant amount of manure that could be used to improve crop yields on low-productivity soils. We wanted to characterize the variability in the chemical composition of poultry manure and its ability to release mineral nitrogen when applied to soils compared to other organic sources of nutrients such as cattle manure and human feces. We conducted a survey in 79 poultry farms to characterize their practices such as the type of poultry raised, the type of feed and the bedding litter. Poultry manure, cattle manure and human feces samples were collected and analyzed to determine their chemical composition. An incubation study was conducted with all three types of organic resources for 91 days to measure mineral nitrogen release. We found that agricultural practices explain more than 60% of the chemical composition of poultry manure. Wood chips were the most common bedding litter (77% of cases) and about 70% of farms use industrial poultry feed. Broiler manure contains more C and N than laying hens that contain more Ca. Poultry manure releases nitrogen faster than cattle manure when applied to the soil. A combination of broiler chicken manure and laying hen manure could be more beneficial to the crops.
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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 | 0.001 | 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".