Decomposition and Nutrient Release of Selected Cereal and Legume Crop Residues
Bibliographic record
Abstract
Crop residues have the potential to enhance soil fertility, but this is dependent on their biochemical quality. A study was conducted at the National Semi-Arid Resources Research Institute-Serere, Uganda to determine the chemical composition, nutrient release patterns and rates of selected crop residues. The design used was randomized complete block design with four replications. The treatments comprised of 20g of finger millet, sorghum, cowpea and groundnut crop residues. Finger millet had the highest initial C (36.28%) and N (3.46%), cowpea had the highest initial P (0.60%) and Ca (0.30%) and groundnut shells had significantly the highest initial K (1.01%). Finger millet husks with the lowest carbon/nitrogen ratio, had significantly the highest rate of dry matter disappearance and nutrient release, while groundnut shells with the highest carbon/nitrogen and carbon/phosphorus ratios had significantly the lowest nutrient release and dry matter disappearance rates. At the end of incubation, finger millet husks had significantly the lowest N (0.29%) and C (10.22%), while cowpea husks had the lowest P (0.27%), K (0.18%) and Ca (0.27%). Nutrient release and dry matter disappearance rates of crop residues occurred in the order of N > K > P > Ca and finger millet husks > cowpea husks > sorghum husks > groundnut shells, respectively. This study has demonstrated that finger millet husks released nutrients faster and this is beneficial for early planted crops, while groundnut shells released nutrients slowly which is appropriate for long term availability of plant nutrients.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| 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 teacher head, 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".