Effects of Organic Residues on Soil Properties and Sesame Water Use Efficiency
Bibliographic record
Abstract
A study was conducted at the National Semi-Arid Resources Research Institute-Serere, Uganda for three seasons (2013 short rains, 2014 long rains and 2014 short rains) to investigate the effect of crop residues and animal manure on soil bulk density (SBD), soil moisture content (SMC) and water use efficiency (WUE) of sesame. The experiment was laid out in a randomized complete block design with three replications. The treatments comprised: control, 4 crop residues, 2 animal manures and combinations of 2 animal manures and 4 crop residues all applied at two rates of 3 and 6 t/ha. Plots treated with 6 t/ha of millet husks produced the highest SMC (37.46%) and lowest SBD (1.1717 g/cm3) across seasons; while plots treated with 3 t/ha of millet husks produced the highest WUE of sesame (9.92 kg ha-1 mm-1) across seasons compared with other crop residue and animal manure treatments applied singly. Soil moisture content was highest (38.09%) and SBD lowest (1.0520 g/cm3) across seasons in plots treated with 6 t/ha of poultry manure plus millet husks; while plots amended with 3 t/ha of poultry manure plus millet husks produced the highest WUE of sesame (9.40 g/cm3) across seasons compared with other treatments. Crop residues influenced SMC and SBD in the order; millet husks > cowpea husks > sorghum husks > groundnut shells. Crop residues affected WUE of sesame in the order; millet husks > sorghum husks > groundnut shells > cowpea husks. This study has demonstrated that poultry manure plus millet husks have a potential to enhance WUE of sesame.
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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 | 0.001 |
| 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 | 0.001 | 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".