Improving the hydraulic and mechanical properties of a degraded paleustult: Potential of combined application of poutry manure and Portland cement
Bibliographic record
Abstract
Physical deterioration of soil poses threat to sustainable agricultural land use and there is the need for proactive management measures to address it. Despite research indicating the benefit of using cement to enhance soil properties, there is a dearth of scientific information on its co-application with organic manure. Therefore, influence of sole and combined applications of poultry manure (PM) and Portland cement (PC) on soil aggregation, hydraulic conductivity (K) and strength was investigated. The treatments consisted of factorial -1 combinations of PM and PC applications at four levels (0, 2.5, 5 and 10 g kg soil) in three replicates to a Typic Paleusult in a screen house. The aggregate stability, K (6, 2 and 0.5 cm suctions) and strength were determined -1 using standard methods. Poultry manure applied at the rate of 5 g kg increased soil macro-aggregate stability -1 (combined total of 40.17%) and the near saturated hydraulic conductivity (0.83 cm day ) of the soil while PC -1 - significantly increased both soil properties (combined total of 38.71% and 0.61 cm day , respectively) at 2.5 g kg 1 -1 rate. Soil strength peaked when PM (0.57 kPa) and PC (0.60 kPa) were applied at 10 and 5 g kg , respectively. The combination of PC and PM significantly increased macro-aggregate stability and soil strength although the PM limited the potential of the PC to strengthen the soil. The treatments showed potential in improving the soil aggregate stability, near saturated hydraulic conductivity and strength of the degraded soil with little to moderate agronomic limitations.
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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 | 0.000 | 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".