Tillage Effects on Selected Soil Physical Properties in a Maize-Bean Intercropping System in Mwala District, Kenya
Bibliographic record
Abstract
A field study was carried out to evaluate the effects of tillage practices on soil physical properties in Mwala district, Eastern Kenya, during the long rains (LR) and short rains (SR) of 2012/13. The treatments were disc ploughing (DP), disc ploughing and harrowing (DPH), ox-ploughing (OX), subsoiling-ripping (SSR), hand hoeing with tied ridges (HTR), hand hoeing only (H). These were investigated under three cropping systems of sole maize, sole bean, and maize-bean intercrop in a split-plot design with four replications. Soil physical properties were monitored at different weeks after planting (WAP) throughout the growing seasons. A four-season average shows that soil moisture content was significantly (P < 0.05) higher in OX > SSR > DPH > H > HTR > DP with values ranging from 13.1 to 14.1%. Soil surface roughness and crust strength varied significantly (P < 0.05) over time within the growing seasons, between the tillage practices, and across the different seasons with values ranging from 26 to 66% and 1.21 to 1.31 MPa, respectively. Tillage practices and cropping systems did not significantly affect bulk density, porosity, or K sat values. It is apparent that long term tillage experiment (>4 seasons) would be required to detect changes in soil physical properties as a result of the soil management practices.
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) | 0.001 | 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".