EFFECTS OF TILLAGE AND CROPPING SYSTEMS ON MAIZE AND BEANS YIELD AND SELECTED YIELD COMPONENTS IN A SEMI-ARID AREA OF KENYA
Bibliographic record
Abstract
Conservation of soil moisture through tillage practices is an important management objective for crop production in semi-arid areas. A study to evaluate the effects of tillage practices on maize and bean yields was conducted in Mwala Sub County, Eastern Kenya, in the long (LR) and short rains (SR) of 2012/13. The tillage treatments were: Disc Ploughing (DP), Disc Ploughing and Harrowing (DPH), Ox-ploughing (OX), Subsoiling – Ripping (SSR), Hand hoeing with Tied Ridges (HTR) and Hand hoeing (H) only. There were three cropping systems of Sole Maize (SM), Sole Bean (SB) and Maize - Bean intercrop (M + B), which were investigated in a Split-Plot Design field experiment with four replications. Data on maize and bean yield and yield components were monitored throughout the four cropping seasons. Maize plant height, leaf area and leaf area index, maize and beans grain and biomass yields were significantly affected by tillage (P < 0.05). No significant effect of cropping systems on the maize height was observed. Higher maize grain yields (P < 0.05) were obtained in the sole maize plots in LR 2012 (5.01 Mg ha-1), SR 2012 (4.19 Mg ha-1) and in the SR 2013 season (2.82 Mg ha-1). A three - season bean grain yield average by tillage shows that DPH > SSR > DP > OX > HTR > H, with values ranging from 0.75 Mg ha-1 to 1.46 Mg ha-1 (P < 0.05). Intercropping reduced the seasonal means of bean grain yields (P < 0.05) with a 54 % decrease by intercropping (0.73 Mg ha-1) compared to the sole bean (1.6 Mg ha-1). Thus, the DP and DPH improved crop yield and yield components and can be recommended as tillage practices in the semi-arid region.
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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 | 0.000 | 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".