Effect of Maize Stover Application as Soil Mulch on Yield of Arabica coffee (Coffee arabica L., Rubiaceae) at Western Hararghe Zone, Eastern Ethiopia
Bibliographic record
Abstract
An experiment was conducted during the dry seasons in western Hararghe Zone, Eastern Ethiopia at the Haramaya University Chiro Campus to determine the effect of maize stover as soil mulch on yield of Arabica coffee. Five levels of maize stover as soil mulch at a rate of: 0t/ha, 2t/ha, 4t/ha, 6t/ha and 8t/ha were applied in randomized complete block design with four replications from 2008 to 2011. Yield data was recorded during specific phenological stage of the plant. Result from the analysis of variance from the application of maize stover as soil mulch over years showed the presence of significant difference among treatments on bean yield of Arabica coffee. The highest bean yield (1070 kg/ha) and the lowest bean yield (520 kg/ha) were noted at 8 tons/ha and 0 ton/ha, respectively. Similarly, the pooled analysis of variance over seasons corroborated that the effect of maize stover as a soil mulch at a rate of 6t/ha and 8t/ha showed the presence of no significant difference on bean yield of coffee (p<0.05). The result of the present study also elucidated that, the unmulched control plots had the lowest coffee bean yield. While application of 8 tons/ha of maize stover as a soil mulch significantly increased coffee yield both in 2010 and 2011 cropping seasons. Therefore, on the basis of these results, it can be concluded that applying maize stover as soil mulch during the dry seasons at west Hararghe can help to sustain Arabica coffee production. Thus, it is recommended that application of 8tons/ha maize stover as soil mulch can significantly increase the yield of Arabica coffee and sustains its productivity over years.
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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 | 0.001 |
| 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".