Effects of Varieties and Nitrogen Fertilizer on Yield and Yield Components of Maize on Farmers Field in Mid Altitude Areas of Western Ethiopia
Bibliographic record
Abstract
Yield of maize hybrids could be low when grown below optimum management practices. Use of improved varieties and optimum nitrogen fertilizer application practices are unlocking the high yielding potential of hybrids maize. With these in view, a field experiment was executed on farmers’ field to determine the effect of varieties and nitrogen fertilizer rate on yield and yield components of maize in two cropping seasons. It is laid out with randomized complete block design in factorial arrangement with three replications. Five maize varieties (BH-540, BH-543, BH-661, BH-660, and BH-140) as main factor and two levels of nitrogen (55 and 110 Kg N ha −1 ) as subfactor were used with one maize variety (BH-543) without fertilizer as control. Leaf area and leaf area index of maize varieties were significantly affected by application of nitrogen fertilizer rates. Interaction of maize varieties with nitrogen fertilizer rates significantly affected all yield and yield components of maize. Application of half and full recommended nitrogen fertilizer produced mean grain yield advantages of 31 and 41% over control. Therefore, application of half and full recommended nitrogen fertilizer for improved maize varieties has significantly improved grain yield and recommended for maize production in midaltitude area of western Ethiopia.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".