Response of Wheat to NP Fertilizer Rates, Precursor Crops and Types of Vertisols in Central Highlands of Ethiopia
Bibliographic record
Abstract
The objective of this work was to evaluate the performance of bread wheat under different NP fertilizer rates, precursor crops and types of Vertisols in order to determine higher agronomic and economic yielding combination of levels of these factors. The first field experiment comparing factorial combinations of 0, 80, 160 and 240 kg N ha-1 with 0, 20, 40 and 60 kg P ha-1 in 2006-2007 on four farmers’ fields, with four replications in each field, resulted in recommendation of 151/40 and 192/60 kg N/P ha-1 for further on-farm evaluation as compared to old recommendation (87/20 kg N/P ha-1) and to the farmers’ highest rate (256/80 kg N/P ha-1) under dominant precursor crops and types of Vertisols. Thus the second field experiment compared the above four treatments on 32 farmers’ fields (no replication in each field) on lentil (Lens culinaris) and tef (Eragrostis tef) precursors on Bushella and Mererie Vertisols in 2012-2013. Grain yield response of bread wheat to N/P rates following lentil precursor on Mererie was significantly (p < 0.01) quadratic while responses following lentil precursor on Bushella, and tef precursor on both types of Vertisols were significantly (p < 0.05) linear. Application of the highest rate (256/80 kg N/P ha-1) on wheat following lentil precursor gave grain yield of 5001 and 3407 kg ha-1 on Mererie and Bushella Vertisols, respectively. The same rate on wheat following tef precursor gave grain yield of 4143 and 3904 kg ha-1 on Mererie and Bushella Vertisols, respectively. However, application of 167/45 kg N/P ha-1 was more economical (79.7-134.1% marginal return) and is suggested to be promoted for bread wheat production following tef and lentil precursors on both types of Vertisols of the test locations and similar areas in the central highlands of Ethiopia. Further studies are also suggested to improve fertilizer use efficiency and reduce such high rate recommendations which could pose environmental risks.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".