Influence of Nitrogen Fertilizer Application on Grain Yield, Nitrogen Uptake Efficiency, and Nitrogen Use Efficiency of Bread Wheat (Triticum aestivum L.) Cultivars in Eastern Ethiopia
Bibliographic record
Abstract
The study was conducted in Eastern Ethiopia, namely Meta and Tullo districts, during the 2015 cropping season to determine the effect of N fertilizer levels on N uptake, N use efficiency (NUE) and grain yield on bread wheat cultivars. Factorial combinations of five N levels (0, 30, 60, 90, and 120 kg N ha-1) and four bread wheat cultivars (Danda’a, Digalu, Kakaba and local cultivar) were laid out as a randomized complete block design (RCBD) with three replications. The interactions of sites, N levels, and cultivars significantly (p ≤ 0.01) affected grain yield (GY), nitrogen uptake by grains, total nitrogen uptake, nitrogen uptake efficiency (NUpE) and nitrigen use efficiency for grain yield (NUEGY). Main effects of sites, N levels and cultivars had significant (p < 0.01) effect on grain and straw N contents, straw N uptake, N biomass production efficiency, N utilization efficiency (NUtE) and nitrogen harvest index (NHI). The cultivar Kakaba at rate of 90 kg N ha-1 produced the highest grain yield (4880 kg ha-1) in Tullo, which was statistically similar with the grain yield (4816 kg ha-1) obtained from the cultivar Digalu with 120 kg N ha-1 in Meta district. The NUEGY was higher with values of 24.2 and 24.1 kg grain kg-1 N in Tullo and Meta districts, respectively, for Digalu cultivar at 30 kg N ha-1 application rate than the remaining N levels and variety interactions at both sites. Cultivars variations in NUEGY under low N application levels were mainly due to higher variations in NUpE than in NUtE. Therefore, there is a need for exploration of the effectiveness of various combinations of N rates with time of applications for improvements of N-use efficiency traits and cost effectiveness in improved wheat cultivars production.
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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 | 0.000 | 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 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".