Performance evaluation of advanced durum wheat genotypes under irrigated condition at Bhairahawa, Nepal
Bibliographic record
Abstract
A field research was carried out at the National Wheat Research Program (NWRP) in Bhairahawa, Nepal in 2022 to investigate elite durum wheat genotypes and key traits contributing to grain yield. The experiment was performed in an alpha lattice design with two replications. Thirty distinct durum wheat genotypes were assessed, focusing on fourteen quantitative traits including days to booting, days to heading, days to maturity, plant height, spike length, peduncle length, number of tillers per square meter, number of spikes per square meter, number of grains per spike, grain weight per spike, thousand kernel weight, grain yield, biomass yield, chlorophyll content. The studied genotypes were grown under irrigated condition. Genotype NL1779 attained the highest grain yield of 3828 kg/ha, followed by NL1769 (3784 kg/ha), NL1772 (3726 kg/ha), NL1789 (3640 kg/ha) and NL1784 (3570 kg/ha). Principal components analysis revealed that eight traits were the major loadings on the first two principal components that describe 53.4% of the total morphological variance at irrigated condition. Cluster analysis grouped the different genotypes into four clusters, with each cluster showing variation in performance for different traits under irrigated conditions. Cluster III is characterized by genotypes exhibiting the highest grain yield, biomass yield, spike length, number of grains per spike, and number of spikes per square meter. Notably, the high-yielding genotypes NL1779, NL1769, NL1772, NL1789, NL1784, and NL1773 identified within this cluster could serve as potential candidates for inclusion in the national breeding program. These superior genotypes could be recommended for irrigated environment after further evaluation. Integrating them into national breeding programs offers an opportunity for genetic improvement, contributing to establishing a robust durum wheat production system in Nepal, meeting the growing demand for durum wheat products while promoting dietary diversity and sustainable agriculture.
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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 | 0.000 | 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 | 0.001 |
| 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".