Multi-Environment Trial Analysis of Elite Rye Cultivars under Rainfed Conditions
Bibliographic record
Abstract
Rye ( Secale cereale L.), as an important crop with both food and feed value, has strong adaptability in arid and semi-arid regions and is a key variety resource for ensuring food security and the development of animal husbandry. However, under dryland conditions, the yield and quality of rye are highly constrained by environmental factors, showing a significant genotype-environment interaction (G×E) effect. To deeply assess the performance of superior rye varieties in different ecological regions, this study, based on the multi-environment test (MET) method, systematically analyzed the yield, quality, stress resistance and resource utilization efficiency of multiple elite rye varieties under typical arid conditions. By comparing the yield differences, stability of quality traits and water and nitrogen utilization efficiency under different ecological environments, Combining GGE-Biplot and stability analysis methods, varieties with high yield, high quality and wide adaptability were screened out. The research results show that multi-environment trials can not only reveal the restrictive factors of the environment on rye production, but also provide a theoretical basis for regionalized variety recommendation and the establishment of breeding goals in dryland farming areas. This study aims to promote the application and popularization of superior varieties by scientifically evaluating the comprehensive performance of elite rye varieties under dryland conditions, and to provide references for future rye breeding and sustainable agricultural development in arid areas.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".