Panicle Angle is an Important Factor in Tef Lodging Tolerance
Bibliographic record
Abstract
(Zucc.) Trotter] is an important crop in the Horn of Africa, particularly in Ethiopia, where it is a staple food for over 60 million people. However, the productivity of tef remains extremely low in part due to its susceptibility to lodging. Lodging is the displacement of the plant from the upright position, and it is exacerbated by rain, wind and the application of fertilizer. In order to address the issue of global food security, especially in the Horn of Africa, greater insight into the causes of tef lodging is needed. In this study, we combine modeling and biomechanical measurements to compare the properties relating to lodging tolerance in high yielding, improved tef genotypes, and lower yielding natural landraces. Our results indicate that the angle of the panicle contributes to the likelihood of lodging in tef. Varieties with compact panicles and reduced height had increased lodging resistance compared to the other varieties. By comparing different varieties, we found that overall, the landraces of tef lodged less than improved varieties. We constructed a model of stem bending and found that panicle angle was an important determinant of the amount of lodging. The findings from this study provide key information to those involved in tef improvement, especially those interested in lodging tolerance.
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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 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".