High-throughput phenotyping of buckwheat (Fagopyrum esculentum Moench.) genotypes under water stress: exploring drought resistance for sustainable agriculture
Bibliographic record
Abstract
BACKGROUND: As global agriculture faces the challenge of climate change, characterized by longer and more severe drought episodes, there is an increasing need for crop diversification and improved plant breeding. Buckwheat is one of the climate-resilient candidates for future important crops with remarkable adaptability to various biotic and abiotic stresses. As an underbred crop, a large number of genotypes should be assessed for the breeding of superior plants. Therefore, this study investigates the response of various buckwheat genotypes to water stress by high-throughput phenotyping and auxiliary plant physiology measurements. RESULTS: We assessed six buckwheat genotypes from different regions under mild and severe water stress, focusing on morphological and physiological changes to understand drought tolerance mechanisms. Our findings revealed that reallocation of assimilated carbon from growth to secondary metabolite production is a common response to drought stress. Among the genotypes tested, Panda emerged as the most drought-resistant, with its morphology remaining the most stable under mild water stress and its ability to rapidly accumulate protective pigments in response to drought. Silver Hull also demonstrated resilience, maintaining its aboveground biomass under mild water stress at levels comparable to the control group. Additionally, the response magnitude to drought stress was linked to the biomass production potential of the genotypes, which was higher for those from warmer regions (Bhutan, Zimbabwe) and lower for those from colder regions (Poland, Canada). CONCLUSION: The diversity in genotypic responses highlights the significant role of genetic variability in shaping drought resistance strategies in buckwheat. This research not only enhances our understanding of buckwheat's physiological responses to water stress but also holds promise for developing drought-resistant buckwheat varieties. These advancements are crucial for promoting sustainable agriculture in the face of climate change.
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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 | 0.001 | 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) | 0.001 | 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".