Bottom‐up redistribution of biomass optimizes energy allocation, water use and yield formation in dryland wheat improvement
Bibliographic record
Abstract
Abstract BACKGROUND Modern wheat cultivars have been developed having distinct advantages in many aspects under drought stress, such as plasticity in biomass allocation and root system architecture. A better understanding of the biomass allocation mechanisms that enable modern wheat to achieve higher yields and yield‐based water use efficiency (WUEg) is essential for implementing best management strategies and identifying phenotypic traits for cultivar improvement. We systematically investigated the biomass allocation, morphological and physiological characteristics of three ploidy wheat genotypes under 80% and 50% field water‐holding capacity (FC) conditions. Some crucial traits were also assessed in a complementary field experiment. RESULTS The diploid and tetraploid genotypes were found to allocate more biomass to the root system, especially roots in the topsoil under drought stress. Our data illustrated that lower WUEg and yield of these old genotypes were due to excessive investment in the root system, which was associated with severely restricted canopy development. Modern hexaploid genotypes were found to allocate smaller biomass to roots and larger biomass to shoots. This not only ensured the necessary water uptake, but also allowed the plant to distribute more assimilates and limited water to the shoots. Therefore, the hexaploid genotypes have evolved a stable plant canopy structure to optimize WUEg and grain yield. CONCLUSION This study demonstrated that the biomass shift from below ground to above ground or a more balanced root:shoot ratio tended to optimize water use and yield of the modern cultivars. This discovery provides potential guidance for future dryland wheat breeding and sustainable management strategies. © 2021 Her Majesty the Queen in Right of Canada Journal of The Science of Food and Agriculture © 2021 Society of Chemical Industry. Reproduced with the permission of the Minister of Agriculture and Agri‐Food Canada.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Bench or experimental | low |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Bench or experimental | low |
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) | 0.001 | 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, unvalidatedLabeled directly by 2 models reading the full record.
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".