An overview of root traits and ideotypes for improving crop productivity and addressing agronomic challenges
Bibliographic record
Abstract
Currently, breeding efforts are focused mainly on shoot traits, which are insufficient to address agronomic challenges complicated by climate change . There is a need to incorporate root traits in breeding strategies, but recent research postulates that, due to root plasticity, breeding for specific root ideotypes is a better and less time-consuming approach. In this review, current studies on root ideotypes are summarized, and a case study on lentil genotypes, discussed. The objectives of this review are to (1) discuss the benefits of incorporating root traits in breeding programs, (2) discuss root traits for enhanced crop productivity i.e., improved nutrient uptake , abiotic stress tolerance, reduced lodging and diseases, and (3) summarize recent root ideotypes studies, and discuss a case study involving ideotypes identified in cultivated versus wild lentil genotypes for their potential implications for moisture and nutrient acquisition. We found that root traits play a significant role in abiotic stress management, root lodging, disease escape, and nutrient acquisition and uptake. The study of individual root traits and their response to biotic and abiotic stress is important to identify root ideotypes. Root ideotypes from domesticated plants (e.g., cultivated lentils) and their wild relatives can contribute significantly to solving agronomic problems when incorporated into breeding programs. Future breeding endeavors should incorporate specific root ideotypes for targeted environments to address agronomic issues.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".