Linkages Between Sorghum bicolor Root System Architectural Traits and Grain Yield Performance Under Combined Drought and Heat Stress Conditions
Bibliographic record
Abstract
Breeding programs often overlook the use of root traits. Therefore, we investigated the relevance of sorghum root traits in explaining its adaptation to combined drought and heat stress (CDHS). Six (i.e., three pre-release lines + three checks) sorghum genotypes were established at two low-altitude (i.e., <600 masl) locations with a long-term history of averagely very high temperatures in the beginning of the summer season, under two management (i.e., CDHS and well-watered (WW)) regimes. At each location, the genotypes were laid out in the field using a randomized complete block design (RCBD) replicated two times. Root trait data, namely root diameter (RD), number of roots (NR), number of root tips (NRT), total root length (TRL), root depth (RDP), root width (RW), width–depth ratio (WDR), root network area (RNA), root solidity (RS), lower root area (LRA), root perimeter (RP), root volume (RV), surface area (SA), root holes (RH) and root angle (RA) were gathered using the RhizoVision Explorer software during the pre- and post-flowering stage of growth. RSA traits differentially showed significant (p < 0.05) correlations with grain yield (GY) at pre- and post-flowering growth stages and under CDHS and WW conditions also revealing genotypic variation estimates exceeding 50% for all the traits. Regression models varied between pre-flowering (p = 0.013, R2 = 47.15%, R2 Predicted = 29.32%) and post-flowering (p = 0.000, R2 = 85.64%, R2 Predicted = 73.30%) growth stages, indicating post-flowering as the optimal stage to relate root traits to yield performance. RD contributed most to the regression model at post-flowering, explaining 51.79% of the 85.64% total variation. The Smith–Hazel index identified ICSV111IN and ASAREACA12-3-1 as superior pre-release lines, suitable for commercialization as new varieties. The study demonstrated that root traits (in particular, RD, RW, and RP) are linked to crop performance under CDHS conditions and should be incorporated in breeding programs. This approach may accelerate genetic gains not only in sorghum breeding programs, but for other crops, while offering a nature-based breeding strategy for stress adaptation in crops.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".