Exploring the role of Peanut (Arachis hypogaea L.) root architecture in enhancing adaptation to climate change for sustainable agriculture and resilient crop production: A review
Bibliographic record
Abstract
Peanut (Arachis hypogaea L.) cultivation is increasingly vulnerable to climate change, with drought and heat stress emerging as major constraints to productivity and food security. This review explores the critical role of root architecture in enhancing peanut adaptation to environmental stressors, and evaluates current strategies and future directions for improving root traits through genetic, physiological, and agronomic approaches. Efficient root systems, characterized by deeper rooting and optimized xylem design, significantly improve water and nutrient acquisition under drought conditions. Key regulators such as abscisic acid (ABA), strigolactones, and specific root-related genes modulate root development and stress responses. Root exudates further enhance soil root interactions, while the peanut root microbiome contributes to nutrient cycling and resilience. Biotechnological tools, including quantitative trait loci (QTL) mapping and CRISPR/Cas-based genome editing, are being harnessed to manipulate root traits at the molecular level. Agronomic practices like mulching and cover cropping synergize with genetic improvements by enhancing soil structure and moisture retention. Strengthening peanut root architecture through the integration of modern breeding, biotechnological advances, and sustainable soil management offers a promising path toward climate-resilient peanut production. Future research should prioritize the convergence of these approaches, alongside microbiome exploration, to secure yield stability and food security in a changing climate.
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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 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".