Microbiome Engineering for Sustainable Rice Production: Strategies for Biofertilization, Stress Tolerance, and Climate Resilience
Bibliographic record
Abstract
The plant microbiome, found in the rhizosphere, phyllosphere, and endosphere, is essential for nutrient acquisition, stress tolerance, and the overall health of plants. This review aims to update our knowledge of and critically discuss the diversity and functional roles of the rice microbiome, as well as microbiome engineering strategies to enhance biofertilization and stress resilience. Rice hosts various microorganisms that affect nutrient cycling, growth promotion, and resistance to stresses. Microorganisms carry out these functions through nitrogen fixation, phytohormone and metabolite production, enhanced nutrient solubilization and uptake, and regulation of host gene expression. Recent research on molecular biology has elucidated the complex interactions within rice microbiomes and the signalling mechanisms that establish beneficial microbial communities, which are crucial for sustainable rice production and environmental health. Crucial factors for the successful commercialization of microbial agents in rice production include soil properties, practical environmental field conditions, and plant genotype. Advances in microbiome engineering, from traditional inoculants to synthetic biology, optimize nutrient availability and enhance resilience to abiotic stresses like drought. Climate change intensifies these challenges, but microbiome innovations and microbiome-shaping genes (M genes) offer promising solutions for crop resilience. This review also discusses the environmental and agronomic implications of microbiome engineering, emphasizing the need for further exploration of M genes for breeding disease resistance traits. Ultimately, we provide an update to the current findings on microbiome engineering in rice, highlighting pathways to enhance crop productivity sustainably while minimizing environmental impacts.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".