CRISPR/CAS9-MEDIATED GENETIC ENGINEERING FOR DEVELOPING SALINITY-TOLERANT RICE VARIETIES FOR INDONESIAN COASTAL AGRICULTURE
Bibliographic record
Abstract
Salinity intrusion in coastal agricultural areas has become a major constraint to rice production in Indonesia, driven by climate change, sea-level rise, and unsustainable land management practices. High soil salinity adversely affects rice growth, yield stability, and food security, particularly in coastal regions that depend heavily on rice cultivation. Conventional breeding approaches for developing salinity-tolerant rice varieties are often time-consuming and limited by genetic complexity. Advances in genome editing technologies, particularly CRISPR/Cas9, offer a precise and efficient alternative for accelerating crop improvement. The objective of this study is to develop salinity-tolerant rice varieties suitable for Indonesian coastal agriculture through CRISPR/Cas9-mediated genetic engineering targeting key genes associated with salt stress tolerance. This research employed an experimental laboratory-based design combined with controlled greenhouse evaluation. Specific salinity-responsive genes were identified and edited using the CRISPR/Cas9 system. Transgenic rice lines were generated and screened for successful gene edits using molecular analysis techniques. Edited lines were subsequently evaluated under saline and non-saline conditions to assess physiological responses, growth performance, and yield-related traits. The results demonstrate that CRISPR/Cas9-edited rice lines exhibited enhanced tolerance to saline stress, indicated by improved germination rates, higher chlorophyll content, better ion homeostasis, and increased biomass compared to non-edited controls. Several edited lines maintained stable growth and yield under moderate to high salinity levels, confirming the effectiveness of targeted gene modification. In conclusion, CRISPR/Cas9-mediated genetic engineering shows strong potential for developing salinity-tolerant rice varieties tailored to Indonesian coastal environments. This approach provides a rapid and precise strategy to enhance rice resilience, support sustainable coastal agriculture, and strengthen national food security under changing climatic conditions.
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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) | 0.000 | 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 | 0.001 |
| 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, 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".