Synergistic Impacts of ZnO-NPs and <i>Pseudomonas fluorescens</i> on Yield, Agronomic traits, and Physiological Factors to Enhance the Drought Resilience in Pakistani Rice Varieties
Bibliographic record
Abstract
Among the abiotic stresses, drought is one of the main contributors to constraint rice productivity in arid and semi-arid areas in Pakistan. The present study aimed to evaluate the individual and combined impact of ZnO-NPs, Pseudomonas fluorescens (PGPR) on growth and biochemical attributes of drought-stressed rice. The experiment had a total of eight treatments like, untreated control, and application of ZnO-NPs alone, PGPR alone and their combination under both normal irrigated as well as drought conditions. Our study showed that drought stress control (DC) significantly decreased the morphological parameters including plant height, number of tillers, fresh biomass, number of panicles, 1000 grain weight and total grain yield as well as chlorophyll content, root length and dry root biomass compared to normal control (NC). In spite of using ZnO-NPs and PGPR individually and in combinations led to significant improve the drought stress effects. The plant height (115 cm), tiller numbers (24), fresh biomass (260 g), number of panicles (16.4), grain yield per plant (55 g.), chlorophyll content (40 SPAD value), root length (30.6 cm) and dry root biomass per plant (35 g) were improved with the combined treatment of drought conditions(Dcomb). In contrast, the highest values of most measured traits were consistently observed in Ncomb, which implied an individual or a combined effect between ZnO-NPs and PGPR. Increased performance of ZnONPs by promoting zinc nutrition, in tandem with conventional helper PGPRs such as phytohormone production and nutrient solubilization. This combination is possibly due to an increase in nanoparticle bioavailability facilitated through PGPR and microbial resilience under drought supported by ZnO-NPs. Among root traits, which are deeply involved in water or nutrient uptake and stress resistance in general, the most positive responses were attributed to root length and biomass under combined treatment. In conclusion, ZnO-NPs + Pseudomonas fluorescens co-application had promising effects in controlling drought stress and rice yield reduction under field condition through meaningful complementary physiological and biochemical mechanisms. The present study demonstrates an integrated nanotechnology and microbial approach for sustainable rice production under a rainfed ecosystem under climate change and water-limited conditions.
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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".