Unlocking the potential of biofilm- forming plant growth-promoting rhizobacteria for growth and yield enhancement in wheat (Triticum aestivum L.): Results from In vitro and in vivo studies
Bibliographic record
Abstract
Abstract Plant growth-promoting rhizobacteria (PGPR) boosts agricultural productivity and alleviates environmental stresses by forming biofilms under natural climatic conditions. In the past few years, microorganisms in biofilm have gained impetus for efficient root colonization. The current work aims to characterize biofilm-associated rhizobacteria for wheat growth and yield enhancement. In this study native rhizobacteria were isolated from the wheat rhizosphere and ten isolates were characterized for plant growth promoting traits and biofilm production under axenic conditions. Among these ten isolates, five potential biofilm-producing plant growth-promoting rhizobacteria on the basis of invitro plant growth promoting trait assays were further tested under controlled and field conditions on wheat growth and yield attributes. Surface-enhanced Raman spectroscopy (SERS) spectra further revealed that biochemical contents of biofilm produced by selected bacterial PGPR strains are associated with proteins, carbohydrates, lipids, amino acids and DNA/RNA. Inoculated plants in growth chamber resulted in longer roots, shoots, and increase in fresh biomass than controls. Similarly, significant increases in plant height (up to 13.3, 16.7%), grain yield (up to 29.6, 46.9%), number of tillers (up to 18.7, 34.8%), nitrogen contents (up to 58.8, 48.1%), and phosphorus contents (up to 63.0, 51.0%) in grains were seen in both pot and field trials, respectively. The two most promising biofilm-producing isolates were identified through 16s rRNA partial gene sequencing as Brucella sp. (BF10), Lysinibacillus macroides (BF15). Moreover, leaf pigmentation and relative water contents increased in all treated plants. Taken together, our results revealed that biofilm forming PGPR can boost crop productivity for sustainable agriculture.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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 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".