Development and characterization of rice bran-gum Arabic based encapsulated biofertilizer for enhanced shelf life and controlled bacterial release
Why this work is in the frame
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Bibliographic record
Abstract
Introduction Currently, microbe-based approaches are being tested to address nutrient deficiencies and enhance nutrient use efficiency in crops. However, these bioinoculants have been unsuccessful at the commercial level due to differences in field and in-vivo conditions. Thus, to enhance bacterial stability, microbial formulations are considered, which will provide an appropriate microenvironment and protection to the bacteria ensuring better rhizospheric-colonization. Methods The present study aimed to develop a phosphobacterium-based encapsulated biofertilizer using the ion-chelation method, wherein a bacterial strain, Myroid gitamensis was mixed with a composite solution containing rice bran (RB), gum Arabic (GA), tricalcium phosphate, and alginate to develop low-cost and slow-release microbeads. The developed microbead was studied for encapsulation efficiency, shape, size, external morphology, shelf-life, soil release behavior, and biodegradability and characterized using SEM, FTIR, and XRD. Further, the wheat growth-promoting potential of microbeads was studied. Results The developed microbeads showed an encapsulation efficiency of 94.11%. The air-dried beads stored at 4°C were favorable for bacterial survival for upto 6 months. Microbeads showed 99.75% degradation within 110 days of incubation showing the bio-sustainable nature of the beads. The application of dried formulations to the pot-grown wheat seedlings resulted in a higher germination rate, shoot length, root length, fresh weight, dry weight of the seedlings, and higher potassium and phosphorus uptake in wheat. Discussion This study, for the first time, provides evidence that compared to liquid biofertilizers, the RB-GA encapsulated bacteria have better potential of enhancing wheat growth and can be foreseen as a future fertilizer option for wheat.
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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 it