Enhancing Melon Growth and Nutrient Uptake with Plant-Growth-Promoting Microorganisms from Cucumis melo
Bibliographic record
Abstract
In an effort to mitigate the environmental impact of chemical fertilizers while promoting soil sustainability, this study investigated the potential of rhizosphere-associated microorganisms to augment nutrient uptake and growth in melon crops.Soil samples were procured from three distinct melon fields and subjected to serial dilution techniques for microbial isolation on Nutrient Agar (NA) and Potato Dextrose Agar (PDA) media.Subsequent screening identified select isolates with capabilities for nitrogen fixation, phosphorus and potassium solubilization, indole-3-acetic acid (IAA) production, and antagonism against pathogens.Two bacterial isolates, designated BS 1A and BK 1, and one fungal isolate, FW 2B, were earmarked for their plant growth-promoting microorganism (PGPM) potential.Sequence analysis revealed homology of BS 1A with Pseudochrobactrum sp., BK 1 with Bacillus cereus, and FW 2B with Trametes polyzona.A greenhouse experiment employing a completely randomized design evaluated the effects of these isolates on melon growth.Control treatments included a positive control (C+), receiving the recommended dosage of NPK fertilizer, and a negative control (C-), devoid of both NPK and microbial treatments.The application of B. cereus, Pseudochrobactrum sp., and T. polyzona was observed to significantly elevate plant biomass and nutrient acquisition, with B. cereus demonstrating the most pronounced effect, rivalling that of chemical fertilization.The results of this study highlight the potential of utilizing select soil microorganisms as biostimulants, which could play a significant role in increasing the productivity of melon crops, thereby supporting the advancement of sustainable agricultural methodologies.
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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) | 0.001 | 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".