Influence of Nitrogen Sources and Plant Growth-Promoting Rhizobacteria Inoculation on Growth, Crude Fiber and Nutrient Uptake in Squash (<i>Cucurbita moschata</i> Duchesne ex Poir.) Plants
Bibliographic record
Abstract
Plant growth promoting rhizobacteria (PGPR, B) have immense potential application in sustainable agriculture as ecofriendly biofertilizers and biopesticides. In this study, the effects of three nitrogen (N) sources (NO3-, NH4+ and NO3NH4) and PGPR on growth, crude fiber and nutrient uptake were investigated in squash plants. Some growth parameters [root dry weight (RDW), shoot dry weight (SDW), total plant dry weight (PDW), number of leaves (NL), shoot length (SL), stem diameter (SD) and number of ramifications (NR)], crude fiber (cellulose content) and nutrient uptake (N, P, K, Ca, Mg, Na, Fe, Cu, Mn and Zn) were determined. Application of NO3-, NH4+ or NO3NH4 singly or in combination with PGPR inoculation led to a significant increase in RDW, SDW, PDW, NL, SL, SD and NR. Na, Cu and Zn contents, on the contrary, decreased in inoculated treated plants while no significant differences were recorded in cellulose contents (CE) of leaves except in plants fed with NO3-. The leaf CE content ranged from 12.58 to 13.67%. The plants supplied with NO3+B, NH4+B and NO3NH4+B showed significantly higher plant biomass and accumulation of N, P, K and Mn concentrations in leaves compared to all other treatments. These results suggest that specific combinations of PGPR with NO3-, NH4+ or NO3NH4 fertilizers can be considered as efficient alternative biofertilizers to improve significantly the squash growth and nutrient uptake.
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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) | 0.001 | 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".