PGPR Inoculated-Seed Increases the Productivity of Forage Sorghum under Fertilized Conditions
Bibliographic record
Abstract
Plant growth promoting rhizobacteria (PGPR) are used for seed inoculation. This study was carried out to determine the influence of seed inoculation with PGPR and different nitrogen (N) and phosphorus (P) levels on the productivity and quality of fodder sorghum. The study was conducted in randomized complete block design (RCBD) under factorial arrangement at Agronomic Research Area, University of Agriculture, Faisalabad during, 2014. The experiment composed of N and P levels such as 0%, 50%, 75% and 100% of recommended doses ha-1 and seed inoculation with PGPR1 (Azotobacter brasilense+Pseudomonas fluorescens) and PGPR2 (Azotobacter chroococcum+Pseudomonas flurescens) with un-inoculated seeds as control. The results revealed that application of N and P, and seed inoculation appreciably increased the productivity and quality of sorghum. Maximum green fodder yield (63.12 t ha-1), dry matter yield (14.51 t ha-1), crude protein (11.02%) and ash contents (8.97%) were recorded with recommended dose of NP. Regarding seed inoculation maximum green fodder yield (62.40 t ha-1), dry matter yield (14.09 t ha-1), crude protein (10.59%) and ash contents (9.07%) were recorded with PGPR1. In conclusion, application of recommended dose of NP and seed inoculation with PGPR1 significantly improved the forage productivity and quality of sorghum.
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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) | 0.000 | 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) | 0.001 | 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".