Comparison of Response of Canola (Brassica napus L. cv Hyola 401) to Biofertilizer Inoculation in Optimal and Delayed Cropping Dates
Bibliographic record
Abstract
In order to compare the effects of phosphate solubilizing bacteria as biofertilizer and mineral phosphorous application in optimal date of sowing with delayed cropping date on canola (Brassica napus L.) growth and productivity, a split-plot experiment using randomized complete block design with three replications was conducted at Dasht-e-Naz Agronomy Research Station in 2014. Four levels of bacterial inoculation (Pseudomonas putida, Pseudomonas fluorescens, singly or in combination) were applied as main plots and five levels of mineral phosphorous application (0, 25, 50, 75, and 100 kg/ha of mineral phosphorous) were applied as sub plots. Canola plant were sown at two dates of sowing, one at optimal date of sowing (Sept. 30, 2014) and the other one month later as delayed cropping (Oct. 30, 2014). The results obtained showed that effects of bacterial inoculation on number of seeds per pod, number of pods on plant, and seed yield were significant at one percent probability level at both sowing dates. Moreover, rates of phosphate application had significant effects on all traits at one percent level. Comparing the means showed that application of mineral P resulted in substantial increase in seed yield. At optimal date of sowing, it was shown that while minimum seed yield obtained at control treatment (1600 kg/ha), the maximum (2980 kg/ha) obtained with interaction effects of application of both bacterial strains along with 75 kg/ha mineral P, having no statistically difference with that of P. fluorescence along with 75 kg/ha mineral P (2940 kg/ha). It was shown that delayed cropping resulted in decreasing canola growth which is reflected in seed yield and yield components. Minimum seed yield at control plot at first date of sowing (1600 kg/ha) decreased to 740 kg/ha in second date of sowing, showing 54% decrease. The maximum seed yield also decreased in delayed cropping, from 2980 kg/ha at Sept. 30, 2014 as compared to 1074 kg/ha at Oct. 30, 2014, showing a 64% decrease. The results obtained showed that an increase in P level, eventually enhanced the seed yield. This increasing trend continued until a threshold level (75 kg/ha of P), after which seed yield showed a declining fashion.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".