Soil microbial and nutrient dynamics under different sowings environment of Indian mustard (Brassica juncea L.) in rice based cropping system
Bibliographic record
Abstract
Farmers are not growing diversified crops and applying huge amounts of agrochemicals and imbalanced fertilizers in the rice-wheat cropping system (RWCS), since the 1960s. The objective of this study was to evaluate the microbial and nutrient dynamics in Indian mustard (Brassica juncea L.) under various sowing environments and nutrient sources during Rabi season (October-March), 2015-2016. The experiment was laid out in the split-plot design with three sowing dates in main-plots, and eight nutrient sources in sub-plots. The maximum bacteria, fungi, and actinomycetes population, soil microbial biomass carbon (SMBC), dehydrogenase activities, and available nitrogen, phosphorus, potassium, and sulphur (NPKS) were recorded on November 17 sown crop, and the lowest was observed on December 7 sowing during both the years, and in the pooled analysis. Furthermore, applied nutrient sources, highest bacteria, fungi, and actinomycetes population, available NPKS, SMBC, and dehydrogenase activity were observed in 75% recommended dose of fertilizers (RDF) + 25% N through pressmud (PM) + Azotobacto + phosphorus solubilizing bacteria (PSB) than other nutrient sources. In conclusion, high demand and cost of chemical fertilizers can be replaced by 25% amount easily and locally available organic manures like PM compost to sustain the soil health and crop productivity. It will be helpful to restore the soil biodiversity in the RWCS and provide a roadmap for the researchers, government planners, and policymakers for the use of PM as a source of organic matter and nutrients.
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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) | 0.000 | 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".