Impact of organic manures on soil health, yield and quality of pit planted sugarcane
Bibliographic record
Abstract
An experiment was conducted at Organic Farm, Navsari Agricultural University, Navsari to study the effect of different proportion of organics on productivity of pit planted sugarcane during three consecutive years of 2013, 2014 and 2015. The experiment was conducted at fixed plot site with 8 set of organics treatments and 1 inorganic treatment as control (250:125:125 kg NPK/ha) arranged outside the experimental plot, laid down in randomized block design replicated thrice. Significantly higher millable cane height, number of internodes/ millable cane and single millable cane weight were recorded when crop nourished with 50% RDN each of vermi compost and castor cake. Further, application of vermi compost (50% RDN) along with neem cake or castor cake (50% RDN) were found equally effective and recorded significantly higher millable cane and trash yields. In organics vs inorganic analysis, application of 100 per cent RDF through inorganic fertilizers recorded significantly higher values of growth and yield parameters and yields of sugarcane crop. For producing higher and profitable cane yield of sugarcane, the crop should be fertilized with 100% RDF (250:125:125 NPK kg/ha) under south Gujarat condition. Further, it is also inferred that for organic production of sugarcane crop, application of 50% RDN through vermi compost and remaining 50% RDN either through castor cake or neem cake was found remunerative.
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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 | 0.001 | 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".