Effect of Integrated Nutrient Management Practices on Soil Nutrient Availability, Growth and Yield of Sunflower in Kailari, Kailali
Bibliographic record
Abstract
The experiment was conducted from February 2024 to June 2024 in Kailari Rural Municipality, Kailali, Nepal to evaluate the effect of different organic fertilizers and sulfur on soil nutrient availability, growth and yield of sunflower. The experiment was carried out in a randomized complete block design with nine treatments and three replications. The nine treatments comprised of T1- Control, T2- Recommended Dose of Fertilizer(RDF), T3- RDF + Compost manure, T4- RDF + Poultry Manure, T5- RDF + Sulfur, T6- RDF + Compost Manure + Poultry Manure, T7- RDF + Compost Manure + Sulfur, T8- RDF + Poultry Manure + Sulfur, T9- RDF + Compost Manure + Poultry Manure + Sulfur. Results showed that combined application of RDF+CM+PM+Sulfur was found significantly superior on plant height (113.59 cm), stem diameter (22.20mm), f lower diameter (11.60cm), grain per flower (570.11), grain yield (1179.95 kg/ha), biological yield (5799.25 kg/ha). Similarly, the treatments that received organic manure significantly affected soil parameters such as pH, organic matter, available nitrogen, water-holding capacity and porosity. The highest yield was observed in RDF+CM+PM+S (1179.95kg/ha) followed by RDF+PM+S (1121.52 kg/ha) which was 37.77 % and 30.95 % higher than control. This combination also enhanced soil quality by improving various soil parameters, underscoring the benefits of integrated nutrient management for sustainable agriculture. Recommended dose of fertilizers alone had a higher BC ratio (1.79), however recommended dose of fertilizers with compost manure (RDF+CM) was more eco-friendly (1.39 BC ratio).
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".