Effect pf Integrated Nutrient Management on Growth and Yield of Potato (Solanum tuberosum L.)
Bibliographic record
Abstract
To study the effect of integrated nutrient management on the growth and yield attributes of potatoes, this field experiment was carried out at Mangalsen Municipality, Thatikhand, Achham district, Nepal from February 2023 to June 2023. The experiment used a single-factor Randomized Complete Block Design (RCBD) with three replications and seven treatments. Different types of organic and inorganic fertilizers and their combination were used as treatment which are; T1: (RDF @ 100:100: 60), T2: (RDF @ 75% + FYM @ 20 ton/ha), T3: (RDF @ 75% + Vermicompost @ 8 ton/ha), T4: (RDF @ 75% + FYM @ 2ton/ha + Sulphur @ 20kg/ha), T5: (RDF @ 75% + FYM @ 2 ton/ha + Zincsulphate @ 20kg/ha), T6: (RDF @ 75% + FYM @ 2 ton/ha + Sulphur @ 20kg/ha + Zincsulphate @ 20kg/ha) and T7: (Control). The variety of potatoes used for research was “Khumal Seto” as it is recommended for cultivation in the high-hill and mid-hill regions of Nepal. A significant difference in plant germination, plant height, leaf number, stem number, canopy diameter, and yield attributing characters such as total number of tuber per hill, average weight of tuber, and total yield was observed among the treatments under observation. The yield parameters such as total number of tuber per hill (8.33), weight per tuber (0.08 gm), marketable tuber yield (10.24 Kg), and total yield (28.33 mt/ha), and growth parameters such as plant germination (21), plant height (39.2 cm), number of leaves (29.2), number of stems (14.26), and plant canopy (69.06) were observed highest in treatment (RDF @ 75% + FYM @ 2 ton/ha + Sulphur @ 20kg/ha + Zincsulphate @ 20kg/ha) at 75 DAS and lowest of these were measured at 45DAS in treatment Control. Therefore, Treatment RDF@ 75% + FYM @ 2ton/ ha + Sulphur@ 20kg/ha + Zincsulphate @ 20kg/ha) is best for farmers in Achham to improve the growth and yield of potatoes.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 teacher head, 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".