Balanced fertilization for yield maximization, profitability and quality of Indian mustard (Brassica juncea L.) in North Western Himalayas
Bibliographic record
Abstract
Indian mustard is one of the major oilseed crops of the country. The productivity of this crop is quite low compared to the rapeseed mustard crops in some other countries, such as Canada and Germany. One of the major reasons for lower productivity is the lack of balanced nutrition in many parts of the country. Sulphur (S), boron (B) and zinc (Zn) play an important role in the metabolism of Rapeseed mustard crops. A field experiment was conducted at Research Farm, SKUAST-Jammu in 2022 and 2023, to investigate the influence of different fertility levels with macro and micro nutrients on productivity, profitability and quality of Indian mustard. The treatment consisted of four fertility levels containing N:P2O5:K2Odoses viz., F1-control, F2- 80:40:20 kg ha-1 , F3- 100:50:25 kg ha-1and F4- 120:60:30 kg ha-1 respectively in main plots and four macro and micronutrient treatments namely, N1- 20 kg S+2.5 kg Zn + 0.5 kg B, N2- 40 kg S+ 5.0 kg Zn +1.0 kg B, N3- N1 + 500 kg FYM ha-1 , N4-N2 + 500 kg FYM ha-1 in sub plots, which were laid out in split-plot design and replicated thrice. The results revealed that the application of N:P2O5:K2O at 120:60:30 kg ha-1 in combination with 40 kg S+ 5.0 kg Zn +1.0 kg ha-1 B enriched with 500 kg FYM ha-1 recorded a significant increase in seed and oil yield along with yield attributes, nutrient uptake, soil microbial population, net returns and B:C ratio of Indian mustard than other treatments in comparison. A significant positive correlation was noted between seed yield and nutrient uptake.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 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".