Productivity, water use and economics as influenced by irrigation scheduling and fertilizer management practices in Broccoli (Brassica oleracea L.) under drip irrigation
Bibliographic record
Abstract
A field experiment was conducted during winter seasons of 2018-19 and 2019-20 at SKUAST-Jammu to study the effect of different irrigation schedules and fertilizer management on productivity, water use and economics in Broccoli. The experiment was laid out in strip plot design with three replications. The treatments comprised of three drip irrigation schedules in vertical strips viz., drip irrigation at 100% ETc; drip irrigation at 75% ETc and drip irrigation at 50% ETc and four fertilizer management practices in horizontal strips viz., 100% RFD of NPK as solid fertilizer; 75% RFD of NPK as solid fertilizer + 25% through liquid fertilizers; 50% RFD of NPK as solid fertilizer + 50% through liquid fertilizers and 25% RFD of NPK as solid fertilizer + 75% through liquid fertilizers and control was IW / CPE=1.0 with RDF as solid fertilizer. Results showed that drip irrigation at 100% ETc in broccoli provided significantly higher yield and saved 13.08% irrigation water over control. Likewise, in sub plots (horizontal strips) application of 50% RFD of NPK as solid fertilizer + 50% through liquid fertilizers using liquid fertilizers recorded significantly higher curd yield in broccoli. This treatment increased curd yield of broccoli by 26.34% over control. The drip irrigation water was saved to the tune of 13.08, 32.25 and 51.40%, respectively under 100% ETc, 75% ETc and 50% ETc irrigation schedules over Control (IW/CPE=1.0). The net returns were highest in the treatment 100% ETc alongwith 50% RFD of NPK as solid fertilizer + 50% through fertigation (Rs. 4,61,341.50) and lowest in control (Rs. 2,31,313.00) whereas the benefit: cost ratio under respective treatments was 4.59 and 2.90.
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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 | 0.002 |
| 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".