Effect of Different Irrigation Water Qualities on Turnip Production and Water Productivity under Furrow Irrigation Method
Bibliographic record
Abstract
A field experiment was conducted at Sindh Agriculture University Tandojam during the year 2015-16, aiming to investigate the response of turnip crop to various salinity levels of irrigation. The experiment was placed applying randomized complete block design (RCBD) with four different treatments i.e. Freshwater (I1), ECw with 2.5, 3 and ECw3.5 dS m-1 (I2, I3 and I4) respectively replicated thrice. The results for experiment placed revealed an average increase in soil ECe 0.09, 0.17, 0.26 and 0.38 dS m-1 under I1, I2, I3 and I4 respectively. An decrease in dry density (g cm-3) of soil profile, decrease in pH 0.19, 0.38, 0.5 and 0.84 in treatments I1, I2, I3 and I4 respectively and an decrease in agronomical data i.e. weight and diameter were also observed with an increase in ECw by the water being irrigated. Crop water productivity with 5.83, 4.35, 2.97 and 1.85 kg m-3 for treatmentsI1, I2, I3 and I4 respectively also decreased with an increase in ECw and Nacl. Average yield of 19.27, 14.37, 9.83 and 6.12 kg was obtained with applied treatments i.e. I1, I2, I3 and I4 respectively, thus a decrease in yield with 25.45%, 31.60% and 37.72% with treatments I2, I3 and I4 was observed when compared as treated by freshwater (I1). Therefore farmers can use irrigation water having ECw 3.5 dS m-1 for the turnip crop at reduction of 37.72% (approximately).
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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 | 0.000 | 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".