Long-term Cyclic Irrigation in Subsurface Drained Lands: Simulation Studies with SWAP
Bibliographic record
Abstract
SWAP (Soil-Water-Atmosphere-Plant) version 2.0 was evaluated for its capability to simulate the crop growth and salinity profile for cyclic irrigation of saline waters at Sampla (India) having shallow water table provided with a subsurface drainage system. Cyclic mode with canal water (EC=0.4 dS m-1) and saline drainage water (EC=12.5-15.5 dS m-1) were used to calibrate and validate the model for the years 1989-91. Canal water was used for pre-sowing irrigation and thereafter, canal and saline drainage waters were used as per pre-decided irrigation modes like all CW, CW:DW, 2CW:2DW, DW:CW, and 1CW:3DW. Absolute deviations and standard error between the SWAP simulated and observed relative yields during calibration ranged from 1.3 to 1.8% and 1.7 to 2.2% respectively. A close agreement was observed between the measured and simulated soil salinity profile. It established the validity of SWAP model under the experimental conditions prevalent at the site. It could also be concluded that the crops could be grown very well under subsurface drainage conditions; but, in dry rainfall years, salinity build-up might occur. To achieve a yield potential exceeding 80%, it could be suggested that cyclic use of saline waters such as 1CW:1DW and 2CW:2DW could be used in such years. A pre-sowing irrigation with canal water could be helpful to overcome the build-up of salts and salt amount washing depends upon the rainfall. Thus, there seems to be no fear of use of cyclic irrigation under drained conditions. The same fact was established through the use of model SWAP.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".