Measured Effect of Agricultural Drainage Water Management on Hydrology, Water Quality, and Crop Yield
Bibliographic record
Abstract
A field scale experiment has been initiated in 2006 to study the effects of controlled drainage on drain flow, nutrient export, and crop yield for a subsurface drained site in eastern Ontario, Canada. Eight paired fields of comparable size (2 to 7 ha), soil (Bainsville silt loam), crop rotation (corn-soybean), and drainage system (subsurface drains 100 cm deep and spaced 15 m apart) were evaluate in the study. For each field pair, controlled drainage (CD) is implemented on one field and conventional (uncontrolled) drainage (UCD) is implemented on the other field. The results of the study showed that controlled drainage substantially reduced subsurface drainage and nutrient (nitrogen and phosphorus) export with drain flow, compared with conventional drainage. On average over the four field pairs and the three-year period, controlled drainage reduced the May-to-November drain flow by 50%, nitrate-nitrogen export by 47% and total phosphorus export by 56%. These results support the contention that nutrient reductions are controlled primarily by reduced drain flow. The results suggest the May-to-November nutrient mass losses were, overall, modest. A very modest increase in crop yield was observed with implementing drainage water management, although results were not statistically significant. Nevertheless, the results do show that controlled drainage does not have an adverse effect on crop yield.
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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 | 0.001 |
| 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 | 0.001 |
| 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 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".