Effect of Water Table Management and Irrigation on Potato Yield
Bibliographic record
Abstract
In Canada, the Province of Manitoba is the second largest potato producer after Prince Edward Island. Potato is a moisture-sensitive crop, and excess or inadequate soil water content can adversely affect the yield and quality. Potato in Manitoba experiences periods of excess as well as insufficient water content within the soil profile during the growing season. The objective of this study was to compare the effect of four different water management treatments on potato yield in a fine sandy loam soil in southern Manitoba: controlled drainage with subirrigation (CDSI), free drainage with overhead irrigation (FDIR), no drainage with overhead irrigation (NDIR), and no drainage with no irrigation (NDNI). In November 2009, tile drains were installed at a depth of 0.9 m. CDSI was done through drainage control structures with a target water table depth of 0.6 m. Overhead irrigation was done using a traveling gun. Groundwater level, drainage discharge, and potato yield data were collected during the 2010 and 2011 growing seasons. In 2010, potato yield was not found to be significantly different between the treatments due to the large variability between the replicates. However, in 2011, potato yield from the FDIR treatment was significantly higher compared to NDNI and CDSI (p <0.05). The NDNI treatment yield was significantly lower (p < 0.05) than the other three treatments. When compared with NDNI, the other treatments showed a yield increase of 15% to 32%. Maintaining adequate soil moisture by overhead irrigation was most effective for increasing potato yield when rainfall is inadequate.
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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 | 0.001 | 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".