Optimum irrigation strategy to maximize yield and quality of potato: A case study in southern Alberta, Canada*
Bibliographic record
Abstract
Abstract The ability to understand how various irrigation levels impact potato productivity could facilitate the introduction of variable‐rate irrigation technology for high‐quality potato production in southern Alberta, Canada. A two‐year field study (2015 and 2016) was therefore conducted to assess the effect of three irrigation levels on yield and quality of potato. Several parameters were measured including climatic data, irrigation amounts, total and marketable potato yield, and tuber quality parameters (specific gravity and glucose content). The Alberta Irrigation Management Model was used to estimate irrigation levels based on soil, crop, and weather variables. The year 2015 was exceptionally dry, resulting in a total of 21 irrigation events, and a total of 12 irrigation events were undertaken in the 2016 growing season. In 2015, the crop in plots receiving normal irrigation (361 mm per season) produced slightly lower total yield than plots receiving high irrigation (480 mm per season), but the normal irrigation plots produced statistically higher marketable yield and better tuber quality in terms of specific gravity and glucose content. In 2016, there were no significant differences between potato yield and quality between irrigation treatments because the rainfall for the year was close to the long‐term average annual rainfall.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".