Reducing Yearly Variation In Potato Tuber Yield Using Supplemental Irrigation
Bibliographic record
Abstract
Abstract This study investigated the influence of supplemental irrigation (SI) on yearly variation in potato yield and associated economics in a humid climate. On-farm trials were conducted in four to five fields annually in Prince Edward Island, Canada from 2019 to 2022. The research involved four different treatments: rainfed production as the control group, irrigation following conventional practices, irrigation guided by soil moisture monitoring, and irrigation guided by soil moisture monitoring coupled with a 20% reduction in fertilizer input. While six commonly-grown russet potato cultivars were used, local standard cultural practices were followed at all sites. In 2019 SI significantly increased marketable yields (MY), which was primarily attributed to a drought period that extended from July to early August. Similarly, in 2020 SI led to a substantial rise in MY due to growing season rainfall being significantly lower than the optimal water demand for the potato plant. Conversely, in 2021 and 2022, when rainfall was relatively sufficient and evenly distributed, farmers either refrained from irrigating or employed minimal irrigation rates, resulting in negligible MY responses. Tuber yield increase as a result of SI varied with rainfall and thus fluctuated yearly. Cross-year comparisons revealed that SI can effectively mitigate annual fluctuations in tuber yield. A cost–benefit analysis indicated that employing SI to minimize yearly variation in tuber yield can be either profitable or unprofitable in the long term, and is contingent on the costs linked to irrigation equipment, the water supply system, operational aspects, field scale, and rainfall distribution. These findings hold significance for guiding decisions in water management for potato production in humid environments.
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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 | 0.000 |
| 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".