Potato Varieties Response to Soil Matric Potential Based Irrigation
Bibliographic record
Abstract
Potato is one of the most cropped plants worldwide. Hundreds of different varieties are cultivated only in North America. Potato growers usually crop multiple varieties on their farms to answer the market demands for potato’s specific physical properties. However, few pieces of information are available regarding the optimal management of irrigation across potato varieties. Knowing that modern potatoes share genetics similarities, the optimal irrigation comfort zone for the potato crop might be the same for different groups of varieties. This study evaluates the effect of precision irrigation thresholds on the potato yields of three varieties (Envol: very early, Kalmia: early, and Red Maria: mid-late) with different maturity classes. In a greenhouse, a soil matric potential sensor network used in combination with a precise irrigation system allows the identification of a common optimal precision irrigation threshold, allowing optimal yields for the three varieties. This paper presents the first identification of an optimal irrigation threshold, −15 kPa, shared by different potato varieties. The optimal irrigation threshold identified in this study is not dependent on the maturity class, plant height or tuber potential production. The determination of an optimal precision irrigation threshold will allow potato growers to adapt their farm management processes to integrate more sustainable water management practices as they will be able to irrigate a field with multiple varieties with the same threshold.
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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 | 0.001 |
| 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".