Deficit Irrigation and Nitrogen Application Rate Influence Growth and Yield of Four Potato Cultivars (Solanum tuberosum L.)
Bibliographic record
Abstract
Potatoes have high nitrogen (N) and irrigation requirements. Increasing water scarcity and environmental concerns highlight the need for efficient resource management. This study evaluated the effects of deficit irrigation and reduced N on yield and growth parameters in four potato cultivars (Canela Russet, Mesa Russet, Russet Norkotah3, and Yukon Gold) at Colorado State University’s San Luis Valley Research Center over two growing seasons. Three irrigation levels (~70%, ~80%, and 100% ET replacement) and two N rates (165 and 131 kg/ha) were evaluated. Measurements included total and marketable yield, tuber size distribution, tuber bulking (TB), leaf area index (LAI), and stem and tuber numbers. Yield losses were absent with ≤18% irrigation reduction in Canela Russet, Mesa Russet, or Yukon Gold but occurred with larger deficits. Russet Norkotah3 experienced yield decline with 16–23% reductions in irrigation. A twenty percent reduction in N application had no effect on Mesa Russet or Russet Norkotah3 yields, while the other varieties experienced a yield decline in one out of two years. Early-season LAI and late-season TB were positively correlated with yield, particularly for Canela Russet and Russet Norkotah3. These findings suggest irrigation and N inputs can be reduced without compromising productivity, but reductions must be determined on a cultivar-by-cultivar basis.
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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 | 0.000 | 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".