Moderate Deficit Irrigation and Reduced Nitrogen Application Maintain Tuber Quality and Improve Nitrogen Use Efficiency of Potato (Solanum tuberosum L.)
Bibliographic record
Abstract
Efficient water and nitrogen (N) management are essential for sustaining potato (Solanum tuberosum L.) production under limited resource conditions. This study investigated the effects of deficit irrigation and reduced N application on tuber quality parameters including specific gravity (SG), starch content (SC), and tuber dry matter (TDM) as well as agronomic water use efficiency (WUE) and nitrogen use efficiency (NUE) in four commercial potato cultivars (Canela Russet, Mesa Russet, Russet Norkotah 3, and Yukon Gold) over two seasons (2016 and 2017) at Colorado State University’s San Luis Valley Research Center. Three irrigation levels (100%, ~80%, and ~70% evapotranspiration replacement) and two N application rates (165 and 131 kg N ha−1) were evaluated using four replications. Moderate deficit irrigation (up to ~18% ET reduction) improved or maintained SG, SC, and TDM in all four cultivars, while severe deficit irrigation (~30–40% reduction) reduced tuber quality. Reduced N application improved NUE in all cultivars without compromising tuber quality or yield. While WUE responded variably to deficit irrigation, NUE was highest under moderate to full irrigation and low N rate. Although effects on WUE were variable, integrating moderate deficit irrigation (18%) with reduced N application (20%) enhanced NUE while maintaining tuber quality.
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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".