Evaluating water use efficiency of marigold in the Issyk-Kul lakeshore
Bibliographic record
Abstract
How much water does a marigold actually need to produce the best blooms while keeping resource waste to a minimum? This research addressed that question by evaluating water use efficiency (WUE) across five deficit irrigation regimes applied to African marigold (Tagetes erecta L.) grown along the Issyk-Kul lakeshore corridor under semi-arid continental conditions. A randomised complete block design with four replications was laid out at the Edmonton Graduate School of Bioagricultural Studies experimental station during June to October 2023. Treatments comprised a fully irrigated control and four levels of crop evapotranspiration (ETc) replacement — 50, 75, 100, and 125 percent — delivered through drip lines. Growth, yield, biochemical quality, and phosphorus cycling parameters were recorded at fortnightly intervals. Results showed that the 50 percent ETc treatment recorded the highest WUE of 5.87 kg m⁻³, though absolute flower yield peaked under the 125 percent ETc regime at 51.7 g plant⁻¹. Vitamin C content responded positively to mild water stress, reaching 53.2 mg 100 g⁻¹ at 50 percent ETc compared with 41.7 mg 100 g⁻¹ in the control. Phosphorus uptake increased linearly with irrigation volume, yet P use efficiency was greatest at 75 percent ETc (41.3%). Transcriptomic screening of hardening-related gene clusters revealed up-regulation of dehydrin and LEA protein transcripts under deficit conditions. The findings point toward 75 percent ETc as a balanced irrigation target that reconciles acceptable yield with strong WUE and favourable nutrient recovery in lakeshore marigold production.
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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".