Climate and management factors influence saffron yield in different environments
Bibliographic record
Abstract
Abstract The economic yield of saffron (Crocus sativus L.) has a wide range in different parts of the world, and it is not clear why this considerable difference exists. In this research saffron yield and yield components of 13 fields with varied geographic and climatic conditions were studied to determine which factor(s) are more important. Among the studied factors, temperature, field age, soil texture, bulk density, soil and water pH, irrigation events, and growth period had the greatest effect on saffron yield. The highest dry stigma weight, as economic yield, was obtained in three regions of Birjand (27 kg ha−1), Sarayan (24 kg ha−1), and Golshan (23.5 kg ha−1), followed by Neyshabur (18 kg ha−1) and Kashmar (17.5 kg ha−1), which had lower temperatures, coarse soil, balanced soil, and water pH, and longer growth periods. The average yields were increased until the sixth or seventh year (20.8 kg ha−1) and then decreased, however, it seems to be economic before the 10th year. Lower temperatures in early fall were important to stimulate flowering and increase yield in that year, and warm and sunny days in the spring are important for next year yields. We found that the optimal temperature for the first irrigation is ∼16°C and for flowering is ∼5°C–10°C. High‐yield fields did not have higher irrigation water volumes but more irrigation events (6.3), resulting in less water volume per irrigation. No direct relationship was observed between manure consumption and yield; however, processed manure increases yield by improving the soil structure and moisture retention ability. Fields with a complete chemical fertilizer composition had higher yields. It was concluded higher yields are achieved in saffron fields where regions are higher in altitude (at least 1300 m) and lower temperature in early autumn with complete fertilizer composition (especially sulfur and iron). There was no evidence of high salinity sensitivity of saffron.
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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 | 0.001 | 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".