Evaluation of Four Sunflower Hybrids (Helianthus annuus) under Three Irrigation Regimes and Two Doses of Fertilization on Flower Production
Bibliographic record
Abstract
Flower production is an income source for farming families. However, their productivity has been limited due to the lack of information on technical managements of crop species. In Mexico, 21,129 hectares are planted every year, with an annual production of 83,000 tons of flowers for decoration, and a total production value of 6,097 million Mexican pesos. In order to design agronomic management practices for sunflower production in the state of Campeche, three factors were evaluated: 1) sunflower hybrids, 2) time of irrigation and 3) fertilization doses, with four (hybrids: “Sunbright”, “Prado Red Shade”, “Full Sun” and “GH-382”), three (soil moisture tension at the start of irrigation of -10, -35 and -60 kPa), and two (Fertilization formulas: 60-50-0, and 30-25-0) levels, respectively using a sub split plot arrangement nested in a complete random block design with three replicates. Response variables evaluated were: plant height (PH), stem diameter (SD), leaf width (LW), leaf length (LL), distance between nodes (DN), number of inflorescences (NF), inner diameter (IDF), external diameter (EDF) and capitulum weight (CW). Irrigation affected PH, SD, LL, IDF, EDF, and CW. In all these variables, the highest values were found when irrigation was conducted at the lowest moisture tension (-10 kPa). Likewise, in all those variables significant effect of hybrids were found, which confirms that the morphology of flowers is defined by genetic factors. Specifically, hybrid “Full Sun” had significantly higher PH, SD, and DN compared to the rest of the hybrids. “Full Sun” and “Sunbright” had the highest values for PH and LL. “Full Sun” and “GH 382” had significantly higher IDF and EDF. “Prado Red Shade” had significantly lower PH, SD, LL, CW and higher NF. Fertilization only had significant effects on LW, DN and CW. In conclusion, irrigation improves morphological characteristics of sunflower plants. If the farmer’s objective is to produce larger flowers, then hybrids “Fun Sun” and “GH 382” are recommended. However, if the objective is to produce small flowers, hybrids “Sunbright” and “Prado Red Shade” are recommended. The latter is also suitable to produce a larger number of capitula.
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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) | 0.001 | 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".