Productive and Nutritional Aspects of Tithonia diversifolia Fertilized With Biofertilizer and Irrigated
Bibliographic record
Abstract
Little is known about the agronomic aspects of Mexican Sunflower (Tithonia diversifolia), in spite of its potential for multiple uses. In this study, we evaluated the effects of application rates of biofertilizer and irrigation on yield, growth, and leaf chlorophyll and nutrient content of Mexican Sunflower. In an experiment in the Brazilian semi-arid region, we used a 5 × 2 factorial arrangement, consisting of five application rates of biofertilizer (0, 40, 80, 120, and 160 m3 ha-1), with and without irrigation. The statistical design was randomized blocks with three replications. Irrigated plants of Mexican Sunflower had greater dry and fresh matter yields, greater height, and greater leaf area index and leaf contents of K, Zn, and B. However, the high concentration of bicarbonate in the irrigation water reduced the leaf contents of N, Ca, S, Fe, and Mn. The mean increase in the two cuttings obtained with the use of irrigation was 350% and 314% for fresh and dry matter, respectively. The increase in the biofertilizer application increased the leaf chlorophyll contents of irrigated plants; however, it did not result in production or nutritional gains. In regions with low availability of rainfall, irrigated cultivation of Mexican Sunflower is recommended.
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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".