Evaluation of Growth and Yield of Purple Coneflower (Echinacea purpurea L.) in Response to Biological and Chemical Fertilizers
Bibliographic record
Abstract
In order to study the effects of biological and chemical fertilizers on quantitative and qualitative yields of purple coneflower, an experiment was carried out during 2010-2012. The morphological traits such as plant height, number of lateral shoots, shoot fresh and dry weight, root fresh and dry weight, number of inflorescences per plant, number of flower buds per plant, and essential oil content and yield were measured. The results showed significant effects of the treatments on the growth parameters. In the second year, the mixture of the three bacteria plus mycorrhizal inoculum improved important parameters such as shoot dry weight (40.42%), root dry weight (60.02%), and number of inflorescences per plant (65.68%). Interestingly, these values were not significantly different from obtained results by the chemical fertilizers. Additionally, the essential oil content in plants treated with the mixture of the three bacteria were 152.14% and 25.11% higher than the control plants in the first and the second year, respectively. The essential oil yield in plants treated with the mixture of the three bacteria was higher than those treated with the chemical fertilizers in two years. The results indicate that using biological fertilizers is a good choice to reduce the use of chemical fertilizers as an important tool to contribute to a sustainable agriculture.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".