Effect of Organic Amendment on the Growth of <i>Artemisia annua</i> in the North of Côte d’Ivoire
Bibliographic record
Abstract
Malaria causes many deaths around the world, particularly in Africa, which ultimately affects the socio-economic development of African countries. The resistance of Plasmodium falciparum to quinine-based drugs led to new studies showing the efficiency of new artemisin-based drugs. The molecule artemisin is extracted from Artemisia annua a plant from China that has been used for decades in traditional Chinese medicine. The purpose of this study is to improve the production of sweet wormwood (Artemisia annua) using organic fertilizers in the north of Cote d’Ivoire. To do so, a morpho-pedological characterization of the study site was firstly performed to determine the soil type and their fertility level. Then, a randomized complete block system including two factors (the quantity of compost and the plant density) was implemented to test the effect of organic amendment and plant arrangement on the growth of Artemisia annua. Six treatments were set up: a control plot (no compost) where the plants are arranged in square (T0D1) and the plants are arranged in staggered (T0D2). Then, a treatment with compost addition of 25 t/ha where the plants are arranged in square (T1D1) and in staggered (T1D2). A treatment with compost addition of 50 t/ha where plants are arranged in square (T2D1) and in staggered (T2D2). Our results showed that the soils hosting our experimentation are Arenithic Plinthic Ferrasols with a very low level of fertility, prone to leaching and erosion. T1D2 and T2D2 treatments obtained the highest yields of 2.82 t/ha and 3.91 t/ha, respectively. Our findings indicate that a high dose of organic amendment combined with a staggered plant arrangement strongly improves the biomass production of sweet wormwood. This is in agreement with previous studies showing that the addition of organic matter can restore the level of soil fertility by increasing soil porosity and the activity of micro and macroorganisms.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".