Mineral Fertilizers Influence the Macrofauna of Soils Under Cocoa Trees in the South-Western Côte d’Ivoire
Bibliographic record
Abstract
A study on mineral fertilization of soils was carried out for two years in three cocoa farms in the Nawa region of south-western Côte d’Ivoire. The aim of this study was to evaluate the effects of mineral fertilizers on soil macrofauna. The experimental design was Fisher block with four treatment with three repetitions. The treatments were control without fertilizer (T0), NPK 0-23-19 fertilizer (T1), NPK 0-15-15 + 17 CaO + 5 MgO + 1 B2O3 + 0.5 Zn (T2) and NPK 4-10-10 + 19 CaO + 4 MgO + 0.8 B2O3 + 0.3 Zn (T3). Twelve soil monoliths were made per treatment and per site, to count the macrofauna of the soil. Each soil monolith was subdivided into three strata 0-10 cm, 10-20 cm and 20-30 cm. The organisms were counted and classified into functional groups. The abundance, species richness and diversity indices of Shannon-weaver and Piélou were determined. The analysis of variance of the numbers of individuals in the macrofauna did not show a significant difference between the treatments on different strata of the monoliths during the second year of the trial. However, during the first year, all the treatments with fertilizer in the 0-10 cm stratum had a more abundant macrofauna than that of the control without fertilizer at Soubré and Mayo. Multivariate analyses (AFC) showed links between functional groups of organisms and treatments. Myriapods and earthworms were linked to the T2 and T3 treatments, microarthropods to T1 and other organisms to T0. As for macrofauna diversity, the highest value of species richness (7 species), Shannon-weaver (2) and Piélou (1) indices were found in the fertilized treatments. Mineral fertilizers thus presented better conditions for the expansion of macrofauna. A reasoned application of mineral fertilizers creates a favourable living environment for certain soil organisms.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".