Associations Between Different Soil Management Practices, Soil Fauna and Maize Yield
Bibliographic record
Abstract
Soil fauna play an important role in ecosystems, and in this context, it is important to better understand how the abiotic and biotic drivers of these organisms interact. We hypothesize that soil fauna are affected by different soil management practices, which has an influence on maize grain yields. The aim of this study was to evaluate the structure of soil fauna under different soil management practices and their associations with maize grain yield. The experiment was conducted in Maranhão, Brazil, in an area divided into 24 plots of 4 × 10 m in a randomized block design with six treatments with four replicates (R). Pitfall traps were placed in the area. The treatments were Leucaena leucocephala-Leucaena (L), nitrogen (N), humic acid + nitrogen (HA + N), nitrogen + Leucaena (N + L), humic acid + Leucaena (HA + L) and humic acid + nitrogen + Leucaena (HA + N + L). The soil fauna dominance, abundance, richness, Shannon-Wiener diversity index, Pielou evenness index and maize grain yield were determined. Formicidae was clearly affected by management with Leucaena, while Coleoptera was affected by management with nitrogen. Despite this, Isopoda and Diplura were the only groups associated with the maize yield. Although fauna abundance did not differ among treatments, it was related to the yield. This study confirms that the abundance and some taxa of soil fauna can influence yield and that these organisms can be used to increase agricultural sustainability.
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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) | 0.000 | 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".