Screening Cover Crops for Soil Macrofauna Abundance and Diversity in Conservation Agriculture
Bibliographic record
Abstract
Soil health is important for sustainable crop production. Frequent soil cultivation has a negative impact on soil health, resulting in loss of soil macrofauna. Conservation agriculture can be practiced to improve soil health by improving the abundance of soil macrofauna. Three leguminous cover crops were tested for soil macrofauna abundance Vigna unguiculata, (cowpea) Lablab purpureus L. (dolichos lablab) and Mucuna pruriens (L.) DC (velvet bean). The experiment was done in two contrasting experimental sites of KwaZulu-Natal (Ukulinga and Bergville) in a randomised complete block design replicated three times. Bare plot and herbicide treatments served as controls. Natural fallow was used to make a comparison to all the other treatments. Cowpea (39 species) had the highest soil macrofauna abundance in Bergville. Lablab (57 species) had the highest soil macrofauna in Ukulinga. Cowpea (0.75 species) and lablab (0.61 species) improved soil macrofauna diversity respectively in Bergville. Natural fallow (0.46 species) had the lowest soil macrofauna diversity in Bergville. Lablab (0.56 species) and velvet bean (0.74 species) had high soil macrofauna species diversity in Ukulinga. Bare plot (0.3 species) had the lowest soil macrofauna species diversity respectively. It can be concluded that cowpea and lablab can be recommended for improving soil macrofauna abundance in conservation 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".