Organic farming practices change the soil bacteria community, improving soil quality and maize crop yields
Bibliographic record
Abstract
Background The importance of organic farming has increased through the years to promote food security allied with minimal harm to the ecosystem. Besides the environmental benefits, a recurring problem associated with organic management is the unsatisfactory yield. A possible solution may rely on the soil microbiome, which presents a crucial role in the soil system. Here, we aimed to evaluate the soil bacterial community structure and composition under organic and conventional farming, considering the tropical climate and tropical soil. Methodology Our organic management treatments were composed by composted poultry manure and green manure with Bokashi. Both organic treatments were based on low nitrogen inputs. We evaluated the soil bacterial community composition by high-throughput sequencing of 16S rRNA genes, soil fertility, and soil enzyme activity in two organic farming systems, one conventional and the last transitional from conventional to organic. Results We observed that both organic systems evaluated in this study, have higher yield than the conventional treatment, even in a year with drought conditions. These yield results are highly correlated with changes in soil chemical properties and enzymatic activity. The attributes pH, Ca, P, alkaline phosphatase, and β- glucosidase activity are positively correlated with organic systems, while K and Al are correlated with conventional treatment. Also, our results show in the organic systems the changes in the soil bacteria community, being phyla Acidobacteria, Firmicutes, Nitrospirae, and Rokubacteria the most abundant. These phyla were correlated with soil biochemical changes in the organic systems, helping to increase crop yields. Conclusion Different organic management systems, (the so-called natural and organic management systems, which use distinct organic sources), shift the soil bacterial community composition, implying changes in their functionalities. Also, our results contributed to the identification of target bacterial groups and changes in soil chemical properties and enzymatic activity in a trophic organic farming system, which may contribute to higher crop yields.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".