Weed Interference in Soybean Crop Affects Soil Microbial Activity and Biomass
Bibliographic record
Abstract
ABSTRACT: Weeds and microorganisms interacting with their rhizosphere may influence nutrient absorption, which is an important factor for plant competition. The purpose of this study was to evaluate the microbiological activity, inorganic phosphorus solubilization (Pi) and acid phosphatase in the cultivated soil, in a combination of soybean (Glycine max) plants and weeds. Soybeans were cultivated in monoculture and in competition with Bidens pilosa, Brachiaria decumbens (Syn. Urochloa decumbens) and Eleusine indica, under two conditions: a) plants competing without contact between the roots b) plants competing with contact between the roots. A nylon screen with a 50 µm mesh was added to prevent contact between the roots of the species in competition so that the substratum could be separated in the vase. The experiment was conducted in randomized blocks, with four replications. The soybeans in competition with weeds led to lower oxidation of organic matter per unit of microbial biomass, resulting in a lower metabolic quotient, compared with the soybean monoculture. The contact between soybean roots and B. pilosa, B. decumbens and E. indica maintained a strong influence, raising the solubilization of Pi, respectively valued at 51, 39 and 31% in relation to the cultivation of each species with a nylon screen. Microbiological activity, inorganic phosphorus solubilization and acid phosphatase were altered by plant species, combinations of weeds and soybean plants in competition; by root contact in some cases. Thus, the microbiological activity of the soil can influence competition strategies and plant development.
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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 | 0.000 |
| 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".