Impact of Different Agricultural Waste Biochars on Maize Biomass and Soil Water Content in a Brazilian Cerrado Arenosol
Bibliographic record
Abstract
Arenosols in the Brazilian Cerrado are increasingly being used for agricultural production, particularly maize. These sandy soils are characterized by low soil organic matter, low available nutrients, and poor water-holding capacity. For this reason, adding biochar as a soil amendment could lead to improved water and nutrient retention. A greenhouse experiment was carried out using twelve biochars derived from four feedstocks (cotton husks, swine manure, eucalyptus sawmill residue, sugarcane filtercake) pyrolized at 400, 500 and 600 °C and applied at 5% w/w. The biochars’ effect on maize biomass was examined, along with their contribution to soil physical properties including water retention, electrical conductivity (EC), and grain size distribution. After six weeks, maize plants in soils with eucalyptus and particularly filtercake biochar had higher biomass compared to those in soils with cotton and swine manure biochars. The latter’s low biomass was likely related to excessive salinity. In general, our biochars showed potential for increasing θ in sandy soils compared to the soil alone. Filtercake and eucalyptus biochars may improve soil aeration and water infiltration, while applying cotton and swine manure biochars at levels <5% to avoid high salinity could contribute to improved soil water retention in Cerrado Arenosols.
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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 | 0.001 | 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".