Biochars from local agricultural waste residues contribute to soil quality and plant growth in a Cerrado region (Brazil) Arenosol
Bibliographic record
Abstract
Abstract Arenosols (sandy soils) in the Cerrado region of Mato Grosso, Brazil, are increasingly used for maize production, the second most important crop in the region after soybean. Yet, these soils are typically nutrient poor with low soil water retention, requiring high fertilizer inputs that are often lost in surface runoff or leached. The addition of biochar, a more recalcitrant organic amendment, may therefore be beneficial in Cerrado Arenosols, contributing to sustainable crop production in the region. To examine biochar contribution to soil nutrient levels and maize growth in a Cerrado Arenosol, we conducted a greenhouse experiment using biochars made from local agricultural waste feedstocks. These were cotton husks, swine manure, eucalyptus sawmill residue, and sugarcane filtercake, pyrolyzed at 400 °C, and applied to soil at five rates: 0%, 1%, 2%, 3%, and 4% by weight. Maize plants were grown under unstressed conditions (e.g., no nutrient or water limitations) to highlight any possible negative effects of the biochars. After 42 days, soils were analyzed for nutrient levels, and plant physical and physiological measurements were taken. Filtercake biochar had the highest plant biomass and physiological properties (e.g., photosynthesis, respiration, nitrogen use efficiency), while cotton biochar had the lowest. Importantly, maize biomass decreased with increasing application rates of cotton and swine manure biochars, while biomass did not vary in response to biochar application rate for filtercake and eucalyptus biochars. In this study, we found that while each biochar exhibited potential for improving chemical and physical properties of Cerrado Arenosols, filtercake biochar stood out as most promising. Biochar application rate was identified a key factor in ensuring crop productivity. Transforming these agricultural residues readily available in the region into more stable biochar can thus contribute to sustainable crop management and soil conservation, providing an alternative form of waste disposal for these residual materials.
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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".