The impact of applications of sugar cane filter cake and vinasse on soil fertility factors in fields having four different crop rotations practices in Brazil
Bibliographic record
Abstract
In this study we examined how the use of vinasse and filter cake as fertilizers at four different sites alters crop yields of sugarcane. The objective was to determine the impacts based on the products fertility and to identify if and how the treatments altered soil microbiology. Using an untreated site as a control (treatment 1) we compared the effect on various soil parameters at sites where soybean was planted after cane production (treatment 2) using just chemical fertilizer, a site which received vinasse as a fertilizer during the sugarcane crop at levels that allowed for replacement of chemical K2O (treatment 3) and a site which received filter cake as a replacement of P2O5 (treatment 4). When all four sites were compared, organic matter was highest at sites amended with filter cake (19.8%) whereas, moisture levels were highest at the vinasse treated sites (18.9%). The phosphorus concentration was also the highest with filter cake amendment. The yields of sugarcane from the control and above described treatments did not differ each other. These findings suggest that vinasse and filter cake improve soil microbiological and fertility conditions although the changes here were not reflected in the yields of sugarcane crop obtained.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".