Domestic Wastewater for Forage Cultivation in Cerrado Soil
Bibliographic record
Abstract
Fertigation of agricultural crops that are not directly used in human food, with domestic wastewater is a viable alternative for the sustainable use of water resources. The development of agricultural practices that provide high productivity with the sustainability of agroecosystems has been a great challenge. Thus, our aims were to use of domestic wastewater in the planting of Brachiaria brizantha cv Marandu, as an alternative for animal feed production in Cerrado soils, and to study the physical-chemical and microbiological impacts of the fertigation. These impacts were evaluated, respectively, by physical-chemical indicators content and diversity of nitrogen fixing bacteria (NFB) and arbuscular mycorrhizal fungi (AMF) in the DGGE profile. The NPK contents of the wastewater were used to determine the five fertigation managements (M1 to M5). M1 and M2 managements had no wastewater and M3 to M5 contained 20, 40 and 60% of NPK from the wastewater. The managements in a completely randomized design with 20 plots and 4 replicates were distributed. Soil samplings prior to fertigation and at the end of the experiment were performed. Leaf biomass productivity was determined in three different grass cuts. After fertigation, changes in physical-chemical indicators and in the viable microbial cells counts were observed. The NPK of wastewater increased the abundance of NFBs and AMFs. Leaf biomass productivity per hectare was directly proportional to NPK concentration. In addition, wastewater did not alter the nutritional composition of Marandu grass. Therefore, the fertigation with domestic wastewater showed to be a viable and promising alternative for reuse of this water in Cerrado soil for animal feed production.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| 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".