Closed-loop organic waste management systems for family farmers in Brazil
Bibliographic record
Abstract
Family farmers in Brazil could diversify their sources of income and improve agriculture practices by adopting circular economy principles on their farms. Closed-loop technological systems can be used to manage organic waste and produce fertiliser and biogas thereby generating revenue. Anaerobic Digestion (AD) is a proven technology that can produce digestate (i.e. fertiliser) and biogas from organic waste, although digestate application in soil and crops without treatment can have adverse effects. However, in practice, there is a lack of knowledge about the benefits of recycling organic waste in farming communities in Brazil. Therefore, the main aim of this paper is to provide conceptual design configurations of closed-loop systems that manage organic waste and generate revenue for small farms in Brazil. A literature review of selected technologies and interviews with Brazilian family farmers were used to inform the components of the proposed conceptual designs. The proposed designs are based on circular economy principles, incorporating AD, pyrolysis for biochar, hydroponics and vermifiltration in various configurations. A complete closed-loop system consisting of a 7.5 m3 digester, pyrolysis unit, a combined hydroponic and vermifilter unit and a shredder is estimated to cost around USD$1600 (R$ 6600). The flexibility of the proposed systems has the potential to increase resilience and income for small-scale farmers, whilst encouraging good practices for waste management. The conceptual designs can be used as a basis for further research and development of small-scale organic waste management solutions in Brazil.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".