Shade’s Benefit: Coffee Production under Shade and Full Sun
Bibliographic record
Abstract
Coffee has major importance in tropical landscapes from agronomic, economic and ecological perspectives. Yet the conversion of shade-coffee into full sun monocultures has deep effect on the potential of those systems to conserve biodiversity and ecosystems services (such as pest control and pollination). Despite of this, effect of shade on production has not been sufficiently addressed, particularly in Brazil, the world major coffee producer. This study compared the performance of shaded coffee and full sun management in terms of productivity and production costs. The survey was conducted in Municipality of Mirante da Serra, in the Brazilian Amazon and eight coffee agroecosystems, four under shade and four under full sun were investigated. The results indicate that shaded systems have lower production costs requiring less working hours than sun plantations. The average production cost of shaded agroecosystems was 49.63%, while in systems under full sun, this value was 82.2%. Shaded and full sun productivity did not differ significantly, with higher variance in the former, showing that shaded systems are more heterogeneous. Shaded coffee agroecosystems presented an economically and environmentally viable alternative. The lower production cost enhances economic viability of these ecosystems in Amazon as well as in the rest of the tropics. Such efficiency may have influenced the persistence of these managements, despite the worldwide agriculture intensification tendency.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".