Do provisioning ecosystem services change along gradients of increasing agricultural production?
Bibliographic record
Abstract
Abstract Context Increasing agricultural production shapes the flow of ecosystem services (ES), including provisioning services that support the livelihoods and nutrition of people in tropical developing countries. Although our broad understanding of the social-ecological consequences of agricultural intensification is growing, how it impacts provisioning ES is still unknown. Objectives We examined the household use of provisioning ES across a gradient of increasing agricultural production in seven tropical countries (Bangladesh, Burkina Faso, Cameroon, Ethiopia, Indonesia, Nicaragua and Zambia). We answered two overarching questions: (1) does the use of provisioning ES differ along gradients of agriculture production ranging from zones of subsistence to moderate and to high agriculture production? and (2) are there synergies and/or trade-offs within and among groups of ES within these zones? Methods Using structured surveys, we asked 1900 households about their assets, livestock, crops, and collection of forest products. These questions allowed us to assess the number of provisioning ES households used, and whether the ES used are functionally substitutable (i.e., used similarly for nutrition, material, and energy). Finally, we explored synergies and trade-offs among household use of provisioning ES. Results As agricultural production increased, provisioning ES declined both in total number and in different functional groups used. We found more severe decreases in ES for relatively poorer households. Within the functional groups of ES, synergistic relationships were more often found than trade-offs in all zones, including significant synergies among livestock products (dairy, eggs, meat) and fruits. Conclusions Considering landscape context provides opportunities to enhance synergies among provisioning services for households, supporting resilient food systems and human well-being.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".