Sustainable Agricultural Intensification – A Perspective from Latin America and the Caribbean
Bibliographic record
Abstract
Sustainable agriculture is broadly established in Latin America and the Caribbean (LAC), but the way that food is being produced and consumed requires rethinking. Sustainable agricultural intensification (SAI) is becoming an approach to address various complex challenges in agriculture. A total of 760 participants (57% LAC) from 101 countries registered for a 2-week e-consultation, which included 3 components with the aim of promoting the dialogue and partnerships on SAI in LAC. In the first component there was an exchange of ideas on its conceptual framework, while in the second component experiences and lessons learned from programs, practices, policies and solutions to address challenges in the region were shared, and the last component served to discuss how to increase regional cooperation through the identification of actors and actions. This paper provides a synthesis report of the e-forum and the main recommendations to consolidate a regional SAI network to exchange experiences and generate joint actions for greater synergies in agricultural research, and better policies, investments and institutions in LAC. Proposed research areas are: analyzing yield gaps, accurate mapping of farming structure of LAC agriculture, rehabilitating degraded lands, curving deforestation, studying the nature of the interphases between sustainable agricultural and food systems, reducing food wastes, adapting to and mitigating climate change, strengthening cooperatives, building local organizations and linking farmers to markets, using information and communication technology to access information and share knowledge on SAI, and defining indicators and metrics to monitor SAI undertakings and assist policy makers for enacting incentives through related policy.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.007 | 0.005 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".