PARTICIPATORY EVALUATION OF IMPROVED GRASSES AND FORAGE LEGUMES FOR SMALLHOLDER LIVESTOCK PRODUCTION IN CENTRAL AMERICA
Bibliographic record
Abstract
SUMMARY Smallholder livestock systems in Central America are typically based on pastures with traditional grasses and associated management practices, such as pasture burning and extensive grazing. With the rise of the global population and a corresponding increase in demand for meat and milk production, research efforts have focused on the development of improved grasses and the incorporation of legume species that can increase productivity and sustainability of Central American livestock systems. However, farmer adoption remains very limited, in part due to the lack of site-specific evaluation and recommendations by local institutions. Using a multi-site participatory approach, this study examined the potential of five improved grasses and five species of forage legumes as alternatives to the broadly disseminated grass Hyparrhenia rufa (cv. Jaragua) in pasture-based cattle systems in western Honduras and northern El Salvador. Improved grasses (four Brachiaria sp. and Megathyrsus maximus ) produced significantly more biomass than H. rufa ; also four of the five legume varieties evaluated ( Canavalia ensiformis , Canavalia brasiliensis , Vigna unguiculata , and Vigna radiata ) demonstrated high adaptability to diverse environmental conditions across sites. Farmer participatory evaluation offers a valuable means to assess performance of forages and will likely contribute to their improved utilization. Future research is needed on more refined management recommendations, pasture system design, costs and environmental benefits associated with the adoption of these forages in local livestock production systems.
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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 | 0.007 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.002 |
| 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".