Evaluación preliminar de los recursos forrajeros en El Salvador: Bases para un inventario nacional
Bibliographic record
Abstract
This study presents a preliminary inventory of forages used in cattle farming systems in El Salvador. The forages were classified as: silages, grazing pastures, cut-and-carry pastures, and other forages (hay, legumes, and agricultural by-products). Standardized protocols were applied for sample collection, which were processed in El Salvador and analyzed at Université Laval, Canada, to determine their concentrations of dry matter (DM), crude protein (CP), ash, neutral detergent fiber (NDF), acid detergent fiber (ADF), and fatty acid (FA) profile, as well as starch content in silages with grain, and in vitro DM digestibility and indigestible neutral detergent fiber (iNDF) in selected forages. The silages included maize, sorghum, sugarcane, and Pennisetum grasses. A wide variability in DM, CP, and fiber fractions was observed, mainly associated with differences in harvest timing and silage management. Many silages had DM content below the optimal level (<30%), suggesting significant nutritional losses. Grazing pastures included Urochloa brizantha, Cynodon nlemfuensis, Digitaria swazilandensis, Megathyrsus maximus, among others, and showed higher DM and CP content than cut pastures, which were mainly composed of Pennisetum and its hybrids. Both types of pastures showed elevated levels of NDF and iNDF, which could negatively affect intake and digestibility.Other evaluated forages included Swazi grass hay, the legume Cratylia, and by-products such as stover and corn husks. Notably, hay showed low nutritional quality, likely due to delayed harvesting. The FA profile revealed a higher proportion of polyunsaturated fatty acids in silages with grain and in fresh pastures, highlighting their potential to improve the lipid quality of animal products. The results underscore the need to improve forage management practices to optimize the nutritional value of forages in tropical livestock 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 | 0.006 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".