Pasture chemoscapes and their ecological services
Bibliographic record
Abstract
Ruminant livestock-production systems are between a rock and a hard place; they are experiencing increasing societal pressure to reduce environmental impacts in a world that demands increased food supply. Recent improvements in the understanding of the nutritional ecology of livestock by scientists may help livestock producers respond to these seemingly contradictory demands. Forages are nutrition and pharmacy centers with primary (nutrients) and plant secondary compounds (PSC; pharmaceuticals, nutraceuticals), which can provide multiple services for the proper functioning of agroecosystems. Legumes with lower contents of fiber and higher contents of nonstructural carbohydrates, coupled with different types and concentrations of PSC (e.g., condensed tannins, terpenes), create a diverse array of chemicals in the landscape (i.e., the "chemoscape") with the potential to enhance livestock nutrition, health and welfare relative to foodscapes dominated by grasses and other conventional feeds. These PSC-containing plants may reduce methane emissions and nitrogen (N) excretion from animals while increasing animal growth rate compared with swards dominated by grasses, and provide meat quality that appeals to consumers. Condensed tannins from sainfoin and saponins from alfalfa and manure of cattle consuming these forages also reduce N mobilization in soils, reduce nutrient leaching, and increase plant-available N stores for future use. The challenge for future pastoral production systems is to design multifunctional spatiotemporal arrangements of forages with "ideal" chemical diversity for specific ecoregions, aiming to achieve sustainability while increasing production goals and improving ecosystem services. Thus, the objective of this review is to stimulate the quest for chemically and taxonomically diverse pastoral feeding systems that optimize overall productivity; reduce environmental impacts; and enhance livestock, soil, and human health.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.024 | 0.002 |
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".