Effect of woody-plant encroachment on livestock production in North and South America
Bibliographic record
Abstract
A large fraction of the world grasslands and savannas are undergoing a rapid shift from herbaceous to woody-plant dominance. This land-cover change is expected to lead to a loss in livestock production (LP), but the impacts of woody-plant encroachment on this crucial ecosystem service have not been assessed. We evaluate how tree cover (TC) has affected LP at large spatial scales in rangelands of contrasting social-economic characteristics in the United States and Argentina. Our models indicate that in areas of high productivity, a 1% increase in TC results in a reduction in LP ranging from 0.6 to 1.6 reproductive cows (Rc) per km(2). Mean LP in the United States is 27 Rc per km(2), so a 1% increase in TC results in a 2.5% decrease in mean LP. This effect is large considering that woody-plant cover has been described as increasing at 0.5% to 2% per y. On the contrary, in areas of low productivity, increased TC had a positive effect on LP. Our results also show that ecological factors account for a larger fraction of LP variability in Argentinean than in US rangelands. Differences in the relative importance of ecological versus nonecological drivers of LP in Argentina and the United States suggest that the valuation of ecosystem services between these two rangelands might be different. Current management strategies in Argentina are likely designed to maximize LP for various reasons we are unable to explore in this effort, whereas land managers in the United States may be optimizing multiple ecosystem services, including conservation or recreation, alongside LP.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".