Determinants of grassland primary production in seasonally-dry silvopastoral systems in Central America
Bibliographic record
Abstract
Grassland primary productivity is the function that underpins the majority of the fodder production in cattle-rearing silvopastoral farms. Hence, understanding the factors that determine grassland productivity is critical for the design and management of silvpastoral systems. We studied the effect of two factors with documented impact on grassland productivity in seasonally dry silvopastures of Nicaragua, rainfall and trees. We assessed the effects of three species that differed in crown size and phenology, one evergreen, Cassia grandis , and two deciduous species, Guazuma ulmifolia and Tabebuia rosea . Overall, grassland ANPP had a quadratic response to rainfall, with a decline at high rainfall that coincided with peak standing biomass and grassland cover. Trees had a predominately negative effect on grassland productivity, and the effect was concentrated in the rainy season at peak productivity. The effect of the trees corresponded with the tree crown area, but not with crown density. Trees reduced the standing biomass of graminoids and increased forb biomass; thus, the effect of trees on grassland ANPP appears in part to respond to changes in grassland composition. We also found higher levels of soil moisture content below the tree canopy, particularly at the peak of the rainy season when soils tend to become waterlogged. The evergreen species, C. grandis , affected grassland ANPP more strongly than the deciduous species.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".