The Global Spatial Co‐Variation Between Crop Diversity and Landscape Heterogeneity
Bibliographic record
Abstract
ABSTRACT The influence of crop and landscape heterogeneity on different components of biodiversity in agricultural landscapes has been assessed at multiple scales. However, how crop species diversity relates to landscape heterogeneity remains underexplored at the global scale. We apply independent global spatial datasets to test the relationship between crop and landscape heterogeneity across 19,505 agricultural landscapes worldwide. We first examine the spatial patterns in crop diversity and landscape diversity (compositional heterogeneity), defined as the effective number of crop species and land cover types, respectively, based on Shannon entropy. Median crop diversity increases on average by 0.36–0.48 effective species for each unit increase in land cover diversity globally. We use quantile generalized additive models (QGAM) to statistically test this relationship. The QGAM approach confirms that crop diversity has a positive but complex relationship with landscape diversity, particularly for landscapes with ≥ 4–5 non‐crop cover types. However, this positive trend is context‐dependent, as the clearest median response generally corresponds to landscapes with moderate cropland extents (25%–75% cropland). We also examine how other components of landscape compositional and configurational heterogeneity, including dominant agricultural field size and patch size, as well as topographic heterogeneity are associated with crop diversity. The relatively highest crop diversity tends to correspond to very small dominant agricultural field sizes (crop configurational heterogeneity) when controlling for cropland extent and non‐agricultural land cover diversity. Mean patch area (landscape configurational heterogeneity) has a strong negative association with crop diversity until patch sizes of > 150 km 2 while topographic heterogeneity has a consistent positive association with crop diversity. Our findings therefore demonstrate how the diversity of non‐agricultural land covers in conjunction with configurational heterogeneity has relevance to understanding existing patterns of spatial crop diversity. Such insights could help inform efforts to design more multifunctional agricultural landscapes, including landscape‐scale farm diversification strategies.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".