Low-hanging fruit? Identifying opportunities to enhance crop diversity in the United States
Bibliographic record
Abstract
Abstract The benefits of crop diversification for key agro-environmental outcomes are well-established, yet the pathways to realizing this diversification at larger scales remain murky. We propose a framework to evaluate possible spatial and temporal crop arrangements that meet specific crop diversity targets by drawing on empirical data across millions of individual crop fields and their recent cropping histories in the US (2015–2022). Specifically, we develop a simple rule-based model to raise crop species diversity to various levels of spatial and temporal crop diversity (driven by crop rotations) already attained by farmers in areas with broadly similar soils and climate (‘attainable diversity’). We demonstrate that small changes on the local scale have sizeable cumulative effects. For example, switching crops in 3.4% of fields (corresponding to 12.4% of US cropland area) in the 75th percentile attainable diversity scenario would raise national-scale crop diversity by over 50% (effective number of crop species increased from 7.6 to 11.5). These simulated changes in crop diversity have clear spatial patterns associated with cropland extent and field size. Finally, we examine how the geographic redistribution of key commodity crops (maize, soybean, and wheat) resulting from these crop switches would influence crop yield and production outcomes. While geographic redistribution has limited influence on yields of these three crops, including some potential yield gains, net national production declines (by ∼10%–13% in our main spatial diversification scenario) due to their reduced share in the cropland portfolio as minor crop groups expand. By drawing on a pool of crops that are already grown in areas with similar field sizes, climate, and soils, our results can help inform realistic strategies toward national-scale crop diversification—illustrating potential ‘low-hanging fruit’ for crop transitions across different landscapes. Our proposed empirical attainable diversity framework adds nuance around the opportunities and barriers associated with efforts to raise agrobiodiversity.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| 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".