Crop Model Ensemble Averaging: A Large But Underappreciated Uncertainty Source for Global Crop Yield Projections Under Climate Change
Bibliographic record
Abstract
Abstract Using an ensemble of crop models have been encouraged for global crop yield projections, which would, however, introduce additional uncertainty from the choice of ensemble averaging methods. Here, we use seven ensemble averaging methods including simple average, regression, Support Vector Regressor, Bayesian model average (BMA), Random Forest (RF), Artificial neural network (ANN) and Long‐short term memory to derive the ensemble mean of eight process‐based crop models for global maize yield projections. Results show that the choice of ensemble averaging methods has a large impact on the projection of long‐term mean yield and year‐to‐year yield variability, with a range of −19.79%–16.62% and −47.92%–55.39% for the globe, respectively. Regionally, the largest uncertainties from the choice of ensemble averaging methods are observed in Indonesia and Canada. Further uncertainty decomposition analysis shows that ensemble averaging methods contributes to 39%–87% of total uncertainties for global yield projections, which is even higher than climate models. These results imply that although using an ensemble of crop models is valuable for informing risk‐based policy‐makings, how we choose to combine and derive the best estimates of crop model ensembles has large influence on the assessment outcomes. This study highlights an important but not well recognized uncertainty source for global yield predictions which arises from the choice of ensemble averaging methods.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".