AMethod to Estimate Climate Drivers ofMaize Yield PredictabilityLeveraging Genetic-by-Environment Interactions in the USand Canada
Bibliographic record
Abstract
Throughout history, the pursuit of diagnosing and predicting crop yields has evidenced genetics, environment, and management practices intertwined in achieving food security. However, the sensitivity of crop phenotypes and genetic responses to climate still hampers the identification of the underlying abilities of plants to adapt to climate change. We hypothesize that the PiAnosi and WagNer (PAWN) global sensitivity analysis (GSA) coupled with a genetic by environment (GxE) model built of environmental covariance and genetic markers structures, can evidence the contributions of climate on the predictability of maize yields in the U.S. and Ontario, Canada. The GSA-GxE framework estimates the relative contribution of climate variables to improving maize yield predictions. Using an enhanced version of the Genomes to Fields initiative database, the GSA-GxE framework shows that the spatially aggregated sensitivity of maize yield predictability is attributed to solar radiation, followed by temperature, rainfall, and relative humidity. In one-third of the individually assessed locations, rainfall was the primary responsible for maize yield predictability. Also, a consistent pattern of top sensitivities (Relative Humidity, Solar Radiation, and Temperature) as the main or the second most relevant drivers of maize yield predictability shed some light on the drivers of genetic improvement in response to climate change.
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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 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".