Evaluating the Contribution of Weather to Maize and Wheat Yield Trends in 12 U.S. Counties
Bibliographic record
Abstract
Evaluating the contribution of weather and its individual components to recent yield trends can be useful to predict the response of crop production to future climate change, but different modeling approaches can yield diverging results. We used two common approaches to evaluate the effect of weather trends on maize ( Zea mays L.) and wheat (Triticum aestivum L.) production in 12 U.S. counties, and investigate sources of disparities between the two methods. We first used the Decision Support System for Agrotechnology Transfer (DSSAT) model from 1984 to 2008 to evaluate the contribution of weather changes to simulated yield trends in six counties for each crop, each county being located in one of the top 10 U.S. producing states for that crop. A parallel analysis was conducted by multiplying inter‐annual weather sensitivity of county‐level yields with observed weather trends to estimate weather contributions to empirical yield trends. Weather had a low (maize) to high (wheat) contribution to simulated yield trends, with rain having the largest effect. In contrast, weather and rain had lower contributions to empirical yield trends. Along with evidence from previous studies, this suggests that DSSAT may be too sensitive to water thus inflating the importance of rain. Moreover, the time period used to compute yield trends also had a large effect on the importance of weather and its individual components. Our results highlight the importance of using multiple computation approaches and different time periods when estimating weather‐related yield trends.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| 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 | 0.000 | 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 teacher head, 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".