Impacts of temperature and precipitation variability in the Northern Plains of the United States and Canada on the productivity of spring barley and oat
Bibliographic record
Abstract
Increasing temperatures and changes in precipitation are expected to effect a change in production of cool-season crops such as spring barley and oat. To determine whether observed changes may already have had an impact on these crops in the Northern Plains of the United States and Canada, first-differences of growing-season temperature and precipitation and of annual yield data were analysed via multiple linear regression for 1980–2012 for the genetically stable cultivars of ‘Robust’ spring barley (Hordeum vulgare L.) at three sites in Minnesota, and for ‘Gopher’ oats (Avena sativa L.) at five sites in Minnesota and neighbouring states and provinces. Temperature and precipitation impacts also were assessed for the top-three yielding barley and oat cultivars at each site to assess whether newer varieties responded similarly to the older varieties. Barley yield at the coolest site showed a modest relationship with climate while the warmer sites showed stronger relationships between climate variability and barley yield, particularly for negative impacts of high temperatures. Climate variability also had a significant impact on yield at the five oat sites. Warm pre-sowing temperatures enhanced yields at cooler sites while high temperatures later in the growing season reduced yields across the sites. Results for the top-three barley and oat cultivars often were similar to those for the older cultivars. Our results suggest that observed climate changes have contributed to the relative decrease in barley and oat yields in the region, that more recent releases have partially compensated for the negative impacts of observed temperature and precipitation trends, and that model projected changes in temperature and precipitation will continue to present both benefits and challenges for barley and oat production in the Northern Plains.
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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 | 0.001 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 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".