Potential impacts of climate change on corn, soybeans and barley yields in Atlantic Canada
Bibliographic record
Abstract
In this paper, relationships between agroclimatic indices and average yields of grain corn (Zea mays L.), soybeans (Glycine max L. Merr.) and barley (Hordeum vulgare L.) in field trials conducted in eastern Canada are explored and then used to estimate potential impacts of climate change scenarios on anticipated average yields and total production of these commodities for the Atlantic region for the 2040 to 2069 period. Average yields of grain corn and soybeans were highly correlated (R 2 = 0.86 and 0.74, respectively) with average available crop heat units (CHU), with yields increasing by about 0.006 t ha -1 CHU -1 for corn and 0.0013 t ha -1 CHU -1 for soybeans. The explained variance was not improved significantly when water deficit (DEFICIT) was included as an independent variable in regression. Correlations between average yields of barley and effective growing degree-days (EGDD) were low (R 2 ≤ 0.26) and negative, i.e., there was a tendency for slightly lower yields at higher EGDD values. Including a second-order polynomial for DEFICIT in the regression increased the R 2 to ≥ 0.58, indicating a tendency for lower barley yields in areas with high water deficits and with water surpluses. Based on a range of available heat units projected by multiple General Circulation Model (GCM) experiments, average yields achievable in field trials could increase by about 2.6 to 7.5 t ha -1 (40 to 115%) for corn, and by 0.6 to 1.5 t ha -1 (21 to 50%) for soybeans by 2040 to 2069, not including the direct effect of increased atmospheric CO 2 concentrations, advances in plant breeding and crop production practices or changes in impacts of weeds, insects and diseases on yield. Anticipated reductions in barley yields are likely to be more than offset by the direct effect of increased CO 2 concentrations. As a result of changes in potential yields, there will likely be significant shifts away from production of barley to high-energy and high-protein crops (corn and soybeans) that are better adapted to the warmer climate. However, barley and other small grain cereals will likely remain as important crops as they are very suited for rotation with potatoes. There is a need to evaluate the potential environmental impacts of these possible shifts in crop production, particularly with respect to soil erosion in the region. Key words: Crop heat units, growing degree-days, water deficits, crop yields, climate change, Atlantic region
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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 | 0.001 |
| 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".