Impacts of Recent Climate Trends on Agriculture in Southwestern Ontario
Bibliographic record
Abstract
In this paper, precipitation, temperature and crop water deficit during the past 80 years in southwestern Ontario are reviewed in terms of their impacts on the region’s present and future field-crop production. Although the generally drier (average annual precipitation ≈ 750 mm) and warmer (average annual air temperature ≈ 9.5 °C) weather in the region during the last decade produced greater average annual crop water deficits (≈ 150 mm yr−1) than during the previous 30 to 40 years, the situation is not as severe as during the 1930s. It is likely, however, that the negative impacts of climate on the region’s field-crop production (yields often ≤ 50% of potential) are greater now than ever before because of more intensive agriculture, fewer pastures/wetlands/woodlots, and greater water demands by increasing population and industry. To mitigate the current negative weather effects, the region’s field-crop sector is adopting drip irrigation technologies (more efficient than the traditional overhead gun systems) which have been shown in recent trials to increase tomato yields by 50–80% over those not irrigated. In addition, combined drainage-surface runoff-irrigation technologies (’closed loop’ water management systems) are being developed which can increase corn and soybean yields by up to 90% and 50%, respectively, while simultaneously decreasing agrochemical and sediment degradation of the off-field environment. If the current climate trend persists, it may be necessary to develop new drought- and heat-resistant field crops as well as updated guidelines for nutrient and pest management.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".