Long-term climate and weather patterns in relation to crop yield: a minireview
Bibliographic record
Abstract
It is accepted knowledge that weather affects yields and climate dictates which crops will grow in a region. Specific weather events, such as drought or hail, that affect yield or quality, are known, as are the general climatic conditions required for groups of crops such as cool season vegetables or tropical crops. However, more precise information on how changes in long-term weather and climate influence the yield of specific vegetable crops is not available. Studies of yield over long periods have the advantage of reflecting the combined effects of numerous environmental variables, including climate effects on physiological stress, pests, and diseases. A study of yields of cool season vegetable crops in Ontario revealed that a decrease in yields is related to an increase in the number of hot days with maximum temperatures over 30 °C. A clear warming trend in Ontario weather is evident, beginning in the mid-1980s, with increasing numbers of hot days and more variable temperatures from year to year. Furthermore, yields of cole crops declined by 10% for every 10 d with temperatures >30 °C in the growing season. Several other studies on agronomic crops worldwide show reductions in yield resulting from warmer temperatures. For example, in a study of rice in the Philippines, yield was reduced by 10% for every 1 °C increase in minimum temperature during the dry season. Methods for evaluating long-term climate effects on crop yield are discussed.
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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 | 0.000 | 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) | 0.000 | 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".