Crop Yield Response to Climate Variables on Dryland versus Irrigated Lands
Bibliographic record
Abstract
Key Points We examine the response of barley, canola, and spring wheat yields to a set of climate variables on both dryland and irrigated lands in southern Alberta, Canada. We find that warming and increased precipitation tend to increase crop yields on dryland, increased precipitation in June and July tends to show opposite effects on crop yields on irrigated lands. Based on regional climate change projection scenarios, we find that climate change decreases crop yields for all the three crops under dryland production. However, yields of canola and spring wheat under irrigation are slightly increased. Few researchers have examined the impact of climate change on irrigated agriculture and crop production. This may be due to an assumption by researchers that irrigation management can offset impacts of climate change. We investigate this issue by examining the response of barley, canola, and spring wheat yields to a set of climate variables on both dryland and irrigated lands in southern Alberta, Canada, with a panel data set at the county level from 1983 to 2007. Our results suggest that warming and increased precipitation tend to increase dryland crop yields, while increased precipitation in June and July tends to show opposite effects on crop yields on irrigated lands. Based on regional projected climate change scenarios, we find that climate change decreases crop yields for all the three crops under dryland production. However, yields of canola and spring wheat under irrigation are increased slightly. Peu de chercheurs se sont penchés sur les impacts des changements climatiques sur l'agriculture irriguée et le rendement des cultures. Il se pourrait que ce soit parce que les chercheurs supposent que les régimes d'irrigation peuvent neutralise les impacts des changements climatiques. Nous examinons cet enjeu en étudiant le rendement de l'orge, du canola et du blé de printemps en fonction de variables climatiques à la fois en sols arides et en sols irrigués au sud de l'Alberta, au Canada avec un ensemble de données de panel provenant des comtés de 1983 à 2007. Les résultats démontrent que le réchauffement et l'augmentation des précipitations semblent accroître le rendement des sols arides mais que cette dernière, lorsqu'elle survient en juin ou juillet, semble engendrer l'effet contraire sur le rendement en sols irrigués. Nous constatons, selon les scénarios hypothétiques de changements climatiques régionaux, une diminution du rendement agricole pour les trois cultures en sols arides. Par contre, le rendement des cultures de canola et de blé de printemps en sols irrigués augmenterait légèrement.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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 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".