Effects of Climate Change on Irrigation Decisions and Low Flow Frequency for a Typical Agricultural River Basin of the Midwestern US
Bibliographic record
Abstract
The Midwest is the largest agricultural area of the United States. Historically, the climate, characterized by moderate temperatures and ample rainfall, has been suitable for un-irrigated agriculture. However, the specter of climate change has created concerns about the future of Midwestern agriculture, regional fresh water resources and the relationship between the two. Implications of climate change for the Midwest are revealed at the river basin scale in a recent study on the Mackinaw River Basin, a typical agricultural Midwestern watershed of central Illinois, through modeling exercises. Generally in this study a future climate with more frequent droughts is envisioned based on the outcome of one of the major General Circulation Models (GCMs), the Canadian Climate Centre model. Climate change in and of itself will affect the vulnerability of regional fresh water resources, by altering the low flow frequencies of stream at reference gauging stations. Moreover, the threats of droughts may motivate farmers to introduce irrigation in this traditionally rain-fed area to maintain high and stable yields. Such irrigations, if any, could exacerbate the effects of the changes in climatic factors. This study shows that the changes in climatic factors of temperature and precipitation will reduce the agricultural productivity, trigger irrigation and increase the low flow frequencies at reference gauging stations. However, the adverse effects of changes in temperature and precipitation may well be counteracted by the effects of elevated CO2 concentration in atmosphere. Thus, the opposing effects of climate change could very well leave agriculture in central Illinois more or less unchanged.
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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 | 0.001 | 0.002 |
| 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 | 0.001 | 0.001 |
| 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".