Modeling Current and Future Climate Effects on Winter Wheat Production in Colorado, USA
Bibliographic record
Abstract
Winter wheat productivity varies geographically depending on severity and duration of cold periods and other factors like altitude, precipitation distribution, and photoperiod. Colorado is an important wheat growing region in the United States. How climate change will affect the winter wheat production is a major concern of Colorado wheat growers. This study explored possible consequences of projected climate change on dryland and irrigated winter wheat at an experimental farm in Colorado, where measured data collected under current climate conditions were available. Root Zone Water Quality Model (RZWQM2), which contains CSM-CERES-Wheat v4.0 crop module, was used for simulating wheat growth under current and projected climates. The model was calibrated and evaluated with experimental field data collected at the USDA-ARS Limited Irrigation Research Farm at Greeley, CO, for six irrigation treatments, over the growing seasons of 2008/09, 2009/10 and 2010/11. Model simulation results agreed with the measured data for leaf area index, biomass, and grain yield, with RMSE values close to one standard deviation of the experimental data and within values reported in the literature. Root mean square errors of the simulated grain yields were 464, 471, and 544 kg ha-1 for the three seasons, respectively. Biomass and grain yield increased with increase in irrigation level. Projected climate change under the lowest IPCC greenhouse gas concentration scenario, Representative Concentration Pathway (RCP) 2.6, did not affect grain yields in 2050 and 2080 for dryland or irrigated crops. However, under higher concentration scenarios, grain yields increased moderately over the current level, mainly due to the CO2 fertilization effect in the C3 wheat plant and more favorable warmer winter and spring temperatures for wheat growth associated with future climates. Winter wheat is a good adaptation crop for climate change in the study area.
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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 | 0.001 |
| 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 | 0.000 |
| Open science | 0.001 | 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".