Controlled Warming Effects on Wheat Growth and Yield: Field Measurements and Modeling
Bibliographic record
Abstract
Climate warming may raise wheat ( Triticum aestivum L.) yields in cooler climates and lower them in warmer climates. To understand these contrasting effects, infrared heating lamps were used to warm irrigated spring wheat by 1.5°C (day) and 3.0°C (night) above unheated controls during different times of the year at Maricopa, AZ. Changes in wheat growth with warming were used to test hypotheses for temperature effects on crop growth in the process model ecosys . Infrared heating substantially raised phytomass growth and grain yield under lower air temperature ( T a ) following plantings from September through December. The same heating, however, lowered growth and yield under higher T a following plantings from January through March. Gains in wheat yield of as much as 200 g C m −2 with heating under lower T a were attributed in the model to more rapid CO 2 fixation and to reduced chilling effects on seed set. These gains were only partially offset by losses from shortened wheat growth periods. Losses in wheat yield of as much as 100 g C m −2 with heating under higher T a were attributed in the model to adverse effects of heating on crop water status and on CO 2 fixation vs. respiration, to greater heat stress effects on seed set, and to shortened crop growth periods. Model hypotheses thus explained contrasting effects of heating on wheat yields under different T a found in the field experiment as well as in many earlier studies. Well‐constrained tests of these hypotheses are vital for models used to project climate change impacts on agricultural ecosystems.
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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".