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 (Ta) following plantings from September through December. The same heating, however, lowered growth and yield under higher Ta following plantings from January through March. Gains in wheat yield of as much as 200 g C m−2 with heating under lower Ta were attributed in the model to more rapid CO2 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 Ta were attributed in the model to adverse effects of heating on crop water status and on CO2 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 Ta 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.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".