Modeling Elevated Carbon Dioxide Effects on Water Relations, Water Use, and Growth of Irrigated Sorghum
Bibliographic record
Abstract
Elevated concentrations of atmospheric CO2 (Ca) are believed to raise sorghum [Sorghum bicolor (L.) Moench] productivity by improving water relations. In ecosys, water relations are simulated by solving for the canopy water potential (ψC) at which water uptake from a model of soil–root–canopy water transfer equilibrates with transpiration from the canopy energy balance. Simulated water relations were tested with ψC, water uptake, and energy exchange measured under ambient (363 μmol mol−1) and elevated (566 μmol mol−1) Ca and high vs. low irrigation in a free air CO2 enrichment experiment during 1998 and 1999. Model results, corroborated by field measurements, showed that elevated Ca raised ψC and lowered latent heat fluxes under high irrigation and delayed water stress under low irrigation. Changes in ψC modeled under ambient vs. elevated Ca varied diurnally, with lower ψC causing earlier midafternoon stomatal closure under ambient Ca. Modeled changes in sorghum water status caused elevated Ca to raise seasonal water efficiency under high and low irrigation by 20 and 26% (vs. 20 and 13% measured) in 1998 and by 9 and 27% (vs. 6 and 26% measured) in 1999. Ecosys was used to generate an irrigation response function for sorghum yield, which indicated that yields would rise by ≈13% for a range of irrigation rates if air temperatures were to rise by 3°C and Ca by 50%. Current high sorghum yields could be achieved with ≈120 mm or ≈20% less irrigation water if these rises in temperature and C a were to occur.
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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 | 0.000 |
| 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) | 0.001 | 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".