RZWQM2 Simulated Irrigation Strategies to Mitigate Climate Change Impacts on Cotton Production in Hyper–Arid Areas
Bibliographic record
Abstract
Improving cotton (Gossypium hirsutum L.) yield and water use efficiency (WUE) under future climate scenarios by optimizing irrigation regimes is crucial in hyper-arid areas. Assuming a current baseline atmospheric carbon dioxide concentration (CO2atm) of 380 ppm (baseline, BL0/380), the Root Zone Water Quality Model (RZWQM2) was used to evaluate the effects of four climate change scenarios—S1.5/380 (∆Tair°=1.5 °C,∆CO2atm=0), S2.0/380 (∆Tair°=2.0 °C,∆CO2atm=0), S1.5/490 (∆Tair°=1.5 °C,∆CO2atm=+110 ppm) and S2.0/650 (∆Tair°=2.0 °C,∆CO2atm=+270 ppm) on soil water content (θ), soil temperature (Tsoil°), aboveground biomass, cotton yield and WUE under full irrigation. Cotton yield and irrigation water use efficiency (IWUE) under 10 different irrigation management strategies were analysed for economic benefits. Under the S1.5/380 and S2.0/380 scenarios, the average simulated aboveground biomass of cotton (vs. BL0/380) declined by 11% and 16%, whereas under S1.5/490 and S2.0/650 scenarios it increased by 12% and 30%, respectively. The simulated average seed cotton yield (vs. BL0/380) increased by 9.0% and 20.3% under the S1.5/490 and S2.0/650 scenarios, but decreased by 10.5% and 15.3% under the S1.5/380 and S2.0/380 scenarios, respectively. Owing to greater cotton yield and lesser transpiration, a 9.0% and 24.2% increase (vs. BL0/380) in cotton WUE occurred under the S1.5/490 and S2.0/650 scenarios, respectively. The highest net income ($3741 ha−1) and net water yield ($1.14 m−3) of cotton under climate change occurred when irrigated at 650 mm and 500 mm per growing season, respectively. These results suggested that deficit irrigation can be adopted in irrigated cotton fields to address the agricultural water crisis expected under climate change.
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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 | 0.001 |
| 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 | 0.001 |
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".