Potential deficit irrigation adaptation strategies under climate change for sustaining cotton production in hyper–arid areas
Bibliographic record
Abstract
Abstract Affected by climate change and elevated atmospheric CO2 levels, the efficacy of agricultural management practices is of particular concern in a hyper–arid area. Herein, the effects of future climate change on cotton (Gossypium hirsutum L.) yield and water use efficiency (WUE) was assessed under deficit irrigation strategies in China’s southern Xinjiang region. A previously calibrated and validated RZWQM2 model simulated cotton production for two time periods ranging between 2061–2080 and 2081–2100, under two automatic irrigation methods [crop evapotranspiration (ET–based) and plant available water (PAW–based)], factorially combined with four irrigation levels (100%, 80%, 60%, and 50%). Weather information was obtained from ten general circulation models, and three Shared Socioeconomic Pathways were tested. Simulation results showed that the irrigation strategy had a greater impact than climatic change on water use and crop production of cotton. Under climate change, both ET– and PAW–based irrigation methods with deficit irrigation showed a simulated decrease in water use and production of cotton compared to the baseline (1960–2019). Under future climate conditions, for a given irrigation level, PAW–based irrigation led to 35.3 mm–135 mm (7.4–53.9%) greater water use for cotton than did ET–based irrigation. For the 2061–2080 period, mean simulated seed cotton yields were 4.47, 3.69, 2.29 and 1.63 Mg ha–1 with the 100%, 80%, 60% and 50% ET–based irrigation protocols, respectively, and 4.46, 4.41, 3.85 and 3.34 Mg ha–1 with the equivalent PAW–based irrigation protocols. Similar yields were simulated for the 2081–2100 period. In addition, the 80% PAW–based or 100% ET–based irrigation protocols under future climate change provided the greatest cotton WUE in southern Xinjiang.
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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".