Potential deficit irrigation adaptation strategies under climate change for sustaining cotton production in hyper–arid areas
Bibliographic record
Abstract
Affected by climate change and elevated atmospheric CO 2 levels, the efficacy of agricultural management practices is of particular concern in a hyper–arid area. The effects of future climate change on cotton ( Gossypium hirsutum L.) yield and water productivity (WP) were 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 automatic irrigation method based on crop plant available water, factorially combined with four irrigation levels (100 %, 80 %, 60 %, and 50 %). Weather data was obtained from ten general circulation models, and two Shared Socioeconomic Pathways were tested. Deficit irrigation under climate change showed a simulated decrease in water use and production of cotton compared to the baseline (1960–2019). For the 2061–2080 period, mean simulated seed cotton yields were 4.43, 4.44, 3.95 and 3.47 Mg ha –1 ( vs. baseline: 4.65, 4.40, 3.58, 2.63 Mg ha −1 ) with the 100 %, 80 %, 60 % and 50 % irrigation levels. A 3.4 %-28.6 % of decrease ( vs. baseline) in seed cotton yield was found under SSP585 scenario in 2081–2100. The 80 %PAW–based irrigation provided the highest WP of 12.8 kg m –3 and 8.4 kg m –3 for 2061–2080 and 2081–2100, respectively, comparing to the baseline WP of 0.82 kg m –3 . Under SSP585 for 2081–2100, the simulated WP declined from 0.19 kg m –3 at 100 % irrigation levels to 0.04 kg m –3 at 50 % irrigation levels. These projections suggests that adequate irrigation is the key to ensure cotton production and moderate deficit irrigation can be applied to mitigate the negative impacts of climate change on cotton yield in a hyper–arid area. • Deficit irrigation reduced cotton yield by 3 %-38 % under climate change. • Deficit irrigation increased cotton water productivity by 4.8 %-12.8 % under SSP245 scenario. • Water productivity decreased by 8.5 %-22 % under SSP585 for the 2081–2100. • The optimal cotton water use was 479–500 mm under climate change in hyper–arid areas.
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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".