Potential deficit irrigation adaptation strategies under climate change for sustaining cotton production in hyper–arid areas
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it