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<p>Accurate projections of climate change impacts on cotton yield in arid regions are essential for sustainable production and risk management. This study employs the Decision Support System for Agrotechnology Transfer-CROPGRO-Cotton(DSSAT-CROPGRO-Cotton)model, integrating 13 CMIP6-GCMs and localized parameters, to simulate cotton yield responses in Alaer, Xinjiang, under SSP2-4.5 and SSP5-8.5 scenarios. Correlation analysis and machine learning method (i.e., Random Forest) identify key yield-influencing factors. Results show: (1) At the growing-season scale, mean air temperature is projected to rise by 1.2–6.7&#xa0;°C, precipitation projections remain highly uncertain, and solar radiation shows an overall decline; the magnitude of climate change varies among seasons. (2) Relative to the baseline, simulated cotton yield changes under SSP2-4.5 reach + 13.0%, + 21.8%, + 28.7% and + 28.6% for the 2030s, 2050s, 2070s and 2090s, respectively; the corresponding increments under SSP5-8.5 are + 15.5%, + 27.1%, + 29.9% and + 18.4%. (3) Elevated CO₂ accounts for 14.9–23.4% of the total yield variation. (4) The fertilization effect of higher CO₂ partly offsets the negative impacts induced by changes in temperature, precipitation and radiation. Over time, the relationship between key drivers (especially temperature and CO₂) and cotton yield shifts from positive to negative, with the transition being more pronounced under SSP5-8.5. (5) The 13 GCMs differ in their simulation outputs and attribution analyses; distilling the consensus patterns can enhance the reliability of the findings. These findings deepen understanding of climate risks to cotton in arid regions and provide a scientific basis for formulating adaptive management strategies.</p> Graphical Abstract <p>This study explores the impacts and uncertainties of climate change on cotton yield in Alaer, Xinjiang, a representative arid region of Northwest China. Using the DSSAT-CROPGRO-Cotton model calibrated with local experimental data, combined with projections from 13 CMIP6 global climate models, we simulated cotton yield responses under two climate scenarios (SSP2-4.5 and SSP5-8.5) across four future periods (2030s, 2050s, 2070s, and 2090s). Results reveal consistent warming trends and declining solar radiation, while precipitation projections remain highly uncertain with potential extreme fluctuations. Elevated CO₂ concentrations enhance yields by 13%–30%, but substantial inter-model variability underscores the uncertainty in future projections. Furthermore, the dominant drivers of yield shift over time: CO₂ fertilization initially promotes yield gains, but under high-emission scenarios, temperature stress becomes the overriding negative factor earlier in the century. By late century, yields decline despite elevated CO₂, particularly under SSP5-8.5. These findings provide new insights into climate-induced risks for cotton production and highlight the urgent need for adaptive strategies tailored to arid agricultural systems.</p>

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Unraveling Climate Risks to Cotton Yield in Arid Northwest China: Projections and Driving Factors

  • 期刊论文

  • 发表日期：

    2026年2月21日

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  • Shengru Yue,
  • Xuefei Hu,
  • Muhammad Waseem Rasheed,
  • Zhuzhu,
  • Abid Sarwar

摘要

Accurate projections of climate change impacts on cotton yield in arid regions are essential for sustainable production and risk management. This study employs the Decision Support System for Agrotechnology Transfer-CROPGRO-Cotton(DSSAT-CROPGRO-Cotton)model, integrating 13 CMIP6-GCMs and localized parameters, to simulate cotton yield responses in Alaer, Xinjiang, under SSP2-4.5 and SSP5-8.5 scenarios. Correlation analysis and machine learning method (i.e., Random Forest) identify key yield-influencing factors. Results show: (1) At the growing-season scale, mean air temperature is projected to rise by 1.2–6.7 °C, precipitation projections remain highly uncertain, and solar radiation shows an overall decline; the magnitude of climate change varies among seasons. (2) Relative to the baseline, simulated cotton yield changes under SSP2-4.5 reach + 13.0%, + 21.8%, + 28.7% and + 28.6% for the 2030s, 2050s, 2070s and 2090s, respectively; the corresponding increments under SSP5-8.5 are + 15.5%, + 27.1%, + 29.9% and + 18.4%. (3) Elevated CO₂ accounts for 14.9–23.4% of the total yield variation. (4) The fertilization effect of higher CO₂ partly offsets the negative impacts induced by changes in temperature, precipitation and radiation. Over time, the relationship between key drivers (especially temperature and CO₂) and cotton yield shifts from positive to negative, with the transition being more pronounced under SSP5-8.5. (5) The 13 GCMs differ in their simulation outputs and attribution analyses; distilling the consensus patterns can enhance the reliability of the findings. These findings deepen understanding of climate risks to cotton in arid regions and provide a scientific basis for formulating adaptive management strategies.

Graphical Abstract

This study explores the impacts and uncertainties of climate change on cotton yield in Alaer, Xinjiang, a representative arid region of Northwest China. Using the DSSAT-CROPGRO-Cotton model calibrated with local experimental data, combined with projections from 13 CMIP6 global climate models, we simulated cotton yield responses under two climate scenarios (SSP2-4.5 and SSP5-8.5) across four future periods (2030s, 2050s, 2070s, and 2090s). Results reveal consistent warming trends and declining solar radiation, while precipitation projections remain highly uncertain with potential extreme fluctuations. Elevated CO₂ concentrations enhance yields by 13%–30%, but substantial inter-model variability underscores the uncertainty in future projections. Furthermore, the dominant drivers of yield shift over time: CO₂ fertilization initially promotes yield gains, but under high-emission scenarios, temperature stress becomes the overriding negative factor earlier in the century. By late century, yields decline despite elevated CO₂, particularly under SSP5-8.5. These findings provide new insights into climate-induced risks for cotton production and highlight the urgent need for adaptive strategies tailored to arid agricultural systems.