As China’s most important cotton production base, Xinjiang plays a crucial role in safeguarding national agricultural and economic security. To quantitatively assess the impact of climate change on cotton growth in this region, daily meteorological data (1990-2020) from 14 agro-meteorological observation stations and 65 meteorological stations, along with cotton growth observation records, were used to calibrate and validate the DSSAT crop model. The validated model was then employed to analyze the spatiotemporal variations in cotton phenology and potential yield across Xinjiang. Additionally, the contributions of key climatic factors were examined using the Mann-Kendall trend test and detrending analysis. The results showed that: (1) For calibration (and validation), the absolute relative errors between simulated and observed values for sowing-flowering date, sowing-maturity date, and yield were 1.80% (1.51%), 0.85% (1.18%), and 5.38% (5.44%), respectively, with normalized root mean square errors of 9.56% (14.06%), 9.71% (11.50%), and 11.30% (11.34%), indicating good model performance. (2) Under fixed sowing dates and cultivar conditions, the durations of sowing-flowering and sowing-maturity significantly decreased (P -1 and 2.54 d 10a-1, respectively, from 1990 to 2020, while potential yield significantly increased at a rate of 159.61 kg hm-2 10a-1. (3) Spatial analysis revealed that changes in cotton phenology and yield were significant at most stations (P maximum temperature > precipitation > minimum temperature. Overall, the DSSAT model effectively simulated cotton growth, development, and yield in Xinjiang, and climate change was found to have a significant impact on cotton phenology and potential yield. These findings provide valuable data support and a theoretical basis for crop model applications, yield forecasting, and climate impact assessments in Xinjiang and similar agro-ecological regions.
Zhou et al. (Fri,) studied this question.