Recently, research conducted by the team led by Dr. Wang Zhanbiao from the Cotton Research Institute of the Chinese Academy of Agricultural Sciences has quantified the impact of climate change on cotton fiber quality using a hybrid machine learning model. They have also proposed strategies for cotton production to adapt to climate change, which is of great significance for improving the quality of cotton in Xinjiang under the background of climate change. The research outcomes, titled "Demonstrating Almost Half of Cotton Fiber Quality Variation is Attributed to Climate Change Using a Hybrid Machine Learning-Enabled Approach," have been published in the internationally renowned academic journal European Journal of Agronomy (IF=4.5, Top Tier in the Agricultural and Forestry Sciences category).

Understanding the effects of climate change on cotton fiber quality will reduce the risks to production caused by global warming. Machine learning algorithms are effective for forecasting climate impacts on crops. However, the impact of climate change on cotton fiber quality is unclear. Hence, a hybrid machine learning-enabled approach, the Bayesian model average (BMA) method with multiple machine learning algorithms (linear regressor, SVR, RFR, GBDT, LightGBM, and XGBoost) and bootstrap resampling, was developed to explore the impact and screen the important climatic factors affecting various traits of fiber quality. On the basis of fiber quality data from 1033 test stations across Xinjiang, China, from 2016 to 2022, the explained variance for climate change in the hybrid machine learning model was as follows: 44.72 %–50.55 % for white cotton grade, 44.06 %–53.95 % for length, 51.72 %–56.81 % for micronaire, 32.70 %–49.50 % for length uniformity, and 45.66 %–53.09 % for strength in the 1000 bootstrapping samples. In addition, recursive feature elimination with cross-validation (RFECV) was used to select the optimal feature set and calculate the contribution of each feature. The variability in micronaire in the hybrid model was affected primarily by climate factors, such as the daily minimum temperature, rainfall, and wind speed, whereas the other quality traits were affected mainly by radiation-related climatic indicators. The climate during the harvest stage in October had a significant effect on cotton quality, explaining 33.0 % of the variance in white cotton grade, 32.1 % in length, and 48.3 % in fiber strength. Conversely, the climate during the boll opening and early harvest stages in September had a greater influence on micronaire and length uniformity, accounting for 21.4 % and 37.2 % of the variance, respectively .  This study highlights that climate change explains nearly 50 % of the variation in fiber quality, with the influence being notably more considerable during the later stages of the cotton growth period. These findings clarify the uncertainty in the impact of climate change on cotton fiber quality considering the uncertainty of the single machine model and model errors. Equally important, this information can be valuable for farmers and growers seeking to improve fiber quality under climate change.

This study was supported by the Natural Science Foundation of the Xinjiang Uygur Autonomous Region (2022D01B224), the Science and Technology Development Program of the Pilot Zone for Innovation-Driven Development along the Silk Road Economic Belt and the Wu-Chan-Shi National Innovation Demonstration Zone (2023LQJ03), the Regional Collaborative Innovation Project of the Xinjiang Autonomous Region - Science and Technology Partnership Program of the Shanghai Cooperation Organization and International Science and Technology Cooperation Program (2022E01061) and the China Postdoctoral Science Foundation (2024T171024).

https://doi.org/10.1016/j.eja.2024.127426