Yield variation in early-maturing cotton in response to sowing dates and growing seasons is associated with differential resource utilization

文献类型： 外文期刊

第一作者： Mi, Yadi

作者： Mi, Yadi;Wang, Yaru;Wu, Fengqi;Han, Yingchun;Yang, Beifang;Lei, Yaping;Xiong, Shiwu;Zhi, Xiaoyu;Li, Yabing;Wu, Fengqi;Li, Yabing

作者机构：

关键词： Cotton; Sowing date; Light and heat resource utilization; Yield

期刊名称：EUROPEAN JOURNAL OF AGRONOMY （ 影响因子：5.5； 五年影响因子：5.9 ）

ISSN： 1161-0301

年卷期： 2025 年 168 卷

页码：

收录情况： SCI

摘要： In the cotton-growing region of the Yellow River Basin in China, cotton sowing dates typically range from early May to early June. The yield of early-maturing cotton varies significantly with sowing dates and growing seasons. We hypothesize that these yield variations are primarily due to differences in resource utilization efficiency. A 3-year field experiment was conducted from 2021 to 2023 with early maturing cotton variety 'Chinese Cotton Research Institute 134' to monitor light and heat resources at different sowing dates. Results showed that cotton sown on May 6, 2023, achieved the highest yield (3455.26 kg ha(-1)), while April 12, 2021, had the lowest yield (1832.41 kg ha(-1)), with the highest yield exceeding the lowest by 28.0 %, 42.8 %, and 46.97 % over three years. LAI negatively correlated with yield, while true leaf number and biomass were positively correlated. PLS analysis showed that light energy utilization (Eu) influences yield through biomass and accumulated temperature utilization efficiency (TPE), while heat resource utilization (HUE) and TPE directly influence yield (r = 0.776, r = -0.971, P < 0.001). Sowing date significantly affected true leaf number, LAI, and yield, with climate factors indirectly impacting yield. Biomass, HUE, and TPE directly influenced yield. 2022 showed higher biomass and LAI peaks in the late sowing, and higher yield in the early sowing. Late sowing resulted in higher yield in 2021 and 2023. This study highlights the role of sowing date and growing season in regulating cotton yield through resource utilization, providing a basis for optimizing cotton production management.

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