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Rice Yield and Nitrogen Use Efficiency Under Climate Change: Unraveling Key Drivers with Least Absolute Shrinkage and Selection Operator Regression

文献类型： 外文期刊

作者： Ma, Yingjun 1 ; Sun, Menglong 1 ; Liang, Xianglong 4 ; Zhang, Huimin 1 ; Xiang, Jinxia 1 ; Zhao, Ling 5 ; Fan, Xiaorong 1 ;

作者机构： 1.Nanjing Agr Univ, Sanya Inst, Sanya 572024, Peoples R China

2.Nanjing Agr Univ, Coll Resources & Environm Sci, State Key Lab Crop Genet & Germplasm Enhancement &, Nanjing 210095, Peoples R China

3.Zhongshan Biol Breeding Lab, Nanjing 210095, Peoples R China

4.Southern Univ Sci & Technol, Dept Stat & Data Sci, 1088 Xueyuan Ave, Shenzhen 518055, Peoples R China

5.Jiangsu Acad Agr Sci, Inst Food Crops, Natl Ctr Technol Innovat Saline Alkali Tolerant Ri, East China Branch, Nanjing 210014, Peoples R China

关键词： climate change; rice; nitrogen use efficiency; LASSO regression; high temperature; rainfall

期刊名称：AGRONOMY-BASEL （ 影响因子：3.4； 五年影响因子：3.8 ）

ISSN：

年卷期： 2025 年 15 卷 3 期

页码：

收录情况： SCI

摘要： Rice (Oryza sativa L.), a staple crop vital to global food security, faces escalating threats from climate change and inefficient nitrogen management. This study employed least absolute shrinkage and selection operator (LASSO) regression to analyze the stage-specific impacts of nitrogen application, temperature, and rainfall on rice yield and nitrogen use efficiency (NUE) across three growing seasons (2020-2022) in Jiangsu Province, China. The key findings revealed the following: (1) the reproductive stages (flowering and filling stages) exhibited extreme thermal sensitivity, with high temperatures (>35 degrees C) causing substantial yield losses (33.1% average) and reducing nitrogen recovery efficiency (NRE: 22.4-60.5% loss) and the nitrogen translocation ratio (NTR: 26.3-61.6% loss); (2) the vegetative stages (tillering and jointing and booting stages) were highly rainfall-sensitive, with rainfall during tillering (2.1-9.7 mm/day) influencing 50% of the traits, including four NUE types; (3) appropriate nitrogen management (250-350 kgNha(-1)) mitigated the heat-induced losses, increasing physiological nitrogen use efficiency (PNUE) by 30.0-41.8% under extreme heat and alleviating the losses of yield. This study further verified the generalizability of LASSO. Compared with the traditional models, LASSO overcomes the issue of multicollinearity and can more effectively identify the key factors driving climate change across different spatial gradients. These findings provide actionable insights for optimizing nitrogen application timing, improving climate-resilient breeding, and developing stage-specific adaptation strategies to safeguard rice productivity under global warming.

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