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

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 °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 kgN·ha−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.