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作物学报 ›› 2025, Vol. 51 ›› Issue (2): 485-502.doi: 10.3724/SP.J.1006.2025.42024

• 耕作栽培·生理生化 • 上一篇    下一篇

基于氮肥运筹下水稻产量与品质协同的农艺生理指标解析

覃金华1,2,**，洪卫源2,**，冯向前1,2，李子秋2，周子榆2，王爱冬2，李瑞杰1,2，王丹英2，张运波1,*，陈松2,*   

  1. 1长江大学农学院, 湖北荆州434025; 2中国水稻研究所 / 水稻生物育种全国重点实验室, 浙江杭州3114001
  • 收稿日期:2024-05-10 修回日期:2024-10-25 接受日期:2024-10-25 出版日期:2025-02-12 网络出版日期:2024-11-19
  • 通讯作者: 张运波, E-mail: yunbo1022@126.com; 陈松, E-mail: chensong02@caas.cn
  • 基金资助:
    本研究由国家重点研发计划项目(2022YFD2300700), 中国农业科学院科技创新工程重大科研任务(CAAS-ZDRW202001), 基于高通量表型检测平台的水稻个体表型鉴定技术研究(2023ZZKT20402)和财政部和农业农村部国家现代农业产业技术体系建设专项(CARS-01)资助。

Analysis of agronomic and physiological indicators of rice yield and grain quality under nitrogen fertilization management

QIN Jin-Hua1,2,**,HONG Wei-Yuan2,**,FENG Xiang-Qian1,2,LI Zi-Qiu2,ZHOU Zi-Yu2,WANG Ai-Dong2,LI Rui-Jie1,2,WANG Dan-Ying2,ZHANG Yun-Bo1,*,CHEN Song2,*   

  1. 1 College of Agriculture, Yangtze University, Jingzhou 434025, Hubei, China; 2 China National Rice Research Institute / National Key Laboratory of Rice Biotechnology and Breeding, Hangzhou 311400, Zhejiang, China
  • Received:2024-05-10 Revised:2024-10-25 Accepted:2024-10-25 Published:2025-02-12 Published online:2024-11-19
  • Supported by:
    This study was supported by the National Key Research and Development Program (2022YFD2300700), the Major Research Tasks of the China Academy of Agricultural Sciences Science and Technology Innovation Project (CAAS-ZDRW202001), the Research on Rice Individual Phenotype Identification Technology Based on High-Throughput Phenotype Detection Platform (2023ZZKT20402), and the China Agriculture Research System of MOF and MARA (CARS-01).

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摘要：

提升水稻产量与品质的协同效应是当前水稻生产面临的重大挑战。深入剖析并清晰界定影响产量与品质协同提升的关键水稻群体特征，对于指导水稻品种的改良和栽培技术的优化具有至关重要的意义。本研究以秀水134 (XS134)和黄华占(HHZ)为材料，分别设置常规固定施氮模式(N0、N1、N2、N3)和基于SPAD阈值的动态施氮模式(RTNM、S34、S37、S40)。通过在水稻关键生育期采集静态与动态农艺生理指标，以及产量和稻米品质数据，采用不同多目标回归预测模型，以深入解析水稻关键农艺生理指标对产量和稻米品质的响应。结果表明：(1) 总体上水稻产量与稻米综合指标(GQI)呈互斥关系。随着施氮量的增加，产量逐渐提高，而GQI则呈降低趋势，尤其在固定施氮模式下，这种负相关性更为明显。然而，与固定施氮模式(N2处理)相比，动态施氮(RTNM处理)在施氮量减少了32.01%~58.02%的同时，能够稳定水稻产量并提升了GQI的3.10%~38.34% (除2022年XS134外)，这凸显了动态施氮模式在缓解水稻产量与品质互斥性，并推动两者协同提升方面的潜力。(2) 相关性分析表明，50个静态农艺指标中，有28个指标与水稻产量和GQI均呈显著相关性，占比56.00%。3种“量质”回归模型对水稻产量(R2：0.74~0.83；RMSE：0.40~0.49)和GQI (R2：0.81~0.90；RMSE：0.63~0.88)的预测能力表现出不同程度的准确性。特征重要性解析表明分蘖期的群体生物量是对产量和品质的预测均为正效应(0.09~6.37)，而株高、叶面积指数和叶重等在预测产量和品质时出现互斥，这表明在构建水稻产量与品质协同提升的分蘖期群体时，需要在确保群体生物量的基础上，对“量质互斥指标”开展适宜度评估与优化。同时，穗发育期的群体净同化率(NAR)对水稻产量和GQI均呈现出不同程度的正效应值(0.06~1.00)，暗示了穗发育阶段水稻的单位叶片光合效率可能是实现二者协同提升的重要群体特征。综上，相较于常规固定施氮模式，基于SPAD阈值的动态施氮策略可在一定程度上实现水稻产量与品质的协同提升；分蘖期干物重和穗发育期NAR可以作为实现这一协同效应的重要参考指标。

关键词: 氮肥, 农艺生理指标, 产量, 品质

Abstract:

Achieving a synergistic improvement in both rice yield and quality remains a major challenge in rice production. A thorough analysis and clear identification of key population traits that influence the coordinated enhancement of yield and quality are crucial for guiding rice variety improvement and optimizing cultivation techniques. In this study, two rice varieties, Xiushui 134 (XS134) and Huanghuazhan (HHZ), were used to evaluate different nitrogen management strategies, including conventional fixed nitrogen applications (N0, N1, N2, N3) and dynamic nitrogen applications based on SPAD thresholds (RTNM, S34, S37, S40). Key agronomic and physiological indicators were collected at critical growth stages, along with yield and grain quality data. Multi-objective regression models were employed to analyze how key agronomic and physiological traits influence rice yield and grain quality. The results showed as follows: (1) A trade-off generally exists between rice yield and grain quality index (GQI); as nitrogen application increased, yield improved, but GQI tended to decrease, especially under fixed nitrogen application. However, compared to N2, the RTNM treatment reduced nitrogen application by 32.01% to 58.02%, while maintaining stable yields and improving GQI by 3.10% to 38.34% (with the exception of XS134 in 2022). This suggests that dynamic nitrogen management can alleviate the yield-quality trade-off, promoting yield-quality synergy. (2) Correlation analysis indicated that 28 out of 50 static agronomic traits were significantly correlated with both yield and GQI (56.00%). The three "yield-quality" regression models demonstrated varying degrees of predictive accuracy for rice yield (R2: 0.74–0.83; RMSE: 0.40–0.49) and GQI (R2: 0.81–0.90; RMSE: 0.63–0.88). Feature importance analysis highlighted that population biomass during the tillering stage positively influenced both yield and quality (0.09–6.37). Conversely, plant height, leaf area index, and leaf weight exhibited trade-offs in predicting yield and quality, suggesting that careful evaluation and optimization of these "mutually exclusive" indicators are necessary, particularly when ensuring sufficient population biomass. Furthermore, the population net assimilation rate (NAR) during ear development showed a positive impact on both yield and GQI (0.06–1.00), indicating that the photosynthetic efficiency per unit leaf during this stage may be a key trait for achieving coordinated improvements in yield and quality. In summary, compared to conventional fixed nitrogen application, a dynamic nitrogen management strategy based on SPAD thresholds can achieve a certain level of synergy between rice yield and quality. Population biomass during the tillering stage and NAR during the ear development stage may serve as important reference indicators for achieving this synergy.

Key words: nitrogen fertilization, agronomic traits, rice yield, grain quality

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