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作物学报

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 311400
  • 收稿日期:2024-05-10 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
  • 作者简介:覃金华, E-mail: 2022710803@yangtzeu.edu.cn;
    洪卫源, E-mail: 1193952013@qq.com第一联系人：**同等贡献
  • 基金资助:
    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);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)

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. 1College of Agriculture, Yangtze University, Jingzhou 434025, Hubei, China
    2China National Rice Research Institute / National Key Laboratory of Rice Biotechnology and Breeding, Hangzhou 311400, Zhejiang, China
  • Received:2024-05-10 Accepted:2024-10-25 Published:2025-02-12 Published online:2024-11-19
  • Contact: E-mail: yunbo1022@126.com; E-mail: chensong02@caas.cn
  • About author:First author contact:**Contributed equally to this work
  • Supported by:
    National Key Research and Development Program(2022YFD2300700);Major Research Tasks of the China Academy of Agricultural Sciences Science and Technology Innovation Project(CAAS-ZDRW202001);Research on Rice Individual Phenotype Identification Technology Based on High-Throughput Phenotype Detection Platform(2023ZZKT20402);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)相比, 动态施氮(RTNM)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 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

图1

试验地概况 该图基于国家地理信息公共服务平台下载的审图号为GS (2024) 0650号标准地图制作, 底图边界无修改。"

表1

2021-2022年不同氮肥运筹对应施肥量"

处理
Treatment		 基肥
Base fertilizer		 分蘖肥(移栽-幼穗分化)
Tillering fertilizer (transplanting to booting stage)		 穗肥(幼穗分化-灌浆)
Panicle fertilizer (booting stage to grain filling)
N0 0 0 0
N1 27 20.25 20.25
N2 54 40.50 40.50
N3 81 60.75 60.75
S34 50 45.00 (SPAD ≤ 34) 30.00 (SPAD ≤ 34)
S37 50 40.00 (SPAD ≤ 37) 25.00 (SPAD ≤ 37)
S40 50 30.00 (SPAD ≤ 40) 20.00 (SPAD ≤ 40)
RTNM 50 30.00 (SPAD ≤ 34) 20.00 (SPAD ≤ 34)
20.00 (34 ≤ SPAD <37) 15.00 (34 ≤ SPAD < 37)
10.00 (37 ≤ SPAD ≤ 40) 10.00 (37 ≤ SPAD < 40)

附表1

2021-2022年不同类型水稻供试品种取样日期"

年份
Year		 品种
Variety		 移栽期
Transplanting period		 分蘖期
Tillering stage		 幼穗分化期
Panicle initiation stage		 齐穗期
Full heading stage		 灌浆中期
Mid-grain filling stage		 成熟期
Maturation stage
2021 黄华占HHZ 06/14 07/05 07/21 08/19 08/26 09/23
秀水134 XS134 06/14 07/05 08/19 09/02 09/17 10/18
2022 黄华占HHZ 06/14 07/13 07/28 08/19 09/02 09/20
秀水134 XS134 06/27 08/03 08/19 09/13 09/28 11/07

附表2

水稻产量、稻米综合指标和各稻米品质的方差分析"

项目
Item		 产量
Yield		 稻米综合指标
Grain quality index		 食味值
Taste value		 直链淀粉含量
Amylose content		 糙米率
Brown rice rate		 精米率
Milled rice rate		 整精米率
Head rice rate		 垩白粒率
Chalky rice rate		 垩白度
Chalkiness degree
年份Year (Y) ** ** ns ** ** ** ** ** **
品种Varity (V) ** ** ** ** ** ** ** ** **
氮肥Nitrogen (N) ** * ** ** ns ns ns ** *
Y×V ** ** ** ** ** ** ** ** **
Y×N ns ns ns ns ns ns * ns ns
V×N ns ns ** ns ns ns ns ns ns
Y×V×N ns ns ** ns ns ns ns ns ns

图2

2021-2022年不同氮肥运筹下施氮量积分图 A代表2021-2022年水稻XS134、HHZ的固定施氮N1、N2、N3处理的施氮累积动态, B、C、D、E分别代表2021-2022年水稻XS134和HHZ动态施氮RTNM、S34、S37、S40处理的施氮累积动态。21XS: 2021年秀水134; 21HHZ: 2021年黄华占; 22XS: 2022年秀水134; 22HHZ: 2022年黄华占。处理及缩写同表1。"

图3

2021-2022年氮肥处理对不同类型水稻产量的影响 A、B分别代表2021-2022年氮肥运筹对水稻XS134产量的影响; C、D分别代表2021-2022年氮肥运筹对水稻HHZ产量的影响。图中误差棒表示标准误, 不同小写字母表示不同氮肥处理间差异显著(P < 0.05, LSD)。处理及缩写同表1。"

附表3

2021年不同氮肥运筹下不同类型水稻的稻米品质"

品种
Cultivar		 处理
Treatment		 加工品质Milling quality 外观品质 Appearance quality 食味品质Tasted quality
糙米率Bown rice rate (%) 精米率Milled rice rate (%) 整精米率Head rice rate (%) 垩白粒率Chalky rice rate (%) 垩白度Chalkiness degree (%) 食味值
Taste value		 直链淀粉含量Amylose content (%)
黄华占HHZ N0 74.98 c 63.18 a 21.53 ab 21.10 a 5.97 a 79.50 a 16.40 a
N1 76.26 ab 64.99 a 26.35 ab 16.18 b 4.81 b 79.00 a 16.17 ab
N2 76.44 ab 64.30 a 24.86 ab 17.43 ab 5.13 ab 77.50 a 15.70 ab
N3 76.79 a 64.25 a 19.37 ab 19.29 ab 4.94 ab 73.50 a 15.47 ab
RTNM 76.18 ab 63.88 a 23.36 ab 19.66 ab 5.30 ab 77.67 a 15.98 ab
S34 75.72 abc 62.81 a 15.47 b 16.81 ab 4.76 b 78.50 a 16.10 ab
S37 75.54 bc 64.91 a 19.88 ab 17.78 ab 4.76 b 78.00 a 15.93 ab
S40 76.82 a 66.39 a 30.31 ab 20.47 ab 5.00 ab 74.50 a 14.97 b
秀水134
XS134		 N0 80.67 b 70.28 a 63.25 ab 27.19 ab 7.98 ab 84.00 abc 16.10 a
N1 81.18 ab 70.85 a 64.56 ab 26.44 ab 7.85 ab 83.67 abc 16.00 a
N2 81.72 ab 69.30 a 59.89 ab 25.74 ab 7.32 ab 82.33 bc 15.87 a
N3 81.94 a 71.70 a 65.46 a 24.26 b 8.74 ab 81.33 c 15.63 a
RTNM 81.38 ab 69.90 a 62.53 ab 28.08 ab 8.51 ab 83.33 abc 15.88 a
S34 81.09 ab 69.70 a 62.81 ab 23.99 b 6.21 b 85.67 a 15.77 a
S37 80.55 b 67.61 a 57.81 b 26.00 ab 8.92 ab 85.00 ab 15.87 a
S40 80.59 b 69.36 a 62.23 ab 31.63 a 10.08 a 82.33 bc 15.63 a

附表4

2022年不同氮肥运筹下不同类型水稻的稻米品质"

品种
Cultivar		 处理
Treatment		 加工品质Milling quality 外观品质Appearance quality 食味品质Tasted quality
糙米率Brown rice rate (%) 精米率Milled rice rate (%) 整精米率Head rice rate (%) 垩白粒率Chalky rice rate (%) 垩白度Chalkiness degree (%) 食味值
Taste value		 直链淀粉含量Amylose content (%)
黄华占HHZ N0 69.58 a 62.75 a 42.01 a 21.06 a 5.47 a 83.00 ab 15.93 ab
N1 68.86 a 62.28 a 41.01 a 18.75 abc 4.92 ab 81.50 abc 15.47 ab
N2 70.29 a 63.42 a 44.89 a 16.19 c 4.09 b 76.50 c 14.80 ab
N3 70.85 a 63.53 a 42.49 a 18.11 abc 4.93 ab 77.00 bc 15.30 ab
RTNM 68.81 a 62.18 a 44.12 a 19.84 ab 5.49 a 82.00 abc 15.33 ab
S34 68.38 a 61.21 a 43.03 a 17.47 bc 4.54 ab 85.50 a 16.30 a
S37 71.51 a 64.62 a 46.10 a 15.68 c 3.92 b 77.50 bc 14.73 ab
S40 67.82 a 61.61 a 41.18 a 19.70 ab 5.36 a 77.50 bc 14.37 b
秀水134
XS134

秀水134
XS134		 N0 76.66 a 65.47 a 60.73 ab 20.64 ab 5.68 ab 83.00 a 18.30 a
N1 77.82 a 65.63 a 60.39 ab 20.39 ab 5.15 b 82.67 a 17.90 a
N2 76.66 a 64.22 a 58.52 b 22.77 b 5.95 ab 81.00 a 17.70 a
N3 76.43 a 63.91 a 58.93 ab 20.35 ab 6.72 a 81.33 a 17.77 a
RTNM 76.26 a 64.51 a 59.94 ab 18.88 b 5.38 b 82.33 a 17.87 a
S34 76.54 a 65.65 a 61.51 a 18.73 b 5.14 b 81.67 a 18.00 a
S37 76.76 a 65.42 a 61.61 a 19.06 b 5.75 ab 82.00 a 18.20 a
S40 75.38 a 63.35 a 59.42 ab 19.10 b 5.81 ab 81.67 a 17.67 a

附图1

2021-2022年不同类型水稻氮肥处理下稻米品质的主成分分析 A、B、C分别代表了主成分PC1与PC2、PC1与PC3、PC2与PC3的载荷分布。PC1: 主成分1; PC2: 主成分2; PC3: 主成分3。BRR: 糙米率; MRR: 精米率; HRR: 整精米率; CGR: 垩白粒率; CD: 垩白度; PC: 蛋白质含量; AC: 直链淀粉含量; TV: 食味值。"

图4

2021-2022年氮肥运筹对不同类型水稻GQI的影响 A、B分别代表2021-2022年氮肥运筹对水稻XS134中GQI的影响; C、D分别代表2021-2022年氮肥运筹对水稻HHZ中GQI的影响。GQI: 稻米综合指标。图中误差棒表示标准误, 不同小写字母表示不同氮肥处理间差异显著(P < 0.05, LSD)。处理及缩写同表1。"

图5

水稻产量及GQI与各生育期的静态农艺指标的相关性图 A、B分别是水稻产量、GQI与各生育期静态农艺指标的相关性, 红色代表显著相关(P < 0.05), 蓝色代表不相关; C是各生育期里的静态农艺指标与水稻产量和GQI显著相关的数量及占比; D是各生育期里同时与水稻产量、GQI显著相关的静态农艺指标。MT: 分蘖期; PI: 幼穗分化期; FL: 齐穗期; MGF: 灌浆中期; FGP: 结实率; GW: 千粒重; PN: 有效穗数; GN: 每穗粒数; BIO: 生物量; LAI: 叶面积指数; SLA: 比叶面积; SNA: 茎鞘氮积累; LNA: 叶氮积累量; PNA: 穗氮积累量; SNC: 茎鞘氮浓度; LNC: 叶片氮浓度; PNC: 穗氮浓度; PH: 平均株高; TILL: 平均分蘖; SW: 单位面积茎鞘重; LW: 单位面积叶重; PW: 单位面积穗重; TW: 单位面积总重; TNA: 总氮积累量; TNC: 全株氮浓度; NI: 氮肥投入量; IEN: 氮素产谷利用率; RE: 氮吸收利用率; AE: 农学氮肥利用效率。"

图6

不同回归模型对水稻产量和GQI 的预测精确度(基于动态及静态农艺指标) A、C、E分别是线性回归、支持向量回归、岭回归模型对水稻产量的预测; B、D、F分别是线性回归、支持向量回归、岭回归模型对GQI的预测。Actual GY: 真实产量; Predict-lrGY: 线性回归模型预测产量; Predict-svrGY: 支持向量回归模型预测产量; Predict-ridgeGY: 岭回归模型预测产量; ActualGQI: 真实GQI; Predict-lrGQI: 线性回归模型预测GQI; Predict-svrGQI: 支持向量回归模型预测GQI; Predict-ridgeGQI: 岭回归模型预测GQI。"

附图2

不同回归模型对水稻产量和GQI的预测精确度(基于静态指标) A、C、E分别是线性回归、支持向量回归、岭回归模型对水稻产量的预测; B、D、F分别是线性回归、支持向量回归、岭回归模型对GQI的预测。缩写同图6。"

附图3

不同回归模型中静态指标对水稻产量和GQI的回归参数排序 A、C、E分别是线性回归、支持向量回归、岭回归模型中静态指标对水稻产量的回归参数排序; B、D、F分别是线性回归、支持向量回归、岭回归模型中静态指标对GQI的回归参数排序。缩写同图5。"

图7

不同回归模型中动态及静态农艺指标对水稻产量和GQI的回归参数排序 A、C、E分别是线性回归、支持向量回归、岭回归模型中动态及静态指标对水稻产量的相关参数排序; B、D、F分别是线性回归、支持向量回归、岭回归模型中动态及静态指标对GQI的相关系数排序。MT: 分蘖期; PI: 幼穗分化期; FL: 齐穗期; MGF: 灌浆中期; MS: 成熟期; CGR: 干重增率; CGRN: 氮积累增率; PBR: 孕穗至齐穗-穗干重增长占比; SBR: 孕穗至齐穗-茎秆干重增长占比; LBR: 孕穗至齐穗-叶干重增长占比; PNR: 孕穗至齐穗-穗氮积累增长占比; SNR: 孕穗至齐穗-茎秆氮积累增长占比; LNR: 孕穗至齐穗-叶氮积累增长占比; LAD: 光合势; NAR: 净同化率。"

表2

不同回归模型中响应水稻产量和GQI的动态及静态农艺生理指标类型"

指标
Indicator		 量质协同 量质互斥 增产不降质 提质不减产
Yield and rice quality synergy Yield and rice quality
antagonism		 Prioritizing yield
over rice quality		 Emphasizing rice quality over yield
静态指标
Static indicator		 TW-MT, TNC-PI,
PNC-FL		 LAI-MT, PH-MT, SW-MT, PNA-FL, TILL-FL, TNA-FL LNC-MT, TILL-PI,
TNC-FL, LW-MGL		 LNA-MT, SLA-MT,
LNA-PI, LNA-MGL
动态指标
Dynamic indicator		 NAR-FL LAD-MT, LAD-PI,
CGR-FL, LBR,
PBR, SBR		 CGRN-MS, NAR-MT,
CGR-PI, LAD-FL		 CGR-MT, CGRN-FL,
CGRN-MS, CGR-MS,
LNR, PNR

图8

动态及静态农艺生理指标对水稻产量和稻米品质的响应 图中红字是“量质协同”类, 椭圆三角是“量质互斥”类, 椭圆是“非量质互斥”类。A是静态农艺生理指标, B是动态农艺生理指标。TP: 移栽期; 缩写同图5和图7。"

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