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, 洪卫源, 冯向前, 李子秋, 周子榆, 王爱冬, 李瑞杰, 王丹英, 张运波, 陈松

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

QIN Jin-Hua, HONG Wei-Yuan, FENG Xiang-Qian, LI Zi-Qiu, ZHOU Zi-Yu, WANG Ai-Dong, LI Rui-Jie, WANG Dan-Ying, ZHANG Yun-Bo, CHEN Song

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。

Fig. 7 Ranking of correlation parameters between dynamic and static agronomic indicators for rice yield and GQI across different regression models A, C and E represent the correlation coefficient rankings of dynamic and static indicators for rice yield in the linear regression, support vector regression, and ridge regression models, respectively; B, D and F represent the correlation coefficient rankings of dynamic and static indicators for GQI in the linear regression, support vector regression, and ridge regression models, respectively. MT: tillering stage; PI: panicle initiation stage; FL: heading stage; MGF: mid-grain filling stage; MS: maturation stage; CGR: crop growth rate; CGRN: crop growth rate of nitrogen accumulation; PBR: proportion of spike dry weight increase from booting to heading; SBR: proportion of stem dry weight increase from booting to heading; LBR: proportion of leaf dry weight increase from booting to heading; PNR: proportion of panicle nitrogen accumulation increase from booting to heading; SNR: proportion of stem nitrogen accumulation increase from booting to heading; LNR: proportion of leaf nitrogen accumulation increase from booting to heading; LAD: leaf area duration; NAR: net assimilation rate.