小麦产量三要素与产量的通径和灰色关联度分析

选用2008—2014年河南省生产上种植面积前15位的小麦品种和部分区域试验或生产示范表现较好的品系,分别采用灰色关联度分析和通径分析2种方法对小麦穗数、穗粒数、千粒质量与产量的相互关系进行分析,并比较了2种分析方法的差异,以期为今后相关研究分析方法的选择提供参考。结果表明,对产量三要素数据分别进行初值化、区间化、均值化处理后,产量三要素与产量的关联度大小顺序各不相同,分别表现为千粒质量穗粒数穗数、穗粒数穗数千粒质量、穗粒数千粒质量穗数;而通径分析结果和前人的研究结果更加一致,产量三要素与产量的相关性表现为穗数穗粒数千粒质量。综上,灰色关联度分析由于受数据转换方法的影响,试验结果波动较大,而通径分析所得结果更加稳定,因此,在进行试验因素相关程度分析时应优先选择通径分析方法。

Grey Relational Analysis and Path Analysis on Wheat Yield and Its Three Key Factors

Wheat cultivated varieties which plant area were in top 15 in Henan province or performed better in regional and productive experiments from 2008 to 2014 were chosed for this experiment. The relationship between wheat spikes, grain numbers per spike, thousand-grain weight and yield were analyzed by grey relational analysis and path analysis, and the difference between two analysis methods were compared for providing reference for method choosing in related studies in the future. The results showed that the correlation degree between yield and its three key factors were different when the primary data was processed through initialization, interval and equalization, the correlation degree orders were thousand-grain weight > grain numbers per spike > spikes number, grain numbers per spike > spikes number> thousand-grain weight, grain numbers per spike > thousand-grain weight > spikes number, respectively. While the result obtained through path analysis were more similar with former studies, the relationship between yield and its key factors was spikes number>grain numbers per spike>thousand-grain weight. The result indicated that experimental result obtained from grey relational analysis was much more variable than that from path analysis due to the influence of different primary data processing methods. In conclusion, the path analysis should be chosen as the main method for analyzing the correlation among different experimental factors.