|Table of Contents|

Establishment of Seedling Overwintering Prediction Model for Seed Production of Chinese Cabbage

《北方园艺》[ISSN:1001-0009/CN:23-1247/S]

Issue:
2024年1
Page:
16-23
Research Field:
Publishing date:

Info

Title:
Establishment of Seedling Overwintering Prediction Model for Seed Production of Chinese Cabbage
Author(s):
MA Chaoxi1; XIAO Xingzhong1; LI Bing1; WANG Mei1; LU Jiaojiao1; YUAN Yuxiang2
(1.Academy of Agricultural Sciences of Jiyuan Demonstration Area in Henan Province，Jiyuan,Henan 459000；2.Institute of Vegetables，Henan Academy of Agricultural Sciences，Zhengzhou，Henan 450002)
Keywords:
Chinese cabbage; overwintering; morphological character; prediction model; grey correlation degree
PACS:
S 634.1
DOI:
10.11937/bfyy.20232921
Abstract:
Taking 20 overwintering Chinese cabbage seedlings as experimental materials,the grey correlation analysis method was used to analyze the morphological characteristics of Chinese cabbage seedlings and select suitable morphological characteristic indicators,in order to establish a prediction model for the overwintering morphological characteristics of Chinese cabbage seedlings in seed production.The results showed that the morphological characteristics of Chinese cabbage seedlings before winter，such as above-ground fresh weight，leaf area per plant，leaf number，rhizome thickness，and lateral root number，were highly correlated with the overwintering rate with the correlation coefficients of 0.798 1，0.750 6，0.735 2，0.707 4，and 0.676 9，respectively)，and all indicators fully reflected the overwintering ability of the seedlings.Using the relative value of the selected ‘pre winter’ morphological indicator as the independent variable，we constructed a prediction model Y=68.885+0.963X1-0.011X2-0.168X3＋10.983X5＋0.116X7.Through back testing，the accuracy rate of the model was more than 97.00%.Then we used the root mean square error (1.405 3) and relative error (0.014 8) to evaluate the model，and its predicted values were highly accurate，which could effectively predict the overwintering rate of Chinese cabbage seedlings in seed production.Overall，establishing the model using the grey correlation analysis method was reliable enough and could be used to predict the overwintering rate of Chinese cabbage seedlings in the open field，which provided strong reference for agricultural sci-tech staff.

References:

［1］陈莹,刘雪莲,孙振营.浅谈大白菜制种技术［J］.北方园艺,2009(10):175-177.［2］刘世河.碎笔闲说大白菜［J］.养生月刊，2019(4):2.［3］王志勇,赵艳艳,杨双娟,等.河南省大白菜露地越冬杂交制种技术［J］.北方园艺,2022(24):153-156.［4］晋四清.大白菜露地越冬制种技术［J］.种子科技,2011,29(5):37-38.［5］李小乔,李广军,李成申.豫西南山区大白菜杂交制种技术［J］.中国种业,2014(8):80-81.［6］孟凡珍.不同季节生态型大白菜对温度胁迫的生理响应［D］.泰安:山东农业大学，2002.［7］王闯,陈中华,张郑伟,等.低温对大白菜幼苗生长及生理指标的影响［J］.中国农学通报,2016,32(13):44-48.［8］徐文玲,王翠花,牟晋华,等.不同浓度脱落酸对大白菜抗冷特性的影响［J］.山东农业科学,2012,44(1):47-50.［9］骆忠伟,马振良,关泰杉,等.不同品种大白菜苗期低温鉴定方法的研究［J］.种子世界,2006(8):33-34.［10］余阳俊,赵岫云,徐家炳,等.大白菜室内苗期低温生长性的鉴定方法［J］.中国蔬菜,2001(5):36-37.［11］闫锋.甜菜主要农艺性状的灰色关联度分析［J］.浙江农业科学,2023,64(5):1174-1176.［12］吴兰芳,岑庆宋,张超,等.不同玉米品种主要农艺性状与产量的灰色关联度分析［J］.耕作与栽培,2022,42(6):5-9.［13］吕伟,文飞,韩俊梅,等.芝麻种质资源苗期抗旱性鉴定及灰色关联度分析［J］.种子,2022,41(12):106-112.［14］唐家瑞,何二良,郭天顺,等.应用灰色关联度分析法评价马铃薯区试品种［J］.中国马铃薯,2010,24(3):132-135.［15］陈晖,周佳民,黄敏,等.中籼迟熟水稻农艺性状与产量的灰色关联度分析［J］.种子,2008(11):105-107.［16］张晓申,韩燕丽,樊永强,等.基于灰色关联度和DTOPSIS法对谷子区域试验的综合评价［J］.种子,2022,41(9):121-126,133.［17］徐超,王明田,杨再强,等.苗期高温对草莓生育期的影响及其模拟［J］.中国农业气象,2020,41(10):644-654.［18］冯敏玉,孔萍,胡萍,等.基于花前物候利用灰色关联分析法建立油菜花期预报模型［J］.中国农业气象,2021,42(11):929-938.

Memo

Memo:
-
Last Update: 2024-01-22