Sains Malaysiana 49(3)(2020): 461-470

http://dx.doi.org/10.17576/jsm-2020-4903-01

 

Multivariate Analysis of Superior Helianthus annuus L. Genotypes Related to Metric Traits

(Analisis Multivariat Genotip Superior Helianthus annuus L. berkaitan Sifat Metriks)

 

ADEEL RIAZ1,2*, MUHAMMAD SHAHID IQBAL3, SAJID FIAZ4, SADARUDDIN CHACHAR2, RAI MUHAMMAD AMIR5 & BISMA RIAZ6

 

1Department of Plant Breeding and Genetics, University of Agriculture Faisalabad, Pakistan

 

2Biotechnology Research Institute, Chinese Academy of Agricultural Sciences, Beijing, 100081, China

 

3Cotton Research Institute, Chinese Academy of Agricultural Sciences, Anyang, China

 

4State Key Laboratory of Rice Biology, China National Rice Research Institute, Hangzhou, 310006, China

 

5Institute of Food and Nutritional Sciences, PMAS-ARID Agriculture University. Rawalpindi, Pakistan

 

6Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing, 100081, China

 

Received: 26 November 2018/Accepted: 11 November 2019

 

ABSTRACT

To increase seed yield and oil contents, variability in breeding material is a pre-requisite. Plant material was comprised of forty-nine sunflower genotypes to investigate the variability and identification of superior genotypes by multivariate analysis. The data were recorded for ten quantitative traits; days to maturity (DM), plant height (PH), stem diameter (SD), head diameter (HD), number of leaves (NOL), achene per head (APH), achene yield per plant (AYP), 100- achene weight (100AW), filled achene percentage (FA) and oil contents (OC). The genotypes showed significant variation for all traits except OC.  A highly significant association of achene yield was observed with 100AW. Principal component analysis (PCA) separated into four components (PC-I, II, III, IV) with Eigenvalue greater than one accounting for 62.63% of the total variation. Total variance percentage was maximum in PC-I (24.4%) followed by PC-II (14.70%). Cluster analysis further classified the sunflower genotypes in three clusters based on seed yield and its related traits. A maximum number of genotypes were included in cluster I (26 genotypes) followed by cluster III (11 genotypes) contributing 65.30%, 24.48%, respectively of total genotypic strength. In addition, maximum number of traits were included in cluster III followed by cluster II. PH and NOL were closest of all the ten traits suggesting their strong correlation. Taken together, these results can be useful for breeders to develop high yielding sunflower hybrids.

 

Keywords: Biplot; cluster analysis; multivariate; PCA; sunflower

 

ABSTRAK

Dalam usaha meningkatkan hasil benih dan kandungan minyak, kepelbagaian bahan pembiakan adalah pra-syarat. Bahan tumbuhan terdiri daripada 49 genotip bunga matahari untuk mengkaji kepelbagaian dan pengenalpastian genotip superior oleh analisis multivariat. Data direkodkan untuk sepuluh ciri kuantitatif; hari kepada kematangan (DM), ketinggian tumbuhan (PH), diameter stem (SD), diameter kepala (HD), bilangan daun (NOL), aken setiap kepala (APH), hasil aken setiap tumbuhan (AYP), berat 100-aken (100AW), peratusan aken tetisi (FA) dan kandungan minyak (OC). Genotip menunjukkan variasi ketara bagi semua ciri kecuali OC. Satu pertalian yang sangat signifikan untuk hasil aken diperhatikan dengan 100AW. Analisis komponen utama (PCA) dipisahkan kepada empat komponen (PC-I, II, III, IV) dengan nilai eigen lebih besar daripada satu untuk mewakili 62.63% daripada jumlah ubahan. Peratus jumlah varians adalah maksimum dalam PC-I (24.4%) diikuti dengan PC-II (14.70%). Analisis kelompok seterusnya mengelaskan genotip bunga matahari dalam tiga kelompok berdasarkan hasil benih dan ciri berkaitan. Bilangan maksimum genotip telah dimasukkan dalam kelompok I (26 genotip) diikuti oleh kelompok III (11 genotip), masing-masing menyumbang kepada 65.30% dan 24.48% daripada jumlah kekuatan genotip. Tambahan pula, bilangan ciri maksimum dimasukkan dalam kelompok III diikuti oleh kelompok II. PH dan NOL adalah paling hampir daripada semua sepuluh ciri yang menunjukkan korelasi mereka yang kukuh. Dengan mengambil kira keputusan ini, penternak boleh membangunkan kacukan bunga matahari untuk hasil yang lebih tinggi.

 

Kata kunci: Analisis kluster; Biplot; bunga matahari; multivariat; PCA

 

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*Corresponding author; email: adeelriaz1991@yahoo.com