European Journal of Agriculture and Forestry Research (EJAFR)

EA Journals

Principal Component Analysis

Morphological Characterization and Estimation of Genetic Parameters in Soya-Bean (Glycine Max (L.) Merr.) Cultivars Grown In Lesotho (Published)

Soya-bean cultivars grown in Lesotho have not been characterized morphologically to distinguish them. A study was conducted in Lesotho, with objectives of  (i)distinguishing the cultivars of soya-beans, (ii) estimating genetic distances among cultivars, (iii) determining the morphological markers with high discriminatory power and (iv) estimating genetic and phenotypic variance among cultivars. Experiment was laid-out using randomized completely block design with 28 treatments and three replications. Data collected using IPGRU descriptor were stem determination, pubescence presence, pubescence density, pubescence colour, pubescence type, leaflets size and leaflet shape. Data were subjected to analysis of variance, cluster analysis and principal component analysis. Analysis of variance revealed a highly significant difference among soya-bean cultivars for pubescent type and pubescent density, and only significant for leaf size and leaf colour. No significant difference was obtained for leaf shape and stem determination. Cluster analysis was able to group cultivars into two groups which further divided into sub-groups. Sub-groups again were divided into smaller groups. Outlier was also obtained. Highest genotypic variance was obtained in pubescence density and pubescence type, while lowest genotypic variance was observed in leaflets shape, leaf size and pubescence colour. Pubescence density and stem determination revealed high phenotypic variance. Leaf size and pubescence colour expressed lowest phenotypic variances. High heritability was expressed in pubescence type and pubescence density. Low heritability was experienced in leaflets shape and stem determination. Highest genetic advance was shown by leaf size, pubescence type, leaflets shape and pubescence density. The lowest genetic advance was experienced with pubescence colour.

Keywords: Cluster analysis, Glycine max, Principal Component Analysis, genotypic variance, morphological markers, phenotypic variance

Genetic Diversity of Maize (Zea Mays L.) Grown in Lesotho Using Morphological Makers (Published)

A large collection of maize germplasm is introduced annually to Lesotho from CYMMIT in Zimbabwe for evaluation of adaptability and yield performance. This collection is not characterized for degree of similarities and dissimilarities using morphological and other markers. The study was conducted with the objectives of (a) estimating genetic distance among maize cultivars using cluster analysis and (b) identifying morphological characters with high discriminatory power to segregate maize cultivars. The study was conducted at National University of Lesotho, Experimental farm. Randomized Complete Block Design was applied with ten treatments and three replications.  Data collected using Descriptor compiled by International Board of Plant Genetic Resource Unit included number of leaves per plant, tassel colour, number of cobs, silk colour, stem colour, plant height, number of ears, ear length, cob diameter, number of kernels, kernel arrangement, kernel colour, shape of upper surface, kernel type, leaf length and tassel length. Data were subjected to GENSTAT software package to generate cluster analysis and perform principal component analysis. The results of cluster analysis revealed two big groups, of which one consisted of six cultivars and another consisted of four cultivars. Besides, there was one outlier. Two big groups were further divided into sub-groups, Three principal component analyses were used to analyze the results, which constituted 65.37% of the total variation. The first one showed variation of 26.93%, the second one showed 20.65% while the third one had 17.79%. The first principal component was constituted by ear length, tillering, maize height, total number of kernels, cross-section of cob and stem colour. The characters comprising second principal component were kernel type, kernel row arrangement, silk colour, number of ear and number of kernel row. Lastly, the character influencing separations along third principal component were number of kernel row, silk colour and number of leaves. The study was able to distinguish the cultivars.


Keywords: Cluster analysis, Lesotho, Maize, Principal Component Analysis

Characterisation of Wheat (Triticum Aestivum L.) Cultivars Grown In Lesotho by Morphological Markers (Published)

Wheat is one of the major cereal crops grown in Lesotho, ranking third after maize and sorghum. Cultivars of wheat are imported from South Africa without characterization.  The study was therefore conducted with the following objectives; (1) to distinguish wheat cultivars grown by farmers, (2) to estimate genetic distance among wheat cultivars and (3) to identify the characters that have high discriminatory power. Complete Randomized Block Design with ten treatments and three replications were applied. Data were collected using Descriptor and analysed using GENSTAT software to perform cluster and principal component analysis. The first three principal components constituted 84.572% of the total variation. First principal component variation accounted for 55.738%, while second principal component contributed 15.737% and third principal component constituted 12.858%. Characters responsible for variation in the first component were spikelets, spike height and tillers. Separation among second component was brought about by plant height, reproductive tillers and seeds per spike. Variation in component three was due to glume hairiness, seed size and plant height. Cluster analysis formed two groups, A and B, and one outlier. Group A comprised of Gariep, Koonap, Elands and Senqu while Group B consisted SST374, SST356, PAN3195, PAN3379 and TugelaDN. Group C was an outlier containing Matlabas. The findings showed that the cultivars were different from each other and as such genetic variation exists that broaden the spectrum of germplasm, from which farmers can make a wider choice.

Keywords: Cluster analysis, Lesotho, Principal Component Analysis, Wheat, morphological characters

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